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Daniel Barabander
@dbarabander
Good crypto x AI projects leverage the features of crypto to overcome limitations faced by AI. Here are three examples of challenges of AI: 1. Resources. To be competitive as an AI company you need to aggregate significant resources (compute and data) and talent. 2. Composability. Agents cannot reliably chain actions together. 3. Native Payments. Agents, while nearly infinite in their ability to act on a web made up of natural language, remain restricted by the fact that payment rails do not exist on internet rails. These challenges can be solved through separate features of crypto: economic ownership, verifiability, and self-custody.
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Daniel Barabander pfp
Daniel Barabander
@dbarabander
Economic Ownership (1/2) Crypto is amazing at using ownership to aggregate resources and talent. This is really just another way of saying crypto solves a coordination problem. Aggregate Resources: Use ownership to incentivize compute and data providers to contribute their resources. These resources can be used to build a shared data layer between siloed applications, bootstrap the high upfront costs of training, or attract new sources of supply for inference/training. Examples: Plastic Labs(shared data layer), Pluralis (training), Hyperbolic (inference/training).
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Daniel Barabander pfp
Daniel Barabander
@dbarabander
Economic Ownership (2/2) Aggregate Talent: Use ownership to aggregate talented participants to exert labor/skillset to a shared layer. There’s two reasons this matters. First, diverse talent at a shared layer leads to high variance — and when paired with composability (which is enabled by another feature of crypto, verifiability) can produce interesting emergent systems, like agent swarms. Second, this human labor can be used to refine data used by AI, like for data labeling or RLHF of taste preferences. Examples: Truffle (agent swarms), Sapien (data labeling), Botto (model taste via RLHF).
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