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Arthur Hayes launches FLOP Network yellow paper for AI settlement

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Arthur Hayes beside FLOP Network logo and yellow paper document
Arthur Hayes beside FLOP Network logo and yellow paper document

Arthur Hayes, co-founder of BitMEX and CEO of Flop Labs, has published a draft version of the FLOP Network v0.5.0 yellow paper. The document describes a network for measuring and settling AI inference tasks, built on the Substrate/FRAME framework with BABE for block production and AlephBFT for finality. The project targets a testnet launch in the fourth quarter of 2026, followed by a mainnet in the first quarter of 2027.

In brief:

  • Arthur Hayes publishes the FLOP Network v0.5.0 yellow paper as an open technical concept for an AI inference settlement layer.
  • The roadmap provides for a testnet in Q4 2026 with token airdrops for participants, and a mainnet in Q1 2027.
  • The document is explicitly a draft: various parameters and mechanisms may still be adjusted.

What is the FLOP Network?

The FLOP Network is designed as a decentralised layer for measuring and financially settling AI inference, the process by which an AI model actually performs computations based on input. Hayes shares the yellow paper via a public GitHub repository from Flop Labs, so that external developers and researchers can provide technical feedback.

The yellow paper on GitHub describes itself as a research draft shared publicly for technical review. The document explicitly distinguishes between protocol requirements, mathematical results under certain assumptions, empirical measurements and points that have not yet been resolved. Readers are explicitly warned to read each piece of evidence together with the stated assumptions and status.

Testnet in Q4 2026, mainnet in Q1 2027

According to Wu Blockchain, the roadmap focuses on a testnet launch in the fourth quarter of 2026. Testnet participants qualify for token airdrops. A mainnet launch is then planned for the first quarter of 2027.

The document itself points to the areas that still deserve the most attention from critical readers: the verification levels and trust domains, the measurement mechanism for AI computing power, the economics for miners and validators, and the dispute resolution system. Appendix E contains a list of all unresolved values and mechanisms. The yellow paper is published under a CC BY 4.0 licence and may be shared and adapted provided that attribution is given.

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