What DeepSeek-R1 Actually Proved About Open Weights and Training Cost
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The buzz over DeepSeek this week crystallized, for many people, a few important trends that have been happening in plain sight: (i) China is catching up to the U.S. in generative AI, with implications for the AI supply chain. (ii) Open weight models are commoditizing the foundation-model layer, which creates opportunities for application builders. (iii) Scaling up isn’t the only path to AI progress. Despite the massive focus on and hype around processing power, algorithmic innovations are rapidly pushing down training costs. About a week ago, DeepSeek, a company based in China, released DeepSeek-R1, a remarkable model whose performance on benchmarks is comparable to OpenAI’s o1. Further, it was released as an open weight model with a permissive MIT license. At Davos last week, I got a lot of questions about it from non-technical business leaders. And on Monday, the stock market saw a “DeepSeek selloff”: The share prices of Nvidia and a number of other U.S. tech companies plunged. (As of the time of writing, some have recovered somewhat.) Here’s what I think DeepSeek has caused many people to realize: China is catching up to the U.S. in generative AI. When ChatGPT was launched…
Context
DeepSeek's R1 release gave developers access to a they could run and adapt. Its repository licenses the principal model's code and weights under MIT and permits commercial use and derivatives. That is a concrete basis for Andrew Ng's argument that expand application builders' choices.
The widely discussed training-cost figure belongs to a different accounting question. The DeepSeek-V3 technical report estimates $5.576 million for V3's official training runs, assuming rented H800 processing at $2 per GPU-hour. It excludes prior research and experiments. That figure describes a defined training workload; it does not establish the total development cost of R1.
- reasoning model — A model trained to think — generating extended internal reasoning before answering — trading time and tokens for accuracy on hard problems.
- open weights — A model whose trained parameters are published for anyone to download and run — unlike API-only models you can access but never possess.
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