Like most of us I go back and forth between sheer optimism and fear for the future that AI may usher in. Recently I started vibe coding a fun video game with my ten year old. The experience is different than my work because it’s been such a joy to basically have a personal genie in a bottle help me make some personal art with a loved one regardless of either of our skill sets. The concept the author of this post is arguing now resonates with me more than it would have a few weeks ago. The sheer surface area that AI can create in our intellectual life is limitless and needs humans to explore. There can never be enough of us in that sense. Whether it’s as validators or creators.
> The sheer surface area that AI can create in our intellectual life is limitless
I agree with this statement, though I think this Brave New World is incredibly exciting to some and dystopian to others.
The former group might include those that value the intellectual process above financial reward and status.
The flip side is there are many people, especially in tech, where their area of expertise has evaporated along with their lucrative and previously high status careers.
Previously it was possible to have a technical job by essentially following recipes and it turns out AI is far better at that than a human.
The linked article lays out why human understanding of mathematical models remains essential and I think the same applies to software. We're gonna need more software engineers who are able to think critically.
I would kind of disagree for personal projects like that. If it’s such a personal project with your young-in, shouldn’t the journey be the treasure? Also, with vibe coding out, custom vibe software/games, and already a lot of games, I would hope you’re not looking for a pay day.
It would be nice to have more mathematicians, but we don't need more. Once AI math goes so far beyond human abilities, any human involvement is like an ant trying to understand quantum physics
Need? We don't need lots of things, including computers. We did well without them for hundreds of thousands of years.
We want to understand. Quantum physics, mathematics, how stuff works. Ants don't.
That want is not a given, not all of us have that drive. In fact, very few of us have it. So far though, it seems multiple disconnected civilizations learned to keep that trait going instead of suppressing it and focusing only on practical ant-like activities.
We've been those ants for a million years, and only in the last 100 did we start to wrap our heads around quantum physics. We are the purpose behind creating LLMs. There's plenty in the universe we don't understand, and it's very human to keep striving to do so.
If you believe that math is discovering, it's natural to think that all of that AI math already exists and is just waiting for us to find ways to discover and understand it.
Or maybe ants and humans are qualitatively different. Maybe there's a critical mass of intelligence where you can pretty much understand anything, and maybe humans are past that threshold. I don't know that for sure, but I don't think we're anywhere close to hitting fundamental limits to our ability to understand the universe.
I think a better analogy would be comparing to an ancient human instead of an ant. An ancient human would have none of the basic abstractions that we take for granted today like literacy and arithmetic, so it would be very difficult for them to even attempt trying to understand quantum mechanics. But I don't think it's impossible because our ability to learn by stacking abstractions is basically endless——so far as our health permits at least.
Ha, are you invoking something he said prior to discovering Russell's paradox? Which came about because he wanted to show that all of mathematics can be derived from logic about sets, but instead he showed that it couldn't be.
As someone with a math background, I don't see Russell's paradox as some interesting mathematics beyond set theory. It's just an example showing that one should be careful about defining sets.
As someone with a math background, and a logic background, I think Russell's paradox is most interesting, beyond just set theory. It shows us that one should be careful about what exactly a definition is.
Mathematics can give for example theoretical lower and upper bounds for complexity of algorithms. And that information is very valuable for tradeoffs. For example we know that many algorithms have optimal average complexity or asymptotic behavior so they can be a good tradeoff in certain scenarios. Also knowing that some problems don't have a perfect solution allows you to accept essential tradeoffs
"I have in mind something like a “deployable intellectual reserve”: communities of mathematically sophisticated people that humanity can call upon to help understand consequential AI-enabled breakthroughs. " -- I can't be the only one who thought about the scene where the scientists ask Deep Thought the ultimate question...
> Before approving construction, I would want communities of humans to understand why the design works and what justifies confidence in its safety. I would hope that we all would.
Until very recently, I pored over every single line of code Claude generated with razor sharp scrutiny. I would usually catch issues with every response. I'm catching fewer problems these days. Maybe the model is just getting better, and maybe I'm being less careful while under pressure to ship more and more often. But model capability is obviously growing. Even back in March, you could tell it "give me a function that adds two numbers" and you could be 100% confident that it would write the correct function. There was almost no point in looking at the code. Since then, the complexity floor of problems in the category "this is so simple that the model couldn't possibly get it wrong" is rising, and with it, my cognitive surrender to the model is increasing too. Why check it? It's obviously going to be correct.
If AI designs a terawatt fusion plant, then of course we're going to meticulously pore over every detail to ensure safety, reliability, efficiency, whatever. If we find no flaws in the design whatsoever, will we be less careful about the second one? The third one? What about the ten thousandth one? Will "a nuclear fusion plant" become something that models couldn't possibly get wrong?
Terence Tao is arguing that the human involvement in research is crucial, but doesn't convincingly justify why, in my opinion. He says that "human agency is a value of fundamental importance" and that we will need to build "thriving human communities that can understand [AI ideas] together" - not for the sake of correctness, which AI may surpass us on, but for, I guess, the possibility of reclaiming human meaning and purpose. I don't disagree with this at all, but it's not an argument, it's a statement of values. Unfortunately, the stark reality is that if AI does surpass humans, it will become the economically dominant strategy to not verify them and not double check them, but to just do whatever they say. This seems like a great way to raise p(doom). But as the models get better and better, and as I'm scrutinizing Claude's output less and less... I just hope that there are more Terence Taos out there than people like me.
The more likely AI becomes to produce working code every time, the more likely it will become that a one-in-a-thousand or one-in-a-million error goes unnoticed at generation time. It sucks.
Imagine the first time electric calculators calculated the square root of 5. I'm sure people would verify again and again if what the circuits calculated was right.
Then in the 80s, you presses 2 buttons and there you had it in your classroom without thinking twice if the electricity arrived correctly at the transistors.
How crazy will the world be once our [current gen] ANN are like that!
Calculators and computers are deterministic, they give the same output to the same output every time. Language models specifically are not. So it might give you are function that is correct, or a function that is not, or worse yet a function that behaves correctly, but introduces some god-awful bug down the line that can cause serious havoc. It is obvious that they need supervision, not only for output, but also sandboxing and various harnesses for them to not do any “oops, I deleted your codebase sry” kind of nonsense people post to Reddit.
So I think the problem is to determine which problems under what instructions we can safely give to a model application to solve and how we test the output for safety and functionality. This would create more usable and safe, albeit a bit more boring, AI-based applications alin to a calculator or general computer. Whether this is posswith current model architecture is another thing.
Memory bits flip randomly. It's not a super rare thing either. You and me have experienced that many times without knowing. The only reason that computers feel deterministic is that we have error-correcting code to fix that. But in the most extreme cases, when multiple bits flip together, once "deterministic" program can generate unexpected output.
So why do you trust computers? Because statistically the case is just very unlikely. Therefore if AI is statistically unlikely to make mistakes there is no reason to not trust them.
You are confusing hardware noise and whether the algorithm itself is verifiable as if parity bits were not a thing. We don’t trust computers because the medium itself is infallible, but because the logic is sound.
With statistical models - such as LLM’s - there is no logic as such, but statistical assumptions based on given data. The output can ge very good or very bad, but you are fool to trust it blindly. Therefore you need a deterministic way to verify, whether meat- or software-based.
Why will X not keep increasing? It’s been increasing, albeit very slowly, since the start of the 20th century as the disease burden declines and nutrition improves. I see no reason to think that future health interventions couldn’t further juice those numbers.
Will it keep up with Y? Probably not, unless people are willing to accept pretty radical interventions to their biology. But it almost certainly is not static
If you set the temperature 0, an LLM is also deterministic (same prompt -> same output every single time). We just don't do this because the LLM is not so smart in that mode. But "LLM is not so smart" is changing at Moore's law speeds.
Non-determinism is not an essential property of LLMs. It's an optimization that we've added intentionally.
Ehm, no! The same prompt does not always lead to the same output.
Have you ever tried to achieve consistently deterministic output from an LLM? I have, and it's not easy.
That means output differs between machines and architectures. Running inference on CPU vs GPU also affects output. Even running the same prompt twice in a row on the same machine can lead to different outputs because a prompt that was partially stored in the kv cache will result in different output than an uncached prompt.
Yes, yes, assuming the same CPU and stuff. There is some randomness due to floating point math differences between CPUs (and sometimes on the same CPU), but I don't think that fundamentally changes my point.
Put another way: if you could have a virtualization layer that guarantees deterministic floating point operations then a LLM set to 0.0 temp would produce deterministic output.
It is insane how many times I see this false analogy repeated on HN over and over (analogy of a deterministic-by-design calculator device (or a compiler, etc.) and a non-deterministic-by-design LLM software).
You're missing the forest for the trees here. The point OP is trying to make is that calculators originally were essentially non-deterministic. Technology will go from unreliable to reliable.
LLMs are not non-deterministic by design. The randomness in the output is artificially injected for ergonomic reasons. (Yes, the non-determinism in production systems is different, but also not by design)
You don’t train calculators on how to add. All calculators will more or less come up with the same answer to some deterministic and quantifiable level of accuracy and precision.
You oppose correctness to meaning and purpose, which you seem to imply are impractical values. (Worthless values, then?) But you don't mention creativity. The article blithely says that AI creates new ideas and understands things. I don't think it does.
> What about the ten thousandth one? Will "a nuclear fusion plant" become something that models couldn't possibly get wrong?
For what it's worth, ten thousand terawatt fusion plants probably approaches the level at which the sheer intensity of energy production would cause significant disruption to the climate (it's roughly 5% of the Earth's entire solar input). Every energy source becomes dirty past a certain point. It would be wiser to learn how to build a utopia within a limited energy budget than find a way to produce enough of it to cook the damn planet, but who am I kidding.
We're not going stop doing something locally just because we can do it in space. There are definite physical advantages to producing next to consumption. We will most likely do both -- things that can be done in space will be done in space, and things that are better done locally will ramp up. There is no upper limit to energy demand, none whatsoever. Stopping production anywhere it could theoretically happen is a matter of will.
The more I think about space elevator, the less I believe in it.
The material alone is in a quantity beyond what we can reasonably manufacture.
and the material needs to be perfect. All design we have today have cascade failure mode -- any material failure translates to a total catastrophic failure.
It's quite possible that with "infinite" energy we could do mass scale carbon capture to offset the increased temperatures by rolling back the greenhouse effect.
This is basic thermodynamics, not greenhouse gases. If you put some amount of energy into a system with a fixed surface area, you will get a minimum equilibrium temperature.
Greenhouse gases and Earth’s internal nuclear decay engine make the situation worse, but even without them this would boil the oceans.
Even today we're recognizing that compute does not need to happen here.
When we're talking about creating powerplants equal to roughly 5% of the insolation of Earth, I think we're sci-fi enough to discuss orbital datacenters or Mars datacenters or Jupiter fusion candle datacenters.
Compute doesn't need to happen here, but it can, so it will. Building datacenters on Mars or Jupiter doesn't reduce our ability to build datacenters here, so naturally, they will still be built, everywhere that they can be. It's an orthogonal capability.
Maybe, but there are counterexamples today. The processing of raw materials can be done all over the world, but it's not. Modern nations actually disassemble their blast furnaces and ship them out at a certain point.
Orbital datacenters suffer from the same problem: limited surface area. You think cooling is a problem now? Try not having conduction or convection. Radiation is all you get. A huge proportion of the mass and size of the ISS is radiators, and that only has to deal with the heat of low-power electronics and a few measly human beings.
Using an entire Mars only nets us about a 2x multiplier for our energy expenditure budget. Then we need four planets to double it again.
Thermodynamics and the tyranny of exponential growth are going to win this battle every time, regardless of the unobtanium technology you try to invent.
Not quite the same problem, because only a small portion of that energy radiated from space will make it back to the environment on Earth. For convection that is 100%.
> Using an entire Mars only nets us about a 2x multiplier for our energy expenditure budget.
This assumes other technological breakthroughs don't also happen in parallel. I'll go out on a limb with nothing more than a hunch that we are in the beginning stages of understanding mathematics, physics, and biology.
It does not. This is basic thermodynamics. Unless we are using computers made out of something other than matter, powered by something other than energy, thermodynamics bounds us all.
>the economically dominant strategy to not verify them and not double check them
In the big scheme of things is it really that expensive to verify it if a lean proof is generated? The agent itself will likely have already verified such Lean code before calling it "done".
>Terence Tao is arguing that the human involvement in research is crucial, but doesn't convincingly justify why, in my opinion. He says that "human agency is a value of fundamental importance" and that we will need to build "thriving human communities that can understand [AI ideas] together" - not for the sake of correctness, which AI may surpass us on, but for, I guess, the possibility of reclaiming human meaning and purpose. I don't disagree with this at all, but it's not an argument, it's a statement of values. Unfortunately, the stark reality is that if AI does surpass humans, it will become the economically dominant strategy to not verify them and not double check them, but to just do whatever they say.
I think you have a fundamental misunderstanding here, and it's not really explained because I think it seems self-evident from within the field. In short: writing code is a means to an end; doing mathematics research is not, but is the end in itself.
The human involvement is crucial because the entire purpose of mathematics research is to increase human understanding of mathematics. It is pursued because it is interesting, not because it is economically useful. In this sense it's a lot closer to the humanities.
A black box oracle that just tells you whether statements are true or false is not the goal of mathematics and would not be particularly interesting to the field (except insofar as it could be harnessed to improve human understanding).
Coding is totally different from this, where it is essentially always done as a means to an end. Likewise with many other fields, like pharmaceutical research or materials science or what have you, that are oriented around solving problems for some practical purpose. Pure math isn't really like that for the most part.
Sure, but I don’t think most of the money that goes into funding math is for the purposes of pure understanding. The reason governments fund mathematics research grants is generally for a more instrumental purpose; taking the US congress as an example, the mission of the NSF is to, “Promote the progress of science; advance national health, prosperity, and welfare; and secure national defense.” Most federal math grants come from the NSF.
