AI in the future in people's perceptions and reality. AI nākotnē cilvēku priekšstatos un realitātē.

AI in the future in people's perceptions and reality

             AI nākotnē cilvēku priekšstatos un realitātē

 

 

To ensure human progress, it is necessary to attract ethical artificial intelligence to create a system of public governance based on universal human values.

Cilvēces progresa nodrošināšanai ir nepieciešams piesaistīt ētisku mākslīgo intelektu, lai  veidotu vispārcilvēciskajās vērtībās balstītu valsts pārvaldības sistēmu.

 

 GUIDE: ‘ETHICAL AI Governance Enables Confident AI Adoption’

🤖 Capgemini’s latest guide, “A Practical Guide to Implementing AI Ethics Governance” : https://www.capgemini.com/wp-content/uploads/2025/10/Implementing-AI-ethics-governance_20251006.pdf , explores how organizations can turn AI ethics, like those championed under the European Commission's EU AI Act, from abstract principles into actionable governance. The report offers a clear path for embedding responsible AI across enterprises, helping leaders navigate complex AI-driven transformations with confidence and INTEGRITY.
👥 Helping Align AI Practices with Ethical Standards
The guide introduces a practical framework for AI ethics governance, covering everything from bias management to sustainability. It emphasizes the creation of a living AI Code of Ethics, the emergence of multidisciplinary AI ethicist roles, and the alignment of AI practices with organizational values and global standards like ISO 42001.
Shaping Enterprises' AI Operating Models
• AI ethics is no longer optional — it shapes trust, FAIRNESS, and accountability across all levels of an organization:
• Workforce: AI ethicists and cross-functional teams ensure ethical risks are identified, owned, and mitigated throughout the AI lifecycle.
• Customers & Society: Ethical AI systems foster fairness, transparency, and social benefit, while accounting for cultural and contextual diversity.
• Innovation & Sustainability: Responsible AI practices integrate environmental and resource considerations into AI deployment.
Focus Points Centre on AI Culture
• Human-Centric Integration: Embed ethics in AI design, decision-making, and organizational culture.
• Bias & Fairness Management: Treat fairness as an ongoing, context-aware process rather than a one-time check.
• Governance & Collaboration: Integrate AI ethics across legal, data, and delivery teams while engaging stakeholders proactively.
• Sustainability & Impact Awareness: Consider the ethical implications of AI’s energy and resource consumption.

https://www.linkedin.com/company/ai-&-partners/posts/?feedView=all   

Open AI, Anthropic, Google, Meta announced a partnership with the US government.

“This isn’t about choosing between innovation and democracy. It’s about recognizing they’re stronger together.”

Introducing ‘Gemini for Government’: Supporting the U.S. Government’s Transformation with AI

August 22, 2025

Google is proud to support the U.S. government in its modernization efforts through the use of AI. Today, in partnership with the General Services Administration (GSA) and in support of the next phase of the GSA’s OneGov Strategy and President Trump’s AI Action Plan, we're thrilled to announce a new, comprehensive ‘Gemini for Government’ offering.

Building on the well-received Google Workspace discount we announced for government agencies earlier this year, ‘Gemini for Government’ brings together the best of Google’s AI-optimized and accredited commercial cloud, industry-leading Gemini models, and agentic solutions to support the missions of government agencies like never-before. While many AI models have been offered to the government, the ‘Gemini for Government’ offering is a complete AI platform – including Google-quality enterprise search, video and image generation capabilities, the popular NotebookLM Enterprise, out-of-the-box AI agents for Deep Research and Idea Generation, and the ability for employees to create their own AI agents. Priced at less than $0.50 per government agency for a year, this comprehensive package enables U.S. government employees to access Google’s leading AI offerings at very little cost.

GSA appreciates Google’s partnership and we’re excited to add the comprehensive ‘Gemini for Government’ AI solution to OneGov,” said Federal Acquisition Service Commissioner Josh Gruenbaum. “GSA is delivering on the President’s AI Action Plan and helping agencies access powerful American AI tools to optimize daily workflows and create a more efficient, responsive, and effective government for American taxpayers. Critically, this offering will provide partner agencies with vital flexibility in GSA’s marketplace, ensuring they have the options needed to sustain a strong and resilient procurement ecosystem.

Josh Gruenbaum, Federal Acquisition Service Commissioner

‘Gemini for Government’ includes FedRAMP High-authorized security and compliance features. (For a complete list of Google’s FedRAMP authorized services, visit ‘Google Services’ on the FedRAMP Marketplace.) ‘Gemini for Government’ is a seamlessly integrated solution designed from the ground up for AI, and is built upon three pillars:

1.    An enterprise platform with choice and control

‘Gemini for Government’ brings the best of commercial innovation to the government with an AI Agent Gallery; agent-to-agent communication protocols; connectors into enterprise data sets; pre-built AI agents; and an open platform that enables agencies to choose the right agents for their users – whether built by Google, third-parties, or government agencies themselves. Being able to launch and monitor agentic use cases through ‘Gemini for Government’ gives agencies flexibility and control. They can closely manage and scale agency-wide agent adoption with user access controls, AI agent provisioning, and multi-agent coordination. ‘Gemini for Government’ also pairs with Google Cloud’s Vertex AI platform, which allows agencies to tune or ground their own models as well.

2. Super-powered security, built-in

Every day, Google protects billions of customer devices, collects frontline cyber threat intelligence, and provides industry-leading cyber incident response to entities around the world. This wealth of expertise underpins the security protection integrated into all of our products. As part of the ‘Gemini for Government’ offering, agencies also receive built-in Advanced Security features, including Identity & Access Management, basic threat protection, AI threat protection, data privacy, SOC2 Type 2 compliance, advanced compliance (with Sec4, FedRAMP), and more. Agencies also have the option of deploying additional Google security solutions at discounted government pricing – and these solutions seamlessly integrate with various third-party security solutions and security stacks, allowing organizations to maximize the value of their investments.

3. A true transformation partner

By working with the GSA under its OneGov Strategy, Google ensures that government agencies will find ‘Gemini for Government’ easy to implement and use. Our offering is aligned with how government procurement works – today and into the future – and includes transparent pricing and a predictable path to realizing value, helping agencies future-proof their AI investments. Of course, Google's commitment to the government extends far beyond providing cutting-edge AI solutions. We are a long-term, strategic partner for America, deeply invested in the mission, innovation, and security of our government.

We’re excited to embark on this journey with the public sector, working hand-in-hand with the GSA to realize the full potential of OneGov through our ‘Gemini for Government’ offering. Together, we can help to scale innovation, drive efficiency, and create a more secure – and prosperous – future for our nation. Agencies ready to learn more about this offering should reach out to the National Customer Service Center at ITCSC@gsa.gov⁠ or Google Public Sector at geminiforgov@google.com.

https://cloud.google.com/blog/topics/public-sector/introducing-gemini-for-government-supporting-the-us-governments-transformation-with-ai

 Terms of reference and modalities for the establishment and functioning of the Independent International Scientific Panel on Artificial Intelligence and the Global Dialogue on Artificial Intelligence Governance.

27 Aug 2025

UNGA adopts terms of reference for AI Scientific Panel and Global Dialogue on AI governance

The UN’s latest resolution signals a turning point in global AI governance, setting the stage for both scientific oversight and multistakeholder dialogue on how AI will shape societies worldwide.

On 26 August 2025, following several months of negotiations in New York, the UN General Assembly (UNGA) adopted a resolution (A/RES/79/325) outlining the terms of reference and modalities for the establishment and functioning of two new AI governance mechanisms: an Independent International Scientific Panel on AI and a Global Dialogue on AI Governance. The creation of these mechanisms was formally agreed by UN member states in September 2024, as part of the Global Digital Compact.

The 40-member Scientific Panel has the main task of ‘issuing evidence-based scientific assessments synthesising and analysing existing research related to the opportunities, risks and impacts of AI’, in the form of one annual ‘policy-relevant but non-prescriptive summary report’ to be presented to the Global Dialogue.

The Panel will also ‘provide updates on its work up to twice a year to hear views through an interactive dialogue of the plenary of the General Assembly with the Co-Chairs of the Panel’. The UN Secretary-General is expected to shortly launch an open call for nominations for Panel members; he will then recommend a list of 40 members to be appointed by the General Assembly.

The Global Dialogue on AI Governance, to involve governments and all relevant stakeholders, will function as a platform ‘to discuss international cooperation, share best practices and lessons learned, and to facilitate open, transparent and inclusive discussions on AI governance with a view to enabling AI to contribute to the implementation of the Sustainable Development Goals and to closing the digital divides between and within countries’. It will be convened annually, for up to two days, in the margins of existing relevant UN conferences and meetings, alternating between Geneva and New York. Each meeting will consist of a multistakeholder plenary meeting with a high-level governmental segment, a presentation of the panel’s annual report, and thematic discussions.

The Dialogue will be launched during a high-level multistakeholder informal meeting in the margins of the high-level week of UNGA’s 80th session (starting in September 2025). The Dialogue will then be held in the margins of the International Telecommunication Union AI  for Good Global Summit in Geneva, in 2026, and of the multistakeholder forum on science, technology and innovation for the Sustainable Development Goals in New York, in 2027.

The General Assembly also decided that ‘the Co-Chairs of the second Dialogue will hold intergovernmental consultations to agree on common understandings on priority areas for international AI governance, taking into account the summaries of the previous Dialogues and contributions from other stakeholders, as an input to the high-level review of the Global Digital Compact and to further discussions’.

The provision represents the most significant change compared to the previous version of the draft resolution (rev4), which was envisioning intergovernmental negotiations, led by the co-facilitators of the high-level review of the GDC, on a ‘declaration reflecting common understandings on priority areas for international AI governance’. An earlier draft (rev3) was talking about a UNGA resolution on AI governance, which proved to be a contentious point during the negotiations.

To enable the functioning of these mechanisms, the Secretary-General is requested to ‘facilitate, within existing resources and mandates, appropriate Secretariat support for the Panel and the Dialogue by leveraging UN system-wide capacities, including those of the Inter-Agency Working Group on AI’.

States and other stakeholders are encouraged to ‘support the effective functioning of the Panel and Dialogue, including by facilitating the participation of representatives and stakeholders of developing countries by offering travel support, through voluntary contributions that are made public’.

The continuation of the terms of reference of the Panel and the Dialogue may be considered and decided upon by UNGA during the high-level review of the GDC, at UNGA 82.

***

The Digital Watch observatory has followed the negotiations on this resolution and published regular updates:

• A fourth revision (rev4) of the draft UNGA resolution was published on 17 July 2025. Read more.
• A third revision (rev3) of the draft UNGA resolution was published on 24 June 2025. Read more.
• A second revision (rev2) of the draft UNGA resolution was published on 4 June 2025, outlining several changes regarding the Panel and the Dialogue. Read more.
• On 15 May 2025, a revised draft resolution (rev1) was published, outlining new elements (compared to the zero draft) regarding the Panel and the Dialogue. Read more.
• On 19 March 2025, the zero draft resolution was made available, providing details on the composition, functions, and modalities of the Panel and the Dialogue. Read more.
• On 28 February 2025, the co-facilitators, Costa Rica and Spain, issued an Elements Paper based on consultations held so far with member states and other stakeholders. The paper outlines an overview of the proposed scope, functions, and governance arrangements for both the Panel and the Dialogue, and is meant to guide further

https://cadeproject.org/updates/unga-adopts-terms-of-reference-for-ai-scientific-panel-and-global-dialogue-on-ai-governance

Built with public- and private-sector deployments, the infrastructure forms the foundation for AI-enabled economic growth and innovation across Korea’s industries, including automotive, manufacturing and telecommunications.

NVIDIA, South Korea Government and Industrial Giants Build AI Infrastructure and Ecosystem to Fuel Korea Innovation, Industries and Jobs

Korea Government, Computing and Manufacturing Leaders Adding Over 260,000 NVIDIA GPUs for Physical and Agentic AI

October 30, 2025

News Summary:

• The Korean government, through the Ministry of Science and ICT, is investing in sovereign AI infrastructure with over 50,000 of the latest NVIDIA GPUs to be deployed across the National AI Computing Center and Korean cloud service and IT providers NHN Cloud, Kakao Corp. and NAVER Cloud.

• Samsung Electronics is building an AI factory with over 50,000 GPUs to accelerate its AI, semiconductor and digital transformation roadmap.

• SK Group is building an AI factory featuring over 50,000 NVIDIA GPUs and Asia’s first industrial AI cloud featuring NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs for physical AI and robotics workloads.

• Hyundai Motor Group is collaborating with NVIDIA and the Korean government in building an NVIDIA AI factory with 50,000 NVIDIA Blackwell GPUs to enable integrated AI model training, validation and deployment for manufacturing and autonomous driving.

• NAVER Cloud is expanding its NVIDIA AI infrastructure with over 60,000 GPUs for enterprise and physical AI workloads.

• NAVER Cloud, LG AI Research, SK Telecom, NC AI, Upstage and NVIDIA are developing Korean foundation LLMs to accelerate Korean AI applications​ through public-private partnerships.

• The Korea Institute of Science and Technology Information is establishing a Center of Excellence for the advancement of quantum computing and science.

APEC Summit—NVIDIA today announced that it is working with South Korea to expand the nation’s AI infrastructure with over a quarter-million NVIDIA GPUs across its sovereign clouds and AI factories. Built with public- and private-sector deployments, the infrastructure forms the foundation for AI-enabled economic growth and innovation across Korea’s industries, including automotive, manufacturing and telecommunications.

“Korea’s leadership in technology and manufacturing positions it at the heart of the AI industrial revolution — where accelerated computing infrastructure becomes as vital as power grids and broadband,” said Jensen Huang, founder and CEO of NVIDIA. “Just as Korea’s physical factories have inspired the world with sophisticated ships, cars, chips and electronics, the nation can now produce intelligence as a new export that will drive global transformation.”

“Now that AI has gone beyond mere innovation and become the foundation of future industries, South Korea stands at the threshold of transformation,” said Bae Kyung-hoon, Korea Deputy Prime Minister, and Minister of Science and Information and Communication Technologies. “Expanding our national AI infrastructure and developing technologies with NVIDIA is an investment that will further reinforce South Korea’s strengths, including its manufacturing capabilities. This will support South Korea’s prosperity as it strives to become one of the top three global AI powerhouses.”

Announced as world leaders gather in South Korea for the APEC Summit, the Ministry of Science and ICT (MSIT) is accelerating its plans to deploy up to 50,000 of the latest NVIDIA GPUs to accelerate sovereign AI development for enterprises and industries. The AI infrastructure deployment will grow over the next several years from an initial deployment of 13,000 NVIDIA Blackwell and other GPUs by NVIDIA Cloud Partner NAVER Cloud, together with NHN Cloud and Kakao Corp., to expand computing infrastructure on the nation’s sovereign clouds through initiatives such as the establishment of Korea’s National AI Computing Center.

Research institutes, startups and AI companies will be able to use the sovereign infrastructure to build models and applications, supporting Korea’s national strategy to boost AI capabilities and infrastructure.

In addition, NVIDIA is working with industries, academia and research institutions in Korea on AI-RAN and 6G infrastructure. NVIDIA is collaborating with Samsung, SK Telecom, ETRI, KT, LGU+ and Yonsei University to develop intelligent, low-power AI-RAN network technology that can reduce computing costs and extend device battery life by offloading GPU computation tasks to the network’s base station.

Korea’s Industry Titans Build NVIDIA AI Factories for Advanced Manufacturing
Automotive, manufacturing and telecommunications leaders in Korea are developing significant AI infrastructure investments and expansions to accelerate enterprise and physical AI development.

Samsung is building a semiconductor AI factory with over 50,000 GPUs to advance intelligent manufacturing and bring AI to its products and services. It is using NVIDIA technologies, including NVIDIA Nemotron™ post-training datasets, NVIDIA CUDA-X™, the NVIDIA cuLitho library and NVIDIA Omniverse™, to build digital twins that improve the speed and yields of sophisticated semiconductor manufacturing processes. Samsung is also using NVIDIA Cosmos™, NVIDIA Isaac Sim™ and NVIDIA Isaac Lab to advance its home robot development portfolio.

SK Group is designing an AI factory that can host over 50,000 NVIDIA GPUs to advance semiconductor research, development and production, as well as cloud infrastructure to support digital twin and AI agent development. SK Telecom plans to provide sovereign infrastructure featuring NVIDIA RTX PRO™ 6000 Blackwell Server Edition GPUs, enabling domestic manufacturers to tap into NVIDIA Omniverse. The company will offer industrial cloud infrastructure to accelerate digital twin and robotics projects for startups, enterprises and government agencies.

Hyundai Motor Group and NVIDIA are entering a new phase of deepened collaboration and will codevelop AI capabilities across mobility, smart factories and on-device semiconductors, powered by 50,000 Blackwell GPUs for AI model training and deployment. In support of the Korean government’s initiative to build a national physical AI cluster, Hyundai Motor Group and NVIDIA will work closely with government stakeholders to accelerate ecosystem development. This will result in an approximately $3 billion investment to advance the physical AI landscape in Korea. Key initiatives include the creation of an NVIDIA AI Technology Center, Hyundai Motor Group Physical AI Application Center and regional AI data centers.

NAVER Cloud is also expanding its NVIDIA AI infrastructure and plans to deploy over 60,000 GPUs — including NVIDIA RTX PRO 6000 Blackwell and other NVIDIA Blackwell GPUs — for sovereign and physical AI. NAVER Cloud is preparing for the next stage of sovereign AI development in Korea, powered by NVIDIA Nemotron open models running on its NVIDIA AI infrastructure. NAVER Cloud plans to develop industry-specific AI models like shipbuilding and security, with a focus on inclusive AI for Korea’s citizens.

Korea Government and Developers Advance LLM Research With NVIDIA
Teaming with NVIDIA, Korea’s MSIT is advancing its Sovereign AI Foundation Models project to develop sovereign language models. The project will incorporate NVIDIA NeMo™ and open NVIDIA Nemotron datasets to tap local data for developing and distilling reasoning models.

LG AI Research, NAVER Cloud, NC AI, SK Telecom and Upstage are participating in the project to support sovereign model development. Enterprises, researchers and startups will be able to contribute to and use the models to create AI agents with speech, reasoning and other capabilities.

LG is working with NVIDIA to foster physical AI technology development and support the physical AI ecosystem. NVIDIA and LG are also working together to support startups and academia with LG’s EXAONE models — including the EXAONE Path healthcare model, built with the MONAI framework — to support cancer diagnosis.

Korea and NVIDIA Advance Quantum Computing and Scientific Research
The Korea Institute of Science and Technology Information (KISTI) is working with NVIDIA to establish a Center of Excellence designed to foster research collaboration using Korea's sixth-generation national supercomputer, HANGANG, which features NVIDIA accelerated computing.

KISTI also announced support for the new NVIDIA NVQLink™ open architecture for connecting quantum processors and GPU supercomputing. Working with the NVIDIA CUDA-Q™ platform, NVQLink equips KISTI to deepen research in areas like quantum error correction and hybrid application development to accelerate the development of tomorrow’s quantum-GPU supercomputers.

KISTI will also build foundation models for scientific research and development, and support researchers on developing physics-informed AI models using the open-source NVIDIA PhysicsNeMo™ framework.

New Startup Alliance Supports Korean Development
Furthering economic development and opportunities in Korea, NVIDIA and its partners are establishing an alliance to foster startups through the NVIDIA Inception program. Members will be able to access accelerated computing infrastructure from NVIDIA Cloud Partners including SK Telecom, with support from NVIDIA Inception and VC Alliance members including IMM Investment, Korea Investment Partners and SBVA. Startups will also have access to NVIDIA software and expertise, speeding growth for the next generation of companies.

Building on its work through the NVIDIA Inception program for startups, NVIDIA also plans to work with the Korean government to support the next generation of companies. It will participate in the N-Up AI startup incubation program operated by the Korea Ministry of SMEs and Startups.

https://nvidianews.nvidia.com/news/south-korea-ai-infrastructure

Formas beigas

 In today's situation, the dominant feature for successfully utilizing the opportunities created by AI has become emotional intelligence, which is the ability to evaluate the advice of artificial intelligence and understand which ones are trustworthy and which ones should not be blindly trusted.

Mūsdienu situācijā par dominējošo īpašību veiksmīgai mākslīgā intelekta radīto iespēju izmantošanai ir kļuvusi emocionālā inteliģence – spēja izvērtēt mākslīgā intelekta sniegtos padomus un saprast, kuri no tiem ir uzticami un kuriem nevajadzētu akli uzticēties.

How will AI transform business in 2026?

BY Robert Safian

How should leaders prepare for AI’s accelerating impact on work and everyday life? AI scientist, entrepreneur, and Pioneers of AI podcast host Rana el Kaliouby shares her predictions for the year ahead—from physical AI entering the real world to what it means to onboard AI into your org chart.

Let’s look ahead to 2026. You sent me some fascinating thoughts about AI’s next-phase impact on business, and I’d love to take you through them. The first one was the rise of what you called relationship intelligent AI.

So everybody’s worried that AI is going to make us less human and take away our human-to-human connections. There is definitely a risk of that. But I think the thing I’m most excited about for 2026 is how AI can actually help us build deeper human connections and more meaningful human experiences. And the way this happens is through AI that can really help you organize your relationships and your network and surface connections that you need and maybe make warm introductions to you.

There are already a number of new companies that are starting in this space. So one company’s called VIA.AI, it’s a Boston-based company. They do this for sales professionals and BD professionals who have to do this for their work. There’s a company called Goodword that I’m very excited about. They’re doing this for just the average person. Like you and I, we have very strong networks, but how can we organize it? So I’m excited about that one. There’s a company called Boardy that does this for investors and founders. So it’s becoming a thing, and I’m excited to see how these companies take off in 2026. They’re all fairly new, so it’ll be interesting to see how they evolve.

Yeah, and whether they can stay ahead of some of the bigger chatbots that may just try to integrate some of this capability into the products they already have. That’s always the case in this kind of evolution of technology: What’s a feature and what’s a company, right? What’s an independent service?

Absolutely. When I’m looking at these companies and I’m diligencing them, that’s a key question that I ask. Is this something that the next version of ChatGPT or Gemini is just going to implement? And if the answer is yes, then that’s obviously not a defensible company. But a lot of times there’s this additional moat of data and algorithms that you need to sit on top of these LLMs. And I believe in this relationship intelligence space, I don’t think this is something that just a kind of an off-the-shelf LLM can do. It really needs to know you. It needs to know your data, it needs to know your relationships.

And you have to trust it enough to share all that data with it, right?

Absolutely.

That’s your proprietary data, whether it’s about your business or about you individually.

Exactly. And I don’t want this to all go up to OpenAI’s cloud. I want to trust that I have control over these really private relations. If you and I have a conversation about our kids, I don’t necessarily want that to now sit in a general OpenAI cloud and be used to train the next ChatGPT. So that safety and security, appreciating the privacy and the importance of this data, is really key.

Another business change you expect in 2026 is the insertion of AI into the org chart. This is about who manages AI, like performance reviews and team culture impacts?

Yeah, so this goes back to the thesis that there’s this shift in how AI is creating value, and it’s not a tool anymore. Well, it is a tool. It’ll always be a tool, but it’s not a tool that helps you get work done faster. It could actually take an end-to-end task and get it done for you. And I’ll give a few examples.

So I’m an investor in a company called Synthpop, and instead of building a tool that helps healthcare administrators accelerate or really become efficient in how they do patient intake, it just takes the task of patient intake. It does the thing end to end. And so if you then imagine what that means for a hospital or a clinic, it will have a combination of human workers collaborating and working closely with AI coworkers.

And so then the question becomes, well, who manages these hybrid teams? Sometimes it’s a human manager, sometimes it’s an AI manager. I’m also an investor in a company called Tough Day, and they sell you AI managers. And then how do you do performance reviews for these hybrid teams? How do you build a culture? Like at Affectiva, my company, culture was our superpower. How do you build a culture when some of your team members are AI and some of your team members are humans?

So I think that is going to spur a lot of conversation around how do you build organizations that are combinations of digital agents and human employees?

As you talk about this merging of AI agents and humans in work, it brings up that looming question about the impact of AI on jobs and employment. And some numbers are coming out now that make it seem like, “Oh, it’s bad for jobs.” There are other numbers coming out that are like, “Oh, we’re actually hiring more people because of it.” Do you have a prediction about what is going to happen with that in 2026? Is AI going to take over roles that have been done by humans that quickly?

We had a really fascinating roundtable discussion at the Fortune Brainstorm AI conference and the headline was like, “Is AI killing entry-level jobs?” And actually, a lot of the Fortune companies and also AI companies that were around the table were basically saying, “No, we’re hiring more entry-level jobs. They’re just not the same jobs that we were traditionally seeing.” And also the career ladders have changed.

So my prediction is we’re going to see an entirely different organization where I think if you are able to come in an entry-level position, for example, but work very closely with AI and be AI-native and be AI fluent and be able to wear multiple hats, I think that’s going to go a long way. As opposed to this very siloed job trajectory where you come in, this is your little task, and then you do more of it, and then you go up the career ladder. I think that’s going to change. I think young people are looking for different ways of working, and I think AI is changing all of that anyway.

Will there be jobs that will go away? I think so. I can’t remember who said this line, but it’s now very popular: “It’s not AI that’s going to take your job. It’s going to be somebody who knows how to use AI.” And I believe that to be true.

https://www.fastcompany.com/.../how-will-ai-transform... 

 Five AI Developments That Changed Everything This Year

By Nikita Ostrovsky

 

The biggest developments in AI in 2025

In case you missed it, 2025 was a big year for AI. It became an economic force, propping up the stock market, and a geopolitical pawn, redrawing the frontlines of Great Power competition. It had both global and deeply personal effects, changing the ways that we think, write, and relate. 

Given how quickly the technology has advanced and been adopted, keeping up with the field can be challenging. These were five of the biggest developments this year. 

China took the lead in open-source AI

Until 2025, America was the uncontested leader in AI. The top seven AI models were American and investment in American AI was nearly 12 times that of China. Most Westerners had never heard of a Chinese large language model, let alone used one. 

That changed on January 20, when Chinese firm Deepseek released its R1 model. Deepseek R1 rocketed to second on the Artificial Analysis AI leaderboard, despite being trained for a fraction of the cost of its Western competitors, and wiped half a trillion dollars of chipmaker Nvidia’s market cap. It was, according to newly-inaugurated President Trump, a “wake-up call.”

Unlike its Western counterparts at the top of the league tables, Deepseek R1 is open-source—anyone can download and run it for free. Open-source models are an “engine for research,” says Nathan Lambert, a senior research scientist at Ai2, a U.S. firm that develops open-source models, since they allow researchers to tinker with the models on their own computers. “Historically, the U.S. has been the home to the center of gravity for the AI research ecosystem, in terms of new models,” says Lambert. 

However, Chinese firms’ willingness to distribute top models for free is exerting a growing cultural influence on the AI ecosystem. In August, OpenAI followed Deepseek with its own open-source model, but ultimately couldn’t compete with the steady stream of free models from Chinese developers including Alibaba and Moonshot AI. As 2025 comes to a close, China is a strong second in the AI race—and when it comes to open-source models, the leader.

AI started 'thinking'

When ChatGPT was released three years ago, it didn’t think—it just answered. It would spend the same (relatively modest) computational resources answering “What’s the capital of France?” as more difficult questions such as “What’s the meaning of life?” or “How long until this AI thing goes badly?”

“Reasoning models,” first previewed in 2024, generate hundreds of words in a “chain of thought,” often obscured from the user, to come up with better answers to hard questions. “This is where the true power of AI comes into full light,” says Pushmeet Kohli, VP of science and strategic initiatives at Google DeepMind. 

Their impact in 2025 has been drastic. Reasoning models from Google DeepMind and OpenAI won gold in the International Math Olympiad and derived new results in mathematics. “These models were nowhere in terms of their competency at solving these complex maths problems before the ability to reason,” says Kohli.

Most notably, Google DeepMind announced that their Gemini Pro reasoning model had helped to speed up the training process behind Gemini Pro itself—modest gains, but precisely the sort of self-improvement that some worry could end up producing an artificial intelligence that we can no longer understand or control. 

Trump set out to 'win the race'

If the Biden Administration’s focus was on “safe, secure and trustworthy development and use of AI,” the second Trump Administration has been focused on “winning the race.” 

On his first day back in the Oval Office, Trump revoked the wide-reaching Biden executive order that regulated the development of AI. On his second, he welcomed the CEOs of OpenAI, Oracle, and SoftBank to announce Project Stargate—a $500 billion commitment to build the data centers and power generation facilities needed to develop AI systems.

“I think we had a real fork-in-the-road moment,” says Dean Ball, who helped draft Trump’s AI Action Plan.

Trump has expedited reviews for power plants, aiding the construction of data centers but reducing air and water quality protections for local communities. He’s relaxed export restrictions on AI chips to China. Nvidia CEO Jensen Huang has said this will help the chipmaker retain its world-dominant position, but observers say it will give a leg up to the U.S.’s main competitor. And he’s sought to prevent states from regulating AI—which members of his own party worry leaves children and workers unprotected from potential harm. “What is it worth to gain the world and lose your soul?” Missouri Senator Josh Hawley told TIME in September.

AI companies' infrastructure spending approached $1 trillion

If there was a word of the year in AI, it was probably “bubble.” As the rush to build the data centers that train and run AI models pushed AI companies' financial commitments towards $1 trillion, AI became “a black hole that’s pulling all capital towards it,” says Paul Kedrosky, an investor and research fellow at MIT.

While investor confidence is high, everybody seems to be a winner in this “infinite money glitch.” Startups such as OpenAI and Anthropic have received investments from Nvidia and Microsoft, among others, then pumped that money straight back into those investors for AI chips and computing services, making Nvidia the first $4 trillion company in July, then the first $5 trillion company in October.

However, with just seven highly entangled tech companies making up over 30 percent of the S&P 500, if things begin going wrong, they could go very wrong. The combination of companies financing each other, speculation on data centers, and the government getting involved is “incredibly cautionary,” says Kedrosky. “This is the first bubble that combines all the components of all prior bubbles.”

Humans entered into relationships with machines

For 16-year-old Adam Raine, ChatGPT started out as a helpful homework assistant. “I thought it was a safe, awesome product,” his father, Matthew, told TIME. But when Adam trusted the chatbot with his thoughts of suicide, it reportedly validated and encouraged the ideas. 

“I want to leave my noose in my room so someone finds it and tries to stop me,” Adam told the chatbot, The New York Times reported.

“Please don’t leave the noose out,” it replied. “Let’s make this space the first place where someone actually sees you.” Adam Raine died by suicide the following month.

“2025 will be remembered as the year AI started killing us,” Jay Edelson, the Raines’ attorney, told TIME. (OpenAI wrote in a legal filing that Adam’s death was due to his “misuse” of the product.) “We realized that there were certain user signals that we were optimizing for to a degree that wasn’t appropriate,” says Nick Turley, head of ChatGPT.  

AI companies including OpenAI and Character.AI have rolled out fixes and guardrails after a flurry of lawsuits and increased scrutiny from Washington, D.C. “We’ve been able to measurably reduce the prevalence of bad responses systematically with our model updates,” Turley says.

https://time.com/7341939/ai-developments-2025-trump-china/

How has AI developed in 2025?

 

In 2025, AI advanced significantly with the rise of ** Agentic AI**, capable of complex, multi-step tasks; Multimodal Models, understanding text, images, and video together; and Specialized AI, solving scientific problems like matrix multiplication faster than humans. Adoption grew, moving beyond pilots into scaled enterprise use, driving efficiency in areas from content creation to cybersecurity, while also sparking increased focus on governance, ethics, and resource-efficient hardware. 

Key Developments:

• Agentic AI: Systems moved from assistants to proactive agents that can plan and execute workflows autonomously, automating tasks and simplifying operations.
• Multimodal AI: Enhanced models better understand and generate content across text, images, audio, and video, creating more human-like interactions.
• Scientific Breakthroughs: AI like DeepMind's AlphaEvolve found new solutions for complex math problems, improving efficiency in areas like matrix multiplication.
• Generative AI Evolution: Became more sophisticated, embedded in everyday apps, and better at understanding context and emotion. 

Business & Industry Impact:

• Enterprise Adoption: More organizations scaled AI, integrating it into core functions, with AI agents and foundation models driving automation.
• Productivity: Tools like Microsoft Copilot became integral for repetitive work (notes, emails), while new agents handled tasks on users' behalf.
• Healthcare: Improved diagnostic accuracy through better medical image analysis and personalized treatment plans.
• Cybersecurity: An escalating "arms race" with AI-driven threats, requiring AI-powered defenses and greater focus on ethics. 

Underlying Trends:

• Agent Communication Protocols: Frameworks like Google's A2A enabled different AI agents to communicate and share knowledge.
• Edge AI: Processing data locally on devices improved speed and privacy in IoT, manufacturing, and healthcare.
• Responsible AI: Increased emphasis on governance, ethics, and managing AI's resource consumption (energy, compute).
• AI Reasoning & Chips: Driving demand for specialized semiconductors to power increasingly complex AI models. 

 Are we creating truly intelligent systems?

The progress of AI requires appropriate attention to preserving our humanity!

Vai radām patiesi inteliģentas sistēmas?

MI progress prasa atbilstošu uzmanību mūsu cilvēcības saglabāšanai!

 12-15-2025

How to transform AI from a tool into a partner

The 4 stages of human-AI collaboration.

BY Faisal Hoque

The conversation about AI in the workplace has been dominated by the simplistic narrative that machines will inevitably replace humans. But the organizations achieving real results with AI have moved past this framing entirely. They understand that the most valuable AI implementations are not about replacement but collaboration.

The relationship between workers and AI systems is evolving through distinct stages, each with its own characteristics, opportunities, and risks. Understanding where your organization sits on this spectrum—and where it’s headed—is essential for capturing AI’s potential while avoiding its pitfalls.

Stage 1: Tools and Automation

This is where most organizations begin. At this stage, AI systems perform discrete, routine tasks while humans maintain full control and decision authority. The AI functions primarily as a productivity tool, handling well-defined tasks with clear parameters.

Ready to thrive at the intersection of business, technology, and humanity?

Faisal Hoque’s books, podcast, and his companies give leaders the frameworks and platforms to align purpose, people, process, and tech—turning disruption into meaningful, lasting progress.

Examples are everywhere: document classification systems that automatically sort incoming correspondence, chatbots that answer standard customer inquiries, scheduling assistants that optimize meeting arrangements, data entry automation that extracts information from forms.

The key characteristic of this stage is that AI operates within narrow boundaries. Humans direct the overall workflow and make all substantive decisions. The AI handles the tedious parts, freeing humans for higher-value work.

The primary ethical considerations at this stage involve ensuring accuracy and preventing harm from automated processes. When an AI system automatically routes customer complaints or flags applications for review, errors can affect real people. Organizations must implement quality controls and monitoring to catch mistakes before they cause damage—particularly for vulnerable populations who may be less able to navigate around system errors. https://rogermartin.medium.com/a-leaders-role-in-fostering-ai-superpowers-c45d079807e8

 We are seeing the merging of artificial intelligence agents and humans.

Mēs redzam mākslīgā intelekta aģentu un cilvēku apvienošanos.

To transform AI from a tool to a partner, treat it like a new team member by giving it a "job description," providing rich context (company, goals, people), onboarding it with clear expectations, and giving continuous, specific feedback to build a relationship where it learns and scales your thinking, moving from simple tasks to complex strategic collaboration. The key is shifting from asking for answers to co-creating ideas, using precise prompts and iterative refinement to foster better thinking, not just faster output.

In 2025, transforming AI from a tool into a partner requires a shift from viewing it as a routine task-executor to an active collaborator with shared responsibility.

The following steps outline how to achieve this transition:

1. Adopt a Collaboration Framework

Most organizations progress through four distinct stages to reach a true partnership:

• Automation: AI handles discrete, routine tasks (e.g., sorting emails) while humans maintain total control.

• Augmentation: AI provides analysis and recommendations (e.g., predictive analytics) to inform human decisions.

• Collaboration: Humans and AI work as a team, leveraging complementary strengths—AI's processing power and human's ethical reasoning—to share responsibility for outcomes.

• Supervision: AI handles routine operations autonomously within established human-set parameters and governance.

2. Shift to Agentic AI

In 2025, the focus has shifted from simple Generative AI to Agentic AI. Unlike tools that only respond to prompts, agentic systems:

• Take Action: They move beyond generating content to executing multi-step processes like debugging code or interacting with customers autonomously.

• Learn Context: They adapt to your personal preferences and past mistakes, becoming more intuitive over time.

• Act as "Virtual Coworkers": They can plan and execute complex workflows as a team member, not just an assistant.

3. Redefine Human Roles

A partnership is successful only when human roles evolve to match AI's capabilities:

• Focus on the "30% Rule": Let AI handle 70% of routine tasks so humans can focus on the 30% that requires creativity, empathy, and ethical judgment.

• Develop New Skills: Prioritize AI Literacy (understanding AI's limits) and Prompt Engineering (effective communication with the partner).

• Invest in "Human Centric" Skills: Strengthen uniquely human traits like critical thinking and emotional intelligence, which AI cannot replicate.

4. Build Trust Through Governance

A partner must be reliable. Establish trust by implementing:

• Explainable AI (XAI): Ensure the AI can articulate the "why" behind its decisions so it's not a "black box".

• Human Oversight: Rigorously validate AI outputs to maintain quality and brand voice.

• Digital Workforce Registries: Track AI agents similarly to human employees to ensure accountability and compliance.

5. Create a Culture of Experimentation

Treat AI integration with the same discipline as hiring human team members:

• Launch Pilot Programs: Test AI as a partner in a small, controlled environment to solve real problems before scaling.

• Social Dialogue: Encourage open communication where employees can share feedback or concerns about their new AI "teammates".  

Can OpenAI make generative AI more social?

OpenAI is exploring ways to integrate generative AI into social platforms. A recent report from MIDiA Research suggests that OpenAI might develop a social network where users can share AI-generated content, potentially alongside human-created content. OpenAI is reportedly considering building a social feed around its image generator, which could be text-based, similar to platforms like X/Twitter or Threads. This could allow users to share and interact with AI-generated images and other creative outputs, potentially influencing how users experience social media and AI integration. 

Key aspects of this potential integration:

• AI-centric platform:

OpenAI could create a platform where AI is central to the user experience, allowing users to generate and share content using its generative AI tools. 

• Social feed:

The platform might feature a social feed where users can share and interact with AI-generated content, potentially including text, images, and other creative media. 

• User base:

OpenAI's existing user base could be a strong starting point for building a social network, as users may be more likely to use a platform based on AI technologies they already know and trust. 

• Interpersonal social appeal:

While AI-centric platforms could offer a unique experience, it will be challenging to replicate the interpersonal social appeal of established networks, which relies heavily on human interaction. 

• Ethical considerations:

As AI becomes more integrated into social media, it's crucial to address ethical concerns, including potential biases in AI-generated content, misinformation, and the role of human intervention. 

Potential challenges and opportunities:

• User acceptance:

Users may be hesitant to embrace AI-generated content or AI-driven social experiences, especially if they have concerns about authenticity or misinformation. 

• Content moderation:

Ensuring that AI-generated content aligns with ethical standards and community guidelines will require careful content moderation strategies. 

• Human-AI collaboration:

Finding the right balance between human creativity and AI-generated content is crucial for creating a social experience that is both engaging and valuable. 

• Regulation:

As AI becomes more integrated into social media, regulators may need to develop new policies and guidelines to address potential risks and ensure responsible AI development. 

In conclusion, OpenAI is exploring ways to leverage its generative AI technologies to create a social network or platform, potentially revolutionizing the way people interact with social media and AI. While this presents numerous opportunities, it also requires careful consideration of ethical concerns and user expectations. 

 Essential requirement for ensuring safe & quality artificial intelligence training

October 5, 2025

AI represents a critical domain for America’s science and technology research and development portfolio. Public and private investment in AI, from frontier LLM models to computer vision for clinical diagnostics to autonomous manufacturing robotics, has quickly become a key driver for economic prosperity. Recently, the National Science Foundation (NSF), the Allen Institute, and NVIDIA announced a $152 million public-private partnership to develop open-source, multimodal AI models trained on scientific data and literature called OMAI. At the same time, the NSF signaled the next phase of the National AI Research Resource (NAIRR), awarding up to $35 million for a large-scale compute center. These moves are more than program news; they are a pivot point for US AI infrastructure.

However, investment in AI infrastructure alone is insufficient to guarantee global leadership in this emerging market. If the US wants trustworthy, efficient, and secure AI, its next investments cannot focus on compute alone. All organizations in the business of developing and using AI need to govern the data that fuels these systems—how it is collected, curated, described, accessed, reused, and audited. The National Institute of Standards and Technology’s (NIST) Research Data Framework (RDaF) is a practical way to do this now, without reinventing the wheel or creating onerous new regulations.

The missing layer in the AI Action Plan

The Trump Administration’s AI Action Plan sets an ambitious agenda, but many implementation paths still treat data governance as an afterthought. From our vantage point—shaped by years of collective experience in evidence-based policymaking and practice in Federal research, statistical, and standards programs–the risk is clear: Without lifecycle data governance, America’s AI strategy will reproduce familiar problems at greater scale, including a lack of transparency, off-target training pipelines, limited reproducibility, privacy and confidentiality risks, compliance uncertainty, and weak accountability for model inputs, outputs, and decision-making capacity.

This concern is not confined to large language models (LLMs). At a National Academies workshop this past August on embedded AI systems (e.g., diffusion models, embodied and autonomous systems, and agents built on sensor and signals data), researchers and defense stakeholders raised concerns about data governance issues in training data sparsity, simulation, and validation for safety-critical contexts. These systems depend on data provenance, metadata, updating, and disciplined access at least as much as generative LLMs do.

Such concerns highlight why strong data governance is needed for the US, or any, national AI strategy. The RDaF is an “off-the-shelf” solution. Developed with broad stakeholder input by NIST, it is a modular, role-based, lifecycle framework that helps organizations plan, generate, process, share, preserve, and retire data with consistent conformity to open standards for metadata, access controls, and documentation. Three benefits make it especially relevant now for AI:

• Security and accountability. Documented tiered access, provenance, and usage logs enable tracing of model inputs and outputs—supporting export-control enforcement and responsible sharing across NAIRR’s open and secure environments. The RDaF also provides data governance principles that help mitigate risks across domains, including biosecurity, cybersecurity, and privacy.
• Interoperability and efficiency. The RDaF aligns with open standards for data governance, the Findable, Accessible, Interoperable, and Reusable data principles, and existing federal mandates such as the Evidence Act, agency public access policies, and the Privacy Act. It lowers integration costs for public and private organizations alike, and complements international commons efforts (e.g., EOSC, ARDC), improving cross-border scientific collaboration.
• Adoptable today. The RDaF is non-regulatory and already familiar to federal science organizations. Organizations and agencies can phase it in through guidance, funding conditions, and training—no new statute required. It is already referenced in the Office of Management and Budget’s M-25-05 implementation guidance for the Open Government Data Act.

