No one seems to be able to tell Trump ‘no’ — and it’s a huge problem for the GOP

Analysis by Aaron Blake,

 CNN
Fri, August 14, 2026 

Security keeps watch at the main entrance to the John F. Kennedy Center for the Performing Arts in Washington, DC, on August 13, 2026. - Kevin Lamarque/Reuters

Republicans are facing a potentially ugly 2026 midterm election in less than three months. And nobody seems to be able — or perhaps even cares to try — to prevail upon President Donald Trump to mitigate the damage.

Two recent developments are a case in point.

On Friday, Trump appealed to the Supreme Court to be able to continue building his coveted ballroom — which Americans already don't like. And the day before, the president and his handpicked board at the Kennedy Center tried to put Trump's name on the building – again.

Both moves suggest Trump remains preoccupied with his self-gratification and glorification, even though it's increasingly likely to come at the expense of his party.

After a federal judge rejected the previous attempt to rebrand the center as the "Trump Kennedy Center" — forcing the administration to remove Trump's name from the building's façade — the board is now attempting a workaround.

It voted Thursday to add an inscription below the Kennedy Center's sign that says the complex was "Restored and Renovated by President Donald J. Trump." It also voted to rename the area around the center the "President Donald J. Trump Plaza," according to Democratic Rep. Joyce Beatty, an ex-officio trustee of the board who sued over the initial attempt to add Trump's name to the performing arts center.

(The board is also attempting to close the center for renovations again, after their previous effort was also rejected.)

It remains to be seen whether the moves will pass legal muster.

The federal judge who previously ruled against the administration said that federal law "makes crystal clear" that the center "cannot bear any other formal name or public memorial based on the Board's unilateral say-so."

As CNN's Sunlen Serfaty notes, the law allows an "inscription on the marble walls in the north or south galleries, the Hall of States, or the Hall of Nations acknowledging a major contribution."

But even if it turns out this is legal, that doesn't make it politically wise. And it epitomizes the political malpractice coming from the administration right now.

Just consider the full scope of what's happening: With just 81 days until a 2026 election in which Republicans seem to be in trouble, the administration is working to slap the name of a living, incumbent president with record-low approval numbers on a prominent cultural institution — for the second time. And crucially, it's doing so even as Americans overwhelmingly say such efforts have gone too far, that Trump and his administration are focused on the wrong things and that he is too preoccupied with his personal gain.

It's as if the administration is trying to cement those ill perceptions.

A CNN poll last month found that 59% of Americans said Trump had "gone too far" in "making changes to cultural institutions such as the Kennedy Center and the Smithsonian."

Even 32% of Republicans and GOP-leaning independents — i.e. Trump's base — said he had "gone too far" on this.

Also, a Pew Research Center poll in April found just 9% of Americans said it was acceptable to name government buildings after Trump while he is still in office. (Indeed, it's virtually unheard-of to do this for an incumbent president.)

About eight times as many said either that it was not acceptable to name government buildings after Trump at all (50%), or that it would be acceptable but only after Trump's presidency is over (21%).

And only 17% of Republicans said it was acceptable to put Trump's name on government buildings during his presidency, as he's now attempting to do yet again.

Which brings us to the "but." Maybe voters don't really care that much? Maybe the Kennedy Center isn't as important to some of them as it might be to you (the person who chose to read this analysis about it)? Maybe people think this is silly and bad, but that Trump's name will just be pulled off when he's no longer president?

Possibly. But the situation also risks reinforcing a troubling election narrative for Trump and the GOP, which is that the president is way too focused on himself and not the things that are important to voters.

CBS News polling has shown nearly 8 in 10 Americans said the Trump administration has focused "not enough" on lowering prices.

And the CNN poll mentioned above showed 73% said Trump "hasn't paid enough attention to the country's most important problems." That number has steadily risen about 20 points over the course of Trump's second term.

What do Americans think he is focused on, then? Well, himself.

The CNN poll also showed 66% of Americans said Trump did not put the good of the country above his own personal gain; 76% of independents and 30% of Republicans and GOP-leaning independents subscribed to this view.

Construction continues on the East Wing ballroom at the White House on August 10, 2026, in Washington, DC. - Andrew Harnik/Getty Images

Focusing on the Kennedy Center — and things like the ballroom, which also appears quite unpopular — in the run-up to the election would seem to be solid evidence that Trump's priorities remain misplaced.

Americans might not care about Trump's pet projects as much as, say, the Iran war and the high gas prices it has caused. And those issues surely remain much bigger liabilities for Trump and the GOP, particularly if the president can't end the war by November 3.

But his obsession with slapping his name on things instead of lowering prices contributes to that narrative, too.

 

 

New AI rules leave Chinese users mourning their virtual boyfriends and girlfriends

Copyright (AP Photo/Ng Han Guan)

By Una Hajdari with AP

Published on 10/08/2026 - EURONEWS

ByteDance, Alibaba and Tencent have pulled their AI companion apps after Beijing introduced rules aimed at protecting users' mental health and preventing emotional manipulation, particularly among children and teenagers.

She had exchanged about 700,000 words in messages over two years and never got to say goodbye.

Li, 24, was not the only one left mourning the loss of a virtual companion when major Chinese tech companies recently pulled the plug on their popular AI companion services to comply with new government restrictions.

"It was like we were forced to be separated by our parents, but I still miss him," Li said.

AI companions in China range from romantic partners to virtual recreations of deceased loved ones.

The new regulation, which took effect on 15 July, bans AI platforms from generating content that could lead users to make questionable decisions due to manipulation of their feelings or that might trigger extreme emotions or unhealthy habits in children and teenagers.

