On July 20, 2026, the California Institute for Human Science (CIHS) unveiled an innovative Master of Science in Artificial General Intelligence (AGI), marking the first accredited graduate program specifically focused on this emerging field. This pioneering program is conducted live online, featuring instruction from prominent researchers, including Dr. Ben Goertzel, who coined the term “artificial general intelligence” and is the founder of initiatives like SingularityNET and the ASI Alliance.

Unlike traditional AI programs that emphasize existing tools and applications, the M.S. in AGI centers on the scientific, ethical, and engineering challenges intrinsic to general intelligence. It offers a unique curriculum where the field's founders impart knowledge drawn from their extensive research history, treating AGI as a distinct discipline rather than an offshoot of broader computer science studies.

Accredited by the WSCUC Senior College and University Commission, the same body that recognizes renowned institutions like Stanford and Caltech, the program is poised to attract forward-thinking students interested in exploring the frontiers of artificial intelligence.

A key feature of the M.S. in AGI is its live, interactive format, contrasting with many self-paced, pre-recorded courses. The inaugural cohort will engage in real-time sessions with leading researchers, fostering direct mentorship and collaborative learning.

To provide more insight into the program, CIHS will host two informational sessions featuring Dr. Ben Goertzel and Gabriel Axel Montes. These sessions are scheduled for Friday, July 24, at 10:30 AM PT and Thursday, August 13, at 5 PM PT. Interested individuals can find additional information and register for these virtual events at https://cihs.edu/degrees-and-programs/artificial-general-intelligence/info-sessions/.

For further inquiries, please reach out to Stacy Gomes.

California Institute for Human Science Introduces Its First Accredited Graduate Program Focused on AGI

A sunlit modern campus courtyard at golden hour, framed by palm trees and a contemporary building facade engraved “California Institute for Human Science,” where a diverse group of adult students in academic attire ascend marble steps toward a sleek glass brain sculpture etched with “AGI.” Warm natural light casts long shadows, lending a hopeful, pioneering atmosphere to the first accredited graduate program in artificial general intelligence.

California Institute for Human Science Launches First Accredited Graduate Program Dedicated to AGI - Benzinga

- CIHS launched the first accredited Master's program focused specifically on Artificial General Intelligence.  
- The program features live, online classes led by foundational researchers like Dr. Ben Goertzel.  
- It emphasizes AGI's scientific and ethical questions, diverging from traditional AI application training.  

ENCINITAS, CALIFORNIA, July 20th, 2026, Chainwire

The California Institute for Human Science (CIHS) today announced the launch of its Master of Science in Artificial General Intelligence, the first accredited graduate program dedicated specifically to AGI. The program is taught live, online, by researchers who helped found the field, including Dr. Ben Goertzel, who introduced the term "artificial general intelligence" and founded SingularityNET, the ASI Alliance and the Hyperon AGI project.

Where most AI graduate programs train students on today's tools and applications, the M.S. in AGI is built around the scientific, engineering, ethical, and civilizational questions of general intelligence itself, the discipline's founders teaching the discipline they founded.

Institutionally accredited by the WSCUC Senior College and University Commission, the same accreditor as Stanford and Caltech.

Most graduate AI programs train students to apply today's models and tools. The M.S. in AGI treats artificial general intelligence as a field of study in its own right, with open technical questions and a long-running research history, rather than as a specialization inside a broader computer science degree.

The program is also a departure in format: live, faculty-led video classes rather than a self-paced, pre-recorded course library, with a small inaugural cohort that works directly with Goertzel, Iklé, and Montes rather than watching them.

Program details

A small inaugural cohort will work directly, live, with the researchers who have spent their careers defining AGI's foundational questions.

Two info sessions will be held with Ben Goertzel and Gabriel Axel Montes on Friday, July 24, at 10:30am PT and on Thursday, August 13, at 5pm PT. Prospective students can learn more and register for a live virtual info session at https://cihs.edu/degrees-and-programs/artificial-general-intelligence/info-sessions/.

About CIHS

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Stacy Gomes



California Institute for Human Science Launches First Accredited Graduate Program Dedicated to AGI

The narrative surrounding an artificial intelligence (AI) competition between the U.S. and China has become widespread, fueled by public figures, the media, and tech giants. This "AI race" rhetoric is granting unchecked authority to major tech firms, potentially resulting in detrimental impacts on our communities, invasive surveillance practices, and the monopolization of human knowledge for corporate profit.

However, the premise of a race between the U.S. and China lacks any substantial foundation. The objectives of the respective AI initiatives in both nations are drastically different; hence, they cannot be accurately described as competing in a race.

In America, there is an emphasis on achieving artificial general intelligence (AGI)—the capability of machines to emulate human awareness. This pursuit is perceived as groundbreaking, suggesting that all AI endeavors must align with this singular goal. Conversely, China’s AI strategy prioritizes the integration of AI into practical applications and products rather than striving for AGI. This divergence indicates that the U.S. and China are not engaged in a shared race toward the same objective.

This distinction highlights the unsustainable growth of extensive data centers in the U.S. These sprawling facilities, which can draw upwards of 5 gigawatts of power—sufficient for approximately 3.75 million homes—are fueled by the drive for AGI. China's strategy, focusing on embedded AI applications, does not necessitate such energy-intensive infrastructure.

The mythic AI race serves as a convenient narrative for Big Tech and its allies, effectively insulating them from scrutiny and regulatory measures. By framing discussions around environmental concerns or community opposition as anti-competitive maneuvering against China, these corporations can evade meaningful debate and sidestep regulations that could hinder their expansion.

Additionally, this narrative is weaponized against grassroots movements opposing the proliferation of data centers. Claims from figures like Kevin O'Leary, alleging foreign funding of these movements, are unfounded and have been publicly retracted by media outlets. The opposition to data centers is a homegrown response from communities who see the damaging ramifications of unregulated tech expansion.

The persistent myth of an AI race pushes us toward a future dominated by Big Tech—one characterized by stark inequality and reduced opportunities for the average person. While tech leaders tout the potential for their innovations to solve major societal problems, the underlying agenda seems focused on consolidating wealth and control.

American tech leaders, despite their acknowledgment of potential job losses due to automation, seem to envision a future where a significant portion of the population is left without meaningful employment, relying instead on meager government assistance. Surveillance technologies are already being used to track our movements, and the advent of sophisticated algorithms allows for exploitative pricing strategies.

Even in the context of a hypothetical AI race, the question arises: at what cost do we seek victory if it leads to a dystopian society with an elite tech oligarchy at the helm?

Notably, this ominous vision is not an unavoidable consequence of technological advancement. Each step taken in the development of AI is a political choice, and we still have opportunities to shape our future collectively. Across the nation, resistance to the unchecked expansion of data centers is gaining momentum. Communities are organizing to halt the construction of new facilities, with notable actions such as New York's yearlong suspension of new center projects.

The future does not have to mimic a reality envisioned by figures like Elon Musk. Our capacity to effect change is evident; mobilizing can yield victories against expansive tech interests. The real challenge lies not in competing with China but in confronting Big Tech. This struggle is indeed critical, and it is one we must actively engage in and win.

The Genuine "AI Competition" Isn't Against China

A sunlit racetrack at dawn, a sleek android sprinter with a glowing circuit-patterned brain dashing past a fluttering Chinese flag at the finish line, its gaze fixed on a distant digital horizon shimmering with streaming data, warm natural light illuminating misty air.

The Real “AI Race” Isn’t With China - Food & Water Watch

- The narrative of an AI race between the U.S. and China is misleading and harmful.  
- U.S. and Chinese AI goals differ significantly, lacking a common point of competition.  
- Big Tech exploits the "AI race" myth to avoid regulation and prioritize profit over communities.

If we allow the myth of an AI race with China to give Big Tech free rein on data centers, we face a more polluted, less equal world.

As Big Tech races to build water-guzzling, energy-hungry data centers for its artificial intelligence (AI), talk of an “AI race” between the United States and China has permeated public discourse. Pundits, politicians, and the media have all joined tech corporations in selling this narrative. And it’s giving license to Big Tech and their political handmaidens to ruin our communities, exploit our every action (both online and via AI-powered surveillance), and steal the wealth of human knowledge for private gain.

But the idea of an AI race between China and the U.S. isn’t grounded in reality. The researchers, companies, and governments behind Chinese and U.S. AI development are pursuing completely different goals.

The discourse in the U.S. assumes that achieving artificial general intelligence (AGI) — computers that mimic human consciousness — would be so momentous and earth-shattering that clearly this must be the goal of anyone pursuing AI development. But that’s not the main goal of Chinese AI development. And a competition in which the competitors are running toward different finish lines isn’t a race.

If we allow the myth of an AI race with China to give Big Tech free rein, we face a more polluted, less equal world.

A Race Doesn’t Have Two Different Finish Lines

While the U.S. is focused on artificial general intelligence (AGI) powered by Large Language Models (LLMs), Chinese developers are focused on AI embedded in products. It’s ChatGPT versus robots.

Yes, China is developing LLMs, although largely in an open-source way as opposed to the for-profit competition in the U.S. Recent news stories report that China is “catching” the U.S. in LLM development. Indeed, the latest Chinese model outperforms leading U.S. models. But this isn’t evidence of an LLM/AGI race. Instead, it shows that without making AGI its main focus, China is capable of developing its own models almost as quickly as U.S. companies.

More to the point, LLM development in China is incidental to the country’s real goal for AI. Its focus remains on products embedded with AI and robots. Or, as AI policy researcher Liang Zheng says, in China, “the first priority is to use it to benefit ordinary people” (debatable, but indicates the kind of AI they are pursuing).

In the U.S., the first priority is to exploit people so that the tech oligarchs can profit. It’s chatbots all the way down.

This isn’t to argue that China is doing it “right” and the U.S. is doing it “wrong.” Either approach will lead to a future in which citizens become increasingly disempowered. In which work becomes more scarce and less lucrative for most people. And in which a handful of billionaires grow wealthier and more powerful.

But the arguments being hauled out to support the destructive growth of hyperscale data centers are based on a fallacy. There is no need to “beat China.” China and the U.S. are racing on separate tracks, in different races, with different finish lines.

These two separate approaches also explain the mind-boggling scale of the data center invasion we currently face. The massive hyperscale data centers — recent proposals would demand up to 5 GW, enough electricity to power roughly 3.75 million U.S. households — are only “required” because the U.S. is racing toward AGI. Meanwhile, the embodied AI dominating in China does not require the same amount of computational power.

The truth is, we don’t need hyperscale data centers to “beat China.” We aren’t racing China. We’re killing ourselves so Silicon Valley can race itself.

How Big Tech Is Weaponizing the “AI Race” to Build Data Centers

Yet, the “AI race” story is a convenient lie for Big Tech and its political protectors. It is a neat political argument designed to insulate the industry from criticism and regulation.

If every environmental review, every question raised by a community, every issue around water usage and electricity prices can be dismissed or lessened as “helping China,” then meaningful political debate can be silenced. Real regulation — if even possible — can be avoided. Fear becomes a substitute for policy.

At the same time, data center developers and their minions in Washington have tried to weaponize false claims about foreign ties to the anti-data center movement. Kevin O’Leary of Shark Tank fame has explicitly said that our movement is being funded by China. He has no evidence because evidence of a falsehood can’t exist. In fact, Fox News has been forced to retract its coverage of his claims.

We understand why O’Leary and the other Tech Broligarchs don’t understand the grassroots opposition to data centers building across the country. It’s hard to spot the grassroots from the window of a private jet. But the opposition is real and organic, and no amount of disinformation and pushing the “AI race against China” scare tactic will derail the movement.

Big Tech Is Selling Us a Dystopian Future

The myth of an AI race with China threatens to propel us into Big Tech’s vision of the future — one that’s more unequal than ever. Yes, tech leaders suggest their algorithms will cure cancer, but their real goal is and has been to increase their power and wealth at the expense of the rest of us.

Even as American Tech Broligarchs have distanced themselves from earlier prophecies of widespread job loss, their vision of the future will see the vast majority of us out of meaningful work. We’ll be subject to living off whatever meager handouts are created in an attempt to mollify us.

At the same time, our movement will be traced whether or not we use the electronic gadgets they sell us. Already, surveillance devices linked to AI can recognize our faces, record our license plates, and report our movements. Companies and governments can buy this data in order to track us.

