Among the headline announcements this year: ChatGPT parent company Open AI, Softbank and Oracle pledged to invest $500 billion (€433 billion) in AI supercomputers, Open AI and chip giant Nvidia announced a $100 billion fund to maintain the United States' dominance in advanced chips, while Chinese tech giants Alibaba and Tencent hiked investments to help speed up China's ambition to lead AI by 2030.

Since ChatGPT’s debut in November 2022, AI-related stocks have added an estimated $17.5 trillion in market value, according to Bloomberg Intelligence, driving around 75% of the S&P 500’s gains and propelling companies like Nvidia and Microsoft to record-breaking valuations.

Corporations are hesitant over AI adoption
 

But signs of a hangover are getting harder to ignore. AI usage by corporations is slipping, spending is tightening and the machine learning hype has massively outpaced the profits.

Many economists think usage concerns, barely three years into AI going mainstream, dropkick the prevailing narrative that AI would revolutionize how businesses operate by streamlining repetitive tasks and improving forecasting.

"The vast bet on AI infrastructure assumes surging usage, yet multiple US surveys show adoption has actually declined since the summer," Carl-Benedikt Frey, professor of AI & work at the UK's University of Oxford, told DW. "Unless new, durable use cases emerge quickly, something will give — and the bubble could burst."

The US Census Bureau, which surveys 1.2 million US companies every fortnight, found that AI-tool usage at firms with more than 250 employees dropped from nearly 14% in June to under 12% in August.

AI’s biggest challenge remains its tendency to hallucinate — generating plausible but false information. Other weaknesses are inconsistent reliability and the poor performance of autonomous agents, which complete tasks successfully only about a third of the time.

"Unlike an intern who learns on the job, today’s pretrained [AI] systems don’t improve through experience. We need continual learning and models that adapt to changing circumstances," said Frey.

Unsustainable capital burn
 

As the gap widens between sky-high expectations and commercial reality, investor enthusiasm for AI is starting to fade.

In the third quarter of the year, venture-capital deals with private AI firms dropped by 22% quarter on quarter to 1,295, although funding levels remained above $45 billion for the fourth consecutive quarter, market intelligence firm CB Insights wrote last month.

"What perturbs me is the scale of the money being invested compared to the amount of revenue flowing from AI," economist Stuart Mills, a senior fellow at the London School of Economics, told DW.

AI’s biggest challenge remains its tendency to hallucinate — generating plausible but false information. Other weaknesses are inconsistent reliability and the poor performance of autonomous agents, which complete tasks successfully only about a third of the time.

"Unlike an intern who learns on the job, today’s pretrained [AI] systems don’t improve through experience. We need continual learning and models that adapt to changing circumstances," said Frey.

Unsustainable capital burn
 

As the gap widens between sky-high expectations and commercial reality, investor enthusiasm for AI is starting to fade.

In the third quarter of the year, venture-capital deals with private AI firms dropped by 22% quarter on quarter to 1,295, although funding levels remained above $45 billion for the fourth consecutive quarter, market intelligence firm CB Insights wrote last month.

"What perturbs me is the scale of the money being invested compared to the amount of revenue flowing from AI," economist Stuart Mills, a senior fellow at the London School of Economics, told DW.

Market leader OpenAI, which is backed by Microsoft, generated $3.7 billion in revenue last year, versus total operating expenses of $8-9 billion. The company says it is on course to make $13 billion this year but is still expected to burn through $129 billion before 2029, news site The Information calculated in September.

Mills thinks generative AI companies like Elon Musk's Grok and ChatGPT are "charging far less than they need to make a profit" and should raise subscription prices.

Few have quantified the AI bubble more starkly than Julien Garran, partner at UK-based research firm MacroStrategy Partnership. He argues that the sheer volume of capital flowing into AI — despite little evidence of sustainable returns — dwarfs previous speculative frenzies.

"We estimate a misallocation of capital equivalent to 65% of US GDP — four times bigger than the housing buildup before the 2008/9 financial crisis and 17 times bigger than the dot-com bust," Garran told DW.

Investors increasingly cautious
 

Recent earnings from Big Tech have sparked cautious optimism, but also fresh doubts about AI’s staying power. Data analytics and intelligence platform Palantir's Q3 revenue surged 63% year-over-year, but its stock price fell by up to 7% on the news. AMD and Meta also saw their strong AI-related earnings overshadowed by market concerns about sustainability.

That disconnect between soaring valuations and shaky fundamentals is exactly what worries Mills, who sees a widening gap between what AI promises and what it actually delivers to businesses.

"The data suggests that AI is not penetrating high enough up the value chain. Loads of people are using it, but it's not being used for tasks that directly contribute to value production," he told DW.

Nvidia's upcoming earnings on November 19 may prove a key test of whether the AI boom still has legs. In the second quarter, Nvidia's data center sales alone made up 88% of total revenue, which hit a record $46.7 billion. For Q3, the company has guided $54 billion, projecting 54% year-on-year growth, which would equate to a full-year total of more than $200 billion.

When will the bubble pop?
 

"With the exception of Nvidia, which is selling shovels in a gold rush, most generative AI companies are both wildly overvalued and wildly overhyped," Gary Marcus, Emeritus Professor of Psychology and Neural Science at New York University, told DW. "My guess is that it will all fall apart, possibly soon. The fundamentals, technical and economic, make no sense."

Garran, meanwhile, believes the era of rapid progress in large language models (LLMs) is drawing to a close, not because of technical limits, but because the economics no longer stack up.

"They [AI platforms] have already hit the wall," Garran said, adding that the cost of training new models is "skyrocketing, and the improvements aren’t much better."

Striking a more positive tone, Sarah Hoffman, director of AI Thought Leadership at the New York-based market intelligence firm AlphaSense, predicted a "market correction" in AI, rather than a "cataclysmic 'bubble bursting.'"

After an extended period of extraordinary hype, enterprise investment in AI will become far more discerning, Hoffmann told DW in an emailed statement, with the focus "shifting from big promises to clear proof of impact."

"More companies will begin formally tracking AI ROI [return on investment] to ensure projects deliver measurable returns," she added.