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Thanks for reading / commenting this post. Initially it seemed like I received a bunch of very negative comments, now I read most of the thread, and there are v

by antirez 9mo ago

Thanks for reading / commenting this post. Initially it seemed like I received a bunch of very negative comments, now I read most of the thread, and there are very good points, articulated with sensibility. Thank you. I wanted to provide some more context that is not part of the blog post. Since somebody may believe I don't enjoy / love the act of writing code. 1. I care a lot about programming, I love creating something from scratch, line by line. But: at this point, I want to do programming in a way that makes me special, compared to machines. When the LLM hits a limit, and I write a function in a way it can't compete, that is good. 2. If I write a very small program that is like a small piece of poetry, this is good human expression. I'll keep doing this as well. 3. But, if I need to develop a feature, and I have a clear design idea, and I can do it in 2 hours instead of 2 weeks, how to justify to myself that, just for what I love, I will use a lot more time? That would be too much of ego-centric POV, I believe. 4. For me too this is painful, as a transition, but I need to adapt. Fortunately I also enjoyed a lot the design / ideas process, so I can focus on that. And write code myself when needed. 5. The reason why I wrote this piece is because I believe there are still a lot of people that are unprepared for the fact we are going to be kinda of obsolete in what defined us, as a profession: the ability to write code. A complicated ability requiring a number of skills at the same time, language skills, algorithms, problem decomposition. Since this is painful, and I believe we are headed in a certain direction, I want to tell the other folks in programming to accept reality. It will be easier, this way.