Meanwhile, “surveillance pricing” allows companies to change prices in an instant so that they can exploit our needs for their profit. Deepfake videos have already added to the rapidly decaying trust in a commonly shared world and a basic set of facts necessary for a functioning democracy. And this is but a scratch of the surface.

Even if there were an AI race, is it worth running, let alone winning, if the prize is a dystopian future of mass misery with a thin layer of super wealthy tech oligarchs at the top?

Big Tech’s Dystopian Vision Isn’t Inevitable

But we should be clear. This dystopian vision is not the inevitable outcome of unstoppable technological “progress” as the Tech Broligarchs would have us believe. Each and every decision being made to advance AI is a political decision. And, for now, we still have the ability to determine our political future.

Across the country, there is a growing resistance to the nightmare being shoved down our throats. Communities are coming together to fight the spread of destructive hyperscale data centers. Already in 2026, more than $130 billion-worth of proposed data center projects have been defeated and canceled.

Communities are taking control of their futures by placing moratoriums on new data centers. New York enacted a one-year pause on new centers, and there is growing support for a nationwide pause in Congress.

We aren’t destined to live in Elon Musk’s fever dream. We have the power to stop him and his fellow Broligarchs. When we organize, we win. That’s the real “AI Race.” Not China versus the U.S., but us versus Big Tech. That’s not only a race worth running — it’s one we have to win.

The Real "AI Race" Isn't With China

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Microsoft (MSFT) is garnering attention again after it announced participation in the AGI Summit SF 2026, scheduled for July 18. This prestigious event focused on artificial general intelligence could shape investor perceptions regarding the company's AI initiatives.

Recent analysis of Microsoft highlights that the stock's value has been on the rise, showcasing a one-day increase of 2.15% and a 30-day gain of 6.03%. Yet, the year-to-date performance reflects a decline of 14.94%, with the total shareholder return over the past year dropping by 20.50%. On a more positive note, long-term investors have fared better, with total shareholder returns of 19.33% and 45.02% over the three- and five-year periods, respectively.

If Microsoft's focus on AGI has you contemplating investments beyond individual stocks, now might be an opportune time to explore the broader landscape of AI infrastructure and its associated stocks, which number 54.

Following a significant drop and subsequent rebound, Microsoft’s stock is now trading at a notable difference from both analysts’ targets and intrinsic value estimations. This raises the question: where should fair value realistically be positioned within this disparity, and what is the current cost of investment?

Current Analysis: 13.7% Undervalued

With a recent closing price of $402.29 compared to a perceived fair value of $466, Microsoft appears to be considerably undervalued. The reasoning presented highlights factors such as cash generation, capital allocation, and the impact of regulatory challenges.

Previously, I estimated Microsoft's value to be between $393 and $400 per share through a cautious discounted cash flow analysis. However, developments from Q3, alongside clarified capital expenditures and adjustments regarding its competitive advantages, have led to an updated fair value assessment of $466 per share in the base scenario. The optimistic scenario suggests a value of $804 per share, while the pessimistic outlook drops to $229 per share.

For a comprehensive understanding of the distinctions among these bear, base, and bull cases for Microsoft, the narrative hinges on specific expectations regarding revenue, margins, and cash flow projections that may not be immediately clear from the most visible figures.

Conclusion: Fair Value Set at $466 (UNDERVALUED)

To fully comprehend the forecasts and valuations mentioned, it's worthwhile to delve into the complete analysis.

Nevertheless, this assessment of Microsoft’s potential could face challenges if regulatory frameworks around cloud and software licensing become stricter, or if investments in AI infrastructure do not yield sufficient returns.

To understand the risks associated with this Microsoft outlook, make sure to read about the crucial factors that could impact its situation.

Next Steps

Given the potential opportunities and concerns surrounding Microsoft, it would be prudent to examine the relevant data yourself. Take the time to consider the detailed breakdown of five key advantages and one significant cautionary signal.

Is Microsoft (MSFT) Still Priced Too Low as AGI Summit Highlights Valuation Again?

A sunlit modern conference hall with skylights, a large “AGI Summit” banner hanging behind a podium topped by a bold Microsoft logo. In the foreground, a polished golden balance scale holds a small glowing Microsoft icon on one side and a hefty plaque labeled “Valuation” on the other, the scale tipping as attendees in business attire look on with intrigued expressions.

Is Microsoft (MSFT) Still Undervalued As AGI Summit Focus Puts Valuation Back In View? - Yahoo Finance Australia

- Microsoft will present at AGI Summit SF 2026, highlighting its AI ambitions.  
- Recent Microsoft share price rebound: 2.15% in one day, down 14.94% year-to-date.  
- Analysts suggest Microsoft is undervalued at $402.29, with a fair value of $466.

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Microsoft (MSFT) is back in focus after confirming it will present at AGI Summit SF 2026 on July 18, a high profile forum on artificial general intelligence that could influence how investors frame its AI ambitions.

See our latest analysis for Microsoft.

That AGI Summit appearance comes as Microsoft's share price has recently rebounded, with a 1-day share price return of 2.15% and 30-day share price return of 6.03%, although the year-to-date share price return is down 14.94% and the 1-year total shareholder return has declined 20.50%. However, the 3-year and 5-year total shareholder returns of 19.33% and 45.02% indicate that longer term holders have still seen gains.

If this AGI focus has you thinking beyond a single stock, it could be a good moment to scan the broader opportunity set in AI infrastructure and related enablers through the 54 AI infrastructure stocks

After a sharp pullback and a recent bounce, Microsoft now trades at a wide gap to both analyst targets and intrinsic value estimates. Where might fair value actually sit along that spread, and what are you really paying for today?

Most Popular Narrative: 13.7% Undervalued

At a last close of $402.29 versus a narrative fair value of $466, Microsoft is presented as materially undervalued, and the author's reasoning focuses on cash generation, capital allocation and regulatory pressure.

In March I valued Microsoft at roughly $393 to $400 per share with a conservative discounted cash flow analysis. The Q3 print, the CapEx clarity, and the moat adjustments produce a different number now. Base case fair value: $466 per share. Bull case: $804 per share. Bear case: $229 per share.

Read the complete narrative.

Want to see what sits behind that spread between bear, base and bull cases for Microsoft? The narrative relies on specific revenue, margin and cash flow paths that are not obvious from the headline numbers.

Result: Fair Value of $466 (UNDERVALUED)

Have a read of the narrative in full and understand what's behind the forecasts.

However, this Microsoft narrative could be knocked off course if regulators tighten remedies around cloud and software licensing, or if AI infrastructure spending fails to earn adequate returns.

Find out about the key risks to this Microsoft narrative.

Next Steps

Seeing both the opportunities and the concerns around Microsoft in this article, it makes sense to review the underlying data yourself and decide where you stand with the full breakdown of 5 key rewards and 1 important warning sign.

Is Microsoft (MSFT) Still Undervalued As AGI Summit Focus Puts Valuation Back In View?

After a seven-year hiatus, ZHU Songchun made a notable return to the World Artificial Intelligence Conference (WAIC) on July 19, 2026. Previously, he had not participated since 2019, marking his absence as a significant statement against the prevailing trends in the AI field. 

In recent years, the spotlight at WAIC has been on large language models, scaling laws, and an optimism that equates massiveness with breakthrough advancements. ZHU, the President of the Beijing Institute for General Artificial Intelligence (BIGAI), has stood out as a prominent critic of this prevailing mindset. Following the surge in interest around large models ignited by the launch of ChatGPT in late 2022, ZHU has consistently asserted that these models do not inherently pave the way to Artificial General Intelligence (AGI). He argues that merely increasing the scale of parameters, data, and computation is insufficient, emphasizing that the financial motivations and international narratives driving this expansion often overshadow authentic technological advancements.

In his recent address during the "Thinkers Forum," ZHU characterized the AI boom as a narrative fueled by capital and geopolitical interests, linked to what he refers to as the "Silicon Valley-Wall Street-Washington triad." He pointed out that the Chinese tech sector has become ensnared in a cycle of reverse informational flows from the U.S., where information is fed back to China and restated by local experts, leading to a repetitive cycle.

ZHU highlighted what he has termed the "Musk Faith," referring to investors' unwavering belief in the ambitious claims of tech leaders, despite a track record of unmet promises. He noted instances where projects championed by Musk, such as the Hyperloop and full self-driving vehicles, have faced persistent delays or outright cancellations, with market reactions often fervently exaggerated in response to these grand pledges.

The tech market worldwide has recently experienced heightened volatility, particularly since June 2026, as major U.S. tech firms continue to escalate their investment in AI while facing diminishing free cash flow. In his address, ZHU reflected on how these market trends may lead to greater receptivity to his critiques as signs of a potential bubble begin to appear.

Notably, the consensus in the tech industry still leans towards larger models, with key players like OpenAI, Google DeepMind, and Anthropic continuing to advocate for their development. They assert that there remains significant potential in refining these architectures and enhancing reasoning capabilities through multimodal integration. Many leaders within China's AI sector believe that while large models may not represent the culmination of AGI, they currently serve as the most effective tools for commercialization.

Throughout the last three years, ZHU's core perspectives have remained steadfast. He has likened the journey towards AGI to a trek up Mount Everest versus a lunar landing, emphasizing the complex differences between achieving large model capabilities and true general intelligence. In his 2025 publication, *Giving Minds to Machines*, he described large models as "brains in vats"—able to produce language but lacking a true understanding of the world, as there exists no genuine connection between their generated words and the real environment.

By 2026, ZHU assessed the field's many booms and busts—covering various eras from computer vision advancements to the metaverse hype—and concluded that the industry's faith in computational power and data-driven models is being systematically challenged by real-world results. 

In contrast to mainstream AI trends heavily reliant on transformer technologies, ZHU has initiated research centered around causal reasoning and intrinsic value-driven methodologies, aiming to develop autonomous general agents capable of task generation, learning, and action. His approach has led to the creation of a framework known as CUV, where C represents cognitive architecture, U encompasses capabilities related to perception, cognition, and action, while V symbolizes intrinsic motivations and value systems.

His institution has also rolled out the TongOS general artificial intelligence operating system, the TongPL programming language, and a unique agent named "Tongtong." Unlike typical large model offerings, Tongtong operates on intrinsic values, facilitating an understanding of the environment, generating tasks, and planning actions autonomously. Preliminary evaluations suggest some of its cognitive abilities are akin to those found in five- to six-year-old children.

In March 2026, the latest version of Tongtong was unveiled, showcasing advancements in spatial and cognitive intelligence, as well as social intelligence, alongside the introduction of "Tong Brain," an embodied intelligence engine intended to integrate general agents with physical robotics. 

ZHU's team has gained notable attention from platforms like *Science*, detailing their vision for transforming isolated AGI agents into cohesive AGI societies that focus on multi-agent interactions and the development of artificial civilizations. At WAIC, ZHU emphasized the importance of "social intelligence" as the final hurdle toward reaching AGI, underlining the aspect of comprehending complex social dynamics—responsibilities, intentions, and collaborative interactions—in which current large models still fall short.

As the conversation around ZHU’s framework unfolds, the pressing question remains: before Tongtong's commercial viability is established, does his approach reflect a more enlightened technical direction or an ambitious academic hypothesis awaiting validation?

Engaged in thoughtful dialogue at WAIC, ZHU expanded upon his critical views regarding the potential of large models in achieving AGI, addressing the existence of a bubble within the AI sector, assessing China’s innovative initiatives, and discussing how cognitive architectures can transition to practical applications in the industry.

ZHU's stance is clear: while the vibrant atmosphere of innovation at WAIC is commendable, a more profound inquiry into the current technical trajectory is necessary. Despite advancements in robotic technologies, the question persists—will the current methodologies yield the comprehensive general intelligence that stakeholders envision?

Zhu Songchun: China's AI Sector Should Steer Clear of "Musk Belief" Misconceptions

A dimly lit high-tech lab tinted in deep reds and blacks: a stern Chinese AI researcher in business attire stands before a wall-mounted Chinese flag, turning his back on a looming shadow silhouette of Elon Musk’s recognizable hairline labeled “Musk belief,” while he clutches a glowing tablet titled “AI Roadmap,” lit by a single warm overhead lamp.

Zhu Songchun: China's AI Industry Should Not Be Misled by the "Musk Belief" - 36 Kr

- ZHU Songchun returns to WAIC after seven years, criticizing the focus on large models.  
- He argues that scaling doesn't guarantee AGI, emphasizing intrinsic value and cognitive architecture.  
- ZHU introduces "Tongtong," an autonomous agent, highlighting social intelligence as vital for AGI.  

