AI Is Bayesian Evidence That We Live in a Simulation
Using Bayes' theorem, the author argues that the development of AI increases the probability that we live in a simulation. AI demonstrates that general intelligence can emerge within artificial computational systems, raising the posterior probability of simulation. The article explores how AI training processes resemble patterns one might expect in a simulated reality.
James Baker
Jul 15, 2026
Let:
\(S=\text{we live in a simulation}\)
\(B=\text{we live in base reality}\)
Bayes’ theorem gives:
\(\frac{P(S\mid E)}{P(B\mid E)}
\frac{P(S)}{P(B)} \cdot \frac{P(E\mid S)}{P(E\mid B)}\)
AI matters because it shows that increasingly general intelligence can arise inside an artificial computational system.
A language model begins by minimizing prediction error:
\(\mathcal{L}(\theta)
-\sum_t \log P_\theta(x_t\mid x_{P(E\mid B)\)
Therefore:
\(P(S\mid E)>P(S)\)
A simulation would not necessarily contain visible pixels or glitches. Its inhabitants would discover internally consistent laws and call them physics. As David Chalmers argues, even a simulated world could still be a genuine reality to the beings living inside it. We are building minds inside machines and worlds inside computers. The provocative question is no longer whether a reality like ours could be simulated. It is why we are so confident that ours was not.