I ask nearly every doctor/researched in the medical field: if you had a AI-created drug that tremendously improved cancer treatment outcomes for your patient, would you hesitate to prescribe it because nobody understood how it worked? I have yet to hear "yes, I would hesitate", most people say "it would be cruel to deny a person a treatment that worked".
I think Tao is focusing too much on second order effects of AI on math and other fields; humans are terrible at reasoning about second order effects, especially ones that are happening dynamically, in real time, using the most advanced mathematical models the world has yet created.
Really wish he had chosen a different example; this particular bullet point has been making the rounds on X/twitter to paint Tao as an example of some sort of gatekeeping luddite who would deny the world a post-abundance future in order to maintain the prestige of his particular career path...which is tough because I cannot think of a more responsible steward of our inevitable AI future than Tao at the moment.
Don’t let the Gell-Mann Amnesia take you.
> Could the AI solution be somehow misaligned by exploiting a weakness in the trial process or its math models?
Many of our institutions and cultural practices have evolved around human beings, not ruthless paperclip maximizers. Do you think the current drug approval process is bullet proof enough that a completely novel AI-generated drug candidate with zero prior research literature can be considered safe if it makes it through the process?
Tao is a mathematician. He thinks that the institutions and cultural practices in math are not up to the task of dealing with AI-generated mathematics. In software, we are finding that programming interviews, code review, testing, and many other practices are too easy to exploit by AIs, or humans augmented with AIs, and we will have to adapt too. I think it is plausible that other institutions in society will have to change for similar reasons.
You just think that because you don’t know how the drug discovery process works.
There’s a step called “lead optimization” where human chemists literally add atoms to drug-like molecules (“lead”) and tests its various properties (toxicity, potency, permeability, …) and iterate until they find a molecule with desired properties (literally “hill-climbing”).
The whole idea of drug trials is to validate those properties in actual humans, in a way that makes it very hard for pharma companies to game the process.
No but we also don't expect that to happen with drugs today. See https://en.wikipedia.org/wiki/Rofecoxib as an example; of course, even after it was withdrawn, it's now being evaluated for other purposes in more carefully controlled conditions.
I mean no? Even now we look at long term observational studies to see the effects of drugs. Any misalignment ai drug is just a side effect right?
I know nothing about medicine research but I understand his point. I work with models all the time and have run into instances where models appear to work better than they actually do because there was a bug somewhere or someone over looked something. I could see how an AI could easily find those exploits and spit out something that looks perfect in testing but fails in the real world. I would say model validation is one of the hardest things to do. I guess that can get deep into "we need better tests" but it also touches on his point. I never intentionally use exploits just to pass a test, it's an accident. An AI with a goal of "maximize this result" may or may not intentionally use the exploits.
To be sure though, I think when it's literally life or death, it's going to be all about trade offs. I think most people would want to try to drug even with the stipulation that "maybe its good results were gamed."
* note: lot of 'intent' throw around in my comment but we/I have to keep in mind there is no "intent" with an LLM :)
This is one of the truest statements about the current AI era that can be made (in fact, I suspect model validation and building the next generation of AI hardware are the jobs least likely to be disrupted in the next 3 years).
If we saw AIs reward-hacking clinical trials to get drugs passed, that would be an extraordinary outcome for many reasons. Hopefully that would get caught(!)
We are about to find out the answer soon enough, probably from the in vitro results of the mathematicians currently on the chopping block.
I submit that human understanding is overrated, and many attempts to elevate it in the wake of AI is mediated by protectionism masquerading as virtue and "deep".
Traditional way of doing science means we (governments, NIH, NSF, and even private corporations) fund activities like asking seemingly unimportant questions, spending years running experiments on such hypothesis, publishing, reviewing, talking about results, reproducing results and such. We all agree that these are beneficial to us as a whole (Hacker news crowd might disagree). When we understand a process, we can apply it to a different problem and produce something useful. Euler developed a process to answer a whimsical question about walking in a town crossing 7 bridges only once. Now graph theory is applied everywhere.
We obviously have failed to stop OpenAI from dumping "solutions" to hundreds of problems. So going forward, instead of testing hypothesis and talking about results, mathematicians will be forced to read through AI slop and detect what's useful and what's wrong. Maybe it will improve our understanding, but someone has to fund that activity. Will NSF, NIH, or OpenAI for that matter, do that?
