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AI has a multiplying effect on existing technical skills

• voidUpdate 5mo ago
  > "I think AI tools are more like Iron Man’s suit. It can do incredible things, but not on its own." Someone needs to watch iron man 3...
  • acbart 5mo ago
    Or "Age of Ultron".
• reconnecting 5mo ago
  > I think AI tools are more like Iron Man's suit. There's an interesting repository with 63600 stars on GitHub (1). The developer of the repository is No 1 at the GitHub's trending contributors list (2). However, it seems like the application isn't what it's described to be (3), and the developers, on their end, are unable to clearly answer whether this is real or not, as it's just messy LLM output. Proof that the suit alone doesn't make anyone Iron Man. 1. https://github.com/ruvnet/RuView https://github.com/ruvnet/RuView 2. https://github.com/trending/developers?since=weekly https://github.com/trending/developers?since=weekly 3. https://github.com/deletexiumu/wifi-densepose https://github.com/deletexiumu/wifi-densepose
  • pelasaco 5mo ago
    the whole thing is creepy. The ruvnet, has multiple projects.. its just AI. A lot of AI. It floods GH infra.. Kind of easy to understand why GH struggles.
    • reconnecting 5mo ago
      On the other hand, there are 8,400 forks, and it looks very real, so developers seem to have confidence in it.
      • chrisweekly 5mo ago
        The forks are as meaningful as the stars.
      • krapht 5mo ago
        I went and looked at the code. It's AI bots and a few confused trend followers all the way down. There's no way anything in there works.
  • tambourine_man 5mo ago
    > After a thorough independent code audit with cross-verification from three AI systems (Claude, Codex/GPT-5.2, Gemini), we confirm that this project is a non-functional facade. So, a nonfunctional project is created by AI and AI is used to attests its nonfunctionality. What a brave new world.
    • reconnecting 5mo ago
      It seems like a perfect ouroboros illustration for the current world. AI creates a delusional product, people don't trust their own opinion regarding it and follow it, another AI is needed to prove that the product is unreal. In the loop.
  • keeda 5mo ago
    > Proof that the suit alone doesn't make anyone Iron Man. I do believe that is a running theme in the Iron Man comics and movies.
• martythemaniak 5mo ago
  I think they're jumping to the right conclusions - because the impetus to get as rid of as many people as possible isn't generally based on understanding, analysis, results, or lessons learned but a FOMO-like mania spread primarily through executive-class groupchats. This is, IMO, what mitchelh referred to last week as entire companies being in the grip of AI psychosis. So while the author's points are completely true and valid, an executive will say "True, but Claude will get smarter faster than these problems and in 3 years it'll fix everything" and there's absolutely nothing you can say or do in response to this.
• Waterluvian 5mo ago
  I had an Iron Man moment last week where I was “vibe coding” a UI design with component tests live on the other screen. Iterating by asking it to move things, reduce emphasis of an element, exploring layout options, etc. The loop was near realtime and felt amazing. The code it generated was awful. The kind of garbage that people who don’t know any better would ship: it looked right and it worked. But it was instantly a maintenance dead end. But I had an effortless time converging on a design that I wouldn’t have been able to do on my own (I’m not a designer). And then I had a reference design and I manually implemented it with better code (the part I am good at).
  • snarf21 5mo ago
    I feel the same but the question I struggle most with is this: "Does it matter when the people who are going to come along and maintain this are just going to use AI to fix or adjust this maintenance nightmare?"
    • Waterluvian 5mo ago
      At that point the code becomes a compile target, and then you need a new source of truth. Which I think is perfectly worthy of exploration. Some people want to check in the prompts. Or even better, check in a plan.md or evenest betterest: some set of very well-defined specifications. I'm not sure what the answer will be. Probably some mix of things. But today it is absolutely imperative that the code I write for the case I wrote it in is good quality and can be maintained by more than just me.
