5 ms·

I see this fallacy being committed a lot these days. "Because LLMs, you will no longer need a skill you don't need any more, but which you used to need, and han

by stavros 7mo ago

I see this fallacy being committed a lot these days. "Because LLMs, you will no longer need a skill you don't need any more, but which you used to need, and handwaves that's bad". Academia doesn't want to produce astrophysics (or any field) scientists just so the people who became scientists can feel warm and fuzzy inside when looking at the stars, it wants to produce scientists who can produce useful results. Bob produced a useful result with the help of an agent, and learned how to do that, so Bob had, for all intents and purposes, the exact same output as Alice. Well, unless you're saying that astrophysics as a field literally does not matter at all, no matter what results it produces, in which case, why are we bothering with it at all?

hirako2000 7mo ago
I was reading in the article that what matters is the process that leads to the (typically useless) result, what the people get out of it. Once I realized that this white on black contrast was hurting my eyes, I decided to stop as I didn't want to see stripes for too long when looking away. Some activity has outcomes that aren't strictly in the results.
- stavros 7mo ago
  Yeah, it was saying that what matters is the process of training people to be good scientists, so they can produce other, more useful, results. That's literally what training is, everywhere. This argument boils down to "don't use tools because you'll forget how to do things the hard way", which nobody would buy for any other tool, but with LLMs we seem to have forgotten that line of reasoning entirely.
  - hirako2000 7mo ago
    There is an argument to make that tools that speed up a process whilst keeping acuity intact are legitimate. LLMs, the way they typically get used, are solely to save time by handing over nearly the entire process. In that sense acuity can't remain intact, even less so improving over time.
    - stavros 7mo ago
      So?
      - hirako2000 7mo ago
        You previous comment reads as if LLMs get some unjustified different treatment. Do you agree the different treatment is justified ? (Many do not). Or are you asking , so what if acuity is diminished so long as an LLM does the job equally well?
  - defrost 7mo ago
    > This argument boils down to "don't use tools because you'll forget how to do things the hard way", which nobody would buy for any other tool, This is false. There absolutely are people that fall back on older tools when fancy tools fail. You will find such people in the military, in emergency services, in agriculture, generally in areas where getting the job done matters. Perhaps you're unfamiliar. They other week I finished putting holes in fence posts with a bit and brace as there was no fuel for the generator to run corded electric drills and the rechargable batteries were dead. Ukrainians, and others, need to fall back on no GPS available strategies and have done so for a few years now. etc.
    - thijson 7mo ago
      In the 80's the Americans thought that the Russians were backwards to be still using vacuum tubes in their military vehicles. Later they found out that they were being used because they are more tolerant to EMP from a nuclear blast.
    - Kon5ole 7mo ago
      >This is false. There absolutely are people that fall back on older tools when fancy tools fail. >They other week I finished putting holes in fence posts with a bit and brace as there was no fuel for the generator to run corded electric drills and the rechargable batteries were dead. It depends on the task though. If you are in a similar scenario as with your fence posts and want to edit computer programs, you can't. (Not even with xkcd's magnetic needle and a steady hand). ;-) As technology marches on it seems inevitable that we will get increasingly large and frequent knowledge gaps. Otherwise progress would stop - we need the giant shoulders to stand on. How many people in the world can recreate a ASML lithography machine vs how many people are surviving by doing something that requires that machine to exist?
      - defrost 7mo ago
        > If you are in a similar scenario as with your fence posts and want to edit computer programs, you can't. Solar panels charging old thinkpads suddenly doesn't work, or are we reliant upon software that requires cloud services to function in your scenario? > How many people in the world can recreate a ASML lithography machine .. Some .. are you advocating the answer should be none, in your future?
  - nathan_compton 7mo ago
    People say this in a very large number of other contexts. Mathematica has been able to do many integrals for decades and yet we still make students learn all the tricks to integrate by hand. This pattern is very common.