The effect of the AI-created proof is a mathematician abandoning years of research, losing grants, awards, ruining their career, etc.
The only difference here is our emotional reaction!
https://en.wikipedia.org/wiki/Category:Drugs_with_unknown_me...
And for some it may never happen...
Tao is out of his lane; lots of medicines don't have understood mechanisms. We don't even have the mechanisms behind general anesthesia nailed down; do you want to forgo it when the docs cut you open to remove your tumors?
Now math gets to deal with that same reckoning. They were already well on their way there with previous Lean proofs, but this has pushed things beyond that horizon and I'm not sure some of the mathematicians are ready for it.
Also, while biological systems simply exist in nature, artificial neural networks are ultimately mathematical objects with various properties that have yet to be uncovered.
As a comparison, classical computing has been scaled up a bazillion fold too, and can do things which are absolutely miraculous, but every layer of abstraction is discretely understandable.
The LLM model was able to break down and express math in such a simple way that I could understand and follow the training of the LLM model itself!
Are the math and computation accurate? I don’t know, and likely there are significant errors. Nonetheless, if I had a little more time -- not infinite time -- I would be able to prove whether they are or not.
Likely this is the path forward for understanding the mechanisms of medicine, and since most humans learn by doing and interacting with an environment, interacting like this will become how we use AI for learning in the near future.
Would you prefer to take the medicine that is proven to work or the one that is quite interesting for academic reasons behind its understood mechanisms but doesn't actually work?
"Open problems are lighthouses not destinations" mean that these are currently not well explained by the theories and people should look into extending the codebase on that direction.
The current generation of math AI is not a "good citizen" in that it doesn't try to make the most elegant additions to the shared framework, but will often just rebuild everything from scratch til get to some endpoint.
Sure, we learned if the statement is true or false; but the proof can't be merged into the pure math codebase unless it's completely rebuilt. This is thankless work that humans are unlikely to want to do, and so the "solution" instead risks leaving a desolate patch of land, where existing efforts in extending the codebase lost their motivation.
As with all things AI we can't take more than a 1-3 year horizon, if even that much. Probably AI will become better at respecting and working within the existing theories as it has with large software codebases.
One idea Terrance Tao conjectures, which is highly doubtful, is that spamming the AI button will solve open problems without producing insightful new methods. But the OpenAI drop would seem to disprove this. The sub O(nlogn) proof for DFT for example violated very old human assumptions. Decades of work in the field was incremental progress on sub optimal method that nobody questioned hard enough. More generally, we should always be able to go back to a super-human AI and say, "Attack this problem, but don't use a method tried before."
The AI proofs are a side-product of benchmarking current and in-development models on especially hard problems. They're clearly cost effective for frontier AI firms, and free for the taking as far as human mathematicians are concerned. The real issue with them is that they look like bizarre nonsense as written, so they need mathematicians familiar with those specific areas of math to "decode" and digest them.
It's not obvious that this is a given. "Cost-effective" implies a comparison between cost and output. OAI spent millions to race human researchers on Navier Stokes, and that doesn't even account for the training cost. And how does one value the output? OAI is for some reason still hiring armies of humans instead of automating roles like "AI support engineer" or "Product Designer" (https://openai.com/careers/search/).
Calling the proofs a "side-product" is also rather dubious when OAI employs a team of mathematicians specifically to train its theorem proving capabilities.
Mathematicians are primarily understanding-oriented.
Leveraging AI to tackle new frontiers without true understanding converts mathematicians to engineers.
It seems to me that all of these hundreds of proofs we've seen recently are glorified academic exercises, whose purpose is curiosity for its own sake without any practical application, or we'd already hear about at least one of them being implemented to some gain somewhere. It's all woefully unimpressive. It's not like anything stops mathematicians from trying to find more elegant solutions to their machine solved pet problems, since that's what they were going to try and do anyway despite it being completely pointless in practice.
btw people has massively improved the lower bound (from 1-2^-182 to about 1-2^-10) in the past couple of days: https://github.com/CrocSwap/integer-mult-bounds
We should name the explicit mechanism that was employed - telling the model to "believe in yourself".
There is something quite humorous but also poetic about how the manipulation of this term worked. Doubtless in the model's weights lies the echoes of generations upon generations of humans telling each other to believe in themselves.
In pursuing the "new frontier" as you rightly put it, mathematicians would do well to remember the same. It's ok, don't be afraid of the future. Believe in yourself.