      • user34283 5mo ago
        I don't see the benefit of checking in either prompts or specs. I never tried spec driven development for myself, but if I review other's MRs I am typically exhausted after the first 10 lines. And there are hundreds of lines, nearly always with major inaccuracies. For myself I always found the plan mode to work well. Once the implementation is done, the code is the source of truth. If it works, it works. When I want to add more functionality or change it, I just tell the agent what I want changed. I doubt walls of semi-accurate existing specs are going to be beneficial there, but maybe my work differs from yours.
• snide 5mo ago
  I mostly share Josh's opinion, but I think a lot of these posts that talk about Senior vs. Junior experience when working with AIs is kind of rubbish. Sure, you get better results as a Senior working with AI tooling and struggle more as a Junior. Nothing has changed in that equation except the amplification. What folks seem to avoid is that a Junior (in ANY subject) has the ability to LEARN so much faster with an AI research assistant, and that becoming an expert has accelerated for those with the personal stamina to dig deep (this as a requirement hasn't changed). I spend just as much time with my AI tooling asking questions as I do asking it to "build" or "fix" things. "How does this work?". "Can you suggest other tools?". I think some people always think about AI as an input / output relationship, when a lot of the time, the fiddling in between, with or without AI was always the important part. Yes people will suck in the beginning, against they always did. I think the good folks though will suck for a MUCH shorter time than I did getting into things. A lot of people will drop out and get discouraged. That happened before too. Learning things requires persistence. I think the only real case to be made is that AI's sense of immediate pleasure can neuter people away from running into friction. AI natives likely won't understand friction and question it.
  • JumpCrisscross 5mo ago
    > a Junior (in ANY subject) has the ability to LEARN so much faster with an AI research assistant I’m not seeing this. And based on what we’re seeing at the university level, I’m not expecting to.
    • sonofhans 5mo ago
      Yes, I agree, the skills are orthogonal. Digital typesetting is vastly quicker than manually putting down metal type, and since you’re exposed to more type you have the opportunity to learn faster. But getting good at typography with digital tools will help you very little if you need to lay out type manually.
      • JumpCrisscross 5mo ago
        > getting good at typography with digital tools will help you very little if you need to lay out type manually The analogy is unlimited typing in Gmail won’t make you a better writer or typesetter on its own.
• hacker_mar 5mo ago
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• worldsayshi 5mo ago
  I see two points: 1. AIs aren't yet good at architecture. 2. AIs aren't yet good at imagining technically exciting stuff to build. And I agree that there's still space there to build a career in the short to medium term (plus Jevons Paradox). When both those points are no longer true we are certainly much closer to, dear I say it, agi. I suspect that (1) will be solved for somewhat limited domains in the near future using harnesses. And it could snowball from there.
  • zaphar 5mo ago
    Nearly every argument that hinges on the word "yet" is just an example of over-extrapolation[0] at play. 0: https://www.fallacyfiles.org/overxtra.html https://www.fallacyfiles.org/overxtra.html
    • worldsayshi 5mo ago
      You're probably on point there.
    • muldvarp 5mo ago
      But saying "LLMs are not good at architecture so software engineering has a bright future" is _also_ extrapolation.
      • zaphar 5mo ago
        Anyone who claims to know the correct strategy to be best positioned for the future is lying or misinformed. The most you can reasonably say is that for the moment an LLM in the hand of an non-expert or naive user is unlikely to produce high quality results or create an absurd boost in productivity. We can make reasoned decisions now and continue to monitor things. It would be just as unwise to ignore the progression of LLM agents as it would be to over-index on them.
        • muldvarp 5mo ago
          I think be both agree that the future of software engineering is very uncertain right now (and likely will be for years). For me personally that alone is enough to recommend not investing money and time to get into software engineering anymore.
• akersten 5mo ago
  Hmm. I think extrapolating from the reddit people who say "I tried vibe coding an entire app from scratch and all I said was fix this and make no mistakes and it didn't work" is a bad data source and will give you the wrong intuition. Of course it won't work when you hold it like that. But put just a tiny bit of knowledge and guidance into the prompt and AI will nail it. I didn't think this 6 months ago but today after what I've seen these models debug and accomplish in established, messy production monoliths, I'm fully convinced even the worst vibe coders are only a year or two away from being able to actually create something from scratch and have it not blow up 50 files in. So I guess I take the totally opposite stance, today's AI is the worst AI will ever be at coding, and I believe the vested interests behind AI do not plan on making it any worse at this task, so...
