Academia always been full of narcissists chasing status with flashy papers and halfbaked brilliant ideas (70%? maybe) LLMs just made the whole game trivial and now literally anyone can slap together something that sounds deep without ever doing the actual grind.
LLMs just speeding up the process, just a matter of time how quickly this shit is exposing what the entire system has been all along
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 thing is, agents aren’t going away. So if Bob can do things with agents, he can do things.
I mourn the loss of working on intellectually stimulating programming problems, but that’s a part of my job that’s fading. I need to decide if the remaining work - understanding requirements, managing teams, what have you - is still enjoyable enough to continue.
To be honest, I’m looking at leaving software because the job has turned into a different sort of thing than what I signed up for.
So I think this article is partly right, Bob is not learning those skills which we used to require. But I think the market is going to stop valuing those skills, so it’s not really a _problem_, except for Bob’s own intellectual loss.
Why not? Once the true cost of token generation is passed on to the end user and costs go up by 10 or 100 times, and once the honeymoon delusion of "oh wow I can just prompt the AI to write code" fades, there's a big question as to if what's left is worth it. If it isn't, agents will most certainly go away and all of this will be consigned to the "failed hype" bin along with cryptocurrency and "metaverse".
Agents may not go away, but they are going to fall off significantly once people wake up to how bad they are at making software. It's like in the early 00s when business execs were stoked about the idea that they could cut costs by hiring bottom rate Indian contractors: it turned out to be a disaster for quality, and eventually there was a shift back towards having staff in the US. The same thing is going to happen with LLMs.
> The thing is, agents aren’t going away. So if Bob can do things with agents, he can do things.
But Bob can't do things with agents.
He can get a project from someone else and ask the agents to do that project. Then give the output of the agents back to that someone else, and that someone else reviews it, says why it's wrong, and sends it back. Bob feeds the review to the agents, gets something back, then gives the output back to that someone else who reviews it, etc. So,
1) The loop requires his advisor to know how to go about doing the thing.
2) Bob is absolutely unnecessary and should be discarded.
3) Alice will eventually be qualified to be an advisor.
edit: And the crisis that the article is really pointing out is that when the advisor is using the LLM (while Bob is driving an Uber), and his productivity goes way up because he's only handling the things only he can handle, what about Alice?
Let's say that pre-AI the advisor could either do the job in 2 months or assign it to Alice who could do it in 12 with a week of the advisor's supervision/review. Now, with the LLM, the advisor can do the job in 2 weeks without Alice. Before, Alice made barely any money and had no health insurance. After, Alice is also driving Uber.
Now the advisor has a heart attack and now the thing just can't be done. Also, Ubers become pretty much self-driving, so Bob and Alice are not only ignorant, but unemployed. They can't even afford to take an Uber.
See also The Profession by Isaac Asimov [0] and his small story The Feeling of Power [1]. Both are social dramas about societies that went far down the path of ignorance.
These themes have been going around and around for a while.
One thing I've seen asserted:
> What he demonstrated is that Claude can, with detailed supervision, produce a technically rigorous physics paper. What he actually demonstrated, if you read carefully, is that the supervision is the physics. Claude produced a complete first draft in three days... The equations seemed right... Then Schwartz read it, and it was wrong... It faked results. It invented coefficients...
The argument that AI output isn't good enough is somewhat in opposition to the idea that we need to worry about folks losing or never gaining skills/knowledge.
There are ways around this:
"It's only evident to experts and there won't be experts if students don't learn"
But at the end of the day, in the long run, the ideas and results that last are the ones that work. By work, I mean ones that strictly improve outcomes (all outputs are the same with at least one better). This is because, with respect to technological progress, humans are pretty well modeled as just a slightly better than random search for optimal decisioning where we tend to not go backwards permanently.
All that to say that, at times, AI is one of the many things that we've come up with that is wrong. At times, it's right. If it helps on aggregate, we'll probably adopt it permanently, until we find something strictly better.
I think this article is largely, or at least directionally, correct.
