Investors are often keen on high-flying tech stocks that trade at attractive valuations, seeing them as prime opportunities for significant returns. However, Tom Essaye, founder of Sevens Report Research, has raised concerns that current low valuations of artificial intelligence (AI) stocks may reflect a broader fear among investors regarding a potential slowdown in the booming data center market.
In a recent note, Essaye emphasized that although high future earnings potential traditionally grants growth stocks higher valuations, the current low valuations of several AI stocks suggest skepticism about their ability to deliver on promised earnings. He provided examples of key stocks in the AI sector alongside their recent performance and forward price-to-earnings (PE) ratios. By comparison, the S&P 500 is trading at a forward PE ratio of 21.5.
Among the stocks highlighted, Nvidia has seen a 44% upside over the past year but is trading at a forward PE ratio of 21. Micron Technology stands out with a staggering 770% upside yet has a much lower forward PE of 10. Broadcom shows a 51% upside with a PE of 24, while SanDisk boasts an incredible 4,490% increase but sits at a forward PE of 14.
Essaye expressed concern that if AI adoption falters, it could lead to significant investment pullbacks, adversely affecting sales for companies heavily linked to AI infrastructure. He provided a hypothetical scenario involving Google (GOOGL), suggesting that if the tech giant decided to halt construction on multiple data centers due to high costs versus expected returns, it could trigger substantial order cancellations for key suppliers like Nvidia, Micron, Broadcom, and SanDisk. This scenario could drastically reduce demand for chips, networking equipment, and processing power.
He pointed to Oracle as a recent example illustrating investor unease; the stock has fallen nearly 25% since June 1 as the company has invested heavily in its AI initiatives. While Essaye is not necessarily warning that the market is approaching a tipping point, he drew parallels to the dot-com bubble of the late 1990s, which ultimately burst in 2000.
Essaye noted ongoing concerns about whether AI technology will be as profitable as anticipated, recalling the earlier internet boom when the profitability of connecting to the web did not meet investors’ high expectations, leading to a halt in infrastructure development.
While fears surrounding the AI sector have persisted for a while, Essaye argues that the current landscape could be indicative of deeper issues if confidence continues to wane, mirroring the trajectory seen in past market bubbles.