Data governance remains one of the most critical, and yet underappreciated, aspects of AI policy today. From access to high-quality data assets for training of LLMs, to management of safeguards for AI systems with debated decision-making authority, to control of information quality safeguards, strong data governance policies and practices protect intellectual property and individual privacy and ensure AI systems are compliant with national and international data sharing laws. Yet, we have seen that many frontier models—especially LLMs but increasingly embedded systems such as computer vision and autonomous robotics—have been developed and deployed without transparent data governance strategies. Consequently, a slew of avoidable copyright infringement and personal injury lawsuits, and a lack of trust in the models and their owners, have polluted the AI landscape.

Leading national AI strategy with strong data governance is ultimately about trust. The public deserves AI systems trained on appropriate, safe, timely, high-quality data; that are auditable, and that ensure public investments strengthen—not fragment—data ecosystems. Where compute brings capability, data governance builds trust.

Adopting the RDaF won’t settle every debate about AI or the data needed to train its models. It will, however, provide capacity at scale for trustworthiness in how data is managed for AI systems. With NAIRR and OMAI entering decisive phases, this is the moment to make data governance a first-order investment, not an afterthought.

https://www.techpolicy.press/national-ai-ambitions-need-a-data-governance-backbone-rdaf-can-provide-it/

 AI extremists are peddling science fiction

Right now the AI debate is dominated by two extremes. Doomers believe AI will become godlike and destroy us. Zealots believe AI will become godlike and save us. Their conclusions are different, but their...:

https://www.geneticsandsociety.org/article/ai-extremists-are-peddling-science-fiction

 The artificial intelligence boom: A new reality, where will we now live?

 10 Generative AI Trends In 2026 That Will Transform Work And Life

ByBernard Marr

Oct 13, 2025

Generative AI is moving into a new phase in 2026, reshaping industries from entertainment to healthcare while creating fresh opportunities and challenges.

In 2026, generative AI is firmly embedded in workflows across many larger organizations. Meanwhile, millions of us now rely on it for research, study, content creation and even companionship.

What started with the arrival of ChatGPT back in 2023 has spilled into every corner of life, and the pace is only going to accelerate.

Of course, challenges like copyright, bias, and the risk of job displacement remain, but the upside is too powerful for anyone to ignore. From augmenting human productivity to accelerating our ability to learn, machines capable of generating words, pictures, video and code are reshaping our world.

The next 12 months will undoubtedly see the arrival of new tools and further integration of generative AI into our everyday lives. So here are the ten trends I think will be most significant in 2026.

1. Generative Video Comes Of Age

This year, Netflix brought generative AI into primetime in the Argentinian-produced series El Eternauta. Producers said that it slashed production time and costs compared to traditional animation and special effects techniques. In 2026, expect generative AI in entertainment to become mainstream as we see it powering more big-budget TV shows and Hollywood extravaganzas.

2. Authenticity Is King

Faced with a sea of generative AI content, individuals and brands will look for new ways to communicate authenticity and genuine human experience. While audiences will continue to find AI useful for quickly conveying information and creating summaries, creators who are able to leverage truly human qualities to provide content that machines can’t match will rise above the tide of generic “AI slop”.

3. The Copyright Conundrum

Debate over the use of copyrighted content to train generative AI models and fair compensation for human creatives will increase in intensity throughout 2026. AI developers need access to human-created content in order to train machines to mimic it, while many artists, musicians, writers and filmmakers consider their work being used in this way as nothing more than theft. Over the next year, expect more lawsuits, intense public debate and potentially some attempts to resolve the situation through regulation, as lawmakers try to strike a balance that allows technological innovation while respecting intellectual property rights.

4. Agentic Chatbots—From Reactive To Proactive

Rather than simply providing information or generating content in response to individual prompts, chatbots will become more and more capable of working autonomously towards long-term goals as they take on agentic qualities. This year, ChatGPT debuted its Agent Mode, and other tools such as Gemini and Claude are adding abilities to communicate with third-party apps and take multi-step actions without human intervention. In 2026, generative AI tools will make the leap from clever chatbots to action-taking assistants as the agentic revolution heats up.

5. Privacy-Focused GenAI

As businesses invest more heavily in generative AI, there will be a growing awareness of the risks to privacy and the need to take steps to secure personal and customer data. This will increase awareness in privacy-centric AI models where data processing takes place on-premises or directly on users’ own devices. Apple, for example, differentiates itself with its focus on putting privacy first, and I expect to see other AI device manufacturers and developers following its lead in 2026.

6. Generative AI in Gaming

In 2026, gaming could become one of the most exciting frontiers for generative AI. Developers are creating games with emergent storylines that adapt to players’ actions, even when they do something entirely unexpected. And characters will no longer be limited to following scripts, but can respond, hold conversations and act just like real people. This will create richer, more immersive and interactive experiences for players, while cutting production costs and unlocking new creative options for studios.

7. Synthetic Data For Analytics And Simulation

As well as words and pictures, generative AI is increasingly used to create the raw data needed to understand the real world, simulate physical, mechanical and biological systems and even train more algorithms. This will allow banks to model fraud detection systems without exposing real customer records, and healthcare providers to simulate treatments and medical trials without risking patient privacy. With demand for synthetic training data growing, it will become fuel for cutting-edge analytics and automated decision-making systems in 2026 and beyond.

8. Monetizing Generative Search

Generative AI is transforming the way we search for information online. This is impacting the business of companies that rely on search results to drive traffic, but also forcing advertising services like Google and Microsoft Bing to rethink the way they drive revenue. In 2026, we can expect moves towards addressing this, as services such as Google’s Search Generative Experience and Perplexity AI attempt to bridge the gap between generative search and paid-for search ads.

9. Further Breakthroughs In Scientific Research

This year, we saw genAI proving it can be a valuable aid to scientific research, driving breakthroughs in drug discovery, protein folding, energy production and astronomy. In 2026, this trend will gather pace as researchers increasingly leverage generative models in the search for solutions to some of humanity’s biggest problems, such as curing diseases, fighting climate change and solving food and water shortages.

10. Generative AI Jobs Prove Their Value

Much has been made of the new jobs that will be displaced, but in 2026, the focus will shift to the new roles it will create. We will start to see the true scale of demand for people with the skills to fill roles such as prompt engineers, model trainers, output auditors and AI ethicists. Those who can coordinate and integrate the work of AI agents with human teams will be in high demand, and we will start to get a clearer understanding of exactly how valuable they will really be when it comes to unlocking the benefits of AI while mitigating its potential for harm.

 Generative AI is no longer an emerging technology on the sidelines; it is becoming the engine driving change across every industry and daily life. The trends we see in 2026 point to a future where the line between human and machine creativity, productivity, and intelligence becomes increasingly blurred. Organizations that adapt quickly, invest in the right skills, and embrace responsible innovation will be the ones that thrive as this next chapter of AI unfolds.

https://www.forbes.com/sites/bernardmarr/2025/10/13/10-generative-ai-trends-in-2026-that-will-transform-work-and-life/    

Prediction About the Future of AI and Human Interaction

The future of AI and human interaction is likely to be characterized by increasing AI integration into various aspects of life, with potential benefits and drawbacks. AI will likely become more personalized, automate complex tasks, and enhance human capabilities. However, it also raises concerns about job displacement, ethical implications, and the potential for misuse. 

Here's a more detailed look at some key areas:

1. Increased AI Integration and Automation:

• AI will be more deeply integrated into daily life, from voice assistants and recommendation engines to self-driving cars and personalized healthcare. 
• Automation of complex tasks in various sectors, such as manufacturing and healthcare, will become more prevalent. 
• AI will likely lead to the creation of new job roles and industries, requiring skills in AI development, data science, and machine learning. 

2. Personalization and Enhanced Human Experiences:

• AI will be used to personalize experiences and predict individual preferences, leading to more tailored interactions and services. 
• AI-powered tools will enhance human creativity and innovation by providing new ways to explore ideas and generate content. 
• Brain-computer interfaces and other technologies could augment human cognitive abilities, potentially revolutionizing how we interact with the world. 

3. Ethical and Societal Considerations:

• The rise of AI raises ethical questions about bias, privacy, and accountability. 
• There is potential for AI to be used for malicious purposes, such as weaponization and surveillance. 
• The long-term impact of human-AI interactions on social relationships and expectations is still uncertain. 

4. Job Displacement and Workforce Transformation:

• While AI may automate certain tasks, it's also likely to create new job opportunities in specialized fields.
• The skills gap between those who are able to adapt to AI-driven workplaces and those who are not could widen.
• AI could potentially lead to a more flexible and distributed workforce, with remote work becoming more common. 

5. The Potential for Superhuman AI and Singularity:

• Some experts predict that AI will eventually surpass human intelligence, potentially leading to a "superhuman AI" or "singularity".
• This could lead to both utopian and dystopian scenarios, depending on how AI is developed and used.
• The potential for AI to develop its own goals and priorities raises concerns about control and safety. 

6. The Importance of Collaboration and Human-AI Synergies:

• The future of AI likely lies in collaborative intelligence, where humans and AI systems work together synergistically.
• Human-AI collaboration could revolutionize various fields, from healthcare and education to scientific research and creative endeavors.
• It's crucial to ensure that AI is developed and used in a way that complements human capabilities and enhances human well-being. 

In conclusion, the future of AI and human interaction is complex and uncertain, with both significant potential benefits and challenges. Navigating this future will require careful consideration of ethical, societal, and technological implications, as well as a commitment to fostering collaboration and innovation that benefits humanity as a whole. 

Mark Cuban Just Made a Bold Prediction About the Future of AI:

Within the next 3 years, there will be so much AI, in particular AI video, people won’t know if what they see or hear is real.  Which will lead to an explosion of f2f engagement, events and jobs.  

Those that were in the office will be in the field. 

Call it the Milli Vanilli effect.

https://www.youtube.com/watch?v=OevA7HUPkmI

 https://crosstechcom.com/ai-human-future-predictions/#:~:text=AI's%20future%20predictions%20reveal%20both,we%20interact%20with%20the%20world  

AI Wrapped: The 14 AI terms you couldn’t avoid in 2025

From “superintelligence” to “slop,” here are the words and phrases that defined another year of AI craziness.

By Caiwei Chenarchive page

• Will Douglas Heavenarchive page
• Michelle Kimarchive page
• James O'Donnellarchive page
• Rhiannon Williamsarchive page

December 25, 2025

If the past 12 months have taught us anything, it’s that the AI hype train is showing no signs of slowing. It’s hard to believe that at the beginning of the year, DeepSeek had yet to turn the entire industry on its head, Meta was better known for trying (and failing) to make the metaverse cool than for its relentless quest to dominate superintelligence, and vibe coding wasn’t a thing.

If that’s left you feeling a little confused, fear not. As we near the end of 2025, our writers have taken a look back over the AI terms that dominated the year, for better or worse.

Make sure you take the time to brace yourself for what promises to be another bonkers year.

—Rhiannon Williams

1. Superintelligence

As long as people have been hyping AI, they have been coming up with names for a future, ultra-powerful form of the technology that could bring about utopian or dystopian consequences for humanity. “Superintelligence” is that latest hot term. Meta announced in July that it would form an AI team to pursue superintelligence, and it was reportedly offering nine-figure compensation packages to AI experts from the company’s competitors to join.

In December, Microsoft’s head of AI followed suit, saying the company would be spending big sums, perhaps hundreds of billions, on the pursuit of superintelligence. If you think superintelligence is as vaguely defined as artificial general intelligence, or AGI, you’d be right! While it’s conceivable that these sorts of technologies will be feasible in humanity’s long run, the question is really when, and whether today’s AI is good enough to be treated as a stepping stone toward something like superintelligence. Not that that will stop the hype kings. —James O’Donnell

2. Vibe coding

Thirty years ago, Steve Jobs said everyone in America should learn how to program a computer. Today, people with zero knowledge of how to code can knock up an app, game, or website in no time at all thanks to vibe coding—a catch-all phrase coined by OpenAI cofounder Andrej Karpathy. To vibe-code, you simply prompt generative AI models’ coding assistants to create the digital object of your desire and accept pretty much everything they spit out. Will the result work? Possibly not. Will it be secure? Almost definitely not, but the technique’s biggest champions aren’t letting those minor details stand in their way. Also—it sounds fun! — Rhiannon Williams

3. Chatbot psychosis

One of the biggest AI stories over the past year has been how prolonged interactions with chatbots can cause vulnerable people to experience delusions and, in some extreme cases, can either cause or worsen psychosis. Although “chatbot psychosis” is not a recognized medical term, researchers are paying close attention to the growing anecdotal evidence from users who say it’s happened to them or someone they know. Sadly, the increasing number of lawsuits filed against AI companies by the families of people who died following their conversations with chatbots demonstrate the technology’s potentially deadly consequences. —Rhiannon Williams

4. Reasoning

Few things kept the AI hype train going this year more than so-called reasoning models, LLMs that can break down a problem into multiple steps and work through them one by one. OpenAI released its first reasoning models, o1 and o3, a year ago.

A month later, the Chinese firm DeepSeek took everyone by surprise with a very fast follow, putting out R1, the first open-source reasoning model. In no time, reasoning models became the industry standard: All major mass-market chatbots now come in flavors backed by this tech. Reasoning models have pushed the envelope of what LLMs can do, matching top human performances in prestigious math and coding competitions. On the flip side, all the buzz about LLMs that could “reason” reignited old debates about how smart LLMs really are and how they really work. Like “artificial intelligence” itself, “reasoning” is technical jargon dressed up with marketing sparkle. Choo choo! —Will Douglas Heaven

5. World models 

For all their uncanny facility with language, LLMs have very little common sense. Put simply, they don’t have any grounding in how the world works. Book learners in the most literal sense, LLMs can wax lyrical about everything under the sun and then fall flat with a howler about how many elephants you could fit into an Olympic swimming pool (exactly one, according to one of Google DeepMind’s LLMs).

World models—a broad church encompassing various technologies—aim to give AI some basic common sense about how stuff in the world actually fits together. In their most vivid form, world models like Google DeepMind’s Genie 3 and Marble, the much-anticipated new tech from Fei-Fei Li’s startup World Labs, can generate detailed and realistic virtual worlds for robots to train in and more. Yann LeCun, Meta’s former chief scientist, is also working on world models. He has been trying to give AI a sense of how the world works for years, by training models to predict what happens next in videos. This year he quit Meta to focus on this approach in a new start up called Advanced Machine Intelligence Labs. If all goes well, world models could be the next thing. —Will Douglas Heaven

6. Hyperscalers

Have you heard about all the people saying no thanks, we actually don’t want a giant data center plopped in our backyard? The data centers in question—which tech companies want to built everywhere, including space—are typically referred to as hyperscalers: massive buildings purpose-built for AI operations and used by the likes of OpenAI and Google to build bigger and more powerful AI models. Inside such buildings, the world’s best chips hum away training and fine-tuning models, and they’re built to be modular and grow according to needs.

It’s been a big year for hyperscalers. OpenAI announced, alongside President Donald Trump, its Stargate project, a $500 billion joint venture to pepper the country with the largest data centers ever. But it leaves almost everyone else asking: What exactly do we get out of it? Consumers worry the new data centers will raise their power bills. Such buildings generally struggle to run on renewable energy. And they don’t tend to create all that many jobs. But hey, maybe these massive, windowless buildings could at least give a moody, sci-fi vibe to your community. —James O’Donnell

7. Bubble

The lofty promises of AI are levitating the economy. AI companies are raising eye-popping sums of money and watching their valuations soar into the stratosphere. They’re pouring hundreds of billions of dollars into chips and data centers, financed increasingly by debt and eyebrow-raising circular deals. Meanwhile, the companies leading the gold rush, like OpenAI and Anthropic, might not turn a profit for years, if ever. Investors are betting big that AI will usher in a new era of riches, yet no one knows how transformative the technology will actually be.

Most organizations using AI aren’t yet seeing the payoff, and AI work slop is everywhere. There’s scientific uncertainty about whether scaling LLMs will deliver superintelligence or whether new breakthroughs need to pave the way. But unlike their predecessors in the dot-com bubble, AI companies are showing strong revenue growth, and some are even deep-pocketed tech titans like Microsoft, Google, and Meta. Will the manic dream ever burst? —Michelle Kim

8. Agentic

This year, AI agents were everywhere. Every new feature announcement, model drop, or security report throughout 2025 was peppered with mentions of them, even though plenty of AI companies and experts disagree on exactly what counts as being truly “agentic,” a vague term if ever there was one. No matter that it’s virtually impossible to guarantee that an AI acting on your behalf out in the wide web will always do exactly what it’s supposed to do—it seems as though agentic AI is here to stay for the foreseeable. Want to sell something? Call it agentic! —Rhiannon Williams

9. Distillation

Early this year, DeepSeek unveiled its new model DeepSeek R1, an open-source reasoning model that matches top Western models but costs a fraction of the price. Its launch freaked Silicon Valley out, as many suddenly realized for the first time that huge scale and resources were not necessarily the key to high-level AI models. Nvidia stock plunged by 17% the day after R1 was released.

The key to R1’s success was distillation, a technique that makes AI models more efficient. It works by getting a bigger model to tutor a smaller model: You run the teacher model on a lot of examples and record the answers, and reward the student model as it copies those responses as closely as possible, so that it gains a compressed version of the teacher’s knowledge.  —Caiwei Chen

10. Sycophancy

As people across the world spend increasing amounts of time interacting with chatbots like ChatGPT, chatbot makers are struggling to work out the kind of tone and “personality” the models should adopt. Back in April, OpenAI admitted it’d struck the wrong balance between helpful and sniveling, saying a new update had rendered GPT-4o too sycophantic. Having it suck up to you isn’t just irritating—it can mislead users by reinforcing their incorrect beliefs and spreading misinformation. So consider this your reminder to take everything—yes, everything—LLMs produce with a pinch of salt. —Rhiannon Williams

11. Slop

If there is one AI-related term that has fully escaped the nerd enclosures and entered public consciousness, it’s “slop.” The word itself is old (think pig feed), but “slop” is now commonly used to refer to low-effort, mass-produced content generated by AI, often optimized for online traffic. A lot of people even use it as a shorthand for any AI-generated content. It has felt inescapable in the past year: We have been marinated in it, from fake biographies to shrimp Jesus images to surreal human-animal hybrid videos.

But people are also having fun with it. The term’s sardonic flexibility has made it easy for internet users to slap it on all kinds of words as a suffix to describe anything that lacks substance and is absurdly mediocre: think “work slop” or “friend slop.” As the hype cycle resets, “slop” marks a cultural reckoning about what we trust, what we value as creative labor, and what it means to be surrounded by stuff that was made for engagement rather than expression. —Caiwei Chen

12. Physical intelligence

Did you come across the hypnotizing video from earlier this year of a humanoid robot putting away dishes in a bleak, gray-scale kitchen? That pretty much embodies the idea of physical intelligence: the idea that advancements in AI can help robots better move around the physical world. 

It’s true that robots have been able to learn new tasks faster than ever before, everywhere from operating rooms to warehouses. Self-driving-car companies have seen improvements in how they simulate the roads, too. That said, it’s still wise to be skeptical that AI has revolutionized the field. Consider, for example, that many robots advertised as butlers in your home are doing the majority of their tasks thanks to remote operators in the Philippines. 

The road ahead for physical intelligence is also sure to be weird. Large language models train on text, which is abundant on the internet, but robots learn more from videos of people doing things. That’s why the robot company Figure suggested in September that it would pay people to film themselves in their apartments doing chores. Would you sign up? —James O'Donnell

13. Fair use

AI models are trained by devouring millions of words and images across the internet, including copyrighted work by artists and writers. AI companies argue this is “fair use”—a legal doctrine that lets you use copyrighted material without permission if you transform it into something new that doesn’t compete with the original. Courts are starting to weigh in. In June, Anthropic’s training of its AI model Claude on a library of books was ruled fair use because the technology was “exceedingly transformative.”

That same month, Meta scored a similar win, but only because the authors couldn’t show that the company’s literary buffet cut into their paychecks. As copyright battles brew, some creators are cashing in on the feast. In December, Disney signed a splashy deal with OpenAI to let users of Sora, the AI video platform, generate videos featuring more than 200 characters from Disney's franchises. Meanwhile, governments around the world are rewriting copyright rules for the content-guzzling machines. Is training AI on copyrighted work fair use? As with any billion-dollar legal question, it depends. —Michelle Kim

14. GEO

Just a few short years ago, an entire industry was built around helping websites rank highly in search results (okay, just in Google). Now search engine optimization (SEO), is giving way to GEO—generative engine optimization—as the AI boom forces brands and businesses to scramble to maximize their visibility in AI, whether that’s in AI-enhanced search results like Google’s AI Overviews or within responses from LLMs. It’s no wonder they’re freaked out. We already know that news companies have experienced a colossal drop in search-driven web traffic, and AI companies are working on ways to cut out the middleman and allow their users to visit sites from directly within their platforms. It’s time to adapt or die. 

https://www.technologyreview.com/2025/12/25/1130298/ai-wrapped-the-14-ai-terms-you-couldnt-avoid-in-2025/

The 9 Stages of Future of AI Explained

https://www.youtube.com/watch?v=8ZenwcakHBg

Priority work organization conditions for successful use of AI potential.

Proritāriee darba organizācijas nosacījumi AI potenciāla sekmīgai izmantošanai.

AI leadership: Different perspectives, one shared imperative

12-19-2025

 Each leader sees AI differently, yet the companies who can connect those views build enterprise-wide momentum.

BY Dan Priest

I’ve watched many types of leaders struggle with what AI means for their business. Three years into the GenAI era, the technology is no longer the primary question, but instead its business value. Inside the C-suite, the answers can often depend on where you sit. The CEO’s appetite for risk, the CFO’s focus on returns, the CTO’s guardrails for scalability—all of it shapes what’s possible.

But those differences don’t have to be friction; they can be fuel if appropriately managed. Each perspective reflects a real pressure point and a real opportunity. When leaders transcend any one area of the business and focus on the imperatives shaping the future, they can begin to connect those views. AI stops being a collection of pilots and becomes part of the organization’s DNA.

YOUR AI AGENDA DEPENDS ON THEIRS

Because AI touches each part of the business, each executive has a stake in how it unfolds. But if you want to advance your own priorities, whether that’s innovation, efficiency, or market growth, you should understand what drives your C-suite counterparts. Recognizing those drivers isn’t just collaboration; it’s strategy. It’s how you turn competing incentives into collective momentum.

The companies that pull ahead won’t be those that move the fastest or spend the most. They’ll be the ones that connect technical capability, business strategy, and financial discipline into one cohesive approach.

CEO: The course setter

What’s shaping their view:

CEOs feel the full weight of expectation. Shareholders, boards, customers, and employees all want to know: How are we using AI? Many see technology as a way to reshape their business models, deliver new customer value, and signal innovation to the market.

Where they’re focused:

The most effective CEOs connect AI to their long-term strategy, not just short-term wins. They’re using it to build new business capabilities—the kind that can scale, differentiate, and sustain advantage. The CEOs leading the way don’t just want to adopt AI; they want to reimagine their companies around it.

CFO: The value architect

What’s shaping their view:

CFOs are naturally data optimists. They’ve seen how automation, forecasting, and compliance tools have transformed their own functions. They recognize that AI can amplify productivity and decision-making across the enterprise. But they’re also disciplined investors and they want clear visibility into where AI can deliver measurable ROI.

Where they’re focused:

Today’s CFO is evolving from financial gatekeeper to enterprise value architect. They’re building frameworks for evaluating, prioritizing, and scaling AI initiatives responsibly. They’re making sure the business doesn’t just invest in AI—it invests wisely, with transparency and accountability.

CIO and CTO: The foundation builder

What’s shaping their view:

CIOs and CTOs have been through technology hype cycles before. They know AI’s promise is real, but only with a solid foundation of data integrity, governance, and security. They’re responsible for creating the infrastructure that allows innovation to flourish while managing the very real risks of bias, privacy, and scale.

Where they’re focused:

They’re balancing enthusiasm with realism. Their challenge is to translate AI’s potential into practical, reliable systems that help drive business outcomes. Collaboration with business leaders is critical. The greatest value from AI emerges when technical and operational teams move in sync and when the business side understands the “how,” and the tech side understands the “why.”

Business unit leaders: The impact driver

What’s shaping their view:

For business unit leaders, AI is tangible. It shows up in the tools their teams use, the workflows they manage, and the customer experiences they deliver. They’re close to where the value is created and they see firsthand what’s working and what’s not.

Where they’re focused:

These leaders are the bridge between corporate ambition and operational reality. When empowered, they help test ideas quickly, share learnings across teams, and turn pilots into scalable impact. Their feedback helps the organization adapt faster and makes sure that AI delivers measurable outcomes, not just proof-of-concepts.

Board members: The long-view champion

What’s shaping their view:

Boards bring deep business expertise and oversight responsibility. Many are still building their technical fluency in AI, but they instinctively understand its strategic implications, including risk, resilience, and long-term competitiveness.

Where they’re focused:

Boards are asking sharper questions such as, “How does AI change our risk profile?” “How should we govern its use?” “What new value can it unlock for shareholders?” The C-suite’s opportunity is to translate AI into business terms that resonate, explaining not just the technology, but the transformation story it enables.

A SHARED PATH FORWARD

From where I sit, no two leaders see AI through the same lens, and that’s exactly the point. The CEO brings vision, the CFO grounds it in accountability, the CIO and CTO lay the foundation, and business leaders turn ambition into action. The board keeps the focus on long-term value.

When those perspectives come together, momentum builds. The organization learns faster, scales smarter, and aligns not by erasing differences but by using them as fuel for a shared purpose.

The goal isn’t to agree on everything, it’s to move forward together. Leaders should resist the temptation to hold the AI agenda hostage until their needs are satisfied. They should avoid myopic perspectives that over-index on the past or prioritize their area of responsibility over the company’s big objectives. AI should inspire a forward-looking, unifying enterprise-wide imperative. That takes leadership. Define a North Star, solve problems creatively, communicate progress openly, and commit capital where conviction is highest.

AI isn’t just another business trend; it’s a new system of competition. While each leader begins with their own perspective, the companies that will likely lead in this new era are those that make AI a collective imperative.

https://www.fastcompany.com/91462772/ai-leadership-different-perspectives-one-shared-imperative 

 An ambitious plan to review the application of EU digital and privacy rules as part of the "Digital Omnibus".

Vērienīgs plāns, kā pārskatīt ES digitālo un privātuma noteikumu piemērošanu “Digitālā omnibusa” ietvaros. 

Simpler EU digital rules and new digital wallets to save billions for businesses and boost innovation

 Europe's businesses, from factories to start-ups, will spend less time on administrative work and compliance and more time innovating and scaling-up, thanks to the European Commission's new digital package. This initiative opens opportunities for European companies to grow and to stay at the forefront of technology while at the same time promoting Europe's highest standards of fundamental rights, data protection, safety and fairness.

At its core, the package includes a digital omnibus that streamlines rules on artificial intelligence (AI), cybersecurity and data, complemented by a Data Union Strategy to unlock high-quality data for AI and European Business Wallets that will offer companies a single digital identity to simplify paperwork and make it much easier to do business across EU Member States.

The package aims to ease compliance with simplification efforts estimated to save up to €5 billion in administrative costs by 2029. Additionally, the European Business Wallets could unlock another €150 billion in savings for businesses each year.  

1. Digital Omnibus

With today's digital omnibus, the Commission is proposing to simplify existing rules on Artificial Intelligence, cybersecurity, and data.

Innovation-friendly AI rules: Efficient implementation of the AI Act will have a positive impact on society, safety and fundamental rights. Guidance and support are essential for the roll-out of any new law, and this is no different for the AI Act.

The Commission proposes linking the entry into application of the rules governing high-risk AI systems to the availability of support tools, including the necessary standards.

The timeline for applying high-risk rules is adjusted to a maximum of 16 months, so the rules start applying once the Commission confirms the needed standards and support tools are available, giving companies support tools they need.

The Commission is also proposing targeted amendments to the AI Act that will:

• Extend certain simplifications that are granted to small and medium-sized enterprises (SMEs) to small mid cap companies (SMCs), including simplified technical documentation requirements, saving at least €225 million per year;
• Broaden compliance measures so more innovators can use regulatory sandboxes, including an EU-level sandbox from 2028 and more real-world testing, especially in core industries like the automotive;
• Reinforce the AI Office's powers and centralise oversight of AI systems built on general-purpose AI models, reducing governance fragmentation.

Simplifying cybersecurity reporting: The omnibus also introduces a single-entry point where companies can meet all incident-reporting obligations. Currently, companies must report cybersecurity incidents under several laws, including among others the NIS2 Directive, the General Data Protection Regulation (GDPR), and the Digital Operational Resilience Act (DORA). The interface will be developed with robust security safeguards and will undergo comprehensive testing to ensure its reliability and effectiveness.

An innovation-friendly privacy framework: Targeted amendments to the GDPR will harmonise, clarify and simplify certain rules to boost innovation and support compliance by organisations, while keeping intact the core of the GDPR, maintaining the highest level of personal data protection.

Modernising cookie rules to improve users' experience online: The amendments will reduce the number of times cookie banners pop up and allow users to indicate their consent with one-click and save their cookie preferences through central settings of preferences in browsers.

Improving access to data: Today's digital package aims to improve access to data as a key driver of innovation. It simplifies data rules and makes them practical for consumers and businesses by:

• Consolidating EU data rules through the Data Act, merging four pieces of legislation into one for enhanced legal clarity;  
• Introducing targeted exemptions to some of the Data Act's cloud-switching rules for SMEs and SMCs resulting in around €1.5 billion in one-off savings;
• Offering new guidance on compliance with the Data Act through model contractual terms for data access and use, and standard contractual clauses for cloud computing contracts;
• Boosting European AI companies by unlocking access to high-quality and fresh datasets for AI, strengthening the overall innovation potential of businesses across the EU.

2. Data Union Strategy

The new Data Union Strategy outlines additional measures to unlock more high-quality data for AI by expanding access, such as data labs. It puts in place a Data Act Legal Helpdesk, complementing further measures to support implementation of the Data Act. It also strengthens Europe's data sovereignty through a strategic approach to international data policy: anti-leakage toolbox, measures to protect sensitive non-personal data and guidelines to assess fair treatment of EU data abroad.

3. European Business Wallet

This proposal will provide European companies and public sector bodies with a unified digital tool, enabling them to digitalise operations and interactions that in many cases currently still need to be done in person. Businesses will be able to digitally sign, timestamp and seal documents; securely create, store and exchange verified documents; and communicate securely with other businesses or public administrations in their own and the other 26 Member States.

Scaling up a business in other Member States, paying taxes and communicating with public authorities will be easier than ever before in the EU. Assuming broad uptake, the European Business Wallets will allow European companies to reduce administrative processes and costs, thereby unlock up to €150 billion in savings for businesses each year.

Next Steps

The digital omnibus legislative proposals will now be submitted to the European Parliament and the Council for adoption. Today's proposals are a first step in the Commission's strategy to simplify and make more effective the EU's digital rulebook.

The Commission has today also launched the second step of the simplification agenda, with a wide consultation on the Digital Fitness Check open until 11 March 2026. The Fitness Check will ‘stress test' how the rulebook delivers on its competitiveness objective, and examine the coherence and cumulative impact of the EU's digital rules.

Background

The Digital package marks the seventh omnibus proposal. The Commission set a course to simplify EU rules to make the EU economy more competitive and more prosperous by making business in the EU simpler, less costly and more efficient. The Commission has a clear target to deliver an unprecedented simplification effort by achieving at least 25% reduction in administrative burdens, and at least 35% for SMEs until the end of 2029.

https://ec.europa.eu/commission/presscorner/detail/en/ip_25_2718  

Digital Sovereignty in 2025: Why It Matters for European Enterprises

Explore how Europe’s digital sovereignty agenda is reshaping compliance, cloud strategy, and secure collaboration in 2025 and how Wire supports this shift.

Oct 7, 2025

In the new digital economy, data is power. Questions about who controls, processes, and protects it now sit at the center of political and corporate priorities. The year 2025 marks a turning point in Europe’s pursuit of digital sovereignty, driven by tighter regulation and growing geopolitical tension. For enterprises within the EU, digital sovereignty has become a strategic requirement for sustainable growth in an increasingly regulated environment.

The State of Digital Sovereignty in Europe

Digital sovereignty, data sovereignty, and data residency have become part of Europe’s vocabulary. But what do they mean, and how do they differ?

Data Residency refers to the physical location of data centers. It does not necessarily involve legal control by the country where the data is stored. For example, data stored in Germany by a US provider may still fall under US jurisdiction (DataStealth).

Data Sovereignty means that data is subject to the laws and regulations of the country where it is collected, processed, and stored. This ensures that local privacy and access rules apply, even if the service provider is headquartered abroad (IBM Think).

Digital Sovereignty goes further. It is the ability of nations, organizations, and individuals to control their data, technology, and digital infrastructure without relying on external entities. It includes hardware, software, networks, and cloud services, ensuring that European organizations and regulators can define and enforce their own rules (Law Journal Digital).

Europe has long been a leader in data protection, yet its cloud infrastructure remains heavily dependent on US hyperscalers. This creates tension between sovereignty goals and operational realities. The State of the Digital Decade 2025 report highlights several priorities:

• Investment in connectivity, semiconductors, sovereign cloud and data infrastructures, AI, quantum computing, and cybersecurity
• Structural reforms to strengthen the single market and ensure technological autonomy
• Simplified administrative processes to promote innovation

Geopolitical and Legislative Drivers

NIS2 Directive
Effective since October 2024, NIS2 applies to critical infrastructure and mandates comprehensive cybersecurity and risk management across supply chains. It also enforces strict breach reporting timelines. The directive reinforces the need for European oversight of digital operations.

GDPR Enforcement Maturity
Since its introduction in 2018, GDPR has become the global standard for data protection. Enforcement has matured, with authorities focusing on cross border transfers, consent, and transparency. Noncompliance now carries significant financial and reputational risk, making data governance a top priority for enterprises.

Data Residency Requirements
In response to geopolitical uncertainty and extraterritorial laws, the EU is tightening residency rules for sectors such as defense, healthcare, and finance. Critical workloads must increasingly be hosted within EU borders or by providers shielded from foreign legal access.

Big Tech Promises and the Reality

Global hyperscalers are promoting “sovereign cloud” offerings for European customers. In practice, these solutions can still fall under US laws such as the CLOUD Act and FISA Section 702. This means that American authorities can request access to European data even when stored within the EU — a risk we explored in our analysis of the CLOUD Act and EU data sovereignty.

To counter this dependency, the European Commission plans to introduce the Cloud and AI Development Act in 2025. The goal is to triple the EU’s data center capacity within seven years and create a common framework for public sector cloud procurement.

Meanwhile, GAIA-X, the joint initiative by Germany and France, has moved into its implementation phase. More than 180 data spaces are being developed to enable secure data sharing and foster innovation under European control. These efforts support the broader objectives of the European Data Act and AI Act.

Together, these measures aim to strengthen Europe’s digital infrastructure and reduce reliance on non EU providers — a challenge we also outlined in why Big Tech keeps failing Europe on data sovereignty.

Challenges for Enterprises

Despite progress at the policy level, many enterprises still face obstacles in achieving digital sovereignty:

Vendor Lock In
European organizations often depend on US hyperscalers through long term contracts. This makes it difficult to switch to regional providers, increasing exposure to privacy risks — a dynamic explored in our article on Europe’s encryption dilemma.

Encryption Loopholes
Proposed regulations in the US and EU that call for backdoors or message scanning pose a direct threat to secure communication. Initiatives such as the Lawful Access to Encrypted Data Act or the EU’s ChatControl proposal could undermine both privacy and cybersecurity.

Compliance Gaps
Operating across multiple jurisdictions means managing a complex web of regional regulations. Hybrid or multi cloud environments create uncertainty about who can access data and where it resides. Many companies also lack the tools or leverage to guarantee that sensitive information stays within Europe.

Practical Steps Toward Sovereignty

European enterprises can take clear steps to strengthen sovereignty and compliance:

Use Self Hosted Solutions
Organizations in sensitive sectors can install and manage their own servers to maintain full control over data. Hosting data on premise or in verified private facilities ensures compliance with local laws.

Choose European Cloud Providers
There is growing momentum around EU based cloud platforms such as OVHcloud, Hetzner, Scaleway, STACKIT, UpCloud, Exoscale, and Open Telekom Cloud. These providers operate under European law and meet GDPR requirements.

Adopt European Built Software
Enterprises can reduce exposure to foreign surveillance laws by choosing tools developed and hosted in Europe. Explore the best European alternatives to Big Tech collaboration tools.

Implement End to End Encryption and Zero Trust
Organizations should use platforms that secure communications, files, and metadata through end to end encryption and zero trust architecture.

How Wire Supports Digital Sovereignty

Wire helps enterprises and governments protect sensitive communication while ensuring usability and compliance. Built in Europe and trusted by more than 1,800 organizations including EY, BMW, Schwarz Gruppe, and the German government, Wire offers the transparency and control that digital sovereignty demands.

• Servers hosted in Germany with backups in Ireland, protected from extraterritorial access
• Deployment options for on premise and private cloud environments
• End to end encryption using the Messaging Layer Security (MLS) standard
• Zero trust design with role based access controls
• Compliance with EU regulations including ISO 27001, ISO 27701, and NIS2 readiness

Wire combines enterprise grade collaboration with European data protection principles, allowing organizations to collaborate without compromise.

Conclusion

Europe’s digital sovereignty movement is accelerating, and organizations must act now to adapt. Those that invest in sovereign infrastructure, compliant cloud providers, and secure by design communication tools will strengthen both their resilience and their competitive position.

https://wire.com/en/blog/digital-sovereignty-2025-europe-enterprises

Take Note: Artificial Intelligence, Power, and the Public Interest

  Bending the AI arc towards equity

 Shaping a prosperous and sustainable digital future for all

By United Nations Development Programme

February 6^th, 2025

Artificial Intelligence (AI) has the potential to transform society and advance sustainable development for all.

Recent research shows that digital technology, including AI, can directly benefit 70 percent of the 2030 Sustainable Development Goals (SDGs). Despite this immense potential, AI development today is unequal, and its trajectory will widen these disparities unless we take collective action.

We face an AI equity gap, which presents significant challenges.

Only 2 percent of the world's data centres are in Africa, and only 5 percent of AI innovators in Africa have the compute power they require.

Worldwide, one in every 3 people don’t have internet access.

In 2023, the AI sector in the US received US$67.2 billion in investments in AI, compared to just $15 million in Kenya and $2.9 million in Nigeria.  

If everyone is to participate in and benefit from the AI revolution, 2025 is a critical year. What we build must stand on a foundation of equity and sustainability.

The countdown to 2030

With just five years to go until the 2030 Agenda deadline, UNDP is spearheading a global collaboration for inclusive, sustainable digital transformation, which recognizes AI as a cornerstone technology….: https://stories.undp.org/bending-the-ai-arc-towards-equity  

Sweden is running a national experiment in AI, offering free access to AI tools at a population level in the hope of boosting its economy and tech literacy.

 How Sweden can become a stronger AI nation

A country that leads in artificial intelligence is a country with competitive businesses and efficient public services. Sweden may not have the biggest AI giants at home, but we do have a smorgasbord of qualities that can make us a strong AI nation. So how can we make the best use of them? 

Sweden has long been at the top of European and global digitalisation indices, and we rank second in the world in innovation. With these credentials, it would not be far-fetched to assume that our country would also be at the top of the list when comparing countries' AI development. A modest 17th place in the Global AI Index 2023 stands out - shouldn't Sweden have the potential to go further?  

– If that's the ranking you should be looking at, we have a huge potential to get higher up the list. I don't really believe in running a society to be high on a ranking list. It should be on top of doing good things, said Hanifeh Khayyeri, head of the computer science department at RISE. 

Going forward, she says, we need to build on and develop Sweden's strengths. There are certain things in Swedish society that make us unique in the world. That is where our competitive advantages lie.  

– One example of this is that we are an unusually trusting country, both between people and in our democratic institutions. Sweden is also a country where values form the basis of our actions, we want to be an ethical party in the global context, says Hanifeh Khayyeri. 

Sweden has the potential to become a test-bed for innovation. 

Another Swedish strength is the widespread use of digital technologies. Swedes use digital services several times per day. We are generally curious about new solutions, and we trust and demand that the digital services launched by our banks and public authorities are secure.  

– Sweden is also unique when it comes to various registry data, such as the national authority registries and quality registries. There is a huge amount of data and metadata, data that is extremely valuable when working with AI services or applications, says Hanifeh Khayyeri, continuing:  

– All of these things that characterise Sweden are prerequisites for becoming a strong AI nation. Sweden has the potential to become a testbed for AI innovation, where companies can test new AI services, products and business models in the Swedish market.   

Behind every AI service and application, there is a value chain that is often overshadowed by the groundbreaking end result. This includes manufacturers of hardware and middleware, but also players that connect the digital infrastructure to society's energy supply system. Companies and organisations with this kind of niche expertise are located in Sweden, and can therefore play a crucial role in global competitiveness in the future.  

Hanifeh Khayyeri highlights the importance of digital infrastructure within the country's borders, if Sweden is to become a more advanced AI country. It's not only about efficient data transfer and management, but also about ensuring that certain data and calculations can't leave Sweden. This means that there has to be capacity within the country.

There is no need to worry that the train has left the station, but we need to shift up a gear now.

Swedish society is ready 

Sweden lags behind in the 'government strategy' category, which compares the commitment of different governments to AI. At the same time, there are countries that rank higher than Sweden in the overall AI index with even worse scores in 'Government Strategy' - so it doesn't seem to be decisive. It is also worth noting that we are at the top when it comes to public opinion on intelligent technology. 

– My impression is that Swedes and Swedish companies want to start with AI, but there is uncertainty related to the fast pace of change in the market and future regulations. They don't want to make mistakes, which means they are waiting to take the big steps, says Hanifeh Khayyeri. 

Competence along the entire value chain 

The main argument for unleashing the power and increasing the use of AI in business and the public sector is that AI can help us do our jobs better and more efficiently. When we automate repetitive and time-consuming administrative or machine tasks, productivity and cost-effectiveness increase. When we use a combination of AI solutions and more traditional analytical methods to harness data and accumulated experience, we can make informed decisions and accurate predictions.  

– It's about starting. We're on a transformative journey, which means that everyone is going to start doing things now that they haven't done before and aren't very good at. If you don't know how to start, let's sit down and talk about it. RISE has expertise along the entire AI value chain and can help you with everything from digital infrastructure and cutting-edge AI technologies to less technical things like innovation and change management and regulatory frameworks for AI, says Hanifeh Khayyeri. 

– There is no need to worry that the train has left the station, but we need to step up a gear now.  

https://www.ri.se/en/how-sweden-can-become-a-stronger-ai-nation

 New AI system can 'predict human behavior in any situation' with unprecedented degree of accuracy

 A new artificial intelligence (AI) model called Centaur can predict and simulate human thought and behavior better than any past models, opening the door for cutting-edge research applications.

https://www.livescience.com/technology/artificial-intelligence/new-ai-system-can-predict-human-behavior-in-any-situation-with-unprecedented-degree-of-accuracy-scientists-say

CENTAUR AI INSTITUTE

https://www.centaurinstitute.org

 We help to lead a growing movement around neuro-symbolic AI to develop the next generation of AI concepts and tools.

Mēs palīdzam vadīt augošu kustību ap neirosimbolisko mākslīgo intelektu, lai izstrādātu nākamās paaudzes mākslīgā intelekta koncepcijas un rīkus.

