Although fully verifying an entire AI model with zero knowledge proofs sounds impressive in theory, it turns out to be extremely slow, resource intensive, and difficult to deploy in practice. When every component must be proven together, performance suffers a massive blow.

That's exactly why the approach from @inference_labs shifts to selective trust rather than an all or nothing verification strategy. Only the truly critical parts of the model are cryptographically proven, while everything else runs at full speed.

This makes verifiable AI practical for real world applications. High stakes decisions get ironclad security, everyday inferences stay lightning fast, and model privacy remains intact. It's a genuine leap toward deploying ZK powered intelligence at scale, far beyond just theoretical concepts.

The vision perfectly matches Inference Labs' focus on efficient, practical ZKML and modular verification systems instead of forcing exhaustive proofs across the board.