Artificial intelligence has models of the physical world, of language, of code. It has no model of the computational world in which it runs. We believe that is the last unmodeled environment, and the one that now decides how much intelligence the world can afford.

A chip is only as useful as the team available to operate it, which means most of the compute humanity has built cannot be reached by the work that needs it.

Our approach is to model that environment directly: learned action-conditioned dynamics across millisecond, second, and minute timescales, constrained planning under hard limits, and an independent safety boundary. We deploy these as AI compute engineers, agents that predict what a placement will do before it touches production.

If this works, a model is deployed once and runs on whatever silicon is available. Trying new hardware costs what the traffic costs instead of a year of porting. Stranded capacity becomes reachable. An institution builds frontier AI on the chips it can obtain rather than the chips one supply chain permits. And hardware competes on physics rather than on how many years of software a company could afford to fund.

To support our mission, we operate GD-X, a bare-metal multi-architecture testbed running unlike silicon side by side, and we publish our measurement protocols and our profiles.

We are a small group of engineers and researchers working on a short list of problems that decide whether compute can be modeled at all: whether a learned representation of one machine transfers to a machine it has never run on, what it costs to buy that knowledge in production traffic, and where planning stops earning its complexity. If this sounds interesting, we would love to hear from you.

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