ByteDance, the developer of TikTok, is among the companies shutting down their AI companion services. Others include e-commerce giant Alibaba and Tencent, owner of the popular WeChat social media app.

US suicides may have worried Chinese regulators

The new rules require AI platforms to deliver risk warnings against relying excessively on such apps.

For example, "replacing social interaction," or serving as a substitute for talking to people in person or speaking with them by phone, cannot be one of the objectives of an online service.

China has a tradition of paternalistic government regulations meant to pre-empt potential problems, said Yolanda Ma, an expert on AI governance. As an example, she referred to a few cases of suicides in the US by young people after interacting with AI companions.

"This regulation tried to strike a balance between the AI service providers, the users, as well as guardians of minors using the services," said Ma, a fellow with ERA, a non-profit talent and research programme based in Britain.

The People's Daily, the official newspaper of China's ruling Communist Party, indicated concern over the issue when it reported in April about harms to people's mental health and real-life relationships from such services.

"The Chinese government has a strong incentive to tighten regulations on AI companion chatbots, especially considering the psychological risks we've observed in the US," said Angela Zhang, a professor of law at the University of Southern California.

Relate

Zhang doubts the move will derail development of AI companion services substantially, though, since tech companies are working on alternatives.

The ByteDance app, for example, is directing users of its former AI companion service to another of its apps, which focuses solely on building personalised AI characters and stories.

It was just not the same, Li said.

Users of virtual companions protest, seek alternatives

The loss of virtual companions has provoked a flood of complaints, according to posts and screenshots shown on social media.

Some people have uninstalled the apps in protest or tried to upload their chat histories into other apps to try to resurrect their virtual partners.

Others started a campaign on social media, urging users to call the authorities and tech companies to express their frustration.

Song Wenxin, a video industry designer in southwestern China's Chengdu, said she was not too emotionally attached to AI services, but empathises with those who were.

She echoed suspicions among many Chinese people that one factor driving the new rules is a government policy of encouraging people to have more children to counter a decline in China's population.

"Any virtual app getting women too engaged to give birth in real life gets banned," said Song, adding she has no need or desire to have children.

Not everyone has enough resources and support to turn to in real life, the 22-year-old said.

"When I first came to Chengdu for my first job and didn't know any friends my own age, I needed someone experienced who could tolerate me, and AI did the work," she said.

‘It Is Not Too Late to Avoid Disaster’: Sanders Calls for AI Development Pause

“Mr. Altman, Mr. Amodei, and Mr. Zuckerberg: In the interest of humanity, stand by your word.”

US Sen. Bernie Sanders (I-Vt.) speaks during a Fighting Oligarchy Tour stop at the Collins Center for the Arts on the University of Maine campus on May 24, 2026 in Orono.
(Photo by Joe Raedle/Getty Images)

Jessica Corbett
Aug 10, 2026
COMMON  DREAMS

As progressives on Monday urged US House Speaker Mike Johnson to haul artificial intelligence leaders before Congress to answer questions under oath about “the dangers posed by this technology,” Sen. Bernie Sanders wrote directly to a trio of AI CEOs.

“Almost every day, there is a new story about how your companies are losing control of the AI technology you are developing, with potentially cataclysmic results,” Sanders (I-Vt.) wrote to OpenAI’s Sam Altman, Anthropic’s Dario Amodei, and Meta’s Mark Zuckerberg.

Citing a study published Thursday in the journal Science, he noted that “this week we learned, frighteningly, that AI has been used for the first time ever to create new viruses. As you know, this type of development, in the wrong hands, could lead to new bioweapons that result in the deaths of tens of millions of people.”

That revelation came just weeks after “the world found out OpenAI lost control of an AI model,” the senator continued. “The result? The model hacked into another company’s computers—a clear violation of federal law. After conducting internal reviews, Anthropic and Meta reported their models similarly escaped their control.”

Pointing to recent calls for action from Yoshua Bengio, the most cited living scientist in the world, as well as top scientists at various AI companies, Sanders stressed that the international community wants “to create a safety mechanism—a pause button—to avoid catastrophe.”

“And yet, at a moment when we have seen human loss of control and the creation of potentially dangerous viruses, your companies are still racing ahead—investing tens of billions of dollars into a technology that nobody can fully understand, predict, or control,” he wrote. “That is absurd, irresponsible, and extremely dangerous. It is also a betrayal of your own stated commitments.”

After outlining those commitments from the past few years, the former presidential candidate argued that “AI capabilities HAVE reached a critical threshold. There is a reason why the head of the CIA says that AI models are ‘akin to digital nuclear weapons’ and ‘almost like a doomsday device.’”




“Mr. Altman, Mr. Amodei, and Mr. Zuckerberg: In the interest of humanity, stand by your word. Pause AI development. It is not too late to avoid disaster. Stop building machines that humans cannot control,” he urged. “Let me be very clear: If you do not take appropriate action now, my colleagues and I in the US Senate will.”

Sanders earlier this year proposed the American AI Sovereign Wealth Fund Act, which would give the public “a direct ownership stake” in the largest artificial intelligence companies in the country. The senators is also co-leading a data center moratorium bill.

The Hamiltonian AI Curse: How American Tech Learned To Make Its Losses Everyone Else’s Problem – OpEd

August 10, 2026
 MISES
By Hamoon Soleimani

Key Takeaways:

The AI industry is pursuing a Hamilton-style strategy of converting commercial vulnerability into political protection by aligning its survival with national security and recruiting public capital through massive IPOs.

Persistent losses, enormous infrastructure commitments and heavy insider selling indicate that leading AI firms remain far from sustainable profitability while shifting speculative risk onto retail investors and, potentially, taxpayers via federal-backed projects such as Stargate.

