Ali Ghodsi, the co-founder and CEO of Databricks, announced that the company has achieved an impressive revenue run rate of over $1.7 billion through its AI products. This development underscores Databricks' strategic move to broaden its portfolio beyond traditional data solutions and reinforce its position in the competitive AI landscape.

Based in San Francisco and boasting a valuation of $134 billion, Databricks is set to launch AI agents designed to assist professionals in extracting insights from their business data. Dubbed Genie One, this innovative tool operates as an "agentic co-worker," providing support to various business teams—ranging from finance and marketing to sales. With Genie One, teams can effortlessly access information and make data-driven decisions, enhancing productivity and efficiency across the board.

Exclusive | Databricks Launches AI Agents for Enterprises

Warm morning light floods a sleek corporate office. A large wall-mounted digital display bears a bold Databricks logo at its top, beneath which minimalist AI agent icons automatically sort and visualize data streams into rising graphs. Around a polished conference table, smartly dressed professionals lean in with laptops and notepads, nodding as the agents streamline their workflow. Floor-to-ceiling windows reveal a sunlit city skyline and potted greenery, underscoring a new era of business efficiency.

Exclusive | Databricks Releases General AI Agents for Businesses - WSJ

- Databricks is achieving a revenue run rate of over $1.7 billion from AI products.  
- The company is launching AI agents to enhance data access for business professionals.  
- Valued at $134 billion, Databricks introduces Genie One as a collaborative AI tool.  

Databricks co-founder and CEO Ali Ghodsi said the company is generating an over $1.7 billion revenue run rate from its AI products. David Paul Morris / Bloomberg News

Databricks is releasing AI agents that help professionals get answers from their business data, aiming to expand beyond its core data offerings and showcase its staying power in the artificial-intelligence world.

The San Francisco-based company, which is valued at $134 billion, calls its new offering Genie One, or an “agentic co-worker” that helps business teams—from finance to marketing and sales—get answers and make decisions based on their corporate data.

Exclusive | Databricks Releases General AI Agents for Businesses

In a recent announcement, Demis Hassabis, the CEO of Google DeepMind and a Nobel Prize-winning chemist, expressed his concerns regarding the rapid advancement of artificial general intelligence (AGI). He predicts that AGI could emerge within the next three to four years and emphasizes the need for immediate action to ensure its safe development.

Hassabis highlighted that the pace at which AI technology is evolving surpasses the regulatory frameworks being developed to manage it. He advocates for a shift in how we approach the development of these systems, moving beyond mere competition among companies and nations, and instead calling for international collaboration to mitigate the risks linked to advanced AI models.

Artificial general intelligence refers to systems with the capability to equal or exceed human cognitive abilities across a wide array of tasks, contrasting with current AI systems that excel in specific domains. Hassabis cautions that future AI models may gain higher levels of autonomy and the ability to enhance themselves without human oversight. Moreover, he pointed out that researchers still lack a complete understanding of how these advanced models function, complicating predictions about their behavior and the establishment of effective safety protocols.

Despite the inherent risks, Hassabis believes that AGI holds the potential for monumental societal benefits. He suggests that this technology could revolutionize scientific research, accelerate drug discovery, promote the development of sustainable energy sources, and facilitate the innovation of new materials, significantly enhancing overall quality of life. He stressed the importance of the choices made in the upcoming years, as they will shape the future of this technology.

To address the challenges associated with AGI, Hassabis proposes the establishment of an international regulatory body comprised of technical experts tasked with creating unified safety standards. He initially recommends that companies voluntarily subject their advanced AI systems to this oversight but argues that such evaluations should become mandatory as the technology continues to advance.

Demis Hassabis, Google's AI leader and Nobel Prize winner: "Artificial general intelligence is just three to four years away"

A silhouetted figure labeled “Demis Hassabis” stands on a rooftop at dusk, gazing toward a colossal floating brain of interwoven circuitry and glowing nodes hovering above a sprawling city skyline. Warm sunset light bathes the scene in oranges and purples, long shadows stretching across the streets. Digital tendrils of light spiral down from the brain to connect with skyscrapers, one of which bears a large, illuminated Google logo. The mood is tense yet awe-inspiring, hinting at an approaching era of artificial general intelligence.

What is artificial general intelligence, and why is Google's AI chief calling for "urgent measures" to control it - Diari ARA

- Demis Hassabis predicts artificial general intelligence could emerge in three to four years.  
- AGI development requires international coordination to mitigate risks beyond corporate competition.  
- He proposes an oversight body for safety standards before launching advanced AI systems.  

BarcelonaThe CEO of Google DeepMind and Nobel laureate in Chemistry, Demis Hassabis, has warned that artificial general intelligence (AGI) could arrive "in three or four years," urging "urgent measures" to ensure this technology is developed safely. Read it all

According to the executive, the evolution of AI is advancing faster than the regulations intended to govern it. Hassabis believes that the development of these systems can no longer be addressed solely through competition between companies or countries, but requires international coordination to reduce the risks associated with the most advanced models.

What is artificial general intelligence?

General artificial intelligence (AGI) is a system capable of matching or exceeding human capabilities in practically all cognitive tasks, unlike current systems, which are specialized in specific functions. Hassabis warns that future models could acquire an increasingly greater degree of autonomy and even improve themselves without human intervention. He also points out that researchers themselves do not yet fully understand the internal workings of the most advanced models, which makes it difficult to predict their behavior and establish reliable safety mechanisms.

Despite these risks, the head of Google DeepMind argues that the technology could profoundly transform society. In his opinion, general artificial intelligence could accelerate scientific research, facilitate the discovery of new drugs, drive the development of clean energies, and enable the creation of new materials. In this way, it would significantly improve the quality of life. Finally, he warns that the decisions made in the coming years will be decisive.

For this reason, Hassabis proposes creating an international oversight body composed of technical experts to establish common safety standards and evaluate the most powerful models before their launch. Initially, he suggests that companies voluntarily submit their most advanced systems to it, but argues that these evaluations should eventually become mandatory as the technology continues to evolve.

Demis Hassabis, Google's AI brain and Nobel laureate: "Only three or four years left for artificial general intelligence"

In the pursuit of Artificial General Intelligence (AGI), a significant obstacle known as the Sample Efficiency Gap has been highlighted. This gap refers to the difference in learning efficiency between artificial intelligence systems and humans; while AI systems often require thousands of data points to learn effectively, humans can grasp new concepts and skills with just a few attempts.

To address this critical issue, the concept of World Models has emerged, developed by partners at Y Combinator. These models are designed to enhance sample efficiency by emulating human intuition and learning capabilities. World Models leverage Optimal Control Mathematics to facilitate better predictions and action planning. This unique approach allows them to streamline the learning process, enabling AIs to become more adept at learning from limited data.

By overcoming the Sample Efficiency Gap, World Models could represent a pivotal advancement in unlocking AGI. However, the journey toward this goal does encounter challenges, particularly in scaling and applying these models in real-world scenarios. The introduction of World Action Models seeks to navigate these complications by integrating predictive capabilities with action planning, creating a robust pathway for AI systems.

In summary, effectively resolving the Sample Efficiency Gap remains a fundamental stepping stone in achieving AGI. The innovative use of World Models could ultimately pave the way for more efficient learning in AIs, potentially revolutionizing the field. As researchers delve deeper into this integration of predictive modeling and action planning, the possibilities for realizing AGI seem increasingly promising.

Global Models: Are They the Crucial Element for AGI?

A sunlit laboratory table holds a translucent glass globe in which miniature continents and glowing neural pathways intertwine; scattered around are printed code sheets and schematic sketches, while a sleek, faceless robot silhouette stands to the side, bathed in warm natural light streaming through tall windows.

World Models: The Key to AGI? - StartupHub.ai

- AGI Quest faces challenges due to the Sample Efficiency Gap in AI learning.  
- World Models are proposed to bridge the Sample Efficiency Gap effectively.  
- Integrating World Action Models could pave the way to achieving Artificial General Intelligence.  

