Why are neural networks and cryptographic ciphers so similar? (2025)

Neural networks and cryptographic ciphers share surprising architectural similarities—both use repeated sequences of linear and nonlinear transformations with alternating row-column mixing patterns—because they solve fundamentally similar problems: creating complex interdependencies from simple primitives under weak correctness requirements and extreme performance constraints. This structural convergence has strategic implications for IT leaders, suggesting that advances in one field (such as hardware accelerators or optimization techniques) may directly transfer to the other, creating opportunities for consolidated infrastructure and cross-domain innovation. Organizations should recognize that investments in cryptographic and AI acceleration infrastructure may yield synergistic returns and that security and ML teams could benefit from closer technical collaboration.