Our new report "Closing the Loop: The Self-Driving Landscape" is out now! AI can generate scientific hypotheses faster than laboratories can test them. Self-driving labs are designed to close that gap by automating the full experimental loop. The model chooses the next experiment, and connected laboratory equipment carries it out. The results feed back into the system and shape what it tests next. The stakes are especially high in drug discovery. Lead optimization alone can consume roughly three years. Bringing a drug to market takes 10–15 years, with average out-of-pocket and time costs of $2.6 billion per drug. Self-driving labs target the earlier experimental bottleneck. They could shrink individual cycles from months to days or hours. Running more experiments can also reduce the cost of each run by spreading the upfront cost of automation further. Every completed experiment adds to a structured record of what worked and what did not. That data improves the model’s next decision, creating a continuous learning loop between AI and the physical lab. AI has accelerated the generation of scientific hypotheses. Self-driving labs could accelerate the experiments that determine which ideas are worth pursuing.