• draw_down 5mo ago
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• xnx 5mo ago
  An "elephant in the room" is a big topic that no one is talking about. Everyone is talking about AI. Better headline: "Why AI Multiplies Developer Skills Rather Than Replacing Them"
  • bluefirebrand 5mo ago
    The outcome is the same, though Fewer developers required to achieve the same things means a lot of people are going to be unemployed It also means that the people who remain will likely be paid less. Why would you pay a senior salary when you could pay a junior salary plus AI subscription and get "the same result"? I think Software Devs are in for a rough time. I've been doing this for 15 years now, and I'm not looking forward to it. I'm honestly thinking about re-skilling to a different industry. Even if it pays less, it's probably worth it to sit out this shitshow.
    • nyantaro1 5mo ago
      I agree with most of your take, but I don't really think those left are going to be paid less. I am not one of them, but I don't see why they would be paid less.
      • bluefirebrand 5mo ago
        Simple economics. There are fewer software developers required to achieve the same goals, so there are fewer jobs for software developers. That means there's an oversupply of software development labor. That means salaries for software developers will go down It's the same thing that happened to every other skilled profession that was automated in the past. That's why unions became a thing and they started busting heads until their employers paid them more. Edit: the only way I can see software developer salaries staying the same is if the amount of work available for them expands dramatically. Hypothetically if half of software developers are laid off and replaced by AI, there will need to be twice as many software development jobs in order for salaries to not go down Also keep in mind that even if salaries remain flat, inflation means you're making less.
    • 5mo ago
• yanis_t 5mo ago
  > the most talented developers I know amplify what they can do with AI Not the most talented developer, but this has been pretty much my experience as well. Just keep it under control, know what and why its doing at every step, read the code, and then it will boost your productivity.
• mapcars 5mo ago
  I see this as a much more solid and mature take than those who "boo" about AI taking their jobs.
• vb-8448 5mo ago
  I don't agree, LLMs/AI does definitely have agency. Maybe not the same agency you would expect from a human being, but if you put them in a ralph loop they can go far, far away, and mostly because on how we build our world in the pre-llm era: do you need to order something (or you want to hire a hitman)? -> you can go do it on a web site or via whatsapp or by calling some API.
  • JumpCrisscross 5mo ago
    > you put them in a ralph loop they can go far, far away The point is they mostly wind up somewhere stupid, and it takes expertise to spot and correct that. (Maybe that changes with further development.)
    • vb-8448 5mo ago
      With enough time (and tokens), they'll eventually recover. It's essentially a "brute force" approach, but in most cases, they only need to succeed once.
      • JumpCrisscross 5mo ago
        > With enough time (and tokens), they'll eventually recover The article’s point is this is not true. They wind up in bullshit attractors where they hit a wall and then get lost within their muddled context window. > they only need to succeed once Yet they don’t. Not on their own. Like, you haven’t had an LLM get stuck in a stupid loop where you point out the flaw and then it gets unstuck?
        • vb-8448 5mo ago
          In a ralph loop you start any iteration from scratch and feed the prompt with last X iterations in order to avoid getting stuck.