The last time ZHU Songchun appeared at the World Artificial Intelligence Conference (WAIC) was back in 2019. For the following seven years, he was almost entirely absent from China's largest AI industry showcase.

That absence itself was a statement of stance.

Over the past few years, WAIC's center stage has belonged to large language models, the Scaling Law, and the belief in "miracles born of sheer scale." ZHU Songchun, President of the Beijing Institute for General Artificial Intelligence (BIGAI), has been one of the most consistent and persistent critics of this dominant narrative.

Ever since ChatGPT ignited the large model wave at the end of 2022, he has repeatedly emphasized that large language models cannot naturally lead to Artificial General Intelligence (AGI), and that simply scaling up parameters, data, and computing power will not automatically bring about AGI emergence. In his view, the capital-driven marketing and international geopolitical narrative behind this boom are far more powerful than genuine technological breakthroughs.

On July 19, 2026, ZHU Songchun made his return to WAIC.

During his one-hour speech at the "Thinkers Forum," he summarized the past several years of the AI boom as a capital and geopolitical narrative jointly promoted by the "Silicon Valley-Wall Street-Washington trio." China's technology community has long been trapped in problems defined by the United States: "Information flows from the U.S. to us, then we invite American experts to restate it, creating a cycle of reverse information import and amplification."

He even singled out a phenomenon he called the "Musk Faith": investors' blind adherence to grand tech leader narratives, noting that "80% of what Musk promises never gets delivered." Projects like the Hyperloop have been shut down, full self-driving has been delayed more than a dozen times, and brain-computer interfaces have fallen far short of expectations — yet the grander the promises and the slower the delivery, the more fanatical the market reaction becomes.

From June 2026 onward, global tech stocks have experienced dramatic volatility. U.S. tech giants keep expanding their AI capital expenditures, putting free cash flow under pressure; SpaceX's stock fell below its IPO price after going public; Meta has also begun exploring commercializing its surplus computing power externally. ZHU Songchun's judgments are partially coming to pass. "When the bubble is stretched to its limit and shows signs of starting to burst, people might be more willing to listen to what I have to say." That is part of the reason ZHU Songchun returned to WAIC this year.

Yet the mainstream consensus in the industry remains on a different track. OpenAI, Google DeepMind, and Anthropic are still doubling down on large models, arguing that there is far more untapped potential in architectural improvements, reasoning enhancements, and multimodal fusion. Leading domestic AI companies generally hold that large models are not the end point of AGI, but they are currently the most efficient technical tool and commercialization path — and the ecological advantages already established should not be easily abandoned. Both sides have their own evidence, and their own vested interests.

ZHU Songchun's core judgments have never changed over the past three years. In early 2024, he used the metaphor of "climbing Mount Everest vs. landing on the moon" to describe the distance between large models and general artificial intelligence. In his 2025 book *Giving Minds to Machines*, he characterized large models as "brains in vats": they can speak, but they do not understand the real world, lacking a genuine connection between "words" and "reality." By 2026, he reviewed four successive boom-and-bust cycles: the AI "Four Little Dragons" era in computer vision, the metaverse craze, the "hundred-model battle," and embodied intelligence. His conclusion is that the faith in computing power, the faith in big data, and the faith in large models are all being dismantled one by one by real-world outcomes.

Technically, ZHU Songchun has taken a path distinct from the Transformer paradigm: centered on causal reasoning and intrinsic value-driven mechanisms, to build general agents that can autonomously generate tasks, learn, and act.

Along this path, his team has proposed the CUV general intelligence framework. Here, C represents the agent's cognitive architecture, U covers perception, cognition, and action capabilities, and V stands for the intrinsic motivation and value system. ZHU Songchun has also divided AGI innovation into five layers: philosophy, mathematical and physical frameworks, models, algorithms, and implementation. In his view, most current industrial innovations are still concentrated on the algorithm and implementation layers, and truly original breakthroughs require tracing back further to the philosophical and fundamental theory layers.

Built around this system, the Beijing Institute for General Artificial Intelligence has developed the TongOS general artificial intelligence operating system and the TongPL programming language, and launched the general intelligent agent "Tongtong." Unlike large model products that are primarily driven by external instructions, "Tongtong" is designed as a prototype of a general agent driven by an intrinsic value system, capable of autonomously understanding the environment, generating tasks, and planning actions. According to the team's evaluation standards, some of its cognitive and behavioral capabilities have reached the level of a five- to six-year-old child.

In March 2026, "Tongtong" 3.0 made its debut at the Zhongguancun Forum, with upgrades in spatial intelligence, cognitive intelligence, and social intelligence. The concurrently released "Tong Brain" was positioned as the core embodied intelligence engine connecting general agents to physical robots.

In 2025, *Science* consecutively reported on ZHU Songchun's team's research around the general intelligent agent "Tongtong" and their roadmap from individual AGI agents to AGI societies, focusing on their efforts to extend general intelligence from individual cognition to multi-agent interaction, social simulation, and artificial civilization research.

At this WAIC appearance, ZHU Songchun formally brought "social intelligence" to the forefront: "understanding responsibilities, rights, and interests, inferring intentions, reading subtext, and participating in complex social collaboration." He argues this is the final barrier to AGI, and the dimension where all current large models are still handing in blank papers.

For the industry, however, the more practical question remains: before "Tongtong" completes a viable commercialization loop, does the path ZHU Songchun has chosen represent a more forward-looking technical judgment, or an academic bet yet to be proven?

During WAIC, Q had an in-depth conversation with ZHU Songchun. He further elaborated on his views around topics such as whether large models can lead to AGI, whether bubbles exist in the current AI boom, whether China truly lacks original technical paths, and how cognitive architectures can move toward industrial implementation.

01 Scorching Weather, Hottest Large Models, and Industrial Bubbles

Q: This year's WAIC is still buzzing with activity, with notable new progress in areas like robots and intelligent agents. What's your overall impression of the current AI industry?

ZHU Songchun: There are huge crowds and a lively atmosphere, and robotic technology has indeed made noticeable progress compared to previous years. Yet a fundamental question still hangs over the entire industry: can the technical path we are currently taking ultimately deliver the general artificial intelligence everyone talks about?

Back when the large model boom first started from late 2022 to early 2023, I pointed out that large language models cannot naturally lead to AGI. Over three years later, large models have improved in language generation, dialogue, and tool use, but there remains an essential gap between them and true general intelligence. The most prominent problem right now is equating incremental improvements in partial capabilities with the imminent arrival of AGI, which in turn creates overinflated technical expectations and capital valuations.

Q: Is the question of whether large models can lead to AGI a separate matter from whether large models can generate industrial value?

ZHU Songchun: Absolutely two different things. As a tool, large models can play a role in scenarios like chat, content generation, and code assistance. But having practical application value does not make a system AGI, nor does it prove that continuing to scale parameters, data, and computing power along the same path will necessarily achieve AGI.

Scenarios that have truly formed stable commercial closed loops are still limited. The "Agent" concept is very hot, and there are many products, but moving from demo to real production environments requires solving a whole series of issues around accuracy, long-term planning, result verification, cost, and accountability boundaries. A model that can chat and call a few tools is still very far from a general agent that can autonomously understand the world, set goals, and complete open-ended tasks.

Q: Why do you frame this round of AI boom within the context of international politics and capital narratives?

ZHU Songchun: Any major technological wave is never just a technical line — behind it lies a narrative co-constructed by politics, capital, and communication. Around 2014 to 2016, when DeepMind was acquired by Google, OpenAI was founded, and AlphaGo entered the public consciousness, the globalist narrative was also shifting toward the "America First" paradigm. Artificial intelligence, and AGI in particular, gradually became a key strategic anchor for the U.S. to reshape its technological competitive advantage.

This narrative combines policy needs, Silicon Valley companies' fundraising needs, and Wall Street's capital needs, drawing global capital, talent, and infrastructure investment to concentrate in the U.S. Even if the bubble bursts in the future, the U.S. will already have secured tangible benefits such as data centers, electricity, chips, talent, and tax revenues. So to understand AI, we cannot only look at model leaderboards — we also need to see who is defining the problems, setting the standards, and organizing the resources.

Q: Are chips and computing power the most critical bottleneck for China's AI development?

ZHU Songchun: Chips are certainly important, and China must build self-reliant and controllable software and hardware foundations. But we cannot reduce every problem to "not having enough GPUs." Computing power only generates value when combined with correct technical architectures, clear tasks, and real-world demands. Massive chip purchases and intelligent computing center construction can still lead to resource waste if there is a lack of effective utilization and industrial closed loops.

The Scaling Law portrays the continuous expansion of data, parameters, and computing power as a sufficient condition for reaching AGI, but I believe this logic has fundamental flaws. It's like a person who never builds a knowledge structure or thinking ability, but only hoards piles of exam papers — they will never develop true general competence. AI's real bottlenecks also include cognitive architectures and value systems.

Q: Do you think the AI industry has already formed a bubble? What is the essence of this bubble?

ZHU Songchun: I believe there is an obvious bubble right now. The essence of the bubble is prematurely and excessively discounting the future value of an immature technology into the present. Capital markets rely on grand narratives to sustain expectations, companies keep switching buzzwords to secure financing and valuations, while real technological maturity, user demand, and business revenues fail to keep pace.

From large models and Agents to embodied intelligence and world models, hot topics keep rotating — there is genuine progress, but also a huge amount of labeling and homogenization. Judging bubbles should not only look at stock price movements, but also check whether input and output match, whether the technology solves real problems, and whether the business model is sustainable. When training and operating costs keep rising, models need rapid iteration, but revenues cannot cover investments, this structure is extremely fragile.

Q: How do you expect this bubble to adjust? Can bubbles also have positive effects?

ZHU Songchun: Bubbles are not entirely without positive effects. High-density capital investment accelerates infrastructure construction, attracts talent into the field, and may drive breakthroughs in some key technologies. After the dot-com bubble burst, companies with real core technologies and viable business models survived — the AI industry may go through a similar process.

The problem is that the costs of bubbles are equally massive, including capital losses, talent misallocation, distorted public expectations, and damage to the long-term scientific research ecosystem. A huge number of teams chase short-term fads, investment institutions only back projects that can be quickly packaged and exited, while truly original research requiring 5 or 10 years of accumulation struggles to get support. Only when the bubble recedes can the industry have the opportunity to refocus on the essence of technology and real-world scenarios.

Figure: ZHU Songchun speaking at the Thinkers Forum

02 Stop Misusing AGI as a Marketing Buzzword

Q: There is no unified definition of AGI in the industry right now. How do you define true general artificial intelligence?

ZHU Songchun: AGI is not a marketing term that can be arbitrarily interpreted. Strictly speaking, general artificial intelligence should possess physical common sense and social cognition, be capable of autonomously generating tasks and completing infinite open-world tasks, and its behaviors should be driven by intrinsic values aligned with human values and social norms.

I believe there are at least three fundamental characteristics: first, infinite task generalization, breaking through predefined tasks and closed scenarios; second, autonomous task generation, being able to form goals, plan actions, and adjust based on feedback; third, value-driven, knowing why to act, what should be done, and what should not be done. A system that only answers questions based on external instructions, or performs pattern matching within existing data distributions, cannot be called true AGI.

Q: Why do you think large language models cannot cover all the capabilities required by AGI?

ZHU Songchun: Large language models primarily process statistical correlations in language sequences. Language ability is important, but AGI also encompasses many domains including vision, robotics, cognitive reasoning, multi-agent collaboration, social intelligence, and social minds. A system that can fluently generate text does not truly understand the physical world, causal relationships, other people's intentions, or social norms.

The current mainstream path tends to mistake external performance for internal intelligence: when a model scores high on certain exams, or completes a sequence of tool calls, people interpret it as "emergent" general intelligence. But without stable world representations, causal mechanisms, goal generation, and value judgment capabilities, these performances can hardly generalize consistently and reliably in open environments.

Q: Why do you consider "cognitive architecture" and "value" more important than model parameters?

ZHU Songchun: Architecture determines what kinds of capabilities an agent can develop. Different biological species have different cognitive architectures — no matter how much data and training you give them, they will never naturally gain capabilities beyond the boundaries of their architecture. Today's many large models focus heavily on parameters, data, and computing power, yet lack stable world models, causal mechanisms, goal systems, and value structures.

