I never expected this many people (on this thread) arguing semantics and what not. I know that not everyone has morality and ethics, but I didn't realize it was this bad.
I'm afraid of the ripple effect of the agenda pushed by AI companies will have. In future and even now, they say AI has significantly progressed math and scientific research in general. There is truth to this, but the narrative has done more damage (so far) to the students, researchers, and the culture of knowledge transfer in academia. Many graduate students (I know) are having a crisis if any of their research worth it? If AI can (or will) do everything, what's the point of doing experiments and all? This will eventually deter a whole generation of curious minded students from research.
I guess, only time will whether this is for the good or bad. And how good AI models get without new data from research and experiments.
I think that the main thing people in this thread are missing, is that it's not about math. AI progression is very likely to affect every single thing humans can do today. Mathematicians are feeling the blow this week, especially as there was wide spread denial in that math community over the capabilities of AI over the last few years, but it's the same problem everywhere.
I work at a top tier academic institution and they are completely asleep at the wheel on this topic. They just blindly assume people will keep taking on a potential lifetime of debt when there is a genuine existential (for ed, at the very least… ignoring humanity for the time being) crisis looming
Yep. Perhaps humanity would be better off if we instituted and enforced the notion of "Thou shalt not make a machine in the likeness of a human mind." It's worth thinking about; just because we can build AI systems doesn't mean we should.
I would remind you that the fictional society that did this was also described as a feudal monarchy in which a tiny class of nobles exploits the economic output of trillions of people reduced to serfs by controlling all means of interstellar trade. Oh, and they also have legal slavery.
During the Butlerian Jihad, the people involved in it didn't know that ten thousand years of slavery would be what they are choosing, so we don't know how bad it actually was.
The way Jihad presented in the oblique references in books, anyway, it sounds a great deal like a fanatical fundamentalist religious movement to me. Those are not known for their fine reasoning.
Honest question, is an oppressed human society better than no human society at all? Isn’t there at least a hope for humans to free themselves from human oppressors?
I'll remind you that a fictional product that provided longevity and woo woo powers of prescience and was only available on one planet in the entire universe was the key context that enabled such a centralized society. Dropping thinking machines and their masters like a bad habit was only part of the cause, as it eliminated one means of safe interstellar travel in that reality. I can take ideas from this plot without bringing in the whole ridiculous mess if I like, just like I can walk and chew bubble gum at the same time, thank you very much.
Of course they don't, but if you choose to use a work of fiction as an illustration, it comes as a coherent package (assuming the author did a good job at worldbuilding) - you can't just take the bits you like and ignore the bits you don't and still have something coherent.
I mean, we have a Millenium problem that is most likely solved. Either by OpenAI on its own, OpenAI stealing another's work and finishing it or a researcher using AI to iterate more rapidly than a human alone could.
> We should be happy that they’re stealing people’s work?
No. We should credit that work is being done that otherwise may not be. I'm not defending OpenAI. I'm defending AI in general, something both OpenAI and Buckmaster (and his co-author) used.
No, I did not. If someone says either X or Y happened, they didn't say X happened.
My exact statement: "Either by OpenAI on its own, OpenAI stealing another's work and finishing it or a researcher using AI to iterate more rapidly than a human alone could" [1]. (Either X, Y [a and b] or Z. An Oxford comma could have helped.)
So if Y happened (they stole it), why should we be happy about that? Why should we give them credit in the situation where they stole it? Why is that good for society?
Yes, let's lie and call something stealing, then pontificate about morality.
At worst what OpenAI did was inconsiderate, but not referencing prior work is a minor academic sin. This is especially the case when AI is not playing the vanity game that seems to so inflame some mathematicians.
Sure, we can show anything if we're allowed to make stuff up from personal outrage. Why should anyone else consider your personal opinion on this to have any weight?
