Three reasons why AI's momentum could stall in 2025

Jordan Times

06-02-2025

LONDON – The rapid pace of technological advances over the past year, especially in artificial intelligence, has provided many reasons for optimism. But as we head into 2025, there are signs that AI's momentum may be waning.
Since 2023, the dominant narrative has been that the AI revolution will drive productivity and economic growth, paving the way for extraordinary technological breakthroughs. PwC, for example, projects that AI will add nearly $16 trillion to global GDP by 2030, a 14 per cent increase. Meanwhile, a study by Erik Brynjolfsson, Danielle Li, and Lindsey R. Raymond estimates that generative AI could boost worker productivity by 14 per cent on average and by 34 per cent for new and low-skilled workers.
Recent announcements by Google and OpenAI seem to support this narrative, offering a glimpse into a future that not long ago was confined to science fiction. Google's Willow quantum chip, for example, reportedly completed a benchmark computation, a task that would take today's fastest supercomputers ten septillion years (ten followed by 24 zeros), in under five minutes. Likewise, OpenAI's new o3 model represents a major technological breakthrough, bringing AI closer to the point where it can outperform humans in any cognitive task, a milestone known as 'artificial general intelligence.'
But there are at least three reasons why the AI boom could lose steam in 2025. First, investors are increasingly questioning whether AI-related investments can deliver significant returns, as many companies are struggling to generate enough revenue to offset the skyrocketing costs of developing cutting-edge models. While training OpenAI's GPT-4 cost more than $100 million, training future models will likely cost more than $1 billion, raising concerns about the financial sustainability of these efforts.
To be sure, investors are eager to capitalise on the AI boom, with venture capital firms investing a record $97 billion in US-based AI startups in 2024. But it appears that even industry leaders like OpenAI are burning through cash too quickly to generate meaningful returns, leading investors to worry that much of their capital has been misallocated or wasted. A back-of-the-envelope calculation suggests that a $100 billion investment in AI would require at least $50 billion in revenue to produce an acceptable return on capital, accounting for taxes, capital expenditures, and operating expenses. But the entire sector's annual revenues, according to my sources, total just $12 billion, with OpenAI accounting for roughly $4 billion. In the absence of a 'killer app' for which customers are willing to pay substantial sums, a significant portion of VC investments could end up worthless, triggering a decline in investment and spending.
Second, the enormous amounts of energy required to operate and cool massive data centers could impede AI's rapid growth. By 2026, according to the International Energy Agency, AI data centers will consume 1,000 terawatt-hours of electricity annually, exceeding the United Kingdom's total electricity and gas consumption in 2023. The consultancy Gartner projects that by 2027, 40 per cent of existing data centers will be 'operationally constrained' by limited power availability.
Third, large language models appear to be approaching their limits as companies grapple with mounting challenges like data scarcity and recurring errors. LLMs are primarily trained on data scraped from sources such as news articles, published reports, social media posts, and academic papers. But with a finite supply of high-quality information, finding new datasets or creating synthetic alternatives has become increasingly difficult and costly. Consequently, these models are prone to generating incorrect or fabricated answers ('hallucinations'), and AI companies may soon run out of the fresh data needed to refine them.
Computing power is also approaching its physical limits. In 2021, IBM unveiled a two-nanometer chip, roughly the size of a fingernail, capable of fitting 50 billion transistors and improving performance by 45 per cent compared to its seven-nanometer predecessor. While undeniably impressive, this milestone also raises an important question: Has the industry reached the point of diminishing returns in its quest to make ever-smaller semiconductors?
If these trends persist, the current valuations of publicly traded AI companies may not be sustainable. Notably, private investment is already showing signs of declining. According to the research firm Preqin, VC firms raised $85 billion in the first three quarters of 2024, a sharp drop from the $136 billion raised during the same period in 2023.
The good news is that should today's AI giants start to falter, smaller competitors could seize the opportunity and challenge their dominance. From a market standpoint, such a scenario could foster increased competition and reduce concentration, preventing a repeat of the conditions that allowed the so-called 'Magnificent Seven', Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, and Tesla, to dominate the US tech industry.
Dambisa Moyo, an international economist, is the author of four New York Times bestselling books, including 'Edge of Chaos: Why Democracy Is Failing to Deliver Economic Growth – and How to Fix It' (Basic Books, 2018). Copyright: Project Syndicate, 2025. www.project-syndicate.org