Anthropic’s self-developed AI chip plan triggers a sharp decline in US semiconductor stocks! SanDisk plunges 12%, Nasdaq 100 turns to fall over 1%

Anthropic’s self-developed AI chip plan triggers a sharp decline in US semiconductor stocks! SanDisk plunges 12%, Nasdaq 100 turns to fall over 1%

Reports say Anthropic has started early work on self-developing AI chips, and has held talks with Samsung about potential manufacturing collaboration, causing shares of U.S. semiconductor companies to fall during Thursday's session, turning the Nasdaq 100 from gains to losses. Early in the U.S. trading session, stocks briefly rose due to weak nonfarm payroll data.

According to The Information, Anthropic PBC is negotiating cooperation with Samsung Electronics, hoping that the latter will become the manufacturing partner for its custom artificial intelligence (AI) chips. Anthropic’s chip project is still in its early stages, and the company has yet to determine the processor’s specific purpose, performance targets, or how it will be integrated into servers.

Anthropic told The Information that Amazon’s Trainium chip, Google’s TPU (Tensor Processing Unit), and Nvidia’s GPU will remain core components of the company’s computing strategy in the future.

After the news was released, semiconductor company stocks fell during Thursday’s session. The Nasdaq 100 index fell 1.3%, Philadelphia Semiconductor Index dropped 4.3%. Sandisk plunged 12%, Western Digital dropped 7.5%, Micron fell 4.3%, AMD dropped 3.9%, Intel fell 2.9%, Nvidia dropped 1.3%.

European chip stocks were also generally under pressure: ASML fell over 3%, ASM International fell over 3%, BE Semiconductor Industries fell over 3%, Aixtron fell over 3%, Nokia dropped 4.1%.

As demand for AI services surges, major AI companies are seeking to diversify chip supply to meet the rapidly growing need for computing power. Last month, OpenAI released its first custom AI chip, developed in collaboration with Broadcom. The generative AI company hopes to improve overall computing efficiency by customizing key hardware required to run its models.

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