gspr 9mo ago
I still really, really, really struggle to see how humans are going to maintain and validate the programs written by LLMs if we no longer know (intimately) how to program. Any thoughts?
- JumpCrisscross 9mo ago
  > how humans are going to maintain and validate the programs written by LLMs if we no longer know (intimately) how to program Short answer: we wouldn’t be able to. Slightly-less short answer: unlikely to happen. Most programmers today can’t explain the physics of computation. That’s fine. Someone else can. And if nobody can, someone else can work backwards to it.
  - gspr 9mo ago
    > > how humans are going to maintain and validate the programs written by LLMs if we no longer know (intimately) how to program > Short answer: we wouldn’t be able to. That's a huge problem! A showstopper for many kinds of programs! > Slightly-less short answer: unlikely to happen. Could you elaborate? > Most programmers today can’t explain the physics of computation. That’s fine. Someone else can. And if nobody can, someone else can work backwards to it. That's not the same at all. We have properly abstracted away the physics of computation. A modern computer operates in a way where, if you use it the way you've been instructed to, the physics underlying the computations cannot affect the computation in any undocumented way. Only a very few (and crucically, known and understood!!) physical circumstances can make the physics influence the computations. A layperson does not need to know how those circumstances work, only roughly what their boundaries are. This is wildly different from the "abstraction" to programming that LLMs provide.
    - munksbeer 9mo ago
      > This is wildly different from the "abstraction" to programming that LLMs provide. I absolutely agree. But consider the unsaid hypothetical here: What if AI coding reaches the point where we can trust it in a similar manner?
      - tete 9mo ago
        At the current time this is essentially science fiction though. This something that the best funded companies on the planet (as well as many many others) work on and seem to be completely unable to achieve despite trying their best for years now, despite an incredible hype. It feels like if those resources were poured in nuclear fusion for example we'd have it production ready by now. The field is also not a couple of years old, this has been tried for decades. Sure only now companies decided to put essentially "unlimited" resources into it, but while it showed that certain things are possible and work extremely well, it also strongly hinted that at least the current approach will not get us there, especially not without significant trade-off (that whole over training vs "creativity" and hallucination topic). Doesn't mean it won't come, but that it doesn't appear a "we just need a bit more development" topic. The state hasn't changed much. Models became bigger and bigger and people added that "thinking" hack and agents and agents for agents, but it also didn't change much about the initial approach and its limitations, given that they haven't cracked these problems after years of hyped funding. Would be amazing if we would have AIs that automate research and maybe help us fix all the huge problems the world is facing. I'd absolutely love that. I'd also love it if people could easily create tools, games, art. However that's not the reality we live in. Sadly.
- ahmadyan 9mo ago
  Very few people have the expertise to write efficient assembly code, yet everyone relies on compilers and assemblers to translate high-level code to byte-level machine code. I think same concept is true here. Once coding agents become trivial, few people will know the detail of the programming language and make sure intent is correctly transformed to code, and the majority will focus on different objectives and take LLM programming for granted.
  - Marazan 9mo ago
    Yes, but compilers (in the main), do not have a random number generator to decide what output to produce.
  - feanaro 9mo ago
    No, that's a completely different concept, because we have faultless machines which perfectly and deterministically translate high-level code into byte-level machine code. This is another case of (nearly) perfect abstraction. On the other hand, the whole deal of the LLM is that it does so stochastically and unpredictably.
    - theshrike79 9mo ago
      We also have machines that can perfectly and deterministically check written code for correctness. And the stohastic LLM can use those tools to check whether its work was sufficient, if not, it will try again - without human intervention. It will repeat this loop until the deterministic checks pass.
      - gspr 9mo ago
        > We also have machines that can perfectly and deterministically check written code for correctness. Please do provide a single example of this preposterous claim.
        theshrike79 9mo ago