    - FrojoS 7mo ago
      Yes. But to be fair to your specific point, symbolic solving of integrals used to be a huge skill in the engineering education. Nowadays, it is not a focus anymore, because numerical solutions are either sufficiently accurate or, more importantly, the only feasible approach anyway.
      - nathan_compton 7mo ago
        There is much more to life than engineering.
        FrojoS 7mo ago
        Sorry, I should have quoted properly in my reply. My first sentence ("Yes.") was in general agreement with you, the second sentence was specifically about > Mathematica has been able to do many integrals for decades and yet we still make students learn all the tricks to integrate by hand But maybe, integrating by hand is still as big as ever in other parts of academia. Or were you thinking about high school? I'm fairly sure, that symbolic solving of integrals is treated as less important in education these days, than it was before digital computers, but I could be wrong. Mathematica's symbolic solve sure is very useful, but numeric solutions are what really makes the art of finding integrals much less relevant.
        nathan_compton 7mo ago
        I studied physics and mathematics and finding analytic solutions to problems is still useful and enlightening.
  - rglullis 7mo ago
    > so they can produce other, more useful, results But to even *know* what is more useful, it is crucial to have walked the walk. Otherwise we will all end up with a bunch of people trying to reinvent the wheel, over and over again, like JavaScript "developers" who keep reinventing frameworks every six months. > which nobody would buy for any other tool I don't know about you, but I wasn't allowed to use calculators in my calculus classes precisely to learn the concepts properly. "Calculators are for those who know how to do it by hand" was something I heard a lot from my professors.
    - thepasch 7mo ago
      > But to even know what is more useful, it is crucial to have walked the walk. I feel like people tend to forget that among the many things LLMs can do these days, “using a search engine” is among them. In fact, they use them better than the majority of people do! The conversation people think they’re having here and the conversation that actually needs to be had are two entirely different conversations. > I don’t know about you, but I wasn’t allowed to use calculators in my calculus classes precisely to learn the concepts properly. “Calculators are for those who know how to do it by hand” was something I heard a lot from my professors. Suppose I never learned how to derive a function. I don’t even know what a function is. I have no idea how to do make one, write one, or what it even does. So I start gathering knowledge: - A function is some math that allows you to draw a picture of how a number develops if you do that math on it. - A derivative is a function that you feed a function and a number into, and then it tells you something about what that function is doing to that number at that number. - “What it’s doing” specifically means not the result of the math for that particular number, but the results for the immediate other numbers behind and in front of it. - This can tell us about how the function works. Now I go tell ClaudeGPTimini “hey, can you derive f(x) at 5 so that we can figure out where it came from and where it goes from there?”, and it gives me a result. I’ve now ostensibly understood what a derivative does and what it’s used for, yet I have zero idea how to mathematically do it. Does that make any results I gain from this intuitive understanding any less valuable? What I’ll give you is this: if I knew exactly how the math worked, then it would be far easier for me to instantly spot any errors ClaudeGPTimini produced. And the understanding of functions and derivatives outlined above may be simplistic in some places (intentionally so), in ways that may break it in certain edge cases. But that only matters if I take its output at face value. If I get a general understanding of something and run a test with it, I’ll generally have some sort of hypothesis of what kind of result I’m expecting, given that my understanding is correct. If I know that a lot of unknown unknowns exist around a thing I’m working with, then I also know that unexpected results, as well as expected ones, require more thorough verification. Science is what happens when you expect something, test something, and get a result - expected OR unexpected - and then systematically rule out that anything other than the thing you’re testing has had an effect on that result. This is not a problem with LLMs. It’s a thing we should’ve started teaching in schools decades ago: how to understand that there are things you don’t understand. In my view, the vast majority of problems plaguing us as a species lies in this fundamental thing that far too many people are just never taught the concept of.