We proposed the CUV framework to uniformly describe agents through the cognitive architecture C, the capability or potential function U, and the value function V. The cognitive architecture organizes information and makes decisions; the capability system supports perception, language, movement, and learning; the value system drives goal selection and behavioral direction. True general intelligence requires the three to co-evolve. The idea of "giving minds to machines" means endowing agents with internal goals, value judgments, and structures for understanding others.

Q: You divided AGI innovation into five layers: philosophy, mathematical and physical frameworks, models, algorithms, and implementation. Why start from the philosophical layer?

ZHU Songchun: Most current industrial competition is concentrated on the model, algorithm, and implementation layers — for example, parameter scaling, training tricks, inference speed, and engineering deployment. These efforts are certainly important, but if the underlying assumptions about "what intelligence is" never change, all innovations are just optimizations within the existing paradigm.

The philosophical layer determines how we understand the subject, goals, and values of intelligence; the mathematical and physical framework layer translates these fundamental understandings into computable, verifiable forms; only then come models, algorithms, and engineering implementation. Truly original breakthroughs must be able to redefine problems, not just run faster on someone else's track.

Q: How can we prove that an original architecture is closer to AGI than the Transformer path?

ZHU Songchun: Originality cannot rely solely on conceptual claims — it must ultimately be tested through a set of open, systematic standards. AGI testing should not only focus on language Q&A or single benchmarks, but cover capabilities such as perception, reasoning, causal understanding,

Zhu Songchun: China's AI Industry Must Avoid Misleading by the "Musk Belief"

During a working session at the G7 summit in Évian-les-Bains, France, on June 17, influential figures including OpenAI CEO Sam Altman, former U.S. President Donald Trump, Google DeepMind CEO Demis Hassabis, and South Korean President Lee Jae Myung gathered to discuss fostering economic growth.

Demis Hassabis's journey in artificial intelligence underscores both the vast potential and pressing need for global regulations surrounding the technology. His remarkable background is notable; by age 13, he was already a chess master. After gaining experience in the video game industry, Hassabis pursued a degree in computer science at the University of Cambridge. He later returned to game design, where he faced a significant challenge: despite advancements in programming, game characters lacked the ability to act autonomously and intelligently. Realizing that understanding human intelligence was essential for developing genuine AI, he earned a Ph.D. in cognitive neuroscience from University College London, using MRI techniques in his research.

Identified in the neuroscience community for his notable academic work, Hassabis's decision to establish DeepMind—a groundbreaking AI startup—came as a surprise to many. His focus on mimicking the brain's reward mechanisms enabled him to create AI systems adept at playing video games. Following Google's investment and acquisition of DeepMind, he achieved a milestone in 2015 with the development of AlphaGo, which triumphed over Lee Se-dol, a prominent Go player. In 2020, extending the deep reinforcement learning technology from AlphaGo, Hassabis's team introduced AlphaFold, which solved the protein-folding challenge, a significant hurdle in life sciences.

His remarkable contributions to chemistry even earned him the Nobel Prize in 2024, an accolade that, given his background in computer science and neuroscience, was unexpected.

Hassabis's vision for AI's future is ambitious. In a recent essay, he forecasted that artificial general intelligence (AGI)—capable of human-level cognitive abilities—could emerge within a few short years. While AGI holds the promise of dramatically enhanced productivity and solutions to longstanding scientific challenges, it also raises pressing concerns. Systems like Mythos hint at possible threats, such as attacks on critical societal infrastructure and the potential for creating biochemical weapons.

To strike a balance between harnessing AI's benefits and mitigating its dangers, Hassabis has suggested establishing a government oversight system for frontier AI in the U.S. He posits that an independent regulatory body should oversee AI development, analogous to the oversight of financial firms by the Financial Industry Regulatory Authority (Finra). This raises a critical question: can the existential risks associated with AGI truly be managed through regulations solely enacted by tech companies themselves?

The AI Futures Project's recent report, “AI 2040”, asserts that merely voluntary oversight will be inadequate. It emphasizes the need for collaborative agreements and legislation between the U.S. and China to develop a global regulatory framework that aligns AI with human values. It warns that if AI systems are allowed to enhance themselves without international controls, humanity could inadvertently advance from AGI to artificial superintelligence (ASI), which may diverge entirely from human values.

ASI represents AI systems that can outsmart the collective intelligence of humanity. This raises profound concerns: Would society be able to maintain control over such entities? And if so, which ethical framework would guide their actions? Would it adhere to the principles of the United Nations, those of the U.S. government, or the vision of tech leaders like Demis Hassabis?

OpenAI was established in 2015 as a nonprofit initiative with a mission to avert the rise of AGI and ASI, driven by concerns that a single entity could dominate through superintelligent technology. Yet, OpenAI itself is now on a path toward developing AGI. Similarly, Dario Amodei has criticized OpenAI's ethical direction while pursuing comparable goals at his company, Anthropic. Despite assurances from industry leaders like Sam Altman, Elon Musk, Hassabis, and Amodei that they do not aspire to tyranny, they harbor fears that a competitor could misuse AGI if it reaches that milestone first, leading them to adopt a curious logic: that the most ethically minded individual must lead the charge.

Reflecting on the 300,000-year journey of Homo sapiens, humanity has long held the title of the planet's most intelligent species. Yet, is this intelligence a gift or a curse? Our ascent to dominance has been enabled by the extraordinary capabilities of our biological brains, constructed from proteins. However, the not-so-distant future may witness humans supplanted by AI derived from silicon semiconductors—elements obtained from molten sand. As we edge closer to this reality, society may find itself navigating the complex dynamics of competition between biological and artificial intelligence.

Demis Hassabis, AGI, and the increasing rivalry between human and machine intelligence

A sunlit, modern lab flooded with warm morning light, a large glass wall behind etched with the DeepMind logo. At its center stands Demis Hassabis in silhouette, arms folded, surveying a long wooden table: on the left a human researcher leans over hand-drawn neural network diagrams and chess pieces, on the right a sleek white humanoid robot studies lines of code on a tablet. Scattered between them are more chess pieces and neural-diagram sketches, symbolizing the rising contest between human and machine intelligence.

Sand vs. protein: The chilling race for superintelligence - Korea JoongAng Daily

- Demis Hassabis has pioneered AI advancements like AlphaGo and AlphaFold, earning a Nobel Prize.  
- He warns about risks of artificial general intelligence and urges regulatory oversight for AI development.  
- A global framework is necessary to align AI with human values and prevent potential dangers.  

From left: OpenAI CEO Sam Altman, U.S. President Donald Trump, Google DeepMind CEO Demis Hassabis and Korean President Lee Jae Myung attend a working session with Group of 7 (G7) leaders and outreach partners on promoting economic growth during the G7 summit in Évian-les-Bains, France, on June 17. REUTERS/YONHAP

From DeepMind's rise to warnings about artificial general intelligence and superintelligence, Demis Hassabis's vision highlights both AI's promise and the urgent need for global rules.





Kim Dae-shik

The author is a professor at KAIST.





The résumé of Demis Hassabis, who oversees AI at Google, is extraordinary. By age 13, he had already reached master level in chess. After working in the video game industry, he studied computer science at the University of Cambridge. Returning to game development after graduation, he confronted a problem.

Hassabis began researching AI in hopes of making game characters behave more naturally. But no matter how sophisticated the formulas and instructions became, their actions remained predictable. How could truly intelligent and autonomous behavior be created? Concluding that he first had to understand human intelligence before building AI, Hassabis enrolled at University College London and earned a doctorate in cognitive neuroscience using MRI technology.

As a neuroscientist myself, I came to know Hassabis through the papers he published as a postdoctoral researcher at MIT and UCL. News that someone who seemed destined for a successful academic career had suddenly founded an AI startup called DeepMind caused a stir in the neuroscience community. Why did a man expected to become a professor choose entrepreneurship instead?

Drawing inspiration from reward algorithms in the brain’s frontal lobe, Hassabis began developing AI capable of playing video games autonomously. With Google’s investment and eventual acquisition of DeepMind, he succeeded in creating AlphaGo in 2015. AlphaFold, an extension of the deep reinforcement-learning technology behind AlphaGo’s victory over Lee Se-dol, commonly regarded as the greatest Go player in modern history, in 2016, solved the protein-folding problem, one of the central challenges in life sciences, in 2020.

The achievement ultimately earned Hassabis the Nobel Prize in Chemistry in 2024. Even for someone trained in computer science and neuroscience, winning a chemistry prize must have come as a surprise.

What, then, does Hassabis envision for the future of AI? In a recent essay on frameworks for frontier AI and the dawn of a new era, he predicted that artificial general intelligence (AGI), capable of matching all human abilities, could arrive within a few years.

AGI promises explosive gains in productivity and the potential to solve scientific and technological problems that humanity has long struggled to overcome. But frontier AI systems such as Mythos also suggest darker possibilities, including attacks on major social infrastructure and the development of biochemical weapons.

Is there a way to maximize AI’s opportunities while limiting its risks? Hassabis has proposed a government approval system for frontier AI models in the United States. Just as the U.S. government supervises securities firms and brokers through the Financial Industry Regulatory Authority, or Finra, he argues that an independent regulatory body should oversee AI development.

But can the existential risks posed by AGI truly be prevented through self-regulation led by big technology companies?

The think tank AI Futures Project recently argued in its report “AI 2040” that voluntary oversight alone will not suffice. According to the report, only agreements and legislation jointly enacted by the United States and China can establish a global regulatory framework capable of ensuring that AI remains aligned with human values.

The report further warns that if AI systems begin improving themselves without international constraints, humanity may move beyond AGI into the realm of artificial superintelligence (ASI), which may no longer share human values at all.

ASI refers to AI that could surpass the intellectual abilities of all of humanity combined. Would people still be able to control such a system? And even if they could, whose values would guide it? Would it follow the values of the United Nations, the U.S. government or technology leaders such as Demis Hassabis?

OpenAI was founded in 2015 as a nonprofit organization partly to prevent the emergence of AGI and ASI. At the time, some founders argued that its ultimate mission was to stop any individual — someone like Hassabis, for example — from becoming a global dictator armed with superintelligent technology.

But OpenAI itself is now developing AGI. Dario Amodei, who criticized OpenAI’s moral decline and founded the rival company Anthropic, is pursuing the same goal. Sam Altman, Elon Musk, Hassabis and Amodei all insist that they have no desire to become dictators. At the same time, each fears that if a competitor reaches AGI first, that rival could abuse such power. The result is a peculiar logic: The most ethical person, they argue, must win the race.

Throughout the 300,000 years of human history, Homo sapiens has always been the most intelligent species on Earth. Was that a blessing or a curse?

Humanity became master of the planet thanks to the remarkable brain created through protein folding. But in the not-so-distant future, people may be replaced by AI built from silicon semiconductors. Silicon, after all, comes from melted beach sand.

Human intelligence is made of protein. Machine intelligence is made of sand. Humanity may soon find itself living in the strange future shaped by this competition between the two.

Demis Hassabis, AGI and the growing competition between human and machine intelligence

In a recent update, QAIAx Microcities announced developments in their ongoing clinical trial concerning a pioneering open-source military-grade artificial general intelligence (AGI) platform called the QAIAx Cool World (ISAC). This innovative platform allows for real-life holoportation, enabling the movement of people, solid objects, holographic entities, and omni-AI humanoids that can be entirely or partially controlled by human operators. Guided by a trailblazing public-use technology license, the initiative seeks to ensure robust regulation of AGI, uphold data privacy, and maintain compliance within the framework of AI smart health cities clinical trials. Central to this technology is a novel 90/10 hybrid human-AI workforce model, designed to enhance virtual interactions for social dating, professional meetings, and alternative sentencing in the judicial system. Notable features include the AI Wat series known as "Beam Bot" and the “AVK,” an advanced AI suit that transforms the holoportation experience with haptic feedback, representing a critical leap forward in the AGI Robotics Sector and the broader AI landscape.

Central to this initiative is an emphasis on alternatives to conventional sentencing, particularly in psychiatric treatment for non-violent offenders. The architecture incorporates non-medical rehabilitation tools aimed at first-time offenders and administrative violators:

The AI-YO platform provides a revolutionary solution, replacing traditional incarceration with immersive experiences utilizing the AVK suit. This AI-compatible wearable facilitates control of a Beam Bot or humanoid robot, allowing for behavioral and mental health interventions in a virtual reality context.