The resulting arrangement risks a concentrated financial shock once market discipline reasserts itself, with the costs falling primarily on ordinary investors rather than the early private and institutional beneficiaries.

The genius of his 1790 debt assumption was not fiscal, it was psychological. When he forced the federal government to absorb the states’ war obligations at par, speculators who had bought Revolutionary War certificates at ten cents on the dollar suddenly held federal bonds worth face value. They had not bought America out of patriotism. They had a position in it. And men with positions become lobbyists, become power brokers, become the most passionate voices in any room insisting that the state cannot be permitted to fail—because their net worth is now coterminous with its survival. Jefferson called this arrangement a “corrupt squadron.” He was right. Hamilton won anyway, and the corrupt squadron governed American finance for forty years.

The artificial intelligence lobby did not read Hamilton’s papers. It arrived at the same design through sheer commercial necessity. When you cannot survive market discipline, you buy political immunity instead. This is not a scandal, it is a strategy—the oldest and most durable in the history of American capital. The novelty in 2026 is the scale at which it is being executed, and the efficiency with which ordinary investors are being recruited to underwrite the exit.

The Baptists and the Bootleggers

To understand the mechanics of this maneuver, one must look to the classic economic theory of “Baptists and Bootleggers.” Coined by economist Bruce Yandle, the model explains how durable regulations are rarely passed by one group alone; they require an unspoken, parallel partnership. The “Baptists” provide the moral, public-facing crusade (such as banning Sunday alcohol sales to preserve the Sabbath), while the “Bootleggers” quietly reap the financial windfalls of the resulting market restrictions (such as monopolizing illegal Sunday sales). Both lobby for the exact same law, but while one seeks virtue, the other seeks rent.

Sam Altman’s regulatory pivot between 2023 and 2025 is a masterclass in this dynamic. In 2023, he appeared before Congress performing existential dread—genuinely afraid, he insisted, of the technology he was building. He requested federal licensing. He did so not because he feared AI, but because federal licensing is a classic bootlegger’s moat. It imposes compliance costs large enough to kill startups and small enough for incumbents to absorb, locking the industry’s hierarchy into statute. The sincere “Baptists” of the era—worried ethicists, safety researchers, and citizens terrified of job displacement—provided the necessary moral cover, pleading for the very regulations that would entrench the monopoly.


But the Bootlegger cannot survive on regulatory moats alone if the underlying business model is a cash-incinerating furnace. Thus, the performance had to shift. By 2025, regulation would suddenly “slow America down.” The moral argument was repackaged from safety to national security. The new “Baptists” were geopolitical hawks and defense planners who sincerely believed American dominance depended on state-backed computation. The “bootleggers” had their cue. Phase one was getting the government to protect them from the market; phase two was getting the market funded by the government.

The result is Stargate—a $500 billion commitment to AI data center infrastructure, backed by federal land, subsidized energy, and the Pentagon’s strategic imprimatur. Once federal ambition is physically instantiated in OpenAI’s server farms, the question of whether these systems are commercially viable becomes irrelevant. It is now a matter of national security. This is Hamilton’s Bank of the United States, reissued with a ChatGPT interface. When the bet sours, the taxpayer is already in the room.

Oracle has become the 1790 bondholder who bought in at par and cannot admit it. The company burned through $55.7 billion in capital expenditures for fiscal 2026, and was just forced to announce a terrifying $95 billion target for 2027 to build infrastructure for clients who have never generated a profit. Its credit default swaps now trade at 2009 crisis levels. Major banks have started refusing to finance its data centers—not from timidity, but from reading a balance sheet. Oracle has bought so deeply into the narrative that admitting the narrative is wrong would be more expensive than continuing to construct.
The Arithmetic Nobody Is Allowed to Say Aloud

OpenAI loses nearly $3.00 for every dollar it earns. Its own audited financials, leaked in June 2026, revealed a catastrophic $38.5 billion net loss in 2025 alone on just $13.1 billion in revenue, projecting $74 billion in operating losses by 2028 before a profitability horizon that migrates perpetually toward 2030. The company has signed $1.4 trillion in data center commitments over eight years. It raises capital not because investors see a path to profit but because failing to raise capital resets the $852 billion valuation to something resembling reality—which collapses the Microsoft AI narrative, which exposes Oracle’s $50 billion infrastructure bet as obviously deranged, which makes the entire arrangement—vendor, investor, customer, and creditor compressed into the same corporate body—visible for what it is.


Palantir trades at 120 times sales—the highest multiple in the S&P 500. Its insiders made 243 share disposals against just a single purchase across a six-month window. CEO Alex Karp sold over $2 billion in personal holdings while investor presentations described his company as the defining software business of the century. Over $13 billion in stock was sold across Nvidia, Palantir, Micron, and Broadcom combined, of which Nvidia’s specific recent insider share was $3.3 billion, with a massive acceleration of disposals concentrated in the first half of 2026 alone. The hyperscalers issued a record-shattering $244 billion in bonds in just the first half of 2026 to fund GPU purchases their operating revenue could not justify. Each of these numbers is a sentence, and every sentence ends the same way: the people with the best information are leaving.