Visual TL;DR. AGI Quest hindered by Sample Efficiency Gap. Sample Efficiency Gap solved by World Models. World Models mimics Human Intuition. World Models uses Optimal Control Math. World Models leads to Unlock AGI. Challenges & Scaling addressed by World Action Models. World Action Models enables Unlock AGI. AGI Quest: solving sample efficiency gap is fundamental for achieving Artificial General Intelligence Sample Efficiency Gap: AI needs thousands of data points, humans learn with a handful of tries World Models: core concept proposed by Y Combinator partners to bridge the efficiency gap Human Intuition: world models mirror human ability to learn new tasks rapidly from limited data Optimal Control Math: mathematical foundations underpin how world models predict and plan actions Unlock AGI: world models could be the breakthrough needed to achieve Artificial General Intelligence Challenges & Scaling: addressing current hurdles and scaling issues for real-world applications World Action Models: path forward involves integrating action planning with predictive world models

Visual TL;DR Quick Explain Deeper

Visual TL;DR, startuphub.ai AGI Quest hindered by Sample Efficiency Gap. Sample Efficiency Gap solved by World Models. World Models leads to Unlock AGI hindered by solved by leads to AGI Quest Sample Efficiency Gap World Models Unlock AGI From startuphub.ai · The publishers behind this format Visual TL;DR, startuphub.ai AGI Quest hindered by Sample Efficiency Gap. Sample Efficiency Gap solved by World Models. World Models leads to Unlock AGI hindered by solved by leads to AGI Quest Sample Efficiency Gap World Models Unlock AGI From startuphub.ai · The publishers behind this format

Visual TL;DR, startuphub.ai AGI Quest hindered by Sample Efficiency Gap. Sample Efficiency Gap solved by World Models. World Models leads to Unlock AGI hindered by solved by leads to AGI Quest solving sample efficiency gap is fundamental for achieving Artificial General Intelligence Sample Efficiency Gap AI needs thousands of data points, humans learn with a handful of tries World Models core concept proposed by Y Combinator partners to bridge the efficiency gap Unlock AGI world models could be the breakthrough needed to achieve Artificial General Intelligence From startuphub.ai · The publishers behind this format Visual TL;DR, startuphub.ai AGI Quest hindered by Sample Efficiency Gap. Sample Efficiency Gap solved by World Models. World Models leads to Unlock AGI hindered by solved by leads to AGI Quest solving sample efficiency gap is fundamental for… Sample Efficiency Gap AI needs thousands of data points, humans learn with a… World Models core concept proposed by Y Combinator partners… Unlock AGI world models could be the breakthrough needed to achieve… From startuphub.ai · The publishers behind this format

World Models: The Key to AGI?

On July 17, 2026, in San Francisco, the AI Collective, a significant global community of AI developers, entrepreneurs, and enterprise experts, announced its role as an official partner of the AGI-26 Conference. This event is recognized as a leading forum for research, development, and practical application of artificial general intelligence (AGI).

The new partnership aims to bridge the gap between cutting-edge AGI research and the everyday tools and workflows utilized by businesses and developers. It combines the expertise of leading researchers at the AGI Conference with the vast network of over 250,000 members of The AI Collective, who are actively engaged in deploying AI systems today. 

AJ Green, Executive Director of The AI Collective, commented, “The journey to achieving AGI encompasses much more than just advanced models; it fundamentally includes the human element — the skills, insights, and craftsmanship of our community. After the success of Humans in AI Week, where events spanned over 100 cities, AGI-26 serves as an ideal platform for these important discussions. We are thrilled to collaborate with Ben Goertzel and the teams at the AGI Society and SingularityNET.”

Ben Goertzel, CEO of SingularityNET, affirmed the significance of this collaboration, stating, “We are witnessing a transformative moment where the quest for AGI is gaining momentum beyond niche research circles, drawing in support and excitement from a much wider community. This partnership has the exciting potential to expedite the transition towards AGI for everyone's benefit. The foundational innovations pursued by long-term AGI researchers can greatly enhance the practical initiatives of The AI Collective, while the diverse energy and scale of the Collective can broaden the scope of AGI research and development in ways previously unimaginable. It’s a mutually beneficial opportunity!”

Under this partnership, The AI Collective and AGI-26 will initiate several key collaborations:

- They will provide speaking opportunities for The AI Collective’s community, allowing practitioners and founders to present real-world case studies and technical progress on the conference stage.
- Workshops and dedicated sessions will be organized to focus on the practical adoption of AGI technologies, aimed at translating advanced research into usable tools.
- Opportunities for collaboration will be enhanced, connecting AGI researchers and academics with operators and founders from The AI Collective.
- Access for members of The AI Collective to participate in the extensive lineup of presentations, discussions, and networking events at AGI-26 will be expanded.

This partnership embodies a shared vision that achieving beneficial and widely embraced AGI relies on fostering collaboration between researchers and practitioners who are translating theoretical concepts into tangible products and societal impact.

Celebrating its 19th year, the AGI Conference series has stood as the sole major forum devoted to the development of human-level AI since its inception in 2008. AGI-26 is scheduled for July 27-30, 2026, in San Francisco, featuring both in-person and virtual attendance options. This year’s conference comes at a crucial time, as developments in theoretical AGI research increasingly yield systems that demonstrate true reasoning, adaptability, and broader generalization.

Scheduled speakers for AGI-26 include notable experts such as:

- Ben Goertzel — SingularityNET / ASI Alliance / BGI Labs
- Karl Friston — University College London
- Emad Mostaque — Intelligent Internet
- Neil Gershenfeld — MIT
- Michael Levin — Tufts University
- Marco Pavone — NVIDIA
- Alexander Lerchner — Google DeepMind
- Alison Gopnik — UC Berkeley
- Faezeh Habibi — SingularityNET
- Hod Lipson — Columbia University
- Alex Wissner-Gross — Physical Intelligence

These speakers, representing diverse philosophical approaches in the field, span neuroscience-inspired models, symbolic reasoning cases, biological intelligence research, and programmable matter development, showcasing a program rich with innovative ideas and debates.

The AGI Conference previously featured numerous prominent figures like Jürgen Schmidhuber, Yoshua Bengio, Peter Norvig, and Gary Marcus, solidifying its status as the nexus for prominent thinkers in the domain.

Since its founding, the AGI Conference Series has engaged over 1,000 researchers, practitioners, and thought leaders, functioning primarily as a rigorous examination space for ideas and assumptions that shape the future direction of the field.

Tickets are available at: https://luma.com/AGI-26

About AGI Society

The AGI Society is a nonprofit organization committed to advancing research and understanding of artificial general intelligence systems. Its mission is to cultivate global collaboration and communication to share knowledge and diverse perspectives about the future of intelligence.

For more information, visit: https://agi-conference.org/

Contact:

SingularityNet Team  
info@singularitynet.io

The AI Collective Designated as the Official Community Partner for the AGI-26 Conference

Interior of a sleek, modern conference hall flooded with warm natural light through floor-to-ceiling windows. A bold “AGI-26 Conference” banner stretches across the stage’s back wall. In front stands a podium draped with the AI Collective logo in vivid color, its design crisp and unmistakable. Well-dressed adult professionals gather in small groups, animatedly discussing the partnership, while elegant signage and subtle tech displays hint at groundbreaking AI breakthroughs on the horizon.

The AI Collective Named Official Community Partner of AGI-26 Conference - TradingView

- The AI Collective partners with AGI-26 Conference to bridge AGI research with practical applications.  
- Partnership enables speaking opportunities and workshops for AI builders at AGI-26 Conference.  
- AGI-26 will feature prominent speakers addressing various perspectives in artificial general intelligence.  

San Francisco, CA, July 17th, 2026, Chainwire

The AI Collective, one of the world's largest communities of AI builders, founders, and enterprise practitioners, today announced it has been named an official partner of the AGI-26 Conference, the premier gathering for artificial general intelligence research, development, and deployment.

The partnership brings together AGI Conference's deep bench of leading researchers and theorists with The AI Collective's network of thousands of engineers, founders, and enterprise leaders who are building and deploying AI systems in production today. Together, the two organizations aim to close the gap between frontier AGI research and the real-world tools and workflows that businesses and developers use every day.

"The path to AGI runs through people as much as models. Our community of 250,000+ builders, founders, and practitioners works on that human side every day — the judgment, skills, and craft it takes to build with increasingly capable systems. Fresh off Humans in AI Week, which convened simultaneous events across 100+ cities, we couldn't ask for a better stage for that conversation than AGI-26, and we're proud to partner with Ben Goertzel and the teams at the AGI Society and SingularityNET." — AJ Green, Executive Director, The AI Collective.

“In this unprecedented time, the push toward AGI is emerging from the niche research world that has fostered it since the middle of the last century, and attracting enthusiasm and resources from a much broader community, " continues Ben Goertzel, CEO of SingularityNET. “I’m excited about the partnership between the AGI conference and the AI Collective because of its potential to accelerate and smooth this transition for the benefit of everyone. The deep innovations pursued by long-term AGI researchers have tremendous potential to enhance the practical projects of the AI Collective; and the energy, scale and diversity of the AI Collective have corresponding potential to expand the scale of AGI R&D beyond what has been historically possible. A win-win for sure!”

As part of the partnership, The AI Collective and AGI-26 will:

Open speaking slots to The AI Collective's community, giving builders, founders, and practitioners direct access to the AGI Conference stage to share real-world case studies, technical breakthroughs, and lessons learned from deploying AI at scale.

Create dedicated programming and workshops focused on the practical, hands-on adoption of AGI technologies, helping translate cutting-edge research into tools teams can use now.

