Emergent Alignment: Economic Mechanisms for the Peaceful Transfer of Work from Humans to AI
Andrei Taranu (Eight Rice) | February 2026
Submitted to the Stanford Journal of Blockchain Law & Policy, special issue for the Kleros-Stanford Symposium on Decentralized Justice and AI.
The paper argues that AI alignment is an economic coordination problem, not a technical constraint problem. It presents an integrated architecture where alignment emerges from the aggregation of individual value structures within governed jurisdictions, enforced through continuous economic gradients rather than binary prohibitions.
- On-Chain Jurisdiction -- Smart contracts for decentralized governance, trustless economies, and the Autonet AI economic layer
- Proof of Intelligence -- Decentralized training and inference protocol (Absolute Zero loop, JEPA, Yuma consensus, Byzantine-resistant aggregation)
- Trustless Economy Simulation -- Game-theoretic simulation of incentive alignment in trustless economies
| Version | Date | Description |
|---|---|---|
| Emergent Alignment (current) | Feb 2026 | Full framework: economic alignment, trustless economy, constitutional governance, world model, decentralized training/inference. ~13,000 words. Deployed infrastructure. |
| Autonet: Economy-as-a-Service | Sep 2021 | Original whitepaper proposing a token economy for deep learning applications. Four stakeholder roles, Proof of Intelligence, service ontology. |
The 2021 whitepaper by Andrei Taranu and Leonhard Horstmeyer introduced the foundational insight that intelligence is the substrate of economic value, and proposed Autonet as an Economy-as-a-Service for deep learning. The current paper builds on that foundation with deployed smart contracts, tested decentralized training protocols, and a formal alignment framework.