The AI Bubble and the U.S. Economy
By Servaas Storm
OpenAI’s Altman boasted that AGI can “discover new science,” because “I think we’ve cracked reasoning in the models,” adding that “we’ve a long way to go.” He “think[s] we know what to do,” saying that OpenAI’s o3 model “is already pretty smart,” and that he’s heard people say “wow, this is like a good PhD.” Announcing the launch of ChatGPT-5 in August, Mr. Altman posted on the internet that “We think you will love using GPT-5 much more than any previous Al. It is useful, it is smart, it is fast [and] intuitive. With GPT-5 now, it’s like talking to an expert—a legitimate PhD level expert in anything any area you need on demand, they can help you with whatever your goals are.”
But then things began to fall apart, and rather quickly so.
ChatGPT-5 is a letdown
The first piece of bad news is that much-hyped ChatGPT-5 turned out to be a dud—incremental improvements wrapped in a routing architecture, nowhere near the breakthrough to AGI that Sam Altman had promised. Users are underwhelmed. As the MIT Technology Review reports: “The much-hyped release makes several enhancements to the ChatGPT user experience. But it’s still far short of AGI.” Worryingly, OpenAI’s internal tests show GPT-5 ‘hallucinates’ in circa one in 10 responses of the time on certain factual tasks, when connected to the internet. However, without web-browsing access, GPT-5 is wrong in almost 1 in 2 responses, which should be troublesome. Even more worrisome, ‘hallucinations’ may also reflect biases buried within datasets. For instance, an LLM might ‘hallucinate’ crime statistics that align with racial or political biases simply because it has learned from biased data.
Building larger LLMs is leading nowhere
The ChatGPT-5 episode raises serious doubts and existential questions about whether the GenAI industry’s core strategy of building ever-larger models on ever-larger data distributions has already hit a wall. Critics, including cognitive scientist Gary Marcus, have long argued that simply scaling up LLMs will not lead to AGI, and GPT-5’s sorry stumbles do validate those concerns. It is becoming more widely understood that LLMs are not constructed on proper and robust world models, but instead are built to autocomplete, based on sophisticated pattern-matching—which is why, for example, they still cannot even play chess reliably and continue to make mind-boggling errors with startling regularity.
Ever-larger GenAI models do not become better, but worse, and do not reason, but rather parrot reasoning-like text. To illustrate, a recent paper by scientists at MIT and Harvard shows that even when trained on all of physics, LLMs fail to uncover even the existing generalized and universal physical principles underlying their training data. Specifically, LLMs follow a “Kepler-esque” approach: they can successfully predict the next position in a planet’s orbit, but fail to find the underlying explanation of Newton’s Law of Gravity. Instead, they resort to fitting made-up rules, that allow them to successfully predict the planet’s next orbital position, but these models fail to find the force vector at the heart of Newton’s insight. LLMs cannot and do not infer physical laws from their training data. Remarkably, they cannot even identify the relevant information from the internet. Instead, they make it up.
95 percent of generative AI pilot projects in companies are failing
Corporations had rushed to announce AI investments or claim AI capabilities for their products in the hope of turbocharging their share prices. Then came the news that the AI tools are not doing what they are supposed to do and that people are realizing it (see Ed Zitron). An August 2025 report titled The GenAI Divide: State of AI in Business 2025, published by MIT’s NANDA initiative, concludes that 95% of generative AI pilot projects in companies are failing to raise revenue growth. As reported by Fortune, “generic tools like ChatGPT [….] stall in enterprise use since they don’t learn from or adapt to workflows”. Quite.
Indeed, firms are backpedaling after cutting hundreds of jobs and replacing these by AI. For instance, Swedish “Buy Burritos Now, Pay Later” Klarna bragged in March 2024 that its AI assistant was doing the work of (laid-off) 700 workers, only to rehire them (sadly, as gig workers) in the summer of 2025. Other examples include IBM, forced to reemploy staff after laying off about 8,000 workers to implement automation. Recent U.S. Census Bureau data by firm size show that AI adoption has been declining among companies with more than 250 employees.
GenAI will not even make tech workers who do the coding redundant, contrary to the prediction by AI enthusiasts. OpenAI researchers have found (in early 2025) that advanced AI models (including GPT-4o and Anthropic’s Claude 3.5 Sonnet) still are no match for human coders. The AI bots failed to grasp how widespread bugs are or to understand their context, leading to solutions that are incorrect or insufficiently comprehensive. Another new study from the nonprofit Model Evaluation and Threat Research (METR) finds that in practice, programmers, using early 2025-AI-tools, are actually slower when using AI assistance tools, spending 19 percent more time when using GenAI than when actively coding by themselves. Programmers spent their time on reviewing AI outputs, prompting AI systems, and correcting AI-generated code.
The U.S. economy at large is hallucinating
The disappointing rollout of ChatGPT-5 raises doubts about OpenAI’s ability to build and market consumer products that users are willing to pay for. But it’s not just about OpenAI: the American AI industry as a whole has been built on the premise that AGI is just around the corner. All that is needed is sufficient “compute”, i.e., millions of Nvidia AI GPUs, enough data centers and sufficient cheap electricity to do the massive statistical pattern mapping needed to generate (a semblance of) “intelligence”. This, in turn, means that “scaling” (investing billions of U.S. dollars in chips and data centers) is the one-and-only way forward—and this is exactly what the tech firms, Silicon Valley venture capitalists and Wall Street financiers are good at: mobilizing and spending funds, this time for “scaling-up” generative AI and building data centers to support all the expected future demand for AI use.