I'd draw a comparison to high-level languages and language frameworks. Yes, 99% of the time, if I'm building a web frontend, I can live in React world and not think about anything that is going on under the hood. But, there is 1% of the time where something goes wrong, and I need to understand what is happening underneath the abstraction.
Similarly, I now produce 99% of my code using an agent. However, I still feel the need to thoroughly understand the code, in order to be able to catch the 1% of cases where it introduces a bug or does something suboptimally.
It's possible that in future, LLMs will get _so_ good that I don't feel the need to do this, in the same way that I don't think about the transistors my code is ultimately running on. When doing straightforward coding tasks, I think they're already there, but I think they aren't quite at that point when it comes to large distributed systems.
As straw men go, this is an attractive one, but...
When I was fresh out of undergrad, joining a new lab, I followed a similar arc. I made mistakes, I took the wrong lessons from grad student code that came before mine, I used the wrong plotting libraries, I hijacked python's module import logic to embed a new language in its bytecode. These were all avoidable mistakes and I didn't learn anything except that I should have asked for help. Others in my lab, who were less self-reliant, asked for and got help avoiding the kinds of mistakes I confidently made.
With 15 more years of experience, I can see in hindsight that I should have asked for help more frequently because I spent more time learning what not to do than learning the right things.
If I had Claude Code, would I have made the same mistakes? Absolutely not! Would I have asked it to summarize research papers for me and to essentially think for me? Absolutely not!
My mother, an English professor, levies similar accusations about the students of today, and how they let models think for them. It's genuinely concerning, of course, but I can't help but think that this phenomenon occurs because learning institutions have not adjusted to the new technology.
If the goal is to produce scientists, PIs are going to need to stop complaining and figure out how to produce scientists who learn the skills that I did even when LLMs are available. Frankly I don't see how LLMs are different from asking other lab members for help, except that LLMs have infinite patience and don't have their own research that needs doing.
> Frank Herbert (yeah, I know I'm a nerd), in God Emperor of Dune, has a character observe: "What do such machines really do? They increase the number of things we can do without thinking. Things we do without thinking; there's the real danger." Herbert was writing science fiction. I'm writing about my office. The distance between those two things has gotten uncomfortably small.
The author is a bit naive here:
1. Society only progresses when people are specialised and can delegate their thinking
2. Specialisation has been happening for millenia. Agriculture allowed people to become specialised due to abundance of food
3. We accept delegation of thinking in every part of life. A manager delegates thinking to their subordinates. I delegate some thinking to my accountant
4. People will eventually get the hang of using AI to do the optimum amount of delegation such that they still retain what is necessary and delegate what is not necessary. People who don't do this optimally will get outcompeted
The author just focuses on some local problems like skill atrophy but does not see the larger picture and how specific pattern has been repeating a lot in humanity's history.
The post was written by Claude. The semicolon in the Dune quote proves it: the only possible reason for that to be there is if the 'author' regex replaced all of the em dashes to obfuscate the source. https://boxobarks.leaflet.pub/3misaejnoqs2k
The exciting and interesting to me is that we'll probably need to engage "chaos engineering" principles, and encode intentional fallibility into these agents to keep us (and them) as good collaborators, and specifically on our toes, to help all minds stay alert and plastic
If that comes to pass, we'll be rediscovering the same principles that biological evolution stumbled upon: the benefits of the imperfect "branch" or "successive limited comparison" approach of agentic behaviour, which perhaps favours heuristics (that clearly sometimes fail), interaction between imperfect collaborators with non-overlapping biases, etc etc
> Lindblom’s paper identifies two patterns of agentic behavior, “root” (or rational-comprehensive) and “branch” (or successive limited comparisons), and argues that in complicated messy circumstances requiring coordinated action at scale, the way actually effective humans operate is the branch method, which looks like “muddling through” but gradually gets there, where the root method fails entirely.