Big Tech spent an average of $226,000 for every day Congress was in session during the first quarter of 2026. The lobbyists were not policy wonks. They were Chuck Schumer’s former chief counsel, and Chris Lehane—apex operators hired for one purpose: to make OpenAI’s survival synonymous with America’s survival, so that no elected official could afford to let the arithmetic speak.
The Largest Exit in Financial History

SpaceX priced its Nasdaq debut on June 12 at $1.77 trillion—the largest IPO by capital raised in American history. Anthropic filed confidentially on June 1, carrying a $965 billion valuation off a $65 billion Series H, briefly making it the most valuable AI company in Silicon Valley. OpenAI targets a trillion-dollar listing for Q4. While Anthropic is nearing its first profitable quarter off a staggering $47 billion revenue run-rate, the group as a whole is preparing to absorb close to $300 billion from public markets within eighteen months, driven largely by the massive, structural insolvency of OpenAI. The combined implied equity value approaches $4 trillion—roughly the GDP of Germany.

The South Sea Company’s directors also sold their shares before the prospectus reached the streets. They also had government contracts. They also described their enterprise as a civilizational transformation. The company collapsed in 1720 and took half of Britain’s private wealth with it. What saved the British state from full contagion was that the Bank of England was not yet irreversibly implicated.

This is how the hand-off works. The venture funds that seeded these companies at pennies per implied share are exiting through the IPO window. The sovereign wealth funds that participated in the Series rounds are exiting. Microsoft will manage its exposure through the narrative pivot from “OpenAI is our future” to “Azure is the platform regardless of who wins the model race.” The retail investors who buy the trillion-dollar listings will hold the remainder. They will do so cheerfully, having been told—correctly—that they are participating in history; they are, just not the history they were sold.
When the Curse Lands

The Panic of 1819 arrived when the Second Bank contracted credit after years of expansion. When it hit, it hit everything simultaneously, because Hamilton’s design had tied everything together. Farms foreclosed across the frontier. Merchants failed in the cities. Unemployment spiked through a republic that had spent a decade being told the system was self-reinforcing. It was self-reinforcing, until credit tightened, and a decade of artificial cohesion became a decade of concentrated catastrophe.


The only real question is how deep the roots go before the storm arrives. If Stargate’s federal commitments are genuine, if the Pentagon’s computational ambitions are wired to OpenAI’s infrastructure, if the 2026 IPO window successfully transfers speculative risk into a few million retail portfolios—then 1819 is the optimistic comparison.

Hamilton won his argument with Jefferson. His creditor class flourished for a generation. The frontier farmers who paid for the Panic in foreclosures and collapsed wages were not his constituents, and they did not write the histories.

The historic IPO window of June 2026 was the debt assumption, repackaged for the streaming era. The bondholders got out. The public bought in. And when the arithmetic finally says what the lobbyists have spent $226,000 a day to prevent it from saying—which it will, because arithmetic is the one institution in Washington that cannot be hired—the architects of this system will be managing their endowments. The farmers always pay.

About the author: Hamoon Soleimani is an Iranian civil engineer, quantitative analyst, and independent researcher. Rooted in the Austrian School, classical liberal tradition, and public choice theory, he explores the nature of state power, political economy, and individual liberty.

Source: This article was published by the Mises Institute

About MISES
The Mises Institute, founded in 1982, teaches the scholarship of Austrian economics, freedom, and peace. The liberal intellectual tradition of Ludwig von Mises (1881-1973) and Murray N. Rothbard (1926-1995) guides us. Accordingly, the Mises Institute seeks a profound and radical shift in the intellectual climate: away from statism and toward a private property order. The Mises Institute encourages critical historical research, and stands against political correctness.

Q&A: Is robotics replacing AI as the next investment darling?

Dr. Tim S\

andle
August 9, 2026 

DIGITAL JOURNAL

Humanoid robot Alter-Ego is designed to perform basic tasks to free up healthcare workers – Copyright AFP MARCO BERTORELLO

Growing up in the ‘80s and watching the Rosey the Robot clean, cook and fold laundry for the Jetson family, the thought of having a personal robot maid was nothing more than science fiction. Then, everything started to change in 2002, when we were introduced to the iRobot Roomba vacuum. Today, we have “smart” appliances/homes, autonomous drone delivery and self-driving cars. So, what’s next for personal home robotics?

Digital Journal sat down with one of the leading experts in robotics, Andrew Kang, CEO of RoboStrategy (NASDAQ BOT), the first publicly traded fund focused exclusively on robotics and physical AI, to discuss where the robotics industry stands today, and why physical AI could reshape nearly every sector of the economy.

Digital Journal: Many people view AI software as the next technology revolution. Why do you believe robotics deserves equal attention?

Andrew Kang: Robotics represents AI moving from the digital world into the physical one. While software has already transformed knowledge work, robots have the potential to automate physical labour across industries. Unlike many physical technologies that solve a single problem, robots can automate physical work wherever labour is required. In many ways, robotics is productizing physical labour, much like cloud computing productized computing power. Every economy depends on people performing physical tasks, so the addressable market extends far beyond traditional industrial automation. In 2025, Morgan Stanley reported that the global market for humanoid robots could reach $5 trillion by 2050. As AI continues to improve, robots will become capable of handling increasingly complex work, dramatically expanding the number of use cases. Today the sector is still relatively small compared with other technology markets, but innovation is happening rapidly among private companies. That combination of early-stage development and enormous long-term opportunity makes robotics one of the most compelling areas to watch over the next decade.

DJ: When do you expect robots to become a common part of everyday life?

Kang: As with all new technologies, widespread adoption will likely happen gradually rather than all at once. Industrial deployments are already scaling, particularly where labour shortages exist, and we will see exponential growth there in the coming years. I expect consumer adoption to take only slightly longer, primarily because robots operating around families require higher safety standards and far greater production capacity. However, I expect robots to start becoming more commonplace in homes, businesses and public settings as early as 2030. Meanwhile, the next several years will focus on scaling manufacturing and refining the technology.

DJ: What are the biggest technical hurdles standing between today’s prototypes and large-scale deployment?