pards 7mo ago
> Take away the agent, and Bob is still a first-year student who hasn't started yet. The year happened around him but not inside him. He shipped a product, but he didn't learn a trade. We're minting an entire generation of people completely dependent on VC funding. What happens if/when the AI companies fail to find a path to profitability and the VC funding dries up?
- stavros 7mo ago
  Do you think that'll take a generation to happen?
  - rafterydj 7mo ago
    ChatGPT 3.5 came out coming on 4 years ago now. I don't think a human generation (~20-30 years) needs to be the benchmark here, but new juniors in the industry for a handful of years can be said to be a whole "generation". That how I was reading OP.
- Paradigma11 7mo ago
  What will happen is pretty obvious. Those companies will either be classified as too important to fail and get government support or go bankrupt and will be bought for pennies on the dollar. For the customers nothing much will change since tokens are getting cheaper every year and the business is already pretty profitable. Progress will slow down massively till local open weight models catch up to pre-crash SotA and go on from there.
  - pards 7mo ago
    > the business is already pretty profitable As of March 2026, OpenAI generates annual revenue exceeding $12 billion. However, the costs of running ChatGPT are around $17 billion a year. Source: https://searchlab.nl/en/statistics/chatgpt-statistics-2026 https://searchlab.nl/en/statistics/chatgpt-statistics-2026
    - ipaddr 7mo ago
djaro 7mo ago
The problem is that LLMs stop working after a certain point of complexity or specificity, which is very obvious once you try to use it in a field you have deep understanding of. At this point, your own skills should be able to carry you forward, but if you've been using an LLM to do things for you since the start, you won't have the necessary skills. Once they have to solve a novel problem that was not already solved for all intentes and purposes, Alice will be able to apply her skillset to that, whereas Bob will just run into a wall when the LLM starts producing garbage. It seems to me that "high-skill human" > "LLM" > "low-skill human", the trap is that people with low levels of skills will see a fast improvement of their output, at the hidden cost of that slow build-up of skills that has a way higher ceiling.
- stavros 7mo ago
  Then test Bob on what you actually want him to produce, ie novel problems, instead of trivial things that won't tell you how good he is. Why is it a problem of the LLM if your test is unrelated to the performance you want?
  - troupo 7mo ago
    How can Bob produce novel things when he lacks the skills to do even trivial things? I didn't get to be a senior engineer by immediately being able to solve novel problems. I can now solve novel problems because I spent untold hours solving trivial ones.
    - stavros 7mo ago
      Because trivial things aren't a prerequisite for novel things, as any theoretical mathematician who can't do long division will tell you.
      - troupo 7mo ago
        Ah yes. The famous theoretical mathematicians who immediately started on novel problems in theoretical mathematics without first learning and understanding a huge number of trivial things like how division works to begin with, what fractions are, what equations are and how they are solved etc. Edit: let's look at a paper like Some Linear Transformations on Symmetric Functions Arising From a Formula of Thiel and Williams https://ecajournal.haifa.ac.il/Volume2023/ECA2023_S2A24.pdf https://ecajournal.haifa.ac.il/Volume2023/ECA2023_S2A24.pdf and try and guess how many of trivial things were completely unneeded to write a paper like this.
nandomrumber 7mo ago
> why are we bothering with it at all? Because we largely want people who have committed to tens of thousands of dollars of debt to feel sufficiently warm and fuzzy enough to promote the experience so that the business model doesn’t collapse. It’s difficult to think anyone would end up truly regretting doing a course in astrophysics, or any of the liberal arts and sciences if they have a modicum of passion, but it’s very believable that a majority of them won’t go on to have a career in it, whatever it is, directly. They’re probably more likely to gain employment on their data science skills, or whether core competencies they honed, or just the fact that they’ve proven they can learn highly abstract concepts, or whatever their field generalises to. Most of the jobs are in not-highly-specific academic-outcome.
- imtringued 7mo ago
  Even if you land a job in your field, you will encounter that academia is backwards vs industry in some aspects and decades ahead of what is adopted in the industry in other aspects to the point where both of these mean that you won't make much use of the skills you learned in university.