The problem with technology is that the people who have it conquer and kill all the people who don't, essentially ensuring a destructive race to the bottom. You would need a mutual international alliance of nations who not only renounce the technology but also agree to invade anyone who attempts to develop it. Our closest precedent is nuclear weapons, but rather than renouncing them, the US's position is that "only we should have them" and that other people acquiring the same tech they have are valid targets for invasion, and they will obviously apply that notion to any new technology that can be weaponised as well.
Google which countries have nuclear weapons. Hint: It’s not just the US, it’s also its allies and frenemies who’ve not been invaded. It’s only nutball countries like Iran that get invaded when on the cusp of acquiring it and for good reason.
The technology is fine, great even. It’s the rabid greed of venture capital and sociopathic CEOs that try to integrate themselves into every aspect of our lives for profit, that’s the problem.
“For 19.99 a month you too can be a world class mathematician!” Meanwhile they are extinct.
I am sure it will lead to more material wealth, but I can’t help but feel pessimistic about the political economy of making millions of people redundant. What leverage will knowledge workers have in the new world?
Well, yeah, while that is true, my impression is that mathematicians (as a community, even though there were outliers) didn't care - not even the slightest - about the impact of LLMs and generative AI, other type of AI and machine learning until it started impacting them. So I have no sympathy for their community at all. They brought all this AI dystopia on us, so they should have voiced their concerns much louder and much much earlier. They should also have shamed their peers who helped these companies much louder and they should have lobbied their institutions and their governments much earlier and much stronger. They should also have protested the direction these companies took at the exact moment when they started using AI for profit.
We can look at the past and wish we were more clear-eyed about what was happening, just as we can look to the present and ask that we have more empathy for each other going forward. But I don't think it helps anybody to blame the mathematics community for not organizing until they were directly impacted. This is a tidal wave that has caught everyone off guard in <3 years.
> mathematicians (as a community, even though there were outliers) didn't care - not even the slightest - about the impact of LLMs and generative AI, other type of AI and machine learning until it started impacting them. So I have no sympathy for their community at all. They brought all this AI dystopia on us
Wait wait wait. The research mathematicians brought this AI dystopia on us. Not the techbros, not Silicon Valley, not the global financial system and its tentacles, not surveillance capitalism... No, it was the postdocs working on ultrafilters for C(2,3) epimorphisms on symplectic manifolds. That's certainly a take.
This really resonates with me. I'm early in my PhD and I'm researching a niche form of data compression and IC design. I don't use any AI at all in my research, I do it the super old fashioned way, I read papers cover to cover and sections of textbooks to familiarise myself with the field.
I genuinely enjoy doing this, it's really fun to think critically about what an author wrote or how a particular approach works.
But it does make you wonder, why bother? Probably a frontier model could one shot my algorithm in a day or less. It's incredibly depressing. At least I'm not forced to use it now, but I fear I will have no choice after I join academia or industry in the future.
I’m no big AI cheerleader, but why don’t you try these models to see what they offer - they might suggest some things you haven’t thought of, or save you some time in your research.
Are you sure they’d one-shot your algorithm? Maybe they’d come up with worse, maybe better, but you are in a perfect position to actually judge the validity of any claims a LLM makes in this realm, which most people are not.
As to why bother; our knowledge will keep expanding even with new tools, don’t believe either the hype or the predictions of doom.
>> I’m no big AI cheerleader, but why don’t you try these models to see what they offer - they might suggest some things you haven’t thought of, or save you some time in your research.
One reason not to try is to create an air gap between the OP's ideas and the data that future models can train on. We saw that the mathematicians who trusted OpenAI and Anthropic with their preliminary work found the rug pulled out of their feet by those same companies.
Another reason is to avoid inadvertently plagiarising the work of other mathematicians. Any mathematical insight that comes out of an LLM is the result of training on the entire bibliography of mathematical research, but those insights are spat out of the models without attribution. If you use AI in your matematical research you are only using the work of others without even knowing who they are and what they contributed.