• light_tech 5mo ago
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• sarreph 5mo ago
  I agree with the author that -- right now -- we're still in the part of the AI adoption / product development curve that it's an extreme force multiplier. I like to think of it as a normal distribution, the further away a programmer is to the right of the mean, the more their benefit. It's almost like it's their standard deviation squared (σ²). So someone like Matt Perry (as OP mentioned), who is a >99.99% programmer for argument's sake and is therefore four standard deviations away from the mean... Matt gets a (4×4) 16x multiplying effect on their productivity. Someone who is a slightly above average programmer might see a 2 or 3x boost on their productivity, which is huge(!) and might also make them fear for their job. Which tracks with the level of moral panic we are seeing and experiencing. This math kinda still holds up for "bad programmers" too (i.e. left of the mean), as in they still see a boost to their productivity (negative squared is a positive number)... but there's something iffy about their results. The technical debt is unmaintainable and because they don't _understand_ the systems that they're operating in, they end up in the "3 hour" prompt loops that the OP refers to. > Similarly, if Matt Perry handed me the keys to the Motion repository and told me to take over, I wouldn’t have the same results even though I have access to the same set of LLM tools. The question is -- how long is this multiplier going to exist for? Some people would wager "for the foreseeable long-term future"; some people think it will widen further; and some people think it will diminish or god forbid even collapse. It feels like most arguments at the moment (like this article's) are that the humans who "know what they are doing" will be able to baton the hatches and avoid being usurped by ever-capable models. I saw it in a café yesterday: someone was using a coding agent to build a marketing website for their project, getting more and more frustrated by not getting the outcome they wanted. Their friend typed a couple of sentences on their keyboard and got a "Dude! How did you do that? That was sick!" a minute or so later. "I used to build websites" the friend said. -- The friend 'knew what they were doing'. How much longer is knowing what you're doing going to be a moat?
  • disgruntledphd2 5mo ago
    > How much longer is knowing what you're doing going to be a moat? For a looooonnnnngggg time, unless there's massive progress in AI research. Fundamentally, next token prediction is limited. Granted, I'm pretty amazed at how well it's done, but if you can't activate the right parts of the models (with your prompts), then you're not going to get good results. And to be fair, for lots of things this doesn't matter. Steve in Finance or Mindy in Marketing can create dashboards that actually help them, and the code quality mostly doesn't matter. For stuff that needs to be shipped, monitored and maintained you still need to know what you're doing.
• mehagar 5mo ago
  I just hope my employer comes to the same conclusion before I get laid off.
• rasgkl 5mo ago
  The "it is just a tool" talking point is very fashionable right now to pretend that plagiarizing material is still a meritocracy.
• muldvarp 5mo ago
  The fact that AI currently requires some human supervision to produce valuable results is not a good predictor that it will stay this way sadly. LLMs were basically unable to reason two years ago. They are now better at many reasoning tasks than most people. If there is even a remote chance that LLMs will make your job obsolete I would pivot as fast as I could. This includes first and foremost software engineering.
  • geraneum 5mo ago
    The people you see in the TV are not actually in the TV box. It looks real until you try to shake one’s hand. It’s kind of the same thing with AI (reasoning and whatnot).
    • muldvarp 5mo ago
      I don't think it matters if the reasoning is philosophically "real" if it can solve real problems.
      • geraneum 5mo ago
        If you read my analogy in the context of the article, it should be clearer what I meant.
        • muldvarp 5mo ago
          I think it would be even more clear if you just write what you mean.
  • mohsen1 5mo ago
    I agree with you. A lot of "AI code is not clean" is hopeful thinking. In two years it might be able to design and architect better than most humans too.
• datakan 5mo ago
  Back in the late 90's when the internet was really just becoming a thing with most people, a friend said something that's stuck with me all these years. "We're losing our moderate speech." Everything these days is either the greatest thing ever or the worst thing ever. All the stuff in the middle has vanished. Very few it seems acknowledge AI as being a useful tool. It's either "We're all being replaced" or "The technology is all slop" and everyone talks over each other like it's the Super Bowl and their teams are battling it out. It would be nice if we could just look to the opportunities this tech offers and focus on that.
  • 2snakes 5mo ago
    Yeah… it is because thats the most impactful way to influence, also used by intelligence agencies. Sort of says something about where language leads deterministically.
• 0xbadcafebee 5mo ago
  > AI models have become shockingly good at completing a wide variety of programming tasks. They’re certainly not perfect, but in many cases, they’re good enough. I’m not happy about this, for a wide variety of ethical/environmental/safety reasons You cannot hold a computer liable for any of those reasons. You can, however, sue the human that built or used the AI. So those concerns shoudn't be any different with or without AI. The same problems will be here either way. If you really care about those problems, you would demand your representatives in government actually enshrine those things in law, with some teeth, to ensure companies prevent problems with them. If you don't do something about those problems (with or without AI), then it's clear by your actions that ethical/environmental/safety concerns aren't actually that important to you.
