Subquadratic Asymptotics

Subquadratic Asymptotics is a visionary, high-level technical book about escaping brute force. It argues that the deepest advances in computation, learning, memory, science, and capital allocation come from discovering structure that reduces exhaustive search into intelligent pathways. Across twelve chapters, the book develops subquadratic thinking as more than an algorithmic idea: it becomes a general theory of efficient cognition, scalable prediction, compressed decision-making, and civilization-level optimization.

Blending complexity theory, geometry, sketching, learning systems, market intelligence, scientific discovery, and long-horizon strategy, the book presents a bold framework for how powerful minds and powerful institutions win by avoiding universal interaction costs. Its central claim is that the future belongs to systems that do not merely compute faster, but think with better asymptotic shape.