With the advent of DeepSeek, the balance of power between the two nations appears to be shifting.
Eric Schmidt, former CEO and chairman of Google, is co-founder of Schmidt Sciences and chair of the nonpartisan think tank Special Competitive Studies Project. Dhaval Adjodah is co-founder and CEO of MakerMaker.AI.
Last week, the AI company DeepSeek released its R1 reasoning model, which is on a par with OpenAI’s o1 (and much better than the ChatGPT models) across a range of logic tasks, including math and coding. The cost of running it is also much lower, only about 2 percent of what OpenAI charges. And on Monday, DeepSeek released Janus Pro, a model small enough to run on your laptop that can generate synthetic images, which it claims outperform OpenAI’s Dall⋅E 3. DeepSeek’s speed of AI innovation is taking the world by storm.
What’s even more remarkable is that DeepSeek’s entire collection of models is open-source — which in this case means they have open weights that anyone can reproduce and build on top of.
It’s a peculiar moment when a Chinese company becomes the de facto open-source leader, while most major American firms, with the exception of Meta, continue to keep their methodologies tightly under wraps. In fact, this is a growing trend for Chinese AI companies — from start-ups such as Minimax to tech giants such as Alibaba — that are giving developers worldwide free access to their AI models.
Until now, closed-source models such as OpenAI’s o3 and Anthropic’s Claude 3 Opus were considered the industry standards with the most advanced capabilities — and they were built in the United States. Open-source and Chinese models were thought to be months behind. But DeepSeek’s R1 and Janus Pro show just how quickly the tides of technological supremacy can turn. The introduction of these models has roiled stock markets and caused U.S. tech stocks to plunge. The balance of power now appears to be shifting along two key axes: one between the United States and China, and another between closed- and open-source models.
Defenders of closed-source models are betting that they can preserve their capability gap by protecting their model weights and training methodologies. Open-source advocates, on the other hand, argue that transparency — allowing others to build on their work — can enable these systems to rapidly catch up with larger, closed models. If the open-source thesis is correct, this would turn the AI ecosystem on its head. Open-source models are generally cheaper to use, so when two equally capable models are available — one open, one closed — the open-source model is likely to gain wider adoption, giving it a strategic advantage.
The United States already has the best closed models in the world. To remain competitive, we must also support the development of a vibrant open-source ecosystem.
The race between open- and closed-source AI, as well as between the United States and China, does not yet have a clear winner. But there is clearly mounting pressure on America’s Big Tech players if DeepSeek can compete with them using far fewer resources. Export controls were aimed at choking off China’s access to the most advanced computer chips, impeding its ability to keep pace. But in fact, the relative dearth of high-performing chips in China might have pushed the nation’s companies and researchers to be more efficient and led them to uncover new methodologies that significantly reduce training costs. For example, DeepSeek demonstrated that large model training could be made more efficient by bypassing the traditional supervised fine-tuning stage. They even created R1-Zero, a model that omits this step in AI training, to challenge the research community’s assumptions about fine-tuning’s indispensability.
DeepSeek’s success has also called into question the importance of pretraining, which involves training ever-larger models that predict the next word based on vast amounts of text. This process requires enormous up-front investment in graphics processing units (GPUs) and data — so much data that OpenAI co-founder Ilya Sutskever recently noted we might soon exhaust all the data available on the internet.
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