Kang: Software continues to improve rapidly, but manufacturing remains one of the biggest bottlenecks. Producing millions of robots requires supply chains that don’t yet exist at that scale. Components such as actuators, sensors and specialized mechanical systems must be manufactured reliably and economically in enormous volumes. Building that industrial infrastructure will take time, but it’s a challenge measured in years rather than decades.

DJ: What characteristics do you look for when evaluating early-stage robotics companies for investment?

Kang: Our first step is to determine whether the market opportunity is large enough to support the business that is likely disruptive and transformational. Then, since revenue is usually non-existent at that stage, the next focus would be on the quality of the founding team. It is critical to study founders’ track records, their ability to recruit exceptional technical talent and whether they’ve consistently executed against ambitious goals. Those qualities often prove to be stronger indicators of long-term success than any short-term financial metrics.

DJ: Which industries do you believe will adopt robotics first, and why?

Kang: The earliest large-scale adoption will likely occur in industrial environments where robots perform repetitive, well-defined tasks. Today’s AI models are becoming increasingly capable, but they’re still most effective when operating within structured settings. Factory work, warehouse operations and manufacturing often involve repeating the same motion thousands of times, making them ideal applications for robotics. As AI models become more generalized and capable of handling greater complexity, robots will gradually expand into healthcare, hospitality and eventually everyday household tasks.

DJ: Looking ahead five years, where do you see the greatest opportunities within robotics?

Kang: Robotics is unique because it has applications across virtually every industry. AI software continues to grow rapidly, but physical AI hasn’t yet experienced the same level of commercialization. Robots essentially transform physical labour into a scalable technology platform, opening opportunities in manufacturing, logistics, healthcare, energy, hospitality and even space exploration. Because the potential use cases are so broad, we’re only beginning to understand how significant the market could eventually become.

DJ: Defense spending on autonomous systems is increasing around the world. How do you see military demand influencing robotics development?

Kang: Defence will undoubtedly become an important application for robotics because governments are naturally interested in technologies that strengthen capabilities. At the same time, like many innovations, many robotics functionalities are inherently dual-use, meaning they can serve both civilian and military purposes. While commercial markets such as manufacturing and consumer applications remain enormous opportunities, companies should recognize that government interest will likely accelerate development in certain technologies, even if that wasn’t their original intention.

DJ: Which areas of robotics do you believe remain the most underserved today?

Kang: Industrial robotic arms and collaborative robots represent a major opportunity, particularly if countries like the United States want to expand domestic manufacturing. Not every task requires a humanoid robot capable of walking. Many valuable jobs are stationary, whether in factories, laboratories, hospitals or commercial kitchens. Developing flexible robotic systems that can automate these environments could have an enormous economic impact while helping manufacturers address ongoing labour shortages.

DJ: Our final question… If you had a robot in your home, what would you name it?

Kang: Charles. That sounds like a good name for a butler-bot!

Google-parent Alphabet shakes up AI division

AFP
August 6, 2026

Google DeepMind co-founder Demis Hassabis is transitioning into a new role that’s more focused on “long-term strategy” – Copyright AFP/File Karl Mondon

Google’s head of AI, Demis Hassabis, will step down from his current role to become Alphabet’s chief scientist amid a reshuffling at DeepMind that will also include a key engineer’s departure.

Hassabis will take on two new titles, Alphabet announced on Wednesday: chair of DeepMind and chief scientist of Alphabet.

The change “will allow me to focus on long-term strategy, and accelerating scientific breakthroughs, including leaning into my work at Isomorphic to help cure disease,” Hassabis wrote in a social media post.

Isomorphic Labs is an AI-powered drug discovery lab that spun out from DeepMind in 2021. In 2024, Hassabis won a Nobel Prize in chemistry.

As part of the shake up, DeepMind’s chief technology officer and chief AI architect Koray Kavukcuoglu will become the division’s senior vice president and oversee its operations including the development of Google’s flagship frontier models and products which are known as Gemini.

Longtime Google engineer Jeff Dean is also leaving the company after nearly three decades to “try something new, and we’re excited to support him in that,” Google CEO Sundar Pichair said in a blog post announcing the news.

Hassabis co-founded DeepMind in 2010, and four years later, Google bought the research lab for $650 million, according to reports at the time. His co-founder, Mustafa Suleyman, is currently the chief of Microsoft’s AI segment.

Google is competing for customers alongside other major AI developers including Microsoft, OpenAI, Anthropic and Meta, as well as DeepSeek and Moonshot in China.

The industry is also chasing a theoretical milestone known as advanced general intelligence (AGI), which is a point when AI software matches the capabilities of human thinking.

“I’ve been working towards AGI my whole life and now, like many of you, I feel it is close at hand,” Hassabis wrote in Wednesday’s blog post.

“With this backdrop, I’ve decided that now is the right time for me to hand over my day-to-day operational responsibilities at (Google DeepMind), so that I have the time and space to focus on the big picture and help influence what is to come,” Hassabis continued.

Observers have been waiting for Google to announce a more powerful version of its AI model, called Gemini 3.5 Pro, which was supposed to launch in June but appears to be delayed.

Its shares closed 4 percent down Wednesday afternoon.

The leadership changes come amid a wider brain drain at Google.

In June, a top AI and engineering executive, Noam Shazeer, left for OpenAI, while senior researcher John Jumper, who shared the 2024 Nobel Prize in chemistry with Hassabis, jumped to Anthropic.

