Dario Amodei — On DeepSeek and Export Controls Dario Amodei Archive Contents On DeepSeek and Export Controls January 2025 A few weeks ago I made the case for stronger US export controls on chips to China. Since then DeepSeek, a Chinese AI company, has managed to — at least in some respects — come close to the performance of US frontier AI models at lower cost. Here, I won't focus on whether DeepSeek is or isn't a threat to US AI companies like Anthropic (although I do believe many of the claims about their threat to US AI leadership are greatly overstated) 1 . Instead, I'll focus on whether DeepSeek's releases undermine the case for those export control policies on chips. I don't think they do. In fact, I think they make export control policies even more existentially important than they were a week ago 2 . Export controls serve a vital purpose: keeping democratic nations at the forefront of AI development. To be clear, they’re not a way to duck the competition between the US and China. In the end, AI companies in the US and other democracies must have better models than those in China if we want to prevail. But we shouldn't hand the Chinese Communist Party technological advantages when we don't have to. Three Dynamics of AI Development Before I make my policy argument, I'm going to describe three basic dynamics of AI systems that it's crucial to understand: Scaling laws. A property of AI — which I and my co-founders were among the first to document back when we worked at OpenAI — is that all else equal , scaling up the training of AI systems leads to smoothly better results on a range of cognitive tasks, across the board . So, for example, a $1M model might solve 20% of important coding tasks, a $10M might solve 40%, $100M might solve 60%, and so on. These differences tend to have huge implications in practice — another factor of 10 may correspond to the difference between an undergraduate and PhD skill level — and thus companies are investing heavily in training these models. Shifting the curve. The field is constantly coming up with ideas, large and small, that make things more effective or efficient: it could be an improvement to the architecture of the model (a tweak to the basic Transformer architecture that all of today's models use) or simply a way of running the model more efficiently on the underlying hardware. New generations of hardware also have the same effect. What this typically does is shift the curve : if the innovation is a 2x "compute multiplier" (CM), then it allows you to get 40% on a coding task for $5M instead of $10M; or 60% for $50M instead of $100M, etc. Every frontier AI company regularly discovers many of these CM's: frequently small ones (~1.2x), sometimes medium-sized ones (~2x), and every once in a while very large ones (~10x). Because the value of having a more intelligent system is so high, this shifting of the curve typically causes companies to spend more , not less, on training models: the gains in cost efficiency end up entirely devoted to training smarter models, limited only by the company's financial resources. People are naturally attracted to the idea that "first something is expensive, then it gets cheaper" — as if AI is a single thing of constant quality, and when it gets cheaper, we'll use fewer chips to train it. But what's important is the scaling curve : when it shifts, we simply traverse it faster, because the value of what's at the end of the curve is so high. In 2020, my team published a paper suggesting that the shift in the curve due to algorithmic progress is ~1.68x/year. That has probably sped up significantly since; it also doesn't take efficiency and hardware into account. I'd guess the number today is maybe ~4x/year. Another estimate is here . Shifts in the training curve also shift the inference curve, and as a result large decreases in price holding constant the quality of model have been occurring for years. For instance, Claude 3.5 Sonnet which was released 15 months later than the original GPT-4 outscores GPT-4 on almost all benchmarks, while having a ~10x lower API price. Shifting the paradigm. Every once in a while, the underlying thing that is being scaled changes a bit, or a new type of scaling is added to the training process. From 2020-2023, the main thing being scaled was pretrained models : models trained on increasing amounts of internet text with a tiny bit of other training on top. In 2024, the idea of using reinforcement learning (RL) to train models to generate chains of thought has become a new focus of scaling. Anthropic, DeepSeek, and many other companies (perhaps most notably OpenAI who released their o1-preview model in September) have found that this training greatly increases performance on certain select, objectively measurable tasks like math, coding competitions, and on reasoning that resembles these tasks. This new paradigm involves starting with the ordinary type of pretrained models, and then as a second stage using RL to add the reasoning skills. Importantly, because this type of RL is new, we are still very early on the scaling curve: the amount being spent on the second, RL stage is small for all players. Spending $1M instead of $0.1M is enough to get huge gains. Companies are now working very quickly to scale up the second stage to hundreds of millions and billions, but it's crucial to understand that we're at a unique "crossover point" where there is a powerful new paradigm that is early on the scaling curve and therefore can make big gains quickly. DeepSeek's Models The three dynamics above can help us understand DeepSeek's recent releases. About a month ago, DeepSeek released a model called " DeepSeek-V3 " that was a pure pretrained model 3 — the first stage described in #3 above. Then last week, they released " R1 ", which added a second stage. It's not possible to determine everythi