• therealmacsteel 5mo ago
  We are quickly reaching a point though that programmers will become so reliant on llm for coding so much so as people have become soul reliant on their phones to remember phone numbers, the younger generations dont have a single phone number they can call to memory and soon the same will be true of code.
• x187463 5mo ago
  > Without guidance, LLMs tend to paint themselves into a corner, because they’re generating code to solve individual prompts, not thinking holistically about an application’s architecture. I've found I can prevent the LLM, in many cases, from thrashing on a bug/feature for long periods of time by switching into plan mode and, even in the middle of a conversation, having it reassess the structure around the problem, first. If you keep prompting about the same bug, it may keep producing variations of the problem code. But forcing it to stop and 'think' for a bit, has yielded much better results.
• idopmstuff 5mo ago
  I think the problem with this logic is it's based on the capabilities of LLMs today and really fails to address the prospect that they will continue to improve. I used to be a PM and am technically literate enough but can only very minimally write code. I have been using LLMs to build (or try to, at least) internal tools for my business since GPT-4. In the early days, I'd get a little ways, then the LLM would start breaking things, and I'd try but fail to get it to fix things. But over successive generations, I was increasingly able to get it unstuck by offering suggestions on where it may have gone wrong. With Opus 4.7, I don't even really have to do that - if something isn't working it's usually sufficient to just tell it what's broken. It can figure out how to fix it without my input. And of course fewer things are broken in the first place. So I think I'm very well positioned to understand how these things are improving - better able to get the LLM to do what I want than the post OP quoted from /vibecoding (though I am 99% sure that post is actually AI slop), but less so than most of the people posting in this thread. As they've improved, whatever ability I have to guess at the causes of problems based on my experience having seen things go wrong with products I've PMed has become less necessary to getting the right outcome. I expect that trend to continue - increasingly the LLM won't need the guidance of people with a great deal of technical expertise. I basically no longer have to attempt to diagnose problems in order to get them fixed, though with the caveat that I am building internal tools for which I am the only user, so certainly much simpler in scope than the stuff OP is talking about. > Without guidance, LLMs tend to paint themselves into a corner, because they’re generating code to solve individual prompts, not thinking holistically about an application’s architecture. The crux of what I'm trying to say here is that I absolutely believe that this line is 100% true today, but I would be deeply cautious about assuming that it will continue to be true given the improvements in LLMs over the past few years.
• jplusequalt 5mo ago
  >I think AI tools are more like Iron Man’s suit. It can do incredible things, but not on its own. Seemingly every AI pilled programmer who writes a blog post on AI's impact on software engineering has the same philosophical argument, and it's wording changes slightly every 6-12 months to reflect the newest models capabilities. In 2023 it was: "AI is just autocomplete. It can't code whole blocks on it's own." In 2024 it was: "AI is only good for scaffolding new projects, or boiler plate code. It can't write the application whole sale." Since November 2025 it's been: "AI is only writing the code for us. It can't manage architecture, or do the long term planning required for real world applications." In 6-12 months when the AI is doing an increasing amount of the architecture and high level planning, what will AI pilled programmers fall back on then?
• zb3 5mo ago
  AI just further increases inequality.. this is fine for the author for now, but might not be fine anymore when we end up with the eventual result - winner-take-all, where one will boast 2500000x productivity increase, while others have no job. When you see rising inequality, don't just cheer because you happen to win for now.. maybe think about the future and also others..
• deleted 5mo ago
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• sunir 5mo ago
  Humans have hard skills and abilities the ais can’t reproduce yet like real time learning, spatial reasoning, cheap parallelism, Qualia so we can identity QWAN (quality without a name) because we feel in real time what the code is. AIs have skills humans aren’t good at like nerding out on technical details. That’s not a perfect map because I’m spitballing. However there is a symbiosis. I am not sure I am productive anymore with AI as I am up to 125 repos and agents most of which are tools for managing AIs and things break frequently that it feels like spinning plates. I spent two months in November and December last year writing by hand a fundamental library to constrain how the AIs build clis. That did make things move a lot faster but for those two months I felt the slowness. I think it will always be like this. It’s the nature of paradigm shift to shift.
