Warp Sciences, founded by ex-OpenAI product chief Kevin Weil to speed up AI drug development, emerges from stealth with $215M at an $865M post-money valuation

Specialized scientific data is now a key bottleneck for AI research, requiring significant new investment.

Kevin Weil, formerly OpenAI's chief product officer and VP for Science, has launched a new AI startup named Warp Sciences. The company has reportedly raised $215 million, with Bloomberg citing a post-money valuation of $865 million, exceeding earlier reports of a $150 million round at a $750 million valuation. Warp Sciences operates in the "AI for science" sector, aiming to improve AI models for research by focusing on specialized scientific data. Weil, who also held product roles at Twitter and Meta, left OpenAI in April 2026. His previous work at OpenAI included leading initiatives like Prism, which integrated AI models into scientific workflows. The launch positions Warp Sciences within a growing wave of AI-for-science companies founded by alumni from major tech firms, focusing on areas such as drug discovery and autonomous research.

The substantial funding and high valuation for Warp Sciences underscore the increasing investor confidence in the "AI for science" sector, particularly for ventures led by experienced AI executives. Such significant seed-stage funding with little room for error implies immense pressure on Weil's new company to demonstrate rapid progress and deliver tangible results that justify the high valuation. The market will be closely watching the final investor lineup and valuation details to gauge the true conviction behind this venture.

As Warp Sciences emerges from stealth, its business model—whether it focuses on selling data, proprietary models, or forming direct partnerships with research organizations—will be a critical indicator for the broader AI-for-science landscape. This move suggests a potential shift towards data specialization as a core differentiator in AI development, impacting how scientific research is conducted and how AI tools are commercialized within academic and industrial R&D settings.