        It's not like testing code is a new thing. Junit is almost 30 years old today. For functionality: https://en.wikipedia.org/wiki/Unit_testing https://en.wikipedia.org/wiki/Unit_testing With robust enough test suites you can vibe code a HTML5 parser - https://ikyle.me/blog/2025/swift-justhtml-porting-html5-parser-to-swift https://ikyle.me/blog/2025/swift-justhtml-porting-html5-pars... - https://simonwillison.net/2025/Dec/15/porting-justhtml/ https://simonwillison.net/2025/Dec/15/porting-justhtml/ And code correctness: - https://en.wikipedia.org/wiki/Tree-sitter_(parser_generator) https://en.wikipedia.org/wiki/Tree-sitter_(parser_generator) - https://en.wikipedia.org/wiki/Roslyn_(compiler) https://en.wikipedia.org/wiki/Roslyn_(compiler) - https://en.wikipedia.org/wiki/Lint_(software) https://en.wikipedia.org/wiki/Lint_(software) You can make analysers that check for deeply nested code, people calling methods in the wrong order and whatever you want to check. At work we've added multiple Roslyn analysers to our build pipeline to check for invalid/inefficient code, no human will be pinged by a PR until the tests pass. And an LLM can't claim "Job's Done" before the analysers say the code is OK. And you don't need to make one yourself, there are tons you can just pick from: https://en.wikipedia.org/wiki/List_of_tools_for_static_code_analysis https://en.wikipedia.org/wiki/List_of_tools_for_static_code_...
- Narkov 9mo ago
  Fair question but haven't we been doing this for decades? Very few people know how to write assembly and yet software has proliferated. This is just another abstraction.
  - gspr 9mo ago
    > Fair question but haven't we been doing this for decades? Very few people know how to write assembly and yet software has proliferated. This is just another abstraction. Not at all. Given any "layperson input", the expert who wrote the compiler that is supposed to turn it into assembly can describe in excruciating detail what the compiler will do and why. Not so with LLMs. Said differently: If I perturb a source code file with a few bytes here and there, anyone with a modicum of understanding of the compiler used can understand why the assembly changed the way it did as a result. Not so with LLMs.
    - Cthulhu_ 9mo ago
      But there's a limit to that. There's (relatively) very few people that can explain the details of e.g. a compiler, compared to for example React front-end developers that build B2C software (...like me). And these software projects grow, ultimately to the limit of what one person can fit in their head. Which is why we have lots of "rules" and standards on communication, code style, commenting, keeping history, tooling, regression testing, etc. And I'm afraid those will be the first to suffer when code projects are primarily written by LLMs - do they even write unit tests if you don't tell them to?
- erelong 9mo ago
  You could use AI to tutor you on how to code in a specific instance you need?
  - gspr 9mo ago
    Tutoring – whether AI or human – does not provide the in-depth understanding necessary for validation and long-term maintenance. It can be a very useful step on the way there, but only a step.
    - Cthulhu_ 9mo ago
      No, that'll always remain a human skill that can only be taught with knowledge (which a tutor can help you gain) and experience.
- Cthulhu_ 9mo ago
  Same how we do it now - look at the end result, test it. Testers never went away. Besides, your comment goes by the assumption that we no longer know (intimately) how to program - is that true? I don't know C or assembly or whatever very well, but I'm still a valuable worker because I know other things. I mean it could be partially true - but it's like having years of access to Google to quickly find just what I need, meaning I never learned how to read e.g. books on software development or scientific paper end to end. Never felt like I needed to have that skill, but it's a skill that a preceding generation did have.
  - gspr 9mo ago
    > Besides, your comment goes by the assumption that we no longer know (intimately) how to program - is that true? I don't know C or assembly or whatever very well, but I'm still a valuable worker because I know other things. The proposal seems to be for LLMs to take over the task of coding. I posit that if you do not code, you will not gain the skills to do so well. > I mean it could be partially true - but it's like having years of access to Google to quickly find just what I need, meaning I never learned how to read e.g. books on software development or scientific paper end to end. I think you've misunderstood what papers are for or what "the previous generation" used them for. It is certainly possible to extract something useful from a paper without understanding what's going on. Googling can certainly help you. That's good. And useful. But not the main point of the paper.
concats 9mo ago
Thanks for the post. I found it very interesting and I agree with most of what you said. Things are changing, regardless of our feelings on the matter. While I agree that there is something tragic about watching what we know (and have dedicated significant time and energy in learning) devalued. I'm still exited for the future, and for the potential this has. I'm sure that given enough time this will result in amazing things that we cannot even imagine today. The fact that the open models and research is keeping up is incredibly important, and probably the main things that keeps me optimistic for the future.
ludicrousdispla 9mo ago