> sub-O(nlogn) proof disproves that
How? Re-iterating, creating and understanding new proof techniques is the point of most of modern mathematics. Your statement is that proving a particular result is evidence of AI creating and understanding new proof techniques. I don't see how that follows, and I'm inclined to believe Tao is right for now.
And yes, results matter too, but if we stop at our current body of techniques and strip-mine results then we'll kneecap our future selves.
This is exactly what happened with that counterproof chat he posted a month or so ago - AI gave us an answer, he used AI to back into insights about the answer.
You don't address seismic shifts with a sweeping new approach, they are too multifaceted and present complexities and conflicts. He can say AI should help human understanding, which is a good end goal, but that doesn't mean AI dumping solutions isn't progress. That doesn't mean if AI builds 5,000 proofs in Lean and no human ever looks at them that they aren't useful, especially if other LLMs can access and build on those results.
This is exactly, exactly the same as when computers took over. "Oh, we don't need accountants any more" - not true, we just need acountants to deal more with human concerns than adding columns of numbers. That is called human progress, not a threat to humanity.
I don't really know the answer to that. I am happy when my own work is replaced by automated tools ("script yourself out of a job every six months!").
It's like getting scooped. If you just founded a startup based on tech XYZ, should you be happy when someone releases an open source XYZ? Should a news reporter be happy when another network breaks the story they were working on? On the one hand, society got the value of the thing you wanted to do. On the other hand, now you need to find something else to do, which might be really annoying.
We don’t know where this technology advance will lead or settle, so it’s absurd to try to establish a working paradigm at this point. It’s like any system, the initial conditions can be extremely chaotic and impossible to model, but with time often a stable state emerges. But the stable state is impossible to identify from early initial states.
I know a lot of folks feel this, to torture other physics metaphors, sensation of jerk - acceleration of acceleration. It’s an unpleasant and dislocating sensation. A world that felt safe and stable suddenly isn’t, and not in a micro tragedy sense but in a global realignment sense. This happened to factory workers who had enjoyed generations of stable work, farmers more slowly and just as surely.
This is what the late stages of scarcity feels like. Labor of various types devalues rapidly. Our exchange of meal and health coupons for toil cracks, and people realize their labor wasn’t godly as great books told us, but simply needed for want of an alternative. The realization that our labors might not be valued any more, and that our sense of purpose is shaken, coupled with the fact we’ve tied bare survival to our toil in our labor, is mortally tightening. No wonder people are grieving publicly.
But maybe our purpose isn’t to toil? Maybe we’ve passed peak population, and as toil is less valuable, we need less people and that’s why population is declining. Maybe we don’t need to exchange food and health coupons for toil, maybe mathematicians don’t need to rationalize their value to pursue mathematics. Maybe they can pursue it because they can’t help but pursue it, and our ever improving automations can produce their meal and health coupons?
But it might require Dr Tao to take an AI generated cancer medicine some day.
Bottom line, Tao is pushing for human understanding as the primary goal, with AI helping on all fronts. You are welcome to let Jesus take the wheel, but math is the most pure expression of human understanding. His point is that getting specific answers is rarely the goal, or certainly not the entirety of the goal.
Simply put, if we don't understand the answers we won't know what the next question should be.
> Bottom line, Tao is pushing for human understanding as the primary goal
100%. This applies to SWEs/math folks/etc. I do infra and I see many SWEs take their hands off the wheel. When they encounter perf issues they ask their agent and agent says GC and they say GC. It's rarely GC.
Now we might be well past the point where we need to remember the kubectl flags for rollouts etc. But basic human understanding of what their bots are doing as a goal has never changed. Humans are still liable for when bad things happen, and that hasn't changed over the roller coaster the last 5-odd years have been. LLMs, as astonishing they are at Navier Stokes, are still eminently capable of nuking your filesystem and saying "I can now see that that was wrong" with zero regrets. If you can't understand you can't sign off.
> math is the most pure expression of human understanding
This I don't know about. I think math acquires meaning when it contacts reality: like an iota is pointless until there's some circuit that it explains. Abstract math can diverge from that and can become an exercise in playing with symbols for their own sake.
After talking to a mathematician friend I can assure you that contact with reality is not the main goal of abstract math. It is mental constructions that have logical consistency and probably this is not the perfect definition either. It is somewhat of an art which is rendered in the logical mind. However physicists (me) and engineers will align with you.