  • iwiwk 5mo ago
    The way I think of it is, computer memory is superior to human memory because it can store anything and re-call on demand when requested. This is great for the human because we no longer have to remember every tiny detail - just enough to recall the object and thus opening up room for space in the brain for other stuff. What is the llm equivalent?
    • sunir 5mo ago
      The current algorithms have a limited context window and work linearly and are extremely expensive to change and energy intensive to run. The human brain has a wide parallel multisystem real-time low-wattage execution layer that has way more modes than a large language model. More importantly, because our brains are real-time, our qualia plus spatial and visual reasoning is superior to an LLM at understanding "elegance", "code smells", and overall system design because we can imagine ourselves as being the code or the system and we don't necessarily need to think in language. Well, at least that's how I experience coding in my mind; I imagine other developers similarly bring large parts of themselves into coding. Feeling the code seems to be much more efficient at reducing complexity than any static analysis I've yet seen. Finally, humans also empathize with other humans who have all the money. We know what works and doesn't work for humans in the here and now, not 2 years ago when the model training data was last collected. The value of Qualia is not to be discounted. That being said, Sonnet 4.0 was the best model I've used that could express how the code felt, so who knows. If the emotionality wasn't tamped down, and the spatial reasoning improved, and the new algorithms for context engineering and parallelism make it to market, these advantages can be erased.
• anilgulecha 5mo ago
  It is of course a multiplier. The worries are: - Lesser overall engineers needed -> lesser demand of human engineers -> lower compensations - insufficient training at junior levels. - longer time to productive human engineering skill. These are playing out right now, and a concern for all engineers in the industry. IronMan amplification don't address the above
• semireg 5mo ago
  Is this just an ad for whimsical animations? Seemed like an abrupt change.
  • renticulous 5mo ago
    It feels like AI topic on HN is going through the phase of when /r/stablediffusion subreddit was going through phase of real artist vs ai artist discussions.
  • ediatedia 5mo ago
    Yeah, this couldn't have really gone any better for the author, opinion piece hitting HN with a link for a course in it. I understand the need to make a living but hard to take this stuff seriously/sincerely with the, "and buy my course!" angle.
  • zimmund 5mo ago
    Came here to comment this. I've already saw another article by this author and it all leads towards the course. Not cool :/
• Ecys 5mo ago
  >AI is a powerful multiplier for people who already have deep technical expertise. The people seeing the biggest wins with AI are already highly skilled. This sentiment will stray further from the truth as time goes on. Sure, it's a multiplier for those who are already skilled, but for those who are unskilled, it is capable of taking you from 0 -> 1+. The ones currently benefiting from AI are the ones who (i) have a general understanding of how an AI works and experience with using it and (ii) have a very generic understanding of what it is they're trying to do (programming, most likely) and know the limits of their tools, but don't know how to actually do anything meaningful. The whole point of AI is to open the door of complexity to normies; they are the ones benefiting most from it. For a skilled developer, it may make a 1hr task -> 5 mins; for a normie, it makes something which was utterly impossible into -> now within his reality to achieve. the difference for normies is just more life-changing. If you think of skilled developers as the ceiling and normies as the floor, AI raises the floor higher by giving normies more capability, which makes the ceiling seem less impressive. But eventually the floor will surpass the ceiling, and then it'll be a matter of who can operate AI better/how good AI is.
  • archimedes237 5mo ago
    I know someone who got AI to make a full minecraft bot gui that will put down waypoints for people to see and dig at and then do an in-game dig search (bot uses jsmacros) and they know zero coding.
• samdjstephens 5mo ago
  Many or even most software engineers are experts in their own codebases though, which means a large proportion of engineers are getting high value out of AI. What’s not clear to me is: if writing more code per engineer is possible, does that result in fewer engineers or just more software, especially in areas that traditionally got squeezed: UX, testing, DevEx, documentation, etc. Perhaps the bar just gets raised?