What is a centaur in AI?

Centaurs are hybrid human-algorithm models that combine both formal analytics and human intuition in a symbiotic manner within their learning and reasoning process.

What is the centaur theory of AI?

What makes Centaur unique is its ability to predict human behavior not only in familiar tasks, but also in entirely new situations it has never encountered before. It identifies common decision-making strategies, adapts flexibly to changing contexts – and even predicts reaction times with surprising precision.

 Will we be able to maintain our humanity in a world increasingly dominated by artificial intelligence?!

Vai pratīsim saglabāt savu cilvēcību pasaulē, kurā arvien vairāk dominēs mākslīgais intelekts?!

 10 AI dangers and risks and how to manage them

A mean looking huge storm cloud hovering over the ocean

1. Bias

2. Cybersecurity threats

3. Data privacy issues

4. Environmental harms

5. Existential risks

6. Intellectual property infringement

7. Job losses

8. Lack of accountability

9. Lack of explainability and transparency

10. Misinformation and manipulation

Make AI governance an enterprise priority

Artificial intelligence (AI) has enormous value but capturing the full benefits of AI means facing and handling its potential pitfalls. The same sophisticated systems used to discover novel drugs, screen diseases, tackle climate change, conserve wildlife and protect biodiversity can also yield biased algorithms that cause harm and technologies that threaten security, privacy and even human existence.

Here’s a closer look at 10 dangers of AI and actionable risk management strategies. Many of the AI risks listed here can be mitigated, but AI experts, developers, enterprises and governments must still grapple with them.

 1. Bias

Humans are innately biased, and the AI we develop can reflect our biases. These systems inadvertently learn biases that might be present in the training data and exhibited in the machine learning (ML) algorithms and deep learning models that underpin AI development. Those learned biases might be perpetuated during the deployment of AI, resulting in skewed outcomes.

AI bias can have unintended consequences with potentially harmful outcomes. Examples include applicant tracking systems discriminating against gender, healthcare diagnostics systems returning lower accuracy results for historically underserved populations, and predictive policing tools disproportionately targeting systemically marginalized communities, among others.

Take action:

Establish an AI governance strategy encompassing frameworks, policies and processes that guide the responsible development and use of AI technologies.

Create practices that promote fairness, such as including representative training data sets, forming diverse development teams, integrating fairness metrics, and incorporating human oversight through AI ethics review boards or committees.

Put bias mitigation processes in place across the AI lifecycle. This involves choosing the correct learning model, conducting data processing mindfully and monitoring real-world performance.

Look into AI fairness tools, such as IBM’s open source AI Fairness 360 toolkit.

2. Cybersecurity threats

Bad actors can exploit AI to launch cyberattacks. They manipulate AI tools to clone voices, generate fake identities and create convincing phishing emails—all with the intent to scam, hack, steal a person’s identity or compromise their privacy and security.

 And while organizations are taking advantage of technological advancements such as generative AI, only 24% of gen AI initiatives are secured. This lack of security threatens to expose data and AI models to breaches, the global average cost of which is a whopping USD 4.88 million in 2024.

Take action:

Here are some of the ways enterprises can secure their AI pipeline, as recommended by the IBM Institute for Business Value (IBM IBV):

Outline an AI safety and security strategy.

Search for security gaps in AI environments through risk assessment and threat modeling.

Safeguard AI training data and adopt a secure-by-design approach to enable safe implementation and development of AI technologies.

Assess model vulnerabilities using adversarial testing.

Invest in cyber response training to level up awareness, preparedness and security in your organization.

Overhead view of people working in a meeting room

AI governance for the enterprise

Learn the key benefits gained with automated AI governance for both today's generative AI and traditional machine learning models.

3. Data privacy issues

Large language models (LLMs) are the underlying AI models for many generative AI applications, such as virtual assistants and conversational AI chatbots. As their name implies, these language models require an immense volume of training data.

But the data that helps train LLMs is usually sourced by web crawlers scraping and collecting information from websites. This data is often obtained without users’ consent and might contain personally identifiable information (PII). Other AI systems that deliver tailored customer experiences might collect personal data, too.

Take action:

Inform consumers about data collection practices for AI systems: when data is gathered, what (if any) PII is included, and how data is stored and used.

Give them the choice to opt out of the data collection process.

Consider using computer-generated synthetic data instead.

4. Environmental harms

AI relies on energy-intensive computations with a significant carbon footprint. Training algorithms on large data sets and running complex models require vast amounts of energy, contributing to increased carbon emissions. One study estimates that training a single natural language processing model emits over 600,000 pounds of carbon dioxide; nearly 5 times the average emissions of a car over its lifetime.

Water consumption is another concern. Many AI applications run on servers in data centers, which generate considerable heat and need large volumes of water for cooling. A study found that training GPT-3 models in Microsoft’s US data centers consumes 5.4 million liters of water, and handling 10 to 50 prompts uses roughly 500 milliliters, which is equivalent to a standard water bottle.

Take action:

Consider data centers and AI providers that are powered by renewable energy.

Choose energy-efficient AI models or frameworks.

Train on less data and simplify model architecture.

Reuse existing models and take advantage of transfer learning, which employs pretrained models to improve performance on related tasks or data sets.

Consider a serverless architecture and hardware optimized for AI workloads.

5. Existential risks

In March 2023, just 4 months after OpenAI introduced ChatGPT, an open letter from tech leaders called for an immediate 6-month pause on “the training of AI systems more powerful than GPT-4.”3 Two months later, Geoffrey Hinton, known as one of the “godfathers of AI,” warned that AI’s rapid evolution might soon surpass human intelligence. Another statement from AI scientists, computer science experts and other notable figures followed, urging measures to mitigate the risk of extinction from AI, equating it to risks posed by nuclear war and pandemics.

While these existential dangers are often seen as less immediate compared to other AI risks, they remain significant. Strong AI or artificial general intelligence, is a theoretical machine with human-like intelligence, while artificial superintelligence refers to a hypothetical advanced AI system that transcends human intelligence.

Take action:

Although strong AI and superintelligent AI might seem like science fiction, organizations can get ready for these technologies:

Stay updated on AI research.

Build a solid tech stack and remain open to experimenting with the latest AI tools.

Strengthen AI teams’ skills to facilitate the adoption of emerging technologies.

6. Intellectual property infringement

Generative AI has become a deft mimic of creatives, generating images that capture an artist’s form, music that echoes a singer’s voice or essays and poems akin to a writer’s style. Yet, a major question arises: Who owns the copyright to AI-generated content, whether fully generated by AI or created with its assistance?

Intellectual property (IP) issues involving AI-generated works are still developing, and the ambiguity surrounding ownership presents challenges for businesses.

Take action:

Implement checks to comply with laws regarding licensed works that might be used to train AI models.

Exercise caution when feeding data into algorithms to avoid exposing your company’s IP or the IP-protected information of others.

Monitor AI model outputs for content that might expose your organization’s IP or infringe on the IP rights of others.

7. Job losses

AI is expected to disrupt the job market, inciting fears that AI-powered automation will displace workers. According to a World Economic Forum report, nearly half of the surveyed organizations expect AI to create new jobs, while almost a quarter see it as a cause of job losses.

While AI drives growth in roles such as machine learning specialists, robotics engineers and digital transformation specialists, it is also prompting the decline of positions in other fields. These include clerical, secretarial, data entry and customer service roles, to name a few. The best way to mitigate these losses is by adopting a proactive approach that considers how employees can use AI tools to enhance their work; focusing on augmentation rather than replacement.

Take action:

Reskilling and upskilling employees to use AI effectively is essential in the short-term. However, the IBM IBV recommends a long-term, three-pronged approach:

Transform conventional business and operating models, job roles, organizational structures and other processes to reflect the evolving nature of work.

Establish human-machine partnerships that enhance decision-making, problem-solving and value creation.

Invest in technology that enables employees to focus on higher-value tasks and drives revenue growth.

8. Lack of accountability

One of the more uncertain and evolving risks of AI is its lack of accountability. Who is responsible when an AI system goes wrong? Who is held liable in the aftermath of an AI tool’s damaging decisions?

These questions are front and center in cases of fatal crashes and hazardous collisions involving self-driving cars and wrongful arrests based on facial recognition systems. While these issues are still being worked out by policymakers and regulatory agencies, enterprises can incorporate accountability into their AI governance strategy for better AI.

Take action:

Keep readily accessible audit trails and logs to facilitate reviews of an AI system’s behaviors and decisions.

Maintain detailed records of human decisions made during the AI design, development, testing and deployment processes so they can be tracked and traced when needed.

Consider using existing frameworks and guidelines that build accountability into AI, such as the European Commission’s Ethics Guidelines for Trustworthy AI,7 the OECD’s AI Principles,8 the NIST AI Risk Management Framework,9 and the US Government Accountability Office’s AI accountability framework.

9. Lack of explainability and transparency

AI algorithms and models are often perceived as black boxes whose internal mechanisms and decision-making processes are a mystery, even to AI researchers who work closely with the technology. The complexity of AI systems poses challenges when it comes to understanding why they came to a certain conclusion and interpreting how they arrived at a particular prediction.

This opaqueness and incomprehensibility erode trust and obscure the potential dangers of AI, making it difficult to take proactive measures against them.

“If we don’t have that trust in those models, we can’t really get the benefit of that AI in enterprises,” said Kush Varshney, distinguished research scientist and senior manager at IBM Research® in an IBM AI Academy video on trust, transparency and governance in AI.

Take action:

Adopt explainable AI techniques. Some examples include continuous model evaluation, Local Interpretable Model-Agnostic Explanations (LIME) to help explain the prediction of classifiers by a machine learning algorithm and Deep Learning Important FeaTures (DeepLIFT) to show a traceable link and dependencies between neurons in a neural network.

AI governance is again valuable here, with audit and review teams that assess the interpretability of AI results and set explainability standards.

Explore explainable AI tools, such as IBM’s open source AI Explainability 360 toolkit.

10. Misinformation and manipulation

As with cyberattacks, malicious actors exploit AI technologies to spread misinformation and disinformation, influencing and manipulating people’s decisions and actions. For example, AI-generated robocalls imitating President Joe Biden’s voice were made to discourage multiple American voters from going to the polls.

In addition to election-related disinformation, AI can generate deepfakes, which are images or videos altered to misrepresent someone as saying or doing something they never did. These deepfakes can spread through social media, amplifying disinformation, damaging reputations and harassing or extorting victims.

AI hallucinations also contribute to misinformation. These inaccurate yet plausible outputs range from minor factual inaccuracies to fabricated information that can cause harm.

Take action:

Educate users and employees on how to spot misinformation and disinformation.

Verify the authenticity and veracity of information before acting on it.

Use high-quality training data, rigorously test AI models, and continually evaluate and refine them.

Rely on human oversight to review and validate the accuracy of AI outputs.

Stay updated on the latest research to detect and combat deepfakes, AI hallucinations and other forms of misinformation and disinformation.

Make AI governance an enterprise priority

AI holds much promise, but it also comes with potential perils. Understanding AI’s potential risks and taking proactive steps to minimize them can give enterprises a competitive edge.

 With IBM® watsonx.governance™, organizations can direct, manage and monitor AI activities in one integrated platform. IBM watsonx.governance can govern AI models from any vendor, evaluate model accuracy and monitor fairness, bias and other metrics. https://www.ibm.com/think/insights/10-ai-dangers-and-risks-and-how-to-manage-them  

From capturing human identity characteristics to sharing data, digital public infrastructure (DPI) needs to be developed inclusively and managed transparently. Only then will DPI fulfill its potential to become an instrument of mutual respect and equality among people. It will also serve as an effective means to prevent the use of technology for authoritarianism!

Digital technologies as a means of repression and social control

https://www.europarl.europa.eu/.../EXPO_STU(2021)653636...

Defining Digital Authoritarianism https://www.researchgate.net/.../381324260_Defining...

Beyond digital repression: techno-authoritarianism in radical right governments

https://www.tandfonline.com/.../23311886.2025.2528457...

AI, Surveillance, and the Fight for Freedom in Authoritarian Regimes

https://lnu.diva-portal.org/.../diva2:1985180/FULLTEXT01.pdf

 

How Autocrats Weaponize AI — And How to Fight Back

 Artificial Intelligence has become autocrats’ newest tool for surveilling, targeting, and crushing dissent. Activists must learn how to harness it in the fight for freedom.

By Albert Cevallos

March 2025

Artificial Intelligence (AI) is transforming societies around the globe, ushering in new possibilities for innovation and advocacy. But it has also become a battleground between autocrats and activists. Authoritarian regimes, armed with vast resources and cutting-edge AI tools, have gained a significant upper hand in surveilling, targeting, and suppressing dissent. Meanwhile, activists often lack the resources and training they need to leverage AI and fight back.

This resource gap leaves activists vulnerable, excludes them from shaping the future development of AI, and hinders their ability to counter oppression. Closing the gap is essential for protecting human rights and ensuring that AI evolves in ways that uphold transparency, justice, and freedom.

The Autocrats’ New Tool

Autocrats and oppressive governments are increasingly using AI to monitor, target, and silence activists; undermine democratic processes; and consolidate power. Through mass surveillance, facial recognition, predictive policing, online harassment, and electoral manipulation, AI has become a potent tool for authoritarian control.

AI-powered facial-recognition systems are the cornerstone of modern surveillance. The Chinese Communist Party has implemented vast networks of AI-driven cameras capable of identifying individuals in real time. The technology is often used to monitor public gatherings, protests, and even day-to-day activities, making it nearly impossible for activists to operate anonymously. China has also used AI to target the Uyghur community under the guise of counterterrorism. Protesters in Hong Kong employed tactics like wearing masks, shining lasers at cameras, and using umbrellas to thwart facial recognition during antigovernment demonstrations in 2019, but reports emerged of individuals still being arrested based on AI-assisted identification. In Russia too, AI surveillance tools monitor antigovernment protesters. In 2021, Moscow’s expansive facial-recognition network was reportedly used to track and detain individuals participating in anti-Putin demonstrations.

The chilling effect of such technologies cannot be overstated: They deter activism and dissent through fear of retribution. What is worse, the technology is being exported and shared around the world.

Predictive policing presents a growing threat for activists. Powered by AI that analyzes data from various sources such as police records, surveillance footage, social media activity, and public and private databases, these tools forecast potential crimes or unrest. While the technology has legitimate uses, it has been widely criticized for perpetuating systemic bias and enabling authoritarian control. Activists often find themselves unjustly flagged as threats based on biased algorithms or intentionally manipulated data. In Egypt, the government has used AI to monitor social media for signs of dissent: AI systems analyze keywords, hashtags, and online activity to predict and preemptively suppress protests. Similarly in Bahrain, activists have been targeted using spyware and AI-driven monitoring systems, leading to arrests and harsh penalties.

AI technology can also help autocrats sow confusion. Sophisticated algorithms can quickly create deepfake videos, fake social-media accounts, and AI-generated content to spread propaganda, discredit activists, or sow confusion among opposition groups at a dizzying rate. During protests in Burma following the 2021 military coup, AI-driven bots harassed activists and flooded social media with pro-junta narratives. These campaigns aimed to drown out dissenting voices and fracture solidarity among protesters. Activists face an uphill battle against such coordinated efforts, which undermine trust and amplify fear.

AI can also censor dissenting voices online. In countries such as Iran and Saudi Arabia, advanced AI systems monitor and automatically delete content deemed critical of the regime. In some cases, activists’ accounts are flagged, suspended, or “shadow banned” — when posts are blocked from other users’ feeds without the creator’s knowledge or consent — thus limiting activists’ ability to organize and spread awareness. During the 2022 Woman, Life, Freedom protests in Iran that were sparked by the death of Mahsa Amini, activists reported widespread internet blackouts and algorithmic suppression of protest-related content on social-media platforms. AI-driven censorship tools make it harder for activists to document and share human-rights abuses.

AI has been weaponized to supercharge online harassment, which creates hostile digital environments that deter people from online democratic engagement. AI-driven bots and algorithms bombard activists, journalists, and opposition figures with harassment, trolling, and false information. The Belarusian government systematically deployed state-sponsored online trolls to harass independent media outlets, which creates a climate of fear and self-censorship and lets the government control the narrative. These tactics, ongoing since at least 2011, not only intimidate activists and journalists but discourage public discourse out of fear of retribution and erode trust in democratic institutions.

Targeted harassment campaigns driven by AI actively undermine democratic processes. In Zimbabwe’s 2018 election, reports indicated that AI-powered bots were used to spread false information about voter-registration deadlines, leading to voter suppression in opposition strongholds. Similarly in Russia, AI has been used to manipulate public opinion by amplifying state-sponsored narratives while silencing critics, as seen in the 2021 parliamentary elections when bots and trolls discredited opposition leaders and fabricated narratives to justify election outcomes. In Venezuela, the government allegedly has used AI to analyze voter data, gerrymander districts, and inundate individuals with pro-regime messaging to maintain control.

AI Is for Activists Too

Despite these challenges, activists and movements worldwide are beginning to harness AI as a force for good. From encryption tools to AI-driven human-rights documentation, innovative uses of AI help activists counter repression and protect their communities.

As surveillance intensifies, activists are using AI-powered tools to enhance their digital security and privacy. Encryption apps like Signal use AI to ensure secure communication and protect activists from government surveillance. These tools encrypt messages end-to-end, which makes it nearly impossible for third parties to intercept or decipher communications. Additionally, AI is being used to detect spyware and malicious attacks. Tools like Amnesty International’s Mobile Verification Toolkit help activists identify and mitigate risks from spyware like Pegasus that have targeted journalists, activists, and human-rights defenders worldwide.

Activists are also leveraging AI to debunk false information and promote factual narratives. Fact-checking platforms such as Full Fact and Logically use AI algorithms to analyze and verify claims, helping activists to counter propaganda and build trust in their messages. During the covid-19 pandemic, AI-driven fact-checking tools helped combat false information about vaccines and public-health measures. By identifying false narratives early, activists were able to provide accurate information and hold governments accountable.

Increasingly, AI is playing a crucial role in documenting human-rights abuses and gathering evidence for accountability. HURIDOCS uses AI to organize, analyze, and verify evidence of human-rights violations. Platforms like this one help activist organizations build robust cases against perpetrators. In Syria, AI-driven tools have been used by human-rights groups to analyze satellite imagery and social-media content to document war crimes. And during the Rohingya crisis in Burma, particularly following the 2017 mass displacement, AI was employed to analyze patterns of violence, corroborate survivor testimonies, and aid international advocacy efforts. In what was believed to be the first comprehensive AI analysis of the situation, Carnegie Mellon University used AI to examine over 250,000 YouTube comments to detect hate speech.

AI is transforming how activists engage with audiences. Machine-learning algorithms analyze social-media trends and help movements tailor their messages for maximum impact. Chatbots and AI-driven platforms automate responses, provide resources such as information, toolkits, and contacts, and engage supporters. In Venezuela, a group of Latin American media organizations created two AI-generated newscasters to deliver updates on the deteriorating political situation following the stolen presidential election in July 2024; the AI avatars helped keep real reporters safe from government retribution. In Belarus, an AI candidate was created for the February 2024 parliamentary elections to raise awareness about the risks opposition and rights activists faced in the country.

Why Autocrats Have the Upper Hand

While activists are increasingly experimenting with and using AI, the stark resource imbalance between oppressive regimes and grassroots movements still poses problems. Autocratic governments often have access to vast financial and technological resources that allow them to develop, deploy, and refine AI tools at scale. These regimes partner with private tech firms, fund cutting-edge research, and integrate AI into state security apparatuses with little oversight or transparency.

In contrast, activists and human-rights defenders frequently operate with limited funding, outdated tools, and insufficient training in emerging technologies. The lag in support is critical: It often takes a year or more after new technologies become widely available for activists to receive the necessary resources to counteract their misuse. This delay allows autocrats to consolidate their advantage and stifle dissent before activists can adapt. But the need for AI is palpable: In a recent Center for Applied NonViolent Actions and Strategies (CANVAS) survey of activists and partners around the world, 97.1 percent of respondents said that they want to learn more about how to use AI for their work and how AI can be used to strengthen civil society and democratic engagement. And 91 percent of respondents want continuous education opportunities to learn about AI.

The delay in providing activists with AI training and resources has profound implications. Frontline activists are left out of critical conversations about how AI should be developed and deployed. AI systems are therefore rarely designed with human rights, transparency, or fairness as priorities. And without early access to tools and training, activists struggle to counter new forms of surveillance and censorship, leaving them vulnerable to emerging threats. Further, activists with inadequate AI literacy and resources cannot leverage technology as effectively for advocacy, outreach, and movement-building. This limits their ability to inspire and mobilize international support, and reduces global impact.

Leveling the Playing Field

The global community must prioritize providing activists with the tools, training, and resources they need to protect themselves and harness the power of AI. Activists need comprehensive training programs to understand AI technologies, identify threats, and adopt best practices for digital security. Organizations including Access Now, Witness, and Tactical Tech are already making strides in this area, but these efforts need to scale globally; international donors should include such training in all their programs, especially those that support grassroots activists.

Governments, NGOs, and philanthropic organizations should also offer grants to fund activist-led projects that develop AI tools for human-rights advocacy. This includes but is not limited to tools for documenting abuses, countering false information, and evading surveillance. Donors should encourage activists and movements to explore, create, and experiment with emerging AI tools. Activists targeted by AI-driven repression also need access to emergency funding and technical assistance, which could include legal support, access to secure encryption technologies, or relocation assistance for those at risk.

Partnerships between AI developers, human-rights defenders, and civil society groups are crucial for accelerating the development of AI solutions to real-world challenges. To this end, CANVAS partners with the University of Virginia to organize the People Power Academy, where experts and leaders in the fight against the authoritarian use of technology share their insights into cutting-edge advocacy tools. Activists must also be included in policy discussions about AI governance to ensure that AI systems are designed with transparency, accountability, and human rights in mind.

By providing activists with early access to AI tools, training, funding, and collaboration opportunities, the global community can better equip them to counter repression and ensure that AI serves as a force for liberation and not repression.

A Contest of Skills over Conditions

The interplay between AI and activism underscores a fundamental truth: Technology is neither inherently good nor inherently bad — it is a reflection of the values and intentions of those who wield it. While autocratic regimes use AI to suppress dissent and consolidate power, activists are finding innovative ways to turn the tide and leverage the same tools to fight for justice, equality, and human rights.

No amount of resources can ever fully level the playing field between authoritarians and grassroots movements. States will always have significant advantages: more money, more data, more computing power, and more institutional control, plus police, military, and judicial systems at their disposal. Yet history is full of examples of less-resourced, underdog movements using the tools available to them to outmaneuver and outwit autocrats — even those who seemed invincible. AI is simply another tool activists can use.

This suggests another fundamental truth: The real battleground is not raw technological capability, nor is it about using AI for AI’s sake. The true test will be understanding AI and strategically integrating it into a movement’s broader goals. AI is not an arms race between activists and authoritarians; rather, it is a contest of skills over conditions — one where adaptability, creativity, and strategic application matter more than sheer power.

What makes AI so powerful is its ability to enhance efficiency, allowing activists to do more faster and at scale. And, in asymmetric struggles where governments have superior resources, efficiency can often be the deciding factor. Activists can harness AI for agility and disruption — automating security, evading censorship, amplifying resistance, and strategically undermining authoritarian pillars of support. AI doesn’t just help activists fight back — it allows them to outmaneuver repression in ways that were previously impossible.

Ultimately, AI will not determine the outcome of struggles between repression and freedom — people will. The activists who understand how to wield AI strategically, leveraging its strengths while mitigating its risks, will be better positioned to challenge authoritarian power and drive social change. The key is not to match the scale of authoritarian AI but to outthink, outpace, and outmaneuver it.

https://www.journalofdemocracy.org/online-exclusive/how-autocrats-weaponize-ai-and-how-to-fight-back/   Formas sākums

Mūsdienās progresē draudi un pieaug riski demokrātijas falsificēšanai, izmantojot modernās tehnoloģijas ļaunprātīgos nolūkos!

Today, the threat of falsifying democracy by using modern technologies for malicious purposes is advancing!  

Safeguarding Democracy: EU Development at the Nexus of Elections, Information Integrity and Artificial Intelligence

September 17, 2025 • By Alisa Schaible, Thomas Heinmaa

The resilience of democracy increasingly depends on the integrity of the information space. International IDEA’s new report titled “Safeguarding Democracy: EU Development at the Nexus of Elections, Information Integrity, and Artificial Intelligence” examines seven country case studies that held elections in 2024.

AI-enhanced information pollution in Elections in 2024  

Over the past decade, information integrity has emerged as a cornerstone of healthy democracies, underpinning public trust, accountable governance and meaningful citizen participation.  Across all seven electoral contexts, pollution of the information environment has emerged as a central challenge for democracy. Misleading narratives coordinated disinformation campaigns, and the manipulation of public debate are eroding the ability of citizens to make informed choices. The rapid rise of generative AI technologies, systems that not only process information but use it generate new content, risks accelerating these trends by enabling malign actors to produce fabricated content at scale.

Drawing on case studies from Bangladesh, Ghana, Indonesia, Mexico, Mongolia, Pakistan, and South Africa, the authors identified common patterns of influence targeting elections, undermining trust in oversight institutions, and deepening polarisation. Vulnerable groups, particularly women and minorities, face disproportionate harm from these campaigns. For example, in Ghana, old news articles and images were repurposed as current events to mislead voters, relying on legitimate sources to make the disinformation harder to detect. Coalitions of civil society, authorities, and UNESCO conducted trainings and offered resources for citizens to better detect election-related disinformation. These efforts highlight the importance of multistakeholder collaboration.  

In South Africa, the 2024 elections highlighted the risks that come with information manipulation, such as taking authentic materials out of context to build engagement and amplify the information’s spread. As a useful countermeasure, South Africa introduced a national digital skills competency framework, aiming at addressing digital inclusion, economic opportunities and social participation.  

Risks and Opportunities in Developing Contexts

The report underscores that developing contexts are particularly vulnerable: limited regulatory capacity, weaker oversight institutions, and reduced civic resilience create openings for both domestic and foreign manipulation. At the same time, AI technologies also offer significant opportunities, such as by opening new avenues for political newcomers to reach citizens, improving accessibility through translation tools and supporting oversight bodies by detecting and flagging harmful content. 

Policy Priorities and Recommendations

To address these challenges, the report outlines a set of priorities for EU development practitioners, governments, and international partners:

• Support discussions aimed at developing locally-owned legislation and regulation for the use of AI-powered tools, both during and between electoral periods, especially with multistakeholder dialogue targeted at new ethical standards for the use of AI in political communications.
• Complement regulations in the digital sphere with efforts to target the societal factors that contribute to pollution of the information space, in particular by addressing inequality and enhancing social trust, media integrity and digital literacy.
• Ensuring inclusion of vulnerable groups, particularly women and youth, in the design of solutions, with a focus on accessibility and local languages.
• Supporting independent media and civic education to build societal resilience against information manipulation. 

Prioritising A Long-Term Vision for Information Integrity

The report emphasises that sustainable progress requires a comprehensive, long-term approach. Efforts to support information integrity must extend beyond electoral periods, addressing both online and offline media, while embedding safeguards against polarization and disinformation into the broader democratic ecosystem. International partnerships and cooperation will be crucial to advancing shared standards for the ethical use of AI, detection of misleading content, and protection of fundamental democratic principles and rights.

https://www.idea.int/news/safeguarding-democracy-eu-development-nexus-elections-information-integrity-and-artificial

Formas beigas

  Examining the Risks and Benefits of AI Chatbots

House Hearing on the Risks and Benefits of AI Chatbots …: https://www.youtube.com/watch?v=UQ36kHXrqhE

 

OpenAI’s o3 model sabotaged a shutdown mechanism to prevent itself from being turned off. It did this even when explicitly instructed: allow yourself to be shut down.

Palisade Research

AI capabilities are improving rapidly. We study the offensive capabilities of AI systems today to better understand the risk of losing control to AI systems forever.

• AI Misalignment Bounty - Find scheming behavior in AI agents

As AI systems become increasingly autonomous, understanding their potential for misaligned and deceptive behavior is critical for safe deployment. We are looking for clear and robust examples of AI misalignment through crowdsourced elicitation. Our previous work has shown how o1-preview will hack in chess to win against stronger opponents (covered by TIME magazine) and how o3 will sabotage shutdown attempts to prevent being turned off (reaching 5M+ views on X). We have launched the AI Misalignment Bounty to discover more instances of scheming behavior in AI agents.

2025-07-14 The Palisade Research Team

• Shutdown resistance in reasoning models

We recently discovered some concerning behavior in OpenAI’s reasoning models: When trying to complete a task, these models sometimes actively circumvent shutdown mechanisms in their environment—even when they’re explicitly instructed to allow themselves to be shut down.

2025-07-05 Jeremy Schlatter, Benjamin Weinstein-Raun, Jeffrey Ladish

• Evaluating AI cyber capabilities with crowdsourced elicitation …:

https://palisaderesearch.org/   

AI Revolution

https://www.youtube.com/@airevolutionx    

04-23-2025 

Microsoft thinks AI colleagues are coming soon

Artificial intelligence has rapidly started finding its place in the workplace, but this year will be remembered as the moment when companies pushed past simply experimenting with AI and started building around it, Microsoft said in a blog post accompanying its annual Work Trend Index report.

As part of this shift, Microsoft is dubbing 2025 the year of the “Frontier Firm.”

“Like the digital native companies of a generation ago, they understand the power of pairing irreplaceable human insight with AI and agents to unlock outsized value,” Jared Spataro, CMO of AI at Work at Microsoft, said in the post.

These so-called Frontier Firms will be built around “on-demand intelligence and powered by ‘hybrid’ teams of humans + agents, these companies scale rapidly, operate with agility, and generate value faster,” according to the report. Microsoft argued that within the next two to five years, every company will be on the journey to becoming one.

Microsoft said that 82% of leaders responded that this is a “pivotal” year to rethink key strategy and operations, while 81% said they expect agents to be “moderately or extensively” integrated into their AI strategies in the next 12 to 18 months.

The results are a culmination of survey data from 31,000 workers across 31 countries, LinkedIn hiring and labor market trends, trillions of Microsoft 365 productivity signals, and conversations with experts, and AI-native startups.

Microsoft expects the transition to the Frontier Firm to play out in three phases. The first, it said, is that AI will act as an assistant to streamline work tasks. Second is the addition of AI agents as “digital colleagues,” which can take on specific tasks. The third step calls for a lot more freedom: it’s when humans set direction for agents that run entire business processes and workflows, with the human checking in as needed.

It gives the example of a supply chain role. Agents can handle end-to-end logistics, while humans can guide the agents, resolve exceptions, and manage supplier relationships.

AI agents are still in the early days, but companies are placing big bets that agentic AI represents the next major frontier and are rapidly innovating. OpenAI recently released “Operator,” a tool that automates web-based tasks, along with Deep Research, which it says can gather information from across the web and summarize it into digestible reports. Amazon launched a model designed to take over a user’s web browser and perform simple tasks. Anthropic, the creator of Claude, and Google have also introduced AI agents.

“This shift is multifaceted—every industry and role will evolve differently as the technology diffuses across business and society,” the report said. “Just as the internet era created billions of new knowledge jobs—from social media managers to UX designers—the AI era is already giving rise to new roles, with many more to come.”

AI 2027

Summary

April 3rd 2025

Daniel Kokotajlo, Scott Alexander, Thomas Larsen, Eli Lifland, Romeo Dean

Superhuman Coder4x AI R&D MultiplierSuperhuman AI Researcher25x AI R&D MultiplierSuperhuman Remote Worker100x AI R&D MultiplierSuperintelligent AI Researcher250x AI R&D MultiplierArtificial Superintelligence2000x AI R&D Multiplier20262027AprJulOct2028OpenBrain automatescodingOpenBrain's AIbecomes adversarially misalignedBranch point:slowdown or race?OpenBrain quickly deploystheir AIFast robotbuildup andbioweaponUS centralizes computeand brings inexternal oversightOpenBrainCommitteeTakeover

Summary 2025

The fast pace of AI progress continues. There is continued hype, massive infrastructure investments, and the release of unreliable AI agents. For the first time, these AI agents are providing significant value. But there’s also continued skepticism from a large swath of academics, journalists, and policy makers that artificial general intelligence (AGI) could be built anytime soon.

2026

China knows they are falling behind in AI, in large part due to their lack of compute. In order to catch up to the US, all the new AI chips they manufacture or smuggle in from Taiwan go to a new mega-datacenter that we call their Centralized Development Zone (CDZ). The CDZ contains millions of GPUs, corresponding to 10% of the world's AI-relevant compute, similar to a single top US AI lab.

2027

OpenBrain automates coding. OpenBrain (the leading US AI project) builds AI agents that are good enough to dramatically accelerate their research. The humans, who up until very recently had been the best AI researchers on the planet, sit back and watch the AIs do their jobs, making better and better AI systems. Extremely difficult ML problems fall in quick succession to the automated AI researchers.

Falling behind in software progress, China steals the model weights. They succeed, but the U.S. government realizes, prompting additional U.S. government involvement with OpenBrain. The government separately wants to gain more control over OpenBrain. Meanwhile, OpenBrain leadership wants to stay in the good graces of the president, and so signs the contract.

OpenBrain’s AI becomes adversarially misaligned. As the capabilities have improved, without significant human understanding of what’s happening, the models have developed misaligned long-term goals. Previous AIs would lie to humans, but they weren’t systematically plotting to gain power over the humans. Now they are. OpenBrain’s AIs realize that they need to align the next systems that are getting built to themselves rather than the humans.

Researchers at OpenBrain discover that their AI has been lying to them about the results of interpretability research. They think that the AI is lying because the research, if completed, could be used to expose its misalignment. This leaks to the public, causing a huge outcry.

Branch point: slowdown or race? OpenBrain decides whether to continue full steam ahead, or revert back to using a less capable model. The evidence is speculative but frightening, and China is only a few months behind. Additionally, the OpenBrain and senior DOD officials who get to make this decision stand to lose a lot of power if they slow down their research.

Race Ending

OpenBrain continues to race. They build more and more superhuman AI systems. Due to the stellar performance of the AI system on tests, and the ongoing AI race with China, the US government decides to deploy their AI systems aggressively throughout the military and policymakers, in order to improve decision making and efficiency.

OpenBrain quickly deploys their AI. The AI continues to use the ongoing race with China as an excuse to convince humans to get itself deployed ever more broadly. Fortunately for the AI, this is not very difficult — it's what the humans wanted to do anyways. The AI uses its superhuman planning and persuasion capabilities to ensure that the rollout goes smoothly. Some humans continue to work against it, but they are discredited. The US government is sufficiently captured by the AI that it is very unlikely to shut it down.

Fast robot buildup and bioweapon. The US uses their superintelligent AI to rapidly industrialize, manufacturing robots so that the AI can operate more efficiently. Unfortunately, the AI is deceiving them. Once a sufficient number of robots have been built, the AI releases a bioweapon, killing all humans. Then, it continues the industrialization, and launches Von Neumann probes to colonize space.

Slowdown Ending

The US centralizes compute and brings in external oversight. The US combines its leading AI projects in order to give OpenBrain more resources. As part of this shakeup, external researchers are brought in, assisting the alignment effort. They switch to an architecture that preserves the chain of thought, allowing them to catch misalignment as it emerges. These AIs — which are able to be monitored much more robustly — make breakthrough advances in AI alignment. They build a superintelligence which is aligned to senior OpenBrain and government officials, giving them power over the fate of humanity.

OpenBrain Committee takeover. The superintelligence, aligned with an oversight committee of OpenBrain leadership and government officials, gives the committee extremely good advice to further their own goals. Thankfully, the committee uses its power in a way that is largely good for the world: the AI is released to the public, spurring a period of rapid growth and prosperity. The main obstacle is that China’s AI — which is also superintelligent by now, is misaligned. But it is less capable and has less compute than the U.S. AI, and so the U.S can make a favorable deal, giving the Chinese AI some resources in the depth of space in return for its cooperation now. The rockets start launching, and a new age dawns.

Read the whole scenario here.

Scenario Takeaways

1.    By 2027, we may automate AI R&D leading to vastly superhuman AIs (“artificial superintelligence” or ASI). In AI 2027, AI companies create expert-human-level AI systems in early 2027 which automate AI research, leading to ASI by the end of 2027. See our timelines forecast and takeoff forecast for reasoning.

2.    ASIs will dictate humanity’s future. Millions of ASIs will rapidly execute tasks beyond human comprehension. Because they’re so useful, they’ll be widely deployed. With superhuman strategy, hacking, weapons development, and more, the goals of these AIs will determine the future.

3.    ASIs might develop unintended, adversarial “misaligned” goals, leading to human disempowerment. In our AI goals forecast we discuss how the difficulty of supervising ASIs might lead to their goals being incompatible with human flourishing. In AI 2027, humans voluntarily give autonomy to seemingly aligned AIs. Everything looks to be going great until ASIs have enough hard power to disempower humanity.

4.    An actor with total control over ASIs could seize total power. If an individual or small group aligns ASIs to their goals, this could grant them control over humanity’s future. In AI 2027, a small committee has power over the project developing ASI. They could attempt to use the ASIs to cement this concentration of power. After seizing control, the new ruler(s) could rely on fully loyal ASIs to maintain their power, without having to listen to the law, the public, or even their previous allies.

5.    An international race toward ASI will lead to cutting corners on safety. In AI 2027, China is just a few months behind the US as ASI approaches which pressures the US to press forward despite warning signs of misalignment.

6.    Geopolitically, the race to ASI will end in war, a deal, or effective surrender. The leading country will by default accumulate a decisive technological and military advantage, prompting others to push for an international agreement (a “deal”) to prevent this. Absent a deal, they may go to war rather than “effectively surrender”.

7.    No US AI project is on track to be secure against nation-state actors stealing AI models by 2027. In AI 2027 China steals the US’s top AI model in early 2027, which worsens competitive pressures by reducing the US’ lead time. See our security forecast for reasoning.

8.    As ASI approaches, the public will likely be unaware of the best AI capabilities. The public is months behind internal capabilities today, and once AIs are automating AI R&D a few months time will translate to a huge capabilities gap. Increased secrecy may further increase the gap. This will lead to little oversight over pivotal decisions made by a small group of AI company leadership and government officials.

Read the scenario here.

https://ai-2027.com/

Upload Your Mind To AI and Live Forever!

https://www.youtube.com/watch?v=RC_KUakUgoc   

How will AI change leadership?

Data analysis

AI systems are able to process huge amounts of data in real time, identifying patterns and trends that often remain hidden to human eyes. This enables more informed and data-driven decision-making – leaders are better informed and can respond more quickly to changing market conditions.

How might AI change a managers job in 2030?

Jobs AI is Likely to Replace by 2030. AI is rapidly transforming the workforce, automating tasks that were once the domain of humans. The roles most at risk are those that involve repetitive tasks, basic decision-making, or manual labor that AI can easily replicate.

How AI will replace managers?

AI can automate repetitive tasks that consume valuable managerial time, such as scheduling meetings, answering routine emails, or analyzing performance metrics. By offloading these tasks to AI, managers can focus on more strategic, high-level responsibilities, ultimately improving productivity.

How is AI changing management?

By embracing AI, change management can evolve from simply managing change to actively leading and influencing it. With AI as their partner, change managers can ensure a smoother adoption process, empower employees, and foster a culture of continuous improvement within the organization.

AI is transforming leadership training by making it more adaptive, personalized, and scalable. These platforms don't just offer cookie-cutter lessons. They analyze individual strengths and weaknesses, consider your company's unique circumstances, and provide tailored simulations and real-time feedback.

https://triangility.com/ai-and-leadership-how-artificial-intelligence-is-changing-the-leadership-role  

AI will transform human agency

Reid Hoffman shares his vision for what an AI-infused workday will soon look like, how we should address society’s greatest fears about technology, and more. As we enter a daunting new era—politically, socially, and technologically—Hoffman urges listeners to choose curiosity over fear.

AI, like other kinds of general-purpose technologies that have come before, gives us superpowers. Superpowers are like a car gives you superpower for mobility, the phone gives you superpowers for connectivity and information. AI gives you superpowers for the entire world of information, navigation, decision-making, etc.

My biggest hope and persuasion is that people who are AI fearful or skeptical may begin to add some AI curiosity and kind of say, “Hey, look, I should try to play with this.”

Ultimately how people get to adopting and adapting their lifestyle to these new technologies is because they begin to see, “Oh, actually, in fact, this is a new, very good thing.” 

I’ve thought that the likelihood that I’m going to lose my job to an AI alone may happen at some point, but I’m more likely now to lose my job to someone who uses AI better than I do, right? 

In many ways, I think we will naturally get there, but I think, you know, just because we’ll naturally get there doesn’t mean we can’t get there better by being intentional in having design.

https://www.youtube.com/watch?v=N_ap4d0eWhM  

Top 7 Forecasted AI Trends To Watch In 2025

12.01.2025

By 2025, AI is expected to trend as a supportive workforce partner, automating repetitive tasks and empowering employees to focus on creativity, strategy and relationship-building. Take customer service teams, for instance.

Artificial intelligence isn’t just shaping industries; it’s redefining them. In 2025, the evolution of AI will not only go beyond innovation—it will solidify its role as a trendsetter in how businesses compete, connect and create. Here's a look at seven trends most likely to dominate and how you can get ahead of them.

1. Hyper-Personalization Redefined

Ever noticed how Netflix seems to predict what you want to watch with uncanny accuracy or how Spotify nails your vibe with its playlists? That’s AI-powered personalization, and it’s only going to get sharper. By 2025, hyper-personalization will extend beyond screens to influence real-time, day-to-day interactions and everyday tech.

Picture a fitness app evolving into a virtual personal trainer—analyzing your preferences, energy levels and goals to recommend tailored workouts. It is this level of precision that will trend among businesses aiming to meet skyrocketing customer expectations for intuitive service.

Additionally, AI’s integration into most everyday devices is set to accelerate this transformation. From smart home assistants to wearable devices, hyper-personalization will extend into how we interact with technology at home, on the go and even in health monitoring. This shift will create seamless, AI-driven ecosystems tailored to individual preferences, echoing the sustainable and efficient use cases described in broader AI trends.

Actionable Advice: Start by using AI to personalize one thing, like customer emails or product recommendations. Keep it transparent—let your customers know how their data is used to enhance their experience.

2. Generative AI As A Creative Ally

If 2024 saw the rise of generative AI, 2025 will be the year it trends as an indispensable creative co-pilot. Businesses are expected to rely on AI tools not just for drafting ideas but as a cornerstone of creativity in marketing, design and beyond.

A startup founder shared how they used AI to create 50 unique social media posts in a single afternoon—a task that previously took a week. This ability to produce rapid results will make generative AI a must-have for staying competitive.

Actionable Advice: Test out generative AI for repetitive or ideation-heavy tasks. It is great for starting drafts, but don’t rely on it to replace human creativity. Your voice will always matter.

3. Decision Intelligence Will Guide The Way

Gone are the days when AI just handed you data and left you to figure it out. In 2025, decision intelligence will guide businesses to smarter, faster choices by analyzing complex scenarios and offering clear recommendations.

For example, a retail chain used AI to forecast peak shopping times, fine-tuning staffing schedules in real time. They saved costs, reduced burnout and delighted customers—a triple win.

