Former OpenAI Executives Found Warp Sciences, Company Accelerates Drug Development with AI and Robotics
Former OpenAI Chief Product Officer Kevin Weil, along with Manohar Paluri and others, founded Warp Sciences, a company that accelerates drug development using AI and robotics, and has completed a $215 million financing round.
Lightspeed led the investment, with participation from IVP, Premji Invest, Lux Capital, among others, resulting in a post-money valuation of approximately $865 million. Angel investors include former OpenAI CFO Sarah Friar and Srinivas Narayanan. Co-founders also include former U.S. Chief Data Scientist DJ Patil and Solomon Reisberg.
The company's goal is to bring some drug chemistry experimental work back to the U.S. from Chinese outsourcing firms, completing molecular preparation and testing through automated laboratories. The team has procured robotic arms and AI chips, and chemists wear cameras and pressure-sensing gloves during experiments to collect training data.
Weil previously served as Chief Product Officer at OpenAI and led OpenAI for Science, leaving in April 2026. The company initially focuses on lead optimization for small molecule drugs.
Market Mechanism: Venture capital injects funds into AI and automated laboratory capabilities to reduce the cycle time and cost of specific experimental steps in drug development; pharmaceutical and biotech companies procure acceleration services as demand; Chinese CROs face potential order transfer pressure, while U.S. laboratories and robotic equipment suppliers benefit.
Source: Public Information
ABAB AI Insight
During his time at OpenAI, Kevin Weil was responsible for product and scientific applications, including workflow tools like Prism, before leaving to establish an independent company. Manohar Paluri comes from Meta's AI research system. Srinivas Narayanan participated as an angel investor, having previously led B2B applications at OpenAI.
On the capital path, institutions like Lightspeed provided $215 million to support the construction of automated laboratories and data collection systems in the U.S. The funds are used for equipment procurement and model training, aiming to localize the experimental data generation process. The company generates revenue by providing services to drug development firms.
This is similar to the path other AI companies are taking by shifting from model layers to experimental and data layers, and is close to the model of automated laboratory startups. It is currently in the laboratory construction and early service phase after completing financing.
Essentially, it involves technological substitution and restructuring of the industrial chain: AI and robotics are used to replace or accelerate experimental steps that originally relied on human labor and outsourcing. The mechanism is that physical experimental data becomes a bottleneck for model improvement, and by generating trainable data through local automated laboratories, the execution of specific steps in drug development shifts from external contractors to internal or new service providers; capital supports equipment and data accumulation to change the experimental cycle and geographical distribution.
ABAB News · Cognitive Law
Model training requires real experimental data; laboratories are the next stop.
The slow steps in drug development are automated, and outsourcing orders may return.
Former executives from large model companies are turning to scientific applications, directly raising the scale of financing.