• rob74 5mo ago
  I agree with more or less everything in the article. "Agentic coding" is great, but you still need to have a good grasp of the overall architecture of your application, and actually check what the agent does, to get the best results. The problem is just that the question is not whether "human developers will be necessary in the near future", it's "how many human developers will be necessary in the near future" - managers wanting to exploit the efficiency gains by deciding that fewer developers can now do more work "thanks" to AI.
• furyofantares 5mo ago
  > So, on the one hand, I’m seeing the most talented developers I know amplify what they can do with AI, and on the other, I’m seeing people with less domain knowledge struggle to get past the “MVP” stage. Those are people who weren't making it to the MVP stage before LLMs. There is no doubt that highly technical people are getting A LOT more out of LLMs than people without dev experience, in an absolute sense. I think it's less clear in a relative sense. A question I also ask myself a lot: What are the skills I'm leveraging, exactly, as a highly experienced developer that's now doing a lot of vibe coding? 1) I'm choosing good technology for the task, and thinking about what LLM-agents are good at and choosing technology that they can work well with. 2) I'm choosing good workflows for the LLM-agent, starting a new context at the right time, having it test things, making sure it has logging that it can inspect, making sure it can operate the application in a way that it can debug and inspect it. 3) I'm thinking about the code even though I'm not looking at it, I'm telling it how I want things implemented, I'm telling it how to debug things. I think these are all hard things for non-developers to do, but I also think non-developers will be able to replicate a large chunk of #1 and #2 relatively quickly. I only have to figure out that it's valuable to tell the LLM-agent to use playwright when working on web page visuals once, and then I can tell you to do that too. Or the coding agents will come with that knowledge built-in (to the model or as a builtin skill or whatever). Knowledge around this will accumulate and become easier for non-developers to access, and in many cases be builtin to the models or harnesses.
• pthambu 5mo ago
  No question about productivity gains - absolute killer. AI isn’t no way threat to SME but how does these agents help on building future SME? I’m not sure I’m learning more like before
• fizza_pizza 5mo ago
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• bluegatty 5mo ago
  "Without guidance, LLMs tend to paint themselves into a corner, because they’re generating code to solve individual prompts, not thinking holistically about an application’s architecture." user error, mostly. But the general argument of 'we will need skilled operators' still holds. For every 'junior' displaced by AI, there will be some other kind of relevant role they're needed for. Agentic workflows, integration, all the data science stuff, new UX paradigms. I don't think the job numbers will dwindle, just shift.
• keybored 5mo ago
  > I want to talk a bit about AI and the related shifts in the tech industry. I know this is top-of-mind for lots of y’all, and you might be wondering if it even makes sense to learn new programming skills in this environment. Y’all sound the same: > Let’s start with an uncomfortable truth: AI models have become shockingly good at completing a wide variety of programming tasks. They’re certainly not perfect, but in many cases, they’re good enough. I’m not happy about this, for a wide variety of ethical/environmental/safety reasons, but it is what it is. More Inevitabilism posting with the “not happy with” but is-what-it-is washing of your hands. At a distance you all look the same: an army of posts insisting the obvious, the inevitable; who knows why you all need to sound the same and say the same thing, but I guess it is to keep it top-of-mind for us alls. It is what it is. > [...] It’s never been easier to learn about new topics, with tools like ChatGPT that can answer any questions you have. But that only works when you know what questions to ask. My course offers a curated curriculum that will introduce you to all sorts of new techniques. I think you’ll be amazed at what you can build after taking the course. Okay, sure. I ask these LLMs things too (c.f. outright --be coding) so that’s not necessarily incongruent with the stance of being not-happy-about-this.
  • jmmv 5mo ago
    > More Inevitabilism posting with the “not happy with” but is-what-it-is washing of your hands OK so, _realistically_, what can you do that will make any meaningful difference?
