The launch of DeepSeek's efficient AI model has triggered a market sell-off in semiconductor companies, raising questions about the future of AI infrastructure spending. While some investors fear a decline in demand for advanced chips, others point to the Jevons Paradox, arguing that increased efficiency will drive higher AI adoption and ultimately boost chip demand.
A week after DeepSeek launched an industry-shaking AI model on Inauguration Day, investors have grappled with its implications, leading to a market reassessment of AI's multi-trillion dollar trajectory. DeepSeek, a spinout from a Chinese hedge fund, appears to have rivaled the capabilities of top AI models but by utilizing fewer, less-advanced chips compared to the billions of dollars invested by American counterparts in capital expenditure.
This efficiency sparked panic among investors who feared a decline in demand for the advanced chips required to power models like OpenAI's ChatGPT or Google's Gemini. The resulting sell-off was most acutely felt by key companies within the AI supply chain. Nvidia, the chip giant that has witnessed a staggering $2.7 trillion surge in its market capitalization since the generative AI boom's inception, plunged by as much as 18% on Monday, suffering the largest US stock market rout in history with $589 billion wiped off its value. Other companies, including ASML, AMD, ARM, and a string of Japanese chipmakers closely tied to the chip-driven AI industry, also experienced significant declines as investors contemplated the possibility of emulating frontier-level AI models like OpenAI's o1 with significantly less computing power.While DeepSeek has challenged trillions of dollars in AI infrastructure spending, not everyone shares the market's dramatic reaction. The debate largely centers on compute. Hamish Low, an analyst at research firm Enders Analysis, told Business Insider that the response to the chip stock sell-off seems 'quite overblown' as 'being able to use compute much more efficiently,' a key claim of DeepSeek's R1 release, 'is by no means bad for compute demand.' Several tech leaders, including Microsoft CEO Satya Nadella, have echoed this sentiment on social media, referencing the Jevons Paradox - the concept that as the cost of using a resource diminishes, demand will surge rather than fall. Nadella stated on X: 'Jevons paradox strikes again! As AI gets more efficient and accessible, we will see its use skyrocket, turning it into a commodity we just can't get enough of.' Former Intel CEO Pat Gelsinger similarly asserted on X, 'Computing obeys the gas law. Making it dramatically cheaper will expand the market for it. The markets are getting it wrong, this will make AI much more broadly deployed.' This suggests that AI leaders anticipate both increased efficiency and greater computing power. Ethan Mollick, a Wharton professor specializing in AI, underscored this point, writing on X, 'Everyone in the space is compute constrained. More efficient models mean those with compute will still be able to use it to serve more customers and products at lower prices & power impact.
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