Thermodynamic Physical AI: when sampling is the compute.
Half of a policy is deterministic — a feedforward pass, which ternary makes multiply-free. The other half is stochastic: sampling an action, a plan, a belief about a half-seen world, the denoising steps of a diffusion policy. And a generative step, underneath, is just drawing from a probability distribution — which a digital chip does the hard way, computing the distribution with matrix-multiply and then sampling it. Thermodynamic hardware inverts the order: it builds the distribution into a physical system and lets thermal noise fall into it, sampling directly. The noise a digital chip spends energy fighting becomes the computation. Extropic's sampling units and Normal Computing's thermodynamic chip claim orders of magnitude less energy for exactly the generative workloads Physical AI leans on, and networks of p-bits already do the same a million at a time. Pair it with ternary and the edge stack sits at its energy floor on both halves — deterministic and stochastic, each done the cheapest way physics allows.
Walkers run Langevin dynamics on an energy landscape — a stand-in for a thermodynamic sampler drawing from a multimodal belief. Turn the temperature down and it freezes into one mode (the mixing–expressivity tradeoff); turn it up and the structure washes out; in between it samples the true distribution. The energy panel is the point: sampling by physics runs orders of magnitude below matmul-then-sample, and the p-bit strip is the free randomness that does it.
In the field · thermodynamic computing is young but real. Extropic builds sampling units that draw from energy-based models in silicon (XTR0 prototype, thrml library, Z1 for 2026), claiming ~10⁴× less energy than a GPU on generative workloads; Normal Computing taped out CN101, a first thermodynamic chip. Underneath sit p-bits — stochastic magnetic tunnel junctions fluctuating at GHz — now networked a million at a time doing >10¹² Gibbs flips per second, and denoising thermodynamic models that borrow diffusion's trick to beat the mixing–expressivity wall. The claims are large and the silicon is early — the honest read is that the deterministic path (ternary) and the stochastic path (thermodynamic) are the two lanes where the energy of intelligence actually falls, and the edge is where they have to meet.
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