Traders made a strong move away from AI stocks in July 2024, leading up to chipmaker Nvidia losing as much as 26% in their share prices between June and August. Other big tech names felt the pain too, with Alphabet’s disappointing earnings report triggering 5% losses for their shares on July 24th, Tesla following suit to the tune of 12%, and, for Meta Platforms, it was 5.6%. All this contributed to pulling down the tech-focused Nasdaq 100 index by 9% in only a few weeks.
Did this signify the beginning of the end for what has appeared to be an AI stock bubble? Some analysts said no because of some key differences with market bubbles in the past. For instance, running up to the real estate crisis in 2008, it was clear that many home purchasers would not be able to pay their mortgages. Looking at internet firms that crashed in 2000, we see that they had been struggling to maintain growth beforehand. In other words, we might account those downturns to fundamental problems in the underlying businesses. By contrast, demand for AI products seemed robust in Q2 2024, which we see from the 29% revenue growth in Microsoft Azure – the company’s cloud segment with heavy AI exposure.
How, then, should we explain the pullback from AI in July? In this view, it was a temporary correction in inflated stock prices, fueled by a rotation into small-cap stocks in anticipation of Fed interest-rate cuts, (since small caps stood to benefit most from the dovish change). All of this sounds well and good, but other analysts see things differently, spying a more fundamental problem in the sector. Join us to find out why, especially if you trade stocks online with iFOREX Europe.
Capital expenditures
Alec Young of Mapsignals interpreted the late-July bearishness differently. “The overarching concern”, he explained, “is, where is the ROI (return on investment) on all the AI infrastructure spending?”. With AI firms spending huge amounts of money in developing their required infrastructure, traders were doubting that these expenditures would yield bigger earnings any time soon. If Young was right, the July correction could be symptomatic of a less temporary issue.
Goldman Sachs’ Jim Covello is one of the economists who feel this way. He writes that “AI technology is exceptionally expensive, and to justify those costs, the technology must be able to solve complex problems, which it isn’t designed to do”. Covello believes the $1 trillion earmarked for AI expansion in coming years will not bring about the economic benefits many are hoping for. Costs for AI applications will not decline as quickly as people expect, which means fewer tasks will be automated with the technology, and companies will not accrue greater value through utilizing it.
Daron Acemoglu of MIT (Massachusetts Institute of Technology) thinks along similar lines, suggesting that “transformative changes [in AI] won’t happen quickly, and few – if any – will likely occur within the next ten years”. Based on this, he questions the narrative of rapid, limitless technological advancement in AI and urges traders to curb their enthusiasm on this score.
Three viewpoints
Peter Oppenheimer, also of Goldman Sachs, agrees with his colleague Jim that the gains seen in tech since 2022 are premised on “the hopes and aspirations around AI”, and that elevated expenditures will hurt the growth rates of these large companies down the line. He insists, though, that AI is not in a bubble because the underlying technology will be taken over by a fresh wave of companies, who will then use it to generate real value in other areas.
Capital Economics believe AI adoption will indeed foster economic growth, and that this will lead to its own undoing. The improved growth will cause higher inflation, which will necessitate interest rate hikes, and these will reign in stock prices dramatically. The bubble, in their view, will pop early in 2026.
The European Central Bank agree AI is in a bubble, but estimate that it could burst much more imminently. If traders continue to be disappointed by earnings reports, we could see precipitous losses in share prices pretty soon and, “in a context of deeply integrated global equity markets, it points to the risk of adverse global spillovers”.
Final thoughts
Returning to our earlier point about the difference between the AI story and previous market bubbles, we mentioned that, in those cases, vital aspects of business operations had deteriorated, while AI demand still appears strong. Here we might point out that the solid demand surrounding cloud services does not imply that the many other use cases priced into the market will materialize. On the contrary, the burden of proof seems to fall on AI firms to prove that their enormous expenditures are likely to yield fruit in other fields.
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