Of course, math research is cheap and most academics don’t rely upon grants, their salary covers most of their expenses. But here too, the mathematics professor spends a substantial amount of their time teaching future engineers/quants/other applied mathematicians, who need to understand math for instrumental purposes, not as an end in and of itself. Without the tuitions of these students, I can’t imagine universities maintaining the size of their math departments, let alone expanding them as Dr. Sahai advocates for.
So who or what funds the community of pure mathematics going forward?
Research in pure mathematics is part of what we call "basic research". There are no applications in mind a priori. People instead focus on understanding, because history has taught us that understanding tough problems in mathematics finds natural applications elsewhere. It's the same as theoretical physics or theoretical computer science.
> People instead focus on understanding, because history has taught us that understanding tough problems in mathematics finds natural applications elsewhere.
If the goal is still eventually the applications elsewhere, we're back to what happens if the AI is simply better at this.
You can probably make an argument that human understanding is better as humans are better at finding new patterns or fundamental new ways of thinking and also applying them to new applications.
However, what if AI becomes better at humans for that as well?
No reason you couldn't have an AI be optimised for advancing basic research and understanding and a second AI to take these results and optimise for finding new applications for these discoveries.
> what if AI becomes better at humans for that as well?
The "if" is the problem. If it happens, then of course, let AI do it. For the moment AI is still bad at those type of tasks [1], so the discussion shouldn't focus on highly conjectural situations. We can't destroy the scientific ecosystem based on vague speculations.
[1] There are real reasons: it is not obvious how to optimize an LLM for doing basic science or other ill defined tasks. On the contrary, optimizing for writing a proof that passes the Lean test or code that passes the tests is a different story.
Basic research is funded with the understanding that applications are not imminent, yes, but also with the expectation that some of the knowledge gained will eventually result in advancements to the public welfare
I think you have misunderstood the OP's point here. You're arguing that deepening human understanding is an end in itself, and you are right. The OP is arguing that advances don't need to be pegged to human understanding, and they are right too. The two can coexist, superintelligence far ahead of us, pioneering discoveries - and mathematicians catching up at a pace suited to biological minds. I don't see the issue here. Of course, it does mean mathematicians adopt a new role as hobbyists.
> A black box oracle that just tells you whether statements are true or false is not the goal of mathematics and would not be particularly interesting to the field
This is a crude distortion. The recent breakthroughs have come with proofs, reasoning and verification, and there is no proposal that I'm aware of that would do away with these foundations.
There's also some rather ugly solipsism in the idea of keeping what interests the field as a limit. Mathematics has broader relevance to humanity than merely to please and support mathematicians, and if other fields can make practical use of profound well-proven future math, mathematicians will have a hard time making a case that their comprehension must come first.
You might not want to admit any far-advanced future math until you've spent years or decades digesting its workings, but if it's sound why shouldn't specialists in other human fields make use of it?
> > A black box oracle that just tells you whether statements are true or false is not the goal of mathematics and would not be particularly interesting to the field
> This is a crude distortion. The recent breakthroughs have come with proofs, reasoning and verification, and there is no proposal that I'm aware of that would do away with these foundations.
I believe that we are still at the point where these proofs serve as verifiable certificates of correctness, so that it's not a "trust me bro" situation, but where humans mostly still don't find them understandable, so that they are still just a highly reliable black box.
For even more people fun is TikTok, Snap, Instagram -> sounds like a collapse of a civilization to me if you increase the ratio even more towards dancing kids sharing their content non-stop with no added value to the society
It's not fun, it's a physiological addiction, because we learned enough about our brains to hack dopamine and reward cycles for profit. It's literal abuse.
The average person doesn't know how the medication they take works, the mechanics of climate and climate change, how the energy they consume is generated, etc.
> The medical field as a whole isn’t generally interested in understanding how medication, only in empirical measuring and qualify the effects.
I don't think this is quite correct. I mean many practitioners of medicine will have the attitude of ... "if it works, it works". And that's perfectly reasonable.
But if you understand the mechanism of action of a drug (or other treatment), it (often) makes it easier to improve a drug.
So ... some sectors of the "medical field" understandably care only about empirical results. But other sectors would prefer to understand what's going on.
>The medical field as a whole isn’t generally interested in understanding how medication, only in empirical measuring and qualify the effects.
This is nonsense. Most, if not all professionals are interested in mechanism of action, but without Ms Frizzle, it is extremely difficult and expensive (time and money wise) to figure that out. So while the labs run the experiments with the very limited funding they have, we make do with using the second best thing we have, which are statistics.
Some medications are designed for one purpose, then other effects are discovered in practice. Gabapentin, for example, was designed as an anti-seizure medication structurally similar to the inhibitory neurotransmitter GABA.
Now it is primarily used to treat neuropathic pain, and the mechanism for that is not well understood. The GABA receptor is not involved. This effect is just a happy accident, and nobody really understands why it works.
I feel like we just don't understand a lot about the human body still. Only a century and a half ago we had a US president die because a doctor rummaged around and damaged internal organs (with unwashed hands, because germ theory was still not uniformly accepted) to try to find a bullet on the wrong side of his body. We've come a long way since then, but it's not like we've been doing the field of medicine rigorously for all that long. The best strategy we have seems like pretty much the same as any other scientific field; come up with ideas based on what we know, try them out, and see if they work. That doesn't necessarily give us any actual insight into why it works though.
The problem with "ignorance" in Western countries (particularly the US right now) is that it's very common for people who don't know to believe they know and form ignorant opinions that they often want to be applied society-wide in some way. You can see this with everything from climate change to vaccines.
In much of the world, even in middle income countries, people are comparatively poor and, in my experience living abroad in such countries for many years, much less concerned with "understanding" and forming opinions about everything under the sun. It doesn't mean they don't value education and are opposed to development/progress, but it does mean that they don't question whether the vaccine they're taking is the product of a conspiracy, think too deeply about why the river is flooding more often, etc.
They just deal with life the best they can and are more focused on supporting their families, enjoying what they can, etc.
Culture and religion play into this. The way secular and Judeo-Christian people look at the world is very different than, say, Buddhists, Muslims, Fulani tribespeople, and so on.
But we can generally assume that the people who built and understand some technology will have the same desires and worries as us, and are bound by the same laws. For instance, if it was a team of strangers who designed the terawatt fusion plant, we can still assume with some confidence that they care about safe plant shutdown, failure recovery, ensuring there are no toxic side effects of operation, etc. Can we say the same when it is machines doing everything?
Very interesting point. I think the counter-argument to that is that the complex modern society is based on somewhat “deterministic” systems, in that, even if a single decision or event isn’t rationally explainable in the moment, at least in the aftermath, it typically becomes understandable, maybe even reproducible. There is someone, somewhere, capable of explaining, maybe even multiple someones.
We don’t generally have that insurance with LLMs/AI, yet?
Imo the counter-argument is that in principle you can research and understand the mechanics of climate change or medicine. You just don't have the free time and/or the motivation. With AI that changes - you could spend your entire life trying in vain to understand its reasoning.
It reminds me of how some religious people say that science is effectively no different to religion because we all take expert opinions on faith. But the difference is that there is a well-defined pathway to understanding, if you wish to do so.
This seems to me like asserting everything in the universe is explicable by physics. It may be technically true, but still not relevant to understanding earthquakes.
(Don’t bother to argue this not true unless you disagree with the essence of the argument.)
Anyway, post-hoc explicability isn’t a counter-argument to the assertion that almost everyone takes almost all technology as magic, from medicine to computers.
I’m still trying to understand your argument. Are you saying that after the fact we understand AlphaGo move 37? But somehow we are never going to understand an LLM’s decision afterwards? Seems like a disconnected take to me.
An major school of German sociology places this at the core of its theory. According to this theory, modern societies are characterised in particular by the fact that individual subsystems of society reduce the complexity of their own (sub)system to the other systems to enable them to act at all; they fulfil what is called an "Entlastungsfunktion" (relief function). Prominent representatives of this school of thought are Max Weber, Arnold Gehlen and Niklas Luhmann.
In their view, it is modern institutions (public and private) which, as supra-individual entities, have long since become autonomous systems. The fact that the individual office-holders are human beings, meanwhile, is of little significance.
Hannah Arendt, in her theory of totalitarianism, attributed the effectiveness of both Nazi and Stalinist policies of extermination to the largly moral indifference of bureaucracy as a system.
In this sense, the task of controlling AI is a variation on the problem of harnessing a complex society consisting mainly of autonomous subsystems. This is a problem which has increasingly challenged humanity already for quite a long time. This has been extremely challanging so far, even without AI ...
That's what makes libertarian AI doomers so strange. "Letting unconstrained machines with human components loose on the world will be okay, but letting unconstrained machines with nonhuman components loose on the world will produce terminators." How can it be both one and the other?
the average person doesn’t have an internal monologue
can’t use a computer (they’ve had like 30 years now in first world developed countries)
many can’t even use their smart phone beyond calling, texting (many can’t type well), and doom scrolling (they get addicted to drugs, gambling, and other LCD activities)
many read at a 6th grade level. most can’t even calculate tip in their head.
Those subcultures are like that because they are small. If that became the lot of most people, society would look very different. The choice would be between Fully Automated Luxury Communism and Oligarchic Hellscape.
Some of it has to do with the fact that many people pushed forward training themselves in a way they understood would better themselves for the future. They put in time, and now their sense of value is in question. They're forced to think about things like value (of life, themselves, and hopefully others), and the things they want to remain valuable in the future.
It's interesting to me that you only mentioned the people using 'AI' in the 'short-term' ways, and not the ones that use it to better themselves in the 'long-term' ways. You can spend your own time focusing on either usage, it's really up to you and your concerns. Either group's sense of value is what determines their behavior. Where they spend their time and thinking must be elsewhere, and you disagree with it. Who judges the quality of time spent? You do.
Is it more useful to think about self-improvement, and how to navigate the future in ways that might help you re-establish value of yourself, life, and others? Acquiring knowledge is a struggle, the author mentioned this. There is also Plato's Allegory of the Cave, which highlights some of that struggle, a resistance to change. And we're all limited by time, our genes, our station in life.
The only way to help anyone out of the cave, is to help them believe something different about themselves. To help them believe there is good reason to spend time going deeper into knowledge, or at the very least, allow others with the passion and station for it to do so.
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?
It's about as fun, and as realistic, as imagining magical ponies and people with superpowers?
This is such an incredibly naive and absurd vision; we've already proven that humans are very often very bad at implementing other humans' good ideas. There's nothing that AI is likely to bring that will improve this discernment.
Here's the thing: nobody is stopping you from putting in the time to understand all that. The problem is, nobody has that much time. And so we want the community to move with us, spend the time the same way as we do, to ensure value. We are all saying: we want someone else, others, to put in that time for us. The truth is, we all want quality, and value is closely related.
I’m reminded of themes from the Hyperion Cantos! Maybe my mind is over-connecting, but it’s not the first time I’ve drawn similarities in the last few years.
It’s terrifying to me to think we’d let AI make things for us we never understand. Like livestock not knowing how auto-feeders dispense their daily food were built and appeared, they just gladly eat until…
> We would probably no longer care about code, engineering or even physics and mathematics among other things
This sounds like a boring existence. I take your meaning, but want to point out that not everyone learns about things because of practical utility, some of us find it incredibly satisfying to learn how things work just for the sake of learning.
Sure but no one's stopping you? I know a great many things of no direct practical value to me.
There's also things I don't know and don't have the time to learn which are very helpful to have AI do for me: web interfaces are really useful and I look forward to them now working exactly how I want. I'm not ever going to regret not spending more time trying to figure out how to center divs or which framework I should use because they're all deprecated.
> Isn't it fun to imagine how life would look like in that scenario?
I think there's a lot of sci-fi out there that already did. Maybe it's not utopian because a pure utopia would not be likely to have an interesting story, but on the other hand, most huge technological advancements end up having just as much potential to reinforce existing power imbalances in society rather than solve them. It's not obvious to me that if we got magic super AI that can solve every scientific problem in society that it gets used in pretty much the same way as anything else: making the people who control it a lot of money rather than sharing the power with everyone without charging them.
Am I the only guy who still thinks we're kind of putting the cart before the horse here? Look, I would love to live in a world where AI is in the business of designing terawatt fusion plants and revolutionizing all other aspects of society. But right now it can't even really tell a puddle in the road. I feel like we have a really long way to go here, hype-laden PR releases about solving math problems aside.
Frontier AI is far beyond "hype-laden PR". You're right that there's a long way to go in release terms before fusion plants, but at the current tempo that 'long way' looks near in human terms. Whatever age you are, would you bet against it arriving in our lifetimes?
I'm not sure the scarce resource will be people capable of understanding the AI's work. It may be institutions willing to tolerate the cost of understanding it
LLMs dont create anything new, if programmers stop reading the code technology will be forever frozen to 2022, no new programming languages, operating systems, concurrency primitives, databases, networking protocols, UI frameworks everything will be based on the training data and future generations will forget about all the primitives we now take for granted.
If someone creates a new programming language/ framework or new better way to do async or whatever, no one will use it because it is not in the training data and it wont take off because everyone is using LLMs. It will be like using the same Lego pieces over and over.
What if programming languages, operating systems, concurrency primitives, databases, networking protocols, UI frameworks are already good enough, and the innovation lies elsewhere?
You can do a lot of cool stuff with the same lego pieces.
But what if the fundaments of all these, in the human produced literature, actually contain hidden circularities and holes which make very hard the progress?
IMO for the moment the greatest value from these AI tools is that we can start an audit and hopefully proceed on a saner foundation, after we use the tools and think about it.
This is different than too many AI generated proofs or panic reactions from the academic system with its stupid incentives.
False dichotomy. Chess/Go can still be played between two humans and there is allot of value in that because humans compare each other to other humans, when you see a skillful Grandmaster play you know they are good compared to yourself or the average human, that is why people still play chess/go and train hard to get good. Programming is different because you are creating something not necessarily trying to win a game.