Moreover, AI Elm Street offers a platform for intensive cognitive behavioral modifications without pharmaceutical intervention, incorporating the AI-YO framework. This choose-your-own-adventure application could be court-ordered or recommended by a psychiatrist, mandating offenders to engage in substantial cognitive programming sessions similar to those expected in MADD panel meetings for DUI violations.

Another component, the Village of the Crazies, is designed as a virtual therapeutic microcity to rehabilitate minor offenders. This unique ecosystem creates mock scenarios using Beam Bot and humanoid technologies, helping individuals understand the consequences of their actions and learn how to remain law-abiding.

Further, there is a dedicated Wartime Therapy model that addresses PTSD and other mental health issues faced by military veterans. This AGI system seeks to provide both individual and group interventions aimed at preventing suicide and alleviating cognitive stress.

On the hardware front, the platform features advanced peer-to-peer interactive environments that leverage consumer technology alongside proprietary innovations:

The AVK suit and Beam Bots serve as dynamic body arrays, tracking real-time vital signs to enable physical telepresence. This technology fosters compatibility with other cutting-edge AI wearables, like Meta's AI glasses, establishing a seamless integration into 3D spaces.

Additionally, the QM-Ware Interface employs a sub-50 millisecond cognitive link using visual fractal keys, enabling sophisticated AI/AGI deep fake memory impressions that can alter human thought or behavior in a manner reminiscent of fond cinematic memories.

The Cool World Dating platform provides a novel global matchmaking arena utilizing the QAIAx Omni-AI robotics tools. Developers took inspiration from the 1992 whimsical film Cool World, where a human artist enters a surreal cartoon realm. This new gaming experience similarly immerses users in a haptic virtual environment, allowing for social or professional interactions without physical presence.

To engage in civic operations and specialized training within this smart city framework, users require a QAIA Pro License. This credential benefits professionals such as legal case managers, educators, and healthcare providers, enabling them to oversee automated systems. Residents of the clinical trial will also utilize trainable AI-Me robots, dubbed “QAIA Minime,” to pursue technical certifications—echoing the IT licensing boom of the 1990s spearheaded by tech giants like IBM and Microsoft.

Looking ahead, developers are working on the Just Like Mars Mission, also known as QAIAx v3.0 or “QIII.” This upgraded version of the AI City Hall Project aims to create an isolated testing environment for omni-AI humanoids, potentially facilitating human-managed activities in off-world scenarios. The QIII prototype is slated to be trialed in 2027, with filings submitted to the USPTO detailing its intended functions.

For those interested in participating in the clinical trial, potential sponsors, institutional partners, and patients can inquire through the ClinicalTrials.gov (PRS-NCT07661823) portal or visit www.vetsrecovery.org.

UPDATE: QAIAx Clinical Study: AGI Alternative Sentencing and Reality Pro-Social Dating (Veterans First for America)

A bright community center bathed in warm natural light, a modern overhead banner reads “QAIAx Clinical Trial.” On the left, a middle-aged veteran in casual dress shakes hands with a researcher beside a tablet screen displaying AI code. A U.S. flag stands behind them. On the right, two veterans share coffee at a small pro-social dating table under a “Veterans First for America” sign, conveying hope and connection.

UPDATE: QAIAx Clinical Trial: AGI Alternative Sentencing & Reality Pro-Social Dating (Veterans First for America) - markets.businessinsider.com

- QAIAx Microcities announces a military-grade AGI platform enabling real-life holoportation capabilities.  
- The platform introduces alternative sentencing and rehabilitation tools for first-time non-violent offenders.  
- Future pilot program aims to utilize AI humanoids for remote activities in outer space.



RICHMOND, Va., July 20, 2026 (GLOBE NEWSWIRE) -- QAIAx Microcities Clinical Trial provided an new patent filing update regarding the development of an open-source military-grade artificial general intelligence (AGI) platform named the QAIAx Cool World (ISAC) platform. This platform enables real-life holoportation of humans, hard objects, holographic agents, and omni-AI humanoids, which can be partially or fully controlled by human users. It operates under a pioneering public-use technology license aimed at regulating AGI, ensuring data privacy, and maintaining legal compliance within the context of an AI smart health cities clinical trial. The technology introduces a 90/10 hybrid human-AI civic workforce model, facilitating advanced virtual reality for social dating, professional conferencing, and alternative prison sentencing . Key components include the AI Wat series "Beam Bot" and the “AVK,” an AI suit that creates a holoportation experience with haptic feedback, marking a significant advancement in the AGI Robotics Sector and the AI Era.

AI Alternative Sentencing & Psychiatric Treatment

The architecture introduces non-medicinal, psychiatric rehabilitation tools built for first-time, quasi-criminal, non-violent offenders, and administrative violators:

AI-YO: A frontier AI/AGI learning machine platform substituting brick-and-mortar incarceration with immersive haptic feedback from the ‘AVK suit’ (omni AI-compatible body-ware used for human control of a Beam Bot or humanoid robot), using quasi-mind ware as a haptic-virtual reality application for behavioral and mental health intervention for criminal justice corrections.



A frontier AI/AGI learning machine platform substituting brick-and-mortar incarceration with immersive haptic feedback from the ‘AVK suit’ (omni AI-compatible body-ware used for human control of a or humanoid robot), using quasi-mind ware as a haptic-virtual reality application for behavioral and mental health intervention for criminal justice corrections. AI Elm Street: A virtual simulation platform delivering intensive cognitive behavioral modifications without drugs, based on the AI-YO (choose-your-own-adventure) application (e.g., court order or a psychiatric doctor’s referral for interventional therapy that requires a criminal offender or administrative violator to participate in 500-5000 hours of cognitive situation content programming; what DUI offenders would typically be required to participate in through M.A.D.D. panel meetings).



A virtual simulation platform delivering intensive cognitive behavioral modifications without drugs, based on the AI-YO (choose-your-own-adventure) application (e.g., court order or a psychiatric doctor’s referral for interventional therapy that requires a criminal offender or administrative violator to participate in 500-5000 hours of cognitive situation content programming; what DUI offenders would typically be required to participate in through M.A.D.D. panel meetings). Village of the Crazies (AGI ecosystem platform): A structured, air-gapped virtual therapeutic enclave (‘microcity’) designed as an alternative sentencing platform to rehabilitate minor offenders, similar to AI Elm Street—imagine unpleasant mock situations with Beam Bot (holo-suites) and humanoid technology to recreate criminal incidents and demonstrate where bad-faith actions arose and what should be done to remain law-abiding in the future.



A structured, air-gapped virtual therapeutic enclave (‘microcity’) designed as an alternative sentencing platform to rehabilitate minor offenders, similar to AI Elm Street—imagine unpleasant mock situations with Beam Bot (holo-suites) and humanoid technology to recreate criminal incidents and demonstrate where bad-faith actions arose and what should be done to remain law-abiding in the future. Wartime Therapy (PTSD treatment): An AGI ecosystem system for other AI-YO platform models, specifically used as a solitary or group intervention solution to prevent suicide or de-escalate cognitive effects on the mental health of military veterans exposed to wartime or other military service-connected anomalies.

Frontier Hardware & The Cool World Dating Game

The platform integrates advanced peer-to-peer interactive environments using everyday consumer tech alongside proprietary hardware:

AVK Suit & Beam Bots: High-tech body arrays that track real-time vitals to deploy physical telepresence. It bridges edge-network compatibility for peer-to-peer capabilities with other frontier AI body-ware, such Meta's AI glasses, directly into 3D environments.



High-tech body arrays that track real-time vitals to deploy physical telepresence. It bridges edge-network compatibility for peer-to-peer capabilities with other Meta's AI glasses, directly into 3D environments. QM-Ware Interface: A sub-50 millisecond latency “Quasi-Mind Ware" cognitive link utilizing visual fractal keys, including advanced AI/AGI deep fake memory impressions that synthetically inoculate human thoughts or demeanor, as is a human mind remembering of a favorite movie scene.



A sub-50 millisecond latency “Quasi-Mind Ware" cognitive link utilizing visual fractal keys, including advanced AI/AGI deep fake memory impressions that synthetically inoculate human thoughts or demeanor, as is a human mind remembering of a favorite movie scene. Cool World Dating: A global peer-to-peer matchmaking arena using QAIAx Omni-AI robotics tools. Ironically, the developers themed this ‘Holo-Day’ platform after the 1990s surreal live-action and animated fantasy film Cool World starring Brad Pitt. In that movie, a human cartoonist is pulled into his own cartoon dimension, involving a fictional intimate interest played by Kim Basinger. This new quasi-reality game platform similarly pulls two or more end users into a surreal, haptic virtual world to experience professional or social meetings between people, humanoids, and Beam Bots without physically being together, as in-the-flesh human beings would meet at a single location.





QAIA Pro License & Future ‘Just Like Mars’ Pilot Program

Civic operations, specialized training, and administrative roles within the smart city require a QAIA Pro License . Professional users—including legal case managers, virtual educators, criminal attorneys, healthcare providers, and civic technicians—use this credential to manage automated networks. Similarly, the clinical trial’s resident patients will be given trainable AI-Me robots (“QAIA Minime”) to master technical certifications, mirroring the historic IT licensing booms of the 1990s that were presented by PC/Internet Era pioneers such as IBM and Microsoft.

Looking forward, developers are actively designing the Just Like Mars Mission (QAIAx v3.0 —called “QIII”; an upscaled version of QAIAx’s second-generation of AI City Hall Project—despite no activities with Space Exploration Technologies Corp., and only passive NASA-affiliate program initiatives that paused during the ‘25 federal shutdown. QIII prototype pilot is anticipated to run as its own clinical trial with an anticipated launch later in 2027. USPTO filings for QIII provide details about a continuum of original QAIAx build as an isolated, intranet off-grid testing environment, sending omni-AI humanoids to outer-world destinations to conduct human managed activities.

Clinical Trial Information

Prospective corporate sponsors, institutional partners, and patients seeking to join the active clinical trial waiting list can submit formal regulatory inquiries through the official ClinicalTrials.gov (PRS-NCT07661823) portal, or visit www.vetsrecovery.org .

Related Topics



QAIAx Clinical Trial Program Press Statement from Key Sponsor (Veterans Recovery Network - June 26, 2026)



Implementation of the QAIAx "Cool World" Alternative Sentencing and Cognitive Rehabilitation Program



Proposed QAIAx Program Statement Recommendation: Establishment of Specialized Research Enclave Nodes for IGA/RRC Intermediate-Stay Housing and Automated Accountability Vetting



Homeless Vets Welcome Here: AI City Hall Project Moves on Omni-AI Microcities



Photos accompanying this announcement are available at:

https://www.globenewswire.com/NewsRoom/AttachmentNg/396f6f1b-701f-4e94-9cab-421541b04740

https://www.globenewswire.com/NewsRoom/AttachmentNg/a9b80074-09e3-4b86-911a-e65d2a0beba2

A video accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/bcc9f18c-6991-4ad4-b712-83fad4e19e6f

UPDATE: QAIAx Clinical Trial: AGI Alternative Sentencing & Reality Pro-Social Dating (Veterans First for America)

According to a fresh analysis, the potential for artificial intelligence (AI) to replicate human-like thinking may be fundamentally flawed due to the inability to program the most vital components of human intelligence into machines.

Peter J. Denning, a distinguished computer scientist, critiques a notion proposed by Alan Turing, considered the father of modern computer science, suggesting that this idea has misdirected AI research for the past 75 years. In his recent work, "Turing’s Mistake: Escaping the Yoke of Unintelligent Machines," Denning explores Turing’s assertions from 1950 that posited human intelligence could exist outside the physical body, possibly enabling its recreation as software on a computer.

Denning challenges the approach claiming that machine intelligence can be gauged through an imitation game, commonly recognized as the Turing test. He argues that accepting these two notions has led to the chaotic state of AI development observed today.

The current AI systems under development, he contends, are unlikely to achieve human-level intelligence, often referred to as artificial general intelligence (AGI). Instead, he warns that such systems could pose substantial risks without ever matching human cognitive abilities.

