Common Risk Factors in Decentralized AI Subnets

Paper: arXiv 2603.29751

Authors: Philip Z. Maymin

Abstract

I derive a size premium from the constant-product automated market maker used to price Bittensor subnet tokens and test the prediction using daily data on 128 subnets. A small-minus-big factor earns 1.01% daily (Newey-West t = 3.28). The December 2025 halving of token emissions, which the theory predicts should halve the premium, reduces it from 1.17% to 0.51% (p = 0.044). Exact slippage calculations show the premium is implementable only below $10K in assets under management; at $100K, transaction costs exceed gross returns.

Complexity vs Empirical Score

• Math Complexity: 5.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper presents a highly original theoretical derivation of a size premium rooted in Automated Market Maker (AMM) mechanics and rigorously validates it using empirical daily data across 128 Bittensor subnets. It stands out by explicitly accounting for real-world transaction costs and exact slippage, establishing clear capacity limits for the trading strategy.

Research Flowchart

  flowchart TD
    A[Research Goal: Evaluate size premium in Decentralized AI subnets] --> B[Data: Daily observations on 128 Bittensor subnets]
    B --> C[Derive size premium from Constant-Product AMM pricing]
    C --> D[Construct Small-Minus-Big Factor & Calculate AMM Slippage]
    D --> E[Key Finding 1: SMB earns 1.01% daily return t = 3.28]
    D --> F[Key Finding 2: Dec 2025 halving reduces premium from 1.17% to 0.51%]
    D --> G[Key Finding 3: Strategy unfeasible >$10K AUM due to slippage costs]