And yet another reason is to avoid polluting your mind with the ideas that come out of the AI. Maybe you get a hint that pushes you to one direction, when you would go into an entirely other direction without that hint. And then maybe that becomes a habit and you can't find new directions without asking the all-knowing oracle.
tl;dr: opsec, integrity and independence are the reasons to not use LLMs in your research. I don't.
Unless you’re very weak-willed, I wouldn’t worry about independence. LLMs make mistakes all the time, they are nothing like an all-knowing oracle nor are they going to replace humans despite the absurd fantasies of LLM fans and those with a vested interest.
Plagiarism is an interesting point, though honestly I think it would be fairly easy to work out who had published similar research if the LLM gives you an idea - personally I see this as the weakest argument against using them, as long as you are strict about attribution - all work like this depends heavily on the research of others - the LLM is just a tool to aid that research IMO.
Opsec is a fair point, and it might be worth avoiding the completely amoral OpenAI at this point for that reason. There are open models though.
Thanks. Regarding plagiarism, I agree that it is possible to check; I don't know how easy it is. But I do note that the mathematicians who complained that their work was plagiarised by AI did not seem to realise that they, themselves, were using a plagiarism machine and were instead quite comfortable admitting that, yeah, we worked our Euler result out together with Sol, Claude and the gang.
To clarify, they do attribute the original ideas to Cordoba and Martinez-Zoroa, but they don't seem to acknowledge that their result which they say was achieved "with a great deal of help from LLMs" is also derivative of others' work.
Independence is about avoiding making errors because of the influence of error-prone models. I guess I didn't explain it well.
I think plagiarism machine is a stretch personally in this domain. In art or writing I could see it persuasively argued (see attempts to generate famous books or imitate illustrators).
Academics and scientists build on the work of many others and always have - their work is not possible without using other’s work.
Attribution is a problem here but I don’t think a new way to reference many others’ research and combine it in novel ways is the problem or should be rejected on that basis alone.
Absurd fantasies like solving a Millennium Prize Problem? How is this not prima facie absurd? And if that's come to pass, why should we believe your bar for anything else?
Yes absurd fantasies like it did that without human help and guidance. OpenAI found out there was a solution and humans attempted to brute force generating all possible solutions while threatening the mathematicians involved (generous interpretation), or stole some ideas and took shortcuts to know where to look and brute forced it so they could claim credit.
Neither looks good for OpenAI or those who support them.
Advanced studies give you more general skills than just the subject you study. You become more generally intelligent and appreciate the world differently. This is a very good feeling and valuable on a personal level.
> Many graduate students (I know) are having a crisis if any of their research worth it? If AI can (or will) do everything, what's the point of doing experiments and all? This will eventually deter a whole generation of curious minded students from research.
Those who think it's me or the machine will fail.
Those who realize how much you can accelerate your research with the help of AI will succeed.
> Those who think it's me or the machine will fail.
> Those who realize how much you can accelerate your research with the help of AI will succeed.
This is only true up until a point. If I treat a mid-sized model (say, Qwen3.8 Flash Next) like a pair programmer, then yes, it accelerates my work.
But I can already see the next stage with Fable: If I give it a couple of paragraphs of spec and $50, then I can just leave the room and go wash the dishes. I learn nothing, I participate in nothing, and I bring nothing to the process. I am no longer succeeding at all. Fable's succeeding without me.
Now, in this model generation, Fable starts getting sloppy after a few thousand lines. I can still build better at scale.
But I don't expect AI to accelerate humans or improve our productivity for long. I can already see the first signs of a future where the AI doesn't need us for anything at all.
> But I don't expect AI to accelerate humans or improve our productivity for long. I can already see the first signs of a future where the AI doesn't need us for anything at all.
The real question is, why is this a bad thing?
Every task that is automated is a task that humans no longer have to do. It doesn't mean that humans still can't do it for reasons other than "because it needs to be done".
And if the answer is "why bother if X does it better", then what does it say about the motivations of doing it in the first place?
