OpenAI has dumped 722 maths papers – now it must clean up the mess

Last week, I asked whether mathematicians can ignore AI. Yesterday, OpenAI fired an answering volley in the form of 722 maths papers dumped onto the code-sharing website GitHub, the output of a mass campaign against 4000 open problems across a broad range of mathematics. Mathematicians are still digesting the release and are likely to be doing so for some time. It is easy to think that this is an unheard-of situation, but actually I think there are some historical precedents that could prove useful in unpacking what has just happened, and what should happen next.

Before I get on to that, there are two big questions to answer here: what does this mathematical torrent mean for AI, and what does it mean for mathematics? On the first question, we now have further confirmation that leading AI models can essentially produce seemingly research-level mathematics at the push of a button. OpenAI used an unnamed internal model and says that each result used the equivalent of an average “three hours of ChatGPT Pro thinking compute” – Pro being its highest-tier product, offered at $100 to $500 a month.

Of course, “average” is doing a lot of work here, and OpenAI’s subscription services are heavily subsidised, so it is hard to give a true cost figure for each of these results, and OpenAI hasn’t provided one. It seems unlikely, however, that the average problem required anywhere near the effort that went into the firm’s Navier-Stokes result last month, which OpenAI says took around 88 hours at a reported cost of $15 million.