In the industry, there is frequent talk about on-chain AI, but the key bottleneck that truly blocks AI from going on-chain.

It's not about computing power or costs, but rather that the AI model cannot operate safely in an open environment.

Once the model is on-chain, the parameters are essentially publicly exposed, equivalent to hanging core assets on a lamp post. This is also why most AI projects can only "run models off-chain and settle on-chain," making it seem like the essence of being on-chain is still Web2.

#The value of Zama becomes very hardcore here, as FHE allows the model to infer in an encrypted state, developers do not need to expose model parameters, and users will not expose input data, with both sides completing interactions at the encryption layer while the model can still provide services.
It can also maintain closed source and keep on-chain native consistency.

This will bring about an industry-level structural change, as AI models now have the feasibility of on-chain deployment for the first time. The models can become a native asset that can be called upon and billed, but will not leak algorithms or data, truly paving the way for "model as a service" on-chain.

The even better part is that none of this requires sacrificing decentralization and public verification; the verifiable properties of FHE computation make transparency and privacy no longer opposites.

@zama has opened up a serious infrastructure gateway for on-chain AI.

Make the model able to safely go on-chain from being unable to go on-chain.

This structural change will redefine the entire business landscape of AI x Web3.
@zama #Zama $ZAMA #ZamaCreatorProgram