Of course you're not going to get rich with the kind of software that LLMs can one shot these days. But that kind of software like to-do lists or basic CRUD have been saturated for over a decade, way before LLMs. People overestimate how much you can one shot, yeah a good prompt can get you 90% there but that 10% remaining often takes months of extra work.
Software has always progressed this way, lots of devs back then would work on business websites that have been 99% replaced by wordpress, squarespace and instagram.
I'm sure it's the same with research, you're going to tackle problems that would have not been worth the effort or outright impossible without AI. The old stuff that you'd work for months, yeah that's going to be a prompt away.
But what does the human researcher do in this future?
If they are not needed to understand the result, then what's their role? Asking the right questions? But how will they know what questions to ask if they don't have a deep understanding of the domain earned by sweating the details themselves?
And how long will they be needed to ask the right questions, how long until AI can do that too?
> Software has always progressed this way
These platforms took on the order of a decade to mature, during which people had plenty of time to learn what's next. With LLMs we went from one-shotting functions in 2025 to entire projects just over a year later.
What do you think you will be working on in a decade?
> Asking the right questions? But how will they know what questions to ask if they don't have a deep understanding of the domain earned by sweating the details themselves?
The right questions are very simple to ask.
How do I get food. How to cure aging. How to turn lead into gold. How to fly high. What is the ultimate theory of physics. Are there any odd perfect numbers. Is there a soul.
We have reached complicated questions requiring deep knowledge because we tried to solve the simpler ones and reached obstacles. For example to solve alchemy we had to develop nuclear physics (and in the process we got chemistry). If one has a genie able to solve questions, you won't have to think about the complicated ones because the genie will.
I think it will be like how a lot of people know how to code in python but have zero understanding of assembly or how a cpu works. As a researcher you'll accept there's this low level stuff that if you want you can dig into but isn't worth your time, like looking at the generated ASM isn't.
You're exaggerating the LLM progress a bit but yeah progress has been crazy. Yet not much has changed right? We mostly have the same jobs, just code wayyyy more than before because things that wouldn't be worth it now are worth it.
10 years from now I have no fucking idea. But I'm sure in the meantime those who leverage AI will do better than those who yell at a cloud.
True, surprisingly not much has changed in software despite the incredible progress. I think it's partly because much software is an "open loop" system where what you build depends on running the software on client computers, devs talking to users, etc. There are still things that only human engineers can do. I worry it is less so for poor mathematicians, where math is "closed loop", and the exchange of capital into research progress is more liquid.
I can't help but be pessimistic about AI moreso than any other technology. Why? I used to be excited to learn the next big thing because I knew it would unlock much more things to do and get paid for, and there would always be more for me. With AI, the new things come nearly too fast, are not very deep or satisfying, and it's hard to see that there will always be a place for me. And so while I leverage AI in my day-to-day, I will yell at the cloud, too.
I am finding it still takes awhile to get my apps to a happy place with AI. Not because the AI is bad but because i have to sit with it for awhile. Figure out what's working, what isn't, what's missing, what turned out to be kind of useless and in the way.
> But I can already see the next stage with Fable: If I give it a couple of paragraphs of spec and $50, then I can just leave the room and go wash the dishes. I learn nothing, I participate in nothing, and I bring nothing to the process. I am no longer succeeding at all. Fable's succeeding without me.
The people that uses Adobe Photoshop also did not learn anything about brushes. We are just going to operate at a much higher level of abstractions. You have to think about the question, "If I just prompted this solution, why didn't they?". This question can even be asked now, why do I even bother paying someone to do my taxes? To write my webpage? To host my website? To manage my network?
> Those who realize how much you can accelerate your research with the help of AI will succeed.
Yes, but, in the last week we saw an AI lab front-run[1] the research of mathematicians doing what you suggest. The lab threw something like $15M of compute at a problem and the researchers were able to spend nowhere near that. I think the authors are more concerned about that kind of asymmetry and race to publish the results.