The flip side I don’t see mentioned very often is that having a product where you know how the code works becomes its own competitive advantage. Better reliability, faster fixes and iteration, deeper and broader capabilities that allow you to be disruptive while everything else is being built towards the mean, etc etc. Maybe we’ve not been in this new age for long enough for that to be reflected in people’s purchasing criteria, but I’m quite looking forward to fending off AI-built competitors with this edge.
Ironically, this article reeks of AI-generated phrases. Lot's of "It's not X, it's Y". eg:
- "The failure mode isn't malice. It's convenience",
- "You haven't saved time. You've forfeited the experience that the time was supposed to give you."
- "But the real threat isn't either of those things. It's quieter, and more boring, and therefore more dangerous. The real threat is a slow, comfortable drift toward not understanding what you're doing. Not a dramatic collapse. Not Skynet. Just a generation of researchers who can produce results but can't produce understanding."
And indeed running it through a few AI text detectors, like Pangram (not perfect, by any means, but a useful approximation), returns high probabilities.
It would have felt more honest if the author had included a disclaimer that it was at least part written with AI, especially given its length and subject matter.
But here it is, on the front page of HackerNews. It produced exactly the result he wanted.
Maybe he won't be able to blog himself out of a wet paper bag tomorrow, but people seem to think he's a great thinker today. Isn't that all that matters?
Those X-Y sentences are like nails on chalkboard to me, but I genuinely wonder why it is so pervasive in LLM arguments. It is like trying very hard to think in binary terms yet failing.
Another threat is that you can find tons of papers pointing out how neural AI still struggles handling simple logical negation. Who cares right, we use tools for symbolics, yada yada. Except what's really the plan? Are we going to attempt parallel formalized representations of every piece of input context just to flag the difference between please DONT delete my files and please DO? This is all super boring though and nothing bad happened lately, so back to perusing latest AGI benchmarks..
The article is well-written and makes cogent points about why we need "centaurs", human/computer hybrids who combine silicon- and carbon-based reasoning.
Interestingly, the text has a number of AI-like writing artifacts, e.g. frequent use of the pattern "The problem isn't X. The problem is Y." Unlike much of the typical slop I see, I read it to the end and found it insightful.
I think that's because the author worked with an AI exactly as he advocates, providing the deep thinking and leaving some of the routine exposition to the bot.
The framing of the essay around learning through "grunt work" is not deep, it's simply that this specific phrase appeared in two of the sources. Anything that looks like insight is plagiarised directly from the sources in some fashion. I've covered in my evidence the pivot phrases where direct summaries of the essay incorrectly appear to transition to the author's own ideas, but there are parts right through the essay that come from the sources. No deep thinking by the prompter required.
"He shipped a product, but he didn't learn a trade." I think is the key quote from this article, and encapsulates the core problem with AI agents in any skill-based field.
I've just started a new role as a senior SWE after 5 months off. I've been using Claude a bit in my time off; it works really well. But now that I've started using it professionally, I keep running into a specific problem: I have nothing to hold onto in my own mind.
How this plays out:
I use Claude to write some moderately complex code and raise a PR. Someone asks me to change something. I look at the review and think, yeah, that makes sense, I missed that and Claude missed that. The code works, but it's not quite right. I'll make some changes.
Except I can't.
For me, it turns out having decisions made for you and fed to you is not the same as making the decisions and moving the code from your brain to your hands yourself. Certainly every decision made was fine: I reviewed Claude's output, got it to ask questions, answered them, and it got everything right. I reviewed its code before I raised the PR. Everything looked fine within the bounds of my knowledge, and this review was simply something I didn't know about.
But I didn't make any of those decisions. And when I have to come back to the code to make updates - perhaps tomorrow - I have nothing to grab onto in my mind. Nothing is in my own mental cache. I know what decisions were made, but I merely checked them, I didn't decide them. I know where the code was written, but I merely verified it, I didn't write it.
And so I suffer an immediate and extreme slow-down, basically re-doing all of Claude's work in my mind to reach a point where I can make manual changes correctly.