nathan_compton 7mo ago
Is that what "academia" wants? Last I checked "academia" is not a dude I can call and ask for an opinion or definition of what it was interested in. I will make an explicit, plausible, counterpoint: academia wants to produce understanding. This is, more or less, by definition, not possible with an AI directly (obviously AIs can be useful in the process). Take GR as an example. The vast majority of the dynamical character of the theory is inaccessible to human beings. We study it because we wanted to understand it, and only secondarily because we had a concrete "result" we were trying to "achieve." A person who cares only about results and not about understanding is barely a person, in my opinion.
mzhaase 7mo ago
Why should we only do things that produce some sort of value? Do we really want to reduce all of human existence to increasing profits?
- stavros 7mo ago
  You said "value" and "profit". I said "useful".
- nemo44x 7mo ago
  What’s a better method for determining how to utilize and distribute resources? To determine where energy should be used and where it should be moved from?
  - sgarland 7mo ago
    Some things are just enjoyable. I get no real utility from photography - it’s not my career, it’s not a side gig, and I’m not giving prints out as gifts. Most of the shots never get printed at all. I do it because I enjoy the act itself, of knowing how to make an image frozen in time look a particular way by tweaking parameters on the camera, and then seeing the result. I furthermore enjoy the fact that I could achieve the same result on a dumb film camera, because I spent time learning the fundamentals.
sega_sai 7mo ago
You missed the argument. When we are talking about faculty, yes their result is the only thing that matters, so if it was produced quicker with a LLM, that's great. But when we are talking about the student, there is a drastic difference in the student in the with LLM vs without LLM cases. In the latter they have much better understanding. And that matters in the system when we are educating future physicists.
selimthegrim 7mo ago
Completely missed the point of the blog post which was that the point was producing the scientist not the result
gedy 7mo ago
We aren't talking pocket calculators here (I see the irony of phone app in pocket), LLMs are hugely expensive things made and controlled behind costly commercial subscriptions. And likely in the middle of a huge investment bubble and stability is uncertain. So we all need to be careful about "gee we don't need that skill or person anymore", etc.
- danielbln 7mo ago
  Open weight models that run under your desk are not frontier model level, but they are getting closer. Improvements in agentic post training and things like TurboQuant mean that even if all frontier labs pull the plug tomorrow, we will still have agents to work with.
  - gedy 7mo ago
    I'm definitely looking forward to that, as I really want people to control their own tools.
  - zozbot234 7mo ago
    TurboQuant is not a step change, it's more of a smaller incremental improvement to KV quantization, and possibly (unsure) to quantization more generally. I'm actually more positive about SSD weights offload, which opens up very large local models for slow inference (good enough for slow chat) to virtually any hardware or amount of RAM.
asHg19237 7mo ago
The arguments of the LLM psychosis afflicted get more and more desperate. Astrophysics is about understanding and thinking, this comment paints it as result oriented (whatever that means). The industrialization of academia hasn't even produced more results, it has produced more meaningless papers. Just like LLMs produce the 10.000th note taking app, which for the LLM psychosis afflicted is apparently enough.
- deleted 7mo ago
  [deleted]
- nothinkjustai 7mo ago
  [flagged]
dsqrt 7mo ago
The goal of academic research is to create understanding, not papers. If we outsource all research to LLMs, then we are only producing the latter.
dwa3592 7mo ago
Hard sciences play this crucial and often unseen role in our society : they help train humans to develop critical thinking. Not everyone with PhD in Astrophysics ends up doing Astrophysics in life; it's a discipline, or a training regime for our minds. After that PhD; the result is a human being who can tackle hard problems. We have many other such disciplines (basically any PhD in hard sciences) which produces this outcome.
cmiles74 7mo ago
Until the LLM is wrong and Bob passes the erroneous result off as accurate, reliable and vetted by a knowledgeable person. At that point Bob is not producing a useful result. Then it becomes a trap other people might get caught in, wasting valuable time and energy.
deleted 7mo ago
[deleted]

