Early this month, OpenAI has released several papers answering decades old problems to which mathematicians have dedicated their own careers. This NYT piece on Terrence Tao (who first supported AI when it was AI to help humans in the process of discovery, but shunned them when it became apparent that they were looking for the discovery for discovery sake, and not for the journey of learning.

Personally, I think it's a matter of changing the training dataset to learn in the process, and AI can do maths the human way very soon.

OpenAI has tried to ask for forgiveness by pledging 5 million to mathematics, maybe hoping that they come back in tokens. On the other side of the duopoly, Anthropic has donated shares to the public good, which seems to me impossible to manage.

AI is here to stay, and it's only a matter of us, humans, mathematicians or software developers finding our raison d'être and learning to communicate with AI in a way that it also advances our knowledge.

At the heart of the crisis in the profession is a menacing question. If solving hard problems is a mission of math, what happens when machines can do it faster? So much of higher math — hiring, promotions, graduate training and acclaim — is based on the difficulty, quality and the impact of papers (the written documents explaining proofs) that a mathematician produces.

The consensus is that A.I.-assisted problem solving is the inevitable future, but no one knows exactly what that new world will look like. “Deep uncertainty will persist in these next years,” a draft report by a group of mathematicians meeting at Harvard on Ph.D. education concluded last month.

— Elisabeth Bumiller on Terry Tao Is on the Front Lines of the Debate Over AI and the Future of Math

This is a completely different subject. Academics created a bean counting system to assess progress that is completely fake. Research is a high-risk activity, like Venture Capital. You invest in many different projects and teams, hoping that one out of 100/1000/1 million succeeds and has a huge impact on society. So you should be okay with the losses you take. It's part of the high-risk/high-reward system.

CoARA and Informatics-Europe provide recommendations to assess researchers: focus on the research and forget bean-counting. But even universities that are trying to adopt CoAra (like my employer, U. Lisbon) forget about the goal and come back to KPIs for fear that funding will be given based on KPIs.

Curiosity-driven research has been responsible for many important discoveries and inventions. So there should be a base funding for that. Then, there should be strategic funding for each country's long-term priorities (healthcare, transportation, defense). Meet with people and fund based on the team and the impact that the project might bring.

To assess researchers, let their peers assess them through conversations and presentations about their work. Do not rely on publication counts and other metrics that can be gamed. And if you are not qualified to evaluate a researcher, maybe you shouldn't had hired them in the first place.