AI-Driven Alpha Decay: Algorithmic Homogenization, Reflexive Signal Erosion, and the Paradox of Intelligent Markets
We show that AI-driven investment strategies are inherently self-defeating at scale. As AI adoption rises, three mutually reinforcing channels -- signal crowding, performative signal erosion, and Red Queen competition -- compress excess returns. We derive the alpha half-life $h(φ) = \ln 2/[θ+ δ(φ)]$, where $θ$ is the natural mean-reversion rate and $δ(φ) = Nφρa/λ(φ)$ is the AI-accelerated decay component, which is convex-decreasing in adoption. At current adoption levels ($φ\approx 0.7$, $ρ\approx 0.6$), the model implies signal half-lives of 18 months versus 5-7 years pre-AI. We establish four theoretical results. First, the alpha half-life theorem: signal lifespans are convex-decreasing...
| Authors | Shuchen Meng, Xupeng Chen |
|---|---|
| Primary category | q-fin.GN |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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