Of the four coding examples you describe, I find none of them compelling either in their utility or as a case for firing a dev (with one important caveat [0]). In each example, you were already very familiar with the problem at hand, and that probably took far longer than any additional time savings AI could offer. 0. Perhaps I consider your examples as worthless simply because you gloss over them so quickly, in which case that greatly increases the odds in most companies that you would be fired.
onetokeoverthe 9mo ago
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tete 9mo ago
All of that makes a lot of sense. And unlike a lot of both pro-AI and anti-AI people I would find it great if it was the case. Unlike maybe a lot of people here I am less attached to this profession as a profession. I'd also love it if I could have some LLM do the projects I always wanted to finish. It would be essentially Christmas. However your experiences really clash with mine and I am trying to work out why, because so far I haven't been able to copy your workflow with success. It would be great if I could write a proper spec and the output of the LLM would be good (not excellent, not poetry, but just good). However the output for anything that isn't "stack overflow autocomplete" style it is abysmal. Honestly I'd be happy if good output is even on the horizon. And given that "new code" is a lot better than working on an existing project and an existing LLM generated project being better than a human made project and it still being largely bad, often with subtle "insanity" I have a hard time to apply what you say to reality. I do not understand the disconnect. I am used to writing specs. I tried a lot of prompting changes, to a degree where it almost feels like a new programming language. Sure there are things that help, but the sad reality is that I usually spend more time dealing with the LLM than I'd need to write that code myself. And worse still, I will have to fix it and understand it, etc. to be able to keep on working on it and "refining" it, something that simply isn't needed at least to that extent if I wrote that code myself. I really wished LLMs would provide that. And don't get me wrong, I do think there are really good applications for LLMs. Eg anything that needs a transform where even a complex regex won't do. Doing very very basic stuff where one uses LLMs essentially as an IDE-integrated search engine, etc. However the idea that it's enough to write a spec for something even semi-novel currently appears to be out of reach. For trivial generic code it essentially saves you from either writing it yourself copy pasting it off some open source projects. Much context, for the question that hopefully explains a lot of stuff. Those 2 hours that you use instead of two weeks. How do you spend them? Is that refining prompts, is that fixing the LLM output, is that writing/adapting specs, is it something else? Also could it be that there is a bias on "time spent" because of it being different work or even just a general focus on productivity, more experience, etc.? I am trying to understand where that huge gap in experience that people have really stems from. I read your posts, I watch video on YouTube, etc. I just haven't seen "I write a spec [that is is shorter/less effort than the actual code] and get good output". Every time I read claims about it in blog posts and so on there appear to be parts missing to reproduce the experience. I know that there are a lot of "ego-centric POV" style AI "fear". People of course have worries about their jobs, and I understand. However, personally I really don't and as mentioned I'd absolutely love to use it like that on some projects, but whenever I try to replicate experiences that aren't just "toying" in the sense of anything that even has basic reliability requirements and is a bit more complex I fail to do so and it's probably me, but I tried for at least a year to replicate such things and it's failure after failure even for more simple things. That said there are productivity gains with autocomplete, transforming stuff and what people largely call "boilerplate" as well as more quickly writing small helpers that I'd otherwise have copied off some older project. Those things work good enough, just like how autocomplete is good enough. For bigger and more novel things where a search engine is also not the right approach it fails, but this is where the interesting bits are. Having topics that haven't been solved a hundred times over. Or is that simply not what you mean/do?
aprilfoo 9mo ago
That's really interesting, but i'm wondering if this is as rational as it looks. > we are going to be kinda of obsolete in what defined us, as a profession: the ability to write code Is it a fact, really? I don't think "writing code" is a defining factor, maybe it's a prerequisite, as being able to write words hardly defines "a novelist". Anyway, prompt writing skills might become obsolete quite soon. So the main question might be to know which trend of technological evolution to pick and when, in order not to be considered obsolete. A crystal ball might still be more relevant than LLMs for that.
- Cthulhu_ 9mo ago
