In their book "Big Math: The Hidden Codes That Run Our World," applied mathematicians Steven Strogatz and Alex Townsend define "big math" as problems exceeding human cognitive capacity—characterized by billions of variables where linearity and sparsity permit tractability, unlike non-linear systems. They trace computing's evolution from servant (calculating known trajectories) to scout (the 1950s Fermi-Pasta-Ulam-Tsingou simulation revealing soliton theory via unexpected recurrence phenomena) to oracle: AI systems like AlphaFold and LLMs with trillions of parameters that produce correct results without explicable mechanisms. Strogatz argues this shift forces mathematics toward empirical validation akin to FDA drug trials—verifying outputs without understanding internal logic—sacrificing the field's traditional certainty of absolute proof. He acknowledges epistemic and cultural losses: the "aha" moment of insight, individual mathematical voice, and the grief of early-career researchers facing potential obsolescence. However, he notes AI currently cannot formulate novel research questions—a critical skill for graduate training—and suggests humans may retain roles in question-posing and verification. The book frames this transition as a Faustian bargain: dangerous yet potentially redemptive if striving toward larger understanding continues, with practical benefits in climate, energy, and medicine already emerging.