The news of Chinese AI startup DeepSeek's advancements sent shockwaves through the market, causing Nvidia investors to panic as the company's shares plummeted almost 17%, resulting in a nearly $600 billion loss in market value. The selloff was sparked by DeepSeek's launch of its V3 and R1 AI models, which seem to match or even surpass the capabilities of leading U.S. AI technologies, yet were developed using significantly fewer resources. This development has led investors to re-evaluate the cost-effectiveness of AI model training, particularly since Nvidia's powerful graphics processing units (GPUs) are a major expense in this process.
However, the narrative around Nvidia's potential downfall might be hasty. DeepSeek's success does not necessarily spell disaster for Nvidia or suggest that U.S. export controls aimed at limiting China's access to advanced AI chips should be abandoned. DeepSeek has claimed access to 10,000 of Nvidia's older A100 GPUs, acquired before export restrictions tightened, and has used Nvidia's H800 chips, designed to comply with U.S. regulations. These chips, while less advanced, have been sufficient for DeepSeek to challenge major U.S. players.
Surprisingly, DeepSeek's R1 model, which excels in math, logic, and coding, can operate on a laptop using a conventional CPU, not requiring the extensive GPU resources typically needed for such tasks. This has stirred debate among critics about the effectiveness of U.S. export controls, suggesting that these restrictions might have inadvertently pushed Chinese developers towards greater efficiency. Angela Zhang from the University of Southern California highlighted this in the Financial Times, arguing that these limitations have actually spurred innovation in China. AI skeptic Gary Marcus also supported this view, discussing how Chinese firms are forced to innovate under constraints.
But there's a twist: the success of DeepSeek might not reduce demand for advanced chips but could increase it due to the Jevons Paradox. This economic principle suggests that when a resource becomes more efficient, its overall consumption increases. As DeepSeek's models are cheaper to run, businesses might find more applications for AI, thereby boosting demand for computing power across the board. Both Satya Nadella and Pat Gelsinger have pointed out on social media that this paradox might lead to a broader deployment of AI, contrary to market fears.
Moreover, the type of computing power needed is shifting. While Nvidia's GPUs are superior for training large language models, other companies like AMD, Groq, Google, and Amazon are making strides in inference, where a trained model is applied to perform tasks. Nvidia's dominance might be challenged here, but its CUDA software ecosystem gives it a strong hold on developers. If AI adoption accelerates, even a smaller market share might mean higher revenues for Nvidia due to the expanding market size.
Regarding export controls, the argument is nuanced. Even if DeepSeek can achieve much with less, access to cutting-edge chips remains a significant advantage. AI policy expert Miles Brundage emphasized that the U.S. should continue restricting China's access to these technologies for national security reasons, as more compute still translates into better AI performance.
Finally, skepticism surrounds DeepSeek's claims about their resource use. Some industry insiders, including Scale AI's CEO Alexandr Wang, suggest that DeepSeek might have access to far more advanced Nvidia chips than disclosed, potentially indicating that the real issue with U.S. export controls might lie in enforcement rather than policy itself.
In conclusion, while DeepSeek has certainly caused a stir in the AI market, the long-term implications for Nvidia and U.S. policy might be less dire than initially feared. The situation highlights the complexities of technological competition and the unintended consequences of policy measures in the global tech landscape.
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