The question is for many employers is, just how do you make money out of the AI hype?
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 said.
“What perturbs me is the scale of the money being invested compared to the amount of revenue flowing from AI,” said Stuart Mills, a senior fellow at the London School of Economics.
AI’s usage by corporations is slipping, spending is tightening and the machine learning hype has massively outpaced the profits.
Billions have poured into AI, sending stock valuations soaring. But there are problems. Slowing adoption, surging costs and elusive profits are fuelling warnings that the boom may be headed for a hard reset.
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 American dominance in advanced chips.
Since Chat GPT’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.
If the boom turns into a bust, it will rock not only the share market but the American economy, sending a tsunami towards Australia.
Many economists think there is serious over-sell about streamlining repetitive tasks and improving forecasting.
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. They don’t adapt to changing circumstances.
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.