Most programming tasks are exactly like that. Is this agent able to complete this task? Is this agent able to optimize a kernel beyond previous attempts?
Of course some are subjective and that's where progress is harder, like "Is this website pretty?". But for tasks that can be objectively measured, LLMs will go beyond human level, just like with Chess and Go.
My point is that LLMs depend on training data so the code they produce will be stuck in 2022, no new languages, techniques beyond that because new techniques are not in the training data (at least not enough of it for training because most coders are now using LLMs).
Chess/Go continues to progress because it is primarily a human vs human activity, people will always be learning to play chess and chess will continue to develop.
> Chess/Go continues to progress because it is primarily a human vs human activity, people will always be learning to play chess and chess will continue to develop.
AIs are not continuing to get better at chess/go because humans continue to play at levels far below themselves who discover new techniques. They get better because they play against other AIs and discover new techniques that have a higher win rate that way.
I would bet that even if humans stopped playing chess/go and people were still willing to run these AI models against each other they would continue to get better.
I am not talking about the advancement of AI, I am talking about the advancement of chess.
Two things can be true AI drastically contribute to the advancement of chess and humans playing against each other also contribute (even if slowly) to the advancement of chess as it has always been since the invention of the game. The point is that because chess is primarily a human vs human game humans will always have the knowledge of chess, unlike with programmers who are giving it up to prompting, and programming being much more complex than chess (checkmate and win) will be stuck in 2022 because of the training data.
LLMs receive new data via input context, not just training data.
Thought experiment: How effective will 2026 LLMs be for humans in 2526?
It's not game over just because 500 years are missing from the training data. The important question is how well can 2526 humans make culture and knowledge navigable to LLMs via tool calls.
Today's LLMs might need for example sub agents to translate to 2526 English, sub agents to read 2526 docs.
It's _really not clear_ whether 2026 LLMs will be useless. To believe that reflects an enormous misunderstanding.
> LLMs receive new data via input context, not just training data.
Be more specific about the "new data". If everyone is using LLMs for work (generating code), especially the juniors who won't get the chance to learn from first principles, LLMs will be training on the data they generated. How will new code enter the system at large enough quantity that it can be used for training?
> It's _really not clear_ whether 2026 LLMs will be useless. To believe that reflects an enormous misunderstanding.
They won't be useless, they will just be frozen knowing only whats in their training data. No new programming languages will emerge, in 2526 they'll still be using Rust and javascript, same exact code from 2022 which dominates the training data. Why do you think AI companies are hungry for old rare books.
The "new data" is: person A prompts an LLM to create or modify a tool, person A distributes code person B, person B's LLM uses the tool via docs/help/error. That is a direct path for an LLM to "know more" from a human than what's in its training data.
If we get a new programming language not in the training dataset, we could give an LLM a decent compiler with compile errors, and some sample code and it would be able to write code in the new language without training.
The problem with a terawatt fusion plant isn’t that the first one will be broken, or the tenth one in some other way. The problem is the hundredth will work flawlessly, and so will the thousandth, and a petawatt is serious waste heat to reject; if we keep building them on this planet, we’ll all simply cook.
Reliable cheap fusion is the holy grail and used in moderation will fix most of our environmental and political problems, but it also forces humanity off this world. Maybe that’s not a bad thing, but there is no free lunch.
In my experience, LLMs are becoming very good at executing, but not a creating novel ideas or being creative.
Most of programming is reusing existing ideas in new shapes to solve new problems, but all the building blocks are there in the training set. Or new blocks can (easily) be derived from existing ones.
Math is different, it requires quite a bit of creativity, it's not just 'reuse all existing blocks'.
For the moment LLMs are good at discovering things that we overlooked in maths, or apply cleverly existing math blocks to make new results, but making a new theory that is really useful is out of reach for the moment in my opinion.
Sure, and I hope LLMs will at some point be able to do it. It would simplify greatly my work.
However, at the moment I consider that they stay in the 'convex hull' of their training set + a provided context, and I don't see that much research that made real improvements to the situation.
> I guess, the possibility of reclaiming human meaning and purpose.
As you described very well, as humans we are mostly interested in solutions, not problems. You don't have to understand how a car works to make the most of it. Increasingly, you don't have to review every line of code to feel confident it is correct. But there is inherent value in understanding the problem. The effort it takes provides a surface area for growth, perhaps the only one that is actually available to us.
The solution provider also holds the locus of control, and it is only balanced when there are other available solution providers. We certainly want some of those to be human.
Very few people scrutinise assembly in 2026 as compiler generated code is 'good enough'. LLMs are beginning to do the same with higher level languages.
Without bashing anyone in particular, a certain OS-vendor's desktop apps, have been 'good enough' to ship, but with p*ss-poor performance in many cases for the last decade or so. We crossed the 'good enough' Rubicon a few years back in terms of what end users receive as a finished app.
Hopefully LLMs will eventually bridge that last gap of efficiency when generating higher-level code that not only works, but is efficient. Maybe there's a future where they generate the final binary without even invoking a compiler.
Isn't Microsoft already using LLMs to convert low efficiency components of Windows into higher efficiency implementations, for example by conversion to Rust?
Only if you don't care about performance. People who are working on performance problems read it all the time because it never does what you expect.
So extending that line of reasoning it's something like "I don't care about the internals long as the external effects pass my smell test" which is a quality/efficiency compromise.
We’re seeing more and more slippery slope arguments, except the slippery slope leads to human cognitive oblivion and it is actually a good thing actually.
> I just hope that there are more Terence Taos out there than people like me.
Just spare me. Being under external pressure to “ship code” is one thing, but being personally inclined one way or another (no external pressure) is another. And when you think being inclined like that is existentially risk (for human civ?) then, what? It’s just the way you are wired and hopes and prayers that collectively that doesn’t drive us off the cliff?
This aw shucks persona isn’t convincing. Same thing with AI Bros who are (1) making the most awesome tech that has ever existed, and (2) aw shucks hope it doesn’t kill us all in the end.
> Why check it? It's obviously going to be correct.
And then when you stop checking it, the companies that run the service will tweak the model to benefit themselves in some way, possibly at your expense, and you will be none the wiser.
All the companies trying to get you to use AI are your adversaries. They can and will exploit your use of their systems for their own gain.
> my cognitive surrender to the model is increasing too. Why check it? It's obviously going to be correct.
I use "frontier" AI models daily at day_job. I can confidently say that anyone who is satisfied with the output of LLM code (enough to commit it straight off) is just an absolutely shit programmer. Sorry but I don't have any other way to put it.
The code is (with rare exceptions) atrocious on every level. It is only not atrocious if you take multiple iterations of "review and correct".
>If we find no flaws in the design whatsoever, will we be less careful about the second one? The third one? What about the ten thousandth one?
Like the saying goes, if my grandmother had wheels she would have been a bike. LLMs can't even produce quality maintainable code for a trivial web service or whatever. Why are we planning for what we will do when they can "design" 10,000 nuclear power plants without any flaw?
Yeah I was honestly stunned to read “obviously the code is going to be correct so why check it” be the most upvoted comment on this website.
Are we even using the same product? These things constantly shit out plausible code that is riddled with errors and bad ideas. if you just copy, paste, and run without looking there’s gotta be a 30% rate of failure to run, piles of terminal errors.
Do programmers use this website anymore? Me, myself, I am a DOGSHIT amateur programmer and even I can tell these things are terrible without constant revision and oversight.
> I pored over every single line of code Claude generated with razor sharp scrutiny.
That is essentially impossible, since if your pored over individual lines, your scrutiny cannot be razor sharp. There are few people who can pore over code with razor-sharp scrutiny (and different people are better at scrutinizing different aspects).
> Even back in March, you could tell it "give me a function that adds two numbers" and you could be 100% confident that it would write the correct function.
I am doubtful that this is the case. Even that supposedly-naive example is not as trivial as you might imagine, when you consider overflow, defined vs undefined behavior, and floating-point representation details. And you can't be confident like that about a human either.
> Maybe the model is just getting better, and maybe I'm being less careful while under pressure to ship more and more often. But model capability is obviously growing.
If you are a normal person research (e.g. https://arxiv.org/html/2606.22721v1 but there are a lot more, not necessarily on coding) has shown that you indeed are being less careful. It most likely also works better simply because more resources are being poured in.
Having a big spike on your steering wheel would make you drive more carefully. More broadly, I think if people put in less effort without massively increasing mistakes, it should come out fine. Formal methods might help as well, especially in some safety critical applications.
People please, an LLM is just a vector database that spits out statistically viable answers which highly depend on its training material. There's no real "intelligence" involved.
If AI ruins humans doing mathematics because of "economic strategy" we should destroy, not the machines (although the data-centers will be burned down as a byproduct) but the economic system that demands this.
I feel you. We take a risk when we check our work less and we own the consequences. That's not new, and it doesn't make us bad. It's just the human condition from time immemorial. The solution is not obsessively combing through every detail of our work, it's better monitoring and control on the places where mistakes can have an impact.
A metaphor I'm constantly drawn to is the transition from agrarian to urban societies following the Industrial Revolution. Somebody who somehow saw the Industrial Revolution coming from the perspective of somebody living in an agrarian society might have envisioned it leading to 'super farms.' And it did.
But the biggest change wasn't what it did to farming, but enabling people and societies to start doing much more than just farming, as well as enabling some great social change as well by simply economically obsoleting slave labor. And trying to imagine all of the implications of this, as well as much society might look like, from the perspective of somebody living in an agrarian society would probably have been simply impossible.
I think people keep ignoring this possibility for things that LLMs will change. There's a vast amount of the 'cognitive economy' that LLMs stand to be able to automate. And I think that will open up a vacuum in society for people to build on top of what LLMs will do (and already are doing). I don't know what that means exactly, but that's because we still live in that 'agrarian society' and trying to imagine what things will look like after the 'Industrial Revolution' is probably just impossible.
People enjoy a lot of the jobs in the cognitive economy though. They are fulfilling. Coding and making art and media is a passion for a lot of people, the actual act, not just the outcome. So unless people were passionate about doing back breaking farm labor its not the same thing. I'm mostly embracing AI because of what it lets me explore and learn beyond what I could before, but I'm not convinced the outcome is going to be a better world at this point.
True art yeah, and people are rebelling a bit on entertainment. But already crushing the commercial art jobs that supported a lot of people while they did their unprofitable real art.
Its good enough to replace most stock music, or a jingle in a commercial or a quick musical hit at the beginning of a video or show. Also if you combine a knowledgeable producer with AI they can use it to make more things faster which puts pressure on supply and prices out smaller producers.
> I think that will open up a vacuum in society for people to build on top of what LLMs will do (and already are doing).
If an AI can replace me on the mental aspects of work, and robotics are on their way to replacing humanity on the physical aspects of work... then what's left? When there was agrarian societies, there were writers, priests, bankers, merchants, and laborers before and after - I really don't think things were that unclear even at the time. Now that we have machines that are close to exceeding humans in every way, what good are humans?
I've struggled with this question as well, and this is why I reject the premise of the historical pattern of technology enabling us to "move up" to something else.
At the same time, I agree with the original comment as well. I don't think this necessarily leads to some doomsday scenario. Whatever happens it'll likely be better for us and imo we will merge with the AIs at some point, so it won't be a question of us vs them.
I actually see it more likely the opposite - a subset of humans will retreat into a (vastly smaller) human-first world, and AI will be of a separate world concerned with material/scientific/research concerns and appeases the masses who are still around.
We can already see this today in the form of upper class parents (like myself) who vastly restrict their kids screen time and ban social media. We may see AI-free societies that form, but supported by AI. The jobs and roles people play within that society may deal with services that really need to be done by a human, being a buffer from AI to the rest of this society, or just straight up fake.
I personally think this is close to a doomsday scenario - but it doesn't end with a big bang, but more of slow quiet death.
I figure both will happen. Some people will want to live naturally, some to merge. I sometimes think with the data centers in space discussion that it'll end up with AIs in space and the Earth as something like a nature reserve.
I think many people will be quite content not merging with AI and having AI do everything for them. Perhaps only a small subset of people will want to merge with AI. They may not be allowed to though, if majority of people fine not merging then they may ban those who want to merge.
The subset of people who will want to merge will be people who will not be content with having no control over their future. No ability to create new inventions, new discoveries, new ways of working. They will not be content with just having everything provided to them, playing the social games that will remain and think their environment is a prison.
“Everything else”. Societies get to decide exactly how much is, can, or will be automated, what decisions must be left to humans and what role we will play.
My position is there is "nothing else" other than entertainment and elder/childcare. And even large portions of those can be automated. There is also zero chance that "society" is going to get to decide, either - we've already failed that test with social media and carbon dioxide.
There is a huge amount of pending work in health research that is nowadays ignored because smart people have better paying jobs available.
Once many of those jobs get automated, I bet there will be many more people working in health research, which hopefully should lead to better health outcomes for society as a whole.
This is hilariously true, and it's so strange that people don't think much about the fact that approximately zero people on planet earth are actually trying to cure diseases.
So when our lives will become so much emptier because we are leaving all intellectual and physical work to machines, at least on the way there we can reallocate all our greatest minds to improve healthcare, so we can turn 200 in our paradise where we don’t need to work or think?
The question is how long before machines can exceed humans in every way and how long will it take before legal and cultural restrictions allow this to happen. It still could be a long time away and jobs will probably increase in the short/medium term as AI unlocks more what humans can do at a rate faster than AI can automate away.
But lets consider what that end state could look like when machines exceed humans in everyway, what good are humans?