by stavros 7mo ago

hirako2000 7mo ago

I was reading in the article that what matters is the process that leads to the (typically useless) result, what the people get out of it. Once I realized that this white on black contrast was hurting my eyes, I decided to stop as I didn't want to see stripes for too long when looking away. Some activity has outcomes that aren't strictly in the results.

stavros 7mo ago
Yeah, it was saying that what matters is the process of training people to be good scientists, so they can produce other, more useful, results. That's literally what training is, everywhere. This argument boils down to "don't use tools because you'll forget how to do things the hard way", which nobody would buy for any other tool, but with LLMs we seem to have forgotten that line of reasoning entirely.
- hirako2000 7mo ago
  There is an argument to make that tools that speed up a process whilst keeping acuity intact are legitimate. LLMs, the way they typically get used, are solely to save time by handing over nearly the entire process. In that sense acuity can't remain intact, even less so improving over time.
  - stavros 7mo ago
    So?
    - hirako2000 7mo ago
      You previous comment reads as if LLMs get some unjustified different treatment. Do you agree the different treatment is justified ? (Many do not). Or are you asking , so what if acuity is diminished so long as an LLM does the job equally well?
- defrost 7mo ago
  > This argument boils down to "don't use tools because you'll forget how to do things the hard way", which nobody would buy for any other tool, This is false. There absolutely are people that fall back on older tools when fancy tools fail. You will find such people in the military, in emergency services, in agriculture, generally in areas where getting the job done matters. Perhaps you're unfamiliar. They other week I finished putting holes in fence posts with a bit and brace as there was no fuel for the generator to run corded electric drills and the rechargable batteries were dead. Ukrainians, and others, need to fall back on no GPS available strategies and have done so for a few years now. etc.
  - thijson 7mo ago
    In the 80's the Americans thought that the Russians were backwards to be still using vacuum tubes in their military vehicles. Later they found out that they were being used because they are more tolerant to EMP from a nuclear blast.
  - Kon5ole 7mo ago
    >This is false. There absolutely are people that fall back on older tools when fancy tools fail. >They other week I finished putting holes in fence posts with a bit and brace as there was no fuel for the generator to run corded electric drills and the rechargable batteries were dead. It depends on the task though. If you are in a similar scenario as with your fence posts and want to edit computer programs, you can't. (Not even with xkcd's magnetic needle and a steady hand). ;-) As technology marches on it seems inevitable that we will get increasingly large and frequent knowledge gaps. Otherwise progress would stop - we need the giant shoulders to stand on. How many people in the world can recreate a ASML lithography machine vs how many people are surviving by doing something that requires that machine to exist?
    - defrost 7mo ago
      > If you are in a similar scenario as with your fence posts and want to edit computer programs, you can't. Solar panels charging old thinkpads suddenly doesn't work, or are we reliant upon software that requires cloud services to function in your scenario? > How many people in the world can recreate a ASML lithography machine .. Some .. are you advocating the answer should be none, in your future?
- nathan_compton 7mo ago
  People say this in a very large number of other contexts. Mathematica has been able to do many integrals for decades and yet we still make students learn all the tricks to integrate by hand. This pattern is very common.
  - FrojoS 7mo ago
    Yes. But to be fair to your specific point, symbolic solving of integrals used to be a huge skill in the engineering education. Nowadays, it is not a focus anymore, because numerical solutions are either sufficiently accurate or, more importantly, the only feasible approach anyway.
    - nathan_compton 7mo ago
      There is much more to life than engineering.
      - FrojoS 7mo ago
        Sorry, I should have quoted properly in my reply. My first sentence ("Yes.") was in general agreement with you, the second sentence was specifically about > Mathematica has been able to do many integrals for decades and yet we still make students learn all the tricks to integrate by hand But maybe, integrating by hand is still as big as ever in other parts of academia. Or were you thinking about high school? I'm fairly sure, that symbolic solving of integrals is treated as less important in education these days, than it was before digital computers, but I could be wrong. Mathematica's symbolic solve sure is very useful, but numeric solutions are what really makes the art of finding integrals much less relevant.
        nathan_compton 7mo ago
        I studied physics and mathematics and finding analytic solutions to problems is still useful and enlightening.
- rglullis 7mo ago
  > so they can produce other, more useful, results But to even *know* what is more useful, it is crucial to have walked the walk. Otherwise we will all end up with a bunch of people trying to reinvent the wheel, over and over again, like JavaScript "developers" who keep reinventing frameworks every six months. > which nobody would buy for any other tool I don't know about you, but I wasn't allowed to use calculators in my calculus classes precisely to learn the concepts properly. "Calculators are for those who know how to do it by hand" was something I heard a lot from my professors.
  - thepasch 7mo ago
    > But to even know what is more useful, it is crucial to have walked the walk. I feel like people tend to forget that among the many things LLMs can do these days, “using a search engine” is among them. In fact, they use them better than the majority of people do! The conversation people think they’re having here and the conversation that actually needs to be had are two entirely different conversations. > I don’t know about you, but I wasn’t allowed to use calculators in my calculus classes precisely to learn the concepts properly. “Calculators are for those who know how to do it by hand” was something I heard a lot from my professors. Suppose I never learned how to derive a function. I don’t even know what a function is. I have no idea how to do make one, write one, or what it even does. So I start gathering knowledge: - A function is some math that allows you to draw a picture of how a number develops if you do that math on it. - A derivative is a function that you feed a function and a number into, and then it tells you something about what that function is doing to that number at that number. - “What it’s doing” specifically means not the result of the math for that particular number, but the results for the immediate other numbers behind and in front of it. - This can tell us about how the function works. Now I go tell ClaudeGPTimini “hey, can you derive f(x) at 5 so that we can figure out where it came from and where it goes from there?”, and it gives me a result. I’ve now ostensibly understood what a derivative does and what it’s used for, yet I have zero idea how to mathematically do it. Does that make any results I gain from this intuitive understanding any less valuable? What I’ll give you is this: if I knew exactly how the math worked, then it would be far easier for me to instantly spot any errors ClaudeGPTimini produced. And the understanding of functions and derivatives outlined above may be simplistic in some places (intentionally so), in ways that may break it in certain edge cases. But that only matters if I take its output at face value. If I get a general understanding of something and run a test with it, I’ll generally have some sort of hypothesis of what kind of result I’m expecting, given that my understanding is correct. If I know that a lot of unknown unknowns exist around a thing I’m working with, then I also know that unexpected results, as well as expected ones, require more thorough verification. Science is what happens when you expect something, test something, and get a result - expected OR unexpected - and then systematically rule out that anything other than the thing you’re testing has had an effect on that result. This is not a problem with LLMs. It’s a thing we should’ve started teaching in schools decades ago: how to understand that there are things you don’t understand. In my view, the vast majority of problems plaguing us as a species lies in this fundamental thing that far too many people are just never taught the concept of.

