Verifiable inference is the piece that joins all three

For most of the last decade, artificial intelligence, applied cryptography and digital settlement developed as separate industries with separate conferences. The thing now drawing them together is narrow and specific: the ability to prove that a particular model produced a particular output, without revealing the model or the input.

That capability is uninteresting on its own. It becomes structural when you notice what it enables — a buyer who can verify a result can pay for it automatically, and a seller who can prove delivery does not need to be trusted.

The loop, stated plainly

• A requester sends work and a payment commitment.
• A provider computes and returns the result with a proof of correct execution.
• The requester verifies cheaply, and settlement releases without a human step.
• No party needs a prior relationship, and no intermediary holds funds.

Why this is still slow

Proving cost scales badly with model size. For small specialised models the economics already work; for frontier models they do not, and no announced improvement closes that gap this year. The deployments that exist run small models on narrow tasks, which is the correct place to start and not the place the marketing points.

Everything in our stack existed two years ago. What changed is that proving got cheap enough for the smallest useful model.

What to watch

Whether proof formats standardise across proving systems. Without that, every buyer verifies in one vendor's scheme, and the trust that was removed reappears one layer up.