Garry Tan wants US open-weight AI labs to 'distill' frontier models, too
Recorded: Sept. 13, 2026, 5:09 p.m.
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Y Combinator’s Garry Tan wants US open-weight AI labs to ‘distill’ frontier models, too Julie Bort 1:59 PM PDT · September 11, 2026
When it comes to Chinese AI labs using distillation techniques to extract knowledge from frontier model makers, Y Combinator CEO Garry Tan is hoping regulators stay out of it. In fact, he thinks U.S. AI labs should perhaps play the same game. He elaborated to TechCrunch that this means he wants smaller, American open-weight AI labs to use the same kind of training techniques on American frontier AI labs, giving the U.S. a more robust set of open-weight options that aren’t Chinese. He also notes that the proprietary AI labs didn’t ask permission when they vacuumed up as much human knowledge as they could to train their models. They famously ingested plenty of copyrighted material without the permission of those intellectual property holders. “They are at the frontier and driving it forward. We want that to be fundable, and be a great business model ongoing,” he told CNBC. “You want open weight models to give people freedom and access.” Topics AI, garry tan, TC, Venture, Y Combinator When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.
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Garry Tan, the CEO of Y Combinator, advocates for a framework where U.S. open-weight artificial intelligence laboratories are also permitted to distill knowledge from frontier models, suggesting the establishment of an American distillation regime. This perspective is offered in contrast to reporting that Chinese AI labs are allegedly using illicit distillation attacks, hiding their identities to extract knowledge from other model makers without authorization. Tan clarifies that his stance is not an endorsement of using stolen credentials; rather, he argues that controlling customer access to information derived from models that have processed broad public data is overly restrictive and should be reconsidered. His reasoning stems from a concern that proprietary AI labs took extensive human knowledge, including copyrighted material, for training their models without seeking permission from the intellectual property holders. Tan posits that access to intelligence trained on public data should be treated as a public good rather than being confined behind restrictive terms of service. He believes there is a legitimate role for government intervention to normalize the concept that access to such intelligence should be more accessible. Furthermore, Tan believes that open-weight models are essential for providing freedom and public access. He expresses a deep concern regarding the potential "doomer scenario" for artificial intelligence, articulating that the most dangerous outcome is the concentration of all immense power of frontier AI into the hands of a single, monolithic proprietary provider who controls access to capital and researchers. Therefore, he aims to foster an environment where open-weight models grant broad freedom and access, balancing the forward momentum of frontier research with equitable distribution of knowledge. |