Progress in AI will come from encoding real work into environments where agents can learn. As models scale, humans remain responsible for defining the tasks, constraints, and ground truth that turn intelligence into economic reality.
This drives our two areas of focus.
Real work, formalized. We encode economically valuable tasks into high-fidelity training environments where agents learn by doing—starting with software engineering.
Tasks drawn from real economic activity—the work that matters, turned into high quality training signal.