AI is starting to function as an autonomous system. But if AI is generating its own data and evolving its own behavior, how do we ensure it stays aligned with human intent?
At our New York College Tour, @DMSKwak (@LazAINetwork & @MetisL2) argued that the core challenge is data alignment. Data doesn’t just record reality anymore, it shapes intelligence itself. As AI agents gain freedom to act, the difficulty of cleaning and constraining training data grows exponentially, especially when the AI begins producing data of its own. Without new safeguards, drift and hallucination become unavoidable.
In his presentation, Daniel introduces LazAI’s approach: verified data pipelines (using TEEs + ZK proofs), Data Anchoring Tokens to bind off-chain AI assets to on-chain state, and individual-centric DAOs to govern each dataset, model, or agent.
It sounds complicated, but the root idea is simple: if AI is autonomous, we must verify not just its outputs, but also the data and policies that drive them.
Dive into Daniel's full presentation to understand the solution in detail:
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