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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.<\/p>\n
\u201cI would do nothing,\u201d he told CNBC in an interview earlier this week<\/a>. \u201cWe could argue that there should be an American distillation regime.\u201d <\/p>\n 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\u2019t Chinese.<\/p>\n Distillation is when a model maker extensively prompts another model in order to learn how it works and reasons. It is commonly, and legitimately, used by AI labs to help train new models.<\/p>\n Anthropic this week released its second report<\/a> alleging that Chinese labs are engaged in \u201cillicit distillation attacks,\u201d hiding their identities to distill without permission and relying on fraud and stolen credentials to do so. Anthropic CEO Dario Amodei had previously publicly called on U.S. regulators<\/a> to crack down on distillation.<\/p>\n It\u2019s notable that the commander of Silicon Valley\u2019s prestigious and prolific startup accelerator doesn\u2019t agree.<\/p>\n To be clear, Tan isn\u2019t advocating for American AI labs to use stolen credentials to distill. He wants them to be free to come in the front door. In fact, his argument is twofold. He feels it\u2019s an overreach for AI labs to dictate what their customers can do with the information their models share with them.<\/p>\n He also notes that the proprietary AI labs didn\u2019t 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<\/a>.<\/p>\n \u201cControlling what users and customers do with API calls to closed weight models feels constraining, and there\u2019s a role government can play here to normalize the fact that access to intelligence that was trained on broad public access data should itself also be more a form of a public good than something locked away behind restrictive terms of service,\u201d he told TechCrunch when asked why American labs should be free to distill, too.<\/p>\n Tan, who is himself such an avid AI user that once described himself as having cyber psychosis<\/a>, wants to see a balance between open-weight AI labs and frontier labs.<\/p>\n \u201cThey are at the frontier and driving it forward. We want that to be fundable, and be a great business model ongoing,\u201d he told CNBC. \u201cYou want open weight models to give people freedom and access.\u201d<\/p>\n To him, the true AI doomer scenario is for all the immense power of frontier AI to wind up in the hands of a single powerful, proprietary provider. \u201cThe nightmare scenario, the doomer scenario for AI is that there\u2019s just one company,\u201d he said. \u201cIt has the best access to capital. It has the best AI researchers. It runs away with it and suddenly there\u2019s one company that\u2019s monolithic. And that would be bad.\u201d<\/p>\n<\/div>\n When you purchase through links in our articles, we may earn a small commission<\/a>. This doesn\u2019t affect our editorial independence.<\/em><\/p>\n