ZK proofs and their applications: SNARKs, STARKs, rollups, and private computation.
MORPH shows that a TPU v6e8 pod runs NTT at 40× CPU — 10× faster than an H100. The same silicon that accelerates AI inference now accelerates ZK proof generation, and the math that explains it is the same in both cases.
ZKLoRA lets a LoRA seller prove adapter compatibility with your base model without exposing the weights. The verifier checks in 1–2 s — but the prover bears 31–74 s per module, and the protocol is interactive, which puts trustless on-chain settlement just out of reach.
An on-chain agent acts on facts it reads from an RPC it has to trust. ZK coprocessors swap that trusted read for a proof — and the punchline is succinctness: proving a query over a year of blocks verifies for the same flat ~300k gas as one slot. The mechanism, the anchor problem, real-time proving.
An AI agent that retrieves from an untrusted vector DB can't tell the true top-k from a cherry-picked or fabricated one. Re-running the search needs the whole corpus. The 2026 fix: commit the snapshot, then prove the k-th distance is a boundary, not the sort. V3DB proves it 22x faster.
An AI agent that acts on your bank balance has to prove it saw that balance — without a trusted API. zkTLS turns any HTTPS session into a portable proof. We dissect MPC-TLS vs proxy vs TEE, the real TLSNotary benchmarks, and read Reclaim's on-chain verifier on Base down to its single attestor.
World Chain reserved top-of-block space for verified humans while 382M smart-account ops poured in. We read the Semaphore tree on Ethereum — 17.59M identities, hourly batches at ~3,300 gas each — decode PBH's month-stamped nullifiers, and find the human-only lane sitting almost empty.
Zero-knowledge proofs promised to make machine learning trustless. A field survey of where zkML actually stands — proving systems, quantization tradeoffs, and what's deployable today.