AI race is really heating up now, with all kinds of agent projects flying everywhere. The calls for decentralized AI to transform the world are endless😂

But on second thought, if AI really goes on the chain, the core bottleneck is trustworthiness. The models themselves are black boxes—who dares to fully trust the reasoning logic? Using ZK proofs for endorsement runs into the same problem: verification takes a long time, consumes a lot of resources, and is simply not scalable.

Recently, I saw a team make interesting optimizations in the NET model area. They reduced proof time from 229 seconds directly to 91 seconds, cutting the verification segment size by 67%. These aren’t just impressive numbers in papers—they’re real, usable performance improvements. The key is their pragmatic approach—they only prove the parts that need proof, leaving the rest out, which ensures a trustworthy foundation without wasting computational power.

This is what a production-level zero-knowledge proof solution looks like. Not a delicate toy from the lab, but something truly usable.

Looking further ahead, the era of AI agents running everywhere will inevitably arrive. By then, on-chain reasoning and proxy trading will become the norm. The supporting technology for all this is precisely these efficient proof techniques. Maybe the next hundredfold opportunity is hiding at the intersection of ZK and AI?

What do you all think? Any ideas?