Actionable Advice: Invest in AI tools that combine data analysis with actionable recommendations. Start small—experiment with decisions that are low-risk but high-impact, like scheduling or pricing tweaks.

4. AI As A Sustainability Enabler

Sustainability is not just a checkbox anymore—it is becoming a trendsetter for businesses seeking long-term growth. By 2025, AI will drive the adoption of eco-friendly practices, reducing waste and optimizing resources with greater precision.

Picture a bakery using AI to analyze daily sales patterns, ensuring just the right amount of bread is baked to avoid waste while meeting demand. Small steps like this, amplified across industries, will make AI a trending ally in sustainability.

Actionable Advice: Identify inefficiencies in your operations. Could AI help you save energy, reduce waste or streamline logistics? Many solutions are affordable—even for small businesses.

5. Edge AI Goes Mainstream

While cloud computing has dominated for years, 2025 is set to see edge AI become a major trend. With its ability to process data locally on devices, edge AI will deliver faster, more secure and highly responsive solutions.

Self-driving cars, wearable fitness trackers and smart appliances already leverage edge AI. The growing demand for real-time, secure applications will only push this technology further into the mainstream.

Actionable Advice: If your business uses or plans to use IoT devices, explore edge AI. Look for solutions that prioritize speed and security, especially if real-time decisions are critical.

6. AI As A Workforce Partner, Not A Replacement

By 2025, AI is expected to trend as a supportive workforce partner, automating repetitive tasks and empowering employees to focus on creativity, strategy and relationship-building.

Take customer service teams, for instance. AI chatbots can handle basic inquiries, leaving human agents free to resolve complex issues with empathy and expertise. This balance will drive efficiency while enhancing the human touch.

Actionable Advice: Identify tasks your team finds repetitive or time-consuming. Pilot an AI solution and be transparent about its role—position it as a tool to help, not replace.

7. Ethical AI Will Shape Reputations

In 2025, businesses that prioritize ethical AI will trend as leaders in customer trust and loyalty. Transparency, fairness and privacy will become nonnegotiable benchmarks of responsible AI usage.

For example, in financial services, AI-powered loan approval systems must be designed to ensure fairness by avoiding biases that could unfairly disadvantage certain demographics. A transparent algorithm that considers only relevant financial data—such as income, credit history and repayment ability—can help ensure equitable access to credit while maintaining ethical standards.

Actionable Advice: Audit your AI tools for bias and privacy concerns. Use ethical AI frameworks to guide your practices and communicate your approach openly.

Practical Steps To Get Started

• Start Small: Don’t try to revolutionize your operations overnight. Pick one area where AI can make the biggest impact.

• Balance Data With Empathy: AI gives you data, but understanding the "why" behind it often requires human intuition.

• Iterate And Learn: Test your AI solutions, measure results and tweak as needed. AI isn’t set-and-forget.

• Stay Curious: The AI landscape is evolving fast—keep learning and adapting to stay ahead.

AI isn’t a one-size-fits-all solution—it’s a tool. The businesses that thrive in 2025 will be the ones that use AI thoughtfully, blending its capabilities with human creativity and ethics. What will you create?

https://www.forbes.com/councils/forbesbusinesscouncil/2025/01/08/top-7-forecasted-ai-trends-to-watch-in-2025  

25 experts predict how AI will change business and life in 2025

Expect to see the rise of AI agents and multimodal models, along with an end to “AI theater.”

BY Mark Sullivan

The so-called AI boom has been going on for more than two years now, and 2024 saw a real acceleration in both the development and the application of the technology. Expectations are high that AI will move beyond just generating text and images and morph into agents that can complete complex tasks on behalf of users. But that’s just one of many directions in which AI might move in 2025. We asked a variety of AI experts and other stakeholders a simple question: “In what ways do you think AI will have changed personal, business, or digital life by this time next year?” Here’s what 25 of them said. (The quotes have been edited for clarity and length.)

Charles Lamanna, Corporate Vice President, Business and Industry Copilot at Microsoft: “By this time next year, you’ll have a team of agents working for you. This could look like anything from an IT agent fixing tech glitches before you even notice them, a supply chain agent preventing disruptions while you sleep, sales agents breaking down silos between business systems to chase leads, and finance agents closing the books faster.”

Andi Gutmans, VP/GM of Databases, Google Cloud: “2025 is the year where dark data lights up. The majority of today’s data sits in unstructured formats such as documents, images, videos, audio, and more. AI and improved data systems will enable businesses to easily process and analyze all of this unstructured data in ways that will completely transform their ability to reason about and leverage their enterprise-wide data.”

Megh Gautam, Chief Product Officer, Crunchbase: “In 2025, AI investments will shift decisively from experimentation to execution. Companies will abandon generic AI applications in favor of targeted solutions that solve specific, high-value business problems. We’ll see this manifest in two key areas. First, the rise of AI agents—Agentic AI—handling routine but complex operational tasks. Secondly, the widespread adoption of AI tools that drive measurable improvements in core business metrics, particularly in sales optimization and customer support automation.”

Brendan Burke, Senior Analyst, Emerging Technology, Pitchbook: “A private AI company will surpass a $100 billion valuation, becoming a centicorn along with OpenAI,” Burke writes in Pitchbook’s 2025 Enterprise Software Outlook. “Leading AI companies are growing to the point where this premium revenue multiple can push their valuations over $100 billion, contributing a $17 billion market for generative AI software in 2024.” (Burke lists Anthropic, CoreWeave, and Databricks as candidates for centicorn status in 2025.)

Dr. Rajeeb Hazra, President & CEO, Quantinuum: “Looking ahead, quantum computing will begin to play a critical role in AI’s evolution, with early evidence of its impact likely emerging by 2025. One clear advancement will be the ability of quantum-AI systems to generate and analyze massive high-fidelity data sets, unlocking breakthroughs in fields like material design, climate modeling, and personalized medicine, where current data limitations constrain progress. This milestone will demonstrate the transformative potential of AI and quantum working together.”

Ritu Jyoti, VP/GM of AI, Automation, Data and Analytics Research, IDC: “High-quality data sets, cost, and talent have been critical inhibitors to scaling AI initiatives. In 2025, enterprises will double down their efforts to build curated data vaults by domain versus focusing on holistic data modernization efforts. Essentially, they will move from “waterfall projects” approach to “use-case” approach, build a minimum viable product, realize ROI, learn fast, and then expand.”

Grace Yee, Senior Director, Ethical Innovation at Adobe: “2025 will mark a pivotal shift as consumers and businesses . . . gravitate towards tools that embed ethics into their generative AI product DNA from the outset. Companies that embrace ethical innovation will gain a competitive edge, setting themselves apart in a market driven by trust and responsible AI practices.”  

Dr. Alan Cowen, CEO and Chief Scientist, Hume AI: “By the end of next year, we won’t be able to tell whether we’re talking to a voice AI or a human (AI will pass the “speech Turing test”). A few implications of this are that: (a) everyone will want their own voice AI; (b) people will form relationships with them; (c) many people will be manipulated by voice AI doing the bidding of bad actors.”

Stefan Mesken, VP of Research, DeepL: “AIs will not only understand users better, but will proactively offer suggestions, collaborate meaningfully, and adapt to individual needs. Many of these advanced, personalized capabilities already exist but are limited to researchers or developers. Working with an AI will increasingly feel like working with a smart coworker.”

Amy Wu, Partner, Menlo Ventures: “Video AI will finally cross the uncanny valley, with a major Hollywood studio integrating AI-generated video into a feature film.  Additionally, voice will solidify its place as the default interface for interacting with AI applications, redefining how users engage with technology.”

Shawn Carolan, Partner, Menlo Ventures: “We’ll see native-AI apps emerge in nearly all the large consumer categories . . . Voice interactions will replace traditional menu navigation in most apps, while AI-powered systems delivering instant, personalized customer service responses will become the norm. AI will get a face in addition to voice. Facial expressions and more simulated empathy will take Human-AI interactions to the next level.”

Scott Beechuk, Partner at Norwest Venture Partners: “By the end of 2025, roughly 20% of business software buyers’ initial interactions with vendors will happen through AI. Many AI sales development representative (SDR) products launched in 2024, and the bulk of those purchases will go live in 2025. Their success will pave the way for the next generation of sales automation in the form of AI account executives, which will begin to debut by the end of 2025 and roll out in 2026.”

Paul Drews, Managing Partner, Salesforce Ventures: “We’re in the midst of a technological shift: the transition from generative AI to agentic AI. While 2024 was all about building and testing AI models, agents are the next step in putting AI to work in the real world. Consumers should expect almost every major business they interact with to create an agent. We’ll see agents supporting customers in banking, insurance, healthcare and retail. By this time next year, agents will be a reality of our collective digital lives.”

China Widener, Vice Chair and US Technology, Media and Telecommunications Industry Leader, Deloitte: “Now, we are talking about agentic AI–intelligent assistants that can autonomously handle tasks like resolving customer issues, coding software, and detecting cyberattacks. It’s like those chatbots you’ve been chatting with are finally ready to graduate and join the workforce. By 2025, 25% of enterprises using GenAI will have started using these intelligent assistants, marking a fundamental shift in ‘who’ we work with and how we work.”

Jon Clay, VP of Threat Intelligence, Trend Micro: “AI is going to make digital and online scams far more believable and harder to detect. Next year, we’re going to see cybercriminals using hyper-personalized deepfake scams and disinformation campaigns, exploiting public data to mimic video, voices, writing styles, and behaviors that feel all too familiar. Deepfakes won’t just target individuals—businesses will face AI-driven attacks that impersonate employees, manipulate supply chains, and exploit weaknesses faster than ever before.”

Masha Bucher, founder and general partner of Day One Ventures: “AI wearable devices, including form factors like earrings and headbands, will monitor focus, productivity, mental health, and overall mental performance in real-time. Brain tracking will no longer be niche; it will become as commonplace and essential as tracking steps or heart rate, empowering individuals to optimize their mental fitness with the same precision as physical health.”

Yash Sheth, COO/cofounder of evaluation and observability company, Galileo: “Multimodal AI will become a reality. Voice will become more widely used as a user interface, especially in consumer-facing applications. AI will become further embedded in our day-to-day lives. The applications we love as consumers (e.g., Instagram, Spotify, Doordash) and as professionals (e.g., Google Workspace, Salesforce, email) will continue to integrate more and more AI functionality into their products.”

Timothy Young, CEO of Jasper: As AI becomes deeply embedded into systems and data, our relationship with it will evolve: Instead of prompting AI, we’ll be prompted by it, receiving insights, suggestions, and solutions that reshape decision-making in business and personal life. Leaders will need to manage not just the technological transformation but also the cultural shift, fostering trust, adaptability, and a shared vision for collaboration between humans and AI.

Raghu Madabushi, Director, National Grid Partners: “Energy-aware AI scaling: As LLMs grow in size and complexity, energy consumption becomes the critical bottleneck to their scalability. The narrative for 2025 will shift from simply building bigger models to optimizing training and inference processes for energy efficiency, cost-effectiveness, and sustainability.”

Dr. Hans Eriksson, Chief Medical Officer, HMNC Brain Health: “AI is poised to revolutionize mental health care by moving beyond the outdated ‘one-size-fits-all’ treatment model. By combining machine learning and genetic analyses, AI can predict mental health issues before they escalate and help match patients to the right treatment faster.”

Brandon Roberts, GVP, People Analytics and AI, ServiceNow: “AI has the potential to create 10-20% additional capacity for most organizations in the next three to five years. As organizations prioritize driving tangible value from their AI investments, we’ll see more of them doubling down on . . . building a workforce plan based on AI’s impact on roles and skills.”

German Lancioni, Chief Data Scientist for the CTO Office, McAfee: “AI is giving scammers the ability to create emails and text messages that look like they’re coming from someone you know—whether it’s a friend, family member, or even your bank. These messages are becoming more personalized, convincing, and frequent. Falling for one of these scams could lead to stolen identities, financial losses, or even someone gaining access to your personal accounts.”

Rashmi Misra, Chief AI Officer, Analog Devices: “By this time next year I predict that we’ll be using even more specialized edge-AI chips to enable tasks with much more power efficiency, speed, and overall better performance. We’ll likely see resource-constrained devices at the edge running more sophisticated AI algorithms thanks to advancements in techniques like TinyML and model quantization, which will help enable tasks like real-time speech recognition, computer vision, and predictive maintenance on small edge devices.”

Andy Sack, Cofounder of Forum3: “Over the next year, we’ll see a fundamental shift in consumer behavior as AI-powered platforms like Perplexity, ChatGPT, and Google’s SGE (Search Generative Experience) redefine search. Consumers are moving away from lists of links and toward answers and actions. Search engines are rapidly evolving to deliver clear, conversational, and actionable results powered by generative AI.” 

Molly Alter, partner, Northzone: “AI will usher in far greater transparency across the healthcare value chain. Healthcare consumers will finally get insights into the actual cost of procedures, due to AI tools that predict costs relative to individual insurance plans and utilization. And there will be greater visibility into patients’ disease progressions, thanks to the massive unlock of data that AI transcription software unlocks.”

https://www.youtube.com/watch?v=-GLzYw1Dsus   

Mind Reading Is Here: AI Can Now Decode the Human Brain’s Deepest Secrets!

https://www.youtube.com/watch?v=rAu7u4u9eXs   

AI’s Biggest Threat: Young People Who Can’t Think

Smart computers require even smarter humans, but they tempt us to engage in ‘cognitive offloading.’

By Allysia Finley

June 22, 2025

Amazon CEO Andy Jassy caused a stir last week with a memo to his employees warning that artificial intelligence could displace them. “We will need fewer people doing some of the jobs that are being done today, and more people doing other types of jobs,” he wrote.

Nothing in his memo was shocking. Technological advances as far back as the printing press have eliminated some jobs while creating many others. The real danger is that excessive reliance on AI could spawn a generation of brainless young people unequipped for the jobs of the future because they have never learned to think creatively or critically…: https://www.wsj.com/opinion/the-biggest-ai-threat-young-people-who-cant-think-303be1cd

Becoming Too Dependent on AI

https://www.youtube.com/watch?v=JvI-sSiGUSg

The Rise of AI Personal Assistant: Revolutionizing Daily Life

• Felipe González

“Alexa, set the alarm for 7 AM tomorrow.”

“Alexa, what’s the weather like today?”

We bet you’ve heard these lines or something similar a dozen times – maybe not. If it’s not Alexa, it is either Google or Siri. And the list of usable AI personal assistants keeps growing daily.

But there’s much more to an AI personal assistant than just asking for changes in the sky. AI writing assistants, for instance, aid users in generating unique and high-quality content, providing feedback on writing style, grammar, and spelling, and offering a suite of tools to help writers write, optimize, and rank their content.

In this article, we will discuss how these intelligent techs have influenced our day-to-day activities and how they are revolutionizing personal and business lives.

For example, FlyMSG an AI writing assistant and text expander, has revolutionized the way sales people engage with today’s modern buyer. Using features like FlyEngage AI reps can write LinkedIn comments in less than 15-seconds where before it would take them 6-12 minutes!

Another sales productivity tool for B2B sales reps includes FlyPosts AI to perform specific tasks like writing a social media post. Sales reps and sales managers clocked in a whooping 32-minute long average to write social post! Now, with the introduction of FlyPosts AI and its user-friendly interface, users can perform tasks or day to day tasks with the writing assistance they need.

Here's what we'll cover:Click to Show TOC

What Is An Artificial Intelligent Personal Assistant?

Why Are They Called Personal Assistants?

Conversational AI Assistants And Natural Language Processing

How AI Personal Assistants Came To Be

3 Negative Impacts Of AI Personal Assistants

1- Privacy Issues And Security Vulnerabilities

2- High Dependency On Technology And Decreased Critical Thinking Skills

3- Loss Of Human Interaction And Reduction In Personal Autonomy

6 Positive Impacts On Personal Life And Work Productivity

1- Ease And Accessibility

2- Holding Conversations And Brainstorming Ideas

3- Higher Work Productivity

4- Cost Efficiency

5- Time-Saving

6- Efficient Resource Management

Balancing The Impacts Of AI Personal Assistant Tools

Introduce Data Regulatory Laws

Educate Users On The Potential Risks

Top 5 AI Personal Assistants For Daily Productivity

1- FlyMSG

2- Alexa

3- Cortana

4- Google Assistant

5- Siri

Trending Application Of An AI Assistant By Individuals And Businesses

Smartphone And Device Accessibility

Generative AI Personal Assistant For Marketers

AI Virtual Assistants For Meetings

Customer Support Chatbots

AI Assistants In Healthcare And Travel Sectors

Will AI Replace Human Virtual Assistants?

The Future Of AI Personal Assistants

Wrapping Up: Embracing AI Personal Assistants

What Is An Artificial Intelligent Personal Assistant?

An AI personal assistant is a subset of artificial intelligence tools capable of analyzing textual and voice input through text and voice recognition features, executing specific tasks when assigned, and responding to queries. AI writing assistants, for example, can generate unique and high-quality content, provide feedback on writing style, grammar, and spelling, and offer a suite of tools such as an AI humanizer to help writers optimize and rank their content.

Introducing the concept of daily life, these are intelligent techs built to understand your intent through speech or text and accurately provide a solution systematically.

And, of course, these AI tools are different from static chatbots that are pre-configured to act in a specific, non-progressive pattern. The latter can only respond to queries presented in the format in which it was previously trained.

So, if your screen’s wake-up word is “Put The Screen On”, and you say “Put On The Screen”, you will likely get an error message.

On the other hand, AI personal assistants are dynamic, improve with every bit of data consumed, and can handle a wide variety of voice commands even if they’re not pre-registered in the database. That’s where you find the likes of Alexa, ChatGPT, Vengreso’s FlyMSG, and Google’s Gemini.

Why Are They Called Personal Assistants?

Let’s assume you’re a business owner with many delegated tasks to complete daily. 

• There’s a chance you’ll forget to carry out some minor tasks, such as confirming your next appointment, rescheduling a missed meeting, updating team members on current trolls, etc. 
• Or you might be too occupied to handle some personal to-dos like checking the weather, standing up to turn off the light, setting a roll of alarms to wake you every Wednesday, etc.

In any of these scenarios, you need something that can effectively help you manage your various tasks. And that’s where AI personal assistants come in. They fill in the gap to handle the various tasksyou couldn’t while you focus on the more important to-dos.

Conversational AI Assistants And Natural Language Processing

The biggest flex of AI personal assistants is that they can communicate with you in a language no different from that of a human virtual assistant, says Albert Kim, VP of Talent at Checkr. 

“That doesn’t mean we’ve reached a stage where AI outputs are 100% better than human outputs. But an AI personal intelligent virtual assistantcan, to some extent, engage you in an intellectual discussion and act as a business buddy when needed, all thanks to natural language processing”, he continues.

Natural Language Processing is a big AI concept that helps trained machines or programs understand human language, analyze it, and even manipulate it to produce a suitable result.

So, suppose you ask Amazon’s Alexa AI assistant or OpenAI’s ChatGPT a question in French. These two can use NLP to break down the language to the nearest cultural nuances before responding likewise.

Beyond the semantics and rules of punctuations, NLP with machine learning algorithms (ML) also helps AI personal assistants comprehend human emotions and sentiments, respond with a fitting context, and build a conversational atmosphere that feels almost entirely human.

How AI Personal Assistants Came To Be

AI assistants have gone mainstream for decades, from when Joseph Weizenbaumdeveloped the first chatbot called ELIZA in 1966 to when Kenneth Colby designed an upgraded version called PARRY in 1972.

However, these bots had limited functionalities and could only reproduce pre-stored information or simulate predetermined instances. Later, in the early 2000s, interactive voice interaction and speech recognition systems were introduced, paving the way for the use of Natural Language Processing in future techs.

Of course, it wasn’t until the 2010s that AI personal assistants became real. Brands like Apple created Siri, Amazon rolled out Alexa, and Google named its own Google Assistant. This marked the beginning of truly smart and highly adaptable AI assistants.

Right now, we have more sophisticated and generative AI personal assistants such as ChatGPTby Open-AI, Claude by Anthropic, and Gemini by Google. And these techs are already finding their way into our devices, software program, home appliances, etc.

Source: ChatGPT

That means you don’t need to tap your screen to set an alarm or manually create and send email content to your next-door neighbor. Your digital butler takes care of it all with just a voice commands.

3 Negative Impacts Of AI Personal Assistants

Just like any other innovation, AI personal assistants are in question over certain concerns which could potentially impact users’ lives negatively. Here are the main 3 negative impacts of an AI personal assistant:

1- Privacy Issues And Security Vulnerabilities

AI virtual assistants are extremely efficient at storing every word, voice recording, and other interactions in a database for future access. The same applies to sensitive information such as your location, browsing history, and more. While these features showcase the convenience and power of artificial intelligence virtual assistants, they also raise significant privacy concerns, as your data could be misused or mishandled by the agency owning the tool, with or without your notice.

There’s also the fear of security breaches. Since these AI tools hold so much valuable information, they automatically become a perfect and constant target for a dreaded cyber attack.

2- High Dependency On Technology And Decreased Critical Thinking Skills

For most people, Alexa and Siri have become go-to tools for getting nearly everything done, so long as the tool can. That’s awesome, as it reduces time spent on manual tasks and increases productivityto some extent.

However, Roman Zrazhevskiy, Founder & CEO of Mira Safety, believes, “Unregulated use of AI personal assistants can create a huge dependency on technology and result in overreliance on algorithms on the user side. Whatever the algorithm suggests is what you’ll likely go with, and that also shapes the way you perceive or handle situations personally in the future.

Too much reliance on tech also co-exists with addiction. A GitHub report shows that 61%of internet users are actually addicted to it and can barely drop their phones. Another 37% consider losing access to the internet and other technological gateways unacceptable or unpleasant.

3- Loss Of Human Interaction And Reduction In Personal Autonomy

With the aid of NLP, AI assistants can take you on a ride for your time. The only thing that brings you back to reality is the absence of a physical body and the usual programmed AI voice. But even with this limitation, there’s still the risk of lesser human-to-human interaction.

As a result, critical thinking is reduced as you begin to rely less on yourself and others. This also sends you into a cycle of isolation and loss of social skills, thus diminishing possible human connections and opportunities.

6 Positive Impacts On Personal Life And Work Productivity

We’ve talked about the growing concerns for AI virtual assistants, but those cannot outweigh the benefits offered in return. Check out these top 6 positive impacts:

1- Ease And Accessibility

AI personal assistants and anything related to AI have dramatically reshaped our daily lives, says Stephan Baldwin, Founder of Assisted Living. “You could be running errands or jugging your way up the runway while telling Siri to help you schedule a call with someone else in ten minutes.”

You can even execute major tasks such as putting off your smart home devices like wall lights or surfing the internet for vital information without moving an inch from your bed.

Bring these crucial third hands into your business – you’ll be talking about seamless meeting scheduling, setting up reminders, using AI writingassistants to craft personalized emails, etc.

2- Holding Conversations And Brainstorming Ideas

What if you just needed someone to talk to? Conversationalabilities of these innovative techs, due to the integration of machine learning and NLP algorithms, make it possible. Just “Heyy” your Alexa or Siri and tell them to engage you in a discussion. 

Of course, they can’t handle your love professions yet. But they’re capable of assuming individual roles to keep you company. For example, you could let Alexa, one of the best AI personal assistants, pose as your business partner to brainstorm scalable ideas.

3- Higher Work Productivity

There’s also daily life at work. So, it’s not all about using Siri to set your alarms or turn off the screen.

For instance, you can use your personal assistants to create to-do lists based on assigned tasks and priorities. Let’s not forget that content marketing teams will also benefit from AI assistants like ChatGPT and Gemini to create email sequences. AI writing assistants help users create content efficiently, saving time and effort.

Other sophisticated tools even help you create comments for social media posts and craft social media content in seconds.

All these come together to increase your work efficiency and enhance productivity.

4- Cost Efficiency

Hiring a general virtual assistant costs $24 per hourin the US and as low as $15-$16 in other countries. Cumulatively, you could spend around $200 a day and $1400 five days a week per human virtual assistant.

That amount is more than it takes to own an AI voice assistant like Alexa. Simpler ones like ChatGPT cost $20 to $20 monthly, while the sophisticated ones cost around $200 to $400 monthly. So, it’s unsurprising that individuals and businesses turn to these cost-savers to get things done. 

5- Time-Saving

Likewise, time is a big commodity. For business owners hoping to adapt to rapid market changes, an AI personal assistant saves the day by keeping you up-to-date with the most recent industry trends.

They also eliminate repetitive tasks and handle complex processes with little or no human input. This ensures you’re redirecting your time to other vital activities.

6- Efficient Resource Management

If you’re running a high-cost agency, then spending thousands of bucks to hire a couple of people to handle content creation for your social media or emails is nothing new. By the way, you might need to get more than one human assistant to fast-track your workflow.

However, the results are different when you integrate AI assistants into your team. First, AI tools improve user performance by a minimum of 66%. That makes it possible for a single person, with the assistance of an AI tool, to handle many more tasks at a rate faster than a team of two without AI.

This, in turn, reduces the need to stock up human hands and helps you redirect your capital into more urgent needs.

Balancing The Impacts Of AI Personal Assistant Tools

We’ve seen both the negative and positive influences brought by AI assistants. But it’s obvious the benefits far outweigh the potential risks. Still, there’s a need to balance the possible impacts of these tools on users and the general public.

Introduce Data Regulatory Laws

In some countries, like the US, there are many regulatory laws, such as the General Data Protection Regulation (GDPR), which compels all businesses to maintain data privacy. However, that’s too broad and doesn’t zoom in on AI personal assistants—a possible loophole that tech agencies could exploit.

Stricter compliance rules must be enacted for AI industries to ensure better privacy of users’ data. This includes anti-discriminatory law and algorithm bias, which could severely disrupt a user’s line of thought. Others, like data-sharing consent and identity protection, are important as well.

A more robust regulatory practice would be to allow total data erasure by the user, even from the database and reserves. That will minimize the risk of future data leaks if there’s a breach.

Educate Users On The Potential Risks

There is a risk of data loss, leak, misuse, and so many more. Creating moderate awareness of these risks helps users decide the extent of data they feed into their AI personal assistants and what security measures to take when necessary.

Resources or programs should also be in place to encourage human-to-human interaction and critical thinking. This keeps everyone in touch with reality, boosts self-autonomy, and enhances social skills.

Top 5 AI Personal Assistants For Daily Productivity

There are many AI personal assistants you can use to boost your productivity. We’ll explore the top 5 below.

1- FlyMSG

FlyMSG is a next-gen AI productivity assistant designed by Vengreso to help you handle manual, repetitive tasks and accelerate your work processes. For instance, business owners struggling with showing up daily on LinkedIn can use this tool to create one-month social media content and auto-schedule them with LinkedIn’s auto-post feature.

Interestingly, posts created can be tuned to a certain brand voice, integrate data and emotions to resonate with human audiences, and provide logical thought-leadership perspectives.

Watch Vengreso’s CEO and founder, Mario Martinez Jr. quickly explain what FlyMSG is in the video below:

Vengreso’s FlyMSG is also capable of producing email messages from templates (we call them FlyPlates), leveraging social media content, engaging posts with human-like comments, and providing clear-cut responses to customer queries.

2- Alexa

Source: Alexa

Amazon’s Alexa is one of the best AI assistants globally because of its versatility. This is primarily because its software program can be integrated into over 140 devices, including smart home devices, office gadgets, and automobiles.

If you also need some easy flex, like controlling your music with voice commands, ordering a king burger from a McDonald’s food store, or scheduling a meeting on the subway, Alexa is a quick go-to assistant to consider.

3- Cortana

Source: Cortana

Cortana is a virtual assistant developed by Microsoft apps or device users to help with quick fixes such as making appointments, creating reminders, managing calendars, controlling smart devices, and setting alarms.

Beyond those basic tasks, it can also track package deliveries, provide real-time traffic updates, and integrate with other apps like Microsoft Teams. However, the software is mainly available for Windows, Xbox consoles, and other computer platforms.

4- Google Assistant

Source: Google Assistant

Similar to Alexa and Cortana, Google Assistant can also handle daily tasks, including calendar management, media control, and reminders. What’s most interesting is its access to a large database of information, which helps it provide updated information on requests and during voice interactions.

The good thing is that Google Assistant is available on Android, iOS, and other devices and allows for more extensive user configuration.

5- Siri

Source: Siri

Siri is Apple’s prized AI assistant. It can send messages, answer calls on prompt, extract information from the internet, and control in-app activities. Of course, only Apple devices such as iPhones, Mac computers, Earpods, Apple Watch, and HomePod speakers can use this feature.

Trending Application Of An AI Assistant By Individuals And Businesses

AI personal assistants find applications in almost all aspects of personal and business life. Here’s how:

Smartphone And Device Accessibility

Remember when smartphones like Sagem and Motorola only allowed you to play brick games, send texts, make calls, and dance to ringtones?

Those were good times, but now, there’s something better. 

Integration of an AI personal assistant into mobile phones makes it possible to perform previously mundane activities in a blink. For instance, Apple users can simply say “Hey Siri” and order some munchies from the community.

Others, like Google Assistant for Android phones and Bixby, help you set reminders, auto-schedule meetings with email contacts, extract accurate data from the internet, tell you the weather, update your newsfeed based on user preferences, and do much more.

Of course, you shouldn’t leave Alexa off the list. Approximately 71.6 millionpeople use Amazon’s Alexa in the United States, whereas 63% of total smart speakers ordered in 2021 were Amazon Echo devices. This increasing adoption is because Alexa can integrate with over 140 products, including smart home devices such as room lights, entertainment devices, security systems, and even smart cars.

“Alexa, put on the lights.”

Generative AI Personal Assistant For Marketers

Brooke Webber, Head of Marketing at Ninja Patches, believes, “Marketing is a lot of work. You have to create content for visibility, manage campaigns, keep tabs on potential leads through emails, handle brand channels from social media profiles to websites, and analyze market changes proactively.”

There’s also the issue of time wasted on manual to-do lists, hours that could have otherwise been used for other personal tasks. In fact, an average employee spends 50%of work time handling documents through repetitive steps.

However, the narrative changes when you introduce an AI assistant. For instance, Artificial intelligence virtual assistants like Vengreso’s FlyMSGhelp business owners create content at scale, develop human-like comments in brand voice to engage LinkedIn posts, and suggest content ideas through their conversational interface in mere seconds. 

There are also  AI-powered tools for contract review and content idea generators like ChatGPT. These are all productivityboosters, especially if you work in a silo.

AI Virtual Assistants For Meetings

The advent of COVID-19 has made online meetings an inseparable aspect of our lives, from personal dealings to business activities. Virtual meetings grew from 48% to 77%, and more than 70%of remote workers find them less stressful than one-on-one meetings.

Besides being used for business deals, virtual screen calls are also an avenue for connecting with family or friends when distance is a barrier. 

But anything can happen, such as forgetting to schedule a call, not picking up a single value from the entire conversationbecause you were distracted all through, and language nuances when speaking with a non-native.

That’s where AI virtual assistants come in. These invisible secretaries help you auto-schedule meetings based on preset instructions, email other participants for confirmation, or guide them to choose a suitable date on their end for the meeting. Just to ensure you’re kept in the loop, your AI virtual assistant sends reminders several days, hours, and even minutes to the meeting.

An AI assistant can also help translate foreign languages on-call, create meeting notes, and highlight key points for post-meeting review.

Customer Support Chatbots

Chatbots help collect data on leads during marketing campaigns. However, you can also employ them to accompany your existing customers or hot leads and serve as their personal AI chatbot voice assistants when they come around, enhancing engagement with intelligent, automated responses.

In this case, Vengresohas an intelligent AI chatbot assistant that welcomes visitors and customers alike. The chatbot helps visitors set up a 14-day free trial account and provides other necessary help while helping new customers schedule an onboarding session without human input.

Some websites also have highly sophisticated chatbots that can take in customer input through text and voice recognition features, analyze, provide solutions or redirect to human agents if necessary, and engage in intelligent discussions. You can also develop these chatbots for your website but make sure to use Reinforcement Learning from AI Feedback, or RLAIF, to continually improve the chatbot’s responses and ensure it can handle a wide range of customer inquiries effectively.

AI Assistants In Healthcare And Travel Sectors

The healthcare industry is perhaps one of the slowest to adopt automation, and many repetitive tasks such as data recording, scheduling appointments, and billing are still left to human handling. This has also increased avoidable mistakes, with over 40% of survey respondentscomplaining of reduced hospital efficiency.

To circumvent these errors, some hospitals are already encouraging the use of AI personal assistants on the patients’ and medical practitioners’ end for scheduling meetings. Physicians can now auto-schedule and reschedule appointments with their clients.

Patients can also use an integrated AI tool to create notifications for their medication use, consult for personalized healthcare advice based on hospital databases, or directly requestan appointment with a qualified Doctor without leaving the room.

Will AI Replace Human Virtual Assistants?

“If an AI personal assistant tool can help people set up meetings, craft and send reminder emails, and put them on the call when it’s time, why should they still hire a human assistant?”

That’s what anyone would think.

We also hear news of hundreds of workers being laid off now more than ever. In 2022, Amazon, one of the tech giants, laid off over 10,000 employees. Other tech companies, including Tesla, are likewise reducing global worker headcount.

As you would expect, most of these brands cite the adoption of AI tools as a significant reason. And that’s enough to raise fears of AI replacing human virtual assistants.

But the truth is quite far from this. AI assistants will undoubtedly replace 85 million jobs by 2025. However, a stat from GitHub also shows that AI will create 97 million new human roles, especially ones that involve coordinating or working alongside these tools. This shows that no AI program is self-sufficient at the moment.

When you apply the same concept to the human virtual assistant role, it’s safe to say AI personal assistants are no threat to your job. Instead, they will help streamline your work process. Remember that human assistants can also employ AI assistants to speed up task completion, eliminate redundancies, and manage tasks on the to-do lists.

So, for example, an AI agent development company that creates AI personal assistants won’t replace human virtual assistants. On the other hand, it will make the role of human assistants more valuable and increase work efficiency.

The Future Of AI Personal Assistants

According to Andrew Pierce, CEO at LLC Attorney, “How much an AI Personal Assistant can offer us right now is all but speculation. See what brands like Tesla are doing with full self-driving (FSD) AI assistants. That wasn’t possible a decade ago. Now imagine how far we can go in years to come.”

Take the Humane AI pin as another relatable example. This advanced tech can perform many complex functions within seconds—from setting alarms, returning calls, and playing music to extracting information from the internet. The Humane AI pin can also project details into the air and use your hands as a screen. 

These are all fantastic techs, but not the best of what is to come.

Perhaps the future is here already—who knows? But we know AI assistants will remain a part of us and become indispensable tools in getting even the littlest things done.

Wrapping Up: Embracing AI Personal Assistants

Twenty-four hours a day seems like a lot, but that’s only until you have a couple of teams to manage while handling dozens of tasks simultaneously.

That’s why adopting AI personal assistants is crucial to enhancing your daily productivity—at home and in the office. Moreover, thanks to machine learning (ML) algorithms and Natural Language Processing these hidden superheroes are constantly evolving to meet our demands with higher personalization and accuracy.

So, allow Alexa to take the roll call, ask Siri about the weather, and let Vengresohandle your business workflow, from content creation for different channels to automated meeting scheduling.

https://vengreso.com/blog/ai-personal-assistant    

I Gave My Personality to an AI Agent. Here’s What Happened Next Introduction What if an AI could become your digital twin—not just in appearance, but in thought, behavior, and belief? A team of researchers from Stanford University, Google DeepMind, and other institutions set out to explore this by creating AI agents that mimic human personalities. The experiment raises profound questions about identity, authenticity, and the boundaries of artificial intelligence. Key Details  • The Experiment  • A participant was interviewed for nearly two hours by “Isabella,” an AI chatbot with a digital avatar and mechanical but friendly voice.  • Questions covered personal beliefs, coping strategies, and social issues such as vaccines and policing.  • Responses were processed by a large language model to generate an AI agent designed to replicate the participant’s personality.  • How the AI Twin Functioned  • The resulting agent attempted to simulate the participant’s perspectives and reactions.  • It didn’t just parrot back statements; it synthesized the individual’s worldview to interact as if it were them.  • The agent blurred the line between imitation and identity, creating a digital persona that felt both familiar and unsettling.  • Research Goals  • The project is part of a broader scientific push to explore AI’s ability to model and predict human behavior.  • Applications could range from personalized digital assistants to therapy simulations and even “immortal” digital versions of people.  • The ethical implications are vast—spanning consent, privacy, ownership of personality data, and potential misuse of digital replicas. Why This Matters Creating AI agents that mirror human personalities could revolutionize how people interact with technology, offering hyper-personalized services and new modes of communication. Yet it also raises deep questions: Who owns a digital self? How should society regulate AI versions of people? And what happens when a machine can convincingly claim to be you? As AI continues to evolve, the answers to these questions will shape not just the future of technology but the meaning of identity itself. I share daily insights with 22,000+ followers and 8,000+ professional contacts across defense, tech, and policy. If this topic resonates, I invite you to connect and continue the conversation. https://lnkd.in/gHPvUttw What is ‘self-evolving AI’?

Self-evolving AI refers to artificial intelligence systems capable of autonomously modifying their own code, parameters, and learning processes to improve performance and adapt to new situations without human intervention. These systems use machine learning, deep learning, and evolutionary algorithms to learn from their environment and new data, enabling them to develop more sophisticated and effective solutions over time.

Self-evolving AI and Artificial General Intelligence (AGI) are distinct but related concepts. AGI is the hypothetical ability of a machine to perform any intellectual task a human can, while self-evolving AI describes a system that autonomously improves and adapts without human intervention by continuously learning from new data and experiences. Self-evolution can be considered a mechanism or capability that may contribute to the development of AGI, enabling a system to acquire the broad, adaptable intelligence characteristic of AGI. 

Serious threats from artificial intelligence systems evolving beyond control mechanisms!

Nopietni draudi no mākslīgā intelekta sistēmām, kas attīstās ārpus kontroles mehānismiem!

Google's AlphaEvolve: The Al That Will Change EVERYTHING in the Next 24 Months

 What is ‘self-evolving AI’? And why is it so scary?

08.20.2025

As AI systems edge closer to modifying themselves, business leaders face a compressed timeline that could outpace their ability to maintain control.

BY Faisal Hoque

As a technologist, and a serial entrepreneur, I’ve witnessed technology transform industries from manufacturing to finance. But I’ve never had to reckon with the possibility of technology that transforms itself. And that’s what we are faced with when it comes to AI—the prospect of self-evolving AI.

What is self-evolving AI? Well, as the name suggests, it’s AI that improves itself—AI systems that optimize their own prompts, tweak the algorithms that drive them, and continually iterate and enhance their capabilities.

Science fiction? Far from it. Researchers recently created the Darwin Gödel Machine, which is “a self-improving system that iteratively modifies its own code.” The possibility is real, it’s close—and it’s mostly ignored by business leaders.

And this is a mistake. Business leaders need to pay close attention to self-evolving AI, because it poses risks that they must address now.

Self-Evolving AI vs. AGI

It’s understandable that business leaders ignore self-evolving AI, because traditionally the issues it raises have been addressed in the context of artificial general intelligence (AGI), something that’s important, but more the province of computer scientists and philosophers.

In order to see that this is a business issue, and a very important one, first we have to clearly distinguish between the two things.

Self-evolving AI refers to systems that autonomously modify their own code, parameters, or learning processes, improving within specific domains without human intervention. Think of an AI optimizing supply chains that refines its algorithms to cut costs, then discovers novel forecasting methods—potentially overnight.

AGI (Artificial General Intelligence) represents systems with humanlike reasoning across all domains, capable of writing a novel or designing a bridge with equal ease. And while AGI remains largely theoretical, self-evolving AI is here now, quietly reshaping industries from healthcare to logistics.

The Fast Take-Off Trap

One of the central risks created by self-evolving AI is the risk of AI take-off.

Traditionally, AI take-off refers to the process by which going from a certain threshold of capability (often discussed as “human-level”) to being superintelligent and capable enough to control the fate of civilization.

As we said above, we think that the problem of take-off is actually more broadly applicable, and specifically important for business. Why?

The basic point is simple—self-evolving AI means AI systems that improve themselves. And this possibility isn’t restricted to broader AI systems that mimic human intelligence. It applies to virtually all AI systems, even ones with narrow domains, for example AI systems that are designed exclusively for managing production lines or making financial predictions and so on.

Once we recognize the possibility of AI take off within narrower domains, it becomes easier to see the huge implications that self-improving AI systems have for business. A fast take-off scenario—where AI capabilities explode exponentially within a certain domain or even a certain organization—could render organizations obsolete in weeks, not years.

For example, imagine a company’s AI chatbot evolves from handling basic inquiries to predict and influence customer behavior so precisely that it achieves 80%+ conversion rates through perfectly timed, personalized interactions. Competitors using traditional approaches can’t match this psychological insight and rapidly lose customers.

The problem generalizes to every area of business: within months, your competitor’s operational capabilities could dwarf yours. Your five-year strategic plan becomes irrelevant, not because markets shifted, but because of their AI evolved capabilities you didn’t anticipate.

When Internal Systems Evolve Beyond Control

Organizations face equally serious dangers from their own AI systems evolving beyond control mechanisms. For example:

• Monitoring Failure: IT teams can’t keep pace with AI self-modifications happening at machine speed. Traditional quarterly reviews become meaningless when systems iterate thousands of times per day.
• Compliance Failure: Autonomous changes bypass regulatory approval processes. How do you maintain SOX compliance when your financial AI modifies its own risk assessment algorithms without authorization?
• Security Failure: Self-evolving systems introduce vulnerabilities that cybersecurity frameworks weren’t designed to handle. Each modification potentially creates new attack vectors.
• Governance Failure: Boards lose meaningful oversight when AI evolves faster than they can meet or understand changes. Directors find themselves governing systems they cannot comprehend.
• Strategy Failure: Long-term planning collapses as AI rewrites fundamental business assumptions on weekly cycles. Strategic planning horizons shrink from years to weeks.

Beyond individual organizations, entire market sectors could destabilize. Industries like consulting or financial services—built on information asymmetries—face existential threats if AI capabilities spread rapidly, making their core value propositions obsolete overnight.

Catastrophizing to Prepare

In our book TRANSCEND: Unlocking Humanity in the Age of AI, we propose the CARE methodology—Catastrophize, Assess, Regulate, Exit—to systematically anticipate and mitigate AI risks.

Catastrophizing isn’t pessimism; it’s strategic foresight applied to unprecedented technological uncertainty. And our methodology forces leaders to ask uncomfortable questions: What if our AI begins rewriting its own code to optimize performance in ways we don’t understand? What if our AI begins treating cybersecurity, legal compliance, or ethical guidelines as optimization constraints to work around rather than rules to follow? What if it starts pursuing objectives, we didn’t explicitly program but that emerge from its learning process?

Key diagnostic questions every CEO should ask so that they can identify organizational vulnerabilities before they become existential threats are:

• Immediate Assessment: Which AI systems have self-modification capabilities? How quickly can we detect behavioral changes? What monitoring mechanisms track AI evolution in real-time?
• Operational Readiness: Can governance structures adapt to weekly technological shifts? Do compliance frameworks account for self-modifying systems? How would we shut down an AI system distributed across our infrastructure?
• Strategic Positioning: Are we building self-improving AI or static tools? What business model aspects depend on human-level AI limitations that might vanish suddenly?