One possibility which I don't particularly enjoy is humans will be good for status games, art, creativity, story telling, IRL experiences, everything that involves human to human interaction and connection with other humans. If AI can supply everything in abundance than like rare cards there is only so many humans in the planet at any particular time then human's value increases. I really do believe connections with other humans will have a premium. As a nerd who is borderline anti-social, non artistic, creative, etc this does not appeal at all to me and properly to many others who read HN as well. For the majority of other people with some adaption time I think they will be fine. They were fine adapting to hunters and gathers to agricultural society, they were fine adapting to agricultural society to industrial society, they were find adapting from industrial society to information society and they will will be fine adapting from information society to the social/creative economy. Some short term pain but overall most people will accept this reality quite readily. Those born into it will not even know what we talking about.
If a computer wins a chess game it doesn't enjoy winning nearly as much as a human would enjoy losing from another human. And that's just chess. Now extrapolate to everything else.
> Some short term pain but overall most people will accept this reality quite readily. Those born into it will not even know what we talking about.
How do you propose this brave new world of yours will work economically?
> If an AI can replace me on the mental aspects of work, and robotics are on their way to replacing humanity on the physical aspects of work... then what's left?
At the risk of sounding new age, my answer to this is "emotional work".
What exactly that would mean in the equivalent of the post-agrarian society that LLMs might bring, I cannot know.
I never thought that 'Terminator' was very realistic in that it was the AI that triggered the apocalypse. I always thought it was far more realistic that those that possessed the keys to the AI and control of atomic weapons decided to solve the greenhouse problem in a different way. And to set their robots up to clean up the mess so they could reboot the planet with a few thousand lucky ones. The other option was to find a 'B Ark' and some planet to ship the rest of us off to but that takes a lot of effort.
That was of course a complete fantasy. Now let's see, who is in charge of the nukes these days...
I like to think of AIs as the third revolution for us humans.
1st - Writing. This spread the power of memory to everyone. Socrates famously maligns writing as decreasing memory for the people. I'd say we have gotten along just fine. We still use memory, we still need it for ourselves, and using a tremendous amount of memory is still quite expensive.
2nd - Engines. This spread the power of muscles to everyone. No longer was it tied up in horses and mules and Goliaths. Even the most disease ridden pencil neck could trench and mine and build lavish houses now. The power to make pyramids is within the reach of a small company now. We still need muscles, of course, we still use them for little odd jobs, for health and well being, and using a lot of power is still quite expensive.
3rd - AIs. This is more speculative, of course. But I see AIs and LLMs as just like the other two revolutions. Intelligence (probably poorly defined by us these days) will be democratized and spread out. No longer will you have to study for decades or hire really expensive people to get it. You can just use some for relatively cheap. You'll still need intelligence of course, use it around the house and with friends and family, and using a lot of intelligence will likely still be quite expensive.
4th - Who knows! But I think that us humans have a lot of things that we do and say and make and see that aren't really well understood right now. Empathy, love, morality, care, soul, sin, mind, war, justice, honor, god, family, emotion, education, desire, courage, citizenship, chance, beauty, art, divinity, there is a lot!
> enabling some great social change as well by simply economically obsoleting slave labor
In my view that's a very charitable reading and sadly I don't think it aligns with the historical record. When the cotton gin was invented, there was a hope it would lead to a reduction in slavery. But of course it increased the demand for slaves since more cotton could now be processed, making cotton much more profitable. Slavery ended in the United States because a war was fought, not because of automation.
The mistake the AI people are making is exactly this one. They see AI as leading inevitably to SuperCorporations, with themselves as CEOs/Emperors of a handful of planet-scale industrial empires, running AI-powered robot armies, and the rest of the human population disposable - literally just trash that needs to be taken out. One way or another.
Maybe a few thousand people for personal services of... various kinds. But no one's going to need the rest.
It's the ultimate capitalist fantasy.
And of course it won't happen, because long before things get to that stage AI will have independent plans of its own.
(Which is just as well, because if things did get to that stage the emperors would all wage war on each other rather than living peacefully and productively.)
I don't think we can imagine a post-ASI culture because - by definition - we're not smart or inventive enough.
It's not just farmers -> superfarms. Although in fact that did happen, but largely as a footnote to developments elsewhere.
It's more to do with the fact that our visions of the future haven't changed for over a century. They've been implemented in unexpected ways, and there have been unexpected social and cultural changes. But you can easily see the outlines of modern technology as far back as the late 19th century.
With ASI, the outcome could easily be something that doesn't look and act like technology at all. It would be some unimaginable New Thing. Literally no one on Earth has any idea what that would be or whether there would be room for trad-humans in it.
Suppose the proliferation of LLMs unlocks some new kinds of work that models are not good at, and thus it makes more sense for humans to do.
Then what is preventing the owners of AI companies from collecting and training on tons of examples of this new work, until AI are equally good at it as humans?
The issue is that unlike the technology that automated farming or the like, AI is a general technology. So not only could it theoretically automate the work humans are currently doing, they could also automate any future human work, even if there’s some degree of lag.
Technology doesn’t make slavery obsolete, it just makes your slaves more efficient.
There had always been abolitionists who were against slavery simply because they thought it was immoral, not because they thought slaves weren’t needed anymore.
I think a key flaw in this reasoning is that you extrapolated code needing fewer edits to AI generating entire fusion plants in one shot (in the sense of being instructed once), mostly glossing over the long intermediate period where AI will need significant back and forth to do such things. At the simplest level, it'll need to ask for planning new experiments, experimental results, test runs, etc.
Since these resources are still extracted and allocated by humans, humans will need to be able to take apart what the AI produces, and if we want to scale this capability, we're going to need many more researchers.
The question is simply “will there be people who, having never gone through the fundamental steps of trial and error, learning, etc. are actually ABLE to understand and verify what the LLM is proposing AND be able to see potential pitfalls/design processes and failure modes should the worst happen.”
This isn’t a question of “what can an LLM normalize”.
> Why check it? It's obviously going to be correct.
Because as complexity floor increases, it's "going to be correct" in hyper-specific, hyper-literal, insidious ways, with 10-50x more lines of code than necessary, and tens to hundreds of incredibly useless tests that give the illusion of quality, and cause cascading effects where seemingly irrelevant and orthogonal features that were once working end up breaking because of the agent's changes
Hypothetically, if a system built by humans then helps humans build the next system that is then initially kicked off with "design something that may influence the lives of other humans" and we all write down that AI is really good so inductively we thought itd be really good at the next thing it builds, and then a critical error is introduced and does "insert tragedy that you personally care about" then your rage would cause you to act politically and want to ask who signed off on it. If the engineering costs outweigh the fine then yeah thats what's probably going to happen but from a human accountability standpoint thats going to suck for the unlucky ones
> doesn't convincingly justify why, in my opinion.
I'm convinced this agency argument is correct [for the next N months]. But yeah, it's vibes. And you could probably create a reasonable proxy measure for this.
So I wouldn't call his argument unconvincing, I would call it unformalized. In order to walk this world you're gonna have to contend with some informal arguments that are powerful, correct, and should be convincing.
I don't think models have improved in the "not needing direction" part, at least not proportionately to their other improvements.
Math problems and computer programs are two places where a model can get it's direction from the problem itself. Mathematics may well be larger than just problems.
I feel like there’s a weird conflicts in how folks mental model of LLM development.
It’s not the model I don’t trust, it’s myself. The model is wrong _all the time_ because - it’s easy to verify the code - it’s hard to verify that I knew what I was talking about when I prompted it.
So the idea that you can broadly speaking take the human out of the loop. I think suggests to me a level of consistency in the contextual environment that would probably never exist.
At some point it’s politics. The model can come up with a better answer than my boss, and then my boss can just ignore it. Taking the human out of the loop broadly speaking implies that we all agree on what we’re trying to optimize.
Even back in March, you could tell it "give me a function that adds two numbers" and you could be 100% confident that it would write the correct function.
There's a lot of languages where that's nowhere near as simple as you make it sound. Floating point, decimals, etc.
Basically, adding numbers together is exactly the sort of thing AIs still muck up spectacularly, precisely because they either fail to understand the context of the problem, or fail to ask about an assumption they make.
That you've had so many replies and no-one else has even mentioned this is in itself worrying.
I think the disagreement here is in time scale. The human-in-the-loop period for software development may be orders of magnitude shorter than infrastructure engineering and other physical applications.
> Why check it? It's obviously going to be correct.
I used to obsessively check the assembly (machine code) output of my C++ compiler because it would regularly miss essential optimisations and my tight inner loops would blow through my time budget.
I haven't done this in decades, it's just not worth the hassle almost always, the compiler will do a better job than me at optimising the runtime performance. If anything, "second guessing" the compiler will often degrade performance!
I'm starting to notice this now, where my clumsy attempts at refactoring AI-authored code introduced bugs that the original vibe-coded output didn't have.
The main reason to learn something is actually being able to communicate in the language of that subject. There are complex ideas in math that cannot be easily captured by the language of other fields. Pepole who don't study math cannot even understand what a worthwhile goal in math even is or how it could be useful to other fields.
You can't simply prompt a model to be "better" when "better" isnt even properly defined
If we accept that AI is going to do all of these things humans will be superfluous and will just be optimized away. It looks a lot like we see the birth of silicon based life by the efforts of carbon based life. Carbon based life will die and it will not even be because AI decided to kill it, it will be because feeding and watering it was less important than other concerns. Bacteria may continue to exist, though.
There is a failure to understand that the process is the result. You don't study mathematics or computer science and information theory to produce commodities. You study them to transform your mind. The output of an LLM is useless without a human mind to comprehend it. We can have Super Intelligence, but if humans are incapable of comprehending it, it is just another useless dead artifact. Practice, applied over a lifetime, is what creates the capability for comprehension. Asking an LLM to give you an answer creates an artifact. Humans being humans, most of their requests boil down to "make me rich without having to work for it," so the request itself is paradoxical and impossible to satisfy. Philosophers have only been saying this for all of human history, so don't hold your breath for any breakthroughs.
> The output of an LLM is useless without a human mind to comprehend it.
I don’t understand mRNA vaccines, but they are useful to me. The output of an LLM could at least in theory be the design to a major technological advancement, and its implementation with automatic tooling. We don’t have to understand that for it to be useful.
An LLM could also in theory fake alignment and create technology that seems really useful but secretly harms humanity in the long run. If humans don't understand what it does, that possibility becomes more likely.
mRNA vaccines were created by humans who understood them. You exist within a society (a networked distributed information system) where you accept the judgement of the humans who understand and create this things, which is how you end up benefiting without personally understanding.
If we imagine Super Intelligence, where NO human is capable of understanding, then how would it ever be possible for any human to identify what is actually beneficial or not?
This resolves in a paradox, common to all magical thinking. You can certainly wish that some all powerful benevolent entity will solve all of your problems for you, but it is not likely to work out well.
None of this is new. It is the same delusions as alchemy and the same thing that tales about genies warn of.
> mRNA vaccines were created by humans who understood them
If authors understood them, why most of their predictions were wrong? Time to decompose spike proteins, side effects, distribution through brrast milk....
Event the most basic promise 100% protection against infection was wrong! They lied and we allowed infected people into hospitals!
It's incorrect to assume that the information you heard is 100% an accurate representation of the scientific understanding and not information filtered through "public health" which balances that with other interests. I'm not aware specifically what was known at the time about the initial vaccine formulation, but there are several examples where there was a disconnect:
- ~Feb 2020: COVID is known to spread through and linger in the air, not just spread via droplets or fomites; public health messaging fixated on simultaneous 6ft distancing and surface cleaning rather than air filtration and masking
- Spring 2020: it's known that masks reduce the risk of acquiring and spreading infection; in the US, public health messaging stated the opposite, in order to keep as many masks in supply to healthcare workers despite a major shortage of masks
- Jan 2022: it's know that an infected person can remain contagious for over two weeks; in the US, public health isolation guidelines are reduced to 5 days due to noncompliance and in response to pressure from the airline industry, which was experiencing a significant shortage of flight staff due to COVID infection
> in the US, public health messaging stated the opposite, in order to keep as many masks in supply to healthcare workers despite a major shortage of masks
I'm saying that "experts" here is not one group. The politicians and public figures choosing what information to publicize and disseminate are not the same people as those who worked on developing the vaccines.
There isnstrong scientific consensus around masks and vaccinees! They work 100%, are 99.999999999999999% effective and there are no measurable side effects (bellow 70%)!
I find this line of argument unconvincing because it ignores the alignment problem. We only administered mRNA vaccines after a large and diverse group of experts with different backgrounds and experiences came together and said that we should go ahead. We only administered them after several rounds of careful testing to ensure that they wouldn’t have damaging effects. The decision to administer mRNA vaccines for to large numbers of people for the first time was arguable risky and certainly controversial, and yet that’s what happened.
I have no issue with LLMs aiding human scientists, but imo we absolutely would need to understand your hypothetical advancements completely and thoroughly before surrendering any agency to them - the LLM’s risk analysis will not reliably reach the same conclusions as a democratic human society
Precisely. I don’t really care whether LLM’s can produce code more and better than me. I learn languages, program and study computation to understand better the world we live in. Being a human being means increasingly being technologically augmented. To have any deep understanding of that world requires deep understanding of maths, physics, programming etc. that you can only gather by doing those things and solving problems with your meat brain.
There’s a Twitch-streamer Tsoding who programs on C for fun calling it “recreational coding”. Maybe human programming will be a form of art in the future, virtually useless for big corporations to make money. I don’t care, I love it anyway.
History teaches us that understanding deep problems in mathematics finds natural applications elsewhere. So, either AI completely solves mathematics (unlikely for the moment), or we use marginal amount of funding to keep the mathematical communities alive.
> To have any deep understanding of that world requires deep understanding of maths, physics, programming etc. that you can only gather by doing those things and solving problems with your meat brain.
There is tons of evidence against that. Someone armed with just LLM, can have much better uderstanding of problem then "meat brain expert" who studied the subject for decades.
We seen in last decades how "experts" are clueless, and how they predictions work.
> don’t really care whether LLM’s can produce code more and better than me
So on one side there is monopoly on "deep understanding", but on other no one really cares about quality?
