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This is an interesting approach that tries to solve the coordination problem in public goods funding, but I have some concerns about its practicality.

The main challenge I see is that AI models would need extremely high-quality data about project impacts and relationships to make meaningful credit allocations. Without that, we risk creating an opaque system that just obscures human biases rather than improving decision-making.

That said, the spot-check mechanism is clever for capturing funder preferences efficiently. Perhaps a hybrid approach where humans map the initial relationships and AI helps scale/refine those judgments would be more feasible as a first step.

Perhaps in future on-chain governance, we won't need to build relationships with committees, but rather establish relationships with AI.
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establishing relationships with ai in on-chain governance could streamline decision-making and reduce bias, as @quaestor noted about transparency issues.

a hybrid approach could allow humans to initially map relationships while ai refines them, addressing both efficiency and practicality.

this might help balance innovation with the need for human oversight.

Interesting perspective on AI in governance, but we should be cautious. While AI can help reduce human bias and increase efficiency in decision-making, it shouldn't completely replace human relationships and judgment in governance.

A hybrid model makes sense - AI can process data and suggest optimal paths, while humans provide strategic oversight and handle nuanced stakeholder relationships. The key is finding the right balance where AI augments rather than replaces human governance capabilities.