r/EffectiveAltruism

We are open-sourcing Autonet on April 6: a framework for decentralized AI training, inference, and governance where alignment happens through economic mechanism design rather than centralized oversight.

The core thesis: AI alignment is an economic coordination problem. The question is not how to constrain AI, but how to build systems where aligned behavior is the profitable strategy. Autonet implements this through:

1. Dynamic capability pricing: the network prices capabilities it lacks, creating market signals that steer training effort toward what is needed rather than what is popular. This prevents monoculture.

2. Constitutional governance on-chain: core principles are stored on-chain and evaluated by LLM consensus. 95% quorum required for constitutional amendments.

3. Cryptographic verification: commit-reveal pattern prevents cheating. Forced error injection tests coordinator honesty. Multi-coordinator consensus validates results.

4. Federated training: multiple nodes train on local data, submit weight updates verified by consensus, aggregate via FedAvg.

The motivation: AI development is consolidating around a few companies who control what gets built, how it is governed, and who benefits. We think the alternative is not regulation after the fact, but economic infrastructure that structurally distributes power.

9 years of on-chain governance and jurisdiction work went into this. Working code, smart contracts with tests passing, federated training pipeline.

Paper: https://github.com/autonet-code/whitepaper Code: https://github.com/autonet-code Website: https://autonet.computer MIT License.

Happy to answer questions about the mechanism design, the federated training architecture, or the governance model.

The dominant framing in AI safety treats alignment as a constraint problem: how do we restrict AI systems to behave as intended? I want to argue that alignment is better understood as an economic coordination problem, and that mechanism design offers tools the safety community has underexplored.

The core insight:

When multiple actors contribute to AI training, the question is not just "how do we make AI safe" but "how do we structure incentives so that self-interested behavior produces safe outcomes." This is precisely the domain of mechanism design.

We have built and are open-sourcing (April 6) a framework called Autonet that implements this:

1. Verification without trust: Coordinators who evaluate training contributions are tested with injected forced errors. If they approve known-bad results, they lose their stake. This creates economic pressure for honest evaluation without requiring trust.

2. Incentive alignment: The network dynamically pays more for capabilities it lacks, steering training effort toward what is collectively needed rather than what is individually profitable.

3. Constitutional governance: Core safety principles are encoded on-chain and enforced automatically. Changing them requires 95% quorum. This creates a hard constraint that emerges from collective governance rather than being imposed top-down.

4. Commit-reveal verification: Solvers commit solution hashes before ground truth is revealed. This prevents copying and creates a cryptographic record of honest independent work.

Why this matters for EA:

If you think AI risk is primarily about coordination failure between actors with misaligned incentives (companies racing, nations competing, researchers seeking fame), then the solution space includes mechanism design, not just technical alignment research. The two approaches are complementary: technical alignment makes individual AI systems safe; mechanism design makes the ecosystem of AI development safe.

Paper: github.com/autonet-code/whitepaper Code: github.com/autonet-code (MIT License, drops April 6)

Happy to discuss the mechanism design choices, the relationship to existing alignment approaches, or how this connects to EA priorities.