In the past two years, AI projects have been advancing rapidly, but once they hit the blockchain, the question immediately becomes: why should I trust that the results you provide are true? Large models can be computed on centralized servers, but on-chain contracts can't see the parameters, can't perform inference, and it's impossible to write a lightweight client for every prediction. The solution I chose in my business is to treat Lagrange as AI's 'verifiable exit': inference still runs off-chain, but I only submit 'conclusion + zero-knowledge proof' on-chain, allowing anyone to verify on different nodes that 'this conclusion was indeed generated according to the established model/process.' This is not theoretical; the official documentation states 'supports any proof types like AI/applications/co-processors,' and the technical documents clearly explain that 'co-processors = moving complex computations off-chain and bringing them back on-chain with mathematical certificates.'
I first created a small PoC: anti-witch scoring. The original features and model run in a private domain, and the output consists of only two things—a 0/1 judgment and a zero-knowledge proof. The on-chain contract doesn't care what features or thresholds you used, only whether verify() passes. This model is very suitable for scenarios that require both privacy and trustworthiness: airdrop screening, task anti-fraud, governance qualifications, NFT whitelists. In terms of experience, users only perceive 'I passed/not passed,' while the community obtains certainty that 'any node can re-verify.'
The second PoC is price anomaly detection. I use a small model to make short-window predictions on the trading behavior of a certain trading pair, outputting 'whether an anomaly threshold is triggered,' and provide a proof for reference to the clearing and settlement contract. Because the proof is verifiable, it provides a basis for post-transaction reconciliation and accountability—you can no longer rely on 'backend logs' to persuade others but must produce mathematical evidence; this is particularly important for risk control trading systems.
The third PoC is content moderation. Many social/UGC protocols want to automatically filter malicious content but are unwilling or unable to expose the model details and labeled samples. I made 'whether it violates' an output of 0/1, accompanied by proof sent on-chain. What goes on-chain is not the 'text itself,' but 'compliance conclusion + mathematical certificate'; contracts can then decide whether to allow distribution or rewards based on this. This approach, in my view, is a feasible path for Web3 content governance: no detailed scrutiny, but requiring every instance of blocking to provide verifiable evidence that can be checked by anyone.
There are two critical thresholds at the engineering level that must be crossed. The first is proof latency. Large models are too heavy, and I don't expect to fit them entirely into the circuit; a more realistic approach is to use lightweight or distilled models to handle the 'whether it triggers' judgment and place the large model off-chain for re-verification. As long as the on-chain portion is made into proof, the experience can be acceptable. The second is versioning and traceability. Every inference must carry 'model version/hash + feature commitment' and record rule changes on-chain; otherwise, if you say 'compliant' today and 'non-compliant' tomorrow, users will have no way to appeal. The 'verifiable database + SQL query' of the co-processor can also enhance the rigor of the data side, allowing me to restore the judgment basis at the time on-chain.
I admit that this route is still far from the comfort of 'plug and play': making AI a verifiable system, with circuit-friendly computations, parallel pipelines, recursion and aggregation, GPU resource scheduling—all of these take time. However, I prioritize the replicability of the paradigm: whether you are dealing with trading risk control, KYC assistance, content governance, or credit assessment, as long as you make 'off-chain inference + on-chain verification' a muscle memory for the team, AI and blockchain will finally move beyond mere conceptual collaboration and form a stable interface within the system.
Why don't I wait for a certain 'AI public chain' to finish everything? Because that path means moving all logic onto the same chain, which is difficult for privacy and goes against the engineering common sense of 'modularity.' A verifiable exit is the correct posture for our connection with the AI world: models continue to run where they should, while the blockchain is responsible for 'providing anyone with an irrefutable conclusion.' When an ecosystem hones this path to maturity, developers will naturally be more willing to 'attach proof' to important processes, rather than allowing 'please trust me' to run rampant in the system. For me, Lagrange's co-processor and proof network have already opened the door; the next step is to see if the ecosystem and toolchain can make the entrance smoother and wider.
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