Anthropic's AI Chip Development Initiative Triggers Sharp Decline in US Semiconductor Stocks; SanDisk Plummets 12%, Nasdaq 100 Turns Negative

Deep News
07/03

Reports that Anthropic has commenced preliminary efforts to develop its own AI chips, coupled with discussions with Samsung regarding potential manufacturing collaboration, led to a decline in US semiconductor company shares during Thursday's trading session, causing the Nasdaq 100 index to reverse from gains into losses. US stocks had initially risen at the open following weaker-than-expected non-farm payrolls data.

According to reports from The Information, Anthropic PBC is in talks with Samsung Electronics to potentially secure the latter as a manufacturing partner for its custom artificial intelligence chips. Anthropic's chip initiative remains in its early stages, with the company yet to finalize the specific applications, performance targets, or server integration methods for this processor.

Anthropic stated to The Information that Amazon's Trainium chips, Google's Tensor Processing Units (TPUs), and Nvidia's GPUs will continue to be core components of its computing strategy moving forward.

Following the news, semiconductor stock prices fell during Thursday's session. The Nasdaq 100 index declined by 1.3%, while the Philadelphia Semiconductor Index dropped 4.3%. SanDisk Corp. shares plunged 12%, Western Digital fell 7.5%, Micron Technology dropped 4.3%, Advanced Micro Devices declined 3.9%, Intel fell 2.9%, and Nvidia decreased 1.3%.

European chip stocks also faced broad pressure: ASML Holding declined over 3%, ASM International fell more than 3%, BE Semiconductor Industries dropped over 3%, Aixtron decreased more than 3%, and Nokia declined 4.1%.

As demand for AI services surges, major AI companies are seeking to diversify their chip supply to meet growing computational power requirements. Last month, OpenAI unveiled its first custom AI chip developed in collaboration with Broadcom. This generative AI company aims to enhance overall computational efficiency by customizing critical hardware required to run its models.

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