DeepSeek Did Not Kill NVIDIA—It Made Powerful AI Cheaper and Expanded the Chip Market
Why did DeepSeek not destroy demand for NVIDIA, but instead make powerful AI cheaper and expand the compute market?
Evaluate DeepSeek and NVIDIA as a physical and industrial system rather than only as software; the next step is to account for chips, energy, cloud capacity, manufacturing, and the cost of scaling.
What to watch for
Key takeaways
The “The market reaction to DeepSeek was immediate” issue should be assessed with one constraint in mind: but that logic is too linear. When computing becomes cheaper, people usually use more of it, not less.
The “DeepSeek: what model, plus and minus is part 1/2” scene leads to a working conclusion: the important signal is not one funding number: the next round, available runway, and closure rate show whether a company can survive the new cost of capital.
The discussion of “How DeepSeek affects the future of AI Part 1/3” yields a practical test: a team that once needed hundreds of millions of dollars now has a chance to run a model locally, fine-tune it, and build it into a product. Even if the public cost figures are incomplete and training used more GPUs than stated, the approach shows that efficiency in architecture and training is nowhere near exhausted.
The decision in “NVIDIA's problem is a temporary landing or a sunset?” depends on one criterion: the important signal is not one funding number: the next round, available runway, and closure rate show whether a company can survive the new cost of capital.
For the “Example of the use of OpenAI Operator” scene, the decisive point is this: the important signal is not one funding number: the next round, available runway, and closure rate show whether a company can survive the new cost of capital.
The “OpenAI Operator: plus and lesss” issue should be assessed with one constraint in mind: this section clarifies the mechanism behind the topic and preserves a constraint that would otherwise be lost in an overly simple conclusion.
The practical meaning of “Conclusion” is that it destroys a more important illusion—that the path to a strong model is known, absurdly expensive, and open to only a few laboratories. Competition will now accelerate and demand for compute will become more distributed. For NVIDIA, that may mean less hysterical growth, but not the disappearance of its market.
What this episode is about
The Chinese model wiped billions from NVIDIA’s market value and forced Silicon Valley to recalculate the cost of training. But a more efficient model does not eliminate demand for compute; it lets more teams launch systems of their own. The real blow was not to hardware, but to the belief that only a handful of companies can build powerful AI.
The market reaction to DeepSeek was immediate: if a Chinese team produced a strong model for less money, then OpenAI’s enormous investments and demand for NVIDIA might be inflated. But that logic is too linear. When computing becomes cheaper, people usually use more of it, not less.
DeepSeek’s main effect is democratization. A team that once needed hundreds of millions of dollars now has a chance to run a model locally, fine-tune it, and build it into a product. Even if the public cost figures are incomplete and training used more GPUs than stated, the approach shows that efficiency in architecture and training is nowhere near exhausted.
For Meta, Mistral, Cohere, Perplexity, and other players, this is both a threat and a gift. It is a threat because a strong open model makes their technology less distinctive. It is a gift because they can build products on a cheaper foundation. NVIDIA still gets to sell equipment to those new participants.
Against the backdrop of DeepSeek, OpenAI is showing Operator and talking about an age of agents. But the product runs in a separate browser and does not always understand the user’s real environment. It is a good example of the gap between a foundation model and an interface: a company can lead in research while releasing an agent that still looks experimental.
DeepSeek does not prove that America has lost or that OpenAI is finished. It destroys a more important illusion—that the path to a strong model is known, absurdly expensive, and open to only a few laboratories. Competition will now accelerate and demand for compute will become more distributed. For NVIDIA, that may mean less hysterical growth, but not the disappearance of its market.
Competition will now accelerate and demand for compute will become more distributed. As a result, for NVIDIA, that may mean less hysterical growth, but not the disappearance of its market.
Episode transcript
The episode is in Russian; below is an English reading guide to the transcript (the full EN transcript is a machine translation). Voice matching applied to 71 segments: 31 identified, 6 mixed, 24 marked with ✓, and 10 unresolved.
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