But wait, I could just use Claude for this! But for now I don't, because I've seen this before. Just a few moments ago. Using Claude has just made it significantly slower when I need to use my own knowledge and skills.
I'm still figuring out whether this problem is transient (because this is a brand new system that I don't have years of experience with), or whether it will actually be a hard blocker to me using Claude long-term. Assuming I want to be at my new workplace for many years and be successful, it will cost me a lot in time and knowledge to NOT build the castle in the sky myself.
> You do what your supervisor did for you, years ago: you give each of them a well-defined project. Something you know is solvable, because other people have solved adjacent versions of it. Something that would take you, personally, about a month or two. You expect it to take each student about a year ...
Is that how PhD projects are supposed to work? The supervisor is a subject matter expert and comes up with a well-defined achievable project for the student?
Nobody actually understands what they're doing. When you're learning electronics, you first learn about the "lumped element model" which allows you to simplify Maxwell's equations. I think it is a mistake to think that solving problems with a programming language is "knowing how to do things" - at this point, we've already abstracted assembly language -> machine instructions -> logic gates and buses -> transistors and electronic storage -> lumped matter -> quantum mechanics -> ???? - so I simply don't buy the argument that things will suddenly fall apart by abstracting one level higher. The trick is to get this new level of abstraction to work predictably, which admittedly it isn't yet, but look how far it's come in a short couple of years.
This article first says that you give juniors well-defined projects and let them take a long time because the process is the product. Then goes on to lament the fact that they will no longer have to debug Python code, as if debugging python code is the point of it all. The thing that LLMs can't yet do is pick a high-level direction for a novel problem and iterate until the correct solution is reached. They absolutely can and do iterate until a solution is reached, but it's not necessarily correct. Previously, guiding the direction was the job of the professor. Now, in a smaller sense, the grad student needs to be guiding the direction and validating the details, rather than implementing the details with the professor guiding the direction. This is an improvement - everybody levels up.
I also disagree with the premise that the primary product of astrophysics is scientists. Like any advanced science it requires a lot of scientists to make the breakthroughs that trickle down into technology that improves everyday life, but those breakthroughs would be impossible otherwise. Gauss discovered the normal distribution while trying to understand the measurement error of his telescope. Without general relativity we would not have GPS or precision timekeeping. It uncovers the rules that will allow us to travel interplanetary. Understanding the composition and behavior of stars informs nuclear physics, reactor design, and solar panel design. The computation systems used by advanced science prototyped many commercial advances in computing (HPC, cluster computing, AI itself).
So not only are we developing the tools to improve our understanding of the universe faster, we're leveling everybody up. Students will take on the role of professors (badly, at first, but are professors good at first? probably not, they need time to learn under the guidance of other faculty). professors will take on the role of directors. Everybody's scope will widen because the tiny details will be handled by AI, but the big picture will still be in the domain of humans.
I like this article and it reads well, but I have to say, that to me it really reads as something written by an LLM. Probably under supervision by a human that knew what it should say.
I don't know if I mind.
Example. This paragraph, to me, has a eerily perfect rhythm. The ending sentence perfectly delivers the twist.
Like, why would you write in perfect prose an argument piece in the science realm?
> Unlike Alice, who spent the year reading papers with a pencil in hand, scribbling notes in the margins, getting confused, re-reading, looking things up, and slowly assembling a working understanding of her corner of the field, Bob has been using an AI agent. When his supervisor sent him a paper to read, Bob asked the agent to summarize it. When he needed to understand a new statistical method, he asked the agent to explain it. When his Python code broke, the agent debugged it. When the agent's fix introduced a new bug, it debugged that too. When it came time to write the paper, the agent wrote it. Bob's weekly updates to his supervisor were indistinguishable from Alice's. The questions were similar. The progress was similar. The trajectory, from the outside, was identical.
134 comments
[ 3.1 ms ] story [ 101 ms ] threadAcademia 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?
