Key Points
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Extending the useful life of an asset brings down its depreciation costs.
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Burry claims that many tech companies are boosting earnings by setting higher-than-realistic useful lives for their chips.
Artificial intelligence (AI) has been a tremendous growth opportunity for tech companies in recent years. It’s led to them investing heavily in data centers, chips, and other crucial infrastructure to ensure they don’t fall behind their rivals. While it’s still debatable just how much of a payoff there will be from all these investments, it has led to surging valuations for many top tech companies.
But one person who is a bit skeptical on the hype in AI is Michael Burry, hedge fund manager and founder of Scion Capital. He’s known for predicting the housing crash in 2008. Now, he’s turned his attention to AI, where he believes that companies are overestimating the useful life of chips. While that may not seem like a big deal, it could mean that companies are underestimating their expenses, and that could be a big problem for investors.
A longer useful life means lower depreciation costs
Estimating the useful life of an asset is crucial in accounting because that’s how long of a period a company will be spreading its cost over. The longer the useful life, the lower cost per year. There can be an incentive there for companies to overestimate useful life for the sake of lowering expenses and boosting profits. Investors might scoff at depreciation expenses since they are further down the income statement and they aren’t directly related to a company’s current day-to-day operating activities, but they can have a material impact on earnings.
If earnings are overstated that means the valuations of these already expensive stocks are even higher than they appear to be. Chipmaker Nvidia (NASDAQ: NVDA), the most valuable company in the world, trades at 45 times its trailing earnings, which may seem a bit high. But its forward price-to-earnings multiple, which is based on analyst expectations, is relatively modest at 23. That’s only slightly higher than the S&P 500 average of 21.It’s a chip supplier, and its sales numbers suggest that demand remains robust, which may indeed suggest frequent refresh cycles.
In Nvidia’s most recent earnings, the company reported $57 billion in sales for the period ending Oct. 26, which was a year-over-year increase of 62%. CEO Jensen Huang says that “Blackwell sales are off the charts, and cloud GPUs are sold out.”
Companies may need to upgrade sooner than investors expect
If Burry’s claims are true, then it may not be too long before a troubling pattern, which is a frequent upgrade cycle. If chips are only useful for two to three years, as Burry suggests they might be (versus the five-plus years that hyperscalers are claiming), then that could mean more frequent refresh cycles. Companies may be ramping up capital expenditures every couple of years to stay relevant in what’s proving to be an intense AI race.
While this might be good news for Nvidia as it could suggest continually high investments into its chips, it could also put pressure on hyperscalers to pull back on some spending, especially if AI projects aren’t paying off but investments remain high.
I believe it’s a plausible scenario that chips may be useful for only a few years and that lifespan estimates may be a bit high. With companies rushing out to get ahead of one another, it seems unlikely that a hyperscaler is going to be willing to sit on five- or six-year-old chips. The AI boom could put pressure on…
Should you be worried about an AI bubble?
If Burry’s prediction turns out to be true and companies are setting longer useful lives for AI infrastructure that’s unrealistic, it could spark concern among investors.
A bubble may have already formed, and the market reaction could influence future valuations.
Now may be a good time to consider your overall exposure to the AI sector as you weigh the potential risks and rewards.
Should you buy stock in Nvidia right now?
Before you buy stock in Nvidia, consider this: