If the AI bubble bursts, what will it mean for research?

After years of hype and ballooning investment, the boom in artificial intelligence technology is beginning to show signs of strain. Many financial analysts now agree that there is an ‘AI bubble’, and some speculate it could finally burst in the next few months.

In economic terms, the rise of AI is unlike any other tech boom in history — there is now 17 times more investment in AI than in Internet companies before the dot-com crash of the early 2000s. And, valued at around US$4.6 trillion, the AI company NVIDIA was worth more than the economies of every nation except the United States, China and Germany.

But AI is not living up the promise of revolutionizing multiple sectors — nearly 80% of companies using AI found it had had no significant impact on their earnings, according to a report from management consulting firm McKinsey, and concerns over the basic architecture of chatbots is leading scientists to say that AI has the potential to harm their research. These doubts over the technology’s utility, and financial viability, is leading analysts and investors to speculate that a crash is coming. Even tech chief executives such as Sam Altman of ChatGPT’s parent company OpenAI in San Francisco, California, have admitted that parts of the field are “kind of bubbly right now”.

So, if a crash is imminent, how will it affect AI research and the scientists and engineers who make it happen?

Lessons from the noughties

Some analysts say that an AI-market collapse would be even more catastrophic than the dot-com crash — a shock that wiped out more than $5 trillion in stock-market value and led to hundreds of thousands of job losses in the tech industry alone. Like those of other tech bubbles before it, the dot-com crash had a lasting impact on computer-science research, says John Turner, an economist and historian at Queen’s University Belfast, UK. “But it wasn’t all bad,” he adds.

“In 2000, a lot of highly skilled electronic engineers and computer scientists lost their jobs” and demand for computer-science graduates plummeted, he says. This led to a drop in the numbers of computer-science graduates — but despite this, research output didn’t falter, and the average number of computer-science publications continued to rise each year during and after the dot-com crash (see Dot-com crash aftermath’). Similarly, the roll-out of telecommunication technologies such as mobile phones and the Internet continued unabated.

Brent Goldfarb, an economist at University of Maryland in College Park, says similar lay-offs in AI researchers and developers would happen were the AI bubble to burst. The biggest impact “would be on the hoard of start-ups jumping on the AI bandwagon, like the tenth AI notetaking app or AI scientist”, he says. OpenAI, Google, NVIDIA and other major AI companies “will likely survive”, he says. “The last thing they’ll do is get rid of their scientific core; that’s the path to the future.”

In fact, crashes can have a silver lining: they can take innovation into other sectors when leading scientists change jobs, Turner says. Take, for instance the British bicycle crash of 1896. “Motorcycles, motorcars, the Wright brothers; all can trace their origins to the bicycle bubble,” he says. “The railway ‘manias’ of the nineteenth century left the legacy of railways for the benefit of people, much like the dot-com bubble gave society the Internet.”

Liberated researchers

Currently, the tech industry eclipses academia when it comes to AI, in terms of both investment and publication output. Some researchers have called this an “AI brain drain”, which sidelines exploratory science in favour of commercial interest¹. “If I’m an AI researcher working at OpenAI, why would I go to a university when I earn ten times the salary?” Goldfarb says.