I mourn the loss of working on intellectually stimulating programming problems, but that’s a part of my job that’s fading. I need to decide if the remaining work - understanding requirements, managing teams, what have you - is still enjoyable enough to continue.
To be honest, I’m looking at leaving software because the job has turned into a different sort of thing than what I signed up for.
So I think this article is partly right, Bob is not learning those skills which we used to require. But I think the market is going to stop valuing those skills, so it’s not really a _problem_, except for Bob’s own intellectual loss.
I don’t like it, but I’m trying to face up to it.
Also, the premise that it took each of them a year to do the project means Bob was slacking because he probably could've done it in less than a month.
Why not? Once the true cost of token generation is passed on to the end user and costs go up by 10 or 100 times, and once the honeymoon delusion of "oh wow I can just prompt the AI to write code" fades, there's a big question as to if what's left is worth it. If it isn't, agents will most certainly go away and all of this will be consigned to the "failed hype" bin along with cryptocurrency and "metaverse".
Now, you don't do thing and do other things when LLMs get stuck. There is no "given enough time I can do it".
I can't see how somebody would go solving slop bugs (slugs :)) in heavy AI generated codebase.
Hope, I'm wrong but that's somehing I personally encountered. Stay sharp.
But Bob can't do things with agents.
He can get a project from someone else and ask the agents to do that project. Then give the output of the agents back to that someone else, and that someone else reviews it, says why it's wrong, and sends it back. Bob feeds the review to the agents, gets something back, then gives the output back to that someone else who reviews it, etc. So,
1) The loop requires his advisor to know how to go about doing the thing.
2) Bob is absolutely unnecessary and should be discarded.
3) Alice will eventually be qualified to be an advisor.
edit: And the crisis that the article is really pointing out is that when the advisor is using the LLM (while Bob is driving an Uber), and his productivity goes way up because he's only handling the things only he can handle, what about Alice?
Let's say that pre-AI the advisor could either do the job in 2 months or assign it to Alice who could do it in 12 with a week of the advisor's supervision/review. Now, with the LLM, the advisor can do the job in 2 weeks without Alice. Before, Alice made barely any money and had no health insurance. After, Alice is also driving Uber.
Now the advisor has a heart attack and now the thing just can't be done. Also, Ubers become pretty much self-driving, so Bob and Alice are not only ignorant, but unemployed. They can't even afford to take an Uber.
[0] http://employees.oneonta.edu/blechmjb/JBpages/m360/Professio...
[1] https://s3.us-west-1.wasabisys.com/luminist/EB/A/Asimov%20-%...
One thing I've seen asserted:
> What he demonstrated is that Claude can, with detailed supervision, produce a technically rigorous physics paper. What he actually demonstrated, if you read carefully, is that the supervision is the physics. Claude produced a complete first draft in three days... The equations seemed right... Then Schwartz read it, and it was wrong... It faked results. It invented coefficients...
The argument that AI output isn't good enough is somewhat in opposition to the idea that we need to worry about folks losing or never gaining skills/knowledge.
There are ways around this:
"It's only evident to experts and there won't be experts if students don't learn"
But at the end of the day, in the long run, the ideas and results that last are the ones that work. By work, I mean ones that strictly improve outcomes (all outputs are the same with at least one better). This is because, with respect to technological progress, humans are pretty well modeled as just a slightly better than random search for optimal decisioning where we tend to not go backwards permanently.
All that to say that, at times, AI is one of the many things that we've come up with that is wrong. At times, it's right. If it helps on aggregate, we'll probably adopt it permanently, until we find something strictly better.
I'd draw a comparison to high-level languages and language frameworks. Yes, 99% of the time, if I'm building a web frontend, I can live in React world and not think about anything that is going on under the hood. But, there is 1% of the time where something goes wrong, and I need to understand what is happening underneath the abstraction.
Similarly, I now produce 99% of my code using an agent. However, I still feel the need to thoroughly understand the code, in order to be able to catch the 1% of cases where it introduces a bug or does something suboptimally.
