A friend of a friend had a grant project written up for the next 10 years, with habilitation on the horizon.

His end goal was one of the things released in the recent OpenAI publications. It took a model 3.5 hours to do.

Things are wild now on math and theoretical faculties now.

And for theoretical fields labs don’t need human data - synthetic one works well if not better.

The best thing you can do if you’re a scientist, imho - wrapup whatever grant you have now asap with ai, use ai to get more grants if possible, and spend the rest od the year learning what new science you can do with the new tools and participate in creating the new paradigm in your field.

That’s when you’re an established scientist. If you’re new in the field then this is the most exciting time you could wish for - an opportunity to make a name for yourself.

That's insane advice. That's like telling CS students to never use a cloud LLM.

Sometimes HN is crazy and out of touch.

But how do they expect to sustain on that?

One natural step of AI usage, the way I see it, is that even mediocre researchers can use AI as a harness to become prolific researchers.

So if you're part of a pure "human only" researchers that publish 1/10th of what the rest are doing, how are you going to survive?

The more prolific researchers will eat your grants for lunch.

This is exactly the same thing a lot of software devs are going through now. AI models have lifted up EVERYONES ability to produce code, so there's simply no premium anymore for those that will only code by hand. And the people paying their salary are asking why they aren't more productive, when even business analyst Joe is pumping out new products left and right.

That's going to be a very short term solution, no?

You can't really swear off looking at problems solved by AI, or keep moving away from fields whenever AI starts contributing, and have a viable career.

Today's frontier models, are everybody's local models in the near future.

If this were a change that had expected stalls or reprieves it would be different.

Doing mathematics looks to be the next chess, er, I mean protein folding challenge. The latter's computation demands have dropped significantly.

That might be like telling early biologists to throw away their microscopes because they are unfair competition to human eyes.

I think we should apply AI into human, so we could be better and then start doing more advanced stuff required for the Universe explorations. Current AI/LLMs can be considered as first step to that. Take and look about our history. We could never achieve so many if we stopped doing great things because of <any reason>.

Why do we ever use PCs, smartphones, Internet, electricity, medicine and so on? Why do we live? For what?

With all the slop being generated I would also like to see the same initiative in the software industry.

ah yes, the ol' bury your head in the sand approach

So others will write their PhD thesis faster, will publish more, you will not publish so you will perish. The rules of the game in this business are brutal. One can count on inventing something entirely new that AI will not be able to figure out, but this is a really high bet.

I was recently working on a project where I had to come up with a novel solution to a problem the company was facing. The problem has been solved before, but in suboptimal ways that wouldn’t work for this specific company. I came up with a pretty interesting solution, one that combines different statistical methods, and was a little more complicated, but gave better results.

I asked Claude what it thought about the technique. It hated it. It loathed the idea that I was not sticking with traditional methods that were pointed to in papers and pre-existing open source packages. Even after talking through the idea and how it was better and showing it the results, it was weirdly hesitant to admit it was a valid method, simply because it seemed nervous that the idea was novel.

I don’t have a real moral to the story, I just thought I would add my anecdote here.

> I have the same optimism as Prof Tao.

This is not written by Tao, but by Álvaro Lozano-Robledo.

It's pretty much the same story with software development. There are two camps and the bigger camp got hit hard. Nowadays I feel entitled to use the "software engineer" title. Essentially, all the decisions-making and nuanced trade-offs which AI is not capable of making; that's engineering. AI can only do programming and code analysis... At least so far.

That said, in the past couple of month, AI has gained a lot of ground on the 'project management' front in terms of planning capability for a lot of simple to medium-complexity projects. It still needs a lot of help for more complex projects. Just today, it almost fooled me into making a major structural mistake but thankfully I asked the right question and saved myself a lot of future problems; it was essentially dancing around a critical point and giving me the illusion that the critical point had been addressed but only when I phrased my question in a particular way, I understood the core issue was not addressed.

Do you care to share then? Because anyone I know with any sense right now is aware that "planning for the next forty or so years" is genuinely impossible at this point, especially in mathematics, and the right approach is to remain on your toes, ride the waves, and diversify as much as possible. Heck, even planning for the next five years seems absurd. This is a time of immense uncertainty, and to think otherwise is foolish.

No one succeeds in making accurate predictions of the future over a 40 year time scale. Look back at the 20th century, and see the range of range of events, and the kind of technological change, that could occur in a span that long.

Go into things expecting you might have to change careers.

It's not a good analogy because the ball is constrained inside a court with fixed rules about how it can travel. No one can predict accurately how AI is going to change things. Look at all the predictions so far. We have the whole spectrum from "the end of humanity" to "utopia".

If it gets better and better for the next 40 or so years, will your career exist?

> At a minimum a discussion like this should acknowledge the possibility that the current rate of AI progress continues apace.

But the post does do this: "[...] even if their capacity becomes far superior, there will always be a need for mathematicians at all levels to guide research in paths that make sense for humans to walk (not run)."

> Of course it will get better at exposition than humans.

I'm not so sure about this, because I don't know how one would train towards better exposition as it's not easy to check for good or bad exposition at scale.

> Just about anyone who has played a sport has heard that they have to target where the ball is moving to, not where the ball is right now.

But the ball is flying out of the court and into the abyss.

What specific direct advice would you give then if you could talk to someone who is currently studying math?

Do you know where “the ball” is going? Do these math students? Do the professors? It’s easy to say a vague nothing platitude like “target where the ball is going to” but such empty vague platitudes do not help anyone and just waste time.

Yes, looking at mathematicians, It's remarkable how such smart people can behave so stupidly. Looks like intelligence is overrated.

> In this regard I am consistently disappointed with the commentary that mathematicians have been producing lately.