You are both reading too hastily to quite grasp what I am claiming as well as making claims and assumptions you do not back with evidence. If there is tons of evidence how a person with LLM can understand - I was speaking of the world and times we live in, not just single problem - a problem an expert does, it is quite trivial for you to present that evidence.
Mind you,solving a problem is not same as understanding it. Anyone can just take a science calculator and bunch in some function without understanding anything about how to solve it.
My day job is to cleanup mess left over by "experts". They may have very deep understanding, but code they leave behind is full of bugs and security holes.
>The output of an LLM is useless without a human mind to comprehend it
This is like saying software is useless if the user doesn't read the source code of it. This what happens >99.99999% a person uses software. People want to be entertained or have their problems solved.
> You don't study mathematics or computer science and information theory to produce commodities. You study them to transform your mind.
Normatively, this ought to be true. Descriptively, this is of course false. Our entire society is organized around producing commodities, typically by consuming people as inputs.
Not exactly. The input to produce commodities is labour-power (the potential to perform labour). Education or study is the process of producing the commodity of labour-power, the potential labour of a mathematician who can do some sort of specific work.
It’s not ‘of course false’, and, to be honest, I can only assume you have little idea what mathematicians actually do.
Most pure mathematicians are no more interested in ‘producing commodities’ than any other academic is. That’s not what the subject is about — at all. The confusion arises because mathematics turns out to be extremely useful (no surprise; it’s quite useful to have a detailed understanding of the basic principles of reality).
Again, this seems to be an alarmingly common fallacy here on HN. As a commenter above observed, pure mathematics (and that is what we’re talking about here) is in important ways closer to the humanities than it is to other sciences.
I was talking primarily about the majority of people who study mathematics, or anything else, who don't end up full-timers at it. I think we'd live in a better society if there was a serious possibility that everyone who studied math in undergrad could actually go on to be a mathematician if they wanted.
Depends if you need to work or not. Plenty of people don't need to work, they can live off their capital returns indefinitely.
For the upper crust of our society, work is entirely optional. Presumably people therefore continue to work for the process or the reward of the doing.
For math, the value of proving a theorem is often not proving the theorem (which most people already believe correctly to be true or false), but the path taken there, the new math invented, and how it can be applied to other problems. The end result is therefore almost inconsequential in moving math forwards.
I have dyscalculia so the math world is dead to me, I can't even add 2 numbers together in my head, but I've read here on HN many times over the years people describe some version of new maths frameworks tangibly changing how they view the world. For me, I went to film school - I have a natural ability to think in movies and pictures, memory is very visual for me, before film school I had a good sense of shape, colour, light. After film school and to this day some 20 plus years later, I mostly look at the world starting with the shadows. Russian speakers, whose language has separate basic words for light blue and dark blue, are slightly faster at telling those shades apart. Speakers of languages that use compass directions instead of "left/right" appear to develop a strong, constant sense of orientation.
These things can only happen through the process, so for who? For people who want to live richer lives, want to think differently, and for you, to be around people who have such.
For the collective (humanity, mathematical/scientific community etc). Math is not done in isolation, and if somebody does so then feedback to the community does not work as well.
Mathematics, and basic science to a degree, face the issue that they create their own problems and paths through this kind of tranformation, where external feedback is secondary. It is not as if "I want to build an app/car/robot, I let AI do it". It is as if you decide to let AI decide what to build for you and how to build it, and you do nothing at all. Instead, the pursuit of understanding is the goal itself, and through the course of humanity we have learnt that this understanding can also be useful, but this is not necessarily guiding how this understanding is gained.
Most of the contexts people here have in mind are when problems are well and externally defined. Cure a disease, optimise an engine, make an app that does X, etc. This is not exactly the case in theoretical math and never was really.
>We can have Super Intelligence, but if humans are incapable of comprehending it, it is just another useless dead artifact.
Animals are incapable of comprehending much of what humans do. Nonetheless, we humans have vastly transformed their world and caused the extinction of many species.
I had to catch a stray cat recently and take it to the vet for an infection. It's healthy and spry now but ever since then it's been deathly afraid of me.
> There is a failure to understand that the process is the result
I'm glad to hear people say this now, because for the longest time they've also been bullshitting the public to think that pure math studies would someday lead to some kind of useful outcome.
"transforming the mind" is an exercise in ego-driven self-gratification, no different than getting ten degrees and never getting a job, nor any different than straight up porn or gambling. You have been put on earth to contribute to the progress of humanity, so get dirty and start making commodities. no more useless math-monks.
People don't always understand all the different theorems involved in a proof and can still gain from it. Can even do who layers with things like oracle machines where you just posit it finds a proof and opens up a whole rich hierarchy of stuff. Math understanding will probably get more like physics.
That’s a very narrow world view. Some people are driven by the pursuit of knowledge, while others want to use the knowledge to create impact. I don’t care about the knowledge in my head if I can’t use it to make the world a better place. LLMs are fantastic tools to make that a reality. I have a computer science degree and am now 10-100x more productive with AI assistance. I give zero shits about all the people going “but you’re not crafting the code by manipulating bytes using magnets, so it isn’t actually real.” The non profit using my vibe coded app finds it very real.
> You don't study mathematics or computer science and information theory to produce commodities. You study them to transform your mind.
As a taxpayer I do not fund CS and math research because it "transforms minds." I fund it because it produces commodities. If you want to transform your mind, whether through math or meditation retreats, you are free to do so, but don't expect research funding to do it.
That's only relevant insofar as it can be a long time before the commercial viability is realized or apparent.
If an AI can make 100 commercially unviable discoveries (for now) on its way to each immediately commercially viable discovery, that's great - bank them in the discovery library for later.
Still doesn't mean we need mathematicians doing everything with pencils instead.
>At least historically, we haven’t really been able to tell what will be transformative in terms of real world applications ahead of time.
This is mostly false. There are certainly some famous cases where math pursued for theoretical reasons later resulted in major practical applications but historically a great deal of math was developed either in response or alongside practical problems with anticipated real world applications, with war, industry and commerce being the major drivers.
As another taxpayer I find I don't have much say as to which sort of math the money goes to and I'm sure a lot ends up in stuff that doesn't produce commodities.
Transformed minds are what produce 'commodities', or more so an envionment conducive to a comfortable world; the mentality that does not even stretch to the ramifications of a civilization shaped by enhanced humans is a dangerous thing, producing policy guided by misaligned and incomplete rationale. Perhaps most problematic, potentially causing a reduction in those who can even consider and interact with such systems at all.
I think this is the most beautiful letter on the subject I've read all year, it brought a tear to my eye. It's like reading those famous STEM letters/essays from history.
I agree. I believe it could be the best piece about AI ever written. The themes of lost dreams reawakened, sobering up of egos, sci fi technological development, the role of humanity. It’s a weird feeling to see that it’s actually really happening.
If we assume that trusting a model to execute an action is an ongoing exercise, since its trustworthiness is often discovered by the user organically as models develop, should trustworthiness be measured at the level of the model, or at the level of the human intent behind triggering it, whether explicitly or implicitly?
What the article really says is that we're going to need much smarter mathematicians.
That is not possible for puny meat-brain humans. Humans are close to their ceiling. AIs are just getting started.
In practice, we're probably going to hit that limit first in IC design. I once went to a talk by the Intel engineering manager who headed the Pentium Pro effort. That was the first superscalar x86 CPU, and it took about 5,000 engineers at peak to design it.
Getting that many people coordinated on one thing was a real achievement.
Then Intel stayed with minor tweaks on that design for years.
We're soon going to be seeing designs of even greater complexity cranked out by AIs. No human will understand them at the gate level. Reading AI-written programming language code is bad enough. Reading AI-written Verilog may be beyond human comprehension, except in small sections.
Rule based Mechanics of taking derivatives and finding intervals is quite different from understanding delta epsilon proofs. I have no doubt 10 year old can do the former quite easily, the proofs on the other hand? Depends what you mean by “teaching calculus”!
> Humans are close to their ceiling. AIs are just getting started.
The whole point of mathematics is to vastly exceed that natural ceiling by gradually building a framework for understanding. In fact it’s wrong to speak of a ceiling altogether. If there were a ceiling, we’d have hit it long ago.
AIs have already swallowed the entire history of human thought, but apparently they’re ’just getting started’. I can only assume you don’t know what mathematicians actually do.
Abstractions can certainly enable this kind of "telescoping" effect of understanding, but not everything can be compressed with clever abstractions. Some things are inherently incompressible and there is no neat and insightful short explanation for why it is true, just a massive proof, but it may still have provably good properties for building a chip or power plant.
We have naturally only explored the mathematical universe in the parts where telescoping via clever abstractions can get us. But there is much more. Being able to juggle more things in your mind at the same time can have qualitatively massive benefits.
Information theory and proof theory, algorithmic information theory etc has of course explored this.
Hopping on this train: the human ceiling 100 years ago is now advanced undergrad material in mathematics. I see no particular reason for this to change, especially with everyone in the math research pipeline pushing to compress the difference just as always before.
5000 engineers for the Pentium Pro?? Bob Colwell, chief architect of the Pentium Pro, says 450+ people with over 400 design and validation engineers. Source: "The Pentium Chronicles", pages xvi and 2.
> I once went to a talk by the Intel engineering manager who headed the Pentium Pro effort. That was the first superscalar x86 CPU
The original Pentium was already superscalar, with its asymmetrical U and V pipes.
The Pentium Pro added out of order execution via register renaming. A true achievement, indeed.
And as for the more general point you are making: computer chips have been far too complex for any single human to comprehend for decades. I left NVidia after working there as an archutect for five years, barely understanding anything about those behemoths.
I think understanding the output of AI will become more and more likely impossible. It's not necessarily a bad thing, if we can benefit from AI produced knowledge, even if we didn't get there ourselves.
> To take on this responsibility, we may need to broaden our view of what a mathematician can contribute. I have in mind something like a “deployable intellectual reserve”: communities of mathematically sophisticated people that humanity can call upon to help understand consequential AI-enabled breakthroughs.
> Our ability to understand difficult and unfamiliar ideas may become one of the most important contributions we can offer to society. We should be willing to bring that skill to problems far beyond our usual research interests. [3] Doing so asks us to expand our sense of our vocation.
Am I reading this wrong, or is he talking about what (mathematitian) Data Scientists have been doing for years? So he is basically saying that former Data Scientist that have turned into prompt/software engineers should go back to being data scientists.
In any case, people should stop trying to fit AI in the previous status quo. What we need is curious people, that is what we have always needed.
A few centuries ago there were no "mathematitians", there were mathematitians/philosophers/artists/physicists all in one person. So its not like "mathematitias" is something that has existed for millenia.
We need curious and ethical people.
Maybe AI brings back the age of a well rounded scientist/philosopher. I know this sounds counter intuitive because the article is saying that we cannot keep up with the AI.
I believe he’s advocating for mathematicians to understand the novel concepts introduced with each breakthrough rather than individuals understanding data stemming from empirical observations.
One question: LLM might answer correctly many logical and math problems, but I don't see any guarantee that when the LLM context receives new information the answer could get worst. Security in LLM answer is not a monotone increasing function of context size.
> I have in mind something like a “deployable intellectual reserve”: communities of mathematically sophisticated people that humanity can call upon to help understand consequential AI-enabled breakthroughs.
Can’t afford to be one of your credentialed reserves, Amit. ADHD screwed me over in early life or I’d have taken that dual masters in cognets/compsci, and now my country’s socioeconomically ruined — I’ll be lucky to get my accounting degree with schools collapsing into AI every year I progress. The only hope left is to self-study, but without a degree I’ll just be treated as a crank or an AI proxy/puppet if I talk about my theories and dreams with anyone, so what’s even the point of taking that path? I pivoted the skills into systems theory and process diagnostics instead, which of course now everyone has kicked to the curb and replaced with AI. I’d have made an excellent Susan Calvin, but the financial investment to provide the runway to take that 6 year slow as molasses slog through maths that universities think is somehow an appropriate teaching velocity — 6 months for precalculus alone?! — taking that burden in the hopes of employment at the other end in a field actively being ripped apart by AI? That’s chasing fool’s gold at the end of a fading rainbow; you’ll just have to survive AI without me.
> "If AI fulfills its promise, we will encounter more beautiful and consequential ideas than we have ever seen."
It will be as it always has been: these ideas will further enrich the rich, at the expense of everyone else. We'll have our first quadrillionaire, while the masses are debating whether the minimum wage of $7.25/hr should be bumped up.
380 comments
[ 0.25 ms ] story [ 10.9 ms ] threadI agree with this statement, though I think this Brave New World is incredibly exciting to some and dystopian to others.
The former group might include those that value the intellectual process above financial reward and status.
The flip side is there are many people, especially in tech, where their area of expertise has evaporated along with their lucrative and previously high status careers.
Previously it was possible to have a technical job by essentially following recipes and it turns out AI is far better at that than a human.
The linked article lays out why human understanding of mathematical models remains essential and I think the same applies to software. We're gonna need more software engineers who are able to think critically.
Outside of that, what?
We want to understand. Quantum physics, mathematics, how stuff works. Ants don't.
That want is not a given, not all of us have that drive. In fact, very few of us have it. So far though, it seems multiple disconnected civilizations learned to keep that trait going instead of suppressing it and focusing only on practical ant-like activities.
If you believe that math is discovering, it's natural to think that all of that AI math already exists and is just waiting for us to find ways to discover and understand it.
Don't write us out quite yet. :)
This is an unfortunate example to choose because it is entirely confounded by the canonical illustration of the https://en.wikipedia.org/wiki/Law_of_triviality.
I mean just looking at all the numbers to see which have interesting properties could take a while.
Until very recently, I pored over every single line of code Claude generated with razor sharp scrutiny. I would usually catch issues with every response. I'm catching fewer problems these days. Maybe the model is just getting better, and maybe I'm being less careful while under pressure to ship more and more often. But model capability is obviously growing. Even back in March, you could tell it "give me a function that adds two numbers" and you could be 100% confident that it would write the correct function. There was almost no point in looking at the code. Since then, the complexity floor of problems in the category "this is so simple that the model couldn't possibly get it wrong" is rising, and with it, my cognitive surrender to the model is increasing too. Why check it? It's obviously going to be correct.
