AI research is bottlenecked by humans. Every advance in ML still runs through a small number of researchers who can only devise and test ideas so fast. Experiments take days, most fail, and the field's progress is gated by how quickly a limited pool of experts can iterate.

We think this is the single biggest lever on the future of AI. Our thesis is that AI progress isn't bounded by compute or data, but it's bounded by the rate at which good ideas get devised and tested. Compute keeps scaling, but the human idea-generation loop doesn't. If you could automate the loop itself: hypothesize, experiment, learn, and repeat, then you could compound discoveries far faster than any human team.

We decided to work on it because we've lived the bottleneck. Between us we spent ~30 years doing ML research at Google, DeepMind, and Luma AI. We kept hitting the same wall: the hard part was never running the code, it was the slow, serial process of engineering the training loops to inform what to try next.

When we built a system that could run that loop autonomously, we found we could shorten the gap between having an idea and knowing whether it was a good one. This has allowed us to explore many exciting ideas that we would have otherwise not tried because it was too time-consuming for us. The results convinced us a self-improvement loop now possible, and that we're the ones to build it.

Long-term, we're building autonomous AI scientists and scaling them from one to millions: a fleet running the scientific method in parallel, each discovery accelerating the next.

And this process will start to compound. Each new method our scientists invent makes the next round of research faster and cheaper, a self-improving loop where discovery accelerates discovery. Concretely, that means a world where breakthroughs that would have taken a field a decade happen in months, where the cost of pushing the frontier drops by orders of magnitude, and where a small team can direct the research output of an entire institution. We start with ML research because it's the loop closest to us and the one that speeds up all the others: an AI scientist that improves AI compounds fastest of all.