This paper argues that (i) we have reached “peak GenAI” in terms of current Large Language Models (LLMs); scaling (building more data centers and using more chips) will not take us further to the goal of “Artificial General Intelligence” (AGI); returns are diminishing rapidly; (ii) the AI-LLM industry and the larger U.S. economy are experiencing a speculative bubble, which is about to burst.

The U.S. is undergoing an extraordinary AI-fueled economic boom: The stock market is soaring thanks to exceptionally high valuations of AI-related tech firms, which are fueling economic growth by the hundreds of billions of U.S. dollars they are spending on data centers and other AI infrastructure. The AI investment boom is based on the belief that AI will make workers and firms significantly more productive, which will in turn boost corporate profits to unprecedented levels. But the summer of 2025 did not bring good news for enthusiasts of generative Artificial Intelligence (GenAI) who were all hyped up by the inflated promise of the likes of OpenAI’s Sam Altman that “Artificial General Intelligence” (AGI), the holy grail of current AI research, would be right around the corner.

Let us more closely consider the hype. Already in January 2025, Altman wrote that “we are now confident we know how to build AGI”. Altman’s optimism echoed claims by OpenAI’s partner and major financial backer Microsoft, which had put out a paper in 2023 claiming that the GPT-4 model already exhibited “sparks of AGI.” Elon Musk (in 2024) was equally confident that the Grok model developed by his company xAI would reach AGI, an intelligence “smarter than the smartest human being”, probably by 2025 or at least by 2026. Meta CEO Mark Zuckerberg said that his company was committed to “building full general intelligence”, and that super-intelligence is now “in sight”. Likewise, Dario Amodei, co-founder and CEO of Anthropic, said “powerful AI”, i.e., smarter than a Nobel Prize winner in any field, could come as early as 2026, and usher in a new age of health and abundance — the U.S. would become a “country of geniuses in a datacenter”, if ….. AI didn’t wind up killing us all.

For Mr. Musk and his GenAI fellow travelers, the biggest hurdle on the road to AGI is the lack of computing power (installed in data centers) to train AI bots, which, in turn, is due to a lack of sufficiently advanced computer chips. The demand for more data and more data-crunching capabilities will require about $3 trillion in capital just by 2028, in the estimation of Morgan Stanley. That would exceed the capacity of the global credit and derivative securities markets. Spurred by the imperative to win the AI-race with China, the GenAI propagandists firmly believe that the U.S. can be put on the yellow brick road to the Emerald City of AGI by building more data centers faster (an unmistakenly “accelerationist” expression).

Interestingly, AGI is an ill-defined notion, and perhaps more of a marketing concept used by AI promotors to persuade their financiers to invest in their endeavors. Roughly, the idea is that an AGI model can generalize beyond specific examples found in its training data, similar to how some human beings can do almost any kind of work after having been shown a few examples of how to do a task, by learning from experience and changing methods when needed. AGI bots will be capable of outsmarting human beings, creating new scientific ideas, and doing innovative as well as all of routine coding. AI bots will be telling us how to develop new medicines to cure cancer, fix global warming, drive our cars and grow our genetically modified crops. Hence, in a radical bout of creative destruction, AGI would transform not just the economy and the workplace, but also systems of health care, energy, agriculture, communications, entertainment, transportation, R&D, innovation and science.

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.

Of note here is that AI chatbots can be and are actively used to spread misinformation (see here and here). According to recent research, chatbots spread false claims when prompted with questions about controversial news topics 35% of the time — almost double the 18% rate of a year ago (here). AI curates, orders, presents, and censors information, influencing interpretation and debate, while pushing dominant (average or preferred) viewpoints while suppressing alternatives, quietly removing inconvenient facts or making up convenient ones. The key issue is: Who controls the algorithms? Who sets the rules for the tech bros? It is evident that by making it easy to spread “realistic-looking” misinformation and biases and/or suppress critical evidence or argumentation, GenAI does and will have non-negligible societal costs and risks — which have to be counted when assessing its impacts.

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 (here and here), 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.

My new INET Working Paper discusses three sobering research studies showing that novel 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 exi