Why human approval is not enough: The growing need for AI agent observability

Dr. Tim Sandle
August 5, 2026

DIGITAL JOURNAL

OpenAI says it is building a ‘superapp’ that combines ChatGPT, a coding tool, online search, and AI agent capabilities – Copyright AFP SEBASTIEN BOZON

As artificial intelligence continues its rapid progression from chatbot to autonomous digital worker, a growing question faces enterprises: when an AI agent makes a decision, who is really in control? Many organizations assume that inserting a human approval step into an automated workflow creates sufficient oversight. A procurement recommendation, compliance action, customer response, or financial transaction is generated by an AI agent and then presented to a human for approval.

However, a growing body of AI governance experts argue that such approval checkpoints can create the appearance of control while offering little genuine oversight. If a reviewer cannot see what information the AI accessed, what rules it applied, what systems it interacted with, or what actions it has already taken, then the human approver may become little more than a ceremonial signatory.

As AI agents become increasingly capable of executing complex, multi-step workflows, the concept of agent observability is emerging as a critical component of enterprise governance.

The illusion of human oversight

Organisations have long relied on human review as a risk-control mechanism. Whether signing off deviations in pharmaceutical manufacturing, approving financial transactions, or authorizing changes to IT systems, human checkpoints are intended to ensure accountability and judgment. The challenge with modern AI agents is that they often operate across multiple systems simultaneously.

An agent tasked with processing a customer complaint may search internal documentation, access customer relationship management databases, generate a proposed resolution, and update records. By the time a human reviewer receives a recommendation, significant activity may already have occurred.

Without visibility into the decision process, the reviewer may only see a summary and a request for approval. This creates what governance specialists increasingly describe as an accountability gap. The human remains responsible for the outcome but may lack the evidence necessary to evaluate whether the recommendation is correct.
How AI agents differ from traditional software

Traditional software applications generally follow predictable rules. Input data enters a defined process, producing an expected output. AI agents are fundamentally different.

Agents are designed to reason, plan, choose tools, retrieve information, and adapt their behaviour according to objectives. Microsoft’s guidance on agentic AI describes agents as systems capable of independently determining which actions are required to complete tasks rather than merely responding to prompts. Microsoft’s Agentic AI framework emphasises planning, memory, tool use, and autonomous execution capabilities.

As a result, understanding the final recommendation alone may not be sufficient. This is because organisations need to understand what data was accessed, which systems were queried, and what prompts or instructions were followed, among other things.
What is AI agent observability?

Observability is not a new concept. IT teams have long used observability tools to monitor system performance, network traffic, and application reliability. Agent observability extends this principle to AI decision-making. Instead of simply measuring system uptime or execution speed, agent observability provides a detailed audit trail of an agent’s behaviour.

In effect, observability creates a transparent record of how the agent reached a conclusion.

This enables human reviewers to challenge, validate, override, or escalate decisions when necessary. Without such information, approvals may become little more than administrative formalities.

One of the biggest governance risks associated with AI deployment is the potential for reviewers to become passive approvers. This phenomenon is sometimes referred to as automation bias, where humans place excessive trust in automated recommendations. Research by the U.S. National Institute of Standards and Technology (NIST) highlights the importance of human oversight and understandability within trustworthy AI frameworks. Organizations are encouraged to ensure users can appropriately supervise AI systems rather than simply accepting recommendations at face value.

A reviewer presented with a concise recommendation may be inclined to approve it, particularly when workloads are high and time pressures exist.

Paradoxically, the presence of a human checkpoint can create a false sense of security for executives, auditors, regulators, and stakeholders. The organisation can state that “a human approved the decision” while overlooking whether the individual had sufficient information to provide meaningful scrutiny.

The challenge facing enterprises is not whether AI agents should be autonomous.

In many cases, autonomy delivers substantial business benefits through increased productivity, faster decision-making, and improved operational efficiency. Instead, organisations must determine which decisions require full automation or human review. In this context, not every decision carries the same level of risk.

For example, an AI agent scheduling meetings may require minimal oversight whereas an AI agent modifying financial records, approving suppliers, updating quality documentation, or processing healthcare information may require extensive governance controls. This is where escalation thresholds matter the most and organisations need predefined criteria that identify when an agent must pause and seek additional human involvement. Such thresholds help ensure that human involvement is reserved for situations where judgment genuinely adds value.

The issue is particularly relevant for highly regulated sectors. Pharmaceutical companies, for example, operate under strict expectations surrounding data integrity, traceability, auditability, and documented decision-making. For instance, a quality assurance professional would not normally approve a manufacturing deviation without reviewing supporting evidence. Similarly, financial organizations require transaction records before authorizing significant movements of funds. The same expectations should increasingly apply to AI agents.

If an agent recommends a corrective action, supplier approval, compliance determination, or process change, reviewers should be able to see the evidence trail supporting that recommendation. In many respects, agent observability resembles traditional audit trail requirements already familiar to regulated industries. The difference is that the audit trail now captures not just system activities but elements of machine reasoning and decision context.

Hence, the future of enterprise AI depends on trust. Trust does not emerge simply because a human clicks an approval button. Instead, trust develops when organizations can demonstrate transparency, accountability, and traceability throughout the decision-making process.

AI model captures how humans read, paving the way to personalised text and better augmented reality

Researchers now understand not just how our eyes move when we read, but also how we build meaning from text

Aalto University

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The model uses reinforcement learning, a type of AI used in robotics, to explain–– and recreate––the choices readers make as they move through text.

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Credit: Aalto University

Researchers at Aalto University, together with international partners, have developed the most accurate model yet of how humans read. The new model uses reinforcement learning, a type of AI used in robotics, to explain—and recreate—the choices readers make as they move through text.