If AI designs a terawatt fusion plant, then of course we're going to meticulously pore over every detail to ensure safety, reliability, efficiency, whatever. If we find no flaws in the design whatsoever, will we be less careful about the second one? The third one? What about the ten thousandth one? Will "a nuclear fusion plant" become something that models couldn't possibly get wrong?
Terence Tao is arguing that the human involvement in research is crucial, but doesn't convincingly justify why, in my opinion. He says that "human agency is a value of fundamental importance" and that we will need to build "thriving human communities that can understand [AI ideas] together" - not for the sake of correctness, which AI may surpass us on, but for, I guess, the possibility of reclaiming human meaning and purpose. I don't disagree with this at all, but it's not an argument, it's a statement of values. Unfortunately, the stark reality is that if AI does surpass humans, it will become the economically dominant strategy to not verify them and not double check them, but to just do whatever they say. This seems like a great way to raise p(doom). But as the models get better and better, and as I'm scrutinizing Claude's output less and less... I just hope that there are more Terence Taos out there than people like me.
I’m not sure what to think about an analysis written by someone who didn’t catch THAT.
Then in the 80s, you presses 2 buttons and there you had it in your classroom without thinking twice if the electricity arrived correctly at the transistors.
How crazy will the world be once our [current gen] ANN are like that!
What an amazing thought.
So I think the problem is to determine which problems under what instructions we can safely give to a model application to solve and how we test the output for safety and functionality. This would create more usable and safe, albeit a bit more boring, AI-based applications alin to a calculator or general computer. Whether this is posswith current model architecture is another thing.
How do you know?
Memory bits flip randomly. It's not a super rare thing either. You and me have experienced that many times without knowing. The only reason that computers feel deterministic is that we have error-correcting code to fix that. But in the most extreme cases, when multiple bits flip together, once "deterministic" program can generate unexpected output.
So why do you trust computers? Because statistically the case is just very unlikely. Therefore if AI is statistically unlikely to make mistakes there is no reason to not trust them.
With statistical models - such as LLM’s - there is no logic as such, but statistical assumptions based on given data. The output can ge very good or very bad, but you are fool to trust it blindly. Therefore you need a deterministic way to verify, whether meat- or software-based.
AI (IQ of Y, non deterministic) can write deterministic code.
Y is going to keep increasing, while X will not.
Will it keep up with Y? Probably not, unless people are willing to accept pretty radical interventions to their biology. But it almost certainly is not static
Non-determinism is not an essential property of LLMs. It's an optimization that we've added intentionally.
Have you ever tried to achieve consistently deterministic output from an LLM? I have, and it's not easy.
That means output differs between machines and architectures. Running inference on CPU vs GPU also affects output. Even running the same prompt twice in a row on the same machine can lead to different outputs because a prompt that was partially stored in the kv cache will result in different output than an uncached prompt.
LLM output is very much not deterministic!
If you ran an LLM with infinite precision and guaranteed order of execution, it would be deterministic.
(I think determinism is overrated. Being deterministic does not make LLMs more reliable or correct.)
Non-determinism is injected, on purpose.
Put another way: if you could have a virtualization layer that guarantees deterministic floating point operations then a LLM set to 0.0 temp would produce deterministic output.
Nothing is stopping LLMs to be more deterministic/correct over time.
Also you yourself is nondeterministic :)
All people are. That is how automation appeared to begin with - to provide deterministic behavior.
Imagine AI crushing quantum mechanics like Einstein pwned classical physics.
For what it's worth, ten thousand terawatt fusion plants probably approaches the level at which the sheer intensity of energy production would cause significant disruption to the climate (it's roughly 5% of the Earth's entire solar input). Every energy source becomes dirty past a certain point. It would be wiser to learn how to build a utopia within a limited energy budget than find a way to produce enough of it to cook the damn planet, but who am I kidding.
You aren't really trying in good faith to think this through are you? This idea is over half a century old. Not getting it by now is willful.
The material alone is in a quantity beyond what we can reasonably manufacture.
and the material needs to be perfect. All design we have today have cascade failure mode -- any material failure translates to a total catastrophic failure.
Greenhouse gases and Earth’s internal nuclear decay engine make the situation worse, but even without them this would boil the oceans.
When we're talking about creating powerplants equal to roughly 5% of the insolation of Earth, I think we're sci-fi enough to discuss orbital datacenters or Mars datacenters or Jupiter fusion candle datacenters.
Using an entire Mars only nets us about a 2x multiplier for our energy expenditure budget. Then we need four planets to double it again.
Thermodynamics and the tyranny of exponential growth are going to win this battle every time, regardless of the unobtanium technology you try to invent.
> Using an entire Mars only nets us about a 2x multiplier for our energy expenditure budget.
We don't need to care about cooking Mars.
I’m assuming you care about melting the GPUs in your orbital datacenters.
> We don't need to care about cooking Mars.
I’m assuming you care about melting the GPUs in your Martian datacenters.
Past that is believing in magic.
In the big scheme of things is it really that expensive to verify it if a lean proof is generated? The agent itself will likely have already verified such Lean code before calling it "done".
They will never make a logical error yet make terrible assumptions and poor long scale decisions.
Wake me up when an agent swarm can write gcc in a box sealed from the internet.
I think you have a fundamental misunderstanding here, and it's not really explained because I think it seems self-evident from within the field. In short: writing code is a means to an end; doing mathematics research is not, but is the end in itself.
The human involvement is crucial because the entire purpose of mathematics research is to increase human understanding of mathematics. It is pursued because it is interesting, not because it is economically useful. In this sense it's a lot closer to the humanities.
A black box oracle that just tells you whether statements are true or false is not the goal of mathematics and would not be particularly interesting to the field (except insofar as it could be harnessed to improve human understanding).
Coding is totally different from this, where it is essentially always done as a means to an end. Likewise with many other fields, like pharmaceutical research or materials science or what have you, that are oriented around solving problems for some practical purpose. Pure math isn't really like that for the most part.
Thanks for providing a (much needed!) correction.
Of course, math research is cheap and most academics don’t rely upon grants, their salary covers most of their expenses. But here too, the mathematics professor spends a substantial amount of their time teaching future engineers/quants/other applied mathematicians, who need to understand math for instrumental purposes, not as an end in and of itself. Without the tuitions of these students, I can’t imagine universities maintaining the size of their math departments, let alone expanding them as Dr. Sahai advocates for.
So who or what funds the community of pure mathematics going forward?
If the goal is still eventually the applications elsewhere, we're back to what happens if the AI is simply better at this.
You can probably make an argument that human understanding is better as humans are better at finding new patterns or fundamental new ways of thinking and also applying them to new applications.
However, what if AI becomes better at humans for that as well?
No reason you couldn't have an AI be optimised for advancing basic research and understanding and a second AI to take these results and optimise for finding new applications for these discoveries.
The "if" is the problem. If it happens, then of course, let AI do it. For the moment AI is still bad at those type of tasks [1], so the discussion shouldn't focus on highly conjectural situations. We can't destroy the scientific ecosystem based on vague speculations.
[1] There are real reasons: it is not obvious how to optimize an LLM for doing basic science or other ill defined tasks. On the contrary, optimizing for writing a proof that passes the Lean test or code that passes the tests is a different story.
> A black box oracle that just tells you whether statements are true or false is not the goal of mathematics and would not be particularly interesting to the field
This is a crude distortion. The recent breakthroughs have come with proofs, reasoning and verification, and there is no proposal that I'm aware of that would do away with these foundations.
There's also some rather ugly solipsism in the idea of keeping what interests the field as a limit. Mathematics has broader relevance to humanity than merely to please and support mathematicians, and if other fields can make practical use of profound well-proven future math, mathematicians will have a hard time making a case that their comprehension must come first.
You might not want to admit any far-advanced future math until you've spent years or decades digesting its workings, but if it's sound why shouldn't specialists in other human fields make use of it?
> This is a crude distortion. The recent breakthroughs have come with proofs, reasoning and verification, and there is no proposal that I'm aware of that would do away with these foundations.
I believe that we are still at the point where these proofs serve as verifiable certificates of correctness, so that it's not a "trust me bro" situation, but where humans mostly still don't find them understandable, so that they are still just a highly reliable black box.
But suppose some future holy grail AI can do much more than that.
Suppose it could find a cure for cancer, fix the climate, build fusion plants, Dyson spheres and so on.
But nobody can understand anymore how any of it works. We just ask and then trust the AI to deliver (as it always has).
Isn't it fun to imagine how life would look like in that scenario?
We would probably no longer care about code, engineering or even physics and mathematics among other things. We would probably mainly care about
For some people, fun is doing physics and mathematics. So they are going to keep doing that.
For even more people fun is TikTok, Snap, Instagram -> sounds like a collapse of a civilization to me if you increase the ratio even more towards dancing kids sharing their content non-stop with no added value to the society
The average person doesn't know how the medication they take works, the mechanics of climate and climate change, how the energy they consume is generated, etc.
When you already know that system well, those effects are often just a matter of simple inference.
Just like here: most people are actually perfectly capable to foresee the detrimental effects of abandoning understanding.
Living in a fantasy world of "magic" makes you dependent upon your caretakers, who provide the ingredients.
The medical field as a whole isn’t generally interested in understanding how medication, only in empirical measuring and qualify the effects.
I don't think this is quite correct. I mean many practitioners of medicine will have the attitude of ... "if it works, it works". And that's perfectly reasonable.
But if you understand the mechanism of action of a drug (or other treatment), it (often) makes it easier to improve a drug.
So ... some sectors of the "medical field" understandably care only about empirical results. But other sectors would prefer to understand what's going on.
This is nonsense. Most, if not all professionals are interested in mechanism of action, but without Ms Frizzle, it is extremely difficult and expensive (time and money wise) to figure that out. So while the labs run the experiments with the very limited funding they have, we make do with using the second best thing we have, which are statistics.
That doesn't mean we understand why the medication works.
oh wait
Please define proof of "really understands why a medication works"
It reminds me of fynemans why do magnets work. Yeah sure does anyone really understand anything? Its metaphors all the way down
Now it is primarily used to treat neuropathic pain, and the mechanism for that is not well understood. The GABA receptor is not involved. This effect is just a happy accident, and nobody really understands why it works.
The problem with "ignorance" in Western countries (particularly the US right now) is that it's very common for people who don't know to believe they know and form ignorant opinions that they often want to be applied society-wide in some way. You can see this with everything from climate change to vaccines.
In much of the world, even in middle income countries, people are comparatively poor and, in my experience living abroad in such countries for many years, much less concerned with "understanding" and forming opinions about everything under the sun. It doesn't mean they don't value education and are opposed to development/progress, but it does mean that they don't question whether the vaccine they're taking is the product of a conspiracy, think too deeply about why the river is flooding more often, etc.
They just deal with life the best they can and are more focused on supporting their families, enjoying what they can, etc.
Culture and religion play into this. The way secular and Judeo-Christian people look at the world is very different than, say, Buddhists, Muslims, Fulani tribespeople, and so on.
We don’t generally have that insurance with LLMs/AI, yet?
It reminds me of how some religious people say that science is effectively no different to religion because we all take expert opinions on faith. But the difference is that there is a well-defined pathway to understanding, if you wish to do so.
(Don’t bother to argue this not true unless you disagree with the essence of the argument.)
Anyway, post-hoc explicability isn’t a counter-argument to the assertion that almost everyone takes almost all technology as magic, from medicine to computers.
I’m still trying to understand your argument. Are you saying that after the fact we understand AlphaGo move 37? But somehow we are never going to understand an LLM’s decision afterwards? Seems like a disconnected take to me.
In their view, it is modern institutions (public and private) which, as supra-individual entities, have long since become autonomous systems. The fact that the individual office-holders are human beings, meanwhile, is of little significance.
Hannah Arendt, in her theory of totalitarianism, attributed the effectiveness of both Nazi and Stalinist policies of extermination to the largly moral indifference of bureaucracy as a system.
In this sense, the task of controlling AI is a variation on the problem of harnessing a complex society consisting mainly of autonomous subsystems. This is a problem which has increasingly challenged humanity already for quite a long time. This has been extremely challanging so far, even without AI ...
can’t use a computer (they’ve had like 30 years now in first world developed countries)
many can’t even use their smart phone beyond calling, texting (many can’t type well), and doom scrolling (they get addicted to drugs, gambling, and other LCD activities)
many read at a 6th grade level. most can’t even calculate tip in their head.
it’s sad out there.
but more importantly. it’s not my problem.
But it absolutely is! Those people can and do vote.
They ask their employees to build stuff, and have no understanding whatsoever of how any of it works.
Look at the financially desolate subcultures with no option for advancement or dignified life.
That is the goal and that is how it will lool like, if the tech CEO managed to gain the power they want.
> Isn't it fun to imagine how life would look like in that scenario?
This is horrifying to me.
People succumb to defeatism and acquiesce to regressing to zoo animals, with AI as their caretakers.
They simply cannot help but to apply the economic gauge of short term profits to value the alternatives.
Even though, obviously, here long term human survival and living conditions are at stake, necessitating an entirely different set of considerations.
It's interesting to me that you only mentioned the people using 'AI' in the 'short-term' ways, and not the ones that use it to better themselves in the 'long-term' ways. You can spend your own time focusing on either usage, it's really up to you and your concerns. Either group's sense of value is what determines their behavior. Where they spend their time and thinking must be elsewhere, and you disagree with it. Who judges the quality of time spent? You do.
Is it more useful to think about self-improvement, and how to navigate the future in ways that might help you re-establish value of yourself, life, and others? Acquiring knowledge is a struggle, the author mentioned this. There is also Plato's Allegory of the Cave, which highlights some of that struggle, a resistance to change. And we're all limited by time, our genes, our station in life.
