Contrary to the "inhuman intelligence" narrative, a top mathematician observes that AI-generated proofs and their reasoning processes are very recognizable and similar to how a human mathematician would think. They are not producing incomprehensible "move 37" style solutions that are common in games like Go.

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.

A common fear is that AIs will produce billion-line proofs of theorems without offering human insight. However, an alternative and perhaps more likely future is that their superhuman capabilities will be applied to explanation. They could take complex, human-incomprehensible proofs and find novel ways to make them intuitive and easy to understand.

Contrary to fears of AI producing thousand-page, unreadable proofs, its current mathematical breakthroughs are often short, elegant, and human-like. The reasoning traces read like a colleague's thought process, making the solutions understandable and building confidence that the AI is not just guessing but reasoning cogently.

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.

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.

A mathematician describes how avoiding a "brutal" 10-page proof forced him to find a more elegant, conceptual solution, leading to deeper insight. As AI becomes capable of tirelessly grinding through such calculations, it might bypass these opportunities for creative discovery that are born from human limitations.

OpenAI's Astra model solving major open problems in mathematics has led to a profound sense of despair among some experts. The sentiment, described as "The dark night of mathematics," reflects a fear that AI is not just automating tasks but devaluing a deeply human field of intellectual discovery.