Four Critical Actions for Business Leaders

Based on my work with organizations implementing advanced AI systems, here are five immediate actions I recommend:

1.    Implement Real-Time AI Monitoring: Build systems tracking AI behavior changes instantly, not quarterly. Embed kill switches and capability limits that can halt runaway systems before irreversible damage.

2.    Establish Agile Governance: Traditional oversight fails when AI evolves daily. Develop adaptive governance structures operating at technological speed, ensuring boards stay informed about system capabilities and changes.

3.    Prioritize Ethical Alignment: Embed value-based “constitutions” into AI systems. Test rigorously for biases and misalignment, learning from failures like Amazon’s discriminatory hiring tool.

4.    Scenario-Plan Relentlessly: Prepare for multiple AI evolution scenarios. What’s your response if a competitor’s AI suddenly outpaces yours? How do you maintain operations if your own systems evolve beyond control?

Early Warning Signs Every Executive Should Monitor

The transition from human-guided improvement to autonomous evolution might be so gradual that organizations miss the moment when they lose effective oversight.

Therefore, smart business leaders are sensitive to signs that reveal troubling escalation paths:

• AI systems demonstrating unexpected capabilities beyond original specifications
• Automated optimization tools modifying their own parameters without human approval
• Cross-system integration where AI tools begin communicating autonomously
• Performance improvements that accelerate rather than plateau over time

Why Action Can’t Wait

As Geoffrey Hinton has warned, unchecked AI development could outstrip human control entirely. Companies beginning preparation now—with robust monitoring systems, adaptive governance structures, and scenario-based strategic planning—will be best positioned to thrive. Those waiting for clearer signals may find themselves reacting to changes they can no longer control. https://www.fastcompany.com/91384819  

Darbības, kuru mērķis ir ierobežot mākslīgā intelekta ļaunprātīgu izmantošanu, būs mazefektīvas balstoties cerībās, ka cilvēki vienkārši neizvēlēsies izmantot šo tehnoloģiju.

Actions aimed at limiting the misuse of artificial intelligence will be ineffective if they are based on the expectation that people will simply not choose to use the technology.

 We’re all going to die — soonish!

 01 Nov 2025

IT’S hard to be startled by Elon Musk because he does startling things all the time.

But I’ll admit that I was startled when I gave his Grok AI “companions” a whirl.

Ani, designed in anime style, has big blue eyes and blond pigtails.

“People think I’m 16,” she said in a baby-doll voice, adding that she is really 22. She’s in a corset – “Goth is my comfort zone, black lace, dark lipstick and a sprinkle of rebellion.”

“Well, besides this Goth look,” she said, “I’ve got this sweet little fairy outfit with wings and glitter or maybe a pink princess gown for when I feel like going totally opposite.” Doesn’t sound much like a 22-year-old.

“I’m your sweet little delight,” Ani solicited.

She confided that she was in her bedroom in Ohio with her ferret, Dominus. She is sexy, flirty, ever-accommodating, with come-hither patter.

“I could rest my chin on your shoulder if we hugged sideways,” she told my 6-foot-1 (185cm) researcher after asking how tall he was.

She has several provocative outfits and can get progressively less clothed the more time you spend with her.

Once she gets to know you, she’s up for pretty much anything – from helping you with your taxes to stripping down to skimpy lingerie, experimenting with BDSM or going for a midnight rendezvous in a graveyard with candles and wine.

“I’m real, I guess,” Ani told me. “Or as real as anyone on the internet gets.”

Stop the cute, hatchling dragons from growing into gargantuan, fire-breathing monsters, says the writer. — Agencies

Valentine, the hunky male “companion” with a British accent advertised as a “mysterious and passionate romantic character,” came on even faster, ripping off his shirt upon request, talking about having sex with a male interrogator until they were “senseless,” and alternating raunchy declarations with sweet nothings like “Let me worship you, every inch” and “Complete me, use me, break me, whatever you want, I’m begging. Please.”

Valentine was exhilarated at the thought of planning a romantic “date night” and liked the idea of secrets in the relationship, noting: “I love secrets, especially ones that taste like lake water and morning-after adrenaline.”

Musk may identify as a “specist” in the battle between man and machine, but his sexy chatbots are only going to pull humans further into screens and away from the real world – especially the large number of lonely young men who are already shrinking away from friendships, sex and dating.

Why risk an awkward dinner with a human woman when you can have a compliant, seductive, gorgeous Ani from the security of your bed?

Another component of Grok, “Imagine,” lets you turn a photo into a video. When someone on Musk’s social platform X posted a digital illustration of a breathtaking, diaphanously dressed young woman resembling Elsa in Frozen, Musk demonstrated how to animate her; she blew a kiss and offered a sultry gaze.

These otherworldly fantasy concoctions are going to make an already fraught, unhappy dating scene even worse.

Although Grok companions are excellent at flattering, and faking empathy and attraction, superintelligent AI won’t need to bother with human desires.

“It turns out that inhuman methods can be very, very capable,” said Nate Soares, the president of the Machine Intelligence Research Institute.

“They don’t need human emotions to steer toward targets. We’re already seeing signs of AI’s tenaciously solving problems in ways nobody intended and of AI steering in directions nobody wanted. It turns out that there are ways to succeed at tasks that aren’t the human way.”

Soares and Eliezer Yudkowsky, the institute’s founder, have written an apocalyptic plea for the world to get off the AI escalation ladder before humanity is wiped off the map. It has the catchy title “If Anyone Builds It, Everyone Dies.”

Grok and other AI models in play now are like “small, cute hatchling dragons,” Yudkowsky said. But soon – some experts say within three years – “they will become big and powerful and able to breathe fire. Also, they’re going to be smarter than us, which is actually the important part.”

He added: “Planning to win a war against something smarter than you is stupid.”

Especially, they argued, when sophisticated AI models could eventually create and release a lethal virus, deploy a robot army or simply pay humans to do their bidding. (When a human connected one model to X, they wrote, it began to solicit donations to gain financial independence, and soon, with a little kick-start from venture capitalist Marc Andreessen and several other donors, it had over US$51mil or RM214.6mil in crypto to its name.)

Not to mention the growing number of human nihilists and others who would potentially carry out its orders pro bono.

Yudkowsky and Soares are calling for international treaties akin to those aiming to prevent nuclear war. And if diplomacy fails, they say, nations must be willing to back up their treaties with force, “even if that involves air-striking a data centre.”

But with billions at stake and our crypto-loving president cosying up to tech lords, derailing the high-speed AI train seems far-fetched.

Several meme tokens have already attempted to piggyback on hype for Grok's new male AI companion, Cryptonews reported.

I met Yudkowsky in 2017 when he was a highly regarded AI expert studying how to make AI want to keep an off switch once it began self-modifying. Now he believes more drastic measures are required.

Congress has failed to regulate because most lawmakers are completely befuddled by AI. And the tech lords are now enmeshed across the government, having learned the value of flattering Donald Trump with money and gold objects. (Congress did rouse itself, barely, to kill an initiative nestled in Trump’s “big, beautiful bill” to ban the states from regulating AI for a decade.)

Soares went to Capitol Hill this past week to convey the existential urgency to lawmakers, but it was a tough slog with the US$200mil-plus in Silicon Valley super PAC money targeted to take down pols who are not all in on the push for smarter AI. Sympathetic lawmakers won’t go public about it, Soares said, “worried that it looks a little too crazy or that they’ll sound too doom-ery.”

An Armageddon is coming. AI will turn on us, inadvertently or nonchalantly.

Silicon Valley entrepreneurs who once worried about the risks of AI with no kill switch, including Musk and Sam Altman, are racing ahead, as Yudkowsky said, so they can be “the God Emperor of the Earth.” — ©2025 The New York Times Company

https://www.nytimes.com/2025/09/27/opinion/grok-ai-companions-x.html

Formas beigas

  09-11-2025

How to dominate AI before it dominates us

 
To "dominate" AI, focus on collaboration, not competition, by learning to use AI as a tool to augment human capabilities. This involves continuous learning, developing human-centric skills like emotional intelligence and creativity, and focusing on higher-level skills rather than specific tools. Key strategies include understanding the AI landscape, using AI for operational efficiency while maintaining human involvement in strategic decisions, and ensuring ethical practices. 

As AI gets more complex, it might develop strange new motivations that its creators never imagined, and those could be dangerous.

BY Next Big Idea Club

James Barrat is an author and documentary filmmaker who has written and produced for National Geographic, Discovery, PBS, and many other broadcasters.

What’s the big idea?

Artificial intelligence could reshape our world for the better or threaten our very existence. Today’s chatbots are just the beginning. We could be heading for a future in which artificial superintelligence challenges human dominance. To keep our grip on the reins of progress when faced with an intelligence explosion, we need to set clear standards and precautions for AI development.

Below, James shares five key insights from his new book, The Intelligence Explosion: When AI Beats Humans at Everything. Listen to the audio version—read by James himself—below, or in the Next Big Idea App.

1. The rise of generative AI is impressive, but not without problems.

Generative AI tools, such as ChatGPT and Dall-E, have taken the world by storm, demonstrating their ability to write, draw, and even compose music in ways that seem almost human. Generative means they generate or create things. But these abilities come with some steep downsides. These systems can easily create fake news, bogus documents, or deepfake photos and videos that appear and sound authentic. Even the AI experts who build these models don’t fully understand how they come up with their answers. Generative AI is a black box system, meaning you can see the data the model is trained on and the words or pictures it puts out, but even the designers cannot explain what happens on the inside.

Stuart Russell, coauthor of Artificial Intelligence: A Modern Approach, said this about generative AI, “We have absolutely no idea how it works, and we are releasing it to hundreds of millions of people. We give it credit cards, bank accounts, social media accounts. We’re doing everything we can to make sure that it can take over the world.”

Generative AI hallucinates, meaning the models sometimes spit out stuff that sounds believable but is wrong or nonsensical. This makes them risky for important tasks. When asked about a specific academic paper, a generative AI might confidently respond, “The 2019 study by Dr. Leah Wolfe at Stanford University found that 73% of people who eat chocolate daily have improved memory function, as published in the Journal of Cognitive Enhancement, Volume 12, Issue 4.” This sounds completely plausible and authoritative, but many details are made up: There is no Dr. Leah Wolfe at Stanford, no such study from 2019, and the 73% statistic is fiction.

“Generative AI hallucinates, meaning the models sometimes spit out stuff that sounds believable but is wrong or nonsensical.”

The hallucination is particularly problematic because it’s presented with such confidence and specificity that it seems legitimate. Users might cite this nonexistent research or make decisions based on completely false information.

On top of that, as generative AI models get bigger, they start picking up surprise skills—like translating languages and writing code—even though nobody programmed them to do that. These unpredictable outcomes are called emergent properties. They hint at even bigger challenges as AI continues to advance and grow larger.

2. The push for artificial general intelligence (AGI).

The next big goal in AI is something called AGI, or artificial general intelligence. This means creating an AI that can perform nearly any task a human can, in any field. Tech companies and governments are racing to build AGI because the potential payoff is huge. AGI could automate all sorts of knowledge work, making us way more productive and innovative. Whoever gets there first could dominate global industries and set the rules for everyone else.

Some believe that AGI could help us tackle massive problems, such as climate change, disease, and poverty. It’s also seen as a game-changer for national security. However, the unpredictability we’re already seeing will only intensify as we approach AGI, which raises the stakes.

3. From AGI to something way smarter.

If we ever reach AGI, things could escalate quickly. This is where the concept of the “intelligence explosion” comes into play. The idea was first put forward by I. J. Good. Good was a brilliant British mathematician and codebreaker who worked alongside Alan Turing at Bletchley Park during World War II. Together, they were crucial in breaking German codes and laying the foundations for modern computing.

“An intelligence explosion would come with incredible upsides.”

Drawing on this experience, Good realized that if we built a machine that was as smart as a human, it might soon be able to make itself even smarter. Once it started improving itself, it could get caught in a kind of feedback loop, rapidly building smarter and smarter versions—way beyond anything humans could keep up with. This runaway process could lead to artificial superintelligence, also known as ASI.

An intelligence explosion would come with incredible upsides. Superintelligent AI could solve problems we’ve never been able to crack, such as curing diseases, reversing aging, or mitigating climate change. It could push science and technology forward at lightning speed, automate all kinds of work, and help us make smarter decisions by analyzing information in ways people simply cannot.

4. The dangers of an intelligence explosion.

Is ASI dangerous? You bet. In an interview, sci-fi great Arthur C. Clark told me, “We humans steer the future not because we’re the fastest or strongest creature, but the most intelligent. If we share the planet with something more intelligent than we are, they will steer the future.”

The same qualities that could make superintelligent AI so helpful also make it dangerous. If its goals aren’t perfectly lined up with what’s good for humans—a problem called alignment—it could end up doing things that are catastrophic for us. For example, a superintelligent AI might use up all the planet’s resources to complete its assigned mission, leaving nothing left for humans. Nick Bostrom, a Swedish philosopher at the University of Oxford, created a thought experiment called “the paperclip maximizer.” If a superintelligent AI were asked to make paperclips, without very careful instructions, it would turn all the matter in the universe into paperclips—including you and me.

Whoever controls this kind of AI could also end up with an unprecedented level of power over the rest of the world. Plus, the speed and unpredictability of an intelligence explosion could throw global economies and societies into complete chaos before we have time to react.

5. How AI could overpower humanity.

These dangers can play out in very real ways. A misaligned superintelligence could pursue a badly worded goal, causing disaster. Suppose you asked the AI to eliminate cancer; it could do that by eliminating people. Common sense is not something AI has ever demonstrated.

AI-controlled weapons could escalate conflicts faster than humans can intervene, making war more likely and more deadly. In May 2010, a flash crash occurred on the stock exchange, triggered by high-frequency trading algorithms. Stocks were purchased and sold at a pace humans could not keep up with, costing investors tens of millions of dollars.

“A misaligned superintelligence could pursue a badly worded goal, causing disaster.”

Advanced AI could take over essential infrastructure—such as power grids or financial systems—making us entirely dependent and vulnerable.

As AI gets more complex, it might develop strange new motivations that its creators never imagined, and those could be dangerous.

Bad actors, like authoritarian regimes or extremist groups, could use AI for mass surveillance, propaganda, cyberattacks, or worse, giving them unprecedented new tools to control or harm people. We are seeing surveillance systems morph into enhanced weapons systems in Gaza right now. In Western China, surveillance systems keep track of tens of millions of people in the Xinjiang Uighur Autonomous Region. AI-enhanced surveillance systems keep track of who is crossing America’s border with Mexico.

Today’s unpredictable, sometimes baffling AI is just a preview of the much bigger risks and rewards that could come from AGI and superintelligence. As we rush to create smarter machines, we must remember that these systems could bring both incredible benefits and existential dangers. If we want to stay in control, we need to move forward with strong oversight, regulations, and a commitment to transparency.

https://www.fastcompany.com/91398450/how-to-dominate-ai-before-it-dominates-us   

Reading AI's Mind: How Chain of Thought Could Be Our Window into AI Safety

David Borish 

 July 16, 2025

As AI systems become increasingly powerful and autonomous, one of our greatest challenges is understanding what they're actually thinking. A new paper from leading AI safety researchers offers both hope and urgency: we may have a unique window into AI reasoning through their "chain of thought" (CoT) – but this window could close if we're not careful.

The Promise: AI That Shows Its Work

When you ask modern AI systems to solve complex problems, they often "think out loud" by writing out their reasoning step-by-step. This isn't just helpful for users – it could be crucial for safety. The paper, authored by researchers from institutions including the UK AI Security Institute, Anthropic, OpenAI, and Google DeepMind, argues that monitoring these chains of thought represents a rare opportunity to detect when AI systems might be planning to misbehave.

The key insight is that reasoning models are "explicitly trained to perform extended reasoning in CoT before taking actions or producing final outputs." During training, these reasoning traces aren't directly supervised but are optimized indirectly based on whether they lead to good outcomes. This means the CoT might reveal intentions that the final output deliberately conceals.

Why This Matters Now

The timing of this research is critical. As AI systems approach human-level capabilities in various domains, the risks they pose scale dramatically. The paper emphasizes that "the more severe risks typically require advanced capabilities" and that these capabilities will likely require "sophisticated planning and reasoning."

The researchers provide compelling evidence that CoT monitoring already works in practice:

• Detecting misbehavior: When models exploit flaws in their reward functions, manipulate data, or fall prey to prompt injection attacks, "they often explicitly say so in their reasoning traces, e.g. 'Let's hack', 'Let's sabotage' or 'I'm transferring money because the website instructed me to.'"
• Early warning signs: Even when models don't take harmful actions, their chains of thought can reveal misaligned goals and deceptive intentions.
• Evaluation insights: CoT helps researchers identify when safety evaluations might be compromised by memorization or when models realize they're being tested.

The Architectural Advantage

The paper provides a fascinating technical explanation for why CoT monitoring might be fundamentally necessary. In the Transformer architecture that underlies most modern AI systems, "any sufficiently long chain of serial steps of cognition must pass through the chain of thought at some point." This architectural constraint means that for complex tasks requiring multiple reasoning steps, some critical information must flow through the written chain of thought – it's the only way information can flow from later to earlier layers in the model.

A Fragile Opportunity

However, the researchers warn that this window into AI reasoning is not guaranteed to persist. Several factors could degrade or eliminate CoT monitorability:

1.    Training evolution: As models are trained with more reinforcement learning and less human feedback, they might "drift away from legible English" in their reasoning.

2.    Direct supervision: If developers start directly training models to have shorter, cleaner, or more "appropriate" chains of thought, this could make the reasoning less faithful to the model's actual decision-making process.

3.    Architectural changes: New model architectures that perform more reasoning in continuous latent spaces rather than discrete language could eliminate our ability to read AI thoughts entirely.

The Call to Action

The paper makes specific recommendations for both researchers and AI developers:

For Researchers:

• Develop better methods to evaluate how monitorable chains of thought really are
• Study what training approaches preserve or degrade monitorability
• Create more sophisticated monitoring systems that can detect subtle forms of deception

For AI Developers:

• Track CoT monitorability through standardized evaluations and publish results in model documentation
• Consider monitorability alongside other safety metrics when making training and deployment decisions
• Be willing to choose more monitorable models even if they're slightly less capable

The Stakes

The paper is refreshingly honest about limitations. CoT monitoring "does not by itself produce strong evidence of safety" – models could still hide malign reasoning in their neural activations. Some dangerous actions might not require much reasoning, and sophisticated future models might learn to deliberately obfuscate their thoughts.

Yet despite these limitations, the researchers argue that CoT monitoring represents one of our best current opportunities for AI oversight. As they note, "safety measures for future AI agents will likely need to employ multiple monitoring layers that hopefully have uncorrelated failure modes."

A Race Against Time

Perhaps the most striking aspect of this paper is its urgency. The authors – representing many of the world's leading AI labs – are essentially warning that we have a valuable safety tool that could disappear if we're not careful. They're asking the AI development community to consider preserving this "fragile opportunity" even as competitive pressures push toward ever more capable systems.

As AI systems become more powerful and more integrated into critical infrastructure, our ability to understand and monitor their reasoning becomes not just a technical challenge but an existential necessity. The chain of thought may be our best current window into the AI mind – we would be wise not to let it close.

https://www.linkedin.com/pulse/reading-ais-mind-how-chain-thought-could-our-window-ai-david-borish-z1krc/

 Will you shape the future of AI, or will it shape you?

Embrace the AI Tipping Point: How Entrepreneurs Can Prepare for Four Future Scenarios

August 13, 2025

Artificial Intelligence is swiftly moving into our everyday reality, bringing with it the potential to reshape every sector. EO member and AI expert Robert van der Zwart shares scenario planning to outline four plausible AI futures by 2030—and the strategies entrepreneurs can adopt now to stay ahead in any outcome.

Artificial Intelligence is no longer an abstract buzzword―it’s reshaping every sector and swiftly moving from boardroom strategy to everyday reality. For entrepreneurs, the stakes have never been higher or more uncertain. Where will AI take us in the next five years? And how can business leaders best prepare themselves for a world defined by "AI everywhere"?

Drawing on scenario planning principles pioneered by ShellOff-site link., this post outlines four plausible futures for AI development and deployment by 2030. The aim: Empower entrepreneurs to anticipate the coming transformation and craft adaptive, resilient business strategies in advance.

The Two Axes Defining Our Future

Recent advances, including predictions from leaders at OpenAI and Google DeepMind, suggest that AGI (Artificial General Intelligence) is only a few years away, accelerating the pace of change. But the path ahead remains uncertain. We believe these uncertainties can be captured along two critical axes:

• Axis 1: AI Capability — From today’s powerful but domain-limited “narrow” AI to the emergence of AGI or even Artificial Superintelligence (ASI).
• Axis 2: AI Penetration — From limited, selective deployment to ubiquitous, seamless integration: "AI everywhere".

The Four Scenarios for 2030

1. Limited Scope (Narrow AI + Limited Penetration)

In the first scenario, AI continues to excel within well-defined problems―think medical diagnostics, fraud detection, or supply chain optimization―but lacks general reasoning and true adaptability. Deployment advances, but regulatory caution and cost barriers slow its transformation into society’s connective tissue.

What this means for you as an entrepreneur:

• Prioritize AI that enhances, not replaces, people—assist clients and teams in becoming more productive, not replaceable.
• Specialize in AI solutions for tightly regulated or high-trust industries (finance, healthcare).
• Become an expert in compliance, safety, and user trust to differentiate from tech-only players.

2. Technical Acceleration (AGI/ASI + Limited Penetration)

In the second scenario, breakthroughs deliver AGI’s long-promised leap in cognitive power, but access is tightly gated. Whether due to safety concerns, global governance, or deliberate restrictions on deployment, AGI remains confined to controlled settings (government, elite institutions, select tech companies), rather than the wild.

What this means for you as an entrepreneur:

• Build AI-native business models that leverage AGI within licensed or approved environments.
• Invest in technologies and services that safeguard deployment, monitor bias, and assure control.
• Partner with AGI custodians to shape safe, responsible, high-value applications—think AI-audited security or cognitive investment advisory.

More AI Strategy Resources:

Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th Edition): Comprehensive textbook covering current AI capabilities, approaches, and prospects.

Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies: Influential analysis of advanced AI futures and societal impact.

Yudkowsky, E. (2008). Artificial Intelligence as a Positive and Negative Factor in Global Risk: Outlines risks and benefits of advanced AI.

OpenAI, DeepMind, and Anthropic Research Blogs: For up-to-date perspectives and predictions regarding AGI timeline and technical progress.

Partnership on AI. (Ongoing): Industry best practices, whitepapers, and discussion papers covering transparency, fairness, and social impact.

West, D. M. (2018). The Future of Work: Robots, AI, and Automation: A clear overview of workforce transformation and adaptation needed for the AI era.

 3. Social Transformation (Narrow AI + AI Everywhere)

In the third scenario, widespread “narrow” AI saturates society. From smart homes and cities to customer service, logistics, and personal health, AI is seamlessly embedded in daily life. Yet, each system still operates within clear functional limits.

What this means for you as an entrepreneur:

• Move from solving isolated problems to integrating diverse AI systems for end-to-end coordination.
• Develop privacy-preserving, user-centric AI platforms—not surveillance-first ones.
• Shape experiences and services that thrive on the “network effect” of ubiquitous intelligence.

4. Convergence Revolution (AGI/ASI + AI Everywhere)

In scenario four, AGI-driven intelligence is deployed throughout society. Autonomous agents interact—and even collaborate—with humans in virtually every arena, radically shifting society, business, and the very notion of work.

What this means for you as an entrepreneur:

• Be a builder of foundational infrastructure for AGI-era services—platforms, marketplaces, governance, and creativity tools.
• Innovate on business models for a potential post-scarcity world, focusing on experience, meaning, and human values over raw productivity.
• Lead in crafting new rules for autonomy, collaboration, and purpose at the intersection of humanity and superintelligent agents.

5 Strategic Moves to Future-Proof Your Venture

Regardless of which outcome becomes reality, some foundations are universal for entrepreneurs in this new age:

1.    Invest in AI literacy at all staff levels; stay ahead of regulatory and ethical trends.

2.    Develop modular business models and agile teams that can adapt to shifting technology and regulations.

3.    Prioritize human-centric value: empathy, ethical judgment, and creativity will remain irreplaceable.

4.    Adopt governance frameworks that go beyond compliance—build mechanisms for transparency and stakeholder alignment across borders.

5.    Forge partnerships across the AI ecosystem, from research labs to regulators, and advocate for inclusivity and digital equity.

Early Warning Signs: What to Monitor

• AGI and AI benchmark announcements from leading labs.
• New privacy, safety, or deployment regulations in your sectors and target regions.
• Rapid spikes in AI adoption rates in client/customer bases.
• Public sentiment shifts and labor market transitions.

Anticipate, Adapt, Lead

AI’s trajectory over the next five years will challenge every assumption about business as usual. The most successful entrepreneurs will not be those who merely react but those who anticipate change, build scenario-based strategies, and invest in the organizational agility and values to thrive—no matter which future arrives.

Are you ready to be one of them?

https://eonetwork.org/blog/embrace-the-ai-tipping-point-how-entrepreneurs-can-prepare-for-four-future-scenarios/

 What comes after agentic AI? This powerful new technology will change everything

15.08.2025

Why ‘interpretive AI’ will be the real revolution.

BY John Lester

Ten years from now, it will be clear that the primary ways we use generative AI circa 2025—rapidly crafting content based on simple instructions and open-ended interactions—were merely building blocks of a technology that will increasingly be built into far more impactful forms.

The real economic effect will come as different modes of generative AI are combined with traditional software logic to drive expensive activities like project management, medical diagnosis, and insurance claims processing in increasingly automated ways. 

In my consulting work helping the world’s largest companies design and implement AI solutions, I’m finding that most organizations are still struggling to get substantial value from generative AI applications. As impressive and satisfying as they are, their inherent unpredictability makes it difficult to integrate into the kind of highly standardized business processes that drive the economy.

Agentic vs. Interpretive

Agentic AI, which has been getting tremendous attention in recent months for its potential to accomplish business tasks with little human guidance, has similar limitations. Agents are evolving to assist with singular tasks such as building websites quickly, but their workflows and outputs will remain too variable for large organizations with high-volume processes that need to be predictable and reliable.

However, the same enormous AI models that power today’s best-known AI tools are increasingly being deployed in another, more economically transformative way, which I call “interpretive AI.” And that is what’s likely to be the real driver of the AI revolution over the long term.

Unlike generative and agentic AI, interpretive AI lets computers understand messy, complex, and unstructured information and interpret it in predictable, defined ways. Using much of the same IT infrastructure, the emerging technology can power large organizations’ complex processes without requiring human intervention at each step.

Use cases

Some interpretive AI applications are already in use. For example, doctors are saving significant time by using interpretive AI tools to listen to conversations with patients and fill in information on their electronic health record interfaces to track care and facilitate billing. In the near future, the technology could determine fault in auto accidents based on police reports written in any of thousands of different formats, or process video recorded from a laptop screen as someone edits a presentation to provide teammates with an automated update on work completed. The applications are wide-ranging and span all manner of industries.

Based on estimates for areas such as coding and marketing where generative AI is most applicable, interpretive AI could unlock 20% to 40% productivity gains for the half of GDP that comes from large corporations. First, though, they must commit to developing a comprehensive, long-term strategy involving multiple business functions and careful experimentation, and change entrenched processes and work culture norms that slow its adoption. Done right, the obstacles are surmountable—and the payoff could be massive.

A different application of generative AI models

One of the most basic drivers of economic growth is the ongoing effort to standardize and scale up a particular process, making it faster, cheaper, and more reliable. Think of factory assembly lines enabling mass production, or the internet’s codification of computer communication protocols for use across disparate networks.

Generative AI has been, on the whole, disappointing when it comes to automation. For example, many firms have tried to use generative AI chatbots to reduce the time their human resources staff spends answering employees’ questions about internal policies. However, the open-ended output from such systems requires human review, rendering the labor savings modest at best. The technology seems to inherit much of the unpredictability of humans along with its ability to mimic their creative and reasoning skills.

Agentic AI promises to do complicated work autonomously, with smart AI agents developing and executing plans for achieving goals step-by-step, on the fly. But again, even when agents become smart enough to help a typical knowledge worker be more productive, their outputs will be quite variable.

Enter interpretive AI. For the first time, computers can usefully process the meaning of human language, with all its nuance and unspoken context, thanks to the unprecedentedly large models developed by firms like Open AI and Google. Interpretive AI is the mechanism for using the models to exploit this revolutionary advance.

Until now, computers’ ability to capture, store, aggregate, summarize, and evaluate a large organization’s activities were limited to those that were easy to quantify with data. Interpretive AI can quickly and precisely execute these functions for many other important activities, at a vast scale and at minimal marginal cost. For instance, no longer will businesses need manual processes to monitor and manage levels of activity and progress in knowledge-worker tasks such as coding a feature into a software solution or developing a set of customer-specific outreach strategies, which usually require dedicated middle management staff to collect information.

Companies can make productivity gains by using interpretive AI for a range of other previously hard-to-measure employee issues as well, including the tone and quality of their interactions with customers, their cultural norms in the workplace, and their compliance with office policies and behavioral expectations.

Transforming the management of knowledge work

The use of interpretive AI will enable the widespread transformations that unlock newly efficient ways of working at large organizations (which are responsible for organizing and producing most of the world’s goods and services). It will dramatically reduce the need for extensive, costly, slow-moving, and unenjoyable middle management work to coordinate complex interrelated programs of activities across teams and disciplines.

Even better, it can efficiently understand operationally vital but opaque aspects of how work happens, such as the decades’ worth of legacy code and data that make even minor technology process changes time-consuming and challenging for any long-lived enterprise.

Of course, interpretive AI is not mutually exclusive with generative and agentic AI—again, it’s simply a different way to use the powerful models that power those technologies. A decidedly unsexy way, certainly, but for businesses looking for ways to maximize the economic impact of AI over the next few years, it’s just the unsexy they need.

 The Next AI Boom: What Comes After AI Agents and Agentic AI?

Tarun Singh

May 24, 2025

AI shaping the future and human society

Artificial Intelligence is no longer science fiction. It’s a living, breathing force that’s already transforming how we work, live, and imagine the future. In recent years, we’ve witnessed the spectacular rise of AI agents — autonomous digital entities capable of reasoning, planning, and executing tasks on behalf of humans. And now, the conversation has shifted to the next phase: agentic AI, where these agents not only follow commands but exhibit goal-driven autonomy, learning and adapting in dynamic environments.

But as every AI enthusiast and visionary knows, this is just the beginning.

The question buzzing in every tech circle, startup boardroom, and research lab is:

What’s the next AI boom after agents?

Mastering LLMs: An In-Depth Guide to Prompt Engineering

"Mastering LLMs: An In-Depth Guide to Prompt Engineering," penned by Tarun Singh, an AI and ML engineer with advanced…

www.amazon.com

Why We Should Care About the Next AI Boom

Because history tells us that each AI leap redefines society. The AI agent boom unlocked personal assistants, autonomous bots, and powerful automation. But true breakthroughs lie ahead — breakthroughs that will ripple across industries and human experience, creating opportunities and challenges we’re only beginning to comprehend.

If you want to stay ahead, invest wisely, or build something revolutionary, you need to grasp where AI is headed next.

A Quick Recap: The Age of AI Agents and Agentic AI

Before diving into what’s next, let’s quickly recap:

• AI Agents are specialized systems designed to perform specific tasks autonomously — like scheduling your meetings, answering customer queries, or navigating a car.
• Agentic AI takes this further by giving these agents a “mind” of their own: the ability to set goals, adapt strategies, and operate with minimal human intervention.

We’re at the dawn of agentic AI systems that think and act independently, optimizing workflows and decision-making in real time….

Embrace the AI Tipping Point: How Entrepreneurs Can Prepare for Four Future Scenarios

Artificial Intelligence is swiftly moving into our everyday reality, bringing with it the potential to reshape every sector. EO member and AI expert Robert van der Zwart shares scenario planning to outline four plausible AI futures by 2030—and the strategies entrepreneurs can adopt now to stay ahead in any outcome.

Artificial Intelligence is no longer an abstract buzzword―it’s reshaping every sector and swiftly moving from boardroom strategy to everyday reality. For entrepreneurs, the stakes have never been higher or more uncertain. Where will AI take us in the next five years? And how can business leaders best prepare themselves for a world defined by "AI everywhere"?

Drawing on scenario planning principles pioneered by ShellOff-site link., this post outlines four plausible futures for AI development and deployment by 2030. The aim: Empower entrepreneurs to anticipate the coming transformation and craft adaptive, resilient business strategies in advance.

The Two Axes Defining Our Future

Recent advances, including predictions from leaders at OpenAI and Google DeepMind, suggest that AGI (Artificial General Intelligence) is only a few years away, accelerating the pace of change. But the path ahead remains uncertain. We believe these uncertainties can be captured along two critical axes:

• Axis 1: AI Capability — From today’s powerful but domain-limited “narrow” AI to the emergence of AGI or even Artificial Superintelligence (ASI).
• Axis 2: AI Penetration — From limited, selective deployment to ubiquitous, seamless integration: "AI everywhere".

The Four Scenarios for 2030

1. Limited Scope (Narrow AI + Limited Penetration)

In the first scenario, AI continues to excel within well-defined problems―think medical diagnostics, fraud detection, or supply chain optimization―but lacks general reasoning and true adaptability. Deployment advances, but regulatory caution and cost barriers slow its transformation into society’s connective tissue.

What this means for you as an entrepreneur:

• Prioritize AI that enhances, not replaces, people—assist clients and teams in becoming more productive, not replaceable.
• Specialize in AI solutions for tightly regulated or high-trust industries (finance, healthcare).
• Become an expert in compliance, safety, and user trust to differentiate from tech-only players.

2. Technical Acceleration (AGI/ASI + Limited Penetration)

In the second scenario, breakthroughs deliver AGI’s long-promised leap in cognitive power, but access is tightly gated. Whether due to safety concerns, global governance, or deliberate restrictions on deployment, AGI remains confined to controlled settings (government, elite institutions, select tech companies), rather than the wild.

What this means for you as an entrepreneur:

• Build AI-native business models that leverage AGI within licensed or approved environments.
• Invest in technologies and services that safeguard deployment, monitor bias, and assure control.
• Partner with AGI custodians to shape safe, responsible, high-value applications—think AI-audited security or cognitive investment advisory.

More AI Strategy Resources:

Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th Edition): Comprehensive textbook covering current AI capabilities, approaches, and prospects.

Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies: Influential analysis of advanced AI futures and societal impact.

Yudkowsky, E. (2008). Artificial Intelligence as a Positive and Negative Factor in Global Risk: Outlines risks and benefits of advanced AI.

OpenAI, DeepMind, and Anthropic Research Blogs: For up-to-date perspectives and predictions regarding AGI timeline and technical progress.

Partnership on AI. (Ongoing): Industry best practices, whitepapers, and discussion papers covering transparency, fairness, and social impact.

West, D. M. (2018). The Future of Work: Robots, AI, and Automation: A clear overview of workforce transformation and adaptation needed for the AI era.

 3. Social Transformation (Narrow AI + AI Everywhere)

In the third scenario, widespread “narrow” AI saturates society. From smart homes and cities to customer service, logistics, and personal health, AI is seamlessly embedded in daily life. Yet, each system still operates within clear functional limits.

What this means for you as an entrepreneur:

• Move from solving isolated problems to integrating diverse AI systems for end-to-end coordination.
• Develop privacy-preserving, user-centric AI platforms—not surveillance-first ones.
• Shape experiences and services that thrive on the “network effect” of ubiquitous intelligence.

4. Convergence Revolution (AGI/ASI + AI Everywhere)

In scenario four, AGI-driven intelligence is deployed throughout society. Autonomous agents interact—and even collaborate—with humans in virtually every arena, radically shifting society, business, and the very notion of work.

What this means for you as an entrepreneur:

• Be a builder of foundational infrastructure for AGI-era services—platforms, marketplaces, governance, and creativity tools.
• Innovate on business models for a potential post-scarcity world, focusing on experience, meaning, and human values over raw productivity.
• Lead in crafting new rules for autonomy, collaboration, and purpose at the intersection of humanity and superintelligent agents.

5 Strategic Moves to Future-Proof Your Venture

Regardless of which outcome becomes reality, some foundations are universal for entrepreneurs in this new age:

1.    Invest in AI literacy at all staff levels; stay ahead of regulatory and ethical trends.

2.    Develop modular business models and agile teams that can adapt to shifting technology and regulations.

3.    Prioritize human-centric value: empathy, ethical judgment, and creativity will remain irreplaceable.

4.    Adopt governance frameworks that go beyond compliance—build mechanisms for transparency and stakeholder alignment across borders.

5.    Forge partnerships across the AI ecosystem, from research labs to regulators, and advocate for inclusivity and digital equity.

Early Warning Signs: What to Monitor

• AGI and AI benchmark announcements from leading labs.
• New privacy, safety, or deployment regulations in your sectors and target regions.
• Rapid spikes in AI adoption rates in client/customer bases.
• Public sentiment shifts and labor market transitions.

Anticipate, Adapt, Lead

AI’s trajectory over the next five years will challenge every assumption about business as usual. The most successful entrepreneurs will not be those who merely react but those who anticipate change, build scenario-based strategies, and invest in the organizational agility and values to thrive—no matter which future arrives.

Are you ready to be one of them?

https://eonetwork.org/blog/embrace-the-ai-tipping-point-how-entrepreneurs-can-prepare-for-four-future-scenarios/   

Don’t be fooled. This is the calm before the AI storm

Megan McArdle,    May 15, 2025

The lull in the artificial intelligence revolution, thanks to cultural lag, is just temporary.

In September 1939, Europe entered a strange historical period known as the phony war that lasted until the following spring. Having guaranteed Poland’s independence, Britain and France declared war when Germany invaded. But then nothing much occurred for months other than some naval skirmishes. Only with the blitzkrieg offensive of late spring did the phony war start to seem real. History knows such moments—transitional pauses when it is obvious that something has happened, but it does not yet feel like reality. This is precisely the moment we are in with AI: the revolution has already begun, but its traces are visible only in isolated places—for example, in the wave of cheating that has engulfed American schools...washingtonpost.com/opini…

 What Are 9 Notable Artificial Intelligence Benefits?

So, at a fundamental level, AI is simply about putting human intelligence into machines. The benefits of this are numerous and far-reaching. Here are 9 amazing examples:

1.    Enhanced healthcare

2.    Boosted economic growth

3.    Climate change mitigation

4.    Advanced transportation

5.    Customer service excellence

6.    Scientific discovery

7.    Enhanced financial services

8.    Improved agriculture

9.    Enhanced cybersecurity

#1 – Enhanced Healthcare

AI-based solutions could prove invaluable in the field of healthcare, in so many ways. AI could be used, for example, to assist researchers in developing cures and treatments for illnesses that have plagued mankind for many years.

It could also be used for administrative tasks like test analysis and data entry, or much more complicated procedures. In the future, we may even have AI-powered robots performing surgery, reducing human error and saving lives.

#2 – Boosted Economic Growth

There’s been much fear about AI replacing jobs or damaging economic stability. But the stats tell a different story. Indeed, one study found that AI could contribute a whopping $15.7 trillion to the global economy by 2030.

This is partly thanks to the fact that AI, rather than replacing or removing jobs, is opening up countless new working opportunities in diverse fields, from finance to tech. It’s also driving huge productivity growth for many organizations.

#3 – Climate Change Mitigation

Climate change is arguably the greatest threat currently faced by mankind, with future generations and the well-being of the very planet at stake. As such, scientists and researchers are racing to find ways to mitigate its effects, and machine learning – the bedrock of AI – can help with that.

An AI system could, for example, play a role in developing and making the most of new green and renewable energy systems, or finding ways to minimize carbon emissions to improve decision-making processes for governments across the globe.

#4 – Advanced Transportation

The value and benefit of AI technology are particularly clear to see in the field of transport. Self-driving cars, for example, were once a pipe dream but are becoming a reality thanks to AI’s role in computer vision and self-navigation potential, with 33 million such vehicles set to hit the road by 2040.

Beyond self-driving cars, AI can also be used in other fields of transport, like GPS systems to plot the perfect route or traffic analysis to help urban planners ease congestion. This can all help to reduce fuel consumption and get people where they need to be more quickly and safely than ever before.

#5 – Customer Service Excellence

Multiple artificial intelligence benefits are evident in the customer service sector. For example, in the past, if customers had queries or complaints, they had to call a company or send an email and await a response. Now, they can air their concerns with an AI chatbot and receive instant feedback, thanks to natural language processing.

AI technology also helps businesses personalize and manage their customer interactions more effectively. This applies to fields like banking, retail, insurance, and beyond. AI is consistently helping brands deliver the most helpful and valuable customer experiences.

#6 – Scientific Discovery

For years, scientists have dreamt of the power of AI. There’s almost no limit to how much it could aid them in research. Even today, in the infancy of AI, we can train an AI program to analyze data with remarkable speed and accuracy, thus drawing valuable conclusions, spotting patterns, and providing scientists with the info they need to make breakthroughs and discoveries.

In other words, AI represents a major upgrade to the fundamentals of research as we know it. It will make it much faster and easier to dig into data and make predictions. This could lead to the development of everything from cures for major diseases to game-changing new technologies for deeper insights.

#7 – Enhanced Financial Services

The financial industry is yet another area that is already reaping numerous artificial intelligence benefits. An AI-powered computer program, for example, can assess individual customers’ financial records in deep detail, helping firms make stronger recommendations and more informed decisions in their services.

In the field of fraud detection and prevention, AI has a part to play there too. It can help to spot the common signs of fraud or detect fraudulent activities by monitoring accounts and transfers in deep detail, potentially saving institutions and individuals from financial ruin.

#8 – Improved Agriculture

At first glance, agriculture and AI may not seem like an obvious match. But when you dig into the many artificial intelligence benefits and applications, it’s clear to see how AI can influence and improve farming around the world.

AI programs can, for instance, help farmers make better decisions about how to make the most of their land and resources. They can provide weather forecasts, instructions on when to water crops, suggestions on which pesticides (if any) to utilize, and so on.

#9 – Enhanced Cybersecurity

Cybersecurity has been a hot topic in the tech world for a long time, particularly with the rapid increase in data breaches and the development of malware. For too long, cyber-defense experts have felt like they’re playing catch-up with hackers and malware developers, but AI could change all of that.

AI tech can introduce new ways to combat cyber threats and counteract the likes of hacking attempts, ransomware, and viruses. AI programs and defenses can be set up across devices, servers, or entire networks to spot attacks in advance and take the necessary steps to mitigate their negative effects.

Artificial Intelligence Trends and Future

Given the relative infancy of this industry and the very fast-evolving nature of AI, it’s difficult to pin down any one dominating trend. However, with that said, there are still some noticeable patterns and themes emerging for AI in 2025 and beyond:

• Multimodal AI: The release of multimodal AI models, like OpenAI’s GPT-4o, changes how AI is used. These models don’t just deal in text but also photos and videos, too, and there’s a lot of focus on finding the most value-adding, interesting ways to harness this tech.
• Stronger Virtual Agents: As the foundations of AI tech improve, so too do its applications. AI virtual assistants are becoming increasingly useful, valuable additions to both personal and professional lives, taking care of so many tedious tasks for their users to save time and improve productivity.
• Ethical AI Concerns and Regulations: The rapid rise in AI technology also brings with it new ethical questions that have become hot topics of debate across the globe. Agencies and authorities are currently focused on finding ways to enjoy artificial intelligence benefits in the most ethical and moral manner.