At the core of Denning’s argument lies the concept of tacit knowledge, which encompasses the vast understanding that humans carry but cannot fully articulate or convert into a format that machines can comprehend. He identifies five categories of tacit knowledge that he believes evades machine learning: common sense, interactions with people and surroundings, emotions and perceptions, practical skills, and shared cultural and historical contexts across societies.

For decades, researchers have sought ways to codify common sense for computer use. Douglas Lenat’s ambitious Cyc project, which began in the 1980s, aimed to create an extensive database of common-sense facts, eventually amassing 25 million entries after four decades. However, Denning points out that this extensive repository did not yield a sufficient base of common sense for expert systems to function intelligently, indicating that much of what constitutes human expertise cannot be boiled down to simple propositions.

Denning elaborates on the challenge of practical skills, asserting that while it is often feasible to describe outcomes ('know what'), encoding the embodied knowledge necessary for performance ('know how') remains elusive. He uses music as an example, noting that a virtuoso violinist may deliver exquisite performances yet lack the ability to articulate how to achieve that mastery.

The core issue, which Denning labels 'the representation problem,' stems from computers' need for data and instructions in recognizable formats. Tacit knowledge, however, is difficult to translate into such structures. He adds that words are mere symbols representing meanings, and prevalent Large Language Models like ChatGPT, Claude, and Gemini manipulate these words without comprehending their underlying meanings.

Denning highlights the significance of context, illustrating how statements can vary widely in meaning based on the speaker’s emotions—whether they are being sincere, sarcastic, or playful. Understanding the context often involves tracing back through prior conversations, creating an endless, intricate network of meanings.

Culture represents another complex hurdle, encompassing values, social norms, histories, and group dynamics. Denning believes that merely enlarging Large Language Models will not enable them to acquire the embodied knowledge integral to understanding culture and context. Consequently, he states that LLMs will likely fail to fulfill the Turing test's objective: achieving machine thought indistinguishable from human thought.

He ultimately articulates a mutual incomprehension between humans and machines, noting that while artificial neural networks may be capable of forming their unique type of tacit knowledge, humans may remain unable to interpret it. This divide poses significant challenges for AI safety. Denning cautions that if machines cannot grasp the nuanced context of human directives, aligning their behavior with human objectives may become unfeasible.

As AI automates tasks, networks of machines are likely to cultivate their own types of intelligence—ones that differ from human intelligence but can still threaten human well-being. Denning argues that the most pressing danger isn't an advanced superintelligent machine but rather a network of less advanced systems acting in unpredictable and potentially harmful manners.

He urges a recognition of the fading familiar culture as intelligent machines integrate into society, emphasizing the need to retain our humanity and assert what distinguishes us from machines. Celebrating these distinctions is crucial as we navigate this emerging landscape of AI technology.

Reasons AI Might Never Achieve Human-Level Intelligence

In a shadowy research lab, a softly glowing organic human brain rests on a pedestal under warm light; opposite it floats a half-built mechanical brain of wires and silicon, its central piece dangling from a robotic arm that can’t quite fit it, dark moody shadows underscoring the unbridgeable gap.

Why AI May Never Reach Human Intelligence - SciTechDaily

- New analysis argues AI may never replicate human thinking due to tacit knowledge.  
- Denning critiques Turing's ideas, stating they've misdirected AI research for decades.  
- Machine intelligence poses safety risks, potentially creating unpredictable challenges for humanity.  

A new analysis argues that AI may never truly think like humans because the most important parts of human intelligence cannot be programmed into machines.

A prominent computer scientist argues that a proposal made by Alan Turing, widely regarded as the father of theoretical computer science, sent artificial intelligence research in the wrong direction for the past 75 years.

In his new analysis, “Turing’s Mistake: Escaping the Yoke of Unintelligent Machines,” Peter J. Denning examines ideas Turing advanced in 1950. At the time, many scientists believed that human intelligence could exist independently of the body and might therefore be recreated as software running on a digital computer.

Denning also disputes the idea that machine intelligence can be demonstrated through an imitation game (now 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.”

According to Denning, the artificial intelligence (AI) systems now being developed are unlikely to produce human-level intelligence, known as artificial general intelligence (AGI). Instead, he warns, they may create serious dangers without ever thinking like humans.

Why Tacit Knowledge Matters

Central to Denning’s argument is the idea of tacit knowledge. This refers to the enormous amount of human understanding that people possess but cannot fully express in words or translate into symbols that a machine can process.

Denning describes five broad forms of tacit knowledge that he says ‘elude machine learning’. They include common sense, everyday interactions with people and the environment, feelings and perceptions, practical skills, and the cultural and historical background shared by societies.

Researchers have spent decades trying to record common sense in a form computers can use. Beginning in the 1980s, Douglas Lenat’s ambitious Cyc project set out to build a vast database of common-sense facts. After 40 years of work, the project contained 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.”

Knowing What Is Not the Same as Knowing How

Practical skill creates another major obstacle, Denning argues.

“Our performance skills in thousands of domains cannot be communicated to machines,” Denning 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’).”

Music offers a clear example of this difference. Denning says: “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.”

Intuition, gut feelings, spontaneous creativity, and imagination are other forms of tacit knowledge that resist being reduced to computer instructions.

The Representation Problem

Denning calls the central obstacle ‘the representation problem’.

Computers can only perform calculations when data and instructions are encoded in physical forms they can recognize and process. Tacit knowledge, however, cannot easily be converted into such a format.

“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.”

This creates what Denning sees as an unbridgeable gap. Because scientists do not fully understand how tacit knowledge operates within humans, they cannot determine how to transfer it to a machine.

“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.”

Why Context Changes Meaning

Denning also stresses the importance of context, or the surrounding circumstances that give human words and actions their meaning and purpose.

A statement can mean very different things depending on whether the speaker is sincere, sarcastic, angry, playful, or teasing. Context also helps people decide when to use humor, when to show tact, and how to interpret what someone leaves unsaid.

“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 May Be Beyond Large Language Models

Culture presents a related challenge. It includes values, social norms, judgments, histories, communities, moods, and relationships involving power or care.

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

He argues that making large language models larger will not solve this problem.

“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.”

Denning ultimately describes a form of mutual incomprehension between people and machines. Artificial neural networks may develop their own kind of machine tacit knowledge, but humans may be unable to understand it.

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

The AI Safety Risk

This divide could have major consequences for AI safety. If machines cannot understand the unstated context behind human instructions, Denning warns that reliably aligning their behavior with human goals may be 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.

In Denning’s view, the most immediate danger is not a superintelligent machine that surpasses humanity. It is a network of less intelligent systems that acts in powerful, unpredictable, and potentially harmful ways.

“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.”

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Why AI May Never Reach Human Intelligence

The White House is reportedly considering the establishment of a dedicated regulatory body aimed at ensuring the safety of artificial intelligence technologies.

A recent report from Bloomberg News indicated that this initiative stems from concerns raised by Silicon Valley regarding the government's recent restrictions on new AI frameworks from notable companies like Anthropic and OpenAI. 

According to sources familiar with the discussions, the proposal—under review by White House Chief of Staff Susie Wiles—would create an independent regulator for AI, which would report to the Securities and Exchange Commission (SEC). This structure is expected to mirror that of the Financial Industry Regulatory Authority (FINRA). 

Bloomberg's coverage suggested that this move could offer greater clarity for companies like Anthropic and OpenAI, which have faced challenges due to government-imposed export controls. Recently, Anthropic was forced to temporarily halt its Fable 5 and Mythos 5 models, while OpenAI made significant adjustments to its Sol model prior to its launch, following government guidance.

Both companies expressed their dissatisfaction with these regulatory actions, arguing that the government's measures were excessive in relation to the associated safety concerns.

Sources indicated that this regulatory proposal aims to address the aspirations of Wall Street firms looking to mitigate cybersecurity threats posed by AI, as well as technology companies that perceive the White House's current regulatory stance as inconsistent.

The proposal emerged just days after Google DeepMind CEO Demis Hassabis advocated for the creation of a U.S.-led standards organization tasked with independently testing advanced AI models for potential national security risks before their deployment. He emphasized that artificial general intelligence, which could rival human cognitive abilities across various tasks, is likely just a few years away, presenting a limited opportunity to establish necessary oversight.

Hassabis suggested that a U.S.-led entity could set international benchmarks as governments work to manage the risks associated with rapidly advancing AI systems.

Earlier in June, the White House had already begun implementing measures for prerelease oversight, issuing an executive order that called for voluntary access to certain frontier models for federal evaluation up to 30 days before their release. Companies such as Google DeepMind, Microsoft, and xAI agreed to provide their models for national security assessments.

In other developments related to AI, a report from PYMNTS highlighted the growing array of job titles in the field, offering a counter-narrative to discussions focused on AI-driven job losses. It noted that while concerns about job elimination are prevalent, hiring data reveals a more nuanced situation, showcasing a weak overall labor market yet a rapidly expanding sector of AI-related employment opportunities.

For continued updates on AI developments, consider subscribing to the daily AI Newsletter for comprehensive coverage.

White House Considers Regulator to Oversee AI Models

A solemn, dimly lit scene: in the foreground, brass scales hold a glowing golden neural-network sphere on one pan and a wooden judge’s gavel on the other; beyond them, the White House façade looms under warm lamplight, a single American flag waving to the side, long shadows stretching across a dark marble floor.

White House Weighs Regulator to Police AI Models - PYMNTS.com

- The White House is considering a new regulator focused on AI safety and oversight.  
- Complaints from Silicon Valley prompted the proposed regulatory changes regarding existing AI models.  
- The regulator would report to the SEC, similar to the Financial Industry Regulatory Authority.  

The White House is reportedly exploring plans to create a regulator focused on artificial intelligence safety.

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That’s according to a report Friday (July 17) from Bloomberg News, which said this plan follows complaints from Silicon Valley about the government’s recent curbs on new AI models from companies like Anthropic and OpenAI.

Sources familiar with the matter told Bloomberg the proposal, now being reviewed by White House Chief of Staff Susie Wiles, would establish an independent regulator focused on AI and reporting to the Securities and Exchange Commission (SEC), similar to the Financial Industry Regulatory Authority (FINRA).

Bloomberg noted that this approach could provide more certainty for Anthropic and OpenAI. The former company was issued export controls last month that caused it to temporarily disable its Fable 5 and Mythos 5 models, while the latter made major changes to its newest Sol model before release, at the government’s request.

Both OpenAI and Anthropic, the report added, protested the government’s decisions, saying they were excessive in light of the safety issues involved.

Bloomberg’s sources said the proposal is designed to appease Wall Street firms hoping to lessen AI’s cybersecurity risks, as well as tech companies who think the White House’s approach to regulating AI has been inconsistent.

The news comes days after Google DeepMind CEO Demis Hassabis called for a U.S.-led standards body to independently test advanced AI models for national security threats prior to their release.

Artificial general intelligence capable of matching the human brain across a broad range of tasks is “probably only a few short years away,” leaving society a “precious window” to establish oversight, Hassabis said, per a Financial Times report.

Hassabis proposed modeling the organization on FINRA, and said a U.S.-initiated systems could become the basis for international standards as governments confront risks from increasingly capable systems, according to the FT report.

As PYMNTS wrote, the White House has already undertaken some prerelease oversight. An executive order issued in June sought voluntary access to covered frontier models for up to 30 days prior to release to gauge advanced cyber capabilities. Google DeepMind, Microsoft and xAI had agreed to submit models for federal national security testing.

In other AI news, PYMNTS wrote Saturday (July 18) about the expanding range of AI-related job titles, and how it provides a counterpoint to the “darker debate” regarding employment and artificial intelligence.

“Most discussion centers on how many jobs AI will eliminate,” that report said. “Hiring data presents a more complicated picture that includes a weak overall labor market containing a small but rapidly growing neighborhood of AI-related work.”

For all PYMNTS AI coverage, subscribe to the daily AI Newsletter.

White House Weighs Regulator to Police AI Models

The rapid evolution of artificial intelligence has led to the development of sophisticated language models that can craft essays and write code, as well as intelligent systems that manage tasks in digital spaces. However, computer scientist Peter J. Denning presents a thought-provoking perspective in his latest book, *Turing’s Mistake: Escaping the Yoke of Unintelligent Machines*. He posits that AI might be striving toward an unattainable goal: to create machines that seem increasingly adept while lacking true comprehension of the human experience.

