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.

Economic Ownership

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), @PluralisHQ (training), @hyperbolic_labs (inference/training)

Aggregate Talent: Use ownership to aggregate talented participants to exert labor/skillset to a shared layer. There are 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: @itsalltruffles (agent swarms), @PlaySapien (data labeling), @bottoproject (model taste via RLHF)

Verifiability

Trust is inefficient: it requires further mechanisms to ensure the trusted party does not abuse that trust, like legal contracting/putting a human in the loop. With cryptographic verifiability, frictionless composability becomes possible. This will be particularly important in the context of agents who need to act autonomously to carry out various tasks, many of which are undetermined at the time the agent is spun up. Composability is a key ingredient to make agent swarms possible/effective.

Examples: @eigenlayer (verifiable agents)

Self-Custody

The primary unlock of self-custody with AI is currently around agents. Giving an agent a wallet — integrating payment rails into internet rails — provides it with financial independence and therefore an unprecedented level of autonomy. This enables great UX, allowing a human to delegate to the agent (within set bounds) a complicated series of transactions without needing to put a human in the loop to pay each step of the way. The best examples of agents that utilize this feature are DeFi agents and services agents. Agents with something closer to true autonomy are also just entertaining (whether for the novelty or intrinsically).

Examples: @AIWayfinder (DeFi agents), @autonolas (services agents), @truth_terminal (entertainment agents)

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