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Kevin Weil

Founder, Warp Sciences; formerly VP, OpenAI for Science and Chief Product Officer, OpenAI, Warp Sciences

Search every verified Kevin Weil interview, podcast appearance, and on-the-record quote — each transcript cross-checked by AI and human review to confirm speaker identity. Kevin Weil has publicly discussed his role as co-founder of Warp Sciences, a robotics-first research lab aimed at bringing small molecule drug discovery back to the United States. In a podcast appearance, he stated that the current reliance on offshore contract research organizations (CROs), largely based in China, creates risks around intellectual property leakage, single-supplier dependence, and geopolitical exposure. He attributed this perspective to documented examples of IP loss and argued that automation and robotics could offer a cost-effective domestic alternative to traditional US CROs, which he said are typically four to five times more expensive. Weil also projected that within ten years, large-scale automated factories could run experiments at a scale previously limited by the number of skilled scientists. In a separate university seminar, Weil addressed language asymmetry in AI models, noting that early models like GPT-1, GPT-2, and GPT-3 were trained predominantly on English web data, leaving many low-resource and minority languages underrepresented. He described efforts to supplement training data for such languages, though he acknowledged the challenge remains. Weil also identified few-shot learning—teaching AI to generalize from a small number of examples—as an unsolved scientific problem in the field. In both appearances, he emphasized his focus on accelerating scientific discovery through AI infrastructure and expressed optimism about the societal benefits of expanding access to AI-driven tools in chemistry, biology, and materials science.

Recent appearances

Verified interview transcripts

Notable quotes

“It rhymes with the manufacturing industry and what happened, right? You start with the manufacturing industry. We started by outsourcing and offshoring the low-skilled manufacturing work, but then over time we pushed more and more out of the country and we lost the muscle, and at the same time China and others became, you know, grew and got better and better at it, and now there's a whole bunch of manufacturing that ”

“The thing that people don't know is that whole 3 to 5 years of making and testing molecules, we've outsourced it entirely to China. So, it all happens in China and it's really dangerous. Right. This is a core part of the process of turning an idea into a new medicine and we cannot depend on what is, you know, a geopolitical adversary to do this work for us.”

“The next step in the process is we would go through 3 to 5 years of making variations of that molecule to try to optimize it to make it more potent, to make it more soluble, to make it less toxic. So there's this 3 to 5 year process and that entire process is outsourced to China. And that's the problem, and that's what we're bringing back. And the only way you're going to bring it back, to your point about economics,”

“The pain point really is around intellectual property, single supplier risk, and geopolitical risk. There are now multiple documented examples where key intellectual property is getting leaked out of these CRO. As a biotech, you are really giving your crown jewel IP and suddenly all of your competitive advantage is lost.”

“The people that we want to have that compute are the scientists who are going to do the most amazing things with it. But those are sometimes the people who have the least ability to pay for that kind of compute. And so that I think is a problem that we're going to want to solve as a as a community, as a you know, as the US. Like we're going to want our scientists in order to accelerate discovery. We're going to want ”

“When we launch a new model, we have an entire safety team — a large number of people that spend all their time doing AI safety research — and we work with third parties and government groups to red team our models up front so we make more small mistakes rather than a big mistake.”

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