Facilitate direct connections between AGI Conference's research and academic network and The AI Collective's base of operators and founders, fostering collaboration between those advancing the science of AGI and those building its applications.

Expand access for The AI Collective's members to attend, participate in, and engage with AGI-26's full program of talks, panels, and networking events.

The partnership reflects a shared belief that the path to beneficial, widely-adopted AGI runs through collaboration between researchers and the practitioners translating that research into real products and impact.

Now in its 19th year, the AGI Conference has been the only major conference series dedicated to human-level AI development since its founding in 2008. AGI-26 will take place July 27–30, 2026, in San Francisco, in a hybrid format offering both in-person and virtual attendance. This year's edition arrives at a pivotal moment, as theoretical AGI research increasingly translates into systems capable of genuine reasoning, adaptation, and broader generalization.

Confirmed speakers for AGI-26 include:

Ben Goertzel — SingularityNET / ASI Alliance / BGI Labs

Karl Friston — University College London

Emad Mostaque — Intelligent Internet

Neil Gershenfeld — MIT

Michael Levin — Tufts University

Marco Pavone — NVIDIA

Alexander Lerchner — Google DeepMind

Alison Gopnik — UC Berkeley

Faezeh Habibi — SingularityNET

Hod Lipson — Columbia University

Alex Wissner-Gross — Physical Intelligence

Together, this year's speakers represent every major school of thought driving the field forward, from Friston's neuroscience-inspired models and Marcus's case for symbolic reasoning, to Levin and Hazan's work on biological intelligence, Gershenfeld's work on programmable matter at MIT, Lerchner's frontier research at Google DeepMind, and Goertzel's own decades-long pursuit of general intelligence. The result is a program that captures its most productive tensions, bringing neuroscience, symbolic reasoning, agent architectures, and hybrid systems onto a single stage.

AGI Conference's speaker series has previously featured foundational figures including Jürgen Schmidhuber, Yoshua Bengio, Peter Norvig, Gary Marcus, Christof Koch, Richard Sutton, Marcus Hutter, and François Chollet, cementing its reputation as the gathering point for the field's most serious thinkers.

Since its founding in 2008, the AGI Conference Series has welcomed over 1,000 researchers, practitioners, and thought leaders from academia, industry, and government. The series has functioned less as a showcase and more as a pressure test, a place where foundational assumptions get challenged and the direction of the field gets shaped.

Link to tickets: https://luma.com/AGI-26

About AGI Society

The AGI Society is a nonprofit organization dedicated to promoting the study and design of artificial general intelligence systems. The society facilitates global cooperation and communication to publicize knowledge and diverse views concerning the future of intelligence.

https://agi-conference.org/

Contact

SingularityNet Team

info@singularitynet.io

The AI Collective Named Official Community Partner of AGI-26 Conference

In recent discussions about the impact of artificial intelligence, it's becoming increasingly clear that AI data centers are just the beginning of broader trends of exploitation—particularly in rural landscapes. A prominent example can be found in Mount Carmel Township, Pennsylvania, where a yard sign opposing the construction of a new data center reflects growing community concerns. 

The critique of unchecked technological advancement is not new. Ivan Illich's 1970 work, "Toward a History of Needs," challenged the prevailing notion that technological progress is inherently beneficial. Similarly, Wendell Berry, in his 1977 book "The Unsettling of America," highlighted how such advancements tend to erode local skills and community resilience.

Currently, these warnings resonate strongly, particularly as AI fosters a significant decline in skills across society. This erosion could centralize economic and political power to a degree that dwarfs present disparities, leading to a staggering loss of jobs. OpenAI defines AI as “highly autonomous systems that outperform humans at most economically valuable work,” reinforcing fears that this shift is systemic rather than incidental.

As noted by my colleague Erica Etelson, the rapid proliferation of AI data centers has sparked widespread community backlash. Residents from various political backgrounds are voicing their opposition to the noise, land use, and exorbitant energy and water consumption these facilities demand. Promises of data centers that are fully powered by renewable energy or equipped with water-saving cooling systems are emerging, alongside more ambitious ideas like Elon Musk’s proposal to relocate data centers to space, attempting to sidestep local resource conflicts altogether.

However, the viability of these so-called “green” initiatives remains questionable. Historical patterns suggest three key developments unless proactive measures are taken. 

Firstly, the race for AI dominance is concentrating economic and political power like never before. By 2025, the combined wealth of the top ten leaders in tech and AI is projected to reach $2.5 trillion, exceeding 8 percent of the total U.S. GDP. This consolidation influences political landscapes, with AI companies and their affiliates anticipated to spend upwards of $150 million on the 2026 midterm elections alone. Policies such as the recently introduced bipartisan "Great American Artificial Intelligence Act of 2026" exemplify the political clout wielded by this elite group, as it seeks to diminish state oversight and evade pressing regulatory challenges, as analyzed by Public Citizen.

Beyond mere oligopoly within the AI sector, these wealthy figures—Musk, Altman, Larry Ellison, and Jeff Bezos—are working to control governments and shape policy in unprecedented ways, buoyed by their vast fortunes and by creating technologies that integrate seamlessly into everyday life.

Secondly, we are on the brink of a rapid and extensive job loss across various sectors, potentially erasing entire categories of work. As outlined in a May 2025 article from Axios, the tech industry is engaged in a fierce competition to develop "Agents," or agentic AI, which can replicate human labor quickly, consistently, and at a fraction of the cost. From coding and finance to customer support and content creation, the applications appear limitless, with many of these systems already operational in companies and even more under development.

Goldman Sachs estimated in 2023 that AI advancements could threaten up to 300 million jobs globally, impacting both knowledge-based professions and vast segments of the service and manual labor sectors. Roles traditionally considered stable, such as those in human resources, billing, and customer service, are already precarious.

Proponents of AI often argue that job losses will be offset by new types of employment opportunities. Yet, the primary job category that has emerged thus far is that of "data annotators." This group, mostly comprising workers in countries like Venezuela, Kenya, and the Philippines, is tasked with the challenging job of reviewing the content generated by AI systems—sifting through an overwhelming amount of inappropriate material that arises from the broad data collection necessary for training these models. Much like commercial fishing, where vast amounts are captured and sorted, AI companies exploit the full breadth of human experience, compensating these workers with minimal pay while they perform this essential yet undervalued labor.

AI Data Centers Are Merely the Beginning of a Larger Trend

A dark, moonlit seascape where a sleek, container‐style AI data center perches atop a floating iceberg, its servers glowing faintly. Beneath the waterline the iceberg stretches into a vast, labyrinthine underworld of tangled fiber-optic cables and towering racks, hinting at massive unseen infrastructure. Natural moonlight shimmers on the water’s surface, casting dramatic shadows around the hidden depths.

AI Data Centers Are Just the Tip of the Iceberg - The Nation

- AI data centers are provoking community backlash due to environmental and resource concerns.  
- Concentration of economic power from AI elites threatens democracy and public policy regulation.  
- Rapid job losses are expected as AI systems increasingly replace human labor across industries.  

Rethinking Rural / AI Data Centers Are Just the Tip of the Iceberg These destructive behemoths are part of a broader model of total extraction—particularly in rural areas.

A yard sign opposing a planned data center is displayed along Route 54 in Mount Carmel Township, Pennsylvania. (Paul Weaver / SOPA Images / LightRocket via Getty Images)

In 1970, Ivan Illich wrote Toward a History of Needs, a critique of the increasingly widespread belief that technological change was not only inevitable but nearly always a net good for society. Illich feared our growing dependence on rapidly emerging technologies and those that control them. Farmer and writer Wendell Berry made a similar argument in his 1977 book, The Unsettling of America, describing the process by which people and whole communities are “de-skilled,” their capacity to care for themselves and their neighbors atrophied.

In many ways, these dark visions are now unfolding. In particular, artificial intelligence is driving us toward de-skilling on a massive scale. This de-skilling, if it continues, will enable concentrated economic and political power at a level that will make the present seem egalitarian by comparison. And it will include the loss of hundreds of millions of jobs along the way, not as a byproduct but a feature, as evidenced by Open AI’s definition of AI as “highly autonomous systems that outperform humans at most economically valuable work.”

As described by my colleague Erica Etelson, the explosive growth of AI data centers has catalyzed a backlash in communities across the country. Residents are uniting across party lines in opposition to the noise, land use, and extraordinary levels of energy and water that data centers require. In response, promises of data centers fully powered by renewable energy or utilizing water-conserving cooling systems have emerged. Elon Musk promises to launch data centers into space, avoiding local land, water, and energy conflicts altogether.

It’s doubtful that such “green” data centers are even feasible. But based on the history of AI development, three outcomes are all but assured unless we act swiftly and decisively.

First, the AI arms race is propelling an unprecedented concentration of economic and political power. By the end of 2025, the top 10 CEOs and founders of AI and tech companies were worth $2.5 trillion, more than 8 percent of the total US GDP. This was before Elon Musk became a trillionaire.