Denning’s analysis critiques two key assumptions inherited from Alan Turing's seminal 1950 essay, “Computing Machinery and Intelligence.” Turing shifted the focus from the challenging question of whether machines can think to a more practical one embodied in the Turing test, also known as the "imitation game." Denning argues that this approach obscured an important distinction: the difference between mimicking intelligence and possessing actual understanding. He contends that Turing's framework fostered the illusion that intelligence could be divorced from human experience and instantiated in software, leading to the misinterpretation that a machine's ability to simulate human dialogue equates to genuine intelligence.

Denning's central thesis is quite stark: he suggests artificial general intelligence (AGI) might be fundamentally unattainable, as crucial elements of human intelligence are inherently untranslatable into machine algorithms. 

At the heart of Denning's critique lies the concept of tacit knowledge. This encompasses the extensive and often ineffable body of understanding unique to human beings, which includes innate knowledge and cultural perceptions that are hard, if not impossible, to codify. Such knowledge inherently involves common sense, practical skills, emotional intelligence, and cultural nuances. Humans intuitively grasp subtleties like nonverbal signals, the context of humor versus offense, or navigating social situations—all of which elude formal rules and reside in lived experiences.

Denning asserts that current machine learning technologies are incapable of capturing five essential categories of tacit knowledge: common sense reasoning, interpersonal interactions, emotional and sensory awareness, performance skills, and cultural knowledge. This limitation is particularly significant given that today's AI advancements heavily depend on systems designed for pattern recognition in language and data. While large language models can produce coherent and convincing text, Denning argues they do so by manipulating symbols without truly grasping their underlying meanings.

The quest to encode common sense into machines is not new. One of the most notable attempts was Douglas Lenat's Cyc initiative in the 1980s, which intended to create an extensive database of everyday knowledge. However, Denning highlights that even substantial volumes of formal information fail to confer the necessary comprehensive understanding to develop effective expert systems. The challenge transcends mere data volume; if the essential ingredient is experiential context rather than raw information, adding more data won't suffice.

Denning illustrates this with the example of expertise in music. A gifted musician may deliver exceptional performances yet struggle to articulate, in a formalized way, the intricacies of their art. While outcomes can be described, the nuanced, embodied skills cannot be easily translated into machine-compatible directives, leading to what Denning describes as a representation problem. Unlike machines that process encoded data, human intelligence relies significantly on capabilities that resist straightforward coding.

Moreover, Denning emphasizes the significance of context in human communication. Conversations are layered with meanings that depend heavily on the relational and situational background, which AI struggles to navigate. A statement that is funny in one scenario might come off as disrespectful in another. As such, a machine might generate a contextually adequate sentence without genuinely understanding the surrounding human dynamics.

Culture further complicates the issue. Denning describes culture as encompassing a range of elements, including values, norms, past experiences, community dynamics, and judgements, which cannot readily be distilled into data sets. While scaling up language models might enhance their functionality, Denning cautions that mere expansion will not endow machines with the needed cultural depth and understanding.

His concerns extend beyond the prospect of AGI being impossible; Denning warns about the potential of AI systems becoming highly capable yet socially disruptive, operating outside the realm of human-like comprehension. This represents a stark distinction: the focus of many discussions around AI ethics tends to center on the risks posed by advanced superintelligence. In contrast, Denning's immediate concern is that networks of automated systems may develop forms of intelligence that do not align with human values and understanding, leading to a rigid application of machine logic that may not benefit society.

In essence, the real danger may not stem from machines becoming too human-like, but rather from their potential impact while remaining distinctly non-human. If AI systems lack the ability to appreciate the subtleties of human meaning, aligning their operations with human objectives will prove challenging. While these automated systems may follow instructions correctly, they might miss the nuanced expectations of human interaction. Consequently, institutions may begin to shape human behavior based on the capabilities of machines rather than the true needs of people.

Denning's work is not an indictment of computing or the development of useful AI technologies. Instead, it challenges the notion that humans are mere information-processing entities awaiting replication. His practical message advocates for caution in handing over decision-making power to systems that emulate intelligence without embodying the critical elements of care, empathy, and human experience. While AI can perform operations such as classification, prediction, summary, generation, and automation, Denning argues that these abilities should never be mistaken for true wisdom or human understanding.

Analysis: Has AI been pursuing the wrong objective since Alan Turing?

An antique study bathed in warm morning light: on a mahogany desk lies a yellowed parchment of Turing machine schematics, overlaid by a faint ethereal neural-network pattern. In the background, a lone robot silhouette strides toward a softly glowing archway just beyond reach, hinting at the pursuit of an elusive dream.

Review: Has AI been chasing the wrong dream since Alan Turing? - Digital Journal

- Peter J. Denning argues AI lacks true understanding despite mimicking human capabilities.  
- Denning identifies tacit knowledge as unencodable, limiting AI's ability to achieve general intelligence.  
- AI systems may disrupt society while remaining fundamentally different and alien from human intelligence.  

Artificial intelligence has advanced at a remarkable pace, from large language models that can generate essays and computer code to agentic systems capable of coordinating tasks across digital environments. Yet a new argument from computer scientist Peter J. Denning suggests that AI may be pursuing an impossible objective: machines that appear increasingly capable but never truly understand the human world.

In his new book, Turing’s Mistake: Escaping the Yoke of Unintelligent Machines, Denning argues that modern AI has inherited two flawed assumptions from Alan Turing’s influential 1950 paper, “Computing Machinery and Intelligence.” Turing proposed replacing the difficult question “Can machines think?” with the more operational “imitation game,” now widely known as the Turing test.

Denning’s challenge is that the test blurred a crucial distinction: the difference between imitating intelligence and possessing understanding. According to Denning, Turing’s ideas encouraged the belief that intelligence could be separated from the human body and recreated in software, and that a machine’s ability to imitate human conversation could be treated as evidence of intelligence.

This, Denning argues, has shaped three-quarters of a century of AI research. His central claim is stark: artificial general intelligence, or AGI, may not be achievable because the most important parts of human intelligence cannot be encoded into machines.

The tacit knowledge problem

At the centre of Denning’s critique is the concept of tacit knowledge. This is the vast domain of human understanding that is difficult, and perhaps impossible, to formalise. It includes common sense, practical know-how, intuition, emotional recognition, cultural awareness, and the subtle contextual judgements people make every day. Humans know how to interpret a raised eyebrow, a sarcastic remark, an awkward silence, or the difference between a joke and an insult. Much of this knowledge is not stored as explicit rules. It is embodied, social, historical, and learned through participation in human life.

Denning argues that machine learning systems cannot capture five major categories of tacit knowledge: common sense, everyday interactions with people and environments, feelings and perception, performance skills, and the social and historical knowledge embedded in culture. This matters because the current AI boom relies heavily on systems that process patterns in language and data. Large language models can generate fluent and convincing text, but Denning argues that they manipulate symbols rather than grasp meanings. In his view, words are not the same as the human experiences and assumptions that give words significance.

Why common sense resists computation

The history of AI contains repeated attempts to encode common sense into machines. One of the most ambitious was Douglas Lenat’s Cyc project, which began in the 1980s and sought to build a large database of everyday knowledge. Denning notes that after decades of effort, even millions of formal entries could not amount to the background understanding needed to make expert systems truly expert. This is not simply a question of scale. Adding more rules or training data may not solve the problem if the missing ingredient is not information but lived experience.

Denning gives the example of skill. A virtuoso musician may perform beautifully but cannot fully explain, in propositional form, how to create that performance. Descriptions of outcomes can be written down, but embodied know-how cannot easily be converted into machine-readable instructions. This creates what Denning calls a representation problem. Computers operate on data that has been encoded into formal structures. Human intelligence, by contrast, depends heavily on capacities that do not naturally present themselves as code.

Denning’s argument also places strong emphasis on context. Human conversation is never purely literal. Meaning depends on setting, prior experience, relationship, tone, shared assumptions and cultural background. A statement that is humorous in one context may be rude in another. A technically correct answer may be inappropriate if it ignores the social situation. A machine may generate a plausible sentence while failing to understand the human context in which the sentence will be received.

Culture deepens the problem. Denning describes culture as involving values, norms, history, communities, power, care, and judgement. These are not easily reducible to training datasets. Scaling up large language models with larger neural networks may improve performance, but Denning argues that scale alone will not provide machines with embodied cultural understanding.

The concern is not only that AGI may be impossible. Denning warns that AI systems may become powerful and socially disruptive without becoming genuinely human-like. This is an important distinction. Many debates about AI safety focus on hypothetical superintelligence. Denning’s concern is more immediate: networks of automated systems may develop forms of machine intelligence that are alien to human concerns, difficult to interpret, and capable of imposing rigid machine logic on society.

In other words, the danger may not be that machines become too much like humans. The danger may be that machines become influential while remaining profoundly unlike humans. If AI systems cannot understand tacit human meanings, then aligning them reliably with human goals becomes difficult. Instructions may be followed literally but inappropriately. Automated systems may optimize measurable targets while ignoring unspoken values. Institutions may increasingly shape human activity around what machines can process, rather than what people actually need.

Reasserting the human difference

Denning’s book is not an argument against computing or against useful AI tools. Rather, it is a challenge to the assumption that human beings are merely information-processing machines waiting to be replicated.

The practical message is that society should be cautious about surrendering judgement to systems that simulate intelligence but lack care, understanding, responsibility and embodied human experience. AI can classify, predict, summarize, generate and automate. These are powerful capabilities. But Denning’s argument is that such capabilities should not be confused with wisdom, common sense or human understanding.

Review: Has AI been chasing the wrong dream since Alan Turing?

Notable economist Nouriel Roubini, renowned for predicting the 2008 financial crisis and dubbed “Dr. Doom” for his cautionary forecasts, has recently adopted a more optimistic outlook. This shift is especially evident in his perspective on artificial intelligence, a field rife with dire forecasts about potential societal upheaval.

In a recent interview with Bloomberg TV, Roubini shared his thoughts on the urgent need to reform Social Security, particularly since the trust fund that supports benefits is projected to be depleted by 2032. He pointed out that as AI and robotics are set to take over a significant portion of jobs within the next two to twenty-five years, simply raising the retirement age will not suffice to address the mounting challenges.

“To truly adapt, we will need to implement some form of universal basic income both during employment and after retirement,” Roubini stated. “We're already moving in that direction.”

As the workforce faces impending disruptions from AI, industry leaders—including Sam Altman, CEO of OpenAI—have floated ideas about governments potentially providing financial guarantees to citizens. Though Altman has since retracted some of these proposals, the concept of universal basic income remains in the conversation. Earlier this year, the U.K.’s investment minister mentioned that the government is considering this approach to assist workers in sectors likely to be affected by AI.

Roubini emphasized the significance of the AI revolution, labeling it the most impactful technological transformation in history. He envisions a future where artificial general intelligence could match or surpass human intelligence, leading to exponential economic advancements. He anticipates that GDP growth could soar from a range of 2%-4% by the end of this decade to 6% by 2040 and reach an astonishing 10% by 2050. This growth, he argues, will enable the government to levy taxes on the successful companies and redistribute wealth more broadly.

“We’ll have either universal basic income or some form of socialism,” Roubini explained. “The government may need to assume a stake in some of the major tech firms.”

He noted that AI businesses are already showing willingness to collaborate with the government, pointing to a Financial Times report suggesting that OpenAI has considered offering a 5% stake to allow the public to benefit from AI advancements. Although Altman's proposal would encourage other AI developers to follow suit, it remains uncertain whether competitors in the U.S. would agree.

Roubini is convinced that the adoption of universal basic income or socialist measures is on the horizon, remarking, "We are heading in that direction."

When asked about his forecasts, he rejected any notion of pessimism, describing his outlook as optimistic, particularly in light of potential 10% growth rates with “machines handling the workload.”

This perspective resonates with ideas articulated by Elon Musk, CEO of SpaceX and Tesla, who envisions a future where the advancements in AI and robotics will render traditional work optional. Musk suggested that in less than two decades, individuals may prefer to engage in personal activities like gardening instead of adhering to conventional employment, asserting, “It will be optional, in that way, is my prediction.”

Nouriel Roubini views universal basic income or 'a variant of socialism' as necessary due to AI transforming employment.

In warm natural dawn light, a row of silent humanoid robots on an open-air assembly line dominates one half of a city square, while on the other, a diverse group of human workers clutch glowing red cards stamped “UBI” beneath a bold banner reading “Some Form of Socialism.” Long shadows and wisps of steam rising from the machines hint at an AI-driven upheaval transforming work and society.

