A significant but underappreciated strength of AI in math is its ability to perfectly execute the minute details of an idea. While humans get lost in the 'epsilon smaller than delta' complexities, AI systems consistently and correctly handle these finicky arguments, which is often the primary barrier to proving a result.

OpenAI's solution to the Navier-Stokes problem illustrates a new paradigm in science. An AI can generate a correct proof without providing any human-intelligible intuition or understanding. This shifts the role of scientists from solely proving things to interpreting the results of an AI's "experiment," which may be correct but not elegant.

There's a critical distinction between a proof (which establishes truth) and an explanation (which provides understanding). Even when a complex mathematical problem is solved, there remains an 'unsolved expository problem' of making the solution comprehensible. This need for clarity and intuition will remain a crucial area for human or AI effort, even after theorems are proven.

An AI model disproved a mathematical conjecture not through a flash of creative genius, but by methodically applying a known technique from a different math subfield. This highlights AI's current strength: synthesizing vast, disparate human knowledge rather than generating truly novel, alien ideas. It's an exhaustive librarian, not an intuitive genius.

Unlike human mathematicians who give up on ideas after weeks of tedious work, AI models are relentlessly dogged. They will execute on a given approach without the human bias of judging it as unlikely or not worth the time, leading to breakthroughs in problems where the solution required immense, finicky detail work.

Expert mathematicians don't just check proofs line-by-line; they assess the overall argument's structure and its broader implications. Current AI models fail at this crucial "big picture" validation. They can follow local logic but miss when a proof's fundamental approach is flawed or "too strong to be true."

Major AI breakthroughs, like OpenAI's solving of a 200-year-old math problem, are often misunderstood as magical insight. In reality, they represent a massive application of computational leverage—equivalent to tens of thousands of human work-years. AI's value is as an engine for brute-force problem-solving, not a mystical god.

OpenAI's Astra model solving major open math problems highlights a critical issue: even experts cannot easily understand or verify the solutions. This forces a reliance on other AIs or formal proof systems for validation, signaling a future where human comprehension is no longer the gold standard for scientific progress.

While AI solving a Millennium Prize problem is a landmark achievement, the mathematical community is concerned it prioritizes answers over understanding. This creates a "misalignment between the outcome... and its initial purpose," which is to build human knowledge, not just generate solutions.