# The Singularity Has a Distribution: Exact Timing Laws and Detection Limits for a Stochastic Intelligence Explosion

arXiv: https://arxiv.org/abs/2610.18445 · HTML: https://mlsanity.com/paper/2610.18445/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 37 references that are in the map.
- First posted on arXiv 13 Oct 2026.

## Builds on (most cited first, 2 of 2)

- [The Dynamics of Intelligence Explosions](https://mlsanity.com/paper/2608.14426/) (2026)
- [What if automating AI R&D triggers an intelligence explosion?](https://mlsanity.com/paper/2609.36054/) (2026)

## Abstract (arXiv)

Automating AI research creates a positive feedback loop between software capability and research effort. We study a stochastic realisation of this loop in which independent, positive, multiplicative improvements arrive at a rate proportional to a power of current capability. Within this model, randomness leaves the deterministic explosion threshold unchanged but replaces the blow-up date by an explicit distribution. For equal-sized improvements the explosion time is a geometrically weighted sum of independent exponentials; its q-series representation gives its transforms, moments and tail behaviour, including a two-term small-time asymptotic. For Lomax-distributed improvement sizes the explosion time is exactly Gamma distributed. Self-similarity implies that, with parameters known, the relative uncertainty about the remaining time does not vanish as explosion approaches. We formulate a fixed-sample test between constant and geometrically increasing idea-arrival rates. If the baseline rate and the alternative log-rate increment ℓ are known, the minimum sample size at error level α is asymptotic to (12zα2)1/3ℓ-2/3, where zα is the upper-α standard normal quantile. Uncertainty about the growth exponent near its critical value produces a 1/t tail with an explicit constant, and hence an infinite conditional mean waiting time, although explosion is certain given a positive exponent. Separate extensions show that a fixed deployment delay prevents explosion in the specified delayed-feedback models, that power-law diversion of effort shifts the growth threshold, and that in a deterministic oversight race a fixed allocation share does not change the asymptotic regime, except at a critical equality of exponents. Numerical illustrations, which are not empirical forecasts, show how timing and detectability depend on the granularity of improvements.