During 2024 and 2025, Big Tech firms invested a staggering $750 billion in data centers in cumulative terms and they plan to roll out a cumulative investment of $3 trillion in data centers during 2026-2029 (Thornhill 2025). The so-called “Magnificent 7” (Alphabet, Apple, Amazon, Meta, Microsoft, Nvidia, and Tesla) spent more than $100 billion on data centers in the second quarter of 2025.
The surge in corporate investment in “information processing equipment” is huge. According to Torsten Sløk, chief economist at Apollo Global Management, data center investments’ contribution to (sluggish) real U.S. GDP growth has been the same as consumer spending over the first half of 2025. Financial investor Paul Kedrosky finds that capital expenditures on AI data centers (in 2025) have passed the peak of telecom spending during the dot-com bubble (of 1995-2000).
Following the AI hype and hyperbole, tech stocks have gone through the roof. The S&P500 Index rose by circa 58% during 2023-2024, driven mostly by the growth of the share prices of the Magnificent Seven. The weighted-average share price of these seven corporations increased by 156 percent during 2023-2024, while the other 493 firms experienced an average increase in their share prices of just 25 percent. America’s stock market is largely AI-driven.
Tech stocks thus are considerably overvalued. Torsten Sløk, chief economist at Apollo Global Management, warned (in July 2025) that AI stocks are even more overvalued than dot-com stocks were in 1999. We all remember how the dot-com bubble ended—and hence Sløk is right in sounding the alarm over the apparent market mania, driven by the “Magnificent 7” that are all heavily invested in the AI industry.
Big Tech does not buy these data centers and operate them itself; instead the data centers are built by construction companies and then purchased by data center operators who lease them to (say) OpenAI, Meta or Amazon. Wall Street private equity firms such as Blackstone and KKR are investing billions of dollars to buy up these data center operators, using commercial mortgage-backed securities as source funding. Data center real estate is a new, hyped-up asset class that is beginning to dominate financial portfolios. Blackstone calls data centers one of its “highest conviction investments.” Wall Street loves the lease-contracts of data centers which offer long-term stable, predictable income, paid by AAA-rated clients like AWS, Microsoft and Google. Some Cassandras are warning of a potential oversupply of data centers, but given that “the future will be based on GenAI”, what could possibly go wrong?
The question therefore is: How long investors will continue to prop up sky-high valuations of the key firms in the GenAI race remains to be seen. Earnings of the AI industry continue to pale in comparison to the tens of billions of U.S. dollars that are spent on data center growth. According to an upbeat S&P Global research note published in June, 2025 the GenAI market is projected to generate $85 billion in revenue 2029. However, Alphabet, Google, Amazon and Meta together will spend nearly $400 billion on capital expenditures in 2025 alone. At the same time, the AI industry has a combined revenue that is little more than the revenue of the smart-watch industry.
So, what if GenAI just is not profitable? This question is pertinent in view of the rapidly diminishing returns to the stratospheric capital expenditures on GenAI and data centers and the disappointing user-experience of 95 percent of firms that adopted AI. One of the largest hedge funds in the world, Florida-based Elliott, told clients that AI is overhyped and Nvidia is in a bubble, adding that many AI products are “never going to be cost-efficient, never going to actually work right, will take up too much energy, or will prove to be untrustworthy.” “There are few real uses,” it said, other than “summarizing notes of meetings, generating reports and helping with computer coding”. It added that it was “skeptical” that Big Tech companies would keep buying the chipmaker’s graphics processing units in such high volumes.
Locking billions of U.S. dollars in into AI-focused data centers without a clear exit strategy for these investments in case the AI craze ends, only means that systemic risk in finance and the economy is building. With data-center investments driving U.S. economic growth, the American economy has become dependent on a handful of corporations, which have not yet managed to generate one dollar of profit on the ‘compute’ done by these data center investments.
America’s high-stakes geopolitical bet gone wrong
The AI boom (bubble) developed with the support of both major political parties in the U.S. The vision of American firms pushing the AI frontier and reaching GenAI first is widely shared—in fact, there is a bipartisan consensus on how important it is that the U.S. should win the global AI race. America’s industrial capability is critically dependent on a number of potential adversary nation-states, including China. In this context, America’s lead in GenAI is considered to constitute a potential very powerful geopolitical lever: If America manages to get to AGI first, so the analysis goes, it can build up an overwhelming long-term advantage over especially China.
That is the reason why Silicon Valley, Wall Street and the Trump administration are doubling down on the “AGI First” strategy. But astute observers highlight the costs and risks of this strategy. Prominently, Eric Schmidt and Selina Xu worry, in the New York Times of August 19, 2025, that “Silicon Valley has grown so enamored with accomplishing this goal [of AGI] that it’s alienating the general public and, worse, bypassing crucial opportunities to use the technology that already exists. In being solely fixated on this objective, our nation risks falling behind China, which is far less concerned with creating A.I. powerful enough to surpass humans and much more focused on using the technology we have now.”
Schmidt and Xu are rightly worried. Perhaps the plight of the U.S. economy is captured best by OpenAI’s Sam Altman who fantasizes about putting his data centers in space: “Like, maybe we build a big Dyson sphere around the solar system and say, “Hey, it actually makes no sense to put these on Earth.” For as long as such ‘hallucinations’ on using solar-collecting satellites to harvest (unlimited) star power continue to convince gullible financial investors, the government and users of the “magic” of AI and the AI industry, the U.S. economy is surely doomed.
Servaas Storm is a Dutch economist and author who works on macroeconomics, technological progress, and climate change. He is a Senior Lecturer at Delft University of Technology.
Source: ineteconomics.org, October 2, 2025. A longer version of this article is available online.