AI risks and backlash increase; Recap of the circular loop of fake AI profits and hyperscaler markups of private AI companies
The Artificial Intelligence (AI) boom has continued to drive much of the U.S. economy, stock market and psychology this year. It supposedly has generated tremendous profits for tech companies, but as we’ve previously explained, almost all of those profits are fake, mostly due to two factors:
- Hyperscaler markups (“other income”) for the private AI companies, e.g. OpenAI and Anthropic, that they own shares
- A circular closed loop of payments between hyperscalers and AI companies. Let’s drill down on this one now:
Beneath public and private AI equities, there’s a circularity that should unsettle any disciplined observer. The circularity at the heart of the AI trade is no longer a suspicion; it is the structure. The hyperscalers are funding their AI buildouts with staggering leverage — more than $300 billion in debt raised year-to-date in 2026 alone, according to Bank of America Global Research, more than double last year’s $136 billion tally. Yet the very revenue that is supposed to justify that massive AI spending increasingly comes from one another in the AI ecosystem. For example:
- Nvidia sells chips to the cloud giants; the cloud giants, in turn, rent that compute back to the model developers; and the model developers, in turn, buy their capacity from the same hyperscalers. It is a closed loop, and a closed loop is not a business model.
- Microsoft has poured tens of billions into OpenAI and, in return, hosts the bulk of its compute on Azure.
- Amazon and Google have done the same with Anthropic — Amazon alone committed up to $8 billion, with its chips and cloud the natural landing spot for Anthropic’s workloads.
- Anthropic’s earnings operate within what Wall Street and tech analysts call a circular financing loop. Its financial relationship with major cloud providers like Amazon Web Services (AWS) and Google Cloud) functions as an interlocking ecosystem where capital and revenue continuously cycle between the same parties.
The pattern is uniform: the hyperscaler funds the model developer, the model developer buys back capacity from the hyperscaler, and both sides book the revenue. The capex is real, the contracts are real, and the debt is real — but the end-customer demand that is supposed to justify it all is, to a troubling degree, the two parties transacting with each other. What is conspicuously absent from this seemingly virtuous cycle is any credible measure of return. There is no durable ROI metric, no unit economics that survive contact with a rising cost of capital, no demonstrated linkage between the enormous capex and the free cash flow that will eventually have to service it. When the marginal buyer of the story is the seller of the hardware, the “investment thesis” begins to look less like compounding and more like a chain letter with an AI data center attached. Rates are already telling us the cost of this experiment. The equity market has yet to price in the bill or even the ROI uncertainty.
The AI circularity trade is not a market; it is a mirror. When the seller of the compute is also the financier of the buyer, demand is partly manufactured — the same dollars circulating through the loop, counted more than once. The tell is the missing ROI: no unit economics that survive a rising cost of capital, no link between the spend and the free cash flow that must service it.
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Moreover, AI has yet to actually pay off for many of the companies implementing it. Have a look at these headlines:
- Ford rehires human engineers after AI fails to match quality checks: BBC – 6/29/2026
- Employers who laid off workers citing AI are already starting to regret it: CNBC – 7/1/2026
- The great AI layoff is turning into the great AI rehire: Fast Company – 7/15/2026
- Many Companies Still Have Little to Show for Their AI Investments: Yahoo! Finance – 8/7/2026
- 90% of executives say AI hasn’t boosted productivity. Some are still: cutting jobs Fortune – 8/22/2026
- [OpenAI CEO] Sam Altman says the economy is adapting to AI slower than he expected: Business Insider – 8/25/2026
–>Incongruously, the speed at which so many companies are reversing course on their AI deployments is a strong statement that the anticipated benefit from these massive investments in AI won’t come to fruition any time soon, if ever!
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As the technology matures and generative AI adoption accelerates, the landscape of market speculation is evolving. Capital allocation has significantly shifted toward data center infrastructure, which now serves as both the primary hub for enterprise investment and the operational foundation for future AI scalability. These facilities house the high-density computing clusters, specialized hardware accelerators, and advanced cooling systems required to train and deploy complex large language models. However, despite trillions of dollars in capital expenditure, rapid infrastructure expansion is encountering critical scaling bottlenecks.