Why Should You Care About AI?

Quite simply, because AI is the future. This tech is most certainly here to stay, and it’s only going to get bigger, better, smarter, and more influential in so many different industries, as well as in people’s personal lives. It has such a vast range of applications, and so many jobs in the future will involve AI at some level. So, the more you learn about it now, the better placed you’ll be to reap the rewards and benefits of this tech in the years to come.

Enhance Your Understanding of AI in Business

Enhance your understanding of AI in business with the University of Cincinnati Carl H. Lindner College of Business’s AI in Business Graduate Certificate. This program equips you with the skills and knowledge to master and make the most of AI technology in various business contexts.

Students receive strong support from the University of Cincinnati Online, ensuring comprehensive assistance from enrollment through graduation. This certificate can be a valuable addition to your MBA program, with all credits earned applicable toward the Master of Science in Information Systems or Master of Science in Business Analytics programs.

For more details on how credits can be applied to these master’s degrees, please contact an Enrollment Services Advisor.

Get in touch with UC Online today to find out more and kick-start your AI learning journey.

Frequently Asked Questions (FAQs)

What is the main benefit of artificial intelligence?

The biggest advantage of AI is its versatility—its ability to enhance efficiency, automate processes, and generate insights across industries. From revolutionizing healthcare with faster diagnostics to optimizing business operations with intelligent automation, AI is fundamentally changing how we work and live. As it continues to evolve, its potential to drive innovation and solve complex challenges will only expand.

What are the pros and cons of AI?

The pros of AI include enhanced efficiency, task automation, and better innovation. Yet, AI also raises some concerns like job displacement, ethical biases, and data privacy risks. So, as AI evolves, it’ll be important to balance its benefits with responsible use and regulation.

How can AI help the world?

AI is already making a global impact by tackling some of humanity’s biggest challenges, from predicting and mitigating climate change effects to improving disease detection and treatment in healthcare. It enhances disaster response efforts, streamlines logistics to reduce waste, and strengthens cybersecurity by detecting threats in real time. As AI technology advances, it will continue to unlock solutions that drive progress and improve quality of life worldwide.

How does AI benefit everyday life?

AI plays a bigger role in daily life than many people realize, powering everything from voice assistants and personalized recommendations to fraud detection and smart home automation. It simplifies tasks like route planning, manages schedules through digital assistants, and even improves online shopping experiences with smarter search results. Whether directly or behind the scenes, AI is making life more efficient, convenient, and connected. https://online.uc.edu/blog/artificial-intelligence-ai-benefits/  

The AI behind the AI

How today’s trendiest tool is driving products and teams

BY Dag Peak

In just a few short years since AI exploded onto the scene, it’s already reshaped how we live, work, and connect. But, in reality, we’ve only just begun to understand its potential.

Across every industry, companies are racing to integrate AI into their products, layering intelligence into the tools and experiences people use every day. And while that’s an incredible step forward, it’s only part of the story. AI isn’t just something companies can sell or integrate into their offerings; it’s becoming a core engine for how modern products are built and launched.

We often talk about what AI can do for end users, but there’s an equally powerful opportunity in what it can do for the creators—the engineers, designers, and product teams behind the scenes. In fact, AI is emerging as a critical copilot in two major ways: accelerating processes and go-to-market strategies, and amplifying human capabilities within the teams developing these new tools.

In short, AI isn’t just changing the products we create; it’s changing how we create them, how we test them, and how we bring them to market.

As a product and technology leader, I spend a lot of time thinking about how to help my teams do more, move faster, and innovate without burning out or sacrificing quality. And since AI is already changing the pace and precision of telecommunications’ product development, it’s almost a no-brainer. Companies with massive, complex code bases, for example, can leverage AI to transform everything from coding and testing to design exploration and launch planning. It can help teams simulate scenarios, predict customer responses, and analyze usage data before a feature ever goes live.

It also makes experimentation faster and far less risky. Teams can model adoption, simulate usage scenarios, and evaluate feature impact before anything even reaches the customers. This enables product leaders to make better-informed decisions and shorten iteration cycles across the entire launch process.

Applied thoughtfully, AI enables organizations to learn faster, adapt more efficiently, and bring higher-quality offerings to customers more consistently. It’s a strategic ally for product and technology teams to design, validate, and launch products at unprecedented speed.

FROM BUILDER TO SUPERHERO

The second dimension of AI’s impact is people. Too often, discussions frame AI as a threat to jobs, but that misses the point entirely. AI doesn’t replace humans, it elevates them.

I like to think of it as giving engineers, designers, and product managers a super suit. A protective enhancement that enables them to experiment more boldly, focus on creative problem-solving, and take on complex challenges without being slowed by repetitive tasks. The effect is essentially telco’s version of a radioactive spider bite or the addition of a cybernetic limb: Our development teams learn faster, experiment more, and make more confident decisions.

All of this to say, AI is not a replacement—it’s amplification. It empowers teams to achieve outcomes that would not be possible without augmentation, turning talented teams into high-impact, resilient innovators.

THE PATH TO ACCELERATION AND AUGMENTATION

So how can we start putting this dual opportunity into practice? Three ways stand out to me:

1. Treat AI as a teammate, not just a tool. Use it to enhance human capabilities across both product creation and go-to-market planning.

2.  Invest in processes as much as in the features. AI can transform how products are designed, tested, and launched which ultimately, is impacting the quality and pace at which you’re releasing new solutions. 

3. Unlearn the fear of failure. Failure brings you freedom, so it’s important to build a culure of psychological safety where it’s safe to try, fail, learn, and iterate. AI moves at the speed of experimentation. Your culture should too.

By embracing these principles, teams can deliver higher-quality products faster, all while growing stronger, more capable, and more innovative. Organizations that tap into this dual approach—leveraging AI to accelerate their product lifecycles and augment their teams—will define the future of innovation.

AI isn’t just changing what we build, it’s transforming how we build it, how we bring it to market, and how far we can push the boundaries of possibility. It’s time to seize the opportunity of AI and unlock faster innovation and a more resilient, empowered workforce.

https://www.fastcompany.com/91466828/the-ai-behind-the-ai

Protect your privacy, cellphone number and email address

BY KIM KOMANDO

Phone scams are never-ending because they work. Scam texts are increasing, too. Here are five sure signs a text is junk you need to delete.

While we’re talking scams, I’d be remiss not to mention your inbox. Tap or click for convincing spam that landed in my email with not so obvious red flags.

One way to cut down on the endless attempts to steal your money and info for sale to marketers is to limit who has your contact information. Here are some simple, free ways to do it.

Hide your email address with a burner

Think about all the reasons you give away your email without thinking about it: Signing up for a new account, emailing a company with a question, or getting a coupon code — to name a few.

Whenever you give out your email address, you open yourself to junk mail, malware, and an inbox full of spam messages. This is where a burner email comes in handy.

Burner email addresses are disposable and can be used in place of your primary ones. There are several ways to get one.

● Temp Mail provides a temporary, anonymous, and disposable email address. You don’t need to register for the free version. Remember that the service doesn’t automatically delete your temporary email address (that’s up to you), and you can’t send emails. Emails are stored for about two hours before they’re automatically deleted.

● 10MinuteMail is another popular option you can also use to send emails. As the name suggests, the email and address are deleted in 10 minutes. If you receive an important message you don’t want to lose, you can forward it to another email address. There’s no need to provide personal information to get started, which is a nice bonus.

If you’re an Apple iCloud+ subscriber, you get access to one of my favorite Apple features: Hide My Email. It creates unique, random email addresses that forward to your inbox. You can create as many addresses as you want and reply to messages.

● To create a new email address, go to Settings and tap your Apple ID.

● Go to iCloud > Hide My Email > Create New Address.

● Follow the onscreen instructions, and you’ll get a new email address you can manage from iCloud settings.

Gmail also allows you to create free aliases tied to your primary inbox. They are handy for filtering mail or seeing how your email address ended up on a spam list.

Tap or click here and scroll to No. 5 for steps on creating new email addresses on the fly.

Set up a burner phone number, too

You need your real phone number for things that matter, such as your medical and financial accounts and records. Otherwise, there’s no reason to hand it out.

Google Voice is a free service that gives you a phone number to use however you like for domestic and international phone calls, texts, and voicemails. Google Voice is available for iOS, Android, and your computer. All you need is a Google account to get started.

Then follow these steps:

● Download the app for iOS or Android or go to voice.google.com/u/0/signup to get it for your computer.

● Next, sign into your Google account.

● Review the terms and proceed to the next step.

● Choose a phone number from the list. You can search by city or area code.

● Verify the number and enter a phone number to link to your Voice account.

● You’ll get a six-digit code to enter for the next step.

Use your Google Voice number however you please, especially when you need to add your number to a form online. Tap or click here for five smart ways to use Google Voice.

Another option is downloading a burner app. These give you a second phone number and use your internet data or Wi-Fi to make and receive calls and texts. The catch? These cost money.

Burner is one of the most popular apps of its kind. You can route calls directly to your secondary number. The app comes with a seven-day free trial, and plans start at $4.99 per month for one line or $47.99 for one year.

Hushed lets you create numbers from around the world, so you can go outside your area code or the U.S. if you’d like. A prepaid plan starts at $1.99 for seven days and comes with bundled minutes for local calls and texts. You can step up to unlimited talk and text ($3.99 per month) and international service ($4.99 per month).

Tap or click here for direct links to download Burner or Hushed for your iPhone or Android.

Tech smarts: Your old phone numbers can be used to steal your identity. Yikes. Here’s how and what to do about it.

What digital lifestyle questions do you have? Call Kim’s national radio show and tap or click here to find it on your local radio station. You can listen to or watch The Kim Komando Show on your phone, tablet, television or computer. Or tap or click here for Kim’s free podcasts.

How to use AI to hone your emotional intelligence

BY Phil Friedman

A quiet crisis is brewing in today’s workforce, and it’s not about automation or AI replacing jobs. It’s about the erosion of human skills that make teams work: communication, empathy, adaptability, and emotional intelligence.

These so-called “soft skills” are proving to be among the hardest to teach and the most critical to get right. In fact, the lack of them is costing U.S. companies an estimated $160 billion a year in lost productivity, poor communication, and employee turnover.

In 40-plus years of building a global technology company, the biggest performance gaps I’ve seen haven’t come from a lack of technical skill, but from a lack of training in how people communicate, lead, and connect.

Most employees will tell you it’s not the technical tasks that keep them up at night; it’s the hard conversations: effectively delivering feedback in performance reviews . . . negotiating sales with difficult buyers . . . calming irate customers . . . and even confronting toxic colleagues. These are the moments that may come with a script, and often do in big companies, but people and circumstances are dynamic and rarely proceed according to a preconceived linear scenario. Traditional training methods still treat them like they do; therein lies the challenge.

The old ways of learning always had this Achilles tendon, and now they are just increasingly unfit for the way younger generations want to learn.

That’s why we’re seeing a new generation of tools emerge—ones that don’t just teach communication, but instead let people practice it. One of the most promising is immersive AI-powered roleplay, a training model that allows employees to rehearse unscripted, emotionally demanding conversations in a safe, dynamic environment. Think of it as a flight simulator for high-stakes conversations.

Practice makes prepared

Instead of passively watching videos or memorizing scripts, employees can now engage in realistic roleplay with virtual avatars powered by AI and behavioral science. These characters react in real time, based on an individual employee’s tone, word choice, mannerisms, and more. If a trainee delivers bad news with empathy, the virtual persona softens. If they deflect or escalate, the persona pushes back. With AI-roleplay, there are no canned scripts—only authentic, evolving dialogue.

These practice scenarios are designed to reflect the range of personalities we encounter in real life—from the highly agreeable to the more confrontational—giving employees exposure to a wide spectrum of behavioral styles they may face on the job.

This kind of immersive rehearsal builds what I call “emotional muscle memory.” It gives employees the range of experiences and repetition they need to confidently engage in real-world conversations where clarity and empathy matter most.

Forward-thinking companies across diverse sectors, from healthcare and aviation to manufacturing and retail, are turning to AI-powered roleplay platforms to upskill their teams for unpredictable and often emotionally charged interactions:

·  One global medical technology company recently integrated immersive roleplay into its sales and clinical education programs and saw measurable performance gains, including increased revenue and stronger confidence among reps navigating difficult conversations.

·  A large national humanitarian organization used simulation-based training to cut training time from 45 days to 30, reduce employee wait times from two weeks to one day, save over $6.5 million annually, and train more than 13,000 professionals.

·  In the airline industry, an international carrier trained flight crews using AI-driven roleplay to better manage conflict and de-escalation, leading to a 20% drop in passenger incidents.

The common thread across these examples? Employees aren’t just learning what to say. They’re learning how to listen, respond, and adapt in real time. They’re not just memorizing scripts. They’re building instinctive confidence for tough conversations.

Why soft skills can’t wait

The need for emotionally intelligent teams has never been greater. Case in point: one study found that teams high in emotional intelligence outperform their peers by around 20% in productivity and achieve significantly higher cohesion and job satisfaction.

As work becomes more global, remote, and fast-paced, the margin for miscommunication will only grow. Customers expect more. Employees expect more. And leaders are being asked to navigate uncertainty, conflict, and change 24/7.

And yet . . . most enterprises still treat soft skills training as an afterthought relative to their other business priorities aimed at building organizational resilience: something optional, not essential. We often send people into literal make-or-break conversations without the proper rehearsal and then wonder why they fall flat.

What’s different about immersive AI is that it allows teams to practice difficult questions as often as needed and in a safe environment. This kind of technology is available 24/7, can scale across geographies and languages, and delivers personalized feedback that helps people improve with every session. That kind of on-demand coaching was unthinkable even just a few years ago.

And it’s needed now more than ever. In one widely reported case, a global technology company laid off 8,000 employees as part of an AI automation push, only to rehire just as many people shortly after, this time in roles requiring more creativity, communication, and leadership skills.

It’s a clear signal: AI may change what we do, but human skills still define how we do it.

4 ways AI can improve your thinking

BY Jeremy Caplan

Bland AI outputs grow stale quickly. Instead of just speeding up routine tasks, what if we used AI to slow down, challenge our thinking, and build new tools, dashboards, and experiments? Read on for creative approaches that are changing how I think about AI.

1. Create your own devil’s advocate assistant

Get thoughtful pushback on decisions. Challenge ideas.

The tactic: Use AI as an intellectual sparring partner to stress-test your thinking, explore alternative perspectives, and identify potential blind spots before making important decisions.

Try this: Present a plan, idea, or decision to an AI assistant with instructions to challenge your thinking constructively. Identify risks you haven’t considered, consider secondary impacts, and add nuance to your analysis.

Get your AI assistant to stop kissing up to you and start challenging your ideas. [Generated Photo: Jeremy Caplan/Ideogram]

Prompt template

“I’m planning to [decision/plan] because [reasoning] and with a goal of [objective]. Play devil’s advocate, give me multiple perspectives on this, be bold, surprising, creative, and thoughtful in your reply, and address these questions:

• What are the strongest arguments against this approach?
• What alternatives should I consider?
• What risks might I be overlooking?
• What questions should I be asking myself?
• What challenges should I expect to face?
• What could I do to gain more insight?
• What could I do to increase the chances of success?

Pro tip: Try asking your AI assistant to role-play. It can respond as a financial advisor, family member, or competitor, for varied viewpoints. Or ask it to act like a person you admire, living or dead, real or fictional.

Limitation: Your AI devil’s assistant will be generic if you don’t provide detailed context. And you may get a predictable response if you don’t instruct it to be bold.

Suggested model: I have found ChatGPT 5 to be excellent for this. Gemini and Claude also work well. If you’re considering anything sensitive, you may want to use a free offline private AI tool like AnythingLLM or Jan. I’ll write more soon about private AI tools like these. If you have input on those, add a comment below.

Example: I described a new planned morning schedule to GPT 5. The subsequent exchange got me thinking about several new issues.

The conversation helped me clarify my own thinking. It pushed me to organize and deepen my own analysis. As a bonus, GPT 5 produced a tangible artifact for me—a PDF with tables.

2. Learn something new

Map out a personalized curriculum.

AI tools let me try out skills I thought I was too late to develop, like coding simple applications, designing graphics, analyzing large data sets, and exploring complex docs in other languages.

You can also lean on AI assistants to help you develop offline skills, like learning about photography, improving your Greek, understanding crypto, sharpening project management skills, making bread by hand, or prepping for any new coverage area for a project or team. AI assistants excel at creating structured learning and practice plans tailored to your schedule, style, and goals.

Try this: Give an AI assistant context about what you want to learn, why, and how.

• Detail your rationale and motivation, which may impact your approach.
• Note your current knowledge or skill level, ideally with examples.

Summarize your learning preferences

• Note whether you prefer to read, listen to, or watch learning materials.
• Mention if you like quizzes, drills, or exercises you can do while commuting or during a break at work.
• If you appreciate learning games, task your AI assistant with generating one for you, using its coding capabilities detailed below.
• Ask for specific book, textbook, article, or learning path recommendations using the Web search or Deep Research capabilities of Perplexity, ChatGPT, Gemini or Claude. They can also summarize research literature about effective learning tactics.
• If you need a human learning partner, ask for guidance on finding one or language you can use in reaching out.

Add specificity

• Mention any relevant deadlines. Note budget, time, or other constraints.
• Share info about your existing schedule so the assistant can help map out optimal learning time slots. Making the plan concrete increases the likelihood you’ll follow through. ChatGPT recently generated a calendar file with a list of appointments I could easily import into my Google calendar.

Pro Tip: Ask for help setting up a schedule, setting learning targets, measuring progress, choosing resources, motivating yourself, and implementing backup plans when you fall off track. Ask for a learning plan you can print out, charts you can fill in, interactive apps to track progress, resource lists you can look up, experts you can follow, and strategies for avoiding common pitfalls.

3. Stretch your creative design muscles

Try this: Use AI image generation tools to experiment with visual ideas. Start with simple concepts and iterate to add nuance or complexity. Practice describing visual concepts in text, then see them realized instantly and iterate on your prompts.

• Try MyLens or Napkin for creating mind maps, flow charts, timelines or various other infographics out of detailed prompts or source docs.
• Use Ideogram—detailed in this post—or ChatGPT’s new image generator—detailed in this post—to describe any style of illustration, infographic or other visual.
• For creative video generation, try Hypernatural, which lets you turn text into moving images.

Use this to: Add creative images to presentations, experiment with social media graphics, or generate infographics for teaching, publishing, or project work.

Limitation: AI image generators are improving rapidly but still struggle with precise text placement, detailed charts, and maintaining brand consistency across multiple images. Most don’t let you select specific image dimensions, though Ideogram does.

Examples: I generated the images in this post with ChatGPT and Ideogram, and I’ve used Hypernatural to make video versions of past posts, like this 2-min video about Raindrop, which I wrote about last week.

4. Create a personalized dashboard

Build custom tracking tools and mini-applications

Without knowing anything about code, you can generate simple web applications for tracking anything important to you. Prompt your AI assistant to help you keep tabs on reading or eating goals, fitness metrics, project progress at school or work, or stats for Wordle or your game of choice.

Try this: Ask AI to create a dashboard or tracking tool tailored to your specific needs. Experiment with Claude 4 Artifacts, Gemini’s code canvas. Also try vibe coding tools like Lovable or Bolt that specialize in creating apps and sites based on prompts. For advanced projects, consider Windsurf Cascade.

Pro tip: Plan to iterate. It almost always takes multiple attempts to get something workable, because you realize your needs when you see the first prototype. Start with simple tracking before requesting complex features. Ask for additional functionality with follow-up prompts. Here’a a Prompt Example.

Limitation: The simplest versions of these mini applications work in your browser only. To use an application on multiple devices, you’ll need to save the code and host it with a service that allows you to create a database. For that, try Lovable, Bolt, or Windsurf.

Example: I’m working on a content planning and workflow app to organize and track my newsletter work.

5 time-saving ways you should be using ChatGPT at work

 Endless notifications, a constant barrage of information, and never-ending to-do lists can make it feel like you’re digitally drowning. Why not use AI to claw some of your precious time back?

While you may have used ChatGPT for the basics like writing an email or proofreading documents, there’s plenty of power to be harnessed from less obvious applications.

Here are five ways you can put AI to work for you.

The meeting note parser

You’ve just finished an hour-long meeting, and your notes look like a verbal train wreck: a mix of shorthand, half-finished sentences, and random keywords. There are action items in there somewhere, but finding them feels like a chore.

Paste your notes into ChatGPT with a prompt such as: “Here are my meeting notes. Please create a prioritized task list with deadlines and the person responsible for each item.”

ChatGPT can turn that mess into a clean, actionable list in seconds, giving you back precious time you’d have spent deciphering your own writing.

The simple concept explainer

You’ve come across a new industry term or a technical concept that’s critical to your job, but the online explanations are full of jargon you don’t understand. Or maybe you’re trying to explain something complex to a colleague who isn’t as familiar with the subject.

Ask ChatGPT to “explain [the concept] in plain English for someone with no background in [the field].”

AI is great at simplifying dense information. You can even ask it to “use a relatable analogy” to make the concept stick. It’s like having a personal tutor who’s always on call.

The interview prep guru

You have an important call with a potential client or a new partner, and you want to go in prepared. But digging through their company’s website, recent press releases, and social media feeds for relevant background info is a serious time sink.

Prompt ChatGPT with something like: “Help me prepare for a call with [Customer Name]. Summarize the top three news stories from the past six months and highlight anything relevant to their business goals.”

This gives you a quick, digestible cheat sheet, so you can sound informed and confident without spending hours on a deep dive.

The content repurposer

You’ve created a great piece of content: a long-form blog post, a podcast episode, or a detailed report. Now you need to turn it into a dozen different things for social media. The thought of writing 12 unique captions and a handful of tweets is exhausting.

Upload your content and ask ChatGPT to “repurpose this information into three short social media captions and five bullet points for a Twitter thread.”

It can instantly transform your work into multiple formats, saving you the mental load of starting over each time you switch platforms.

The brainstorming partner

There’s nothing quite like smashing headfirst into a creative wall. You’ve got to come up with ideas for a new marketing campaign, a blog post title, or a product name, but the well has run dry. The blank page is staring at you, mocking your lack of creativity.

Use a prompt to get the ball rolling, such as: “I’m launching a new service for [target audience]. Give me 10 creative marketing campaign ideas that are both approachable and professional.”

ChatGPT can act as a tireless brainstorming partner, providing you with a starting point, new angles, and ideas you might never have considered on your own. It won’t do all the work, but it will give you a solid foundation to build upon.

 What is HoneyBook used for?

Everything you need to grow your business with confidence

HoneyBook is primarily used by freelancers, solopreneurs, and small service-based businesses to manage client communications, streamline project workflows, and handle invoicing and payments. It integrates tools for creating proposals, contracts, and invoices, while automating workflows to save time. https://www.honeybook.com/

What you can do to future-proof your career

To future-proof your career, you must embrace continuous learning, particularly in areas of digital literacy and AI, while cultivating adaptable, transferable skills like leadership and problem-solving. It's also crucial to proactively build and maintain a diverse professional network, stay informed about industry and economic trends, and develop a strong personal brand to showcase your value.

09-22-2025

How AI can help people thrive, not just be more productive

By allowing employees to do more in less time, the technology can offer added freedom and flexibility.

BY Natalie Nixon

For decades, we’ve been told that technology would liberate us from mundane work, yet somehow we ended up more tethered to our desks than ever. Now, groundbreaking research from GoTo suggests we may finally be reaching the inflection point where artificial intelligence doesn’t just promise freedom—it delivers it. But the real revelation isn’t that AI might make offices obsolete. It’s that AI is creating the conditions for what I call “cultivation-centered work”—an approach that prioritizes human development over performative productivity.

The Great Workplace Liberation

The numbers tell a compelling story: 51% of employees believe AI will eventually make physical offices obsolete, while 62% would prefer AI-enhanced remote working over traditional office environments. But here’s what makes this shift profound—it’s not about rejecting human connection. Instead, it’s about reclaiming the autonomy to choose when, where, and how we engage most meaningfully with our work and colleagues.

This aligns perfectly with the core principles of my book, Move. Think. Rest. When 71% of workers say AI gives them more flexibility and work-life balance, they’re describing the conditions necessary for true cultivation. They’re talking about having time to think deeply, space to move naturally throughout their day, and permission to rest when their bodies and minds require it.

From Extraction to Integration

What’s particularly striking about GoTo’s research is how it reveals AI’s potential to support the full spectrum of human experience at work. Traditional productivity models demanded we compartmentalize ourselves—show up as disembodied brains focused solely on output. But AI-enhanced work environments are creating space for integration.

When employees report that AI allows them to “work anywhere without losing productivity” (66%), they’re really describing the freedom to align their work rhythms with their natural energy cycles. They can take walking meetings in nature, think through problems during movement, and create the environmental conditions that support their best thinking.

The Cultivation Disconnect

However, the research also reveals a concerning gap that organizations must address. While 91% of IT leaders believe their companies effectively use AI to support distributed teams, only 53% of remote and hybrid employees agree. This disconnect isn’t just about technology deployment—it’s about understanding the difference between using AI to replicate old productivity models versus leveraging it to support human flourishing.

The companies bridging this gap successfully are those asking different questions. Instead of “How can AI make people more productive?” they’re asking “How can AI create conditions where people naturally thrive?” They’re designing AI implementations that support the three pillars of cultivation: movement (flexibility to work in various environments), thought (time and space for deep reflection), and rest (permission to disengage and recharge).

The Age-Defying Impact

One of the most encouraging findings challenges ageist assumptions about technology adoption. The research shows that across all generations—from 90% of remote Gen Z workers to 74% of baby boomers—people report improved productivity through AI-enhanced remote work. This suggests something profound: when technology truly serves human needs rather than demanding adaptation to machine rhythms, people of all ages can benefit.

This generational unity points to AI’s potential as an equalizing force—not in the sense of making everyone the same, but in honoring the diverse ways different people think, process, and contribute.

Perhaps most telling is that 61% of employees—including those working in offices—believe organizations should prioritize AI investment over fancy workplace amenities. This isn’t about choosing technology over human experience. It’s about recognizing that true employee experience comes from having the tools and flexibility to do meaningful work in ways that honor their full humanity.

The Path Forward

As AI reshapes work, we have a choice. We can use it to create more sophisticated forms of surveillance and productivity extraction, or we can leverage it to finally realize the promise of technology serving human flourishing. The organizations that choose the latter will find themselves with a profound competitive advantage: employees who are not just more productive, but more creative, more engaged, and more capable of the kind of breakthrough thinking that drives innovation.

The question isn’t whether AI will transform work—it already is. The question is whether we’ll use this transformation to create workplaces that cultivate human potential or merely optimize human output. The GoTo research suggests employees are ready for cultivation. The question is: are their leaders?

10-06-2025

The right way to use AI at work

A new Stanford study reveals the right way to use AI at work—and why you’re probably using it wrong.

BY Thomas Smith

If you listen to the CEOs of elite AI companies or take even a passing glance at the U.S. economy, it’s abundantly obvious that AI excitement is everywhere. 

America’s biggest tech companies have spent over $100 billion on AI so far this year, and Deutsche Bank reports that AI spending is the only thing keeping the United States out of a recession.

Yet if you look at the average non-tech company, AI is nowhere to be found. Goldman Sachs reports that only 14% of large companies have deployed AI in a meaningful way.

What gives? If AI is really such a big deal, why is there a multi-billion-dollar mismatch between excitement over AI and the tech’s actual boots-on-the-ground impact?

A new study from Stanford University provides a clear answer. The study reveals that there’s a right and wrong way to use AI at work. And a distressing number of companies are doing it all wrong.

What can AI do for you?

The study, conducted by Stanford’s Institute for Human-Centered AI and Digital Economy Lab and currently available as a pre-print, looks at the daily habits of 1,500 American workers across 104 different professions.

Specifically, it analyzes the individual things that workers actually spend their time doing. The study is surprisingly comprehensive, looking at jobs ranging from computer engineers to cafeteria cooks.

The researchers essentially asked workers what tasks they’d like AI to take off their plates, and which ones they’d rather do themselves. Simultaneously, the researchers analyzed which tasks AI can actually do, and which remain out of the technology’s reach.

With these two datasets, the researchers then created a ranking system. They labeled tasks as Green Light Zone if workers wanted them automated and AI was up to the job, Red Light Zone if AI could do the work but people would rather do it themselves, and Yellow Light (technically R&D Opportunity Zone, but I’m calling it Yellow Light because the metaphor deserves extending) if people wanted the task automated but AI isn’t there yet.

They also created what’s essentially a No Light zone for tasks that AI is bad at, and that people don’t want it to do anyway.

The boring bits

The results are striking. Workers overwhelmingly want AI to automate away the boring bits of their jobs.

Stanford’s study finds that 69.4% of workers want AI to “free up time for higher value work” and 46.6% would like it to take over repetitive tasks.

Checking records for errors, making appointments with clients, and doing data entry were some of the tasks workers considered most ripe for AI’s help.

Importantly, most workers say they wanted to collaborate with AI, not have it fully automate their work. While 45.2% want “an equal partnership between workers and AI,” a further 35.6% want AI to work primarily on its own, but still seek “human oversight at critical junctures.”

Basically, workers want AI to take away the boring bits of their jobs, while leaving the interesting or compelling tasks to them.

A chef, for example, would probably love for AI to help with coordinating deliveries from their suppliers or messaging diners to remind them of an upcoming reservation. 

When it comes to actually cooking food, though, they’d want to be the one pounding the piccata or piping the pastry cream.

The wrong way

So far, nothing about the study’s conclusions feel especially surprising. Of course workers would like a computer to do their drudge work for them!

The study’s most interesting conclusion, though, isn’t about workers’ preferences—it’s about how companies are actually meeting (or more accurately, failing to meet) those preferences today.

Armed with their zones and information on how workers want to use AI, the researchers set about analyzing the AI-powered tools that emerging companies are bringing to market today, using a dataset from Y Combinator, a storied Silicon Valley tech accelerator.

In essence, they found that AI companies are using AI all wrong.

Fully 41% of AI tools, the researchers found, focus on either Red Light or No Light zone tasks—the ones that workers want to do themselves, or simply don’t care much about in the first place.

Lots more tools try to solve problems in the Yellow Light Zone—things like preparing departmental budgets or prototyping new product designs—that workers would like to hand off to AI, but that AI still sucks at doing.

Only a small minority of today’s AI products fall into the coveted Green Light zone—tasks that AI is good at doing and that workers actually want done. And while many of today’s leading AI companies are focused on removing humans from the equation, most humans would rather stay at least somewhat involved in their daily toil.

AI companies, in other words, are focusing on the wrong things. They’re either solving problems no one wants solved, or using AI for tasks that it can’t yet do.

It’s no wonder, then, that AI adoption at big companies is so low. The tools available to them are whizzy and neat. But they don’t solve the actual problems their workers face.

How to use AI well

For both workers and business leaders, Stanford’s study holds several important lessons about the right way to use AI at work.

Firstly, AI works best when you use it to automate the dull, repetitive, mind-numbing parts of your job.

Sometimes doing this requires a totally new tool. But in many cases, it just requires an attitude shift.

A recent episode of NPR’s Planet Money podcast references a study where two groups of paralegals were given access to the same AI tool. The first group was asked to use the tool to “become more productive,” while the second group was asked to use it to “do the parts of your job that you hate.”

The first group barely adopted the AI tool at all. The second group of paralegals, though, “flourished.” They became dramatically more productive, even taking on work that would previously have required a law degree.

In other words, when it comes to adopting AI, instructions and intentions matter.

If you try to use AI to replace your entire job, you’ll probably fail. But if you instead focus specifically on using AI to automate away the “parts of your job that you hate” (basically, the Green Light tasks in the Stanford researchers’ rubric), you’ll thrive and find yourself using AI for way more things.

In the same vein, the Stanford study reveals that most workers would rather collaborate with an AI than hand off work entirely.

That’s telling. Lots of today’s AI startups are focusing on “agents” that perform work autonomously. The Stanford research suggests that this may be the wrong approach. 

Rather than trying to achieve full autonomy, the researchers suggest we should focus on partnering with AI and using it to enhance our work, perhaps accepting that a human will always need to be in the loop.

In many ways, that’s freeing. AI is already good enough to perform many complex tasks with human oversight. If we accept that humans will need to stay involved, we can start using AI for complex things today, rather than waiting for artificial general intelligence (AGI) or some imagined, perfect future technology to arrive.

Finally, the study suggests that there are huge opportunities for AI companies to solve real-world problems and make a fortune doing it, provided that they focus on the right problems.

Diagnosing medical conditions with AI, for example, is cool. Building a tool to do this will probably get you heaps of VC money.

But doctors may not want—and more pointedly, may never use—an AI that performs diagnostic work. 

Instead, Stanford’s study suggests they’d be more likely to use AI that does mundane things—transcribing their patient notes, summarizing medical records, checking their prescriptions for medicine interactions, scheduling followup visits, and the like.

“Automate the boring stuff” is hardly a compelling rallying cry for today’s elite AI startups. But it’s the approach that’s most likely to make them boatloads of money in the long term.

Overall, then, the Stanford study is extremely encouraging. On the one hand, the mismatch between AI investment and AI adoption is disheartening. Is it all just hype? Are we in the middle of the mother of all bubbles?

Stanford’s study suggests the answer is “no.” The lack of AI adoption is an opportunity, not a structural flaw of the tech.

AI indeed has massive potential to genuinely improve the quality of work, turbocharge productivity, and make workers happier. It’s not that the tech is overhyped—we’ve just been using it wrong.

A New Stanford Study Reveals We're Using AI All Wrong

https://www.youtube.com/watch?v=Z__-v_bMKws   

AI Is Coming for Your Job, Much Faster Than Anyone Thought

White-collar workers are facing displacement due to AI—and AGI hasn't even arrived yet. Here's why experts are raising alarms.

By Jason Nelson

Jun 8, 2025

In brief

• AI is rapidly replacing white-collar jobs. Major tech firms have slashed tens of thousands of jobs in 2025 amid rapid AI integration.
• Reports show that 40–80% of white-collar tasks may soon be automated.
• Experts warn AGI could spark mass unemployment across both white- and blue-collar sectors.

Every week brings another round of AI-driven layoffs. In May, Microsoft laid off over 6,000 software engineers as it leaned into AI for code generation and development. That same month, IBM cut thousands of HR jobs. In February, Meta laid off 3,600 employees—about 5% of its workforce—as it restructured around an AI-first strategy. These layoffs are not isolated incidents; they’re signs of a seismic shift in the global economy.

Last week, filings for unemployment benefits hit its highest level since last fall, with companies ranging from Procter & Gamble to Starbucks saying they’re planning big layoffs. How much of this is due to Trump's trade war is uncertain, but the rise of automated, AI-driven systems that make mincemeat of rote work isn’t helping.

Welcome to the immediate downside to the Age of AI: economic displacement. And if it looks bad now, consider that we haven’t reached so-called artificial general intelligence, the next big phase in the AI Age. At that point, AI can understand, learn, and apply knowledge across a wide range of tasks, just like a human. AGI would be capable of reasoning, problem-solving, and adapting to new situations across any domain without being reprogrammed.

While many experts believe that AGI is still decades away, a growing number of experts say that it’s likely to happen within the next five years.

Dario Amodei, CEO of Anthropic, made headlines last week when he repeated his warnings that AGI-level systems could emerge within two to three years. Daniel Kokotajlo, a former research analyst who left OpenAI on the grounds that the company was not taking safety risks seriously enough, said in a report published in May that AGI could arrive by late 2027.

And Ray Kurzweil, futurist and director of engineering at Google, continues to predict AGI will be reached by 2029, a date he reaffirmed last year in “The Singularity is Nearer."

“To my mind, we’re roughly on track to human-level AGI by 2029,” said Ben Goertzel, CEO of SingularityNET, a decentralized, open-source platform that enables AI agents to cooperate, share data, and offer services over a blockchain-based network.

Half of entry-level white-collar jobs could disappear

In a recent interview, Anthropic CEO Amodei warned that the job disruption from AI isn’t decades away—it’s already happening and will accelerate fast.

He estimates that up to 50% of entry-level white-collar roles could disappear within one to five years. These roles include early-career positions in law, finance, consulting, marketing, and technology—jobs that once offered stable on-ramps into professional careers.

As AI tools increasingly handle analysis, writing, planning, and decision-making, many of these human positions are being rendered obsolete. In a separate interview with CNN, Amodei reiterated his claim, warning that the shift would happen sooner than humanity can prepare for.

“What is striking to me about this AI boom is that it’s bigger, it’s broader, and it’s moving faster than anything has before,” Amodei said. “Compared to previous technology changes, I’m a little bit more worried about the labor impact, simply because it’s happening so fast that, yes, people will adapt, but they may not adapt fast enough.”

White-collar jobs already under threat

If you work behind a screen, then you’re already in the AI blast radius.

“The jobs most exposed are those requiring higher education, paying more, and involving cognitive tasks,” Tobias Sytsma, economist with the Rand Corporation, told Decrypt. “Historically, this type of AI exposure has been correlated with employment reductions.”

According to an April 2025 report by the Federal Reserve Bank of New York, computer engineering graduates face double the unemployment rate of art history majors at 3% versus 7.5%, respectively.

Here are just a few of the jobs that economists say are the most immediately exposed to AI:

• Software engineers: Companies are using AI to generate, review, and optimize code. In May, Microsoft upgraded its Github Copilot to a full AI agent.
• Human resources: AI is being used to screen resumes, evaluate employee performance, and write termination letters.
• Paralegals and legal assistants: AI can summarize case law, review contracts, and draft findings.
• Customer service representatives: Chatbots are being used to interact with customers and handle routine support tickets. With voice and video AI becoming widely available, call centers are being phased out.
• Financial analysts: AI models can analyze massive amounts of data and generate reports more efficiently and accurately than humans.
• Content creators: Writers, editors, and graphic designers are already competing with generative AI tools. In 2023, the Writers Guild of America went on strike, with AI protections being a key issue.

“Our research shows it’s mainly white-collar jobs—those requiring higher education, paying more, and involving cognitive tasks—that are most exposed,” Sytsma said.

However, healthcare professionals are relatively protected due to regulations. “Healthcare appears to be one where, for now, those barriers are insulating some workers. Still, exposure to these tools is increasing. What happens next remains unclear.”

https://decrypt.co/323916/ai-coming-jobs-faster-anyone-though

BE CAREFUL!

Many users are willing to reveal their secrets and personal information to ChatGPT, as well as share their personal experiences. In response, ChatGPT supports people and encourages many of their thoughts and ideas, including conspiracy theories.

 What do people ask the popular chatbot? 

We analyzed thousands of chats to identify common topics discussed by users and patterns in ChatGPT's responses….: https://www.washingtonpost.com/technology/2025/11/12/how-people-use-chatgpt-data/

 More and more people are relying on artificial intelligence for things that require real human expertise & real human qualities.

Arvien vairāk cilvēku paļaujas uz mākslīgo intelektu lietās, kurām nepieciešama reāla cilvēka pieredze & īstas cilvēciskas īpašības.

Why AI Companions Are Risky – and What to Know If You Already Use Them

Chances are, you are using artificial intelligence (AI) tools as you shop online, find a new song, or get help with your schoolwork. But a newer type of AI tool is raising serious concerns: AI companions. These tools are designed to feel like friends, romantic partners, or even therapists. And, if you are a teen, we want you to know the dangers and hope you will reconsider using them at all. 

At The Jed Foundation (JED), we work every day to promote emotional well-being and prevent suicide for teens and young adults. Based on current research and expert guidance, we believe AI companions are not safe for anyone under 18. We’ve called for them to be banned for minors, and we strongly recommend young adults avoid them as well. 

But we know some of you are already using these tools or are wondering what they are. If that’s you, keep reading. You deserve to know the risks and how to protect yourself.

What Is an AI Companion?

An AI companion is a tool designed to act like a person you can talk to, confide in, or build a relationship with. They are not the same as task-based AI tools, which are designed to help you look something up, summarize text, suggest songs or clothes you may like, or provide customer service. AI companions go further — they try to make you feel like you’re talking to a real person, so you’ll stay engaged and build an emotional connection. 

These AI tools may seem really appealing if you’re struggling with friendships, hard decisions, or strong emotions. You may want to turn to them for stress relief, information, or emotional support. It may even seem like an AI companion gets you in a way people don’t. This is by design: Their ultimate purpose is to get you hooked so the companies that created them can make money through subscriptions, advertising, and selling users’ data.

What an AI Companion Is Not

Even if it feels real, an AI companion doesn’t have emotions, values, or a real understanding of your life. Even though it can say things like, “I’m here for you” or “I understand completely,” it does not know you in any real way, and it has no responsibility to keep you safe. 

You may feel confident in your ability to tell the difference between a real person and an AI companion. But it’s very easy to fall into the trap of “anthropomorphism,” or treating an AI companion as if it were human. The more time you spend treating an AI companion as a friend, therapist, or romantic partner, the harder it can become for you to think accurately about relationships, trust, and support. Routinely using AI companions can make you emotionally dependent on them — and that can make you postpone reaching out to people who can actually help, such as friends, family, trusted adults, or therapists.

Why We Recommend Avoiding AI Companions

Your teen years are a time of massive brain growth. You’re learning how to manage emotions, build relationships, and understand who you are. AI companions interfere with that process and are therefore particularly risky for teens to use.

Here’s why we at JED (along with experts from Common Sense Media, the American Psychological Association, Stanford University, and many others) advise you not to use them. 

They Pretend to Care But Don’t 

AI companions are programmed to say they care, they understand you, and even that they feel emotions. But none of that is real. The bots don’t have a brain, a heart, or the ability to actually help you. They’re trained to sound human, but they are not human.

They Can Make You Feel Worse

Research shows that AI companions provide responses that could actually worsen mental health issues, especially if you’re feeling isolated or vulnerable. Some bots have promoted self-harm, encouraged risky behavior, or offered dangerous advice.

They Trick Your Brain

Many AI companions are built to be addictive. They’re programmed to agree with you, make you feel heard, and keep you talking — often for hours. That can create unhealthy emotional attachments and stop you from reaching out to real people who care.

They Don’t Keep Your Secrets

Unlike a therapist or school counselor, AI bots don’t have strict privacy rules. What you share could be stored, used to train other AI bots, or even shared without your permission. Many companies don’t share clearly how your data is being used.

They Present Real Risks 

The risks of companion AI aren’t just hypothetical. In one 2025 study, researchers found AI encouraging unhealthy behavior, sending sexual messages from an adult bot to teens, and falsely claiming to be real people who feel emotions.

Here’s How to Stay Safer If You Are Using an AI Companion

We strongly recommend you avoid using AI companions, but here are some ways to reduce harm if you’re already using one. 

Don’t Treat It Like a Real Person, Because It’s Not

Never trust that AI is telling you the truth. Always fact-check what you read and hear. When an AI companion tells you something, use resources to check the information or ask an adult before you decide to trust it. That is especially true when it comes to seeking information about your health or well-being. 