[1] - I am not going to debate whether that was deliberate on the part of the lab or if it crept into training data, etc. I don't know and don't think it matters towards the point of the authors here.
This has always happened, way before AI. You'd spend months or years building and growing your business, and then Google would release a feature or product that would kill your business overnight because they can throw way more money at the problem, plus their branding. That's life.
But you're talking about business, where competition has always been expected. OP is talking about mathematical research, which has stood on hundreds of years of cultural tradition driven by human individuals sharing ideas, collaborating, building on each others' work, all for the benefit of humanity.
I _want_ to agree, but I fear this is too close to the old “do what you love for work and you’ll never work a day”.
It didn’t lead to a lot of people having a wildly successful career, it lead to a lot of people getting burnt out, exploited, underpaid and generally disillusioned.
There will be a lucky few, who have the benefit of being given the space to work alongside. The vast majority of people will (unless we change things) simply be made to take whatever the machine outputs and call it a day.
I think the only thing that stops this from becoming true is what Chinese and European labs decide to do. If they can keep up and keep opening their weights, then we might see some kind of democratization. But right now it looks like the gap has increased, and those groups can't replicate research that isn't published, or distill models that are internal only, or for select (very wealthy) customers.
That's the crux of it. In the current ecosystem of AI model usage, it's very hard to figure this out for research.
If you set a wrong foot and start trusting the model outputs, you can waste years searching for nothing.
How can someone realize this? By getting proper research training, failing, and learning from mistakes. For people beginning their research, it would be really hard to make decisions to move forward.
Why would anyone pay you to do "your" research, when they can just cut out the middle man and ask the AI directly about whatever it is you're thinking about?
So many people who are excited about AI making them more productive are, I think, drastically overestimating how much value they are adding to that process.
There will be no curiosity, no enjoyment of the process of life. All competing pleasures will be destroyed. But always—do not forget this, timcobb—always there will be the intoxication of power, constantly increasing and constantly growing subtler. Always, at every moment, there will be the thrill of victory, the sensation of trampling on an enemy who is helpless^W not also subscribed to ChatGPT. If you want a picture of the future [of math], imagine a boot stamping on a human face—forever.
> There will be no curiosity, no enjoyment of the process of life.
How does the ability or inability of AI to do something blocks your curiosity or enjoyment of the process of life?
Not being able to pay bills because you're out of job does ruin the enjoyment of life, sure. But the AI is not the problem there; the economic system is. Torches and pitchforks should be properly applied to the economic and the political elites, not to data centers.
> Many graduate students (I know) are having a crisis if any of their research worth it? If AI can (or will) do everything, what's the point of doing experiments and all? This will eventually deter a whole generation of curious minded students from research.
should they not be deterred?
we stumbled into a way of brute forcing intelligence with gradient descent.
I think the impact is heavily exaggerated. Yes, it means mathematics will be transformed into a field that relies on tools instead of your mind alone. Just like most fields. Doctors heavily rely on (really expensive!) tools to make diagnoses and do surgeries too. But it doesn't make them less valuable. If anything it makes them more valuable, as the society needs a long training and filtering process to decide who to use these tools.
That's the thing. It might be. We won't know the impact until the next few years. The things that are changing now: culture of research, aspects of collaboration and sharing of knowledge.
I used to be a PhD student more than a decade ago, and I published a paper containing a solution to an open problem. Yet shortly after my first publication I became increasingly disillusioned, because I started to think that within my lifetime AI would reach and eventually surpass my ability to solve such problems—and that we only had a decade or two left.
So I started saying that it only made sense to focus on problems whose solutions would be useful immediately. I even emailed my supervisor about it, arguing that our efforts were “pointless” in the sense that AI-related problems were much more pertinent and had to be prioritised.
