# AI is breaking our proxies for expertise DevFeed: [AI is breaking our proxies for expertise]() Original publisher: [Read original article]() Published: 2026-09-13T00:00:00Z Content type: opinion Language: en Sources: [Sean Goedecke]() Topics: [Artificial Intelligence](), [Mathematics]() Tags: [ai](), [mathematics]() ## AI overview The article examines how AI's success in solving prestigious mathematical problems may disrupt traditional proxies for expertise. It distinguishes puzzle-solving from idea-generating mathematics and argues that this distinction can clarify AI's effects on other fields. ## Source excerpt Mathematicians are broadly not anti-AI. They're more culturally open to using AI as a tool than, say, artists or writers1. However, now that more and more genuinely prestigious problems have fallen to AI, that might be changing. Almost five thousand mathematicians (including twenty-five Fields medalists) have signed a declaration called A Severe Misalignment of AI in Mathematics. The core argument goes something like this: In recent months, the success of AI in solving major mathematical problems has made headlines even outside mathematical circles. But solving problems is only a tool and proxy for achieving the primary goal of conceptual understanding and insight. Forgetting this in the world of AI may turn the tool against the primary goal. Indeed, the mass production at faster and faster pace of "true/false" statements could destroy fertile ground instead of breathing life into new ideas. A lot of people online have interpreted this as the expected complaint from any field that gets automated: translators did it, artists and programmers have been doing it, and now it's the turn of the mathematicians. I think this is too dismissive. Understanding the concrete problem mathematicians are upset about can help us better understand the impact of AI on our own fields, and what we'll have to do about it. Puzzle-solving and idea-generating There are two types of mathematics. Most people are familiar with the first, which we might call "puzzle-solving": you take a problem and try to find a solution to it. When you're a student, these problems are typically easy, like simplifying some algebraic expression. When you're a researcher, these problems can be nearly impossible, like proving Fermat's Last Theorem. Puzzle-solving is easy to understand but hard to do, which makes it impressive to non-mathematicians, which makes it highly prestigious. In other words, puzzle-solving is legible. The second type of mathematics is "idea-generating": coming up with new ways of thinking ab