Autonomous AI sounds compelling—until trust enters the equation.

Agents can trade, robots can move, and systems can act autonomously, but none of it truly matters if those actions cannot be verified.

This is the gap @inference_labs is addressing.

Inference Labs is building the missing verification layer for autonomous AI: cryptographic proofs that an AI-generated outcome was produced by the correct model, using the correct inputs, without exposing private or sensitive information. This is what Proof of Inference enables.

As zkML has matured, one reality has become clear: the core challenge is no longer cryptography itself, but scalable proving and verification. Full-model proofs are computationally expensive and impractical at scale, so the approach has been inverted.

Instead of proving everything, Inference Labs proves only what truly matters—critical decision gates, safety constraints, and verification checkpoints that determine outcomes.

The result is not theoretical:

Over 302 million proofs already processed

Subnet 2 operating as the largest decentralized zkML aggregation layer

This is not a vision waiting to be deployed.
It is verification infrastructure already running in production.