It's possible that in future, LLMs will get _so_ good that I don't feel the need to do this, in the same way that I don't think about the transistors my code is ultimately running on. When doing straightforward coding tasks, I think they're already there, but I think they aren't quite at that point when it comes to large distributed systems.
When I was fresh out of undergrad, joining a new lab, I followed a similar arc. I made mistakes, I took the wrong lessons from grad student code that came before mine, I used the wrong plotting libraries, I hijacked python's module import logic to embed a new language in its bytecode. These were all avoidable mistakes and I didn't learn anything except that I should have asked for help. Others in my lab, who were less self-reliant, asked for and got help avoiding the kinds of mistakes I confidently made.
With 15 more years of experience, I can see in hindsight that I should have asked for help more frequently because I spent more time learning what not to do than learning the right things.
If I had Claude Code, would I have made the same mistakes? Absolutely not! Would I have asked it to summarize research papers for me and to essentially think for me? Absolutely not!
My mother, an English professor, levies similar accusations about the students of today, and how they let models think for them. It's genuinely concerning, of course, but I can't help but think that this phenomenon occurs because learning institutions have not adjusted to the new technology.
If the goal is to produce scientists, PIs are going to need to stop complaining and figure out how to produce scientists who learn the skills that I did even when LLMs are available. Frankly I don't see how LLMs are different from asking other lab members for help, except that LLMs have infinite patience and don't have their own research that needs doing.
The author is a bit naive here:
1. Society only progresses when people are specialised and can delegate their thinking
2. Specialisation has been happening for millenia. Agriculture allowed people to become specialised due to abundance of food
3. We accept delegation of thinking in every part of life. A manager delegates thinking to their subordinates. I delegate some thinking to my accountant
4. People will eventually get the hang of using AI to do the optimum amount of delegation such that they still retain what is necessary and delegate what is not necessary. People who don't do this optimally will get outcompeted
The author just focuses on some local problems like skill atrophy but does not see the larger picture and how specific pattern has been repeating a lot in humanity's history.
If that comes to pass, we'll be rediscovering the same principles that biological evolution stumbled upon: the benefits of the imperfect "branch" or "successive limited comparison" approach of agentic behaviour, which perhaps favours heuristics (that clearly sometimes fail), interaction between imperfect collaborators with non-overlapping biases, etc etc
https://contraptions.venkateshrao.com/p/massed-muddler-intel...
> Lindblom’s paper identifies two patterns of agentic behavior, “root” (or rational-comprehensive) and “branch” (or successive limited comparisons), and argues that in complicated messy circumstances requiring coordinated action at scale, the way actually effective humans operate is the branch method, which looks like “muddling through” but gradually gets there, where the root method fails entirely.
And indeed running it through a few AI text detectors, like Pangram (not perfect, by any means, but a useful approximation), returns high probabilities.
It would have felt more honest if the author had included a disclaimer that it was at least part written with AI, especially given its length and subject matter.
Maybe he won't be able to blog himself out of a wet paper bag tomorrow, but people seem to think he's a great thinker today. Isn't that all that matters?
(I tried faking some AI style.)
Interestingly, the text has a number of AI-like writing artifacts, e.g. frequent use of the pattern "The problem isn't X. The problem is Y." Unlike much of the typical slop I see, I read it to the end and found it insightful.
I think that's because the author worked with an AI exactly as he advocates, providing the deep thinking and leaving some of the routine exposition to the bot.
The framing of the essay around learning through "grunt work" is not deep, it's simply that this specific phrase appeared in two of the sources. Anything that looks like insight is plagiarised directly from the sources in some fashion. I've covered in my evidence the pivot phrases where direct summaries of the essay incorrectly appear to transition to the author's own ideas, but there are parts right through the essay that come from the sources. No deep thinking by the prompter required.
How this plays out:
I use Claude to write some moderately complex code and raise a PR. Someone asks me to change something. I look at the review and think, yeah, that makes sense, I missed that and Claude missed that. The code works, but it's not quite right. I'll make some changes.
