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As of now, $RDAC coin has not yet been established as the first decentralized infrastructure token specifically for AI model storage on-chain, but the idea is technically and conceptually feasible—and it could become a leading project in that space if it delivers on key innovations. Here’s a breakdown of the considerations: 1. What would make $RDAC the first of its kind? To claim that position, $RDAC would need to provide: • Decentralized, scalable storage for AI models (like neural network weights or transformer checkpoints). • On-chain accessibility or verifiable off-chain storage with cryptographic proofs (since full models are often too large to store entirely on-chain). • Tokenized infrastructure incentives, meaning miners/validators or node operators are rewarded in $RDAC for storing or serving model data. • Interoperability with AI execution layers, enabling decentralized model deployment and querying. If it can provide all the above in a unified framework, it could indeed become the first true infrastructure token purpose-built for AI model storage and access. 2. Is storing AI models fully on-chain realistic? Currently, full on-chain storage of large AI models is not practical on most blockchains due to: • Size constraints: Even mid-sized models like GPT-2 are several hundred MBs to multiple GBs. • Gas/fee limitations: Uploading large binaries to chains like Ethereum is extremely expensive. Instead, most approaches focus on: • IPFS/Arweave/Filecoin-style decentralized storage, with on-chain pointers and integrity proofs. • Using zk-proofs or FHE (Fully Homomorphic Encryption) for privacy-preserving model inference. So a successful $RDAC approach would likely: • Store metadata and verification hashes on-chain. • Keep heavy model weights in decentralized storage networks off-chain. • Use the blockchain to orchestrate access control, ownership, and rewards. 3. Are there competitors or similar projects? There are projects working on adjacent ideas: • Ocean Protocol – decentralized data marketplace (not model storage per se). • Bittensor (TAO) – decentralized machine learning, where models are trained and rewarded in a peer-to-peer network. • Gensyn – decentralized ML training compute, though still off-chain for storage. But none of them focus explicitly on model storage as infrastructure in a way that is tied directly to an on-chain AI deployment framework. This is where $RDAC could carve a niche. 4. What would $RDAC need to succeed? • Strong partnerships with decentralized storage layers (IPFS, Filecoin, Arweave). • Incentivization model that rewards both storage providers and model contributors. • Tooling for devs: APIs or SDKs that make it easy to register, retrieve, and run models. • Security and integrity guarantees: cryptographic proofs, access controls, etc. Conclusion: $RDAC could become the first decentralized infrastructure token for AI model storage on-chain, if it can solve both technical and incentive-layer challenges effectively. Right now, it’s a promising concept in an emerging niche—but it would need to show a real working prototype, meaningful adoption, and a clear technical roadmap to take that leading role. If you want, I can help analyze $RDAC’s whitepaper (if available) or compare its roadmap with competitors.

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Viewing the Technological Game of FHE, TEE, ZKP, and MPC in the Sub-Second MPC Network Launched by Sui