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Chip stocks plunge 7% on AI demand cooling fears

Yahoo Finance AI2d ago4 min read
Chip stocks plunge 7% on AI demand cooling fears

Key takeaway

The Philadelphia Semiconductor Index fell over 7% as investors grew concerned that large cloud providers may have over-invested in AI infrastructure and cannot demonstrate sufficient returns to justify continued high spending. Reports that Meta has spare AI computing capacity to lease and that Apple is exploring Chinese chip suppliers amplified fears of future oversupply, which could shrink orders for chips and memory components that chipmakers depend on.

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3 Key Points

  • What happened

    The Philadelphia Semiconductor Index fell over 7% after a Citi analyst questioned whether large cloud platforms would sustain high spending on AI infrastructure without proven returns. Reports that Meta plans to lease out spare AI computing capacity and that Apple is in talks with Chinese chip suppliers also weighed on the sector. Amkor fell 13.5%, Penguin Solutions fell 12.4%, and Entegris fell 13.6%.

  • Why it matters

    For two years, the semiconductor sector has traded on the assumption of an insatiable GPU and memory shortage. If Meta—which guided to as much as $145 billion(約23兆円) of capex for the year—has enough spare capacity to lease it out, the market reads that as a signal hyperscalers may have over-built. This could mean future orders for GPUs, HBM, and NAND memory shrink, threatening chipmaker revenues.

  • What to watch

    Entegris has had 36 moves greater than 5% over the last year, making this decline significant even for a volatile stock. Two days prior, analysts at Mizuho and UBS had raised price targets on Entegris, with UBS lifting its target to $205 from $185 while keeping a Buy rating, reflecting an improved outlook for wafer fab equipment spending.

FAQ

Which semiconductor stocks fell the most?
Amkor fell 13.5%, Penguin Solutions fell 12.4%, and Entegris fell 13.6%.
Why are investors worried about AI chip demand now?
A Citi analyst questioned whether large cloud platforms would continue their high rate of spending on AI infrastructure if they could not show investors the cost was generating returns. Additionally, Meta's plan to lease out spare AI computing capacity suggested hyperscalers may have over-built capacity.

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