  I don't think our profession was writing code to begin with (and this may be a bit uuhh. rewriting history?); what we do is take an idea, requirements, an end goal and make it reality. Often times that involves writing code, but that's only one aspect of the software developer's job. Analogy time because comment sections love analogies. A carpenter can hammer nails, screw screws, make holes, saw wood to size. If they then use machines to make that work easier, do they stop being carpenters? It's good if not essential to be able to write code. It's more important to know what to write and when. Best thing to do at this point is to stop attaching one's self-worth with the ability to write code. That's like a novelist (more analogies) who praises their ability to type at 100wpm. The 50 shades books proved you don't need to either touch type (the first book was mostly written on a blackberry apparently) or be good at writing to be successful, lol.
  - mpyne 9mo ago
    As a specific example of this, the U.S. 18F team had helped the Forest Service a decade ago with implementing a requirement to help people get a permit to cut down a Christmas tree. Although there was a software component for the backend, the thing that the actual user ended up with was a printed-out form rather than a mobile app or QR code. This was a deliberate design decision (https://greacen.com/media/guides/2019/02/12/open-forest-launch-post/index.html https://greacen.com/media/guides/2019/02/12/open-forest-laun...), not due to a limitation of software.
  - danparsonson 9mo ago
ManuelKiessling 9mo ago
This is by far the best summary of the state of affairs, or rather, the most sensible perspective that one should have on the state of affairs, that I've read so far. Especially point 3 hits the nail on the head.
deltarholamda 9mo ago
I think there is a danger in the enthusiasm for AI inside of these excellent points, namely that the skills that make a good programmer are not inherent, they are learned. The comparison would be a guy who is an excellent journeyman electrician. This guy has visual-spatial skills that makes bending and installing conduit a kind of art. He has a deep and intuitive understanding of how circuits are balanced in a panel, so he does not overload a phase. But he was not born with them. These are acquired over many years of labor and tutelage. If AI removes these barriers--and I think it will, as AI-enhanced programmers will out-perform and out-compete those who are not in today's employment market--then the programmer will learn different skills that may or may not be in keeping with language skills, algorithms, problem decomposition, etc. They may in fact be orthogonal to these skills. The effect of this may be an improvement, of course. It's hard to say for sure as I left my crystal ball in my other jacket. But it will certainly be different. And those who are predisposed for programming in the old-school way may not find the field as attractive because it is no longer the same sort of engineering, something like the difference between the person that designs a Lego set and the person that assembles a Lego set. It could, in fact, mean that the very best programmers become a kind of elite, able to solve most problems with just a handful of those elite programmers. I'm sure that's the dream of Google and Microsoft. However this will centralize the industry in a way not seen since perhaps IBM, only with a much smaller chance of outside disruption.
- cirelli94 9mo ago
  Maybe solving more trivial problems with AI will left novice programmer to do more depth problems and will make them better faster, because they will spend time solving problems that matter.
  - deltarholamda 9mo ago
    That is possible, for sure. But think of it like a person learning the piano. You could practice your arpeggios on a Steinway, or you can buy a Casio with an arpeggiator button. At a certain point, the professional piano player can make much better use of the arpeggiator button. But the novice piano player benefits greatly from all the slogging arpeggio practice. It's certainly possible that skipping all that grunt work will improve and/or advance music, but it's hardly a sure thing. That's the experiment we're running right now with AI programming. I suppose we'll see soon enough, and I hope I'm utterly wrong about the concerns I have.
endymion-light 9mo ago
Really enjoyed your article, and it reflects a lot of the pain-points I experience with models. I tend to still write and review LLM code after creation, but there is definitely a shift within how much code I create "artisinally" and how much is reviewd in terms of scale. If I need to implement a brand new feature for the project, I will find myself needing to force a view into a LLM because it will help me achieve 80% of the feature in 1% of the time, even if the end result requires a scale of refactoring, it's rarely the time that the original feature would've taken me. But, I think that's also because I have solid refactoring foundations, I know what makes good code, and I think if I had access to these tools 5 years ago, I would not be able to achieve that same result, as LLMs typically steer towards junior level coding as a consequence of their architecture.
aagha 9mo ago
I'm curious if you saw the top voted post: https://news.ycombinator.com/item?id=46583507 https://news.ycombinator.com/item?id=46583507 What's happening here? Why is there such a massive disconnect between your experience and there's? If you assume they're a good programmer, why is your experience so vastly different?