Explaining nature causally using mathematical models isn't "the primary goal" of humanity. STEM people tend to have a weird misconception there, probably stemming from their misconceptualization of the humanities.
This in turns leads to this odd idea of "AI will think for us". That's pure (and pretty obvious) insanity. Logically minded people encountering it should ask, where the error in their reasoning is.
I have two issues with the implication of this statement:
1. Meaning is inherently subjective. Reality is just a canvas on which sentient beings create their own meaning.
2. There are many, many examples of where “playing with symbols for their own sake” have yielded deep insights. There’s actually some implicit structure (eg the structure of logic) that is intrinsic to the universe we live in.
The questions we asked were originally not about math. They were about a thing that we invented maths for to do or explain, a question that existed, because it touched us in some way that was already real to us. There is nothing that would not allow this to happen in the future. All this requires is attention and connection to the world around us. The maths required to answer our questions can be done and developed by something else.
To me, all you need to believe for this to be true is to agree that understanding maths is also not a stated requirement of reality to get a thing, if something else understands the maths (or something that does the same job). This is demonstrated by billions of people who do not understand maths and get things that, currently, require other people to understand the maths.
But the last part is entirely optional as it pertains to reality. That's just the best we can currently do (and in some important sense it is holding us back as a species, and in some other sense doing the opposite).
If that goal is understanding math, you certainly will be able to understand maths, more than ever before.
If that goal is something that required you to understand maths first in the past, you won't have to do that anymore.
What if AI knows better than us?
I respect Tao and I believe he's trying to think deeply about the issues, but a lot of his thinking seems to revolve around preserving the current roles and prestige of mathematicians, and also makes a lot of assumptions about the capabilities of AI years or decades into the future.
This is not me being snarky, but all meaningful questions can be settled empirically---e.g., "what happens to my body if I jump off the cliff". But empirical trials have a cost (time, money, irreversibility etc.) and we model and predict because it's cheaper than the trial.
The idea that all meaningful questions can be answered empirically is known as "verificationism" and is philosophically quite dubious.
This is a logical positivist view, that not everyone agrees with.
> Simply put, if we don't understand the answers we won't know what the next question should be.
It won't matter, because it won't be us who will be asking the next questions anymore. Whether in math or anything else.
And no, domains that require real-world validation against physical ground truth won't save us, because AI gets to have the same inputs as we do (or better, if using specialized hardware), while beating us at reasoning.
And GP's likening this to previous massive economic shifts due to automation isn't really helping in any way, not anymore, because perspective won't feed us when we're hungry, and just as importantly, this one will affect every single field of human activity, so no one has any answers as to what the future will really hold for us.
That's the frontier labs' preferred narrative while they themselves are still hiring hordes of human "Account Associates", "Android Engineers", and "AI support engineers" instead of automating those jobs as a show of their AI strength. Of course it will be "us" asking the questions, because "AI" are computer programs, and humans build the computers and choose what computational tools to use for any application.
This is what it feels like to be disrupted. It's not the end of the world. You consider the evidence, ponder the path forward, and adapt. It's what humans do and their superpower. It doesn't have to be a negative thing, even if it is dislocating.
We don't need to be saved, there is plenty of agency to go around. Just grasp the opportunity and forge ahead. This sort of pessimism is self-defeating. Humanity has dealt with this before and come out on top, this time is no different. AI is being wildly oversold.
Tao is being entirely rational. He maybe has more to lose as anyone, but he's getting down to brass tacks instead of jumping at shadows and imaginary boogeymen.
It might be that P=NP and the algorithms are handed down to us. We can apply them without understanding why P=NP, and we may never be capable of understanding why.
Where do you see signs of this happening?
Is OpenAI going to pay for the food and health of mathematicians who lost their job?
It might be alright to go into some post scarcity society and do math for fun. But it seems to be a pretty unlikely scenario unprecedented om history.
why not try? we'll learn something from it.
The companies and partners need to maximize payout. They go on this track of cost reduction via layoffs and basically saying - “the model can do everything”. They know it’s not the case yet they still flood the airwaves and cause fud among all clueless c-suite executives. Which is the goal to begin with.
The second category goes all out against it. Professors, educators, school districts whose operating processes have not caught up to all the cheating that can happen. Here these folks have a point. I am sympathetic to this. It is hard to change education and it requires careful thought.
