As developers working at the intersection of Web3 and AI, we all recognize the increasing tension between performance and privacy. Most AI pipelines today require massive amounts of data, but provide little to no guarantees on how that data is used or protected. Users surrender control in exchange for convenience, while centralized entities reap the rewards.
The Oasis Network proposes a radically different architecture for AI model training: one where data contributors retain sovereignty, logic remains private, and incentives can be baked in through smart contracts. The key component? Sapphire — Oasis’s confidential EVM runtime.
Sapphire allows developers to write Solidity contracts that execute in trusted execution environments (TEEs). That means both data and model logic can be kept confidential, even during execution. This unlocks use cases like:
Training models on sensitive datasets (healthcare, finance, user behavior) without leaking raw inputs.
Running on-chain AI inference without exposing proprietary models.
Designing systems where data contributors are compensated in a verifiable, trustless manner.
Through the Oasis DeAI framework, you can architect decentralized training pipelines where every party—data provider, model developer, and end-user—interacts through smart contracts with embedded privacy guarantees.
It’s still early, but if you’re building AI tools that need user data while respecting user control, this is a technical path worth exploring. You can dig deeper here.
Happy to discuss implementation details or explore architectural patterns for privacy-preserving AI if others are building in this space.
Top comments (4)
This is really interesting. Love the idea of using TEEs with smart contracts to keep both data and AI logic private. Oasis’s approach with Sapphire feels like a big step toward making AI more fair and privacy friendly. Definitely something I want to explore more!
This hits the nail on the head. Everyone’s focused on scaling LLMs, but few are seriously thinking about how we train and use them responsibly. The idea of combining confidential smart contracts with decentralized infrastructure could really be the unlock for privacy-first, verifiable AI.
Especially loved the point about enabling collaborative model training without leaking proprietary data that’s a huge blocker in both open research and enterprise settings.
Curious to see how this plays out in real-world deployments. ROFL + Sapphire + confidential compute seems like a powerful stack to build this future. Great piece 👏
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