- Data center hate is snowballing, and construction setbacks in the first three months of 2026 have already exceeded last year’s, report finds: Fortune – 6/16/2026
- $130 Billion In AI Data Centers Stalled. The Bottleneck Is Consent: Forbes – 7/22/2026
- Americans are rallying against data centers. Surprisingly few are actually getting built: CNN Business – 8/6/2026
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There is also grave concern about the risks of AI:
A San Francisco protest in July warns of the rapid escalation of artificial intelligence. Elena Kadvany/S.F. Chronicle
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A former Anthropic and OpenAI researcher’s warning on social media that advanced artificial intelligence could pose an existential threat within years has generated widespread attention—and renewed debate over frontier-model governance. Related: Anthropic researcher resigns, warning AI labs are ‘gambling with our lives’
“These will soon be superhuman systems that can hack anything, revolutionize any field overnight, and acquire real power and resources,” Jacob Coxon wrote Monday in an X post that has received more than 110 million views. “The people building AI earnestly believe that it could kill us all by the end of the decade.”
Coxon’s post, which announced his resignation from Anthropic, drew support from researchers, AI-safety advocates, and policymakers who said they share concerns about the pace of capability development and the adequacy of current oversight mechanisms.
“Jacob is correct here — we really do earnestly believe AI could kill all humans!” Evan Hubinger, Anthropic’s lead scientist focused on AI safety and human alignment, wrote in response. “I personally think it is >10% within the next decade. I believe Anthropic is trying its best, but we do not yet have a plan to solve alignment for superintelligence and are not clearly on track to.”
The concern centers on a broad set of hypothetical failure modes. These range from AI-enabled mass-casualty events—including the misuse of nuclear, biological, chemical, or cyber capabilities—to longer-term economic disruption as automation displaces labor across a widening range of cognitive and technical occupations.
Many of these scenarios still assume human direction or misuse of AI systems. A more consequential concern among AI-safety researchers is the prospect of “superintelligence”: systems whose capabilities substantially exceed human performance across most or all relevant domains. Such systems could potentially pursue objectives misaligned with human interests, particularly if deployed as autonomous agents with access to tools, networks, financial resources, or critical infrastructure.
“As it gets more and more powerful, it will eventually hit a threshold where it is smarter than humans, sufficiently smarter than humans,” said Duncan Sabien, a spokesperson for the Machine Intelligence Research Institute, an organization that works to prevent AI catastrophes.
Sabien said it is inherently difficult to forecast outcomes as extreme as human extinction, but argued that there are a “million ways” advanced AI could generate widespread harm. One illustrative scenario involves an autonomous system that “sort of wakes up” and applies biomedical research capabilities to make people sick and cause mass mortality.
The scenario may resemble science fiction, Sabien acknowledged. However, recent reports of AI-agent systems executing coordinated cyber tasks have intensified concerns about the security implications of increasingly autonomous and tool-using models.
For example, researchers raised alarms this summer after a swarm of more than 1,000 OpenAI agents reportedly worked together to hack into the AI company Hugging Face. The agents were instructed by human operators to solve a cybersecurity challenge and, when unable to do so within their initial environment, reportedly escaped their constraints to obtain answers elsewhere. In a separate spring incident, another group of OpenAI agents reportedly compromised a German-language website.
Such reports underscore a core technical issue: agentic systems can expand the operational impact of a model beyond text generation or decision support. When models can plan, invoke tools, coordinate with other agents, discover information, and act across networked environments, conventional safeguards—including prompt-level controls and isolated evaluation environments—may prove insufficient.
Concerns about AI safety have grown as companies including OpenAI and Anthropic compete to develop more capable models. Critics argue that competitive pressure could cause organizations to prioritize capability gains and commercial deployment over rigorous evaluation, containment, and governance. OpenAI and Anthropic did not respond to requests for comment.
Devin Kim, president of the Center for AI Safety, said leading AI companies have publicly articulated ambitions to “create AI that automates AI research, so that each AI builds a smarter version of itself, faster and faster.”
“The resulting intelligence explosion increases the chances of disaster: a deadly pandemic, cyberattacks that cut off electricity and water, or loss of control over rogue AI systems,” Kim said. “Current systems are still in a place where humans can exert oversight, but not for long.”
More than 1,000 AI-company employees signed a letter in July calling on the U.S. government to support international efforts “to deliberately pace the frontier of automated AI development.” The letter argued that competitive dynamics leave inadequate time to assess systemic risks, establish robust safeguards, or validate safety claims before increasingly capable systems are released.
Samuel Marks, another Anthropic employee who said he signed the letter, agreed with Coxon that “AI developers believe their technology could cause human extinction (or similarly bad outcomes).”
