Issued on: 12/11/2025 - FRANCE24
44:31 min VIDEO
Could the artificial intelligence boom already be running out of road? We examine the warning signs. To think that three short years ago, the commercial launch of ChatGPT took the world by storm. AI has since sparked a global race for cash, energy resources and data – all to feed the seemingly insatiable appetite of large language model computing systems.
With a few US companies dominating the AI race – and a US president who's all-in with billionaires – market watchers worry about investors tempted by the easy money of rising tech stocks at the expense of the entire rest of the economy. Is it a bubble? Is it about to burst?
And with what consequences? How should Europe and the rest of the world prepare? More broadly, is AI changing humanity and our world for better – or for worse?
Produced by François Picard, Rebecca Gnignati, Charles Wente, Ilayda Habip & Jean-Vincent Russo
OUR GUESTS
Simon McGARR Solicitor with McGarr Solicitors and director of Data Compliance Europe
Leïla MörchPartner, Maresquier Partners
Billions have poured into AI, helping stock valuations soar. But the cracks are starting to show. Slowing adoption, surging costs and elusive profits are fueling warnings that the boom may be headed for a hard reset.
The artificial intelligence (AI) party is still in full swing, with tens of billions globally pouring into infrastructure, startups and attracting the best talent.
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.
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.
How AI boom is impacting Latin America, US 07:31
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.
Edited by: Uwe Hessler
Nik Martin is one of DW's team of business reporters.
AI’s energy usage is less than previously thought
Energy consumption in the U.S. shifts perception of the environmental risks of AI
University of Waterloo
Contrary to popular belief, new research finds that the use of artificial intelligence has a minimal effect on global greenhouse gas emissions and may actually benefit the environment and the economy.
For their study, researchers from the University of Waterloo and the Georgia Institute of Technology combined data on the U.S. economy with estimates of AI use across industries to determine the environmental fallout if AI use continues its current trajectory.
According to the U.S. Energy Information Administration, 83 per cent of the U.S. economy is powered by petroleum, coal and natural gas, all of which contribute to climate change when burned. The study authors found that while power usage from AI in the U.S. equalled the energy consumption for all of Iceland, the amounts were not noticeable on a global or national scale.
“It is important to note that the increase in energy use is not going to be uniform. It’s going to be felt more in the places where electricity is produced to power the data centres,” said Dr. Juan Moreno-Cruz, a professor in the Faculty of Environment at Waterloo and Canada Research Chair in Energy Transitions. “If you look at that energy from the local perspective, that's a big deal because some places could see double the amount of electricity output and emissions. But at a larger scale, AI’s use of energy won’t be noticeable.”
While this paper did not examine the effects on local economies where the data centres are located, the researchers found some encouraging results.
“For people who believe that the use of AI will be a major problem for the climate and think we should avoid it, we're offering a different perspective,” Moreno-Cruz said. “The effects on climate are not that significant, and we can use AI to develop green technologies or to improve existing ones.”
To reach their conclusions, environmental economists Moreno-Cruz and Dr. Anthony Harding examined different sectors of an economy, the jobs within those sectors, and what portion of them could be done by AI.
Moreno-Cruz and Harding plan to repeat the study for other countries to measure the impacts of AI adoption in other parts of the world.
The paper, Watts and Botts: The Energy Implications of AI Adoption, appears in Environmental Research Letters.
Journal
Environmental Research Letters
Method of Research
Data/statistical analysis
Subject of Research
Not applicable
Article Title
Watts and bots: the energy implications of AI adoption
Article Publication Date
11-Nov-2025
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