The industry loves to talk about on-chain AI, but the real bottleneck preventing AI from going on-chain isn’t computing power or costs—it’s the fact that AI models can’t run securely in open environments. Once a model goes on-chain, its parameters are essentially exposed publicly, like hanging your core assets on a streetlight. This is why most AI projects can only “run models off-chain and settle on-chain.” It looks like it’s on-chain, but in essence, it’s still Web2. This is where Zama’s value becomes hardcore. FHE (Fully Homomorphic Encryption) allows models to perform inference while encrypted. Developers don’t need to expose model parameters, and users won’t expose input data. Both sides interact within the encryption layer, enabling models to provide services while remaining closed-source and maintaining native consistency with the chain. This brings a structural shift to the industry. For the first time, AI models can feasibly be deployed on-chain. Models can become native assets that can be called and monetized without leaking algorithms or data, truly paving the way for “Model-as-a-Service” on-chain. What’s even cooler is that none of this sacrifices decentralization or public verification. The verifiable nature of FHE computation ensures that transparency and privacy are no longer opposites. @zama is essentially opening up a legitimate infrastructure gateway for on-chain AI. Turning models from “unable to go on-chain” to “securely on-chain.” This structural shift will redefine the entire AI x Web3 business landscape. @zama Zama ZAMA ZamaCreatorProgram