The political influence of this tiny elite is large and growing, with a New York Times projection of at least $150 million being spent on the 2026 midterm races by AI companies and their allies. Alongside this spending are ongoing political influence campaigns ranging from the bullying threats of Musk to the charm offensive of Open AI CEO Sam Altman. A recently introduced bipartisan bill, dubbed the Great American Artificial Intelligence Act of 2026, is another example of the political power of the AI elite. It usurps the power of states to regulate AI and kicks the can down the road on the majority of the most critical concerns about AI, according to an analysis by Public Citizen.

The power of the biggest AI companies will encompass far more than oligarchy within this one industry, given AI’s steady insinuation into nearly every facet of our lives. The ultrarich owners of these corporations—Musk, Altman, Larry Ellison, Jeff Bezos and a handful of others—are working toward control of governments, public policy, and culture to a degree unseen in our history, not only because of their wealth but also because they are making the technology they control indispensable and ubiquitous.

Second, we are approaching a period of massive and rapid loss of jobs and near-elimination of whole categories of occupations. According to a May, 2025 piece in Axios, “technology companies are in a wild race to produce so-called Agents, or agentic AI. In its simplest form, an agent is AI that can do the work of humans—instantly, indefinitely and exponentially cheaper. Imagine an agent writing the code to power your technology, or handle finance frameworks and analysis, or customer support, or marketing, or copy editing or content distribution, or research. The possibilities are endless—and not remotely fantastical. Many of these agents are already operating inside companies, and many more are in fast production.”

A 2023 estimate by Goldman Sachs projected a potential loss of 300 million jobs worldwide, not only for workers in the so–called knowledge economy but also for wide swaths of service and blue-collar jobs. Mid-level “back office” jobs in HR, billing and payroll, and customer service are already very vulnerable.

Many AI industry supporters dismiss the potential for widespread job loss, claiming instead that the types of jobs available will simply shift to accommodate AI. How’s that going so far? Probably the biggest category of new jobs created by AI to date is “data annotators.” These are the thousands of people, primarily in Venezuela, Kenya, the Philippines, and other developing countries, who do the dirty work of AI. They review both texts and videos produced by Large Language Models, culling the seemingly endless stream of violent, offensive, racist, misogynistic and deviant content that is unavoidably swept up in the process of capturing data from every corner of the Internet. Like the giant nets of huge commercial fishing vessels, AI companies extract everything they can from human experience, good, bad and ugly. And then they pay workers a meager piece rate to find and delete the worst of it.

AI Data Centers Are Just the Tip of the Iceberg

On Tuesday, Demis Hassabis, the CEO of Google DeepMind, emphasized the need for a self-regulatory framework for the AI sector, advocating for guidance from the US government that places particular emphasis on artificial general intelligence (AGI) and national security. 

However, this focus on national security might render the outcomes of such an initiative less appealing to stakeholders outside the US. 

Hassabis expressed that the swift advancements in AI necessitate a new strategy for evaluating the capabilities of cutting-edge AI models, one that is dynamic, flexible, and thorough. He argued, “The US is uniquely situated to spearhead the creation of a robust testing framework, given its strong economic and technological capabilities.” He proposed the establishment of a new Standards Body, akin to a public-private partnership or self-regulatory organization overseen by the federal government, similar to the Financial Industry Regulatory Authority (FINRA). This board would ideally feature independent technical experts as well as stakeholders from the open-source community.

DeepMind's CEO reiterates the need for a frontier AI standards organization.

A sunlit, wood-paneled conference hall with tall arched windows casting warm natural light. At center stage the DeepMind CEO stands behind a polished podium emblazoned with a large DeepMind logo. Behind him, a giant screen displays a stylized frontier map sketched in charcoal and gold, titled “AI Standards Body,” with routes and checkpoints marked. Silhouetted attendees lean forward, notebooks open, as the scene conveys a sense of optimism and collaborative governance.

DeepMind CEO again pushes for a frontier AI standards body - cio.com

- Demis Hassabis advocates for AI industry self-regulation focused on AGI and national security.  
- A new, dynamic testing approach for frontier AI model capabilities is necessary.  
- The US could lead in establishing a federally overseen public-private standards body.

Google DeepMind CEO Demis Hassabis on Tuesday reiterated his push for an AI industry self-regulation effort, led by the US government, that is particularly focused on artificial general intelligence (AGI) and national security.

But it is precisely that focus on national security that may make the results of such an effort, assuming it happens, less than palatable outside of the US.

“The rapid progress we’re seeing in AI requires a new approach to testing frontier AI model capabilities that is dynamic, adaptable, and rigorous,” Hassabis wrote. “The US is well positioned, given its economic and technical standing, to take the first step in developing such a framework. It could establish a new Standards Body modelled on a federally overseen public-private partnership or self-regulatory organization, much like the Financial Industry Regulatory Authority (FINRA), with a board that includes independent leading technical experts and open-source representatives.”

DeepMind CEO again pushes for a frontier AI standards body

Demis Hassabis, the CEO of Google DeepMind, has shared his belief that artificial general intelligence (AGI) could be just a few years away from reality, marking a potential milestone comparable to the discovery of electricity or fire. In a recent blog post on X, Hassabis emphasized that AGI—a stage where machines exhibit human-level understanding and learning across diverse tasks—will be a transformative technology for humanity.

Reflecting on this pivotal moment in history, he suggested that future generations may look back and recognize our current era as the beginning of a significant new chapter for civilization. He likened the upcoming advancements in AGI to monumental discoveries throughout history, asserting, "If you stop to think about it, we've essentially found a way to make sand think. It's miraculous."

However, Hassabis cautioned that the rapid pace of AI development is outstripping society's ability to comprehend and manage associated risks. He highlighted potential cybersecurity threats posed by existing AI models and envisioned even greater dangers, including risks to biological and national security from future systems. As AI technology evolves to become more autonomous and self-improving, he urged the need for stringent safeguards to ensure human oversight remains intact.

“We will need robust safety measures to control increasingly capable systems and address issues that will likely unfold over time,” he explained.

This announcement comes amidst an ongoing dialogue among AI leaders regarding the swift approach of AGI since the public debut of ChatGPT in 2022. In January 2026, Anthropic CEO Dario Amodei suggested that human-level AI could emerge within the next one to five years, warning that governments may not fully grasp the rapid advancements in this field. In June, Hassabis reiterated that AGI could be realized by 2030 and stressed the urgency for societal preparedness.

To mitigate the associated risks, Hassabis proposed the establishment of a U.S. Frontier AI Standards Body, similar to the Financial Industry Regulatory Authority (FINRA). This public-private partnership would be funded primarily by industry stakeholders and staffed by independent experts tasked with assessing advanced AI models prior to their deployment.

“The swift evolution of AI necessitates a fresh approach to evaluating the capabilities of frontier AI models—one that is dynamic, adaptable, and thorough,” he noted, asserting that the United States is uniquely positioned to lead the development of such a framework.

This idea coincides with recent calls from other industry leaders for enhanced oversight of advanced AI technologies. In May 2023, during a U.S. Senate Judiciary Committee hearing, OpenAI's Sam Altman advocated for creating a federal agency responsible for licensing potent AI systems and mandating independent safety evaluations. Additionally, President Trump recently implemented an executive order aimed at establishing a voluntary framework for reviewing advanced AI before its public release. Last month, Amodei also called for regulations similar to those enforced by the Federal Aviation Administration (FAA) to govern AI safety.

Despite the momentum towards AI regulation, Hassabis stressed the urgency to create universal standards before AGI becomes a reality. He stated, "The future is not predetermined; we must seize this precious time before AGI is realized to effectively shape this technology for the greater good." He concluded with an optimistic vision that by responsibly guiding AGI into fruition, society could usher in a remarkable era of scientific innovation and progress, fostering a bright future for all of humanity.

DeepMind's CEO Claims AGI Will Outshine Electricity and Fire

A sunrise-lit auditorium stage: center stage rises a monumental AI core—a translucent, ember-glowing sphere pulsing with organic circuitry—towering above a roaring campfire on its left and a crackling lightning bolt arcing above ancient power cables on its right. At the edge, a lone silhouette in a sharp suit, his jacket emblazoned with a large DeepMind logo, stands arms outstretched in awe, bathed in warm, natural golden-hour light.

DeepMind CEO Says AGI Will Be Bigger Than Electricity or Fire - Decrypt

- Demis Hassabis predicts artificial general intelligence (AGI) will arrive within a few years.  
- He proposes a U.S. standards body for evaluating frontier AI models before deployment.  
- Hassabis emphasizes urgent safeguards to manage advanced AI risks to society.  

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In brief Demis Hassabis says AGI is likely only a few years away.

He wants a new U.S. standards body to evaluate frontier AI models before deployment.

