A month ago, I mapped who supplies, routes, and tokenizes AI inference. Now I'm looking at the next piece of the market: Verifiable Inference. As agents start paying for compute, proving what actually ran becomes essential. 12 projects building across 4 key pillars: Pillar 1 - Verifiable Execution > @lagrangedev - $LA builds DeepProve for cryptographic inference proofs. > @inference_labs makes autonomous AI execution auditable. > @OpenGradient - base:0xfbc2051ae2265686a469421b2c5a2d5462fbf5eb combines zkML proofs and TEE attestations. Pillar 2 - Private Execution > @PhalaNetwork - ethereum:0x6c5ba91642f10282b576d91922ae6448c9d52f4e runs private inference inside GPU TEEs. > @0G_labs - $0G builds private, verifiable inference infrastructure. > @nesaorg enables secure inference on its AI Layer 1. Pillar 3 - Reproducibility > @gensynai - $AI develops REE to make AI execution reproducible and verifiable. > @PrimeIntellect built TOPLOC, where validators recompute hidden states to verify inference. Pillar 4 - Compute Provenance > @prlnet - $PEARL turns GPU AI compute into proof of useful work that secures a chain. > @AethirCloud - $ATH runs Checker nodes that guard compute integrity on its network. > @darkbloomai routes requests to physical Macs across a live compute network. > @eigencloud - $EIGEN builds open compute infrastructure and verifiable agent tooling. As inference becomes a tradable service, buyers will demand proof of execution, privacy, and reliability. These four pillars are where the next wave of onchain AI gets built.