Some people are anti-AI because they can already see the downside, potentially losing their job or watching the craft they spent years learning get automated, while the promised land, curing cancer and treating diseases, still feels very far away. AI gets incredibly useful when it has good data and a way to test whether it’s right. But biology has an enormous space of possibilities, much of the relevant data hasn’t been generated yet, and you can’t answer every question by thinking harder. At some point you have to run the experiment. So here’s something I’d love to see someone build: a giant warehouse, basically a data center for biology, filled with lab equipment and eventually tens of thousands of robots running experiments around the clock. Liquid handlers, plate readers, microscopes, incubators, all connected to models that propose experiments, look at the results and decide what to try next. I call the idea bounded serendipity. A lot of discovery comes from exploring something and finding something you weren’t looking for. CRISPR came out of research into how bacteria defend themselves against viruses. RNA interference also emerged from unexpected experimental observations. These were years of careful science, but the surprise mattered. You can’t schedule the breakthrough, you can create more opportunities to encounter one. So why wouldn’t we massively scale those opportunities? Pick a scientifically meaningful area, define what can be tested and measured reliably, then explore far more of it than a human team could. Give the models room to try unusual ideas, follow surprising results and update their understanding, with scientists deciding which questions matter and checking what the results actually mean. Two things make me think the economics could change. 1/ Robotics. Lab automation already exists, but integrating different instruments and workflows often takes expensive custom engineering. My bet is that increasingly capable general-purpose robots, including humanoids where they make sense, could reduce how much bespoke hardware you need by using equipment designed for humans. Keep specialized machines for the tasks they do best, and use flexible robots to connect everything and handle the work between them. We’re not yet at the point where you can buy 10,000 humanoids and expect them to run a biology lab reliably. But that’s the direction I’d bet on. Once a workflow works, you can replicate it and run shifts around the clock. Scaling that capacity could become much easier than recruiting and training thousands of lab technicians for every new facility. 2/ The data itself can be a product. Historically, the economics of an experiment often depended on whether it helped produce a valuable discovery. Now there’s another potential buyer: someone training a biology model. A well-run experiment that shows something doesn’t work can still be useful training data. A broken experiment is different, but a clean negative result tells you something about the world. That creates a possible business model where you sell high-quality experimental datasets while also using them to build better models and pursue your own discoveries. Companies are already selling biological data for AI training, so this part is more than a hypothetical. And parts of the loop already work. OpenAI and Ginkgo connected GPT-5 to an automated lab, tested more than 36,000 protein-synthesis reaction compositions and reported a 40% reduction in production cost. That’s a narrow result, but it’s a concrete example of a model proposing experiments, getting real measurements back and improving through iteration. The flywheel is more experiments, more useful data, better models, smarter experiments, and data revenue funding more capacity. We’re building enormous data centers for compute. I’d love to see us build the equivalent for biology, so the promised land gets here faster. Bounded serendipity at industrial scale.