by antirez 9mo ago

gspr 9mo ago

I still really, really, really struggle to see how humans are going to maintain and validate the programs written by LLMs if we no longer know (intimately) how to program. Any thoughts?

JumpCrisscross 9mo ago
> how humans are going to maintain and validate the programs written by LLMs if we no longer know (intimately) how to program Short answer: we wouldn’t be able to. Slightly-less short answer: unlikely to happen. Most programmers today can’t explain the physics of computation. That’s fine. Someone else can. And if nobody can, someone else can work backwards to it.
- gspr 9mo ago
  > > how humans are going to maintain and validate the programs written by LLMs if we no longer know (intimately) how to program > Short answer: we wouldn’t be able to. That's a huge problem! A showstopper for many kinds of programs! > Slightly-less short answer: unlikely to happen. Could you elaborate? > Most programmers today can’t explain the physics of computation. That’s fine. Someone else can. And if nobody can, someone else can work backwards to it. That's not the same at all. We have properly abstracted away the physics of computation. A modern computer operates in a way where, if you use it the way you've been instructed to, the physics underlying the computations cannot affect the computation in any undocumented way. Only a very few (and crucically, known and understood!!) physical circumstances can make the physics influence the computations. A layperson does not need to know how those circumstances work, only roughly what their boundaries are. This is wildly different from the "abstraction" to programming that LLMs provide.
  - munksbeer 9mo ago
    > This is wildly different from the "abstraction" to programming that LLMs provide. I absolutely agree. But consider the unsaid hypothetical here: What if AI coding reaches the point where we can trust it in a similar manner?
    - tete 9mo ago
      At the current time this is essentially science fiction though. This something that the best funded companies on the planet (as well as many many others) work on and seem to be completely unable to achieve despite trying their best for years now, despite an incredible hype. It feels like if those resources were poured in nuclear fusion for example we'd have it production ready by now. The field is also not a couple of years old, this has been tried for decades. Sure only now companies decided to put essentially "unlimited" resources into it, but while it showed that certain things are possible and work extremely well, it also strongly hinted that at least the current approach will not get us there, especially not without significant trade-off (that whole over training vs "creativity" and hallucination topic). Doesn't mean it won't come, but that it doesn't appear a "we just need a bit more development" topic. The state hasn't changed much. Models became bigger and bigger and people added that "thinking" hack and agents and agents for agents, but it also didn't change much about the initial approach and its limitations, given that they haven't cracked these problems after years of hyped funding. Would be amazing if we would have AIs that automate research and maybe help us fix all the huge problems the world is facing. I'd absolutely love that. I'd also love it if people could easily create tools, games, art. However that's not the reality we live in. Sadly.
ahmadyan 9mo ago
Very few people have the expertise to write efficient assembly code, yet everyone relies on compilers and assemblers to translate high-level code to byte-level machine code. I think same concept is true here. Once coding agents become trivial, few people will know the detail of the programming language and make sure intent is correctly transformed to code, and the majority will focus on different objectives and take LLM programming for granted.
- Marazan 9mo ago
  Yes, but compilers (in the main), do not have a random number generator to decide what output to produce.
- feanaro 9mo ago
  No, that's a completely different concept, because we have faultless machines which perfectly and deterministically translate high-level code into byte-level machine code. This is another case of (nearly) perfect abstraction. On the other hand, the whole deal of the LLM is that it does so stochastically and unpredictably.
  - theshrike79 9mo ago
    We also have machines that can perfectly and deterministically check written code for correctness. And the stohastic LLM can use those tools to check whether its work was sufficient, if not, it will try again - without human intervention. It will repeat this loop until the deterministic checks pass.
    - gspr 9mo ago
      > We also have machines that can perfectly and deterministically check written code for correctness. Please do provide a single example of this preposterous claim.
      - theshrike79 9mo ago
        It's not like testing code is a new thing. Junit is almost 30 years old today. For functionality: https://en.wikipedia.org/wiki/Unit_testing https://en.wikipedia.org/wiki/Unit_testing With robust enough test suites you can vibe code a HTML5 parser - https://ikyle.me/blog/2025/swift-justhtml-porting-html5-parser-to-swift https://ikyle.me/blog/2025/swift-justhtml-porting-html5-pars... - https://simonwillison.net/2025/Dec/15/porting-justhtml/ https://simonwillison.net/2025/Dec/15/porting-justhtml/ And code correctness: - https://en.wikipedia.org/wiki/Tree-sitter_(parser_generator) https://en.wikipedia.org/wiki/Tree-sitter_(parser_generator) - https://en.wikipedia.org/wiki/Roslyn_(compiler) https://en.wikipedia.org/wiki/Roslyn_(compiler) - https://en.wikipedia.org/wiki/Lint_(software) https://en.wikipedia.org/wiki/Lint_(software) You can make analysers that check for deeply nested code, people calling methods in the wrong order and whatever you want to check. At work we've added multiple Roslyn analysers to our build pipeline to check for invalid/inefficient code, no human will be pinged by a PR until the tests pass. And an LLM can't claim "Job's Done" before the analysers say the code is OK. And you don't need to make one yourself, there are tons you can just pick from: https://en.wikipedia.org/wiki/List_of_tools_for_static_code_analysis https://en.wikipedia.org/wiki/List_of_tools_for_static_code_...
Narkov 9mo ago
Fair question but haven't we been doing this for decades? Very few people know how to write assembly and yet software has proliferated. This is just another abstraction.
- gspr 9mo ago
  > Fair question but haven't we been doing this for decades? Very few people know how to write assembly and yet software has proliferated. This is just another abstraction. Not at all. Given any "layperson input", the expert who wrote the compiler that is supposed to turn it into assembly can describe in excruciating detail what the compiler will do and why. Not so with LLMs. Said differently: If I perturb a source code file with a few bytes here and there, anyone with a modicum of understanding of the compiler used can understand why the assembly changed the way it did as a result. Not so with LLMs.
  - Cthulhu_ 9mo ago
    But there's a limit to that. There's (relatively) very few people that can explain the details of e.g. a compiler, compared to for example React front-end developers that build B2C software (...like me). And these software projects grow, ultimately to the limit of what one person can fit in their head. Which is why we have lots of "rules" and standards on communication, code style, commenting, keeping history, tooling, regression testing, etc. And I'm afraid those will be the first to suffer when code projects are primarily written by LLMs - do they even write unit tests if you don't tell them to?
erelong 9mo ago
You could use AI to tutor you on how to code in a specific instance you need?
- gspr 9mo ago
  Tutoring – whether AI or human – does not provide the in-depth understanding necessary for validation and long-term maintenance. It can be a very useful step on the way there, but only a step.
  - Cthulhu_ 9mo ago
    No, that'll always remain a human skill that can only be taught with knowledge (which a tutor can help you gain) and experience.
Cthulhu_ 9mo ago
Same how we do it now - look at the end result, test it. Testers never went away. Besides, your comment goes by the assumption that we no longer know (intimately) how to program - is that true? I don't know C or assembly or whatever very well, but I'm still a valuable worker because I know other things. I mean it could be partially true - but it's like having years of access to Google to quickly find just what I need, meaning I never learned how to read e.g. books on software development or scientific paper end to end. Never felt like I needed to have that skill, but it's a skill that a preceding generation did have.
- gspr 9mo ago
  > Besides, your comment goes by the assumption that we no longer know (intimately) how to program - is that true? I don't know C or assembly or whatever very well, but I'm still a valuable worker because I know other things. The proposal seems to be for LLMs to take over the task of coding. I posit that if you do not code, you will not gain the skills to do so well. > I mean it could be partially true - but it's like having years of access to Google to quickly find just what I need, meaning I never learned how to read e.g. books on software development or scientific paper end to end. I think you've misunderstood what papers are for or what "the previous generation" used them for. It is certainly possible to extract something useful from a paper without understanding what's going on. Googling can certainly help you. That's good. And useful. But not the main point of the paper.

