LIVE MARKETS-AI scarcity effect gives chip stocks pricing power, edge over Big Tech

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AI SCARCITY EFFECT GIVES CHIP STOCKS PRICING POWER, EDGE OVER BIG TECH

Semiconductor stocks continue to outpace the broader market as strong demand for generative AI infrastructure gives chipmakers pricing power and clearer near-term earnings visibility. The PHLX Semiconductor Index .SOX closed at a record high on Wednesday, highlighting investors’ growing preference for the sector.

Jessica Rabe, co-founder of DataTrek Research, points to the VanEck Semiconductor ETF SMH.O, as an example of the theme’s strength. While the ETF is heavily concentrated in its top 10 holdings — notably Nvidia NVDA.O and Taiwan Semiconductor Manufacturing TSM.N — performance has remained resilient. Through Wednesday’s close, SMH was up 17.6% year-to-date, compared with a 1.1% decline for the S&P 500 .SPX, even as geopolitical risks remain elevated following the U.S.-Israeli conflict with Iran.

Rabe notes that investors in the semiconductor theme must have high conviction in leading names such as Nvidia, TSM, Broadcom AVGO.O and Advanced Micro Devices AMD.O, particularly given elevated valuations. Still, only a subset of holdings needs to outperform for the ETF to generate strong overall returns, as seen so far this year.

The four major chipmakers are also seeing much stronger earnings estimate momentum than U.S. Big Tech hyperscalers. Rabe says that average estimates for the chipmakers have risen 9.9% for this year and 17.9% for next year, vs. gains of 2.8% and 2.4%, respectively, for hyperscalers. Reflecting that divergence, through Wednesday’s close, the chipmakers were up 6.9% year-to-date, while hyperscalers were down 8.4%.

“Heightened demand for gen AI infrastructure has benefited semis, creating a scarcity effect and pricing power,” Rabe writes. She adds that with fabs running near capacity and backlogs high, chipmakers have locked in revenue for coming quarters, giving investors greater confidence in their near-term earnings vs. an uncertain payoff timeline for hyperscalers’ AI investments.

(Terence Gabriel)

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