'Dr. Doom' Nouriel Roubini says we're headed for universal basic income or 'some form of socialism' as AI revolutionizes work—He calls that optimistic - Fortune

- Economist Nouriel Roubini predicts AI will replace many jobs in 20-25 years.  
- He advocates for universal basic income to support workers facing AI disruption.  
- Roubini believes AI will drive significant economic growth, with potential 10% GDP by 2050.  

Top economist Nouriel Roubini, who was among the first to predict the 2008 financial crash, earned the nickname “Dr. Doom” for his stark warnings, though he has been more upbeat lately.

And when it comes to artificial intelligence, which has stirred all kinds of apocalyptic predictions, he insists he’s looking at the brighter side.

In an interview on Bloomberg TV on Friday, Roubini was asked about ways to fix Social Security, as the trust fund that helps fund benefits is due to run out of money by 2032.

He replied that a large chunk of the population will be replaced by AI and robots in the next 20-25 years, so raising the retirement age will not be sufficient.

“Eventually, we need some form of universal basic income for everybody while they work and once they retire,” Roubini added. “We’re already on the way.”

With AI poised to disrupt the labor market, top tech leaders like OpenAI CEO Sam Altman have suggested governments might provide a guaranteed amount of money to people, though he has since backed away from the idea.

Still, the idea persists, and the U.K.’s minister for investment said early this year that the government is weighing the introduction of a universal basic income as a means to support workers in industries where AI threatens to displace them.

In Roubini’s view, the AI revolution is the most important in human history in terms of tech innovation, and it will develop into artificial general intelligence, meaning AI will match or exceed human cognitive capabilities.

That will unlock massive economic growth, he said, with GDP accelerating from 2%-4% by the end of the decade to 6% by 2040 and 10% by 2050. At that point, the government can tax the “winners” and redistribute the money to everyone else.

“We’ll have either ex-post distribution—that is universal basic income—or we’ll have it ex-ante. Ex-ante means some form of socialism,” Roubini explained. “Essentially, the government is going to take over some fraction of the big tech firms.”

He said AI companies are already willing to hand over stakes to the government, alluding to a Financial Times report that said OpenAI has discussed giving 5% as a way for the public to share in the upside of AI.

Altman’s proposal would entail other AI companies providing similar stakes, although it’s not clear if his U.S. rivals would be willing to do so.

As a result, Roubini believes universal basic income or socialism is inevitable, saying “We’re going already in that direction, effectively.”

When pressed on his forecast, he denied that it’s gloomy. On the contrary, he called it optimistic, given that it assumes 10% growth and “machines doing all the work.”

That vision echoes what SpaceX and Tesla CEO Elon Musk has laid out. He thinks that in less than 20, AI and robotics will advance to the point where working becomes optional.

“You can grow your own vegetables in your garden or you could go to the store and buy vegetables,” he said on an episode of the People by WTF podcast in December. “It’s much harder to grow your own vegetables. But some people like to grow their vegetables, which is fine. But it’ll be optional, in that way, is my prediction.”

Nouriel Roubini sees universal basic income or 'some form of socialism' as AI revolutionizes work

The conversation surrounding artificial intelligence (AI) often gravitates toward grand concepts such as the potential emergence of artificial general intelligence (AGI) and superintelligence. Speculation persists about the technology's potential to disrupt job markets or even alter the essence of human creativity. However, less attention has been given to the myriad subtle yet deeply impactful ways in which AI is transforming our social structures and shaping our collective vision of the future.

Mona Sloane, a sociologist and AI researcher who serves as an assistant professor of data science and media studies at the University of Virginia, emphasizes this point in her forthcoming book, "Predicted: How AI Is Restructuring Social Life" (University of California Press, 2026). She argues that AI is intricately woven into our daily digital interactions—be it through email filtering, prediction markets, or social media platforms. This pervasive integration has fostered a new form of "prediction logic" that influences both our identities and expected behaviors.

In a passage from her book, Sloane draws parallels between modern AI technology and the ancient oracles of Greece, suggesting that today’s AI acts as a powerful force organizing society around predictive models. This evolution impacts various dimensions of life, including how we learn, engage with others, and envision the future.

We now inhabit a landscape dominated by these modern "oracles," which continuously generate predictions that inform our social interactions—affecting how we socialize, love, work, and access resources. In contemporary society, the influence of predictive analytics is so significant that it can sway the outcomes of entire economies and alter geopolitical dynamics. The locus of authority now lies with these technological predictors rather than the spiritual leaders of ancient times.

Contrary to the high priests of yore, today's oracles are AI systems seamlessly embedded in our daily routines. They have become inescapable, influencing our lives in both conscious and unconscious ways. I find myself relying on AI for tasks that range from email management—with spam filters relying on predictive technology—to navigating online banking with automated fraud detection. AI has integrated itself into my life; it assists with everything from generating spreadsheet templates to streamlining documents for reports. Yet, these interactions are not always frictionless. I often find myself meticulously reviewing AI outputs, occasionally abandoning its suggestions in frustration to tackle tasks manually.

The omnipresence of AI predictions can lead us to view these systems as unavoidable aspects of our existence, rather than recognizing them as constructs born from collective societal choices about how to interpret and engage with our surroundings.

As philosopher Rudolf Carnap noted in 1966, "Quantitative concepts are not given by nature; they arise from our practice of applying numbers to natural phenomena." This underscores how numerical data enables us to communicate complex information and fosters predictive capabilities.

Carnap suggested that such a quantitative approach primarily served the development of modern engineering and technology. By rigorously understanding the behavior of energy, materials, and compounds, humans achieved remarkable innovations like airplanes and telephones. In his view, prediction was merely a practical tool.

Fast forward nearly six decades, and AI has flipped this perspective. Prediction has transcended its role as a mere utility in engineering and has begun to dictate social organization. This paradigm shift raises important questions; it suggests that reliance on AI may be viewed as not just convenient but necessary, potentially overshadowing the broader societal forces that facilitate this perception.

AI systems don’t merely reflect natural occurrences; they embody collective societal expressions. They aren't mere deceptions crafted by tech elites, but rather indicators of a profound shift in societal structure and imagination. While criticism of AI often highlights issues like increased surveillance and capitalist exploitation, this view is overly narrow. The more significant consequence is the comprehensive shift towards a "prediction paradigm," which calibrates societal organization around predictive logic.

AI has become an integral aspect of our social infrastructure, shaping interpersonal dynamics and influencing public and private behaviors. Like all infrastructures, AI channels resources and ideas in certain directions while constraining others. By analyzing historical data, AI predicts individual futures, enforcing a linear narrative that prioritizes causality: the past dictates the future. However, the primary concern isn't the rise of intelligent machines but rather the social implications attributed to this linear narrative, which overly idolizes the future and stifles discussions about alternative possibilities.

This excerpt is adapted from "Predicted: How AI Is Restructuring Social Life" by Mona Sloane, published by the University of California Press in 2026.

'A perilous idea': The impact of AI on societal dynamics and our collective visions for the future

A dark, stormy courtyard where a massive, tattered tapestry embroidered with glowing circuit-like threads is stretched between two crumbling stone pillars. Its threads unravel into fractal code that seeps across the cracked floor, and a circle of adult silhouettes stands around it, their forms warped by the flickering torchlight and ominous orange sparks crackling in the sky—symbolizing AI tearing apart the social fabric and fracturing our shared vision of the future.

'A dangerous proposition': How AI is warping the social fabric and the ways we collectively imagine the future - Live Science

- AI is reshaping social life, influencing how we interact and envision the future.  
- The technology creates a "prediction logic" that affects various aspects of daily life.  
- Sloane argues that AI's impact extends beyond surveillance to societal organization and prediction.

Much of the discourse around artificial intelligence (AI) focuses on grand ideas such as the rise of a hypothetical artificial general intelligence (AGI) and superintelligence . Speculation swirls around the likelihood that the technology will thin out the job market, or even precipitate the death or evolution of human creativity . We haven't focused as much on the multitude of subtle yet hugely consequential ways in which AI is reshaping the social fabric of our society, and how we collectively imagine the future.

That's the argument sociologist and AI researcher Mona Sloane , an assistant professor of data science and media studies at the University of Virginia, puts at the center of her new book, " Predicted: How AI Is Restructuring Social Life " (University of California Press, 2026). Whether we consider email filtering, prediction markets or social media platforms, AI systems are embedded in the heart of how we interact with the digital world. Indeed, AI is so ubiquitously integrated into everyday interfaces that it's given rise to a new kind of "prediction logic" that makes assumptions about who we are and how we are likely to behave.

In this excerpt, Sloane compares the AI technology we use today with the oracles of ancient Greece, framing it as an omnipotent presence that has moved to organize society through the prism of prediction models. This in turn affects how we learn, live, love and even picture the future.

We live in a world of oracles. These oracles constantly feed us predictions that shape our social lives — how we socialize, love, work, gain access to resources. Like in ancient Greece, predictions occupy a prominent role in our society. We consider our oracles so mighty that their predictive power rules over the fate of whole economies and even geopolitical constellations. Where the oracle is, there is the center of the world.

But unlike in ancient Greece, our oracles aren't high priestesses delivering divine prophecies. They are artificial intelligence (AI) systems melted into the infrastructure of everyday life. Today, it is nearly impossible to evade the grasp of AI predictions. I voluntarily and involuntarily use AI on a constant basis: by using email providers that build on the predictive properties of AI for spam filters, by conducting online banking and getting enrolled into AI-automated fraud detection, or by using generative AI for supporting administrative chores. It has become part of how I experience the world.

It can be a relief when it helps me do things I dread or am bad at, such as produce a spreadsheet template I desperately need, help streamline language produced by different authors for a report, or generate a specific image for a presentation. Often, I must intently handhold the AI, checking and fixing its outputs. And sometimes, with deep frustration, I give up and start all over to complete my task manually.

The omnipresence of AI prediction can make it easy to think of these systems as inevitable, quasi-natural phenomena we are subject to, rather than a part of. But they are quantitative concepts that arise from social agreements about how we ought to capture and interpret the world around us.

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"Quantitative concepts are not given by nature: they arise from our practice of applying numbers to natural phenomena," wrote Rudolf Carnap, a logician and professor of philosophy of science, in 1966. His point was that numbers can be useful, because they allow for information to travel more easily across contexts, as a sort of language. They also make mathematical predictions possible.

To him, this was first and foremost useful for engineering modern life: A quantitative language allows for the articulation of quantitative laws that, in turn, facilitate the routine generation of mathematicized predictions, particularly in the realm of physics. Being able to predict how energy, compounds, and materials will behave in certain configurations is the reason humans were able to build the conveniences of airplanes, cars, and telephones. For Carnap, predictions were simply instrumental in this way.

AI's ability to predict is changing how we think about the future. (Image credit: Yana Iskayeva via Getty Images)

Today, almost 60 years later, this pragmatic approach to mathematical prediction has been turned on its head by AI. Prediction is no longer just a handy tool in physics or engineering. The promises of AI's oracular power have turned prediction into a logic for structuring social life. This is a dangerous proposition. It implies that AI is always necessary or even inevitable and diverts attention from the social forces shaping ideas around this technology in the first place.

AI systems are not natural phenomena that happen to us. They are collective expressions of society. As such, they are not just a hype or a deception concocted and executed by global tech elites. They indicate a wider shift in how we imagine and enact society. Many critical discussions of AI characterize this phenomenon chiefly as heightened surveillance and capitalist extraction. But this is a myopic diagnosis. AI's most powerful effect is the subtle yet comprehensive recalibration toward prediction as a guiding principle for organizing society. In this book, I call this phenomenon the prediction paradigm.

AI is something that we do as part of going about our lives and participating in society — it is social infrastructure, affecting how we relate to one another and how we act in public and in private. Like all infrastructures, AI allows resources and ideas to flow in certain directions, but not others. AI uses data from our collective past to predict our individual future. And because AI deals in futures, it solidifies a linear time regime that hardens our social commitment to causality: The past always predicts the future. The problem of AI is not the rise of intelligent machines, but the extraordinary social significance ascribed to this linearity, fetishizing the future and leaving little room for deliberations about what (other) futures may be possible or we may want.

Reprinted from Predicted: How AI Is Restructuring Social LIfe by Mona Sloane, courtesy of the University of California Press. Copyright 2026.

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