concats 9mo ago

Thanks for the post. I found it very interesting and I agree with most of what you said. Things are changing, regardless of our feelings on the matter. While I agree that there is something tragic about watching what we know (and have dedicated significant time and energy in learning) devalued. I'm still exited for the future, and for the potential this has. I'm sure that given enough time this will result in amazing things that we cannot even imagine today. The fact that the open models and research is keeping up is incredibly important, and probably the main things that keeps me optimistic for the future.

ludicrousdispla 9mo ago

Of the four coding examples you describe, I find none of them compelling either in their utility or as a case for firing a dev (with one important caveat [0]). In each example, you were already very familiar with the problem at hand, and that probably took far longer than any additional time savings AI could offer. 0. Perhaps I consider your examples as worthless simply because you gloss over them so quickly, in which case that greatly increases the odds in most companies that you would be fired.

onetokeoverthe 9mo ago

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tete 9mo ago

All of that makes a lot of sense. And unlike a lot of both pro-AI and anti-AI people I would find it great if it was the case. Unlike maybe a lot of people here I am less attached to this profession as a profession. I'd also love it if I could have some LLM do the projects I always wanted to finish. It would be essentially Christmas. However your experiences really clash with mine and I am trying to work out why, because so far I haven't been able to copy your workflow with success. It would be great if I could write a proper spec and the output of the LLM would be good (not excellent, not poetry, but just good). However the output for anything that isn't "stack overflow autocomplete" style it is abysmal. Honestly I'd be happy if good output is even on the horizon. And given that "new code" is a lot better than working on an existing project and an existing LLM generated project being better than a human made project and it still being largely bad, often with subtle "insanity" I have a hard time to apply what you say to reality. I do not understand the disconnect. I am used to writing specs. I tried a lot of prompting changes, to a degree where it almost feels like a new programming language. Sure there are things that help, but the sad reality is that I usually spend more time dealing with the LLM than I'd need to write that code myself. And worse still, I will have to fix it and understand it, etc. to be able to keep on working on it and "refining" it, something that simply isn't needed at least to that extent if I wrote that code myself. I really wished LLMs would provide that. And don't get me wrong, I do think there are really good applications for LLMs. Eg anything that needs a transform where even a complex regex won't do. Doing very very basic stuff where one uses LLMs essentially as an IDE-integrated search engine, etc. However the idea that it's enough to write a spec for something even semi-novel currently appears to be out of reach. For trivial generic code it essentially saves you from either writing it yourself copy pasting it off some open source projects. Much context, for the question that hopefully explains a lot of stuff. Those 2 hours that you use instead of two weeks. How do you spend them? Is that refining prompts, is that fixing the LLM output, is that writing/adapting specs, is it something else? Also could it be that there is a bias on "time spent" because of it being different work or even just a general focus on productivity, more experience, etc.? I am trying to understand where that huge gap in experience that people have really stems from. I read your posts, I watch video on YouTube, etc. I just haven't seen "I write a spec [that is is shorter/less effort than the actual code] and get good output". Every time I read claims about it in blog posts and so on there appear to be parts missing to reproduce the experience. I know that there are a lot of "ego-centric POV" style AI "fear". People of course have worries about their jobs, and I understand. However, personally I really don't and as mentioned I'd absolutely love to use it like that on some projects, but whenever I try to replicate experiences that aren't just "toying" in the sense of anything that even has basic reliability requirements and is a bit more complex I fail to do so and it's probably me, but I tried for at least a year to replicate such things and it's failure after failure even for more simple things. That said there are productivity gains with autocomplete, transforming stuff and what people largely call "boilerplate" as well as more quickly writing small helpers that I'd otherwise have copied off some older project. Those things work good enough, just like how autocomplete is good enough. For bigger and more novel things where a search engine is also not the right approach it fails, but this is where the interesting bits are. Having topics that haven't been solved a hundred times over. Or is that simply not what you mean/do?

aprilfoo 9mo ago

That's really interesting, but i'm wondering if this is as rational as it looks. > we are going to be kinda of obsolete in what defined us, as a profession: the ability to write code Is it a fact, really? I don't think "writing code" is a defining factor, maybe it's a prerequisite, as being able to write words hardly defines "a novelist". Anyway, prompt writing skills might become obsolete quite soon. So the main question might be to know which trend of technological evolution to pick and when, in order not to be considered obsolete. A crystal ball might still be more relevant than LLMs for that.

