curt15

Why should that make material difference to the IPO? What is the economic value of those results?

The entire US federal budget for math research is something like $100M annually. And mathematicians in other countries are hardly making bank either. How does one reconcile how the market has historically valued mathematics with the cash-strapped frontier labs ploughing so much money into that enterprise?

How well do you think someone will understand fractions or trigonometry if they always punch their math homework into Wolfram alpha?

The increasing pervasiveness of technology in US education has not produced more capable graduates.

> This reads like a comment from 2025. LLMs are solving novel math problems and they successfully emulated a PS5, this week lol.

It's curious seeing "math" and "productivity" in the same sentence when the market assigns much less economic value to math than to other endeavors. The entire annual US federal budget for mathematics a mere ~$100M on average.

No one is going to get tenure by spending 24 years on a problem with no results. The profs who have that much free time on their hands are already in the later stages of their careers with records of impactful results. By that time, a problem like that is more of a curiosity than sometimes expected to have broad concrete impact.

"AI" is largely a marketing term for a particular type of computer program that uses a statistical language model.

Computers and computer programs are tools. Humans always remain sovereign over their tools.

If it's their own computer, what better way for them to learn than by making mistakes? And if it's a shared computer, Linux container tools (e.g. https://github.com/containers/toolbox) would let them "sudo rm -rf /" in their own environment without jeopardizing the base system or other users.

"Most" involves great subjectivity, but how about laying the mathematical foundations for quantum mechanics?

Perhaps so for the most junior grunts, but officers with people under their command are definitely expected to think.

The people reaping AI's benefits today generally developed their skills as juniors through old-fashioned struggling in the pre-ChatGPT era -- actually reading docs and articles, trial and error, puzzling over mysterious bugs, and generally doing lots of mental lifting.

How will skill building happen when LLMs and coding harnesses seemingly provide all the answers at one's fingertips?

If math is a "burden" on AI labs, why do they willingly spend tens of millions of dollars for a purely theoretical and unphysical result? What's the ROI for that when the frontier labs are already notoriously cash-strapped? Their motives pervade the entire discussion and cannot be ignored here.

> The math result is a result only when it includes proof. The proof is the value here. The way somebody came to it isn't really important - we don't know how Newton came to his results, whether it was apple or pear, and it isn't really important. Or how Einstein was walking the city streets looking at the tower watches - it is just historic curiosity having no real value for science.

This mischaracterizes the role of rigourous proofs in mathematical understanding. While undergrads and early grad students focus primarily on proofs, formalism recedes into the secondary role of honing intuition as mathematicians transition to their "post-rigourous" stage of development[1].

The importance of intuition in mathematics cannot be overstated. That's why people go to math talks even when the actual results are codified in papers. In a less formal setting, they get to pick the author's brain to learn their mental pictures and heuristics that don't make their way into papers. Those would be analogous to chain-of-thought traces and agent-to-agent messages for a computer generated result.

[1]: https://terrytao.wordpress.com/career-advice/theres-more-to-...

Your monopoly theory lacks a credible motive and also ignores the fact that your quote specifically targets the frontier labs, not LLMs or computational tools in general. The group (https://agmai.org/) comprises of world-renowned mathematicians, including several Fields medalists, who have nothing left to prove. If they felt like it they could quit mathematics today and take private sector jobs paying far more than their professor salaries.

The basic purpose of mathematics has always been human understanding (see for example https://mathoverflow.net/a/44213), and the working group's various recommendations simply aim to ensure that computer-generated mathematical activity aligns with that purpose.

New norms are only worth adopting if they are clearly better, and that is far from obvious for whatever norms the proprietary AI companies are trying to push. Also their letter specifically targets proprietary AI companies, not computational tools in general which mathematicians do use when they advance mathematical understanding.

The problem with proprietary models is that you don't get to poke inside and see what it is doing and how it arrives at its answer, which is precisely what mathematicians do. Mathematical understanding derives less from any particular result than the insights and methods that pave the road to results. Whenever a theorem is proved, researchers seek to unpack the proof and get inside the author's mind to learn their ways of thinking.

LLM generated results might benefit mathematical understanding if people can inspect their intermediate reasoning traces to discover erroneous human biases or patterns that they might have previously overlooked. Otherwise, the results might as well be produced by oracles.

What will the path to a tech lead look like when it's no longer paved by thousands of hours of experience internalizing code? How do future tech leads avoid becoming like people who never got comfortable with fractions because they offloaded all arithmetic to calculators from an early age?

Before extrapolating that far, take a look at the frontier labs' own job boards (https://openai.com/careers/search/?). Isn't it curious that they are still recruiting human "Android Engineers", "Account Associates", "Consumer Marketing Leads" instead of automating them with their world-beating models?

How about

> Cluster mirroring embeds cross-cluster replication directly into the Kafka broker. No external processes, no offset translation tables, no recompression.

I think the OP was referring not to their public image, but to the incongruity of paying human influencers instead of deploying computer-generated influencers created by their world-beating models supposedly worth $30T.