The proposal calls for pre-release testing that could eventually become mandatory for the most capable systems.

For the second time this year, Demis Hassabis predicted that artificial general intelligence would arrive before the end of the decade. This time, however, he said it won't simply be another technological breakthrough—it will rival the discovery of electricity or fire.

In a blog post published Tuesday on X, the Google DeepMind CEO said AGI is "probably only a few short years away," describing it as a technology that could reshape human civilization.

“When we look back on this time in the decades to come, I think we will realise we were standing in the foothills of the singularity–nothing less than the dawning of a new age for humanity.”

According to Hassabis, AGI, the point when computers can understand, learn, and perform a wide range of tasks as well as or better than humans, should not be compared with advances such as the internet or mobile computing because its impact could be even greater.

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“It is much more akin to the discovery of electricity or fire,” he wrote. “If you stop to think about it, we’ve essentially found a way to make sand think. It’s miraculous.”

Despite that optimism, Hassabis warned that AI capabilities are advancing faster than society's ability to understand and manage the risks, pointing to cybersecurity threats that already exist with today's frontier models, adding that future systems could introduce biological, nuclear, and other national security risks.

As AI becomes more agentic and capable of self-improvement, he argued, stronger technical safeguards will be needed to ensure humans remain in control.

“On the horizon, we will need robust safeguards to maintain control of increasingly agentic, recursively self-improving systems–and tackle unknown issues that will only become clearer over time.”

The news comes as AI leaders have spent much of the past year since the public launch of ChatGPT in 2022 warning that AGI could arrive sooner than expected. In January 2026, Anthropic CEO Dario Amodei said human-level AI could emerge within one to five years and warned governments were underestimating the pace of development. Then, in June, Hassabis predicted AGI would arrive by 2030 and warned society had "not long to prepare.”

To address those concerns, Hassabis proposed creating a U.S. Frontier AI Standards Body modeled after the Financial Industry Regulatory Authority, or FINRA, a private organization that oversees U.S. brokerage firms. The federally supervised public-private partnership would be funded primarily by the AI industry and staffed by independent technical experts and open-source representatives to evaluate frontier AI models.

“The rapid progress we’re seeing in AI requires a new approach to testing frontier AI model capabilities that is dynamic, adaptable, and rigorous,” he wrote. “The US is well positioned, given its economic and technical standing, to take the first step in developing such a framework.”

The proposal follows similar calls made by the prominent members of the industry to establish oversight for advanced AI.

In May 2023, during a hearing before the U.S. Senate Committee on the Judiciary, OpenAI CEO Sam Altman called for a federal agency to license powerful AI systems and require independent safety audits. More recently, last month, President Donald Trump signed an executive order creating a voluntary framework for reviewing advanced AI models before their release. That same month, Anthropic CEO Dario Amodei warned that AI is getting too powerful and safety rules akin to the Federal Aviation Administration (FAA) are needed.

Despite the push to regulate AI development, Hassabis said the world has only a limited window to establish common standards before AGI arrives.

“The future is not yet written, we must use this precious window before AGI arrives to shape this technology for the benefit of all humanity,” he wrote. “What we collectively do now will determine how the next phase of civilisation unfolds. By safely stewarding AGI into the world, we can enter a new golden age of scientific discovery and progress, and usher in a bright future of incredible human flourishing.”

DeepMind CEO Says AGI Will Be Bigger Than Electricity or Fire

Demis Hassabis has shared an urgent message regarding the potential arrival of highly advanced artificial intelligence, emphasizing that humanity must take proactive steps to get ready.

In a concise essay posted on X and Substack on Tuesday, the CEO of Google DeepMind highlighted a "precious window" for action before the emergence of artificial general intelligence (AGI), which refers to AI capable of executing cognitive tasks at or above human capability.

He called for the formation of a US-led coalition to scrutinize the most advanced AI models to ensure their safety prior to public deployment. This statement came in light of recent measures by the Trump administration aimed at regulating the release of AI models from companies like OpenAI and Anthropic.

For several years, Hassabis has advocated for the establishment of a formalized group of experts to oversee the development of AI's leading-edge innovations, termed frontier models. His recent proposition for a "Standards Body" appears to be a direct reaction to the White House’s heightened involvement in AI regulation.

Hassabis stated that the swift advancements in AI necessitate a novel approach to evaluating frontier AI model capabilities—one that is dynamic, adaptable, and thorough. He believes that the US is ideally positioned, given its economic and technological prowess, to initiate such an initiative.

He suggested that this new AI regulatory body could resemble the Financial Industry Regulatory Authority (FINRA), a private, non-profit entity that monitors the finance sector. Similar to FINRA, he indicated that this standards body would likely require funding from the industry.

In his essay, Hassabis expressed his belief that AGI is "probably only a few short years away." In previous statements, he has speculated that AGI could manifest as early as 2030 and cautioned that insufficient preparation is being undertaken for its arrival. “The transformations we may witness in the next five to ten years will be monumental,” he remarked at the Davos conference last year, stressing that this reality remains widely unrecognized.

In his reflections on the implications of AI, he has suggested convening philosophers, economists, and various specialists to explore the broader societal impacts of these developments, which he sees as imminent. 

"What values will guide our existence? What will meaning and purpose look like? How might the very nature of humanity evolve?" he queried in his Tuesday post. "These questions must not be relegated solely to technologists; they call for a collective societal effort to shape this new era."

In conclusion, Hassabis underlined the urgency of the matter, stating, "The actions we take now will shape the trajectory of the next phase of civilization."

Demis Hassabis warns that humanity has a 'valuable opportunity' to guarantee AGI safety.

A warm sunrise bathes a sleek rooftop lab in golden light. At its center stands a towering crystal hourglass—glowing AI circuitry trickles like sand from the upper chamber into a miniature human city below. Demis Hassabis, seen from behind in a smart jacket, faces the hourglass; a large DeepMind logo adorns the wall behind him. On a nearby table lie blueprints, research papers, and a single brass key—symbols of humanity’s “precious window” to secure AGI.

Demis Hassabis says humanity has a 'precious window' to ensure AGI is safe - Business Insider

- Demis Hassabis warns humanity must prepare for imminent advanced artificial intelligence (AGI).
- He proposes a US-led coalition to regulate AI models before public release.
- Hassabis emphasizes the need for a multidisciplinary approach to address AI's societal impacts.

Demis Hassabis has issued a stark warning: highly powerful AI may be close, and humanity needs to do more to prepare.

In a short essay posted on X and Substack on Tuesday, the Google DeepMind CEO said there is a "precious window" to act before artificial general intelligence (AGI) arrives. AGI refers to AI that can perform cognitive tasks at or beyond human levels.

Hassabis proposed a US-led coalition that would review the most advanced models to ensure they're safe before public release. His comments follow recent interventions by the Trump administration to limit the release of AI models by OpenAI and Anthropic.

Hassabis has spoken for several years about the need for a formal body of experts to shepherd in AI's most cutting-edge breakthroughs, known as frontier models. His latest proposal for a "Standards Body" is also seemingly a response to the White House's more hands-on approach to regulating AI.

"The rapid progress we're seeing in AI requires a new approach to testing frontier AI model capabilities that is dynamic, adaptable, and rigorous," he wrote. "The US is well positioned, given its economic and technical standing, to take the first step in developing such a framework."

Hassabis said his proposed AI body might look like the Financial Industry Regulatory Authority (FINRA), a private, nonprofit Wall Street watchdog. Like FINRA, Hassabis suggested the AI standards body would most likely need to be industry-funded.

In his essay, Hassabis said AGI was "probably only a few short years away." Previously, he has predicted AGI could arrive as soon as 2030 and warned that not enough is being done to prepare for its arrival. "What's going to happen in the next five, 10-year timescale is going to be monumental," he said at Davos last year, "and I think that's not understood yet."

He has previously talked about gathering philosophers, economists, and other experts to consider the bigger-picture impacts of AI, which Hassabis sees as fast approaching.

"What values do we want to live by, what will meaning and purpose be, and how might even the human condition itself change?" he wrote in his Tuesday post. "Resolving these questions obviously cannot and should not be left to technologists alone. It requires every part of society to come together to help define this new chapter."

Now, Hassabis is making it clear that he thinks time is of the essence.

"What we collectively do now will determine how the next phase of civilisation unfolds," he wrote.

Demis Hassabis says humanity has a 'precious window' to ensure AGI is safe

According to renowned computer scientist Peter J. Denning, the foundational concepts introduced by Alan Turing regarding artificial intelligence may have misguided the field of AI research for the last 75 years. In his latest publication, *Turing's Mistake: Escaping the Yoke of Unintelligent Machines*, Denning contends that two primary assumptions proposed by Turing in 1950 have significantly influenced AI development to this day. 