Cthulhu_ 9mo ago
I don't think our profession was writing code to begin with (and this may be a bit uuhh. rewriting history?); what we do is take an idea, requirements, an end goal and make it reality. Often times that involves writing code, but that's only one aspect of the software developer's job. Analogy time because comment sections love analogies. A carpenter can hammer nails, screw screws, make holes, saw wood to size. If they then use machines to make that work easier, do they stop being carpenters? It's good if not essential to be able to write code. It's more important to know what to write and when. Best thing to do at this point is to stop attaching one's self-worth with the ability to write code. That's like a novelist (more analogies) who praises their ability to type at 100wpm. The 50 shades books proved you don't need to either touch type (the first book was mostly written on a blackberry apparently) or be good at writing to be successful, lol.
- mpyne 9mo ago
  As a specific example of this, the U.S. 18F team had helped the Forest Service a decade ago with implementing a requirement to help people get a permit to cut down a Christmas tree. Although there was a software component for the backend, the thing that the actual user ended up with was a printed-out form rather than a mobile app or QR code. This was a deliberate design decision (https://greacen.com/media/guides/2019/02/12/open-forest-launch-post/index.html https://greacen.com/media/guides/2019/02/12/open-forest-laun...), not due to a limitation of software.
- danparsonson 9mo ago

ManuelKiessling 9mo ago

This is by far the best summary of the state of affairs, or rather, the most sensible perspective that one should have on the state of affairs, that I've read so far. Especially point 3 hits the nail on the head.

deltarholamda 9mo ago

I think there is a danger in the enthusiasm for AI inside of these excellent points, namely that the skills that make a good programmer are not inherent, they are learned. The comparison would be a guy who is an excellent journeyman electrician. This guy has visual-spatial skills that makes bending and installing conduit a kind of art. He has a deep and intuitive understanding of how circuits are balanced in a panel, so he does not overload a phase. But he was not born with them. These are acquired over many years of labor and tutelage. If AI removes these barriers--and I think it will, as AI-enhanced programmers will out-perform and out-compete those who are not in today's employment market--then the programmer will learn different skills that may or may not be in keeping with language skills, algorithms, problem decomposition, etc. They may in fact be orthogonal to these skills. The effect of this may be an improvement, of course. It's hard to say for sure as I left my crystal ball in my other jacket. But it will certainly be different. And those who are predisposed for programming in the old-school way may not find the field as attractive because it is no longer the same sort of engineering, something like the difference between the person that designs a Lego set and the person that assembles a Lego set. It could, in fact, mean that the very best programmers become a kind of elite, able to solve most problems with just a handful of those elite programmers. I'm sure that's the dream of Google and Microsoft. However this will centralize the industry in a way not seen since perhaps IBM, only with a much smaller chance of outside disruption.

cirelli94 9mo ago
Maybe solving more trivial problems with AI will left novice programmer to do more depth problems and will make them better faster, because they will spend time solving problems that matter.
- deltarholamda 9mo ago
  That is possible, for sure. But think of it like a person learning the piano. You could practice your arpeggios on a Steinway, or you can buy a Casio with an arpeggiator button. At a certain point, the professional piano player can make much better use of the arpeggiator button. But the novice piano player benefits greatly from all the slogging arpeggio practice. It's certainly possible that skipping all that grunt work will improve and/or advance music, but it's hardly a sure thing. That's the experiment we're running right now with AI programming. I suppose we'll see soon enough, and I hope I'm utterly wrong about the concerns I have.

endymion-light 9mo ago

Really enjoyed your article, and it reflects a lot of the pain-points I experience with models. I tend to still write and review LLM code after creation, but there is definitely a shift within how much code I create "artisinally" and how much is reviewd in terms of scale. If I need to implement a brand new feature for the project, I will find myself needing to force a view into a LLM because it will help me achieve 80% of the feature in 1% of the time, even if the end result requires a scale of refactoring, it's rarely the time that the original feature would've taken me. But, I think that's also because I have solid refactoring foundations, I know what makes good code, and I think if I had access to these tools 5 years ago, I would not be able to achieve that same result, as LLMs typically steer towards junior level coding as a consequence of their architecture.

aagha 9mo ago

I'm curious if you saw the top voted post: https://news.ycombinator.com/item?id=46583507 https://news.ycombinator.com/item?id=46583507 What's happening here? Why is there such a massive disconnect between your experience and there's? If you assume they're a good programmer, why is your experience so vastly different?