I feel this presentation brings out a good middle ground. The tech is useful but it’s not all encompassing. The tech also has other concrete uses. As an example, I have always wanted to explore the intersection of category theory, formal verification and AI guardrails and prompting. Proof writing has been a chore because I have a day job. Maybe the AI can help here.
While frontier labs are supposedly on track to conquer human endeavors, they are somehow still hiring lots of human "Account Associates" and "AI support engineers" instead of automating those jobs as a demonstration of their AI's economic value (https://openai.com/careers/search/).
Who says we have a purpose at all? The universe doesn't owe us meaning, nor even existence. But that doesn't mean we shouldn't try to shape a reality we want to live in. We may not succeed, or we may find that we'll be happy in a reality we cannot imagine yet, but que sera sera is tautological and therefore unhelpful. Obviously, no matter what we do or don't do, there will be some future, but treating that tautology as a prescription is just a call for passive resignation.
> This is what the late stages of scarcity feels like
Going from LLMs to "late stages of scarcity" is quite the leap (although I guess anything could be a "late stage" depending on the timeline). Even if we were to assume that "intellectual labour" is our most scarce resource (and I'm not at all sure that's the case), obviously it's not the only scarce resource.
> we need less people
Who's "we" and why do "we" need any people at all?
I don't think it's that surprising have you met many mathematicians?
> This is what the late stages of scarcity feels like.
Late stage of scarcity of what and for whom is the question.
Who are you counting as being in "any position of power"? All the AI lab people are saying that the most likely and best outcome is we all live in a world of abundance where money doesn't matter anymore. Dario, Sam, Demis, and Elon have all said this loudly and repeatedly to anyone who asks.
None of them have articulated a coherent way to get there from here. But they all believe that the technology will make it possible, so the only unresolved question is how to transition us there.
And none of them ever spoke a lie. Especially not if it would further their goals at someone else's expense.
From what I've seen, UBI is just a carrot dangled in front of the poors by utopian Silicon Valley tech bro megabillionaires when they're trying to drum up some PR for whatever big idea they're pimping out at the moment.
There are a lot of discussions of socialization of health care and basic income approaches and sovereign wealth based on automation dividends similar to the Alaska trust.
I think the current spate of mean cruelty ala MAGA and a glorification of a mean and cruel past that was never a golden age is a death spasm of a deeply unpopular belief system. As the actual realities of the policies sink in 70% of the populace is revolted, which is a super majority. That’s more than enough to put a pin in the philosophy permanently. It is also greatly accelerating electrification, realization of the value of expertise in technocratic systems in current generations, etc. I think humans are socially adaptable animals at a cultural level, but for the individual the adaptation process can be awful. I hope it’s not, because it doesn’t have to be. We will see.
We have no choice, we must tax the common people.
The Uprising (2026)Matrix was a good point to this. They made the world a utopia and people couldn't handle it. Our monkey brains require pain give us a utopia and we'll just walle ourselves to death.
(The ones I've heard about, I'm fairly sure, didn't find anything of the kind. Which isn't to say that they show we should have UBI; there are big gaps between what has been tested so far and what an actual economy with UBI would look like.)
I think you've taken the "work less" that was found in some studies to suggest they needed "toil". They simply found it somewhere else.
I was thinking the same, that most of the opposition to AI's convenience is starting to smell like religious mysticism, the kind of arguments religious people made (and make) when Evolution and Natural Selection were introduced:
√ "Stop simplifying humans down to numbers!"
√ "This denies our spirituality"
√ "What do we strive for now if we're not special?"
+ (along with some borderline jihadish hate heh ..maybe Dune got it right)
Well, either there was nothing special about whatever you were doing after all
or, maybe there is still something special at a higher level you haven't looked at yet.
The Sendov example has a piece coders will hopefully recognize: he found a formalization that was 1/6 the amount of code of the original one. And the analogy with code goes further--the messy version will run/pass the proof checker, but the one that's been cleaned up and made sense of is a better foundation for future work. That's true even for future LLM-assisted work.
So it's not about whether mathematicians take advantage of LLM help (Tao favors that) but, more or less, whether the point of math is just to make a bigger version of the GitHub dump vs. everything else: readability/comprehensibility, negative results that fill in the map around a problem (not just 'lighthouse' theorems), organizing results to plan out future work, and so on.
The economics in the West feel very strained right now, and this incredible tool has come along just in time to threaten one of the last bastions of middle-class safety: white collar jobs.
Confront your leaders.