For all the arrogance the hard sciences have, in the end they are just as human as their humanities counterparts. They just thought they were above becoming worthless because science was elevated above the softer sciences.

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You could being playing best jai alai player always knowing where the ball goes. Problem is, nobody cares.

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As the article points out, though, all of this fundamentally depends on current AI approaches "hitting a wall", that is where there are some inherent limitations in LLM-based tech that leaves humans some areas where we are still superior.

It's definitely (again, to the article's credit as it points out) up in the air whether LLMs have inherent limitations. But I can't fathom how someone can say "I don’t expect a jobs apocalypse, not even in math." Because if AI does end up surpassing humans, what exactly will there be left for humans to do? And even if they don't surpass humans, there will still be a jobs apocalypse. Probably the majority of people today work in jobs where AI can surpass their performance.

Simple case in point: years ago I used TurboTax to do my taxes, but I eventually needed to hire a CPA because I had some complicated situations that TurboTax couldn't handle, and I also needed some tailored advice. I ended up finding a great CPA. Now though, AI agents can literally do 100% of the job I hired my CPA to do, including asking me the right questions and offering advice. I'm sure there may still be tasks for a CPA in the corporate world, but for personal taxes, I literally can't imagine a CPA providing value over what an AI agent can provide. And to emphasize, I would have probably considered that a ludicrous statement a year ago with all the mistakes LLMs made. But so many of those mistakes have been fixed, and you can get better-than-human performance by having agents check each other's work. And AI will only get better when tax season rolls around next year.

> Transition of college teaching from tenured professors to gig workers

This is a huge, and likely permanent, change.

Philip Greenspun has a 2-decade old essay that is relevant:

https://philip.greenspun.com/careers/women-in-science

Basically, it's arguing that science (really academia) is generally an awful career path for most Americans.

Not even a compiler, but a search engine. That's what LLMs are: an NLP search engine. Maybe even slightly worse than that since the only option is the "I'm feeling lucky" button.

That may be true, but mathematicians still need to eat, and for the they'll need jobs. 'Will there be more or less demand for mathematicians in the future?' and 'What will the work entail?' I think are valid concerns

What economic value is there in a small group of people understanding something, if they provide no added value by proving things?

You must be thinking of physics and other sciences. Mathematics has very little to do with the real world and can be better described as a game, similar to chess, but much more complex. It's no wonder it now went the way of chess, go and similar games

I know most people here are already well into their careers so they might not even notice at this point, but the CS job market isn't just 'going through a tough spot' for new grads. The entire market is down in general, but entry level? Entry Level Hirings have literally collapsed. Roughly 65-75% of entry level hiring dissapeared since 2019 at major segments of the tech industry.

One thing with programming is that LLMs are actually pretty bad at it, and use of them for that purpose is driven entirely by hype. I'm not remotely qualified to judge the mathematical realm, but mathematicians seem to think that LLMs can do the math well. So there's a significant difference there. In programming, we will see a major pull back from LLMs as the abysmal quality of vibe software becomes too prominent to ignore, but the same might not happen in math if they can actually do the job.

There are lots of similarities currently within software engineering and mathematics and a lot of analogies which apply to Software engineering apply to mathematics and vice versa.

> At least for now someone still has to decide what to prove and why. Like why are you trying to prove that thing to begin with? Presumably it's a step along some journey, right? Maybe the journey is where you need to start deriving your satisfaction from, then.

Beautifully said. I had been thinking about the same thing and the analogy b/w CS and mathematics and these were some that I had found:

1.) to prove/disprove from the proofs that OAI created, you needed an mathematician to do so and OAI had to withdraw three mathematical proofs.[0]

But it was only because an expert within the field could verify if it was true or not, I feel as if software engineering is the same as well. We are/can be paid to prove/disprove if a software is working as intended or not.

2.) for someone to be that said mathematician who disproved it, he had to learn the basics of mathematics and multiple branches of it to then perhaps specialize in one thing that he most strongly resonated with and within all this learning, there was some struggle definitely involved. They had to learn algebra etc.,

this analogy can also extend to how we teach children algebra/calculations and other things even though we have had calculators for a long time, yet, we teach children how to do calculations because it is still valuable enough and either teaching maths can help them perhaps in future make a mathematician or it can help them be less reliant on simply calculators and more confident on on the spot calculations and help them within this skill.

As knowing calculation has become the norm rather than exception, even though we have calculators. In fact knowing how to do calculation by hand can perhaps better help you write a problem to calculator. Knowing the technical aspects of CS can help you express a problem to AI with much more depth and effectiveness as well.

[0]: https://news.ycombinator.com/item?id=50002650

Yes. I wrote about it here in the other post: https://news.ycombinator.com/item?id=50000421#50000660

I’m frankly a bit disappointed that Tao published the blog post I linked to above.

Telling OpenAI they shouldn’t test frontier math on their internal models is just plain nuts and illogical. It’s surprising that the advanced math community can lack so much logic.

If the mathematicians refuse to use LLMs they will be superseded by a new set of Mathematicians 2.0 willing to do so. Eventually, the flow of money and students for bespoke artisanal manual math will dry up.

Most of my fellow math students work now in software engineering or consulting. There aren't a lot of professor positions available.

Sounds like a pyramid scheme

I am not sure what you are trying to imply, but quasi-Riemann hypothesis is in fact not Riemann hypothesis and resolution of the former, while truly fucking amazing (it wouldn't be an exaggeration to say it is a discovery of century), gives no hint how to resolve the later.

Confusing since it's on a terrytao's wordpress.com blog. I wonder if he's aware of the drama at Automattic.

Funny because monks discovered a lot. Genetics and the Bayes theorem come to mind.