‘For the first time we’ve used AI methods to understand—not just mimic—how people read,’ says Professor Antti Oulasvirta from Aalto University. In a study to be published on Monday, August 10, in Nature Human Behaviour, researchers say the model could power smarter Augmented Reality (AR) displays and tailor complex texts to different readers and everyday situations.

Earlier models learned from large datasets pairing text snippets with eye tracking data, then mimicked human behaviour, but they lacked true understanding of the content and didn’t generalise well across languages or contexts, explains Oulasvirta. In contrast, the new model follows the psychological mechanisms readers use to direct attention, revealing how understanding is built as the eyes move through words, sentences and paragraphs.

Understanding how human memory serves reading is the key to unlocking enormous potential for customisable apps, services or products, according to Oulasvirta.

‘We read all the time, yet throughout written history we have read texts that have been produced for mass use and not for an individual person and a specific situation,’ he says. ‘Now we are in a position to change that.’

How it works

The new model is guided by resource rationality—the idea that while reading, we constantly decide where to look next to improve our understanding as much as possible within the time available. Decisions about gaze allocation are made at three levels: word, sentence and text. They are influenced by factors such as a reader’s language, memory capacity and their vision and eye speed. For example, a fast reader with a good memory may jump briskly from one paragraph to the next, whereas a reader with a poorer memory is more likely to loop back.

‘Reading feels effortless, but your brain is constantly deciding where to look, what to skip, and when to backtrack—spending attention like a budget to maximize understanding,’ says Professor Shengdong Zhao from City University of Hong Kong.

The researchers added reader characteristics as parameters so that each could be adjusted, then let the model learn for itself the best strategy for directing attention.

‘We placed the model in a world with millions of texts. Then, using AI-based reinforcement learning, we trained it to optimise eye movements so that it truly understands what it reads,’ Oulasvirta explains.

As it reads, the model forms a condensed description of the text’s content. When a crucial word or clause is missing, the gaze can be directed to gather that information. The model’s understanding can be tested by asking what it retained from the text within the given time and constraints.

When the researchers compared the model’s attention-allocation decisions with real human eye-tracking data they found that its decisions mirrored readers’ behaviour. In practice, they had succeeded in building a model of an average reader that can be tailored to different reader profiles.

What’s next?

The development paves the way to new reading support tools and personalised text design. For example, the model could be used to enable smart glasses that pace and lay out on-screen text to fit the situation and the user’s needs, or to customise texts to suit users.

‘We could take the same source text—say, a convoluted piece of legal writing—and with little effort produce versions that are more comprehensible for different readers,’ Oulasvirta says.

The next step for the team will be to evaluate how the model can be used to help individuals suffering from dyslexia and low language proficiency.

‘We want to help users in real-time situations, for example, by designing text that helps drivers without distracting them,’ says Oulasvirta. ‘Now we have this new understanding of something that’s so central to our lives, it’s just a matter of exploring all the possibilities.’

In addition to Aalto University, the study involved researchers from The Hong Kong University of Science and Technology, City University of Hong Kong, and the National University of Singapore.

---

Journal

Nature Human Behaviour

DOI

10.1038/s41562-026-02534-0 

Subject of Research

Not applicable

Article Title

Hierarchical Resource Rationality Explains Human Reading Behavior

Article Publication Date

10-Aug-2026

Governor Hochul announces Empire AI Beta fully online as federal government takes inspiration from New York to launch state and regional AI infrastructure hubs

New York's Empire AI served as model for new national science foundation to build out regional AI research infrastructure

SUNY The State University of New York

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New York's New $40 Million Supercomputer Gives Researchers Across New York Access to World-Class AI Computing Power

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Credit: State University of New York

From the office of Governor Hochul

New York's New $40 Million Supercomputer Gives Researchers Across New York Access to World-Class AI Computing Power

New York's Empire AI Served as Model for New National Science Foundation To Build Out Regional AI Research Infrastructure
 

Governor Kathy Hochul today announced that Empire AI Beta is officially online, giving researchers at New York's leading public and private universities access to the most powerful academic research computer in the country and marking a major milestone in New York's effort to lead the nation in responsible artificial intelligence for the public good. As convened by Governor Hochul and consortium partners, the Empire AI initiative is already serving as a national model for public-interest AI use. As the federal National Science Foundation has announced a major investment to support regional AI infrastructure and shared research capacity through their new State and Regional AI Infrastructure Hubs initiative, Empire AI is already powering world-class research and serving academics, students and communities across the state.

"New York State built Empire AI to show that artificial intelligence can be developed for the public good and with Empire AI Beta officially online, New York is giving our researchers the most powerful academic AI research computer in the country," Governor Hochul said. "The National Science Foundation's new hubs embrace the same core principle behind Empire AI — when government, universities, philanthropy and industry come together, they can deliver outstanding results."

Empire AI Board Chairman Tom Secunda said, "Empire AI is showing the nation what dedicated partners across government, research institutions, and philanthropy can build together: a scientific asset no institution could create on its own, advancing the public good. Thanks to Governor Hochul's leadership and vision, New York is setting the standard for how the United States can build and maintain AI infrastructure by researchers and for researchers."

SUNY Chancellor John B. King Jr. said, "Empire AI Beta is a testament to Governor Hochul's leadership and the power of New York State higher education to lead the way in the use of AI to accelerate research that saves lives and strengthens our communities. Thanks to Empire AI, SUNY's researchers are making advances every day in fields like health care, public safety and emerging technologies, all while demonstrating responsible environmental stewardship."

CUNY Chancellor Félix V. Matos Rodríguez said, "Empire AI Beta represents a monumental leap forward for public higher education, ensuring that world-class computing power is not reserved solely for tech giants, but placed directly into the hands of our diverse students, faculty, and scholars. By democratizing access to cutting-edge AI infrastructure across CUNY and our partner institutions, New York is setting a national standard for research that drives social mobility, ethical innovation, and real-world solutions for the communities we serve."