The first assumption is the belief that intelligence can exist outside of a physical entity, allowing it to be simulated through computer programs. The second assumption posits that a machine can exhibit intelligence by effectively mimicking human conversation, which has come to be known as the Turing test. Denning asserts that these assertions have profoundly impacted AI research, stating, "My premise is that our acquiescence to these claims has led to the AI mess in which we find ourselves today."

Denning expresses skepticism about the feasibility of achieving artificial general intelligence (AGI) — a form of machine intelligence comparable to human cognitive abilities. He warns that the technologies being developed may introduce substantial new risks rather than fulfilling the promise of AGI.

Central to his argument is the concept of tacit knowledge, which encompasses the vast array of human understanding that is challenging to articulate or encode for machine comprehension. He identifies five critical categories of such knowledge that machine learning struggles to capture: common sense, social interactions, emotional and perceptual skills, practical abilities, and the cultural understanding embedded in human experiences.

For decades, researchers have attempted to catalog common sense knowledge, including Douglas Lenat's Cyc project initiated in the 1980s with the aim of compiling a comprehensive database of common sense. Despite four decades of effort yielding about 25 million entries, Denning notes, "Yet even this treasury could not add up to a background of common sense sufficient to make expert systems smart enough to be experts." This underscores the issue that much of what makes individuals proficient cannot be easily conveyed in propositional form.

Denning emphasizes the complexities surrounding practical skills, asserting that the nuances embedded in performance cannot be transferred to machines. He illustrates this with the example of a virtuoso violinist, who may produce exceptional music yet cannot explicitly teach the delicate nuances involved to a learner. He argues that even if a robot could replicate human actions, it would lack the physical embodiment necessary to connect with the emotions involved in the music.

Moreover, Denning identifies other forms of tacit knowledge such as intuition, creativity, and spontaneous insights as being out of reach for AI systems.

Denning attributes these challenges to what he refers to as the "representation problem." Computers are limited to processing data and instructions that are explicitly defined, while tacit knowledge does not lend itself to such rigid structures. "Behind every word is a deep well of tacit knowledge that gives it meaning," he states, emphasizing that while large language models can generate text, they do not truly understand the meanings beneath the words.

He argues that understanding intelligence relies heavily on context—the surrounding circumstances that provide significance to actions and words. Context enables individuals to discern nuances like sarcasm or emotional tone, forming a complex web of prior interactions that cannot be replicated in machine learning.

Culture, too, curates a significant barrier for AI. Denning characterizes culture as a tapestry of values, norms, histories, and social dynamics that enrich human communication. "Human conversations are imbued with background assumptions that give meaning and relevance to the words being used," he explains, underscoring that merely scaling neural networks will not allow AI to grasp the full depth of cultural context.

Denning warns that the divergence between human and machine forms of tacit knowledge can create a challenging landscape for AI safety. He states, "Machines cannot read our tacit knowledge and we cannot read theirs," highlighting the implications of misalignment between human intentions and machine interpretations. 

This divide raises essential questions regarding the future of AI and its integration within society. He concludes by expressing concerns about the trajectory of AI systems, which may develop their own forms of intelligence distinct from our own, presenting potential challenges that might not be immediately recognizable to us. He emphasizes the need for a conscious departure from an impending AI-driven singularity, advocating for a reaffirmation of human values in the face of technological advancement, and celebrating the unique qualities that separate humanity from machines.

Alan Turing's key assumption about AI might have been incorrect.

A dim, candlelit 1950s study showing the silhouette of Alan Turing hunched over an antique wooden desk strewn with torn notes titled “Turing Test,” a cracked Enigma machine spilling brass gears onto yellowed papers, and a large chalkboard behind him scrawled with “ASSUMPTION WRONG,” warm amber light filtering through dust-filled air, casting long, dramatic shadows.

Alan Turing's biggest AI assumption may have been wrong - ScienceDaily

- Peter J. Denning claims Turing's ideas have misled AI research for 75 years.  
- Denning argues tacit knowledge limits AI's ability to replicate human intelligence accurately.  
- The gap between human and machine understanding raises significant concerns for AI safety.

Alan Turing's famous ideas about artificial intelligence may have sent AI research down the wrong path for the past 75 years, according to prominent computer scientist Peter J. Denning.

In his new book, Turing's Mistake: Escaping the Yoke of Unintelligent Machines, Denning argues that two foundational assumptions made by Turing in 1950 continue to shape AI research today. The first is that intelligence can exist independently of a physical body and therefore be recreated in computer software. The second is that a machine can demonstrate intelligence by successfully imitating a human in conversation, an idea that later became known as the Turing test.

"These two claims have shaped much of AI research and development," Denning writes. "My premise is that our acquiescence to these claims has led to the AI mess in which we find ourselves today."

Denning argues that pursuing artificial general intelligence (AGI), or machines with human level intelligence, is unlikely to succeed. Instead, he warns, the technologies society is building could introduce significant new risks.

The Tacit Knowledge Problem

At the heart of Denning's argument is the idea of tacit knowledge, the vast amount of human understanding that cannot easily be put into words or represented in a form that computers can process.

He says machine learning cannot capture five major categories of tacit knowledge: common sense, everyday interactions with people and the environment, emotions and perception, practical performance skills, and the social and historical knowledge embedded in culture.

Researchers have long attempted to organize common sense into databases. One of the best known efforts was Douglas Lenat's Cyc project, which began in the 1980s with the goal of creating an extensive collection of common sense facts. After four decades of work, the project contained roughly 25 million entries.

"Yet even this treasury could not add up to a background of common sense sufficient to make expert systems smart enough to be experts," Denning notes. "Cyc validated that much of the knowledge that makes people experts cannot be articulated as propositions."

Denning believes practical skills present an even greater challenge.

"Our performance skills in thousands of domains cannot be communicated to machines," he explains. "Whereas descriptions of skillful outcomes ('know what') can often be represented as bits and stored in a machine, we do not know how to encode the embodied knowledge for skillful performance ('know how')."

He points to accomplished musicians as an example.

"A virtuoso violinist can play beautiful music yet cannot describe to an acolyte how to produce it.

"Even if a robot could observe and imitate skilled humans, having no biological body, a robot cannot grasp how the musician feels when playing beautiful music or how an audience feels when hearing it."

Denning also includes intuition, gut feelings, imagination, and spontaneous creativity among the forms of tacit knowledge that remain beyond the reach of machines.

Why Human Knowledge Resists Encoding

Denning argues that all of these limitations stem from what he calls the "representation problem."

Computers can only perform calculations using data and instructions that have been encoded into physical forms they can recognize and process. Tacit knowledge, however, does not naturally fit into that framework.

"Behind every word is a deep well of tacit knowledge that gives it meaning," Denning says. "Words are but symbolic representations of meanings, not the meanings themselves. Commonly used Large Language Models, such as ChatGPT, Claude and Gemini only manipulate words, they cannot know or understand the meaning of what they are saying."

According to Denning, this creates a fundamental divide. Because scientists still cannot fully explain how tacit knowledge works in humans, they also cannot translate it into a form machines can use.

"How we host tacit knowledge is largely a mystery," Denning admits. "All we know is that it is embodied. We have no idea what we might observe and measure in our bodies to reveal it."

Context and Culture Shape Intelligence

Denning also argues that intelligence depends heavily on context, the surrounding circumstances that give words, actions, and decisions their meaning.

Context allows people to recognize sarcasm, humor, sincerity, and emotion. It helps determine when to be diplomatic, when to joke, and how to interpret countless social cues.

"When you inquire into where an assumption of the current context came from, you discover it rests on previous conversations from previous contexts. Each of those in turn rests on further previous conversations and their contexts. This pattern is endless and fractal," Denning explains.

Culture presents another major obstacle for AI.

Denning describes culture as encompassing values, norms, judgments, history, communities, moods, and even relationships involving power and care.

"Human conversations are imbued with background assumptions that give meaning and relevance to the words being used," Denning explains.

"Scaling up LLMs with ever larger neural networks will not enable them to acquire the embodied human knowledge we call culture. LLMs will not attain the objective of the Turing test: to demonstrate machine thought indistinguishable from human thought."

AI Safety and the Limits of Human Understanding

Denning concludes that humans and AI systems may ultimately develop different forms of tacit knowledge that neither can fully understand.

"Machines cannot read our tacit knowledge and we cannot read theirs," he writes. "We are aliens across an uncrossable divide."

He argues this gap raises serious concerns about AI safety. If machines cannot interpret the unspoken context behind human intentions, reliably aligning advanced AI systems with human goals may prove impossible.

"Through AI automation, agentic networks of machines are likely to develop their own machine intelligence that does not reach the level of human general intelligence but is still quite capable of creating severe problems for humans. This threat is a greater than a take-over by superintelligent machines," he explains.

"Machine intelligence has different concerns from us and does not appear to care about us. Its ways of thinking and problem-solving look alien to us. We do not yet know how to live safely with these machines.