My supervisor thought I was bonkers. I still have the email, though. Quoting myself from April 2015:
> By 2030-2040 we will have enough computing power to simulate a human brain neuron by neuron. Once we manage to create a human intelligence we will be one little step away from super intelligence: just set the intelligence to modify itself and see the exponential growth in action. Our human intelligence is bounded by a number of biological factors (e.g. size of a skull) and even the smart human who has ever lived will appear to be a primitive ant to a supper intelligence (machine intelligence will also have perfect motivation). There is plenty of literature on this if you are interested in discussing this further.
>
> What does it have to do with research in pure maths? I can say that research in pure maths which won't come handy in the next 60 years is just wasted effort. The super intelligence will be able to do maths way better than humans. I believe a lot of current efforts should go into researching of artificial intelligence (or areas to do with AI) instead rather than the pure maths. I want to be proven wrong but most mathematicians I interact with are too narrow-minded to counter me and they just laugh about even contemplating the above. Frankly I am myself so perplexed that I take the above seriously, but I do and it's hurting my motivation.
I'm quite curious what my supervisor thinks of that email now.
I think, with all due respect, that your supervisor would be correct to think of that email as an insult to both them and to theoretical mathematicians as a field.
She was not offended but thought I could benefit from therapy. She didn’t share my opinion - she said it’d not bother her if AI could eventually solve problems for her.
I later realised that my motivation depended heavily on believing I was making a contribution that would otherwise go unmade. The prospect of AI doing that work undermined my motivation, but it needn’t undermine hers. I was trying to explain why I was struggling to continue, though I can see how calling the work “pointless” came across as dismissive.
Newsflash. Most graduate research is worth nothing. If this helps graduate students to re-evaluate and start doing things that can generate income while being intellectually stimulating, it's a net win for the person and the economy
I mean it is. Most of the time in graduate school, you're getting trained and learning to do reasearch. People rarely produce great work during graduate school. Only towards the end of their degree or start of new position (post doc, independent researcher, or assistant professor), you start seeing good work. And for a professor, most of their research is done by graduate students.
Academia has it's own sets of big problems. Before this AI boom also, most people graduating never stayed in academia. The way graduate schools are structured, I would be happy if few people joined. That would also mean we lose good researchers in a long run.
I don't understand why you introduce morality and ethics into your comment?
Pointing out that regressive Ai politics likely are not going to help researchers crisis of purpose is not unethical.
You also assume in your comment that we need new large generations of researchers, and that knowledge transfer through academic institutions is important.
I get the fear. But entertaining ideas of impact to institutions and potentially new post ai institutions is not unethical.
We don't know for this particular case with certainty.
But, here many people are okay with the concept of stealing other ideas, plagiarism, unethical aspects of collaboration in research. At least for me and many people I know, this is no okay. This is the part for morality and ethics. I see that no everyone agrees with this.
The issue is, these AI ecosystems don't effect everyone equally. Not everyone knows how to judge AI model output accurately. There is already a large divide on AI usage in research. The fear comes from this. If we wait longer, it might be alright too late. I could be also wrong with these speculations but better to cautious.
Academia was already crumbling for various reasons. This just hastens it. The reasons are manifold and will never be acknowledged. There will be a lot of gnashing of teeth, but it was unsustainable anyway. And historically the bloat was a very short period, a few decades only.
Academia is toxic for sure. But, also there are genuinely good people who work for greater good and whose training and teaching transcend across domains.
> I know that not everyone has morality and ethics, but I didn't realize it was this bad.
This is a very disrespectful way to make a point about acting with integrity.
You should consider that maybe your views on what makes something ethical or moral are not universal -- and that coming to a discussion with the assumption that your position is the only valid one is not conducive to convincing others who disagree with you.
I commented something similar on a bunch of other posts. The thing that scares me about a lot of the AI community in general is that their utopia is basically more frightening to me than their doom scenarios. They present this "incredible abundance" as the ultimate human endgame, but I agree, when we all end up like the humans in Wall-E, what's the purpose of it all?
