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Rola Khalaf, editor of the FT, selects her favorite stories in this weekly newsletter.
There are many realities when it comes to tech bubbles. One is that it's often hard to see when you're in it: each individual's spending or investment decision may seem rational, even if the net effect is extreme.
While it is generally agreed that a bubble is forming, it is often inflated and investors who bail out early. And after a bubble bursts, it can take years to work out whether it was just a product of hype or a precursor to an even bigger tech boom.
As U.S. tech companies head into their latest earnings season, a distinct spiral in tech valuations has begun on Wall Street. Mid-2024 was always destined to be an “air pocket” for creative artificial intelligence. The boom in technology-driven investment is huge, but it takes time for all of this new potential to be put to productive use by the end users of the tech industry. Wall Street's patience is about to be tested.
Software companies, which are best positioned to capture the value of new technology as features in their existing products, struggle under a stock market cloud.
Nor has AI provided any compelling new services for consumers, even if Apple has recently promised to sprinkle the technology liberally across its devices. For many people, discovering ChatGPT was a source of interest. But the first time they picked up an iPhone, used Google's search box or looked up their friends on Facebook, it didn't change their digital lives.
At best, it signals a break in the widespread use of generative AI. At worst, it shows that technology is not as transformative as the tech companies claim. The longer the lag, the sharper the gap between booming investment and waning demand.
Underscoring this is the narrowness of the base on which these investments are rising. Nvidia said late last year that cloud companies — a market dominated by a handful of big players — account for more than half of its data center chip sales. Any hint from the big tech companies that they are moderating their spending this earnings season would be a serious blow.
Yet as tech companies prepare to announce their latest earnings, all signs are that the bullishness is still in full swing. Many business users have just launched their first pilot projects using the technology and will continue to test the technology in the coming months, even if it's unclear what their final use will be. will Investing in big language models and the infrastructure to support them has become a strategic imperative even for the big tech companies themselves. If machines that can “understand” language and images represent an entirely new computing platform, as many in the tech world expect, all the major players will need to have a strong foothold in the technology.
It is also worth noting that these companies have enough financial power to sustain and even expand the war. The combined operating cash flow of Apple, Microsoft, Alphabet, Amazon and Meta grew 99% over the past five years to reach $456 billion in 2023. That was enough to offset capital spending, which rose 96 percent to $151 billion.
Meanwhile, the next major product cycle for chipmaker Nvidia, based on its new Blackwell chip architecture, isn't even slated to begin until the second half of this year. The promised low cost of training and running large AI models has guaranteed a rush from consumers, even as demand for the earlier generation of chips remains strong.
Here's a paradox that all new tech platforms have in common. With the cost of using new technology reduced, customers could theoretically avoid making fewer purchases. But rapidly falling costs usually lead to the search for new uses, and demand instead increases. As with everything to do with generative AI, it's happening at a rapid pace, and it's hard to tell how closely it will mirror other disruptive tech cycles.
At some point, of course, all that investment has to pay off. If not, chief executives who have been forced by their boards and fear of competition to demand that their companies find uses for generative AI will eventually lose patience and move on. But all indications are that we are not at that point yet.
richard.waters@ft.com