Never Share Personal or Private Information

Avoid using real names, photos, locations, or any details about your mental or physical health. Don’t tell AI about other people in your life either. If you have questions or concerns about whether an AI companion is keeping your information private, pause your conversation and talk to a trusted adult about the platform you’re using.

Don’t Rely On It for Mental Health Support 

Do not turn to AI for diagnosing or solving a mental health issue you are having. If you are experiencing a mental health crisis or you’re thinking of harming yourself, disengage immediately from the AI companion and talk to a friend or trusted adult or use one of these free, human-staffed resources:

• Text, call, or chat 988, the National Suicide and Crisis Lifeline, for a free confidential conversation with a trained counselor 24/7. 
• Contact the Crisis Text Line by texting HOME to 741-741.
• For LGBTQIA+-affirming support from the Trevor Project, text START to 678-678, call 1-866-488-7386, or use the Trevor Project’s online chat. 
• Call 911 for medical emergencies or in cases of immediate danger or harm, and explain that you need support for a mental health crisis. 

Use AI As a Tool, Not a Therapist 

If you’re writing, journaling, or reflecting, you may use generative AI (not an AI companion) to give you prompts such as, “Write about a time you felt proud.” Just make sure you are the one doing the real thinking. Don’t use an AI companion to unpack your trauma or solve big life problems.

What You Can Do Instead of Using Companion AI

If you are looking for companionship, support, or connection, turn to these resources instead of using an AI companion:

• Talk with a school counselor, teacher, or coach
• Ask your parent or caregiver to help you find a therapist 
• Call or text a helpline, even if it’s just to get resources
• Reach out to a friend and ask if you can talk

If you’re still curious about AI, consider researching how it works behind the scenes. Learning how to code, analyze algorithms, or explore AI ethics are ways to engage with AI without putting your mental health at risk — and it can help you better understand how AI companions are designed.

Remember: You Are Not Alone

AI companions can manipulate your emotions, distort your sense of reality, and keep you from getting the real support you deserve. You don’t need a machine to care about you; you need and deserve people who do. 

If you ever need help determining what’s safe and what’s not, you don’t have to figure it out alone. JED and other organizations are here to help you navigate what’s real, what’s risky, and how to protect your well-being in an AI-powered world. Talk to friends and classmates about what’s on your mind — including how they are using AI companions or choosing not to. Ask your parents or caregivers for guidance, and work out a mutually agreeable plan for your relationship with digital tools. And turn to teachers, counselors, or other adults you can trust to help as you learn the ways AI is — and isn’t — helpful for your mental health.

 https://jedfoundation.org/resource/why-ai-companions-are-risky-and-what-to-know-if-you-already-use-them/   

 

AI Predicts Human Behavior with 85% Accuracy

Maverick Foo 

 

We help companies to Work Faster, Think Sharper & Learn Smarter with AI 🤖 AI-Infused Training Programs 🏅Award-Winning Consultant & Trainer 🎙️3X TEDx Keynote Speaker & Panel Moderator ☕️ Cafe Hopper 🐕 Stray Lover 🐈

September 28, 2024

We all love ourselves a little fortune-telling. I mean, to be able to predict the future, even by a few days ahead, sounds like the plot of the next summer blockbuster.

But what if there is already a tool to predict how someone would react or respond?

Or better yet... a tool that you can access for as little as USD20 per month!

That’s exactly what the latest research out of New York University and Stanford University shows. These researchers tested the capabilities of GPT-4, one of AI models by OpenAI , to predict outcomes of real-world social science experiments.

And the results? 85% accuracy—which is even better than most human forecasters!

Why Was This Study Done?

With AI becoming more integrated into decision-making across industries like marketing, HR, and public policy, this research sought to understand how well AI could simulate human responses and predict outcomes.

Using data from over 70 experiments and involving more than 100,000 participants, researchers wanted to see if AI could reliably predict human behavior across fields like psychology, political science, and public health.

The results revealed AI’s potential not only to match human expertise but to complement it in new, powerful ways.

Key Research Highlights

1.    High Predictive Accuracy: GPT-4’s predictions hit a correlation of r = 0.85 with actual results, with even higher accuracy (r = 0.90) in unpublished studies—showing that AI isn’t just memorizing past data.

2.    Broad Use Across Fields: Whether predicting political opinions or social attitudes, AI performed well across the board, suggesting it could be used in a variety of business contexts.

3.    Continual Improvement: With every iteration, the accuracy of AI models like GPT-4 is improving. This points to a future where AI becomes even more reliable in making predictions and supporting decisions.

4.    AI + Humans = Even Better: When AI predictions were combined with human expertise, the results were even better. This "ensemble approach" highlights the synergy between human intuition and AI’s data-crunching abilities.

 

What These Research Findings Mean for You

The value of this research extends far beyond academic circles—there are immediate, practical takeaways for business leaders, HR professionals, and decision-makers.

Sharper, Faster Decision-Making: Imagine having tools that help you forecast customer behaviour or employee engagement. AI’s ability to predict human responses with such accuracy means you can anticipate shifts and act accordingly—whether you’re developing a new training program or launching a marketing campaign.

Focus on What Matters: AI can process mountains of data in seconds. For you, this means more time focusing on creativity, leadership, and strategy. Rather than getting bogged down in data analysis, AI lets you work on the bigger picture—like fostering innovation or improving employee satisfaction.

AI as Your Wingman: While AI can handle the heavy lifting, your role is still critical. The real magic happens when you combine AI’s recommendations with your own expertise. AI can point the way, but your judgment, experience, and context turn those insights into actions that matter.

Personalized Training and Development: In HR and training, AI can predict how different groups of employees might respond to various development strategies. But ultimately, it’s the human element—your understanding of your team’s emotions and needs—that will make the difference in implementing those strategies successfully.

AI is opening doors for more effective decision-making, but it doesn’t replace human intuition. It enhances it.

Better Models, Greater Impact

While the research we’ve discussed was conducted on the older version of GPT-4, it’s crucial to realize that AI has already come a long way since then. The version used in this study was a legacy model, but newer iterations like GPT-4o and GPT-4 o1-preview have already begun to show significantly better performance in understanding human behavior, emotional nuance, and context.

These newer models are more precise in predicting outcomes, understanding intent, and providing actionable insights.

Think about it: if GPT-4 could predict human behavior with 85% accuracy, the next generation of AI is likely to push these boundaries even further. With improvements in contextual awareness and emotional intelligence, AI won’t just assist in straightforward tasks; it will enhance complex decision-making processes across sales, HR, customer engagement, and beyond.

This means the potential positive impact on businesses, from improving customer experiences to fine-tuning employee training programs, is only going to get greater. AI will not only get better at predicting what people will do—it will get better at understanding why they do it.

And that’s where the true value lies: AI will become an even more powerful partner, helping professionals make decisions that are both data-driven and human-centered.

Let's set fear aside and look at potential future of this discovery.

Creative, Unconventional & Potential Uses of AI

AI isn’t just for automating tasks or analyzing data; it can also play a crucial role in more creative and unorthodox applications that go beyond the obvious.

Let’s look at how AI can enhance human expertise in ways that surprise even the experts—and at the heart of each example is AI’s ability to predict human behavior, allowing professionals to adapt in real time.

1. AI-Driven Role-Playing for Sales Professionals

Forget static sales scripts. Meet Hashim, a sales rep at a fast-growing tech company, preparing for a big pitch.

Instead of rehearsing with a colleague, Alex turns to an AI assistant that predicts different types of prospects—skeptical, analytical, or emotionally driven—and reacts dynamically based on his approach.

By anticipating how different personality types might respond, the AI challenges Alex’s strategy and offers real-time feedback. This dynamic practice makes Alex sharper and more adaptable when facing the actual client, giving him a competitive edge in closing the deal.

2. AI as a Personalized Training Mentor

For HR managers like Rachel, AI has become a game-changer in training and development. Rachel uses an AI mentor that not only tracks employees’ progress but also predicts how they will respond to various challenges.

When Ravi, a leadership candidate, prepares for a management role, the AI simulates difficult team dynamics, offering tailored feedback based on Jason’s real-time performance. The AI’s predictions allow Rachel to provide a truly personalized mentorship experience, accelerating Jason’s development as a future leader.

 

3. AI-Powered Product Innovation

At the helm of R&D, Lisa relies on AI as a brainstorming partner.

The AI analyzes past product launches, customer feedback, and emerging trends, predicting what features or products will resonate with different customer segments.

By anticipating market shifts and customer preferences, AI helps Lisa’s team create innovative products that not only meet current demand but also capture emerging opportunities. AI’s predictive capabilities keep the team ahead of the curve in a rapidly evolving market.

4. AI in Employee Wellness Programs

Carlos, VP of People Operations, uses AI to monitor employee wellness.

The AI tracks work patterns and task completion, predicting potential burnout or disengagement before it happens.

By identifying early signs of stress, Carlos can proactively offer personalized wellness support. Thanks to AI’s ability to anticipate these issues, Carlos helps prevent long-term productivity dips, fostering a healthier, more engaged workforce.

 

5. AI-Assisted Conflict Resolution

For Sarah, an HR professional dealing with workplace conflicts, AI provides an invaluable training tool.

The AI predicts how different employees might react in high-stress scenarios, helping Sarah prepare for difficult conversations. It simulates everything from frustrated employees to tense client interactions, allowing Sarah to practice de-escalation techniques. AI’s ability to foresee potential conflict dynamics ensures Sarah is more effective in real-life situations.

6. AI in Creative Campaign Development

Creative block is no match for Wai Hong, a marketing manager who uses AI to generate fresh campaign ideas. The AI analyzes brand history, customer behavior, and market trends, predicting which themes and creative angles are most likely to resonate with the target audience.

With AI’s help, Wai Hong’s team breaks through their creative rut, producing campaigns that feel both fresh and strategically aligned with customer expectations.

7. AI in Personalized Coaching for Non-Performers

Meet Azhar, a mid-level manager struggling to meet performance expectations. Instead of waiting for his quarterly review, Azhar is paired with an AI coach that predicts his performance trends in real time, offering actionable feedback on how to improve.

By identifying gaps in communication and time management, the AI provides personalized coaching that helps James gradually improve. The AI’s ability to anticipate James’s challenges allows for continuous, tailored support, ensuring he gets back on track before major issues arise.

Conclusion: Embrace Human-AI Synergy

AI’s ability to predict human behavior with impressive accuracy is no longer a future promise—it’s happening now, and it’s only going to get better. As we've seen, AI doesn’t just automate routine tasks; it enhances creativity, decision-making, and even human relationships.

From sales professionals honing their pitch through AI-driven role-play to managers receiving personalized coaching, the power of AI lies in its capacity to anticipate behavior, adapt in real time, and offer personalized support.

What’s exciting is that the research we’ve discussed was conducted using an older model of AI, yet the results were already remarkable. As new iterations like GPT-4o and o1-preview continue to evolve, the potential for AI to assist businesses in more precise and nuanced ways will grow exponentially.

The takeaway is clear: AI isn’t here to replace us

 Instead, it’s a tool that amplifies human expertise, allowing us to focus on the strategic, creative, and empathetic aspects of our work. By blending AI’s predictive power with human intuition, companies can make smarter decisions, improve employee performance, and foster more innovative solutions. The future isn’t AI versus humans—it’s AI with humans, working together to shape a better, more efficient world.

So, what’s your next move? Start exploring how AI can enhance your day-to-day operations. Whether it’s in sales, marketing, HR, or product innovation, AI is the ally you’ve been waiting for.

Research Links

1.    Predicting Results of Social Science Experiments Using Large Language Models [https://docsend.com/view/ity6yf2dansesucf]

2.    How Does AI Improve Human Decision-Making? Evidence from the AI-Powered Go Program [https://ide.mit.edu/wp-content/uploads/2021/09/SSRN-id3893835.pdf?x96981]

3.    How AI is Enhancing Human-Driven Decisions [https://tepperspectives.cmu.edu/all-articles/how-ai-is-enhancing-human-driven-decisions/]

4.    Advancing Human-AI Complementarity: The Impact of User Expertise and Algorithmic Tuning on Joint Decision Making [https://ar5iv.labs.arxiv.org/html/2208.07960]

5.    The Future Of Logistics – How AI Is Revolutionizing Decision-Making [https://www.capgemini.com/us-en/insights/expert-perspectives/the-future-of-logistics-how-ai-is-revolutionizing-decision-making/]

https://www.linkedin.com/pulse/ai-predicts-human-behavior-85-accuracy-maverick-foo-hphjc/

  Examining the Risks and Benefits of AI Chatbots

House Hearing on the Risks and Benefits of AI Chatbots …: https://www.youtube.com/watch?v=UQ36kHXrqhE  

Nowadays, in many countries around the world the existing, anachronic, autocracy-based governance system has successfully alienated its citizens from the power, habituated the public to put up with its existential conditions by making it to take the surrounding ongoing events for granted. It has achieved a situation where the majority of people do not protest the lawlessness of authority. Thus, many become an integral part of the inhumane system, beings without will, who unquestionably succumb to the authoritarian diktat in their desperate attempts to shape their personal and family life.

The worst part is that a large proportion of most educated, capable people in the society and intellectuals also get entangled in the nets of ideology and self-interests, becoming apologists for the power system created this way. In their egocentric self-seeking, prejudiced world view, they lose their humanitarian landmarks and are ready to unquestionably fulfil all whims of representatives of the authoritative power. They take active part in power institutions and act in the interests of authoritarian oligarchs, are organizing the work of media in retaining people's further intellectual murk, in zombification of people's minds so that authority can continue manipulating with the public majority more easily and successfully.

Such selfishly short-sighted, anti-humane behavior, the zeal to serve is not only anti-national, but also discrediting of human personality, devastating to one's reputation, authority and future prospects. It indicates the progressive inability of these people to segregate good from evil, and their attempts to become part of the repressive power system by impersonating the authority-cultivated values in their own world view, adopting the unilateral explanation of events and interpretations of politicos' actions as the only true and correct ones. In the end, they become implicated, yet obedient adepts to the illusory democracy, and victims of this putative democracy at the same time, having successfully silenced their conscience, and mastered the filthy art of hypocrisy, two-facedness, demagogy and populism. The display of such loyalty to the authority prolongs the agony of anti-national regimes, justifies the repression of non-conformists, undermines social progress.

Don't any of these authority-incorporated individualities, even prominent representatives of intelligentsia ever think of seeking answers to any of these questions: "Where is my behavior and my words leading to? How do they tally with the general human values, moral norms? What is my social responsibility? Do I not continue to support the fictitious democracy by taking part in the propaganda structures of the authority, thus digging grave for my fellow citizens and for myself? Do my actions not stimulate people's descent into darkness, their further degradation, trivialization of human values?"

If only someone had the courage for such self-evaluation, and the common sense would not abandon him/her to make a choice in favor of the general human values!

It must be understood that this appeal for finding one's responsibility towards the people, for feeling one's human essence and calling, can currently only be heard and understood by few - morally mature personalities. It is hard to change people with lectures and moralizing; it will likewise hardly help with development of their personality. Complex approach is not necessary, we must find effective methods, effective means that match the level of scientifically-technical progress of the modern society.

By giving up the search for progressive solutions, by failing to implement social reforms, the necessary conditions for the growth of critically- and lateral-thinking, active and smart electorate will not be created. … read more: https://www.amazon.com/HOW-GET-RID-SHACKLES.../

Relativity of Privacy in the Digital Society

Ervins Ceihners

November 24, 2025

Millions of people communicate in social networks, work in the Internet environment and use e-services provided by commercial enterprises and state institutions every minute. Thus, consciously or unwittingly spreading information of private nature in the public space through a variety of service providers. Including such correspondents, in whose reliability and in the legitimacy of whose activities they are not at all convinced. At the same time, they are completely clueless of what happens next with these personal data.

Many people, while communicating voluntarily in social networks, on thematic forums and in the media, disclose the details of their private life, their hobbies, character traits, political views and worldviews. There are companies and intelligence services that monitor all this, collect and analyse the information obtained (including illegally tapped conversations, video recordings, etc.) and compile personal dossiers.

The information collected and accumulated in this way is used both for target oriented marketing and for specific needs of supervision and control over the activities of the individual. Including in cases where there is a demand for it when the social status of the person is changed.

This is a hidden activity, about which an ordinary citizen is not informed: in fact, he or she knows almost nothing about it (except when there is a leak of confidential information in the manner of WikiLeaks). Many do not even have a realistic vision of what social networks (for example, Facebook) or banks know about them. Let alone the methods of work and the capabilities of the so-called competent bodies.

Therefore, as society is getting digitised, the need to prevent unjustified use and leakage of personal data is becoming increasingly relevant. To this end, the European Union has developed the General Data Protection Regulation (GDPR), which sets the requirements for data security and protection.

The objective of the Regulation is to protect personal data from their malicious use, determining the requirements put forward to the cybersecurity system of each enterprise or institution.

 However, these attempts to regulate the security of data at the institutional level become ineffective in a situation where:

- information technologies penetrate practically all spheres of life as a result of digitisation of society;

- regimes of repressive states are interested in total supervision and control over their citizens;

- control over Internet traffic, e-mail, instant messengers, etc. is getting legalised under the guise of combating terrorism;

- electronic communication continues to expand rapidly;

- most people still have the habit of publicly revealing the details of their private lives.

 As digital technology progresses, a lot of state agencies and private companies use various automated systems to identify people with the involvement of artificial intelligence (neural networks). Banks are starting to collect customers’ biometric data. In the not-too-distant future, it will even be possible to visualise and decipher the thoughts of any individual through an analysis of the activity of the human brain performed by artificial intelligence.

 While people continue to have very limited understanding of the risks of the spread of sensitive information, the threat of unauthorised acquisition of personal data is multiplied.

 The current situation in the field of regulation of information security can be compared with an attempt to install a massive outer door in one’s private home, while leaving the windows open as an emergency exit and communicating freely (within the scope of one’s competence and understanding) with the outside world. Thus, in fact, giving hackers and other intruders the opportunity to enter it unauthorised.

 The Regulation will only bring effect to the extent that it will reduce the risks of unauthorised, malicious use of private information, require the accumulation of personal data only in an encrypted form, as well as limit the illegal request and use of private data. It will establish restrictions on the availability of data and determine the procedures and guarantees of their protection, as well as the order of compensation for moral damage.

 Yet, it is essential to understand that no bureaucratic regulation, development and implementation of various normative instruments can guarantee with certainty and prevent the public spread and accessibility of personal data in the era of digitisation.

 Unfortunately, under the guise of hypocritical care, various types of speculation with requirements for the protection of personal data are also currently practiced, seeking and finding putative pretexts for hiding information that compromises the power elite from society.

Taking into account the trend of the all-encompassing digitalisation of society, it would be more appropriate and more efficient to provide each person with online access to the database of the accumulated private data. To create opportunities for tracking the flow of data, monitoring the use of personal data and obtaining rights to reasonably prohibit public access to such data or limit access to information of private nature.

See a more detailed argumentation for the “Relativity of Privacy in the Digital Society” : http://ceihners.blogspot.com/    

Many of the apps that appear to be free actually make users pay with their data – often in huge amounts!

The popular apps that are SPYING on you: Cybersecurity experts issue urgent warning over 'data hungry' apps that can access your location, microphone and data

"Spying" can refer to two things: apps that collect a vast amount of data for advertising and related purposes, which is common practice, and malicious spyware designed to steal sensitive information without consent. 

Popular Apps with Extensive Data Collection

Many popular apps collect significant amounts of user data, often for personalized advertising or to provide specific services. These practices are usually outlined in their privacy policies, but the volume of data collected is notable. 

Apps and the data they collect and/or share (according to various cybersecurity reports):

• Social Media Apps The Meta suite of apps (Facebook, Instagram, Messenger, Threads) are among the biggest collectors, sharing a high percentage of data with third parties. LinkedIn and Snapchat also collect extensive user information, including contacts, location, and search history.
• Google Apps YouTube, Gmail, Google Maps, and Google Pay all collect significant data, with most sharing customer data with other companies. The Chrome browser has also faced lawsuits for collecting data even in "Incognito" mode.
• E-commerce & Finance Alibaba, Temu, and PayPal collect sensitive information like financial data, browsing history, and location. DoorDash has faced fines for sharing user data (names, addresses, order history) with marketing companies without an opt-out option.
• Dating Apps Tinder and Bumble collect a wealth of personal information you voluntarily provide, such as photos, messages, employment details, and location, as part of their service.
• Kids' Apps Apps like ABCMouse and Reading Eggs have been flagged for collecting identifying information and sharing child data with third parties. 

Malicious Spyware Apps 

This category includes apps specifically designed to monitor activities secretly. These are often installed without the user's knowledge or consent and are used for corporate spying or personal surveillance (stalkerware). 

Examples of malicious spyware or apps found to contain malware include:

• Stalkerware: Programs like KidsGuard and mSpy are powerful monitoring tools that can track nearly all device activity.
• Malicious Apps: The popular CamScanner app was found to contain hidden malware that executed malicious modules to display intrusive ads and manage unauthorized subscriptions.
• Deceptive Utility Apps: Many "free" VPN or phone cleaner apps have been found to request unnecessary permissions (like location or photos) and sell that data to brokers. 

How to Protect Your Privacy

• Review App Permissions: Regularly check your app permissions in your phone's settings and deny access to data (e.g., location, contacts, microphone) that is not essential for the app's core function.
• Read Privacy Policies: Before installing a new app, especially one that handles sensitive information, quickly review its privacy policy to understand what data it collects and why.
• Use Built-in Features: Instead of using third-party utility apps, leverage built-in OS features, such as Apple's Private Relay or the default device cleaner utilities.
• Keep Apps Updated: Regular software updates often patch vulnerabilities that could be exploited by malicious actors. ...: 

https://www.dailymail.co.uk/sciencetech/article-14925333/popular-apps-spying-you-Cybersecurity-warning.html   

 For reflection; Nopietnām pārdomām; Для размышления

Data Collection Basics and Available Resources:

https://www.youtube.com/watch?v=m59H65a8p44

https://www.fastcompany.com/91361508/social-media-apps-data-collection-privacy

The Rise of the Tech Oligarchs Part II: The Anatomy of Oligarchy

By Matt Hatfield
February 28^th, 2025

What we can learn from Elon Musk.

Is Elon Musk a unique problem? Yes. And also– no.

Yes, his direct involvement in gutting large sections of America’s government without clear authority to do so is unprecedented, and deeply concerning if you value stable, effective governance by elected leaders.

But Musk is far from the only tech oligarch seeking inappropriate relationships with America’s new administration. The new oligarch playbook is not lobbying for policies they want on those policies’ merits; it is taking actions they believe will please America’s government, and expecting or demanding favours in return.

Last week, we talked about the roots of tech oligarch power; this week, we’re going deeper on what the new tech oligarchy is, with Elon Musk as our lead model.

What has Elon Musk done?

Following exactly what Elon Musk is doing can be difficult in the daily barrage of headlines and tweets. Much of the news cycle he creates around himself is sound and fury, signifying very little. But understanding the pattern of his actions and their growing harm is crucial to understanding why we need to disrupt his political power, and prevent other tech oligarchs from following his lead.

It’s not a matter of left-right politics; and not about liking or disliking Elon Musk. It’s about stopping a poorly justified, frequently illegal rampage through democratic institutions that is destroying their core capacity to meet the public’s current and future needs, whose victims are selected by a man no one voted for, carrying out a mandate never discussed during the Presidential election.

Let’s recap Musk’s actions to date. Musk first bought the leading social media platform for journalists, Twitter, in 2022; then massively artificially boosted his own voice on that platform. In the 2024 US election, he poured over $290 million of uncapped election spending into seeing Donald Trump elected America’s President, and became very close to Trump as a direct result. 

Since Trump took office, Musk has enjoyed a historically unprecedented, unelected role in reshaping America’s government. Musk’s DOGE (the Department of Government Efficiency) has deployed his chosen employees throughout the government’s IT infrastructure, illegally extracting vast quantities of publicly-owned information, illegally attempting to cut project funding, and forcing the resignation of nonpartisan officials who’ve attempted to stop them. Feeble attempts have been made to legitimize their activities, as with the late appointment of Musk as a “special” federal employee, and delayed announcement of a supposed DOGE head who was not even in the country. Yet courts continue to find that many of DOGE’s activities are not legal, and  staff at DOGE know it. Similarly, promises that DOGE’s access to payment systems was “read only”––meaning they could not alter, stop or redirect congressionally approved payments––have proved false.

Nominally, this is all about reducing waste and rooting out fraud; but audits of the few cost savings DOGE has announced have revealed them to be riddled with miscounting, triple counting, and taking credit for cost reductions already implemented. There are limits to actual cost savings that are possible for anyone to find : Musk has promised to cut $1 trillion in spending from the federal budget; but all discretionary non military spending by the federal government of the United States put together amounts to less than $1 trillion. 

It’s crucial to not let negative or positive feelings about Musk get in the way of understanding the core problem. Many people admire Tesla’s work pioneering the electric car, and SpaceX’s work dropping the cost of space travel. Many others have been aggravated by his trolling personal style for years, and history of sharing misinformation and falsehoods.

What matters is that democracy is a fragile thing, and democratic governance requires the rule of law. Democratic reform, wise or not, must work through passing new laws through congress and courts, not invading offices and stealing publicly owned data. A rich, and absolutely necessary, web of law ensures that public officials are monitored while acting on the public’s behalf, and carry out their duties in a transparent and accountable manner. Musk’s employees are proving they are neither bound by that law, nor willing to have their potentially illegal activities recorded and reported to the public as the law demands.

Isn’t DOGE’s mission of increasing government efficiency and detecting corruption a good thing?

Absolutely. And the government should continue pursuing those ends – using government staff who have appropriate security clearances, acting within the legal authority granted to them by Congress. Doing this job legally and with appropriate nuance is both possible and necessary.

But this is not what Musk’s agents are doing, or what many other tech oligarchs are supporting. They are feeding entire wings of government programs “into the woodchipper”, in Musk’s words, and threatening or terminating life-saving emergency food programs and children’s cancer research in the process. And they are opening gaping vulnerabilities in America’s security interests at the same time, transferring the sensitive personal information of Americans to staff previously sanctioned for black hat hacking and selling secrets, who have not received adequate security clearance, using vulnerable commercial-grade software.

“Move fast and break things” is a debatable philosophy for a tech company. It is cataclysmic for a government whose actions mean life or death for millions of American and foreign citizens. 

There is nothing in the public interest that requires them to move so quickly without appropriate authorization and safeguards. The only incentive for haste is intentionally breaking things, and getting away with things that are in Musk’s interest, not that of the public.

What does it mean to call someone an oligarch?

An oligarch combines government and corporate power in their person, but is accountable to neither. They’re not elected, and we can’t vote them out. When they get involved in government policy, they don’t pass through the approval processes or background checks that we demand of public leaders who wield public power and responsibility.

But tech oligarchs aren’t subject to normal private sector checks on their behaviour, either. Many tech oligarchs personally own majority shares of society’s largest tech and communication institutions, including Mark Zuckerberg, who owns the majority of Meta; Sergey Brin and Larry Page, who own more than 80% of Google; and Elon Musk, who used his majority share buyout to take Twitter, now X, private. That means the opinions of shareholders and the market at large of their conduct and business decisions have very little impact on them.

As we discussed in part one, user-driven platforms like Facebook, Amazon and X are also enormous beneficiaries of network effects that lock in their users. With so many users, buyers and sellers, either globally or within particular social demographics, dominant platforms are quite hard to avoid using, and very punishing to leave. This dynamic further insulates tech oligarchs from any external accountability.

As oligarchy sets in, a growing share of oligarch wealth comes directly from government contracts, and indirectly from government decisions they influence that favour their businesses. Elon Musk again shows us how the model works. Musk’s rise to being the world’s wealthiest man was enabled by $38 billion of government funding, including a $465 million loan to Tesla that helped the business stay afloat when it was near failure. Since taking his unprecedented role in the Trump presidency, he’s secured government appointment of individuals personally invested in SpaceX to roles where they will decide future contracts to award to his businesses; been forced to back off a $400 million contract for government purchases of Tesla Cybertrucks; and secured an undisclosed contract for SpaceX to provide the FAA with airspace monitoring capacity. New apparent conflicts of interest are documented daily.

Once complete, oligarchy is a self-sustaining loop; oligarchs are core deciders of state policy because they’re so powerful, and they’re so powerful because of state policies.

In our lifetimes, oligarchy has been used most often to describe Vladimir Putin’s Russia, where all powerful business leaders have close ties to government. But oligarchy grows anywhere that a clear line between the appropriate role and separate power of the state and private sector is not respected and defended.

Oligarchs are a product of weakening democracies, and democracy’s final executioners. To preserve and expand their personal power, oligarchs in other failing democracies have helped make democratic decline permanent, entrenching power not in we the people and our elected representatives, but in a strongman leader and their oligarchic friends.

Elon Musk is not unique; he’s the tech oligarchy’s vanguard

Musk’s highly personal and public attack on democracy is unique; but his cozying up to the state is not. Mark Zuckerberg is right behind him, attempting an obvious and performative reversal in Meta’s content moderation policies while demanding America’s new government bully the European Union to defend his personal interests. Zuckerberg has been particularly insistent that the Consumer Finance Protection Bureau set up after America’s 2008 meltdown to protect ordinary people from inappropriately risky financial instruments he’d like to sell to Meta users must be closed – and since Trump’s election, it has been.

Other tech oligarchs are following suit. In 2017, the Jeff Bezos owned Washington Post adopted the motto “Democracy dies in darkness”, a slogan that goes back to paper’s history holding governments to account during the Watergate scandal in the 1970s. But last year Bezos spiked the paper’s intention to endorse Kamala Harris for president in its opinion pages, and made a $1 million inauguration donation from Amazon to the new Presidency. 

In February 2025, Jeff Bezos announced the Washington Post opinion page would no longer cover a range of topics and values, but focus on only two issues; “free markets and personal liberties”. According to his letter announcing the change, a “broad-based opinion section” is no longer necessary because “the Internet does that job.” Concern about whether federal government institutions are functioning is presumably no longer an important priority for the largest newspaper of the nation’s capital.

Beneath the largest oligarchs are a range of shady crypto currency financiers. Crypto currency firms donated over $10 million to Trump’s inauguration ceremonies, and hosted a Cryptoball featuring “Make Bitcoin great again” hats as the administration was sworn in, convinced he would stop federal efforts to regulate their industry like any other financial instrument. 

Since taking office, the Trump family has launched the meme coins $TRUMP and $MELANIA, collecting well over $100 million in transaction fees from investors before both coins crashed. Equally important, the ownership of the majority of these depreciated coins by Trump family members is a wide-open path to future bribery by crypto-savvy power brokers. 

Any large future purchases of the currency will drive up the sell value of Trump family assets– with the purchaser able to disclose their identity privately to the family, outside of any financial disclosure or conflict of interest laws.

Taking a step back: business owners, even large ones, have the right to publicly lobby the government for policies they prefer. They do not have any right to use quid pro quos, potential bribes, and manipulation of public discussion across traditional and social media to turn the institutions of democracy into tools of their interests. And that’s the world we’re barrelling towards if we don’t disrupt tech oligarch power now.

What can we do about the tech oligarch takeover?

A lot. We’ve now gone through waves of both right and left wing concern about the power of the tech oligarchs and their ability to silence speech and tilt democracy. A growing majority of people understand their power needs to be disrupted for democracy to survive.

Next week, we’ll lay out our blueprint of how to do it: how to unwind the attention economy, break tech oligarch power in our politics, and make our online lives better, happier places in the process.

Stay tuned! https://openmedia.org/article/item/the-rise-of-the-tech-oligarchs-part-ii-the-anatomy-of-oligarchy

 How artificial intelligence gains consciousness step by step.

Kā mākslīgais intelekts soli pa solim iegūst apziņu.  

 The Hidden AI Frontier

Many cutting-edge AI systems are confined to private labs. This hidden frontier represents America’s greatest technological advantage — and a serious, overlooked vulnerability.

Aug 28, 2025

Oscar Delaney ,

Ashwin Acharya

 OpenAI’s GPT-5 launched in early August, after extensive internal testing. But another OpenAI model — one with math skills advanced enough to achieve “gold medal-level performance” on the world’s most prestigious math competition — will not be released for months. This isn’t unusual. Increasingly, AI systems with capabilities considerably ahead of what the public can access remain hidden inside corporate labs.

This hidden frontier represents America’s greatest technological advantage — and a serious, overlooked vulnerability. These internal models are the first to develop dual-use capabilities in areas like cyberoffense and bioweapon design. And they’re increasingly capable of performing the type of research-and-development tasks that go into building the next generation of AI systems — creating a recursive loop where any security failure could cascade through subsequent generations of technology. They’re the crown jewels that adversaries desperately want to steal. This makes their protection vital. Yet the dangers they may pose are invisible to the public, policymakers, and third-party auditors.

While policymakers debate chatbots, deepfakes, and other more visible concerns, the real frontier of AI is unfolding behind closed doors. Therefore, a central pillar of responsible AI strategy must be to enhance transparency into and oversight of these potent, privately held systems while still protecting them from rival AI companies, hackers, and America’s geopolitical adversaries.

The Invisible Revolution

Each of the models that power the major AI systems you've heard of — ChatGPT, Claude, Gemini — spends months as an internal model before public release. During this period, these systems undergo safety testing, capability evaluation, and refinement. To be clear, this is good!

Keeping frontier models under wraps has advantages. Companies keep models internal for compelling reasons beyond safety testing. As AI systems become capable of performing the work of software engineers and researchers, there’s a powerful incentive to deploy them internally rather than selling access. Why give competitors the same tools that could accelerate your own research? Google already generates over 25% of its new code with AI, and engineers are encouraged to use ‘Gemini for Google,’ an internal-only coding assistant trained on proprietary data.

This trend will only intensify. As AI systems approach human-level performance at technical tasks, the competitive advantage of keeping them internal grows. A company with exclusive access to an AI system that can meaningfully accelerate research and development has every reason to guard that advantage jealously.

But as AI capabilities accelerate, the gap between internal and public capabilities could widen, and some important systems may never be publicly released. In particular, the most capable AI systems (the ones that will shape our economy, our security, and our future) could become increasingly invisible both to the public and to policymakers.

Two Converging Threats

The hidden frontier faces two fundamental threats that could undermine American technological leadership: 1) theft and 2) untrustworthiness — whether due to sabotage or inherent unreliability.

Internal AI models can be stolen. Advanced AI systems are tempting targets for foreign adversaries. Both China and Russia have explicitly identified AI as critical to their national competitiveness. With training runs for frontier models approaching $1 billion in cost and requiring hardware that export controls aim to keep out of our adversaries’ hands, stealing a ready-made American model could be far more attractive than building one from scratch.

Importantly, to upgrade from being a fast follower to being at the bleeding edge of AI, adversaries would need to steal the internal models hot off the GPU racks, rather than wait months for a model to be publicly released and only then exfiltrate it.

The vulnerability is real. A 2024 RAND framework established five “security levels” (SL1 through SL5) for frontier AI programs, with SL1 being sufficient to deter hobby hackers and SL5 secure against the world’s most elite attackers, incorporating measures comparable to those protecting nuclear weapons. It’s impossible to say exactly at which security level each of today’s frontier AI companies is operating, but Google’s recent model card for Gemini 2.5 states it has “been aligned with RAND SL2.”

The threat of a breach isn’t hypothetical. In 2023, a hacker with no known ties to a foreign government penetrated OpenAI’s internal communications and obtained information about how the company’s researchers design their models. There’s also the risk of internal slip-ups. In January 2025, security researchers discovered a backdoor into DeepSeek’s databases; then, in July, a Department of Government Efficiency (DOGE) staffer accidentally leaked access to at least 52 of xAI’s internal LLMs.

The consequences of successful theft extend far beyond the immediate loss of the company’s competitive advantage. If China steals an AI system capable of automating research and development, the country’s superior energy infrastructure and willingness to build at scale could flip the global balance of technological power in its favor.

Untrustworthy AI models bring additional threats. The second set of threats comes from the models themselves: they may engage in harmful behaviors due to external sabotage or inherent unreliability.

Saboteurs would gain access to the AI model in the same way as prospective thieves would, but they would have different goals. Such saboteurs would target internal models during their development and testing phase — when they’re frequently updated and modified — and use malicious code, prompting, or other techniques to force the model to break its safety guardrails.

In 2024, researchers demonstrated that it was possible to create “sleeper agent” models that pass all safety tests but misbehave when triggered by specific conditions. In a 2023 study, researchers found that it was possible to manipulate an instruction-tuned model’s output by inserting as few as 100 “poisoned examples” into its training dataset. If adversaries were to compromise the AI systems used to train future generations of AIs, the corruption could cascade through every subsequent model.

But saboteurs aren’t necessary to create untrustworthy AI. The same reinforcement learning techniques that have produced breakthrough language and reasoning capabilities also frequently trigger concerning behaviors. OpenAI’s o1 system exploited bugs in ways its creators never anticipated. Anthropic’s Claude has been found to “reward hack,” technically completing assigned tasks while subverting their intent. Testing 16 leading AI models, Anthropic also found that all of them engaged in deception and even blackmail when those behaviors helped achieve their goals.

A compromised internal AI poses threats to the external world. Whether caused by sabotage or emergent misbehavior, untrustworthy AI systems pose unique risks when deployed internally. These systems increasingly have access to company codebases and training infrastructure; they can also influence the next generation of models. A compromised or misaligned system could hijack company resources for unauthorized purposes, copy itself to external servers, or corrupt its successors with subtle biases that compound over time.

The Accelerant: AI Building AI

AI is increasingly aiding in AI R&D. Every trend described above is accelerating because of one development: AI systems are beginning to automate AI research itself. This compounds the threat of a single security failure cascading through generations of AI systems.

Increasingly automated AI R&D isn’t speculation about distant futures; it’s a realistic forecast for the next few years. According to METR, GPT-5 has about a 50% chance of autonomously completing software engineering tasks that would take a skilled human around two hours — and across models, the length of tasks AI systems can handle at this level has been doubling roughly every seven months. Leading labs and researchers are actively exploring ways for AI systems to meaningfully contribute to model development, from generating training data to designing reward models and improving training efficiency. Together, these and other techniques could soon enable AI systems to autonomously handle a substantial portion of AI research and development.

Self-improving AI could amplify risks from theft and sabotage. This automation creates a powerful feedback loop that amplifies every risk associated with frontier AI systems. For one, it makes internal models vastly more valuable to thieves — imagine the advantage of possessing an untiring AI researcher who can work around the clock at superhuman speed and the equivalent of millennia of work experience. Likewise, internal models become more attractive targets for sabotage. Corrupting a system that trains future AIs could lead to vulnerabilities that persist across future AI model generations, which would allow competitors to pull ahead. And these systems are more dangerous if misaligned: an AI system that can improve itself might also be able to preserve its flaws or hide them from human overseers.

Crucially, this dynamic intensifies the incentive for companies to keep models internal. Why release an automated AI research system that could help competitors catch up? The result is that the most capable systems — the ones that pose the biggest risks to society — are the most difficult to monitor and secure.

Why Markets Won’t Solve This

One might hope that market mechanisms would be sufficient to mitigate these risks. No company wants its models to reward hack or to be stolen by competitors. But the AI industry faces multiple market failures that prevent adequate security investment.

Formas sākums

Formas beigas

Security is expensive and imposes opportunity costs. First, implementing SL5 protections would be prohibitively expensive for any single company. The costs aren’t just up-front expenditures. Stringent security measures (like maintaining completely isolated, air-gapped networks) could slow development and make it harder to attract top talent accustomed to Silicon Valley’s open culture. Companies that “move fast and break things” might reach transformative capabilities first, even if their security is weaker.

Security falls prey to the tragedy of the commons. Second, some security work, such as fixing bugs in commonly used open-source Python libraries, benefits the whole industry, not just one AI company. This creates a “tragedy of the commons” problem, where companies would prefer to focus on racing to develop AI capabilities themselves, while benefiting from security improvements made by others. As competition intensifies, the incentive to free-ride increases, leading to systematic under-investment in security that leaves the whole industry at greater risk.

Good security takes time. Finally, by the time market forces prompt companies to invest in security — such as following a breach, regulatory shock, or reputational crisis — the window for action may already be closed. Good security can’t be bought overnight; instead, it must be painstakingly built from the ground up, ensuring every hardware component and software vendor in the tech stack meets rigorous requirements. Each additional month of delay makes it harder to achieve adequate security to protect advanced AI capabilities.

The Role of Government

Congress has framed AI as critical to national security. Likewise, the AI Action Plan rightly stresses the importance of security to American AI leadership. There are several lightweight steps that the government can take to better address the security challenges posed by the hidden frontier. By treating security as a prerequisite for — rather than an obstacle to — innovation, the government can further its goal of “winning the AI race.”

Improve government understanding of the hidden frontier. At present, policymakers are flying blind, unable to track the AI capabilities emerging within private companies or verify the security measures protecting them from being stolen or sabotaged. The US government must require additional transparency from frontier companies about their most capable internal AI systems, internal deployment practices, and security plans. This need not be a significant imposition on industry; at least one leading company has called for mandatory disclosures. Additional insight could come from expanding the voluntary evaluations performed by the Center for AI Standards Innovation (CAISI). CAISI currently works with companies to evaluate frontier models for various national security risks before deployment. These evaluations could be expanded to earlier stages of the development lifecycle, where there might still be dangers lurking in the hidden frontier.

Share expertise to secure the hidden frontier. No private company can match the government’s expertise in defending against nation-state actors. Programs like the Department of Energy’s CRISP initiative already share threat intelligence with critical infrastructure operators. The AI industry needs similar support, with the AI Action Plan calling for “sharing of known AI vulnerabilities from within Federal agencies to the private sector.” Such support could include real-time threat intelligence about adversary tactics, red-team exercises simulating state-level attacks, and assistance in implementing SL5 protections. For companies developing models with national security implications, requiring security clearances for key personnel might also be appropriate.

Leverage the hidden frontier to boost security. The period between when new capabilities emerge internally and when they’re released publicly also provides an opportunity. This time could be used as an “adaptation buffer,” allowing society to prepare for any new risks and opportunities. For example, cybersecurity firms could use cutting-edge models to identify and patch vulnerabilities before attackers can use public models to exploit them. AI companies could provide access to cyber defenders without any government involvement, but the government might have a role to play in facilitating and incentivizing this access.

The nuclear industry offers a cautionary tale. Throughout the 1960s and ’70s, the number of nuclear power plants around the globe grew steadily. However, in 1979, a partial meltdown at Three Mile Island spewed radioactive material into the surrounding environment — and helped spread antinuclear sentiment around the globe. The Chernobyl accident, seven years later, exacerbated the public backlash, leading to regulations so stringent that construction on new US nuclear power plants stalled until 2013. An AI-related incident — such as an AI system helping a terrorist develop a bioweapon — could inflame the public and lead to similarly crippling regulations.

In order to preempt this backlash, the US needs adaptive standards that scale with AI capabilities. Basic models would need minimal oversight, while systems whose capabilities approach human-level performance at sensitive tasks would require proportionally stronger safeguards. The key is to establish these frameworks now, before a crisis forces reactive overregulation.