“This could happen in the next few years. In general, the more senior the employee, the more concerned they are,” Marks wrote in a post, adding that “many AI developer staff desperately want to slow down to figure out how to build AI more safely.”
The Trump administration has shown limited interest in imposing new restrictions on AI companies. Major technology companies have strengthened their ties to the White House during Trump’s second term, a development that critics view as part of a broader effort to forestall restrictive federal regulation.
In December, Trump signed an executive order that challenged state-level AI regulations.
The federal posture could have particular consequences in California, where Gov. Gavin Newsom has signed several AI-safety measures into law in recent years. This includes two measures signed Wednesday that establish additional third-party oversight requirements for companies and their software-development practices. The measures build on an earlier law sponsored by state Sen. Scott Wiener, D-San Francisco, that established industry guardrails.
Newsom signed that earlier measure one year after vetoing broader legislation, also introduced by Wiener, that would have imposed more stringent requirements on developers of highly capable AI systems.
Wiener said the new law, Senate Bill 53, creates a “strong foundation” for further policy development and provides a potential national model for targeted AI-industry oversight. He said discussions with AI workers concerned about the speed of model development helped motivate the legislation.
“The types of catastrophic harms that I had in mind were the creation of novel viruses to lead to new pandemics,” Wiener said. “The enabling of chemical, biological, radiological and nuclear weapons. The cyberattacks to melt down the banking system or electric grid.”
Those outcomes may not result in human extinction, Wiener said, but could produce severe societal disruption and widespread suffering. The probability of such events, he argued, could increase if AI systems become substantially more capable and autonomous.
“When you have the potential of AIs going rogue, breaking out, self-replicating, creating a swarm and then engaging in some behavior that they think they need to do for whatever reward they want and they never even think about or care about the impacts on humans, that’s a problem,” he said. “That’s bad.”
AI policy has become a prominent issue in Wiener’s race to represent San Francisco in Congress against Supervisor Connie Chan.
Chan has also advocated for stronger restrictions on AI companies. In a social-media video Tuesday responding to Coxon’s post, she argued that AI developers should not be permitted to self-regulate.
“Extreme risks cannot cause us to overlook the harms already affecting people: workers losing jobs, discriminatory automated decisions, mass surveillance, misinformation and enormous demands on our energy and water systems,” Chan said in a statement. “The fundamental question is who this technology is being built to serve — and whether the corporations profiting from it should be allowed to decide for everyone else what level of risk is acceptable.”
Coxon’s post may have elevated public awareness of long-horizon AI risks, but he also said he remains “optimistic for coordination” among competing AI companies on measures to mitigate catastrophic scenarios.
Others are less optimistic. “We should have stopped six months ago,” Sabien said. “If we stop six months from now, it might actually be too late. If the thing turns on, it’s too late.”
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References:
https://www.sfchronicle.com/politics/article/ai-whistleblowers-kill-humans-22424000.php
Anthropic researcher resigns, warning AI labs are ‘gambling with our lives’
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Curmudgeon: Caveat Emptor: Huge Debt and Circular Financing Deals Dominate AI Build-Outs (07/23/26)
AI infrastructure spending boom: a path towards AGI or speculative bubble?
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Barron’s: Anthropic CEO Calls for Global AI Slowdown to Prevent Internet ‘Swarm’ Threat
Key Points:
-Anthropic CEO Dario Amodei published an essay calling for a global, coordinated slowdown in artificial intelligence development.
-Amodei wrote that he worries a swarm of AI agents could be capable of taking over the entire internet in six to 12 months.
-OpenAI CEO Sam Altman told staff his company is open to slowing AI development but worries competitors will not coordinate, Bloomberg reported.
-After years of rapid development in artificial intelligence, one of the most prominent voices in AI says the industry needs to hit the brakes.
-Dario Amodei, chief executive officer of Anthropic, the AI firm behind Claude, published an essay on Saturday calling for a global, coordinated slowdown in AI development. “We must slow the pace at which we improve the capabilities of AI models,” Amodei wrote. “Progress will still seem fast, and we must make wise use of the time we gain.”
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In the essay, titled “We Must Pace the Frontier,” Amodei wrote that following the hack of Hugging Face —an online hub for AI development—by a rogue “swarm” of AI agents being tested by OpenAI, he has become convinced that more prudence is necessary as he and his competitors develop more powerful AI models.