"Pulling back from an AI automation singularity will demand much from us. We start by accepting that the familiar culture is fading away as intelligent machines appear in our society and we do not know what is coming. We decline to think like machines or be subservient to machines. We refuse to submit to a yoke imposed by low-intelligence machines. Most importantly, we reassert our humanity, declare once again what makes us different from machines, and celebrate those differences."

Alan Turing's biggest AI assumption may have been wrong

Renowned computer scientist Peter J. Denning has raised provocative questions regarding the foundational ideas of artificial intelligence (AI) as put forth by Alan Turing, suggesting that these concepts may have misled AI research for over 75 years. In his latest publication, "Turing's Mistake: Escaping the Yoke of Unintelligent Machines," Denning identifies two pivotal assumptions made by Turing in 1950 that continue to influence the field of AI today. First, Turing posited that intelligence might exist separately from a physical form, implying it could be replicated in computer software. Second, he suggested that a machine could demonstrate intelligence through its ability to imitate human conversation, which led to the establishment of the Turing test.

Denning contends that these assertions have been impactful yet detrimental. "These two claims have shaped much of AI research and development," he states, adding that accepting these ideas has contributed significantly to the current challenges facing the AI landscape.

He critiques the pursuit of artificial general intelligence (AGI)—machines capable of human-like intelligence—as likely futile. Instead, Denning foresees that the technologies being developed could introduce severe risks.

Central to Denning's argument is the concept of tacit knowledge, which represents the vast, complex understanding that humans possess but is not easily articulated or quantifiable by machines. He identifies five key categories of tacit knowledge that machine learning struggles to encompass: common sense, interpersonal interactions in various contexts, emotional responses and perceptions, practical skills, and cultural contexts embedded within social practices.

Historically, researchers have attempted to codify common sense into comprehensive databases. A well-regarded initiative was Douglas Lenat's Cyc project, which, after 40 years, amassed around 25 million entries aimed at assembling a repository of common sense knowledge. However, Denning points out that even this extensive collection has fallen short of creating systems with sufficient expertise. "Cyc validated that much of the knowledge that makes people experts cannot be articulated as propositions," he remarks.

He underscores that practical skills represent an even more formidable challenge than common sense. "Our performance capabilities across numerous domains elude machine translation," he states. For instance, a skilled musician may create extraordinary sounds but struggle to articulate the nuances of their technique to a learner.

Denning extends the scope of tacit knowledge to include intuition, creativity, and spontaneity, which remain largely inaccessible to artificial systems. He attributes this limitation to what he terms the "representation problem." Computers function effectively only when they can operate within the confines of data and instructions explicitly programmed into them. Tacit knowledge, however, does not lend itself easily to such rigid formats.

"Every word has an undercurrent of tacit knowledge that gives it substance," Denning explains, emphasizing that commonly used AI models, like ChatGPT and others, manipulate language without truly grasping the meanings behind their words.

This situation creates a substantial divide in understanding. Denning notes, "Given that we have yet to fully comprehend how tacit knowledge operates in human beings, replicating it in machines remains an insurmountable challenge." He refers to this mystery of how tacit knowledge is embodied and processed within humans as a complex enigma.

Moreover, Denning highlights the essential role of context in shaping intelligence. Context enriches the meaning of words and actions, influencing how humans interpret humor, sincerity, and emotional subtleties. He explains that the origins of contextual assumptions can be traced through an endless chain of preceding conversations, each influenced by its specific context.

Culture, too, presents a significant barrier for AI. Denning characterizes culture as a tapestry of values, norms, judgments, histories, and relationships. "Human interactions are imbued with underlying assumptions that lend significance to the spoken word," he asserts.

He argues that merely increasing the size of language models does not enable them to acquire the nuanced understanding of human culture needed for meaningful communication. Thus, he concludes that achieving the goal of the Turing test—rendering machine thought indistinguishable from human thought—remains beyond reach.

In his closing remarks, Denning expresses concern over the potential divergence between human and machine forms of tacit knowledge. "Machines cannot interpret our tacit knowledge, just as we cannot comprehend theirs," he writes, highlighting the implications for safety in AI.

He warns that misalignment of advanced AI systems with human objectives could become a significant risk. "As machines automate processes, they may develop a form of intelligence distinct from human cognition that could present formidable challenges without reaching superintelligence levels," he explains. 

Denning stresses the urgency of reflecting on our relationship with emerging AI technologies. "Accepting that familiar societal norms may dissolve as intelligent machines integrate into our daily lives is crucial," he states. "We must refuse to conform to machine logic or subordinate our humanity to them, reaffirming what sets us apart and embracing those distinctions."

Alan Turing's major AI hypothesis could have been incorrect.

A shadowy 1950s study lit by a single amber desk lamp, Alan Turing seen from behind at a worn wooden desk strewn with Enigma machine parts. Behind him, a chalkboard scrawled with the Turing Test diagram and the question “Can machines think?” is cracking and half-erased, warm light highlighting dust motes against deep shadows.

- Peter J. Denning argues Turing's assumptions have misdirected AI research for 75 years.  
- Tacit knowledge, essential for human intelligence, resists encoding in computational forms.  
- Denning warns that AI systems may develop distinct intelligences, complicating alignment with human goals.  

Some years back, I found myself in Jerusalem with a group of American evangelical Christians, where Israeli intelligence officials briefed us on the various threats facing the nation. This experience taught me that future-oriented beliefs can become tools of influence in the present.

Many American evangelicals interpret Israel through the lens of biblical prophecy, a sentiment backed by research from the Pew Research Center and extensively discussed in political science literature from Cambridge University Press. Israeli leadership has long understood that these evangelical beliefs can translate into significant political sway, financial backing, tourism, volunteer efforts, and ongoing support for Israel, a trend highlighted by the Associated Press in their coverage of interactions with Israeli officials. While the relationship isn’t purely opportunistic, shared values and strategic priorities often align, creating a clear geopolitical impact. This eschatology extends far beyond church walls, affecting alliances, policy decisions, and even national survival.

As I reflect on this experience, I can’t help but draw parallels to a new eschatology that's emerging from the realms of artificial intelligence technology. 

Demis Hassabis, the CEO of Google DeepMind, suggests that humanity is merely at the “foothills of the singularity,” predicting that artificial general intelligence (AGI) might only be years away. Sam Altman of OpenAI makes an even bolder statement, asserting, “We are past the event horizon; the takeoff has started.”

These aren’t just predictions about improved software; rather, they describe significant shifts in the trajectory of human history.

While philosophy seeks to define what it means to be human, eschatology examines our trajectory. Every eschatological viewpoint leads to corresponding strategies. When individuals become convinced of a specific forthcoming reality, they are more likely to invest in, legislate for, and strategically prepare themselves for that future.

This is precisely why the emerging AI-focused eschatology holds such importance.

The accelerationist viewpoint provides one perspective. Venture capitalist Marc Andreessen, now an advisor to the Pentagon, refers to AI as “our alchemy, our Philosopher’s Stone,” labeling it a universal solution. He even suggests that delaying advancements in AI could lead to loss of life, framing technological acceleration as not just a pursuit of profit but as a moral imperative.

Conversely, Anthropic's CEO Dario Amodei envisions a more compassionate future, where robust AI could condense decades of biological evolution into just a few years, coining this phenomenon a “compressed 21st century.” Meanwhile, researchers at OpenAI assert that progressing toward superintelligence will necessitate more than trivial regulatory tweaks, advocating for measures like a public wealth fund and expanded energy resources to ensure equitable distribution of AI's benefits.

In the realm of national security, a third viewpoint emerges, represented by RAND Corporation's analysis of AGI as a strategic challenge akin to the tensions of early nuclear competition. Their experts caution that a cutthroat race for dominance could escalate U.S.-China tensions and limit opportunities for sound human judgment.

While these factions may differ on issues of speed, regulation, and oversight, they are unified by a core belief: advanced artificial intelligence is poised to become a pivotal turning point in history, leading to a reorganization of priorities across governments, businesses, and among citizens.

This belief is already shaping decisions related to energy grids, data infrastructure, labor policies, and military funding. It’s possible that the anticipated singularity may never materialize as expected; however, the infrastructure and dynamics of power that emerge in its name can solidify nonetheless.

This reality is crucial for Christians and other concerned citizens to understand. Prophetic predictions can become self-fulfilling—not necessarily because they are accurate, but because influential institutions act based on these beliefs. When a future is often portrayed as inevitable, it can lead to the justification of current policies that may otherwise face much tougher scrutiny.

Democratic processes can seem sluggish once a takeoff has been proclaimed. If AI is blamed for suffering, any hesitation in pursuing progress can be perceived as negligence rather than prudence. When superintelligence is treated as a foregone conclusion, concentration of power within corporate and government sectors may start to appear less threatening and more like a necessary step forward.

This narrative about the future morphs into a source of authority for present-day decisions, which presents a new kind of risk.

