<p>Artificial intelligence (AI) increasingly powers safety-critical systems that demand robust, energy-efficient computation, often under conditions of data scarcity and uncertainty. Traditional AI approaches are limited in their ability to quantify confidence, leaving them vulnerable to unreliable predictions. In this Perspective, we introduce Bayesian electronics, which harnesses the intrinsic randomness of emerging nanodevices for on-device Bayesian computations. By encoding probability distributions at the hardware level, these devices naturally estimate uncertainty and reduce overhead compared with purely deterministic designs. We examine how Bayesian networks and Bayesian neural networks can be implemented in this framework to enhance sensor fusion and out-of-distribution detection. We also describe how hardware training via Markov chain Monte Carlo or Langevin dynamics yields energy-frugal sampling-based learning. Finally, we draw parallels with biological systems that are hypothesized to similarly exploit noise for probabilistic computation. By integrating device engineering, algorithmic design and system-level optimization, Bayesian electronics offers a path towards more trustworthy and adaptive AI hardware.</p>