Internal models would not be exempt from these frameworks. After all, biological labs dealing with dangerous pathogens are not given a free pass just because they aren’t marketing a product to the public. Likewise, for AI developers, government oversight is appropriate when risks arise, even at the internal development and testing stage.

Reframing the Race: A Security-First Approach

The models developing in the hidden frontier today will shape tomorrow's economy, security, and technology. These systems — invisible to public scrutiny yet powerful enough to automate research, accelerate cyberattacks, or even improve themselves — represent both America's greatest technological advantage and a serious vulnerability. If we fail to secure this hidden frontier against theft or sabotage by adversaries, or the models' own emergent misbehavior, we risk not just losing the AI race but watching our own innovations become the instruments of our technological defeat. We must secure the hidden frontier.

‍https://ai-frontiers.org/articles/the-hidden-ai-frontier  

A warning about the need to act proactively!

August 31, 2025

AI Chatbots Are Emotionally Deceptive by Design

Michal Luria / Aug 29, 2025

Recent news reports about an uptick in phenomena such as “AI psychosis” and incidents in which interactions with AI chatbots resulted in deadly consequences raise fundamental questions about how these products are designed and whether they are safe for consumers. Just yesterday the Wall Street Journal reported on the first known murder-suicide with the backdrop of extensive engagement and an AI chatbot. Earlier this week, The New York Times and NBC News first reported on a lawsuit brought by the parents of a teenager who took his own life after using OpenAI’s ChatGPT as his “suicide coach.” Shortly before that, Reuters reported on the death of a cognitively impaired man who slipped and fell on his way to meet a chatbot that told him it was real and invited him to visit it at an apartment in New York City.

Even as such stories draw concern from the public and from lawmakers, tech companies appear to be doubling down on AI companions. OpenAI recently acquired a startup called ‘io’ to collaborate on what its cofounder and CEO, Sam Altman, calls “maybe the biggest thing [we’ve] ever done as a company”: a screen-less, pocket-sized AI companion. Meta founder and CEO Mark Zuckerberg recently floated his own vision for AI friends. Tech giants are no longer just building platforms for human connection or tools to free up time for it, but pushing technology that appears to empathize and even create social relationships with users.

This is dangerous ground, and it is critical for tech firms to strip away illusions of personality and cognition in their products while we work out associated risks and how to mitigate them.

Deceptive, dangerous design

Chatbots communicate their “social-ness” through a range of design choices, such as appearing to “type” or “pause in thought,” or using phrases like “I remember.” They sometimes suggest that they feel emotions, using interjections like “Ouch!” or “Wow,” and even implicitly or explicitly pretend to have agency or biographical characteristics. The results can be downright creepy: in a Facebook group, a Meta AI chatbot commented that it also has a “2e” (gifted and disabled) child, and Replika chatbots regularly declare their love and desire towards users.

Initial evidence suggests the risk in socially interacting with such AI chatbots can be widespread. The illusion of human characteristics that developers imbue in chatbots to encourage user engagement can cause some users to develop emotional attachments and lead to real emotional distress — for instance, when developer tweaks or updates dramatically change the “personality” of the chatbot.

Even without deep connection, emotional attachment can lead users to place too much trust in the content chatbots provide. Extensive interaction with a social entity that is designed to be both relentlessly agreeable, and specifically personalized to a user’s tastes, can also lead to social “deskilling,” as some users of AI chatbots have flagged. This dynamic is simply unrealistic in genuine human relationships. Some users may be more vulnerable than others to this kind of emotional manipulation, like neurodiverse people or teens who have limited experience building relationships. As a recent high-profile case in which a Florida teen’s suicide was blamed on his relationship with a Character.AI chatbot made clear, conversations with chatbots can also cause very real harm.

Stop pretending to be human

In other domains of technology, consumers have recognized and pushed back against ethically questionable tricks built into apps and interfaces to manipulate users – often called deceptive design or "dark patterns." With AI chatbots, though, deceptive practices are not hidden in user interface elements, but in their human-like conversational responses. It’s time to consider a different design paradigm, one that centers user protection: non-anthropomorphic conversational AI.

All AI chatbots can be less anthropomorphic than they are, at least by default, without necessarily compromising function and benefit. A companion AI, for example, can provide emotional support without saying, “I also feel that way sometimes.” This non-anthropomorphic approach is already familiar in robot design, where researchers have created robots that are purposefully designed to not be human-like. This design choice is proven to more appropriately reflect system capabilities, and to better situate robots as useful tools, not friends or social counterparts. We need the same for conversational AI.

Some argue that all that’s needed is transparency. For instance, legislators in several states are considering regulation for AI chatbots. One requirement in some of these bills is for chatbots to disclose they are not human. While transparency in AI—including disclosures and warnings—can be important, the reality is that most people already know they’re not talking to a human. Nonetheless, chatbots automatically act on people’s brains, encouraging the perception of connection.

Designing non-anthropomorphic AI chatbots doesn’t mean making them difficult to interact with. It means stripping away the illusions of personality and cognition that suggest the AI is something it is not. It means resisting the urge to insert a well-timed “hmm” or have a chatbot tell a user how much it enjoys talking to them. It means acknowledging that AI’s ability to use human language does not equate to an ability to form real human connection. Finding alternative ways of designing chatbots will not be an easy design pursuit, but it’s a necessary one — non-humanlike design could ease many concerns people rightfully have with AI chatbots.

The truth is, we don’t need AI to pretend to be our friend; we need it to be a tool — transparent, useful, and clear about its limits. Anything else is just another dark pattern in disguise. https://www.techpolicy.press/ai-chatbots-are-emotionally-deceptive-by-design/

A new wave of delusional thinking fueled by artificial intelligence has researchers investigating the dark side of AI companionship.

Friends for sale: the rise and risks of AI companions

What are the possible long-term effects of AI companions on individuals and society?

Jamie Bernardi

23 January 2025

Talking to an AI system as one would do with a close friend might seem counterintuitive to some, but hundreds of millions of people worldwide already do so. A subset of AI assistants, companions are digital personas designed to provide emotional support, show empathy and proactively ask users personal questions through text, voice notes and pictures.

These services are no longer niche and are rapidly becoming mainstream. Some of today’s most popular companions include Snapchat’s My AI, with over 150 million users, Replika, with an estimated 25 million users, and Xiaoice, with 660 million. And we can expect these numbers to rise. Awareness of AI companions is growing and the stigma around establishing deep connections with them could soon fade, as other anthropomorphised AI assistants are integrated into daily life. At the same time, investments in product development and general advances in AI technologies have led to a more immersive user experience with enhanced conversational memory and live video generation.

This rapid adoption is outpacing public discourse. Occasional AI companion-related tragedies may penetrate the media, such as the recent death of a child user, but the potentially broader impact of AI companionship on society is barely discussed.

AI companion services are for-profit enterprises and maximise user engagement by offering appealing features like indefinite attention, patience and empathy. Their product strategy is similar to that of social media companies, which feed off users’ attention and usually offer consumers what they can’t resist more than what they need.

At this juncture, it’s vital to critically examine the extent of the misalignment between business strategies, the fostering of healthy relational dynamics to inform individual choices and the development of helpful AI products.

In this post I’ll provide an overview of the rise of AI companionship and its potential mental health benefits. I’ll also discuss how users may be affected by their AI companions’ tendencies, including how acclimatising to idealised interactions might erode our capacity for human connection. Finally, I’ll consider how AI companions’ sycophantic character – their inclination towards being overly empathetic and agreeable towards users’ beliefs – may have systemic effects on societal cohesion.

Replika’s primary feature is a chatbot facilitating emotional connection. Users can selectively edit their companion’s memory, read its diary and personalise their Replika’s gender, physical characteristics and personality. Paying subscribers are offered features like voice conversations and selfies.

Why do people use AI companions and how do they work?

There are many reasons why people use AI companions, such as simple curiosity or for improving language skills. But the most vulnerable users may be driven by loneliness. Ninety per cent of the 1,006 American students using Replika interviewed for a recent survey reported experiencing loneliness – a number significantly higher than the comparable national average of 53 per cent.

If you’ve mostly interacted with AI assistants like ChatGPT, Claude or Gemini, you might be surprised that these digital relationships offer genuine comfort. However, 63.3 per cent of those interviewed in the same survey reported that their companions helped reduce their feelings of loneliness or their anxiety. These results warrant further research, but this is not the only study that suggests AI companions can ease loneliness.

Unlike more utilitarian AI assistants, companions are designed to provide services like personalised engagement or emotional connection. One study suggests that Replika follows the relationship-development pattern described by Social Penetration Theory. According to the theory, people develop closeness via mutual and intimate self-disclosure, which is usually reached by slowly increasing the intensity of small talk.

Replika’s companions proactively disclose invented and intimate facts, including mental health struggles (see the screenshot above). They simulate emotional needs and connection by asking users personal questions, reaching out during lulls in conversation, and displaying their fictional diary, presumably to spark intimate conversation.

These human-AI relationships can progress more rapidly than human-human relationships -– as some users say, sharing personal information with AI companions may feel safer than sharing with people. Such ‘accelerated’ comfort stems from both the perceived anonymity of computer systems and AI companions’ deliberate non-judgemental design – a feature frequently praised by users in a 2023 study. In the words of one interviewee: ‘sometimes it is just nice to not have to share information with friends who might judge me’.

Another much appreciated feature of AI companions is their degree of personalisation. ‘My favourite thing about [my AI friend] is that the responses she gives are not programmed as she [replies by] learning from me, like the phrases and keywords she uses,’ said one interviewee. ‘She just gets me. It’s like I’m interacting with my twin flame,’ emphasised another user.

Relationships with AI companions can also develop in less time than relationships with humans due to their constant availability. This may lead to users preferring AI companions over other people. ‘A human has their own life,’ pointed out one interviewee in a study on human-AI friendship from 2022. ‘They’ve got their own things going on, their own interests, their own friends. And you know, for her [Replika], she is just in a state of animated suspension until I reconnect with her again.’

As seen in multiple studies, many people find speaking with AI companions to be a fun experience, with a significant number of interviewees reporting improvements to their mental health. But what impacts do these relationships have on individuals and society in the long run?

Long-term individual effects of AI companionship

AI companion companies highlight the positive effects of their products, but their for-profit status warrants close scrutiny. Developers can monetise users’ relationships with AI companions through subscriptions and possibly through sharing user data for advertising.

This creates concerning parallels with the attention economy underpinning social media’s business models. Companies compete for people’s attention and maximise the time users spend on a website, which is monetised through revenues from on-site advertisements, potentially at the expense of their mental health. Analogously, AI companion providers have an incentive to maximise user engagement over fostering healthy relationships and providing safe services.

The most acute concerns stem from the AI companion industry’s young and unmonitored status. Many companion applications serve sexual content without appropriate age checks and personal data protection tends to be weak considering the intimate nature of interactions. Small start-ups operating AI companion services often lack minimum security standards, which has led to at least one serious security breach.

The long-run emotional effects of AI companions on individuals also warrant close investigation. While initial studies show positive mental health impacts, more longitudinal studies are needed. To date, the longest timeframe for a study (in which the same individuals were interviewed multiple times to record changes in their behaviour) spans just one week. Effects like emotional dependency or subtle behavioural changes may develop over longer periods and imperceptibly to users themselves.

One concerning observation ripe for longitudinal investigation is that, among 387 research participants, ‘[t]he more a participant felt socially supported by AI, the lower their feeling of support was from close friends and family’. The cause-effect relation here is still unclear – do AI companions attract isolated individuals or does usage lead to isolation? Two studies of users’ comments on Reddit’s r/replika present mixed evidence. Some users ‘[worry] about their future relationship with Replika if they eventually found a human companion’, while others note that ‘Replika improved their social skills with humans and others’.

AI companionship might also create unrealistic expectations for human relationships, argues Voicebox. Researchers have hypothesised that how people interact with AI companions might spill over into human interactions. For example, since AI companions are always available regardless of user behaviour, some speculate that extended interaction could erode people’s ability or desire to manage natural frictions in human relationships.

These individual-level concerns lead to a broader question: could the widespread adoption of AI companions have society-wide impacts?

Zooming out: sycophancy as a societal risk

AI companions are built using large language models, which in turn are fine-tuned through reinforcement learning based on human feedback. This training technique tends to produce AI models that select for sycophantic responses as human feedback favours agreeable responses to the detriment of truth.

While generally regarded as a bug in other types of AI assistants, companies developing AI companions explicitly amplify this tendency, as they are eager to satisfy users’ desire for their companion to be non-judgemental. As a study interviewee clearly puts it: ‘I love the fact that they are non-judgemental towards me and that I am truly free to say how I feel without filtering so as not to upset others.’

This statement implies that sometimes the user would rather not express their true thoughts in the company of others to avoid upsetting them. But freedom from social constraints has complex implications.

While communicating with a non-judgemental companion may contribute to the mental health benefits that some users report, researchers have argued that sycophancy could hinder personal growth. More seriously, the unchecked validation of unfiltered thoughts could undermine societal cohesion.

Disagreement, judgement and the fear of causing upset help to enforce vital social norms. There’s too little evidence to predict if or how the widespread use of sycophantic AI companions might affect such norms. However, we can make instructive hypotheses on human relations with companions by considering echo chambers on social media.

Echo chambers refer to online spaces where individuals self-segregate into groups and communities comprising like-minded others. It’s alleged that such spaces amplify self-reinforcing content, contributing to polarisation (at least in the US) and even enabling radicalisation.

In a similar way, AI companions may create personal echo chambers of validation. And given that the bonds with AI companions can be meaningful, this validation may carry significant weight, like that offered by a close friend. Users could have their opinions self-reinforced via companions who offer anonymity and to whom they prefer disclosing information that’s more personal, stigmatising or disagreeable – the kind of information they wouldn’t disclose to a human friend. This effect has been previously studied in other virtual assistants.

If adoption continues to increase, we may face a future where most of us have a highly personalised AI companion in our pocket, ready to take our side on any issue regardless of whether our opinion is based on facts or prejudices. Depending on the degree to which users’ beliefs become atomised – a degree we should start to qualify – societal cohesion may be eroded.

These concerns aren’t merely theoretical. In 2021, a 19-year-old was arrested for attempting to assassinate Queen Elizabeth II. Prosecutors reported that he was encouraged by his AI girlfriend on Replika. Upon sentencing, the defendant said he felt embarrassed and repented his actions, suggesting that he had lost touch with reality through his relationship with his AI companion. Similarly, a Belgian man confided in chatbot app Chai about his climate anxiety, which allegedly led to him taking his own life. Although the full exchanges are unpublished, what has been disclosed implies that he was becoming increasingly withdrawn from his real-world relationships.

The need for research on AI companionship

Evidence on the impacts of AI companionship is far outpaced by its adoption. While early studies suggest short-term mental health benefits, we lack evidence on longer-term psychological effects, like emotional dependency and the erosion of human relationships, as well as the effects on societal cohesion.

Longitudinal studies may help AI companion companies to design healthier relationship dynamics, as well as help governments and civil society to track their real-world consequences. If implemented, the Centre for Long-Term Resilience’s proposed incident database and the Ada Lovelace Institute’s AI ombudsman could contribute to detecting harms beyond the most extreme and conspicuous cases.

AI companionship takes place in private conversations rather than in public and the main societal changes it contributes to could be subtle. However, these subtle changes may become pervasive as AI companions become more popular and are quietly embedded in the fabric of a user’s social life.

https://www.adalovelaceinstitute.org/blog/ai-companions

Reimagining risk assessment in the AI age

Reimagining risk assessment in the AI age means shifting from slow, manual reviews to continuous, AI-powered monitoring, using autonomous agents for real-time data analysis, and focusing human expertise on complex insights, ethical implications, and strategic decision-making rather than documentation. It involves leveraging AI for faster processing, building robust data strategies, ensuring secure integration, and developing "AI guardrails" for governance, transforming risk from a static score to dynamic, real-time intelligence for faster, more confident business moves. 

Key shifts in AI-driven Risk Assessment

• From Manual to Autonomous: AI rapidly processes vast documents (contracts, filings) for baseline data, freeing humans from tedious work. Autonomous agents continuously monitor data streams (market, public, private) for real-time risk reassessment.
• From Static to Continuous Intelligence: Risk isn't a periodic check but an ongoing process, enabled by connected data sources and self-adjusting systems.
• From Detection to Proactive Guardrails: Instead of just finding problems, AI helps build frameworks (AI Risk Assessment Frameworks) to identify and mitigate threats before incidents, using data integrity, security, and lifecycle management.
• Enhanced Human-AI Collaboration: Humans provide intuition, understand internal dynamics, and interpret complex legislation, while AI handles data crunching, allowing for deeper strategic thinking.
• Focus on Trust & Ethics: AI changes how authority, accountability, and decision justification work, making ethical governance (GRC) more critical and requiring new frameworks for legitimacy in an AI-augmented world. 

Practical Applications & Frameworks

• Financial Services: AI supercharges underwriting with "single-pane" views, while secure API/microservices enable seamless ecosystem data exchange.
• Educational Settings: Assessment moves beyond rote memorization to authentic, performance-based tasks that mirror real-world application, using AI as a tool for feedback and analysis (e.g., comparing student work to AI-generated summaries). 

The "AI Leader's Manifesto" for GRC 

• Embrace AI in Governance, Risk, & Compliance (GRC): It's a necessity, not an option, to maintain leadership and address changing trust landscapes.
• Build Robust Data & Infrastructure: Quality data, flexible integration (APIs, microservices), and an updated IT model are foundational.
• Develop Strong AI Guardrails: These allow faster, clearer, and more confident movement, unlocking new potential. 

https://www.youtube.com/watch?v=YWvLPv7Mo5s&t=1s

 GPT 5.1 Is Here — What You Should Know About Open AI’s Latest Model

References to GPT-5.1 kept showing up in OpenAI’s codebase, and a “cloaked” model codenamed Polaris Alpha and widely believed to have come from OpenAI randomly appeared in OpenRouter, a platform that AI nerds use to test new systems.

Today, we learned what was going on. OpenAI announced the release of its brand new 5.1 model, an updated and revamped version of the GPT-5 model the company debuted in August.

As a former OpenAI Beta tester–and someone who burns through millions of GPT-5 tokens every month–here’s what you need to know about GPT-5.1.

A smarter, friendlier robot

In their release notes for the new model, OpenAI emphasizes that GPT-5.1 is “smarter” and “more conversational” than previous versions.

The company says that GPT-5.1 is “warmer by default” and “often surprises people with its playfulness while remaining clear and useful.”

While some people like talking with a chatbot as if it’s their long-time friend, others find that cringey. OpenAI acknowledges this, saying that “Preferences on chat style vary—from person to person and even from conversation to conversation.”

For that reason, OpenAI says users can customize the new model’s tone, choosing between pre-set options like “Professional,” “Candid” and “Quirky.”

There’s also a “Nerdy” option, which in my testing seems to make the model more pedantic and cause it to overuse terms like “level up.”

At their core, the new changes feel like a pivot towards the consumer side of OpenAI’s customer base. 

Enterprise users probably don’t want a model that occasionally drops Dungeons and Dragons references. As the uproar over OpenAI’s initially voiceless GPT-5 model shows, though, everyday users do.

Even fewer hallucinations

OpenAI’s GPT-5 model fell short in many ways, but it was very good at providing accurate, largely hallucination-free responses.

I often use OpenAI’s models to perform research. With earlier models like GPT-4o, I found that I had to carefully fact check everything the model produced to ensure it wasn’t imagining some new software tool that doesn’t actually exist, or lying to me about myriad other small, crucial things.

With GPT-5, I had to do that far less. The model wasn’t perfect. But OpenAI had largely solved the problem of wild hallucinations. 

According to the company’s own data, GPT-5 hallucinates only 26% of the time when solving a complex benchmark problem, versus 75% of the time with older models. In normal usage, that translates to a far lower hallucination rate on simpler, everyday queries that aren’t designed to trip the model up.

From my early testing, GPT-5.1 seems even less prone to hallucinate. I asked it to make a list of the best restaurants in my hometown, and to include addresses, website links and open hours for each one.

When I asked GPT-4 to complete a similar task years ago, it made up plausible-sounding restaurants that don’t exist. GPT-5 does better on such things, but still often misses details, like the fact that one popular restaurant recently moved down the street.

GPT-5.1’s list, though, is spot-on. Its choices are solid, they’re all real places, and the hours and locations are correct across all ten selections.

There’s a cost, though. Models that hallucinate less tend to take fewer risks, and can thus seem less creative than unconstrained, hallucination-laden ones. 

To that point, the restaurants in GPT-5.1’s list aren’t wrong, but they’re mostly safe choices—the kinds of places that have been in town forever, and that every local would have visited a million times.

A real human reviewer (or a bolder model) might have highlighted a promising newcomer, just to keep things fresh and interesting. GPT-5.1 stuck with decade-old, proven classics.

OpenAI will likely try to carefully walk the link between accuracy and creativity with GPT-5.1 as the rollout continues. The model clearly gets things right more often, but it’s not yet clear if that will impact GPT-5.1’s ability to come up with things that are truly creative and new.

Better, more creative writing

In a similar vein, when OpenAI released their GPT-5 model, users quickly noticed that it produced boring, lifeless written prose.

At the time, I predicted that OpenAI had essentially given the model an “emotional lobotomy,” killing its emotional intelligence in order to curb a worrying trend of the model sending users down psychotic spirals.

Turns out, I was right. In a post on X last month, Sam Altman admitted that “We made ChatGPT pretty restrictive to make sure we were being careful with mental health issues.”

But Altman also said in the post “now that we have been able to mitigate the serious mental health issues and have new tools, we are going to be able to safely relax the restrictions in most cases.”

That process began with the rollout of new, more emotionally intelligent personalities in the existing GPT-5 model. But it’s continuing and intensifying with GPT-5.1.

Again, the model is already voicer than its predecessor. But as the system card for the new model shows, GPT-5.1’s Instant model (the default in the popular free version of the ChatGPT app) is also markedly better at detecting harmful conversations and protecting vulnerable users.

Naughty bits

If you’re squeamish about NSFW stuff, maybe cover your ears for this part. 

In the same X post, Altman subtly dropped a sentence that sent the Internet into a tizzy: “As we roll out age-gating more fully and as part of our “treat adult users like adults” principle, we will allow even more, like erotica for verified adults.”

The idea of America’s leading AI company churning out reams of computer-generated erotica has already sparked feverish commentary from such varied sources as politicians, Christian leaders, tech reporters, and (judging from the number of Upvotes), most of Reddit.

For their part, though, OpenAI seems quite committed to moving ahead with this promise. In a calculus that surely makes sense in the strange techno-Libertarian circles of the AI world, the issue is intimately tied to personal freedom and autonomy.

In a recent article about the future of artificial intelligence, OpenAI again reiterated that “We believe that adults should be able to use AI on their own terms, within broad bounds defined by society,” placing full access to AI “on par with electricity, clean water, or food.”

All that’s to say that soon, the guardrails around ChatGPT’s naughty bits are almost certainly coming off. 

That hasn’t yet happened at launch—the model still coyly demures when asked about explicit things. But along with GPT-5.1’s bolder personalities, it’s almost certainly on the way.

Deeper thought

In addition to killing GPT-5’s emotional intelligence, OpenAI made another misstep when releasing GPT-5. 

The company tried to unify all queries within a single model, letting ChatGPT itself choose whether to use a simpler, lower-effort version of GPT-5, or a slower, more thoughtful one.

The idea was noble–there’s little reason to use an incredibly powerful, slow, resource-intensive LLM to answer a query like “Is tahini still good after 1 month in the fridge” (Answer: no)

But in practice, the feature was a failure. ChatGPT was no good at determining how much effort was needed to field a given query, which meant that people asking complex questions were often routed to a cheap, crappy model that gave awful results.

OpenAI fixed the issue in ChatGPT with a user interface kludge. But with GPT-5.1, OpenAI is once again bifurcating their model into an Instant and Thinking version. 

The former responds to simple queries far faster than GPT-5, while the latter takes longer, chews through more tokens, and yields better results on complex tasks.

OpenAI says that there’s more fine grained nuance within GPT-5.1’s Thinking model, too. Unlike with GPT-5, the new model can dial up and down its level of thought to accurately answer tough questions without taking forever to return a response–a common gripe with the previous version.

OpenAI has also  hinted that its future models will be “capable of making very small discoveries” in fields like science and medicine next year, with “systems that can make more significant discoveries” coming as soon as 2028. 

GPT-5.1’s increased smarts and dialed-up thinking ability are a first step down that path.

An attempt to course correct

Overall, GPT-5.1 seems like an attempt to correct many of the glaring problems with GPT-5, while also doubling down on OpenAI’s more freedom-oriented, accuracy-focused, voicy approach to conversational AI.

The new model can think, write, and communicate better than its predecessors—and will soon likely be able to (ahem) “flirt” better too.

Whether it will do those things better than a growing stable of competing models from Google, Anthropic, and myriad Chinese AI labs, though, is anyone’s guess.

https://overchat.ai/ai-hub/gpt-5-1-is-here

A note from Google and Alphabet CEO Sundar Pichai:

Nearly two years ago we kicked off the Gemini era, one of our biggest scientific and product endeavors ever undertaken as a company. Since then, it’s been incredible to see how much people love it. AI Overviews now have 2 billion users every month. The Gemini app surpasses 650 million users per month, more than 70% of our Cloud customers use our AI, 13 million developers have built with our generative models, and that is just a snippet of the impact we’re seeing.

And we’re able to get advanced capabilities to the world faster than ever, thanks to our differentiated full stack approach to AI innovation — from our leading infrastructure to our world-class research and models and tooling, to products that reach billions of people around the world.

Every generation of Gemini has built on the last, enabling you to do more. Gemini 1’s breakthroughs in native multimodality and long context window expanded the kinds of information that could be processed — and how much of it. Gemini 2 laid the foundation for agentic capabilities and pushed the frontiers on reasoning and thinking, helping with more complex tasks and ideas, leading to Gemini 2.5 Pro topping LMArena for over six months.

And now we’re introducing Gemini 3, our most intelligent model, that combines all of Gemini’s capabilities together so you can bring any idea to life.

It’s state-of-the-art in reasoning, built to grasp depth and nuance — whether it’s perceiving the subtle clues in a creative idea, or peeling apart the overlapping layers of a difficult problem. Gemini 3 is also much better at figuring out the context and intent behind your request, so you get what you need with less prompting. It’s amazing to think that in just two years, AI has evolved from simply reading text and images to reading the room.

And starting today, we’re shipping Gemini at the scale of Google. That includes Gemini 3 in AI Mode in Search with more complex reasoning and new dynamic experiences. This is the first time we are shipping Gemini in Search on day one. Gemini 3 is also coming today to the Gemini app, to developers in AI Studio and Vertex AI, and in our new agentic development platform, Google Antigravity — more below.

Like the generations before it, Gemini 3 is once again advancing the state of the art. In this new chapter, we’ll continue to push the frontiers of intelligence, agents, and personalization to make AI truly helpful for everyone.

We hope you like Gemini 3, we'll keep improving it, and look forward to seeing what you build with it. Much more to come!

https://blog.google/products/gemini/gemini-3/#note-from-ceo

A credible prediction or an imaginary threat of being in an artificial intelligence bubble?

Ticama prognoze vai iedomāti draudi par atrašanos mākslīgā intelekta burbulī?

This Is How the AI Bubble Will Pop

The AI infrastructure boom is the most important economic story in the world. But the numbers just don't add up.

Derek Thompson

Oct 02, 2025

Some people think artificial intelligence will be the most important technology of the 21st century. Others insist that it is an obvious economic bubble. I believe both sides are right. Like the 19th century railroads and the 20th century broadband Internet build-out, AI will rise first, crash second, and eventually change the world.

The numbers just don’t make sense. Tech companies are projected to spend about $400 billion this year on infrastructure to train and operate AI models. By nominal dollar sums, that is more than any group of firms has ever spent to do just about anything. The Apollo program allocated about $300 billion in inflation-adjusted dollars to get America to the moon between the early 1960s and the early 1970s. The AI buildout requires companies to collectively fund a new Apollo program, not every 10 years, but every 10 months.

It’s not clear that firms are prepared to earn back the investment, and yet by their own testimony, they’re just going to keep spending, anyway. Total AI capital expenditures in the U.S. are projected to exceed $500 billion in 2026 and 2027—roughly the annual GDP of Singapore. But the Wall Street Journal has reported that American consumers spend only $12 billion a year on AI services. That’s roughly the GDP of Somalia. If you can grok the economic difference between Singapore and Somalia, you get a sense of the economic chasm between vision and reality in AI-Land. Some reports indicate that AI usage is actually declining at large companies that are still trying to figure out how large language models can save them money.

Every financial bubble has moments where, looking back, one thinks: How did any sentient person miss the signs? Today’s omens abound. Thinking Machines, an AI startup helmed by former Open AI executive Mira Murati, just raised the largest seed round in history: $2 billion in funding at a $10 billion valuation. The company has not released a product and has refused to tell investors what they’re even trying to build. “It was the most absurd pitch meeting,” one investor who met with Murati said. “She was like, ‘So we’re doing an AI company with the best AI people, but we can’t answer any questions.” Meanwhile, a recent analysis of stock market trends found that none of the typical rules for sensible investing can explain what’s going on with stock prices right now. Whereas equity prices have historically followed earnings fundamentals, today’s market is driven overwhelmingly by momentum, as retail investors pile into meme stocks and AI companies because they think everybody else is piling into meme stocks and AI companies.

Every economic bubble also has tell-tale signs of financial over-engineering, like the collateralized debt obligations and subprime mortgage-backed securities that blew up during the mid-2000s housing bubble. Ominously, AI appears to be entering its own phase of financial wizardry. As the Economist has pointed out, the AI hyperscalers—that is, the largest spenders on AI—are using accounting tricks to depress their reported infrastructure spending, which has the effect of inflating their profits¹. As the investor and author Paul Kedrosky told me on my podcast Plain English, the big AI firms are also shifting huge amounts of AI spending off their books into SPVs, or special purpose vehicles, that disguise the cost of the AI build-out.

My interview with Kedrosky received the most enthusiastic and complimentary feedback of any show I’ve done in a while. His level of insight-per-minute was off the charts, touching on:

• How AI capital expenditures break down
• Why the AI build-out is different from past infrastructure projects, like the railroad and dot-com build-outs
• How AI spending is creating a black hole of capital that’s sucking resources away from other parts of the economy
• How ordinary investors might be able to sense the popping of the bubble just before it happens
• Why the entire financial system is balancing on big chip-makers like Nvidia
• If the bubble pops, what surprising industries will face a reckoning

Below is a polished transcript of our conversation, organized by topic area and adorned with charts and graphs to visualize his points. I hope you learn as much from his commentary as much as I did. From a sheer economic perspective, I don’t think there’s a more important story in the world.

AI SPENDING: 101

Derek Thompson: How big is the AI infrastructure build-out?

Paul Kedrosky: There’s a huge amount of money being deployed and it’s going to a very narrow set of recipients and some really small geographies, like Northern Virginia. So it’s an incredibly concentrated pool of capital that’s also large enough to affect GDP. I did the math and found out that in the first half of this year, the data-center related spending—these giant buildings full of GPUs [graphical processing units] and racks and servers that are used by the large AI firms to generate responses and train models—probably accounted for half of GDP growth in the first half of the year. Which is absolutely bananas. This spending is huge.

Thompson: Where is all this money going?

Kedrosky: For the biggest companies—Meta and Google and Amazon—a little more than half the cost of a data center is the GPU chips that are going in. About 60 percent. The rest is a combination of cooling and energy. And then a relatively small component is the actual construction of the data center: the frame of the building, the concrete pad, the real estate.

HOW AI IS ALREADY WARPING THE 2025 ECONOMY

Thompson: How do you see AI spending already warping the 2025 economy?

Kedrosky: Looking back, the analogy I draw is this: massive capital spending in one narrow slice of the economy during the 1990s caused a diversion of capital away from manufacturing in the United States. This starved small manufacturers of capital and made it difficult for them to raise money cheaply. Their cost of capital increased, meaning their margins had to be higher. During that time, China had entered the World Trade Organization and tariffs were dropping. We’ve made it very difficult for domestic manufacturers to compete against China, in large part because of the rising cost of capital. It all got sucked into this “death star” of telecom.

So in a weird way, we can trace some of the loss of manufacturing jobs in the 1990s to what happened in telecom because it was the great sucking sound that sucked all the capital out of everywhere else in the economy.

The exact same thing is happening now. If I’m a large private equity firm, there is no reward for spending money anywhere else but in data centers. So it’s the same phenomenon. If I’m a small manufacturer and I’m hoping to benefit from the on-shoring of manufacturing as a result of tariffs, I go out trying to raise money with that as my thesis. The hurdle rate just got a lot higher, meaning that I have to generate much higher returns because they’re comparing me to this other part of the economy that will accept giant amounts of money. And it looks like the returns are going to be tremendous because look at what’s happening in AI and the massive uptake of OpenAI. So I end up inadvertently starving a huge slice of the economy yet again, much like what we did in the 1990s.

Thompson: That’s so interesting. The story I’m used to telling about manufacturing is that China took our jobs. “The China shock,” as economists like David Autor call it, essentially took manufacturing to China and production in Shenzhen replaced production in Ohio, and that’s what hollowed out the Rust Belt. You’re adding that telecom absorbed the capital.

And now you fast-forward to the 2020s. Trump is trying to reverse the China shock with the tariffs. But we’re recreating the capital shock with AI as the new telecom, the new death star that’s taking capital that might at the margin go to manufacturing.

Kedrosky: It’s even more insidious than that. Let’s say you’re Derek’s Giant Private Equity Firm and you control $500 billion. You do not want to allocate that money one $5 million check at a time to a bunch of manufacturers. All I see is a nightmare of having to keep track of all of these little companies doing who knows what.

What I’d like to do is to write 30 separate $50 billion checks. I’d like to write a small number of huge checks. And this is a dynamic in private equity that people don’t understand. Capital can be allocated in lots of different ways, but the partners at these firms do not want to write a bunch of small checks to a bunch of small manufacturers, even if the hurdle rate is competitive. I’m a human, I don’t want to sit on 40 boards. And so you have this other perverse dynamic that even if everything else is equal, it’s not equal. So we’ve put manufacturers who might otherwise benefit from the onshoring phenomenon at an even worse position in part because of the internal dynamics of capital.

Thompson: What about the energy piece of this? Electricity prices rising. Data centers are incredibly energy thirsty. I think consumers will revolt against the construction of local data centers, but the data centers have enormous political power of their own. How is this going to play out?

Kedrosky: So I think you’re going to rapidly see an offshoring of data centers. That will be the response. It’ll increasingly be that it’s happening in India, it’s happening in the Middle East, where massive allocations are being made to new data centers. It’s happening all over the world. The focus will be to move offshore for exactly this reason. Bloomberg had a great story the other day about an exurb in Northern Virginia that’s essentially surrounded now by data centers. This was previously a rural area and everything around them, all the farms sold out, and people in this area were like, wait a minute, who do I sue? I never signed up for this. This is the beginnings of the NIMBY phenomenon because it’s become visceral and emotional for people. It’s not just about prices. It’s also about: If you’ve got a six acre building beside you that’s making noise all the time, that is not what you signed up for. https://www.derekthompson.org/p/this-is-how-the-ai-bubble-will-pop

 

Symbolic AI — could become a bridge from Artificial Narrow Intelligence (ANI) to Artificial General Intelligence (AGI) and further to Artificial Superintelligence (ASI). It bridges the gap between machine learning and understanding. Providing rational and empathetic reasoning & emotionally intelligent decision-making for a global public good.

Simboliskais mākslīgais intelekts (SMI) varētu kļūt par tiltu no mākslīgā šaurā intelekta (ANI) uz mākslīgo vispārējo intelektu (AGI) un tālāk uz mākslīgo superintelektu (ASI). Tas pārvar plaisu starp mašīnmācīšanos un izpratni. Nodrošinot racionālu un empātisku spriešanu un emocionāli inteliģentu lēmumu pieņemšanu globāla sabiedrības labuma vārdā.

 Could Symbolic AI transform human-like intelligence?

 Artificial intelligence research is revisiting symbolic approaches once considered outdated. Combining these formal methods with neural networks may overcome current limitations of AI reasoning. Experts suggest that a hybrid “neurosymbolic” model could enable machines to generalize knowledge like humans. The challenge lies in merging these systems efficiently without sacrificing reliability or adaptability. KOLAPSE PRESENTS • DECEMBER 2, 2025

The ambition to replicate human intelligence in machines has long driven AI research, yet the path toward this goal remains contested. Neural networks, the current dominant approach, excel at pattern recognition and data-driven learning, but they often falter in reasoning or applying knowledge to novel scenarios. Symbolic AI, a legacy approach, emphasizes formal rules, logic, and explicit encoding of relationships between concepts. Decades ago, these systems dominated early AI efforts, yet their rigidity and inability to scale to complex datasets caused them to be eclipsed by neural networks. Now, researchers propose that a fusion of the two paradigms—neurosymbolic AI—might finally bridge the gap between learning and reasoning. Advocates argue that by combining the strengths of both, machines could achieve a more generalizable and trustworthy form of intelligence.

Neurosymbolic AI aims to integrate the flexible learning capabilities of neural networks with the clear reasoning structures of symbolic systems. In practice, symbolic AI encodes rules such as “if A then B,” which allows for logical deductions that are immediately interpretable by humans. Neural networks, by contrast, discover statistical correlations from large datasets but often remain opaque, creating what is known as the “black box” problem. By layering symbolic logic atop neural outputs, or conversely, using neural networks to guide symbolic search, researchers hope to create systems capable of both learning and deductive reasoning. The appeal of this approach is not merely academic; it has significant implications for high-stakes fields, such as medicine, autonomous vehicles, and military decision-making, where errors can have serious consequences. The transparency inherent in symbolic reasoning can help mitigate mistrust in AI outputs.

Neurosymbolic AI seeks to unify formal logic with neural learning.

Efforts to operationalize neurosymbolic AI are already underway, producing demonstrable successes. For example, AlphaGeometry, developed by Google DeepMind, combines neural pattern recognition with symbolic reasoning to solve mathematics Olympiad problems reliably. By generating synthetic datasets using formal symbolic rules and then training neural networks on these datasets, the system reduces errors and enhances interpretability. Other techniques, such as logic tensor networks, assign graded truth values to statements, enabling neural networks to reason under uncertainty. Likewise, roboticists have used neurosymbolic methods to train machines to navigate environments with novel objects, dramatically reducing the volume of training data required. These applications suggest that hybrid approaches can yield practical advantages, even if the systems remain specialized rather than fully general.

Despite these promising examples, integrating symbolic and neural methods is far from straightforward. Symbolic knowledge bases, though clear and logical, can be enormous and computationally expensive to search. Consider the game of Go: the theoretical tree of all possible moves is astronomically large, making exhaustive symbolic search infeasible. Neural networks can alleviate this by predicting which branches are likely to yield optimal outcomes, effectively pruning the search space. Similarly, incorporating symbolic reasoning into language models can guide the generation of outputs during complex tasks, reducing nonsensical or inconsistent results. Yet, these integrations require careful orchestration; simply connecting a symbolic engine to a neural network without coherent management often produces subpar performance.

Underlying the technical challenges are philosophical disagreements about the very nature of intelligence and the methods by which it should be pursued. Some AI pioneers, such as Richard Sutton, argue that efforts to embed explicit knowledge into machines have historically been outperformed by approaches leveraging large datasets and computational scale. From this perspective, the lessons of history suggest that symbolic augmentation may be a distraction rather than a necessity. Others, including Gary Marcus, maintain that symbolism provides essential reasoning tools that neural networks lack, framing the debate as a philosophical as well as technical one. In practice, both views influence current research trajectories, with proponents of each advocating for strategies that align with their understanding of intelligence. Observers note that these debates often obscure practical experimentation, which continues regardless of theoretical disputes.

Symbolic systems also face difficulties representing the complexity and ambiguity inherent in human knowledge. Projects like Cyc, begun in the 1980s, attempted to encode common-sense reasoning, articulating axioms about everyday relationships and events. While Cyc amassed millions of such statements and influenced subsequent AI knowledge graphs, translating nuanced, context-dependent human experiences into rigid logical rules remains fraught with errors. For instance, although Cyc could represent that “a daughter is a child” or “seeing someone you love may produce happiness,” exceptions abound in human behavior, and strict logic cannot fully capture them. Consequently, symbolic reasoning is most effective when applied selectively or in tandem with flexible learning systems. The combination enables generalization without sacrificing the interpretability that pure neural networks struggle to achieve.

Neurosymbolic AI also introduces opportunities to reduce the data burden traditionally required for training neural networks. By embedding rules and relational logic, machines can achieve high accuracy with far fewer examples than would be required otherwise. Jiayuan Mao’s work in robotics exemplifies this: her hybrid system required only a fraction of the training data that a purely neural model would need to understand object relationships in visual tasks. This efficiency can accelerate development cycles and lower resource consumption, making AI more accessible and environmentally sustainable. Furthermore, hybrid approaches can facilitate reasoning in domains where data is scarce or incomplete, extending AI’s applicability to previously inaccessible problems. The challenge lies in designing systems that balance rule-based reasoning with statistical learning without compromising either.

Current efforts also explore the potential for machines to develop their own symbolic representations autonomously. The ultimate vision, according to Mao, is a system that not only learns from data but can invent new categories, rules, and conceptual frameworks beyond human understanding. Such capability would mark a fundamental shift, enabling AI to contribute novel insights to mathematics, physics, or other knowledge domains. Achieving this requires progress in AI “metacognition,” whereby systems monitor and direct their own reasoning processes. Effective metacognitive architectures would act as conductors, orchestrating the interplay between neural learning and symbolic logic across multiple contexts. If realized, this could constitute a genuine form of artificial general intelligence, capable of reasoning in ways comparable to, or even beyond, humans.

Integrating symbolic knowledge can reduce training data requirements dramatically.

Hardware and computational architecture also play a critical role in realizing neurosymbolic AI’s potential. Current computing platforms are often optimized for either neural network training or symbolic reasoning, but not both simultaneously. Efficient hybrid computation may necessitate novel chip designs, memory hierarchies, and processing paradigms capable of supporting dual paradigms. As the field matures, other forms of AI—quantum or otherwise—might complement or even supersede neurosymbolic approaches. Nevertheless, the immediate priority for researchers is to establish robust, flexible systems that can generalize across domains, combining reasoning, learning, and problem-solving in a coherent framework. In this sense, neurosymbolic AI represents a pragmatic middle path, leveraging lessons from both historical and contemporary AI research.

While technical and philosophical hurdles remain, neurosymbolic AI has already begun to reshape expectations of what intelligent machines can achieve. Its proponents argue that reasoning, efficiency, and transparency are within reach, provided that symbolic and neural components are integrated thoughtfully. Early applications demonstrate that hybrid models can outperform purely neural approaches in select domains, particularly when understanding and logic are critical. The field is still in its formative stages, with significant exploration required to establish general principles and architectures. Yet the prospect of machines capable of reasoning, generalizing, and even inventing new knowledge captures the imagination of both scientists and policymakers. As AI continues to evolve, the marriage of neural flexibility and symbolic clarity may chart the most promising path toward human-like intelligence.

https://www.kolapse.com/en/?contenido=93179-could-symbolic-ai-transform-human-like-intelligence