Comment by stavros
11 hours 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?
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.
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?
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.
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What people forget about programming is it is a notation for formal logic, one that can be executed by a machine. That formal logic is for solving a problem in the real world.
While we have a lot of abstractions that solve some subproblems, there still need to connect those solutions to solve the main problem. And there’s a point where this combination becomes its own technical challenge. And the skill that is needed is the same one as solving simpler problems with common algorithms.
This whole argument can be made for why every programmer needs to deeply understand assembly language and computer hardware.
At a certain point, higher level languages stop working. Performance, low level control of clocks and interrupts, etc.
I’m old enough dropping into assembly to be clever with the 8259 interrupt controller really was required. Programmers today? The vast majority don’t really understand how any of that works.
And honestly I still believe that hardware-up understanding is valuable. But is it necessary? Is it the most important thing for most programmers today?
When I step back this just reads like the same old “kids these days have it so easy, I had to walk to school uphill through the snow” thing.
Teaching how computer hardware works is pretty smart. There is no need to do it in depth though.
Writing assembly is probably completely irrelevant. You should still know how programming language concepts map to basic operations though. Simple things like strict field offsets, calling conventions, function calls, dynamic linking, etc.
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> 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.
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.
> 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?
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.
> 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
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Do you think that'll take a generation to happen?
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.
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.
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.
> 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.
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> 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.
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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.
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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.
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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.
this user is also a massive AI booster on this platform
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?
You said "value" and "profit". I said "useful".
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?
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.
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.
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.
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.
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.
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.
Completely missed the point of the blog post which was that the point was producing the scientist not the result
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.
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.
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.
I'm definitely looking forward to that, as I really want people to control their own tools.