The world needs 10× more
inference than it can build.

For seventy years, digital silicon solved every hard problem, from weather and fluid flow to option prices and protein folding, by running numerical approximations of stochastic math on deterministic transistors. It was brilliant, and it led us blind into the inference era. Modern AI is stochastic too, but the silicon still pretends it isn't.

Every GPU on earth is paying a tax to do an arithmetic impression of what a physical system would do for free, and roughly eighty percent of its energy moves data rather than computes with it, which works out to six hundred billion dollars this decade. The ten-times shortfall above is not a resource problem; it sits on the wrong assumption, that stochastic math belongs on deterministic silicon, and you cannot out-spend an assumption.

So we stopped simulating. We built silicon where the equations are the circuit, where inference is what happens when the physics settles, and where the transistor-level design already shows roughly eighteen thousand times the energy efficiency of an H100. One human heartbeat, fifteen hundredths of a joule, powers fifty-eight million inferences on that chip.