Common Risk Factors in Decentralized AI Subnets
Philip Z. Maymin
TL;DR
This paper analyzes the size premium in decentralized AI subnets using a constant-product AMM model, showing how token emission halving impacts returns and transaction costs limit profitability at higher AUM levels.
Contribution
It derives a theoretical size premium from AMM pricing and empirically tests its behavior across 128 subnets, linking emission halving to reduced premiums.
Findings
A small-minus-big factor earns 1.01% daily with statistical significance.
Token halving reduces the size premium from 1.17% to 0.51%.
Transaction costs outweigh gross returns at assets over $10K.
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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.