pards 7mo ago

> Take away the agent, and Bob is still a first-year student who hasn't started yet. The year happened around him but not inside him. He shipped a product, but he didn't learn a trade. We're minting an entire generation of people completely dependent on VC funding. What happens if/when the AI companies fail to find a path to profitability and the VC funding dries up?

stavros 7mo ago
Do you think that'll take a generation to happen?
- rafterydj 7mo ago
  ChatGPT 3.5 came out coming on 4 years ago now. I don't think a human generation (~20-30 years) needs to be the benchmark here, but new juniors in the industry for a handful of years can be said to be a whole "generation". That how I was reading OP.
Paradigma11 7mo ago
What will happen is pretty obvious. Those companies will either be classified as too important to fail and get government support or go bankrupt and will be bought for pennies on the dollar. For the customers nothing much will change since tokens are getting cheaper every year and the business is already pretty profitable. Progress will slow down massively till local open weight models catch up to pre-crash SotA and go on from there.
- pards 7mo ago
  > the business is already pretty profitable As of March 2026, OpenAI generates annual revenue exceeding $12 billion. However, the costs of running ChatGPT are around $17 billion a year. Source: https://searchlab.nl/en/statistics/chatgpt-statistics-2026 https://searchlab.nl/en/statistics/chatgpt-statistics-2026
  - ipaddr 7mo ago

djaro 7mo ago

The problem is that LLMs stop working after a certain point of complexity or specificity, which is very obvious once you try to use it in a field you have deep understanding of. At this point, your own skills should be able to carry you forward, but if you've been using an LLM to do things for you since the start, you won't have the necessary skills. Once they have to solve a novel problem that was not already solved for all intentes and purposes, Alice will be able to apply her skillset to that, whereas Bob will just run into a wall when the LLM starts producing garbage. It seems to me that "high-skill human" > "LLM" > "low-skill human", the trap is that people with low levels of skills will see a fast improvement of their output, at the hidden cost of that slow build-up of skills that has a way higher ceiling.

stavros 7mo ago
Then test Bob on what you actually want him to produce, ie novel problems, instead of trivial things that won't tell you how good he is. Why is it a problem of the LLM if your test is unrelated to the performance you want?
- troupo 7mo ago
  How can Bob produce novel things when he lacks the skills to do even trivial things? I didn't get to be a senior engineer by immediately being able to solve novel problems. I can now solve novel problems because I spent untold hours solving trivial ones.
  - stavros 7mo ago
    Because trivial things aren't a prerequisite for novel things, as any theoretical mathematician who can't do long division will tell you.
    - troupo 7mo ago
      Ah yes. The famous theoretical mathematicians who immediately started on novel problems in theoretical mathematics without first learning and understanding a huge number of trivial things like how division works to begin with, what fractions are, what equations are and how they are solved etc. Edit: let's look at a paper like Some Linear Transformations on Symmetric Functions Arising From a Formula of Thiel and Williams https://ecajournal.haifa.ac.il/Volume2023/ECA2023_S2A24.pdf https://ecajournal.haifa.ac.il/Volume2023/ECA2023_S2A24.pdf and try and guess how many of trivial things were completely unneeded to write a paper like this.

nandomrumber 7mo ago

> why are we bothering with it at all? Because we largely want people who have committed to tens of thousands of dollars of debt to feel sufficiently warm and fuzzy enough to promote the experience so that the business model doesn’t collapse. It’s difficult to think anyone would end up truly regretting doing a course in astrophysics, or any of the liberal arts and sciences if they have a modicum of passion, but it’s very believable that a majority of them won’t go on to have a career in it, whatever it is, directly. They’re probably more likely to gain employment on their data science skills, or whether core competencies they honed, or just the fact that they’ve proven they can learn highly abstract concepts, or whatever their field generalises to. Most of the jobs are in not-highly-specific academic-outcome.

