Trustworthy AI systems increasingly rely on probabilistic computation, shifting performance bottlenecks from arithmetic to memory, which must deliver both data and randomness. This paper introduces a unified data-access perspective, treating deterministic access as a limiting case of stochastic sampling, to analyze and address these new memory challenges.