Or people with a vested interest in pushing the frontier labs' preferred narrative that they've achieved "AGI"...even while frontier labs continue to hire human "Account Associates", "Android Engineers", "Applied AI Engineers"...

At least Android allows truly alternative web browsers with ad blockers that aren't handicapped by Manifestv3 limitations.

You can call it survivorship bias, but another way to phrase it is that it's very difficult to forecast the practical benefit of any one piece of mathematics even while the long-term impact of mathematics as a whole is undeniable. And any schemes to further ration resources among mathematicians ignores the reality that for such foundational subject, math research already one of the least funded compared to other disciplines or domestic priorities.

> And why the hell would anyone want a job where a mistake results in personal ruin?

People will do nearly anything if the price is right.

The primary goal of all basic sciences is human understanding. "Truth" is no more a goal for mathematicians than the physical laws are a goal to physicists; they simply exist in nature. The goal is rather to develop useful language and conceptual frameworks for reasoning and communicating. That understanding underpins all practical applications.

How do you normally verify the work product of a "few-shot" development process? Do you scrutinise the source code like with human developers? Or do you just run the test suite and click around the app to check if it seems to work?

Since OpenAI is undoubtedly also under economic pressure, why is it still hiring humans for Account Associates, Android Engineers, Data Scientists instead of demonstrating its AI prowess by automating those roles?

https://openai.com/careers/search/

> He’s also always argued that mathematics is fundamentally about human understanding.

It's also worth pointing out he didn't come up with this viewpoint just to "cope" with the headlines. Thurston articulated this way back in the 90s (https://arxiv.org/pdf/math/9404236) and also more recently (https://mathoverflow.net/questions/43690/whats-a-mathematici...):

> The product of mathematics is clarity and understanding. Not theorems, by themselves. Is there, for example any real reason that even such famous results as Fermat's Last Theorem, or the Poincaré conjecture, really matter? Their real importance is not in their specific statements, but their role in challenging our understanding, presenting challenges that led to mathematical developments that increased our understanding.

> I thought the reason we were encouraging people to go into STEM, and providing clever people with large salaries, was that mathematical results were of practical value to the wider human race. In which case, it’s surely very good news that AI can get those results.

The impact of mathematics on other disciplines goes much further than specific results. Just as important, if not even more so, are the language and ways of thinking that typically come out of the process of establishing those results. Results without the accompanying conceptual understanding are about as useful as a mere oracle for math theorems.

Another point that seems to be frequently missed or mischaracterized is that mathematicians are not opposed to computational tools as a matter of principle and in fact do use them when they help their research. The current controversy is not about a hypothetical future where mathematicians have easy access to open-source, open-weight, auditable natural-language assistants to help them internalize a new result or search for counterexamples. It's primarily about the recent behavior of certain for-profit companies suddenly trying to disrupt mathematics by caricaturing it in the public eye as a game they can "solve" or "beat" for headlines and valuation.

> Math, on the other hand, was paid for because people and states believed progress in math might lead to meaningful improvements in other branches of science, and, in turn, in our lives. If this is better served by AI, should we keep paying for the same number of tenure positions?

That is a pretty huge "if". The ultimate purpose of mathematics and really all scientific inquiry is human understanding of the natural world. It's not at all clear whether large language models can replace that any more than calculators can replace human mastery of arithmetic. Is society ready for engineers to design bridges and airplanes without understanding the underlying mathematics by simply handing off the entire process to a black box "AI architect"? Are people ready to ingest drugs "vibe-designed" by human drones pushing buttons on an "AI drug discovery" machine and just going "meh, seems about right!"

It might also be useful to take a step back from the hype that the frontier labs are obviously incentivised to incite. Before speculating about how "AI math" capabilities might supplant cutting-edge research in mathematics and other sciences, take a look at OpenAI's own job postings (https://openai.com/careers/search/?). Why doesn't OpenAI demonstrate its world-beating AI capabilities by automating more routine roles like "Account Associate", "Systems Architect" or "Android Engineer"?

Where do you draw the line between system packages and user facing apps? Some software defies such an easy categorization. If your default install doesn't come with docker and you install docker later for development, does that make docker a user-facing app? What about language toolchains like golang, rust, npm, etc?

> So let’s say OpenAI cure cancer and put every cancer researcher out of work depriving them of intellectual satisfaction, this would also be a problem? It would certainly impact the field.

Would you expect Fields medalists to cure cancer if you moved them from the math department to a medical research lab? This is precisely the fallacy that the frontier labs are counting on to inflate their valuation as their IPO approaches. They want to use headline-grabbing problems in pure maths to make their models look "smart" in the public eye. But what does "smartness" in mathematics really mean in terms of economic value? It is not at all obvious whether success in abstract mathematics should translate to successes and, more importantly, profitability, in more grounded endeavors.

Look at OpenAI's job postings (https://openai.com/careers/search/). Those roles involve far more pedestrian yet profitable duties than research mathematics. So why isn't OpenAI automating them with their vaunted models? Success in one field, no matter how "difficult", does not predict results in another field.