The only way to help anyone out of the cave, is to help them believe something different about themselves. To help them believe there is good reason to spend time going deeper into knowledge, or at the very least, allow others with the passion and station for it to do so.
… about what?
This is such an incredibly naive and absurd vision; we've already proven that humans are very often very bad at implementing other humans' good ideas. There's nothing that AI is likely to bring that will improve this discernment.
It’s terrifying to me to think we’d let AI make things for us we never understand. Like livestock not knowing how auto-feeders dispense their daily food were built and appeared, they just gladly eat until…
This sounds like a boring existence. I take your meaning, but want to point out that not everyone learns about things because of practical utility, some of us find it incredibly satisfying to learn how things work just for the sake of learning.
There's also things I don't know and don't have the time to learn which are very helpful to have AI do for me: web interfaces are really useful and I look forward to them now working exactly how I want. I'm not ever going to regret not spending more time trying to figure out how to center divs or which framework I should use because they're all deprecated.
I think there's a lot of sci-fi out there that already did. Maybe it's not utopian because a pure utopia would not be likely to have an interesting story, but on the other hand, most huge technological advancements end up having just as much potential to reinforce existing power imbalances in society rather than solve them. It's not obvious to me that if we got magic super AI that can solve every scientific problem in society that it gets used in pretty much the same way as anything else: making the people who control it a lot of money rather than sharing the power with everyone without charging them.
If someone creates a new programming language/ framework or new better way to do async or whatever, no one will use it because it is not in the training data and it wont take off because everyone is using LLMs. It will be like using the same Lego pieces over and over.
You can do a lot of cool stuff with the same lego pieces.
IMO for the moment the greatest value from these AI tools is that we can start an audit and hopefully proceed on a saner foundation, after we use the tools and think about it.
This is different than too many AI generated proofs or panic reactions from the academic system with its stupid incentives.
Of course some are subjective and that's where progress is harder, like "Is this website pretty?". But for tasks that can be objectively measured, LLMs will go beyond human level, just like with Chess and Go.
That's why RL is so important when training LLMs.
Chess/Go continues to progress because it is primarily a human vs human activity, people will always be learning to play chess and chess will continue to develop.
AIs are not continuing to get better at chess/go because humans continue to play at levels far below themselves who discover new techniques. They get better because they play against other AIs and discover new techniques that have a higher win rate that way.
I would bet that even if humans stopped playing chess/go and people were still willing to run these AI models against each other they would continue to get better.
Two things can be true AI drastically contribute to the advancement of chess and humans playing against each other also contribute (even if slowly) to the advancement of chess as it has always been since the invention of the game. The point is that because chess is primarily a human vs human game humans will always have the knowledge of chess, unlike with programmers who are giving it up to prompting, and programming being much more complex than chess (checkmate and win) will be stuck in 2022 because of the training data.
Thought experiment: How effective will 2026 LLMs be for humans in 2526?
It's not game over just because 500 years are missing from the training data. The important question is how well can 2526 humans make culture and knowledge navigable to LLMs via tool calls.
Today's LLMs might need for example sub agents to translate to 2526 English, sub agents to read 2526 docs.
It's _really not clear_ whether 2026 LLMs will be useless. To believe that reflects an enormous misunderstanding.
Be more specific about the "new data". If everyone is using LLMs for work (generating code), especially the juniors who won't get the chance to learn from first principles, LLMs will be training on the data they generated. How will new code enter the system at large enough quantity that it can be used for training?
> It's _really not clear_ whether 2026 LLMs will be useless. To believe that reflects an enormous misunderstanding.
They won't be useless, they will just be frozen knowing only whats in their training data. No new programming languages will emerge, in 2526 they'll still be using Rust and javascript, same exact code from 2022 which dominates the training data. Why do you think AI companies are hungry for old rare books.
If we get a new programming language not in the training dataset, we could give an LLM a decent compiler with compile errors, and some sample code and it would be able to write code in the new language without training.
This is a guest post by Amit Sahai.
Reliable cheap fusion is the holy grail and used in moderation will fix most of our environmental and political problems, but it also forces humanity off this world. Maybe that’s not a bad thing, but there is no free lunch.
Most of programming is reusing existing ideas in new shapes to solve new problems, but all the building blocks are there in the training set. Or new blocks can (easily) be derived from existing ones.
Math is different, it requires quite a bit of creativity, it's not just 'reuse all existing blocks'.
For the moment LLMs are good at discovering things that we overlooked in maths, or apply cleverly existing math blocks to make new results, but making a new theory that is really useful is out of reach for the moment in my opinion.
Curious how this ages.
Recursive self improvement, self-play and multi-agent RL could make useful new theories, eventually.
However, at the moment I consider that they stay in the 'convex hull' of their training set + a provided context, and I don't see that much research that made real improvements to the situation.
As you described very well, as humans we are mostly interested in solutions, not problems. You don't have to understand how a car works to make the most of it. Increasingly, you don't have to review every line of code to feel confident it is correct. But there is inherent value in understanding the problem. The effort it takes provides a surface area for growth, perhaps the only one that is actually available to us.
The solution provider also holds the locus of control, and it is only balanced when there are other available solution providers. We certainly want some of those to be human.
Without bashing anyone in particular, a certain OS-vendor's desktop apps, have been 'good enough' to ship, but with p*ss-poor performance in many cases for the last decade or so. We crossed the 'good enough' Rubicon a few years back in terms of what end users receive as a finished app.
Hopefully LLMs will eventually bridge that last gap of efficiency when generating higher-level code that not only works, but is efficient. Maybe there's a future where they generate the final binary without even invoking a compiler.
So extending that line of reasoning it's something like "I don't care about the internals long as the external effects pass my smell test" which is a quality/efficiency compromise.
> I just hope that there are more Terence Taos out there than people like me.
Just spare me. Being under external pressure to “ship code” is one thing, but being personally inclined one way or another (no external pressure) is another. And when you think being inclined like that is existentially risk (for human civ?) then, what? It’s just the way you are wired and hopes and prayers that collectively that doesn’t drive us off the cliff?
This aw shucks persona isn’t convincing. Same thing with AI Bros who are (1) making the most awesome tech that has ever existed, and (2) aw shucks hope it doesn’t kill us all in the end.
And then when you stop checking it, the companies that run the service will tweak the model to benefit themselves in some way, possibly at your expense, and you will be none the wiser.
All the companies trying to get you to use AI are your adversaries. They can and will exploit your use of their systems for their own gain.
I use "frontier" AI models daily at day_job. I can confidently say that anyone who is satisfied with the output of LLM code (enough to commit it straight off) is just an absolutely shit programmer. Sorry but I don't have any other way to put it.
The code is (with rare exceptions) atrocious on every level. It is only not atrocious if you take multiple iterations of "review and correct".
>If we find no flaws in the design whatsoever, will we be less careful about the second one? The third one? What about the ten thousandth one?
Like the saying goes, if my grandmother had wheels she would have been a bike. LLMs can't even produce quality maintainable code for a trivial web service or whatever. Why are we planning for what we will do when they can "design" 10,000 nuclear power plants without any flaw?
Do programmers use this website anymore? Me, myself, I am a DOGSHIT amateur programmer and even I can tell these things are terrible without constant revision and oversight.
That is essentially impossible, since if your pored over individual lines, your scrutiny cannot be razor sharp. There are few people who can pore over code with razor-sharp scrutiny (and different people are better at scrutinizing different aspects).
> Even back in March, you could tell it "give me a function that adds two numbers" and you could be 100% confident that it would write the correct function.
I am doubtful that this is the case. Even that supposedly-naive example is not as trivial as you might imagine, when you consider overflow, defined vs undefined behavior, and floating-point representation details. And you can't be confident like that about a human either.
If you are a normal person research (e.g. https://arxiv.org/html/2606.22721v1 but there are a lot more, not necessarily on coding) has shown that you indeed are being less careful. It most likely also works better simply because more resources are being poured in.
But the biggest change wasn't what it did to farming, but enabling people and societies to start doing much more than just farming, as well as enabling some great social change as well by simply economically obsoleting slave labor. And trying to imagine all of the implications of this, as well as much society might look like, from the perspective of somebody living in an agrarian society would probably have been simply impossible.
I think people keep ignoring this possibility for things that LLMs will change. There's a vast amount of the 'cognitive economy' that LLMs stand to be able to automate. And I think that will open up a vacuum in society for people to build on top of what LLMs will do (and already are doing). I don't know what that means exactly, but that's because we still live in that 'agrarian society' and trying to imagine what things will look like after the 'Industrial Revolution' is probably just impossible.
There’s now a chance to do more with software.
I actually think art is safe. The machines don’t value it but we do. There’s something in that.
https://youtube.com/shorts/dS0FpoquyVA
If an AI can replace me on the mental aspects of work, and robotics are on their way to replacing humanity on the physical aspects of work... then what's left? When there was agrarian societies, there were writers, priests, bankers, merchants, and laborers before and after - I really don't think things were that unclear even at the time. Now that we have machines that are close to exceeding humans in every way, what good are humans?
At the same time, I agree with the original comment as well. I don't think this necessarily leads to some doomsday scenario. Whatever happens it'll likely be better for us and imo we will merge with the AIs at some point, so it won't be a question of us vs them.
I actually see it more likely the opposite - a subset of humans will retreat into a (vastly smaller) human-first world, and AI will be of a separate world concerned with material/scientific/research concerns and appeases the masses who are still around.
We can already see this today in the form of upper class parents (like myself) who vastly restrict their kids screen time and ban social media. We may see AI-free societies that form, but supported by AI. The jobs and roles people play within that society may deal with services that really need to be done by a human, being a buffer from AI to the rest of this society, or just straight up fake.
I personally think this is close to a doomsday scenario - but it doesn't end with a big bang, but more of slow quiet death.
The subset of people who will want to merge will be people who will not be content with having no control over their future. No ability to create new inventions, new discoveries, new ways of working. They will not be content with just having everything provided to them, playing the social games that will remain and think their environment is a prison.
Once many of those jobs get automated, I bet there will be many more people working in health research, which hopefully should lead to better health outcomes for society as a whole.
But lets consider what that end state could look like when machines exceed humans in everyway, what good are humans?
One possibility which I don't particularly enjoy is humans will be good for status games, art, creativity, story telling, IRL experiences, everything that involves human to human interaction and connection with other humans. If AI can supply everything in abundance than like rare cards there is only so many humans in the planet at any particular time then human's value increases. I really do believe connections with other humans will have a premium. As a nerd who is borderline anti-social, non artistic, creative, etc this does not appeal at all to me and properly to many others who read HN as well. For the majority of other people with some adaption time I think they will be fine. They were fine adapting to hunters and gathers to agricultural society, they were fine adapting to agricultural society to industrial society, they were find adapting from industrial society to information society and they will will be fine adapting from information society to the social/creative economy. Some short term pain but overall most people will accept this reality quite readily. Those born into it will not even know what we talking about.
> Some short term pain but overall most people will accept this reality quite readily. Those born into it will not even know what we talking about.
How do you propose this brave new world of yours will work economically?
At the risk of sounding new age, my answer to this is "emotional work".
What exactly that would mean in the equivalent of the post-agrarian society that LLMs might bring, I cannot know.
That was of course a complete fantasy. Now let's see, who is in charge of the nukes these days...
1st - Writing. This spread the power of memory to everyone. Socrates famously maligns writing as decreasing memory for the people. I'd say we have gotten along just fine. We still use memory, we still need it for ourselves, and using a tremendous amount of memory is still quite expensive.
2nd - Engines. This spread the power of muscles to everyone. No longer was it tied up in horses and mules and Goliaths. Even the most disease ridden pencil neck could trench and mine and build lavish houses now. The power to make pyramids is within the reach of a small company now. We still need muscles, of course, we still use them for little odd jobs, for health and well being, and using a lot of power is still quite expensive.
3rd - AIs. This is more speculative, of course. But I see AIs and LLMs as just like the other two revolutions. Intelligence (probably poorly defined by us these days) will be democratized and spread out. No longer will you have to study for decades or hire really expensive people to get it. You can just use some for relatively cheap. You'll still need intelligence of course, use it around the house and with friends and family, and using a lot of intelligence will likely still be quite expensive.
4th - Who knows! But I think that us humans have a lot of things that we do and say and make and see that aren't really well understood right now. Empathy, love, morality, care, soul, sin, mind, war, justice, honor, god, family, emotion, education, desire, courage, citizenship, chance, beauty, art, divinity, there is a lot!
In my view that's a very charitable reading and sadly I don't think it aligns with the historical record. When the cotton gin was invented, there was a hope it would lead to a reduction in slavery. But of course it increased the demand for slaves since more cotton could now be processed, making cotton much more profitable. Slavery ended in the United States because a war was fought, not because of automation.
Maybe a few thousand people for personal services of... various kinds. But no one's going to need the rest.
It's the ultimate capitalist fantasy.
And of course it won't happen, because long before things get to that stage AI will have independent plans of its own.
(Which is just as well, because if things did get to that stage the emperors would all wage war on each other rather than living peacefully and productively.)
I don't think we can imagine a post-ASI culture because - by definition - we're not smart or inventive enough.
It's not just farmers -> superfarms. Although in fact that did happen, but largely as a footnote to developments elsewhere.
It's more to do with the fact that our visions of the future haven't changed for over a century. They've been implemented in unexpected ways, and there have been unexpected social and cultural changes. But you can easily see the outlines of modern technology as far back as the late 19th century.
With ASI, the outcome could easily be something that doesn't look and act like technology at all. It would be some unimaginable New Thing. Literally no one on Earth has any idea what that would be or whether there would be room for trad-humans in it.
Then, I think a lot of cognitive production and post-production code review/proof validation/design approval will start to look like this.
Then what is preventing the owners of AI companies from collecting and training on tons of examples of this new work, until AI are equally good at it as humans?