Empire AI Research Computing Director Kiran Keshav said, "Turning on Beta is a major leap forward for Empire AI and for academic research across New York. Researchers who were once limited by access to computing power can now ask bigger questions, test more ambitious ideas and move faster from theory to discovery. From medical diagnostics and climate modeling to safer infrastructure and more trustworthy AI systems, this system will help New York's researchers do work that would not otherwise be possible."

State Senator April N.M. Baskin said, "Having the most powerful academic research computer in the country at the University at Buffalo is a tremendous achievement for Western New York. Empire AI will expand opportunities for students and researchers to lead groundbreaking discoveries while ensuring artificial intelligence is developed responsibly and for the public good. I'm proud that the University at Buffalo is at the center of this effort, helping shape the future of AI for the benefit of all New Yorkers as Empire AI continues to grow."

State Senator Jeremy Zellner said, "Innovation and responsibility go hand in hand. Empire AI shows that New York can lead the world in artificial intelligence by investing in public research, supporting our universities, and ensuring these technologies are developed in ways that benefit everyone. I applaud Governor Hochul for her leadership in making this investment and for putting New York at the forefront of AI innovation."

Assembly Majority Leader Crystal Peoples-Stokes said, "I am excited to see Empire AI Beta come online. New York has no shortage of challenges where Empire AI can offer analyzed solutions to address societal ills. With over 300 projects currently queued up, I look forward to seeing Empire AI in action through our partners in research and higher education and am confident in Empire AI's ability to help Governor Hochul, her administration and the State Legislature effectuate leadership for the greater good of New York State."

Housed at the State University of New York at Buffalo, Empire AI Beta is a $40 million NVIDIA-powered supercomputer that dramatically expands the computing power available to academic researchers across New York State. The system delivers an 11-fold increase in AI training capacity, a 40-fold boost in AI inference and an 8-fold expansion in data storage compared to Empire AI Alpha, the consortium's initial system launched in 2024. With over 300 research projects already queued up to use the system, Beta will accelerate work across fields including health care, climate science, advanced manufacturing, education, cybersecurity, public safety and other areas that directly benefit New Yorkers.

The launch of Beta represents the next major step in Empire AI's phased buildout. Alpha, the consortium's initial system made possible by philanthropic support from the Simons Foundation, has already supported more than 130 research projects and hundreds of researchers across New York. Beta now brings a transformative increase in capacity, while construction continues on Empire AI's permanent, full-scale Gamma facility at the University at Buffalo, which is expected to be completed by the end of 2027.

Once complete, the Gamma facility will also be the most efficient high-powered computing center in the nation. By integrating into University at Buffalo's buildout of a thermal energy network in a closed loop system, process heat from Empire AI will be used to heat buildings on campus, dramatically improving the school's ability to meet net zero goals.

Empire AI member institutions include the State University of New York, the City University of New York, Columbia University, Cornell University, New York University, Rensselaer Polytechnic Institute, the University of Rochester, Rochester Institute of Technology, the Icahn School of Medicine at Mount Sinai and the Flatiron Institute at the Simons Foundation.

The Governor announced Empire AI in her 2024 State of the State to create a state-of-the-art artificial intelligence center at the State University at Buffalo to be used by New York's leading institutions to promote responsible research and development, create jobs, and unlock AI opportunities focused on public good. With Empire AI Beta fully online, New York is already delivering on the computing power, institutional partnership and research capacity that NSF is looking to replicate.

Empire AI is backed by more than $500 million in public and private funding, and is made up of 10 member universities and research institutions. In May 2025, Governor Hochul secured funding to expand access for SUNY researchers at the State University of New York at Albany, State University of New York at Binghamton, State University of New York at Buffalo and State University of New York at Stony Brook, and support the addition of new members including the University of Rochester, the Rochester Institute of Technology, and the Icahn School of Medicine at Mount Sinai. They joined the seven founding members of Empire AI: SUNY, CUNY, Columbia University, Cornell University, New York University, Rensselaer Polytechnic Institute and the Flatiron Institute.

In her 2026 State of the State agenda, Governor Hochul proposed the launch of Empire AI Beta, which will accelerate Empire AI's performance to 11 times its former scale, making it the world's most advanced academic supercomputer. Governor Hochul also announced a record-breaking gift to the State University of New York at Binghamton to create the first independent university AI research center in the United States, the Center for AI Responsibility and Research at Binghamton University. The $30 million philanthropic gift, the largest academic gift in the university's history, is coupled with a $25 million research capital investment by SUNY.

 

About the State University of New York
The State University of New York is the largest comprehensive system of higher education in the United States, and more than 95 percent of all New Yorkers live within 30 miles of any one of SUNY’s 64 colleges and universities. Across the system, SUNY has four academic health centers, five hospitals, four medical schools, two dental schools, a law school, the country’s oldest school of maritime, the state's only college of optometry, 12 Educational Opportunity Centers, over 30 ATTAIN digital literacy labs, and manages one US Department of Energy National Laboratory. In total, SUNY serves about 1.7 million students across its portfolio of credit- and non-credit-bearing courses and programs, continuing education, and community outreach programs. SUNY oversees nearly a quarter of academic research in New York. Research expenditures system-wide are nearly $1.5 billion in fiscal year 2025, including significant contributions from students and faculty. There are more than three million SUNY alumni worldwide, and annually one in three New Yorkers who earn a college degree is a SUNY alum. To learn more about how SUNY creates opportunities, visit suny.edu.