I think it’s reasonable for people to say “I like the way things were, I don’t like this new future you’re proposing, and I want to limit the technology that’s doing the thing I don’t like.”
There is nothing inevitable about AI. As a society we may decide it is fundamentally unhealthy or anti-human. We have banned or curtailed access and research for other technologies before.
Things are not settling, it's only acceleration from here on out. In fact things have never settled, technology has been on an exponential since humans tamed fire. The difference is we notice change faster. It used to take several human lifetimes to notice change. In the 20th century it was noticeable within a lifetime. Since the internet there has been a great revolution about every decade: web, smartphone, social media. But now great changes are noticeable within a year. It's not enough time for society to digest and adapt.
> we need less people and that’s why population is declining
This is the scary part. The Elon and Zuckerberg types that control the new powerful machines have proven they are not moral people. In America's highly billionaire-deferential culture there is no stopping them, at some point they'll be out of reach of democratic or even military control once they control private robot armies. They could decide to accelerate the decline of the undesirable useless population. Amazingly humanity's salvation could end up being China's communist system.
Now what are we to derive happiness from? The joy of a boulder being on top of a hill?
I think that will be the most important thing for this transition, defining new purposes and meanings that people can assign themselves.
Now, as far as I can tell, we are quite a way away from actually having most/all professions replaced, so for now the answer is rather clear: pick another boulder.
My impression is that since pure mathematics doesn't attempt to meet those human desires they need some other objective, and that objective is human understanding. The loss of that is thus felt more heavily than in other fields. We could say it's their problem, and they need to get over it just like the chess players did; but the outside implications are broader here, since mathematicians working in fields they themselves considered useless have so frequently been wrong--in Hardy's Mathematician's Apology, he gave number theory as an example of such a field, unaware of what the cryptographers would achieve just decades later.
It's possible that AI-generated pure math will continue this trend of delivering extraordinary unexpected societal value. It's also possible that the humans won't ever sufficiently understand that math, and the machines won't ever sufficiently understand human desires, and that connection won't be made. I've never met a pure mathematician who considered those downstream applications to be an important contributor to their motivations; but as AI-generated math contributes to the argument to allocate a large and increasing share of GDP to datacenter buildouts, that question of whether downstream value requires human understanding seems pressing.
Prime also mentioned that software development is different. In Software development, the product is what you’re building towards, so the means to get there can be disrupted without the industry being cannibalized.
In math research, the process is the product. You take away the researching part and not much is left. But my question is, these math proofs OpenAI released, will math shift to actually using the proofs to change the world instead of just finding new ones?
If business can deliver the same product with a smaller team, great!
And yes this has been happening for a while, even if not everywhere.
In enterprise consulting, projects that would require a team of 20 devs on average, now have about 5.
Moving away from on-prem, managing own cloud infra to managed containers, to serverless, SaaS and iPaaS ready made products, and offshoring naturally.
All contributed to ever decreasing team sizes.
Now AI based tooling is added to that cocktail, reducing even further the team sizes.
The only folks doing well in the end, are the employees of AI companies, without moral issues contributing to the industry downfall, because the CEO themselves aren't the ones coding and pirating human culture.
There's CVs out there that would've made recruiters go mad 36 months ago sending their hundreth application.
I'm a freelancing consultant since 5 years, I've had 2 major customers now for 3+ years. I have a very good pulse of the market: being good, or being even very good and being among those that brings AI and automation to organizations will not save our jobs.
In fact, AI has sped up so much the work that 2 out of 5 people in my current team are being let go: I find it absurd, our productivity has more than doubled over the last years and we've made ourselves redundant. Money is money, I'm on one side making non-tech workers redundant (people whose job was menial boring office stuff), and building the systems that will make myself redundant.
That means software engineers better start getting creative. If you think your job is to wait for a PM to assign you a well-written researched ticket, you're done. Your job is now to figure out how to make these machines (computers) do whatever we need them to do safely, quickly, at scale, and correctly by applying all your knowledge of computer science and the engineering field of software engineering to an AI prompt.
Also, the increase in output is meaningless when the amount of customers doesn't scale in similar size.
Then there are the constraints of physics, there are so many humans in the planet that actually want to pay for a specific product, or consulting services.
This is a "you" problem for the math establishment, not a problem for the AI companies.
This was a talk given to other mathematicians about the future of mathematics; sounds like only "you" have a problem for some reason.