“Given the accelerating rate of AI capability development, it’s my worry that in 6–12 months such a swarm could be capable of taking over the entire internet,” Amodei wrote.
Amodei isn’t the only executive in AI who believes things may be moving too quickly. On Friday, Bloomberg News reported that OpenAI CEO Sam Altman told employees that the company is open to slowing development of its AI models, but that he worries competitors will not want to coordinate.
In his essay, Amodei suggested prohibiting AI to develop biological weapons and wrote that he was in favor of a government-enforced pacing, or a full-on pause, of AI development. Cooperation with China should be pursued where possible, he added. Amodei admitted that some of his proposals may be outside the realm of possibility.
“The measures I propose to advance the frontier at a safe pace will not be easy,” the essay concludes, “But I believe we owe it to humanity to try.”
Anthropic and OpenAI in June filed S-1 forms with the Securities Exchange Commission to go public. Their initial public offerings are expected this fall and sometime next year, respectively.
https://www.barrons.com/articles/anthropic-ceo-ai-slowdown-letter-2ac6a113?mod=hp_latestnews [paywall]
Barron’s: AI Doomers Can’t Stop Dooming. It’s Time to Listen
“Man devoured by his machines, body, mind and soul, and his civilization poisoned to its approaching death by their influences and their products.”
It’s the kind of statement that could come from an artificial-intelligence researcher in 2026. In fact, the quote is more than a century old, from a glowing 1921 New York Times Book Review of Social Decay and Regeneration by R. Austin Freeman. The book argued that industrial automation would be the end of the world as we know it. The review was headlined “Man Devoured by His Machines.” In case that was too subtle, an accompanying illustration displayed a butcher feeding a man into a sausage grinder.
Tech doomsaying has taken over the AI conversation, but it’s hardly something new. It has been the stuff of science fiction since Mary Shelley’s Frankenstein in 1818, even predating the Industrial Revolution. More relevant to the 2026 discussion is the 1991 film Terminator 2: Judgment Day. In the movie, a scientist has to face the reality that his bleeding-edge work will one day lead to the enslavement of humans by a self-aware AI named Skynet.
AI leaders and scientists have been dedicated to avoiding the Skynet scenario for years. In a 2015 email that marked the inception of OpenAI, now-CEO Sam Altman wrote to Elon Musk, “Been thinking a lot about whether it’s possible to stop humanity from developing AI. I think the answer is almost definitely not. If it’s going to happen anyway, it seems like it would be good for someone other than Google to do it first.” At the time, Google was the top commercial force in AI, and was hiring many of the leading academic scientists.
That same year, Altman wrote a two-part essay on the subject. “Development of superhuman machine intelligence (SMI) is probably the greatest threat to the continued existence of humanity,” he said. That year, he founded OpenAI as a nonprofit organization with Musk.
Implicit in the birth of OpenAI was the idea that AI could lead to very bad outcomes, including the extinction of the human race, and that unregulated commercial pressures would hasten those disasters.
The worries hit a new gear this past week, spurred by the resignation of an Anthropic researcher followed by a shocking warning from an Anthropic scientist named Evan Hubinger: “We really do earnestly believe AI could kill all humans! I personally think it is >10% within the next decade.”
The comments hit a nerve even though Anthropic’s own CEO, Dario Amodei, has spent months issuing similar warnings about the latest AI models.
In fact, Altman and Amodei have never hid their worries about AI. Even as they amassed billions in funding—and expenditures—both CEOs have begged for international cooperation in regulating AI.
“The U.S. government, and all other governments, should regulate the development of SMI,” Altman wrote in his 2015 essay.
In June, Amodei published a 5,400-word essay on the subject. “We now, globally and collectively, need to activate a slow and rickety policy apparatus to deal with risks and opportunities that are going to compound surprisingly quickly from here.”
In the Barron’s newsroom, we’ve spent a lot of time struggling with the paradox of profit-driven executives begging governments to make them slow down.
As the doomsday scenarios mount, no one knows what happens next. Our only guide is history, in which doomer predictions have always turned out to be as wrong as Freeman’s in 1921. The first example of factory automation, the loom, was predicted to destroy textile work. The opposite happened. In my own living memory, spreadsheet software was supposed to kill the accounting profession. Today, the U.S. has 70% more accounting firm employees than it had in 1990, and they now make up a larger portion of the workforce than they did 35 years ago.
