> The future of mathematics — “Math 2.0” — will require both expanding the research frontier, while simultaneously decentering the traditional role of problem solving.

Seems to be the crux of the argument, but "use your imagination" isn't a great thing to tell people who are looking at degree irrelevancy, concerned about getting tenure or a research position. How do we measure if someone is a good mathematician or not, if they are one of the sanctioned few who get access to the biggest AIs?

I'm learning so much about specific vs. generalized intelligence through this entire ordeal.

The cancer arguement on slide 20 is pretty weak. I first need to be alive in the long term to worry about the long term effects. If I had terminal cancer I'd gladly take an AI developed 'cure'.

There are still a lot of treatments/medicines in medical science where we dont know 100% the real reason as to why it does what it does but we still prescribe them because the intended effect is what we are interested in.

> This is in stark contrast to current AI performance on tasks which are subjective, dependent on real world interactions, or for which data is scarce. AI performance is thus extremely jagged: astounding in some directions, while inadequate in others. This is true both within mathematics, and more broadly.

underrated buried comment based in reality

Folks really want to decide if this is pro or anti and respond to that. It's neither.

In a world with only LLMs doing the day-to-day of proving, you still want more than just proofs dumped out as soon as they pass adversarial review or formalization. The first proof code that passes checks may be a mess that's hard to build on; the path to a result, including failures, is full of interesting stuff; naming, generalizing, linking, and organizing things are tasks that move us from today's raw material to tomorrow's future directions.

Folks get this about code: "it works" isn't the same as "it's good." We knew it before LLMs, out of practical necessity as projects became hard to build on.

An odd thing to me is it would not be that big a concession for the labs to openly talk about the difference between the math artifacts they're releasing and what mathematicians do. It's analogous to the Claude's C Compiler post noting it wouldn't replace a prod C compiler. They could talk about paths from a rough initial product to 'production' math, say they hope future models will continue to improve as math collaborators and expositors, and so on.

For the compiler, the gaps would be trivially obvious to their customers, so it was an obvious call to acknowledg them. With their math output, the difference between a proof dump and the kind of well-written, contextualized mathematical program the community pursues is apparently subtle and deep enough the companies can't get themselves to acknowledge there is any difference. So the companies self congratulate and then are yelled at on the Internet instead.

(Ant seems to have wised up a tiny bit about this compared to OAI, and e.g. Ant handed a recent result to some experts in complexity theory to understand and refine and publish rather than just dumping a Claude-written PDF.)

And yes the drug discovery piece was weird. There are good relevant points: it'd be good to understand pharmaceuticals better (helps us find others, understand side effects, etc.); when we get a mystery result, it's worth effort to try to work our way to proper understanding; anything that could help us understand (reasoning etc.) shouldn't be hidden. But as presented it's both not a lot like math and accidentally a lot like those "would you choose..." questions that always get so much engagement on social media.

Doesn't feel good slipping into irrelevance does it. This dude was complaining about having no funding and planning to leave the US just a few years ago, now he's all over the place giving talks and lecturing people about AI. He's got the classic case of epistemic trespassing

Doesn't this assume that humans stop trying to understand problems and AI-provided solutions? Yes, the field is going to change and will require certain rethinking of mathematicians' motivation, but what stops humans from keeping to work on problems they want to solve and understand? The fun from math comes not from solving cancer, but from understanding something new with every approach you take

I think a better analogy would be conducting a marathon in a fog. If you can't see the path of the proof, how do you know it is completely true in all scenarios? If you can't see whether the AI runner ran through every part of the race, how do you know it didn't draw hallucinated shortcuts in the parts where humans can't see? Or worse, create obscurity and blow smoke to hide the shortcut section? If the same AI was to guide the last living humans to a star, because it found a path clear of danger, could you trust it to get in that ship? Or did it just forgot mentioning an asteroid belt the ship is not built to navigate? Truth is verifiable truth that multiple parties can agree upon. Can you trust with your life something you can't verify?

Reads in a lot of places a bit like a mix of anger and bargaining. There is no putting the genie back into the bottle.

But I understand that for people whose whole life was math and solving math problems this will lead to an identity crisis. Seen the same in my area of work (software engineering)

And what software developers should do?

Basically people are vibe coding their personal apps and anything that's expensive is being vibe coded open in the public. I don't see many software companies staying profitable for long.

DHH is the biggest proponent of AI and let me know which of the 37 signals products can't be vibe coded in a month at a $200 plan that are suitable for that organization alone that just has to be accessible internally only? Hence scale and security aren't such an issue.

In that climate - for how long software companies would stay profitable and when not, who'll be employing developers?

PS: Don't underestimate vibe coded apps. Take a look at PDFCraft, VectorCraft, WordCraft. And imagine the feature parity in a year.