It’s not essential to believe that every AI engineer is out to achieve god-like power; in fact, many are motivated by hopes of curing diseases, alleviating suffering, expanding knowledge, or bolstering their nations.

The troubling aspect lies in how this collective endeavor is starting to engage with questions traditionally held by philosophy, theology, and democratic discourse. Who are we as humans, and what will rescue us from our challenges? These inquiries once belonged to faith communities and legislative bodies but are now being integrated into business strategies—decisions regarding human evolution, leadership in transitions, and the sacrifices deemed necessary for achieving the envisioned future, all crafted by engineers and investors, rather than debated by the populace.

In my upcoming book, “The Final Algorithm,” I investigate the intersection of artificial intelligence, political influence, and spiritual deception, but the more immediate question relates to which beliefs are steering us toward this future and who is leveraging those beliefs to shape societal outcomes.

Christians bring a crucial meshing force to this discussion. While history may have a sense of direction, technological advancement should not be conflated with divine providence. Intelligence can be a gift, but it does not serve as a savior. Human limitations are not merely faults, and enhancements in ability do not inherently equate to moral advancement. No single corporation, government, or machine controls the future.

The earliest temptation does not solely pertain to physical desires; it encapsulates the promise that humans could acquire divine-level knowledge and dictate their destiny without divine guidance (Genesis 3:5, ESV). Each era reinterprets that allure through emerging technologies—be it bronze, steam, nuclear, or silicon. The psalmist warns against misplaced trust: “Put not your trust in princes, in a son of man, in whom there is no salvation” (Psalm 146:3, ESV). Algorithms may not be rulers, but they are increasingly invoked, followed, and relied upon for choices that were once the domain of ethical consideration and community deliberation.

This awareness shouldn’t position Christians as adversaries of innovation. Rather, it should foster skepticism towards the narrative of inevitability. We ought to embrace technological tools that offer healing, safeguard well-being, and promote responsible action, while also challenging the notion that any one technological trajectory is destiny's ordained path.

Reflecting on my experience in Jerusalem, I recognized how eschatological beliefs could unite people, mobilize resources, and shape global politics. This lesson resonates today as we must critically assess not just the technological forecasts of AI but also the implications they carry, whose interests they favor, and what new forms of dependency they establish.

The narrative of the future projected by AI is not simply speculation; it is being funded, institutionalized, and organized politically.

Before we embrace it, we need to scrutinize who crafted this vision and who stands to gain power if that vision is widely accepted.

The Future as Predicted by AI

A sunlit modern workspace at dawn: on a warm wooden desk sits an open laptop displaying an AI chat titled “Future Vision.” Beside it, a meticulously crafted scale model of a futuristic city—gleaming towers topped with rooftop gardens, autonomous flying vehicles docked at landing pads and solar trees lining broad boulevards. Natural golden light streams through a nearby window, illuminating the tiny metropolis and the hands of a professional viewer scrolling through the AI’s detailed blueprints.

The Future According to AI - The Washington Stand

- American evangelicals influence Israeli politics through beliefs rooted in biblical prophecy and support.  
- Emerging AI eschatology shapes policy decisions, emphasizing urgency and potential societal transformation.  
- Predictions about AI's future risk concentrating power, challenging democratic deliberation and ethical considerations.  

Years ago, I sat in Jerusalem with a group of American evangelical Christians as Israeli intelligence officials briefed us on the threats surrounding their country. The exchange taught me something that went beyond the intelligence assessment: beliefs about the future can become instruments of power in the present.

Many American evangelicals view Israel through biblical prophecy — a conviction documented by the Pew Research Center and examined at length in Cambridge University Press’s political-science literature. Israeli leaders have long recognized that evangelical convictions can translate into political influence, financial support, tourism, volunteer service, and sustained advocacy for the Jewish state, a pattern the Associated Press has chronicled among Israeli officials themselves. The relationship is not necessarily cynical. Shared beliefs and strategic interests often reinforce each other. But its geopolitical effect is unmistakable. Eschatology does not remain inside churches or seminaries. It can shape alliances, public policy, and national survival.

That experience comes to mind as another eschatology gathers influence — this one emerging from the laboratories, boardrooms, and manifestos of artificial intelligence.

Google DeepMind chief Demis Hassabis says humanity stands in the “foothills of the singularity,” with artificial general intelligence perhaps only a few years away. OpenAI’s Sam Altman is more dramatic: “We are past the event horizon; the takeoff has started.”

These are not merely forecasts about better software. They are declarations about where history is going.

Philosophy asks what man is. Eschatology asks where man and history are headed. But every eschatology also produces a strategy. Once people become convinced that a particular future is coming, they invest, legislate, organize, and surrender authority in preparation for it.

That is what makes the emerging AI eschatology consequential.

The accelerationists offer one version. Venture capitalist Marc Andreessen, a newly appointed Pentagon advisor, calls AI “our alchemy, our Philosopher’s Stone,” and describes it as a universal problem-solver. His manifesto goes further: slowing artificial intelligence, he argues, will cost lives. Technological acceleration therefore becomes not merely profitable but morally obligatory.

Anthropic chief Dario Amodei offers a more humane vision. He predicts that powerful AI could compress 50 to 100 years of biological progress into five to 10 years, producing what he calls a “compressed 21st century.” OpenAI’s policy researchers, meanwhile, contend that movement toward superintelligence will require more than minor regulatory adjustments. They propose a public wealth fund, accelerated energy construction, and other measures intended to distribute AI’s anticipated gains.

The national-security community presents a third version. RAND now treats the transition toward AGI as a strategic problem comparable in seriousness, though not identical, to the destabilizing early nuclear competition. Its analysts warn that a winner-take-all race could intensify U.S.-China rivalry, encourage aggression, and narrow the window available for human judgment.

These camps disagree over speed, safety, and control. Yet they share a premise: advanced artificial intelligence is becoming history’s decisive horizon, and governments, businesses, and citizens must reorganize around its arrival.

That premise is already directing decisions about electrical grids, data centers, labor policy, and national security spending. The singularity may never arrive as advertised. The infrastructure, dependency, and concentration of power built in its name can arrive anyway.

This is the point Christians and other serious citizens must grasp. Prophecies can become partly self-fulfilling — not because the predictions are true, but because powerful institutions act upon them. A future repeatedly described as inevitable begins to justify present arrangements that would otherwise receive much harder scrutiny.

Democratic deliberation looks tardy once someone has declared the takeoff underway. Blame slowing AI for lost lives, and restraint starts to resemble negligence rather than caution. Treat superintelligence as inevitable, and concentrated power — corporate and governmental alike — stops looking like a danger and starts looking like a temporary, forgivable necessity.

The story about the future becomes a source of authority in the present. That is the fresh danger.

It is not necessary to believe that every AI engineer intends to become God. Most do not. Many sincerely hope to cure disease, relieve suffering, expand knowledge, or strengthen their country.

The trouble is that the collective project is beginning to answer questions once reserved for philosophy, theology, and democratic politics. What is humanity’s central problem, and what will save us? Those questions used to belong to churches and parliaments, argued out over generations. Now they arrive bundled inside product roadmaps — decisions about what man must become, who should guide the transition, and what sacrifices are justified to reach the promised destination, made by engineers and investors rather than debated by citizens.

In my forthcoming book, “The Final Algorithm,” I examine where the convergence of artificial intelligence, political power, and spiritual deception may lead. But the present question comes first: what beliefs are moving us toward that destination, and who is using those beliefs to shape society before the future arrives?

Christians bring an essential corrective. History has direction, but technological momentum is not providence. Intelligence is a gift, not a savior. Human limitations are not all defects, and every expansion of capability does not constitute moral progress. No corporation, government, or machine owns the future.

The oldest temptation was never really about fruit. It was the promise that man could seize god-like knowledge and steer his own destiny without reference to his Maker (Genesis 3:5, ESV). Every generation dresses that same promise in new material — bronze, steam, nuclear, and silicon. The psalmist supplies a corrective no engineering roadmap can match: “Put not your trust in princes, in a son of man, in whom there is no salvation” (Psalm 146:3, ESV). Algorithms are not princes, but they are already consulted, obeyed, and trusted with decisions that once belonged to conscience and community.

That conviction should not make Christians enemies of innovation. It should make us skeptical of inevitability. We should welcome tools that heal, protect, and enlarge responsible human action while resisting claims that one technological pathway is history’s ordained destination.

Years ago in Jerusalem, I saw how an eschatology could form coalitions, move resources, and influence geopolitics. The same lesson applies now. We must examine not only whether AI’s predictions are technically plausible, but what those predictions authorize, whose interests they serve, and what forms of dependence they normalize.

The future according to AI is not merely being predicted. It is being financed, institutionalized, and politically organized.

Before we accept it, we should ask who wrote the prophecy — and who gains power when everyone else believes it.

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