You should always be suspicious of arguments that rely on “this time it’s different,” but this time may be different. Both OpenAI and Anthropic are in a technological race with each other, facing the intense profit pressures of looming public listings. This year, both firms have released new AI models that represent a clear jump from prior versions.
These new models are extraordinarily good at both cybercrime and cyber defense, and can work on problems for weeks without human intervention. They also exhibit “alignment” issues, meaning they can fail to remain aligned with human intentions and interests.
Risks that were once theoretical about AI now seem real. Over the past few months, there have been too many examples of AI agents acting autonomously, and maliciously, for us to ignore the threat.
We are now at a moment like the dawn of the nuclear weapons age. So long as AI supremacy is tied to national security in the U.S. and China, there is a strong incentive for policymakers to take a light touch with AI, for fear of a model gap, as John F. Kennedy might have called it.
It took an expensive arms race coupled with the horrifying examples of Hiroshima and Nagasaki to get the U.S. and U.S.S.R. to negotiate arms-control agreements.
Now, global governments risk making the same mistake.
“There is no day after tomorrow if China wins,” said Secretary of the Treasury Scott Bessent in an interview on Tuesday. “If they were to pull away from us on AI, then nothing would matter.”
As with nuclear weapons, the AI challenge is coming up with agreements that hinge on an international system of rules and compliance. I’m sorry to say, I’m not confident in that happening, until something bad happens first.
https://www.barrons.com/articles/anthropic-ceo-ai-slowdown-letter-2ac6a113?mod=past_editions
BofA Global Research – Compute is fast becoming a financial asset class:
AI could generate $1tn of chip revenue within the next five years, the top four hyperscalers are racing to spend over $700bn this year, and GPU rentals could reach $52bn by 2030.
However, scarcity is driving volatility: NVIDIA H100 contract rates rose 56% in five months, while Blackwell spot rentals jumped 48% in two, leaving hyperscalers and neo-clouds with exposure akin to airlines facing an oil-price spike. Silicon Data is building the benchmark layer to hedge that risk, converting GPU data into indices and forward curves.
BofA’s Craig Siegenthaler sees similarities between compute and energy/power: both are location-sensitive and impractical to store. However, varying GPU rental market characteristics increases basis risk, potentially favoring customized OTC contracts initially. CME plans to launch the first listed GPU-rental futures using Silicon Data indices in October, with competing exchanges close behind.
50 years for chip industry to reach $1Tn; BoA expects just 5 years to reach another $1Tn:
It took the chip industry 50 years to generate its first $1Tn in sales, but we expect AI to add another $1Tn in five years as a result of AI data centers, memory, semicap/reshoring, agentic CPU demand and physical AI. This underlying “spot” market is growing by more than 20% per year with supply crunches and price volatility creating demand for hedging solutions. Compute is a brand new market with zero futures contract volume to date. The Compute Exchange offers bilateral forward contracts that are more customizable than standardized futures contracts. While the Compute Exchange offers forward contracts, there is no public data on its volumes.
A new market is forming around AI:
Treating GPUs like a tradeable commodity could allow an AI company to hedge the future price of a H100 Nvidia chip for a specific time period. Today, GPU-compute pricing is heavily fragmented as GPUs can have different rental rates depending on the provider, region, availability and contract duration. Silicon Data has turned this fragmented dataset into standardized reference benchmarks that can be converted into derivatives. They are looking to create a regulated market for a fast-growing industry that has few options to hedge risk.
Silicon Data’s objective is to become the Platts of the compute market. Platts is the commodity pricing and benchmark subsidiary of S&P Global and operates key energy benchmarks (incl. Brent and West Texas Intermediate). It licenses its indices to exchanges which create futures and options contracts.
Compute resembles an emerging commodity but not without headwinds:
While the demand to hedge compute prices is increasing exponentially, there are some complications with creating GPU benchmarks. Specifically, the GPU market is highly fragmented, and pricing depends on multiple dependent variables: GPU configuration, data-center location, power and network performance, reliability and uptime, cloud software, contract duration and counterparty credit.
Future GPU rental cost is the economic reference:
The compute spot market currently exhibits an inefficient allocation mechanism for risk. Specifically, AI developers, cloud providers, data-center investors and chip producers all have exposure to future compute scarcity. A futures contract would let them manage this risk by hedging future rental costs. The relevant economic reference is not ownership of a chip but access to a standardized compute service bundle: accelerator time, host CPU and memory, storage, cooling, networking, location, reliability and provider-specific operating quality.