Execution Proof vs. Truth Proof: What Zero-Knowledge ML Actually Verifies

Zero-knowledge ML proofs verify that a computation was executed correctly, not that the result is true or useful—a critical distinction for developers accustomed to trusting black-box AI servers. The strength of zkML lies in proving execution integrity across transparent artifacts (model commitment, input, output, proof), making it most valuable for narrow, verifiable claims rather than broad assertions about model quality or training reliability. This fundamentally reshapes how crypto-native systems approach AI trust: from opaque server reliance to checkable computational boundaries.