Darwin's theory of evolution, published in 1859, came out two centuries after Newton's Principia (1687), despite being conceptually simpler, because evolutionary evidence is cumulative and retrospective while gravitational predictions can be immediately verified, and because natural selection lacked an experimental test comparable to measuring the moon's orbital period.

Tao predicted in 2023 that by 2026 AI would be like a trustworthy co-author if used correctly, which prediction is holding up well in retrospect.

Lucretius proposed the idea that species adapted to their environment in the first century BC, but this idea received no attention or development until Darwin because Lucretius could not provide experimental evidence or run tests that would compel others to pay attention to his theory.

Fields with tight data loops where predictions can be easily verified will likely see more progress from AI systems than fields that rely on cumulative retrospective evidence, suggesting a bias toward progress in hard sciences over soft sciences.

Jane Street posed a puzzle to the audience where a ResNet neural network had all 96 layers shuffled, and solvers needed to recover the correct order using only model outputs and training data, without brute force (which is infeasible due to combinatorial explosion).

A listener named Shawn solved the Jane Street puzzle by decomposing it into two parts: first identifying which layers pair together by looking for negative diagonal patterns in weight matrices, then ordering the pairs by sorting blocks by residual contribution size, combined with local swaps for refinement.

The process of identifying which scientific ideas will have broad unifying implications across multiple fields—like the concept of the bit, which emerged from Bell Labs work on signal transmission and propagated to probability theory and computer science—requires mechanisms beyond peer review and is difficult to systematize at scale.