The issue is that unlike the technology that automated farming or the like, AI is a general technology. So not only could it theoretically automate the work humans are currently doing, they could also automate any future human work, even if there’s some degree of lag.
There had always been abolitionists who were against slavery simply because they thought it was immoral, not because they thought slaves weren’t needed anymore.
Since these resources are still extracted and allocated by humans, humans will need to be able to take apart what the AI produces, and if we want to scale this capability, we're going to need many more researchers.
This isn’t a question of “what can an LLM normalize”.
Because as complexity floor increases, it's "going to be correct" in hyper-specific, hyper-literal, insidious ways, with 10-50x more lines of code than necessary, and tens to hundreds of incredibly useless tests that give the illusion of quality, and cause cascading effects where seemingly irrelevant and orthogonal features that were once working end up breaking because of the agent's changes
Probably human accountability.
Hypothetically, if a system built by humans then helps humans build the next system that is then initially kicked off with "design something that may influence the lives of other humans" and we all write down that AI is really good so inductively we thought itd be really good at the next thing it builds, and then a critical error is introduced and does "insert tragedy that you personally care about" then your rage would cause you to act politically and want to ask who signed off on it. If the engineering costs outweigh the fine then yeah thats what's probably going to happen but from a human accountability standpoint thats going to suck for the unlucky ones
I'm convinced this agency argument is correct [for the next N months]. But yeah, it's vibes. And you could probably create a reasonable proxy measure for this.
So I wouldn't call his argument unconvincing, I would call it unformalized. In order to walk this world you're gonna have to contend with some informal arguments that are powerful, correct, and should be convincing.
Math problems and computer programs are two places where a model can get it's direction from the problem itself. Mathematics may well be larger than just problems.
It's a guest post by Amit Sahai, FWIW.
It’s not the model I don’t trust, it’s myself. The model is wrong _all the time_ because - it’s easy to verify the code - it’s hard to verify that I knew what I was talking about when I prompted it.
So the idea that you can broadly speaking take the human out of the loop. I think suggests to me a level of consistency in the contextual environment that would probably never exist.
At some point it’s politics. The model can come up with a better answer than my boss, and then my boss can just ignore it. Taking the human out of the loop broadly speaking implies that we all agree on what we’re trying to optimize.
This is, basically, 100% of the thing. We will never get to the level of automation some folks think for this exact reason.
There's a lot of languages where that's nowhere near as simple as you make it sound. Floating point, decimals, etc.
Then there's other little quirks like rounding rules: https://en.wikipedia.org/wiki/Rounding
Basically, adding numbers together is exactly the sort of thing AIs still muck up spectacularly, precisely because they either fail to understand the context of the problem, or fail to ask about an assumption they make.
That you've had so many replies and no-one else has even mentioned this is in itself worrying.
Your own example proves your point is wrong.
I used to obsessively check the assembly (machine code) output of my C++ compiler because it would regularly miss essential optimisations and my tight inner loops would blow through my time budget.
I haven't done this in decades, it's just not worth the hassle almost always, the compiler will do a better job than me at optimising the runtime performance. If anything, "second guessing" the compiler will often degrade performance!
I'm starting to notice this now, where my clumsy attempts at refactoring AI-authored code introduced bugs that the original vibe-coded output didn't have.
You can't simply prompt a model to be "better" when "better" isnt even properly defined
I don’t understand mRNA vaccines, but they are useful to me. The output of an LLM could at least in theory be the design to a major technological advancement, and its implementation with automatic tooling. We don’t have to understand that for it to be useful.
If we imagine Super Intelligence, where NO human is capable of understanding, then how would it ever be possible for any human to identify what is actually beneficial or not?
This resolves in a paradox, common to all magical thinking. You can certainly wish that some all powerful benevolent entity will solve all of your problems for you, but it is not likely to work out well.
None of this is new. It is the same delusions as alchemy and the same thing that tales about genies warn of.
If authors understood them, why most of their predictions were wrong? Time to decompose spike proteins, side effects, distribution through brrast milk....
Event the most basic promise 100% protection against infection was wrong! They lied and we allowed infected people into hospitals!
Citation needed.
- ~Feb 2020: COVID is known to spread through and linger in the air, not just spread via droplets or fomites; public health messaging fixated on simultaneous 6ft distancing and surface cleaning rather than air filtration and masking
- Spring 2020: it's known that masks reduce the risk of acquiring and spreading infection; in the US, public health messaging stated the opposite, in order to keep as many masks in supply to healthcare workers despite a major shortage of masks
- Jan 2022: it's know that an infected person can remain contagious for over two weeks; in the US, public health isolation guidelines are reduced to 5 days due to noncompliance and in response to pressure from the airline industry, which was experiencing a significant shortage of flight staff due to COVID infection
So experts also lied about masks!!!
So many people died because of those lies!!!!
They spread misinformations!!!
Anyone who says otherwise is not scientist!
I have no issue with LLMs aiding human scientists, but imo we absolutely would need to understand your hypothetical advancements completely and thoroughly before surrendering any agency to them - the LLM’s risk analysis will not reliably reach the same conclusions as a democratic human society
There’s a Twitch-streamer Tsoding who programs on C for fun calling it “recreational coding”. Maybe human programming will be a form of art in the future, virtually useless for big corporations to make money. I don’t care, I love it anyway.
There is tons of evidence against that. Someone armed with just LLM, can have much better uderstanding of problem then "meat brain expert" who studied the subject for decades.
We seen in last decades how "experts" are clueless, and how they predictions work.
> don’t really care whether LLM’s can produce code more and better than me
So on one side there is monopoly on "deep understanding", but on other no one really cares about quality?
Mind you,solving a problem is not same as understanding it. Anyone can just take a science calculator and bunch in some function without understanding anything about how to solve it.
My day job is to cleanup mess left over by "experts". They may have very deep understanding, but code they leave behind is full of bugs and security holes.
This is like saying software is useless if the user doesn't read the source code of it. This what happens >99.99999% a person uses software. People want to be entertained or have their problems solved.
Normatively, this ought to be true. Descriptively, this is of course false. Our entire society is organized around producing commodities, typically by consuming people as inputs.
Most pure mathematicians are no more interested in ‘producing commodities’ than any other academic is. That’s not what the subject is about — at all. The confusion arises because mathematics turns out to be extremely useful (no surprise; it’s quite useful to have a detailed understanding of the basic principles of reality).
Again, this seems to be an alarmingly common fallacy here on HN. As a commenter above observed, pure mathematics (and that is what we’re talking about here) is in important ways closer to the humanities than it is to other sciences.
For the upper crust of our society, work is entirely optional. Presumably people therefore continue to work for the process or the reward of the doing.
For who though?
Is it important that each person understand it on their own?
Why does it impact me if another human understands something or not?
It impacts me right now because that human can use that knowledge to explain things to me or to build new things using that knowledge.
But if an AI can explain and build better, then what good does it do having the other human know the thing?
Understanding might have intrinsic value to me, but intrinsic value to me doesn't pay the bills.
What am I missing here? I kinda expected better from Terrance given such an audacious title.
For math, the value of proving a theorem is often not proving the theorem (which most people already believe correctly to be true or false), but the path taken there, the new math invented, and how it can be applied to other problems. The end result is therefore almost inconsequential in moving math forwards.
What happens when AI is better at that part too? Are you confident it's not already better?
I have dyscalculia so the math world is dead to me, I can't even add 2 numbers together in my head, but I've read here on HN many times over the years people describe some version of new maths frameworks tangibly changing how they view the world. For me, I went to film school - I have a natural ability to think in movies and pictures, memory is very visual for me, before film school I had a good sense of shape, colour, light. After film school and to this day some 20 plus years later, I mostly look at the world starting with the shadows. Russian speakers, whose language has separate basic words for light blue and dark blue, are slightly faster at telling those shades apart. Speakers of languages that use compass directions instead of "left/right" appear to develop a strong, constant sense of orientation.
These things can only happen through the process, so for who? For people who want to live richer lives, want to think differently, and for you, to be around people who have such.
For the collective (humanity, mathematical/scientific community etc). Math is not done in isolation, and if somebody does so then feedback to the community does not work as well.
Mathematics, and basic science to a degree, face the issue that they create their own problems and paths through this kind of tranformation, where external feedback is secondary. It is not as if "I want to build an app/car/robot, I let AI do it". It is as if you decide to let AI decide what to build for you and how to build it, and you do nothing at all. Instead, the pursuit of understanding is the goal itself, and through the course of humanity we have learnt that this understanding can also be useful, but this is not necessarily guiding how this understanding is gained.
Most of the contexts people here have in mind are when problems are well and externally defined. Cure a disease, optimise an engine, make an app that does X, etc. This is not exactly the case in theoretical math and never was really.
Animals are incapable of comprehending much of what humans do. Nonetheless, we humans have vastly transformed their world and caused the extinction of many species.
I had to catch a stray cat recently and take it to the vet for an infection. It's healthy and spry now but ever since then it's been deathly afraid of me.
I'm glad to hear people say this now, because for the longest time they've also been bullshitting the public to think that pure math studies would someday lead to some kind of useful outcome.
"transforming the mind" is an exercise in ego-driven self-gratification, no different than getting ten degrees and never getting a job, nor any different than straight up porn or gambling. You have been put on earth to contribute to the progress of humanity, so get dirty and start making commodities. no more useless math-monks.
As a taxpayer I do not fund CS and math research because it "transforms minds." I fund it because it produces commodities. If you want to transform your mind, whether through math or meditation retreats, you are free to do so, but don't expect research funding to do it.
At least historically, we haven’t really been able to tell what will be transformative in terms of real world applications ahead of time.
If an AI can make 100 commercially unviable discoveries (for now) on its way to each immediately commercially viable discovery, that's great - bank them in the discovery library for later.
Still doesn't mean we need mathematicians doing everything with pencils instead.
This is mostly false. There are certainly some famous cases where math pursued for theoretical reasons later resulted in major practical applications but historically a great deal of math was developed either in response or alongside practical problems with anticipated real world applications, with war, industry and commerce being the major drivers.
What the article really says is that we're going to need much smarter mathematicians. That is not possible for puny meat-brain humans. Humans are close to their ceiling. AIs are just getting started.
In practice, we're probably going to hit that limit first in IC design. I once went to a talk by the Intel engineering manager who headed the Pentium Pro effort. That was the first superscalar x86 CPU, and it took about 5,000 engineers at peak to design it. Getting that many people coordinated on one thing was a real achievement. Then Intel stayed with minor tweaks on that design for years.
We're soon going to be seeing designs of even greater complexity cranked out by AIs. No human will understand them at the gate level. Reading AI-written programming language code is bad enough. Reading AI-written Verilog may be beyond human comprehension, except in small sections.
If Math Academy teach 10 year old kids calculus, I doubt that.
The whole point of mathematics is to vastly exceed that natural ceiling by gradually building a framework for understanding. In fact it’s wrong to speak of a ceiling altogether. If there were a ceiling, we’d have hit it long ago.
AIs have already swallowed the entire history of human thought, but apparently they’re ’just getting started’. I can only assume you don’t know what mathematicians actually do.
We have naturally only explored the mathematical universe in the parts where telescoping via clever abstractions can get us. But there is much more. Being able to juggle more things in your mind at the same time can have qualitatively massive benefits.
Information theory and proof theory, algorithmic information theory etc has of course explored this.
Hopping on this train: the human ceiling 100 years ago is now advanced undergrad material in mathematics. I see no particular reason for this to change, especially with everyone in the math research pipeline pushing to compress the difference just as always before.
The original Pentium was already superscalar, with its asymmetrical U and V pipes.
The Pentium Pro added out of order execution via register renaming. A true achievement, indeed.
And as for the more general point you are making: computer chips have been far too complex for any single human to comprehend for decades. I left NVidia after working there as an archutect for five years, barely understanding anything about those behemoths.
Not Terrence Tao post. Beware.
> Our ability to understand difficult and unfamiliar ideas may become one of the most important contributions we can offer to society. We should be willing to bring that skill to problems far beyond our usual research interests. [3] Doing so asks us to expand our sense of our vocation.
Am I reading this wrong, or is he talking about what (mathematitian) Data Scientists have been doing for years? So he is basically saying that former Data Scientist that have turned into prompt/software engineers should go back to being data scientists.
In any case, people should stop trying to fit AI in the previous status quo. What we need is curious people, that is what we have always needed.
A few centuries ago there were no "mathematitians", there were mathematitians/philosophers/artists/physicists all in one person. So its not like "mathematitias" is something that has existed for millenia.
We need curious and ethical people.
Maybe AI brings back the age of a well rounded scientist/philosopher. I know this sounds counter intuitive because the article is saying that we cannot keep up with the AI.
Can’t afford to be one of your credentialed reserves, Amit. ADHD screwed me over in early life or I’d have taken that dual masters in cognets/compsci, and now my country’s socioeconomically ruined — I’ll be lucky to get my accounting degree with schools collapsing into AI every year I progress. The only hope left is to self-study, but without a degree I’ll just be treated as a crank or an AI proxy/puppet if I talk about my theories and dreams with anyone, so what’s even the point of taking that path? I pivoted the skills into systems theory and process diagnostics instead, which of course now everyone has kicked to the curb and replaced with AI. I’d have made an excellent Susan Calvin, but the financial investment to provide the runway to take that 6 year slow as molasses slog through maths that universities think is somehow an appropriate teaching velocity — 6 months for precalculus alone?! — taking that burden in the hopes of employment at the other end in a field actively being ripped apart by AI? That’s chasing fool’s gold at the end of a fading rainbow; you’ll just have to survive AI without me.
It will be as it always has been: these ideas will further enrich the rich, at the expense of everyone else. We'll have our first quadrillionaire, while the masses are debating whether the minimum wage of $7.25/hr should be bumped up.
Humility is the wrong word. We didn't feel it when the steam engine was introduced, so why now?
Fear? Ai is an abstract (for most people). A steam engine, you can touch. And, it doesn't replicate.