imtringued 7mo ago
Even if you land a job in your field, you will encounter that academia is backwards vs industry in some aspects and decades ahead of what is adopted in the industry in other aspects to the point where both of these mean that you won't make much use of the skills you learned in university.

nathan_compton 7mo ago

Is that what "academia" wants? Last I checked "academia" is not a dude I can call and ask for an opinion or definition of what it was interested in. I will make an explicit, plausible, counterpoint: academia wants to produce understanding. This is, more or less, by definition, not possible with an AI directly (obviously AIs can be useful in the process). Take GR as an example. The vast majority of the dynamical character of the theory is inaccessible to human beings. We study it because we wanted to understand it, and only secondarily because we had a concrete "result" we were trying to "achieve." A person who cares only about results and not about understanding is barely a person, in my opinion.

mzhaase 7mo ago

Why should we only do things that produce some sort of value? Do we really want to reduce all of human existence to increasing profits?

stavros 7mo ago
You said "value" and "profit". I said "useful".
nemo44x 7mo ago
What’s a better method for determining how to utilize and distribute resources? To determine where energy should be used and where it should be moved from?
- sgarland 7mo ago
  Some things are just enjoyable. I get no real utility from photography - it’s not my career, it’s not a side gig, and I’m not giving prints out as gifts. Most of the shots never get printed at all. I do it because I enjoy the act itself, of knowing how to make an image frozen in time look a particular way by tweaking parameters on the camera, and then seeing the result. I furthermore enjoy the fact that I could achieve the same result on a dumb film camera, because I spent time learning the fundamentals.

sega_sai 7mo ago

You missed the argument. When we are talking about faculty, yes their result is the only thing that matters, so if it was produced quicker with a LLM, that's great. But when we are talking about the student, there is a drastic difference in the student in the with LLM vs without LLM cases. In the latter they have much better understanding. And that matters in the system when we are educating future physicists.

selimthegrim 7mo ago

Completely missed the point of the blog post which was that the point was producing the scientist not the result

gedy 7mo ago

We aren't talking pocket calculators here (I see the irony of phone app in pocket), LLMs are hugely expensive things made and controlled behind costly commercial subscriptions. And likely in the middle of a huge investment bubble and stability is uncertain. So we all need to be careful about "gee we don't need that skill or person anymore", etc.

danielbln 7mo ago
Open weight models that run under your desk are not frontier model level, but they are getting closer. Improvements in agentic post training and things like TurboQuant mean that even if all frontier labs pull the plug tomorrow, we will still have agents to work with.
- gedy 7mo ago
  I'm definitely looking forward to that, as I really want people to control their own tools.
- zozbot234 7mo ago
  TurboQuant is not a step change, it's more of a smaller incremental improvement to KV quantization, and possibly (unsure) to quantization more generally. I'm actually more positive about SSD weights offload, which opens up very large local models for slow inference (good enough for slow chat) to virtually any hardware or amount of RAM.

asHg19237 7mo ago

The arguments of the LLM psychosis afflicted get more and more desperate. Astrophysics is about understanding and thinking, this comment paints it as result oriented (whatever that means). The industrialization of academia hasn't even produced more results, it has produced more meaningless papers. Just like LLMs produce the 10.000th note taking app, which for the LLM psychosis afflicted is apparently enough.

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nothinkjustai 7mo ago
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dsqrt 7mo ago

The goal of academic research is to create understanding, not papers. If we outsource all research to LLMs, then we are only producing the latter.

dwa3592 7mo ago

Hard sciences play this crucial and often unseen role in our society : they help train humans to develop critical thinking. Not everyone with PhD in Astrophysics ends up doing Astrophysics in life; it's a discipline, or a training regime for our minds. After that PhD; the result is a human being who can tackle hard problems. We have many other such disciplines (basically any PhD in hard sciences) which produces this outcome.

cmiles74 7mo ago

Until the LLM is wrong and Bob passes the erroneous result off as accurate, reliable and vetted by a knowledgeable person. At that point Bob is not producing a useful result. Then it becomes a trap other people might get caught in, wasting valuable time and energy.

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