Except I can't.
For me, it turns out having decisions made for you and fed to you is not the same as making the decisions and moving the code from your brain to your hands yourself. Certainly every decision made was fine: I reviewed Claude's output, got it to ask questions, answered them, and it got everything right. I reviewed its code before I raised the PR. Everything looked fine within the bounds of my knowledge, and this review was simply something I didn't know about.
But I didn't make any of those decisions. And when I have to come back to the code to make updates - perhaps tomorrow - I have nothing to grab onto in my mind. Nothing is in my own mental cache. I know what decisions were made, but I merely checked them, I didn't decide them. I know where the code was written, but I merely verified it, I didn't write it.
And so I suffer an immediate and extreme slow-down, basically re-doing all of Claude's work in my mind to reach a point where I can make manual changes correctly.
But wait, I could just use Claude for this! But for now I don't, because I've seen this before. Just a few moments ago. Using Claude has just made it significantly slower when I need to use my own knowledge and skills.
I'm still figuring out whether this problem is transient (because this is a brand new system that I don't have years of experience with), or whether it will actually be a hard blocker to me using Claude long-term. Assuming I want to be at my new workplace for many years and be successful, it will cost me a lot in time and knowledge to NOT build the castle in the sky myself.
> You do what your supervisor did for you, years ago: you give each of them a well-defined project. Something you know is solvable, because other people have solved adjacent versions of it. Something that would take you, personally, about a month or two. You expect it to take each student about a year ...
Is that how PhD projects are supposed to work? The supervisor is a subject matter expert and comes up with a well-defined achievable project for the student?
- Caddyshack
This article first says that you give juniors well-defined projects and let them take a long time because the process is the product. Then goes on to lament the fact that they will no longer have to debug Python code, as if debugging python code is the point of it all. The thing that LLMs can't yet do is pick a high-level direction for a novel problem and iterate until the correct solution is reached. They absolutely can and do iterate until a solution is reached, but it's not necessarily correct. Previously, guiding the direction was the job of the professor. Now, in a smaller sense, the grad student needs to be guiding the direction and validating the details, rather than implementing the details with the professor guiding the direction. This is an improvement - everybody levels up.
I also disagree with the premise that the primary product of astrophysics is scientists. Like any advanced science it requires a lot of scientists to make the breakthroughs that trickle down into technology that improves everyday life, but those breakthroughs would be impossible otherwise. Gauss discovered the normal distribution while trying to understand the measurement error of his telescope. Without general relativity we would not have GPS or precision timekeeping. It uncovers the rules that will allow us to travel interplanetary. Understanding the composition and behavior of stars informs nuclear physics, reactor design, and solar panel design. The computation systems used by advanced science prototyped many commercial advances in computing (HPC, cluster computing, AI itself).
So not only are we developing the tools to improve our understanding of the universe faster, we're leveling everybody up. Students will take on the role of professors (badly, at first, but are professors good at first? probably not, they need time to learn under the guidance of other faculty). professors will take on the role of directors. Everybody's scope will widen because the tiny details will be handled by AI, but the big picture will still be in the domain of humans.
I don't know if I mind.
Example. This paragraph, to me, has a eerily perfect rhythm. The ending sentence perfectly delivers the twist. Like, why would you write in perfect prose an argument piece in the science realm?
> Unlike Alice, who spent the year reading papers with a pencil in hand, scribbling notes in the margins, getting confused, re-reading, looking things up, and slowly assembling a working understanding of her corner of the field, Bob has been using an AI agent. When his supervisor sent him a paper to read, Bob asked the agent to summarize it. When he needed to understand a new statistical method, he asked the agent to explain it. When his Python code broke, the agent debugged it. When the agent's fix introduced a new bug, it debugged that too. When it came time to write the paper, the agent wrote it. Bob's weekly updates to his supervisor were indistinguishable from Alice's. The questions were similar. The progress was similar. The trajectory, from the outside, was identical.