It should be noted that optimization of a convex bounded lipschitz function is exactly what most modern statistical learning (AI) models are based on.

Around here AI isn't really more of a threat to juniors than it is to seniors. It's a threat to the people who have been taught "recipies" rather than applied computer science. You can have excellent seniors who can do TDD, DRY, SOLID and so on, who also happen to have no idea what a L1 cache miss is. The current AI models know all of those things, but they struggle applying them correctly without someone piloting them. Even in the energy industry where I work, where you'd think it would be obvious from the context that you should prioritize runtime safety over debug safety, the current AI models struggle to do so. As far as seniority goes, though. If we can find a young developer with little experience who actually knows computer science, we're much more likely to hire them... Since they are cheaper.

This isn't something which is unique to software development though. We're currently building enterprise AI apps that we can deploy into the AI agents working for anyone of our employees. The key thing we're currently seeing is that the people in a team who are the ones that everyone turn to for advice, are the only people who aren't in "danger". Even people who are great at their jobs are being outperformed by AI in many cases.

I think it'll be a massive challenge for our society in the coming years. Maybe we're even going to get to the point where the AI will also be capable of replacing a lot of the "domain experts". Right now that seems far out, but then, if you had asked me about AI four months ago I would've told you it was all hype.

My experience may not be entirely representative because to be entirely honest I’m not exactly a great researcher and there are brilliant PhD students. That said it indeed was my experience that in the pre-PhD / early PhD period ( or even longer … ) your advisor proposes (gives) you pretty low hanging stuff that he mostly already knows how to solve, at least at a high level, with the expectation that it will teach you to use the mathematical tools you need.

This apparently required a 10-page prompt. It seems like someone needs to know enough to write it?

Math is way more automatable than programming.

In math, a proof is a proof. We don't know if we can get there and so getting there is the hard part.

In software, we always know that we can solve the problem. So HOW to solve the problem is the hard part. Because the type of solution involves maintainability, which involves planning, LLMs suck at it. This leads to "LLM slop code" whereby the LLM creates ad-hoc convoluted logic with redundancies and no reuse of existing standard library batteries.

Unless you're a Grothendieck who gets mad at Deligne for not solving the Weil's conjecture "THE RIGHT WAY", software is fundamentally different than math in this respect.

So I'll say it again, AI will win a fields medal for before managing a McDonald's simply because there are enough big problems within arms reach than their current capacity to plan over time

It was rejected for being wrong (or most charitably, incomplete).

If it were commonplace, there wouldn't be a post and discussion about it. Cheap? Arguable - while it didn't cost thousands, it wasn't free. Cheap is in the eye of the beholder. Efficient...How do we even measure that? The massive infrastructure and training to take a product to the point where someone could do this is massive. Ignoring everything behind the scenes and acting like one session and result is the whole picture of efficiency doesn't seem right. And no, nothing produced by AI makes skills irrelevant. That is the whole ongoing argument of whether people are losing cognitive ability by moving their thinking to AI.

Overall, this is an impressive proof of capability. But I wouldn't take that proof as anything more than what it is.

It's still clear that LLMs lack spatial reasoning, either in the concrete or abstract, and while that sort of reasoning has been downplayed by academia for at least a century it is fundamental to technology and industry. (And many would say for science and mathematics too).

They will, however, get there as well either directly or as interfaces to models that do, and your core point stands.

Ever heard of the infinite monkey theorem?

This is basically what LLMs do on really hard tasks. Prompt it a million times on a really hard problem and it might output the correct answer once.

Everybody can be an armchair mathematician now. Just fling some thoughts in the direction of your AI setup and let it do breadth first search with AI based pruning heuristics.

Intelligence was always relatively cheap. You can pick up a phone and get answers for free in most academic settings.

Oh brother

AI hasn’t even taken the class of jobs associated with customer service lmao

yeah...right. Go touch some grass

Once we figure out the pesky problem of how we're going to pay for housing, food, and healthcare.

Mathematics is a human-designed game that involves rearranging symbols.

I actually think people who are great at understanding problems, coming up with requirements and designing solutions (all things I would expect someone who is good at churning out MVPs would be good at) are exactly the people most empowered by the current batch of LLMs. Its the people who are only good at working on small chunks of problems that I'm concerned about..

You’re at least 18 months out of date claiming that prompting will be the new hot skill. Turns out LLMs are also good at prompting other LLMs.

I think there's a lot of interesting things to the side of development that don't get the resources they deserve

Debuggers, testing techniques, testing layers

Essentially things that could be used to ground your ai back to reality and work good for humans too

> I'm starting to feel like there's no place left for programmers like me who focus on quickly churning out MVPs.

Of course there is. The same way this was only possible as a result from the professor who prompted it with his specialized 10 page prompt and most importantly his deep knowledge of the problem space, the muscle memory and intuition you've built over the years is what will allow you to get more out of any AI than some guy who says "make a door dash clone" as the entire prompt

From the post lol

>So I wouldn't really say that this result is using or creating some fundamentally new techniques in convex geometry or optimization theory. What this means from my perspective is that if a result is attainable with existing techniques, modern AI methods will be able to solve those problems. I don't think researchers in math/TCS will be made obsolete, but I think it will instead no longer make sense to work on any low-hanging, or even medium-hanging (you know what I mean) fruit. We'll be needed for problems where actual novel approaches are needed.

HEH. Don't know why you're getting downvoted. It's painfully obvious that there is a vicious AI backlash now, where every amazing advancement is met with denial and loathing.

Oh wait, sorry, I do know why you're getting downvoted. Fear.

Oh brother I can tell you didn't read the entire article.

It is lean-verified, so it can be trusted unless the Lean statement of the hypothesis is not an accurate description of the hypothesis.

Saying “solve this problem” doesn’t get good results most of the time with humans either, it’s entirely underspecified so the person assigned that problem may solve it in a variety of unacceptable ways or not at all or perhaps worse solve the wrong problem because you weren’t clear about its definition. This actually happens all the time. What matters is the ability to communicate clearly and with precision as well as the “harness” which for humans is procedure, training, planning and management.

Lean is the Mizar here. For those who have no clue what this is about, Mizar [1] was an early automated theorem prover. Can't wait for HN to add AI features to explain concepts in the sideline, and autovoting.

[1] https://en.wikipedia.org/wiki/Mizar_system

It's interesting to see the old "Why would we go to space when there are still uncured diseases" show up in a place like this. Science and discovery are singular, all discovery aids all discovery.

Cancer is also bottleknecked by a lot more than just intelligence. If you have 100 of the smartest PHd students working on a cancer problem you have to wait for funding, lab experiments, and clinical trials etc. Math is deterministic and requires nothing like that.

Have you not heard of things like AlphaFold?