Y Combinator CEO Garry Tan has publicly called on US open-weight AI labs to make greater use of distillation techniques applied to frontier models. Distillation involves training a smaller, cheaper model using outputs generated by a more powerful, often proprietary model, allowing it to capture a portion of the larger model's capabilities without the cost of full-scale training.
The comments come amid growing attention to how Chinese labs, particularly DeepSeek, have used this approach to produce competitive open-weight models at a fraction of the cost typically associated with frontier-level training. These results have fueled debate in the US about whether its AI ecosystem can maintain a lead when rivals can replicate cutting-edge performance so cheaply and quickly.
As a prominent figure in startup investment through Y Combinator, Tan's argument carries both strategic and economic weight: if distillation allows broader access to near-frontier capabilities, failing to adopt it domestically risks ceding a meaningful competitive advantage to labs abroad already using the technique at scale.
The statement fits into a wider discussion about the United States' position in the open-weight AI race, at a time when major American labs largely favor closed, proprietary models. It highlights an ongoing tension between the commercial interests of leading generative AI companies and calls, notably from the venture capital community, for faster and broader diffusion of advanced AI capabilities.