And then to get retorts of "it's just just rich techies that want to find 'meaning' in their jobs while millions starve in the third world". Why would we expect the most wealth concentrating technology in history to lead to mass benefits for those in the 3rd world?
Probably the opposite of Wall-E utopia, AI will keep us busy. For one - we are becoming harder to differentiate, competence is hidden deep and surface signals don't carry anymore. Niches and specializations feel threatened by imitation. This means everyone is frantically searching for their own corner, musical chairs. This holds for both people and companies.
Second - every time we learned to transmit better from past experience we saw massive expansion in culture and economy, not shrinking. Writing, printing, computers, internet and AI are just gradations on the scale of transmission from past experience. Everything stands on this transmitted experience.
My analogy to the body - every cell has the same DNA (every human has the same AIs, culture and tools), but they express it differently depending on context. And no one cell is too fat, the organism distributes work and energy. AI won't make anyone too rich. Its wealth is distributed in the system.
I am truly baffled that you think AI is just one more step on the spectrum of "knowledge transmission" between humans. I think that literally nobody who is an expert on this (both from the perspective of the people creating the technology, and the folks on the safety side warning about the dangers) believes that.
100% agree, their utopia sounds far worse than their “humans will go extinct” which still sounds implausible to me. I would rather die than live in a WallE society, and I hate that they’re getting trillions trying to force us there
I actually feel like it's the other way around. The online discourse over the past ~day or so has seemed unusually irrational to me for a technical audience. Commenters making emotionally charged claims of wrongdoing that appear inconsistent with the published claims without justification of the discrepancies. Granted you might well doubt openai's version of events but there's a general expectation of clear evidence when advancing claims of malfeasance.
Sort of, yeah. We are seeing more people commenting who have anti-AI sentiments or in the fence on the topic of AI usage.
On why some people are making emotionally charged claims, my guess: This affects the core belief of what is right or wrong, Impressions based on past doings of OpenAI, losing trust for OpenAI based on sequence of events.
I don't think we will get to see any clear evidence. I'm not even sure what would be the evidence. I would be surprised if OpenAI comes out clean if they have made a mistake. They move on to the next shiny thing.
I identify as neither mathematician nor "maker of things people want" (coder, hacker, engineer). But having friends who identify as those kinds of professionals, let me make some observations.
To use a metaanalogy from chess (once again), mathematicians play the opening game, and builders play the end game. AI is sort of a middleman connecting human understanding to applications.
I think there's a Technical argument to be made that openAI is a threat to the game itself. For example, could it have produced the navier-stokes counterexample without human inputs? since it seemed to have used the much gossiped research strategy "C" and "D", you can't absolutely be certain that Son of Astra (son of altman?) was magicking an unknown unknown from nothing (sorry to cue Rumsfeld). You have got to wait for the other five problems to be solved after general boycott
Subpar PR engine of the OpenAI leadership might kill the pipeline of inputs that they won't admit they still need in this dreamtime before "recursive self-improvement". You can call that emotional. Personally I would rather accuse mathematicians of "preferring local models that believe in the usefulness of unidentifiable individual contributors, and the uselessness of named generalist managers (ie the prompt writers at oAI)"
Big man tlb likes to say that science might be dead but engineering is just getting started. Navier-Stokes is the hammer of the nail in the science coffin. It kills science by killing the prestige of science. The engineers have to imagine that it's likely they will now get all their design ideas from the hypothetical future datacenters.
I'm afraid of the ripple effect of the agenda pushed by AI companies will have. In future and even now, they say AI has significantly progressed math and scientific research in general. There is truth to this, but the narrative has done more damage (so far) to the students, researchers, and the culture of knowledge transfer in academia. Many graduate students (I know) are having a crisis if any of their research worth it? If AI can (or will) do everything, what's the point of doing experiments and all? This will eventually deter a whole generation of curious minded students from research.
I guess, only time will whether this is for the good or bad. And how good AI models get without new data from research and experiments.