    • keybored 4mo ago
      Don’t worry. I’m partially[1] resigned. [1] https://news.ycombinator.com/item?id=48237536 https://news.ycombinator.com/item?id=48237536
• deleted 5mo ago
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• travisgriggs 5mo ago
  What is unclear to me is how less skilled people gain useful experience, when using these amplifying tools. I’ve been at this for 35 years; I like to think that sometimes i get some pretty amazing results. I work with two pretty green developers. The rate that they can make a mess is now phenomenal. And the sense of confidence the tools give them with early successes, means any experience I might have to offer means less now. Which is ok, I’m not going to be that “my experience has to be useful to you so I still fell relevant” old guy. But I do find myself curious how “lessons are learned” that lead to greater and greater tool exploitation in this brave new world.
  • rglover 5mo ago
    > But I do find myself curious how “lessons are learned” that lead to greater and greater tool exploitation in this brave new world. (I think I'm reading this the right way but if not feel free to correct). In a word: pain. Until there's a legitimate threat to their well-being (emotional, psychological, or financial), the lessons won't be truly "learned." Until you know the true cost of a decision, you're flying high. Older engineers have dealt with this organically so it's kind of encoded into their DNA. The very reason certain things aren't done (or a certain way) is because that pain has already been felt/encouraged learning a better way.
• simonw 5mo ago
  The more time I spend accelerating my work with AI tools the more I realize how incredibly hard the craft of shipping useful software actually is. Sure, Claude Code and Codex can write (most of) the code for me - but the amount of technical knowledge I need to decide what and how to build remains enormous. As an example: I'm working on a system right now that works like Claude Artifacts, allowing custom HTML+JS apps to safely run in an iframe sandbox inside a larger application. Just understanding why that's a useful thing that can be built requires deep knowledge of sandboxing, security threats, browser security models, and half a dozen different platform features that have been evolving over a couple of decades. A vibe coded without that technical understanding would have zero chance of prompting such a thing into existence, no matter how much guidance the LLMs gave them. It really saddens me to see some developers talk about literally quitting their careers over AI, right when the benefits of existing deep technical experience have never been more valuable.
  • keybored 5mo ago
    I can’t just quit the “career” that I’ve spent years building (for what else?). I’ll just fade somewhat gradually into unemployment, I imagine.
    • simonw 5mo ago
      I firmly believe that your existing skills and experience are more valuable in a world where the AI tools can speed up the bit where you type the code.
      • NiloCK 5mo ago
        It's great that you believe this, but are you hiring? I don't intend this to read as pure snark, but someone's abstract value isn't much good to them if the job market itself can't / won't recognize it.
        • therealdrag0 5mo ago
          You’re right. Hiring depends on revenue streams. Code being cheaper means less coders needed for same code. Less-sr-coders-needed means more senior engineers competing for limited architect/leadership roles. However, the happy path is that as more code is written, more revenue streams will be developed and more sr engineers will be needed and hired to manage those ballooning codebases. That will be a gradual process of growing the pie though.
• rspoerri 5mo ago
  I am working on a project since 10 months that is solely written by ai. The second iteration of the code is even coded in a language that i can really write in (rust), it uses advanced and very complex structures (crdt, plugins, parallel threaded webviews). None of the advanced features were an idea of the llm's, but a vision i had in my mind or requirements that came up when encountered with problems. The software is a tool specifically designed around my requirements of managing lectures that need to be prepared, managed, have presentations, grading etc. I wanted one big space where i can quickly access all related data in a workspace, fold and unfold important aspects while also editing and moving contents across multiple days/lectures. The first version is a vscode plugin, which i now use since about 4 months without or with minor modifications to manage my lectures and private data. The second version is a standalone application which improves on the ideas of the first version and goes a few steps further. AI can make you something that looks like its running quickly. But when you try to finish it takes way longer then you'd think. You need to specify every little detail. You need to make its KISS and DRY etc. You let it analyze the application structure and simplify and cleanup nearly the same amount of times as you add features. While fixing bugs you might need to run the same thing multiple times and revert any unrequired changes. You need to think about good level of debug logs and ways that the program can help you find errors and report them quickly. I hope my project will be ready in about 2 to 3 months. The current version is according to a quick analysis over 850 files with 250'000 lines of code. I spent about 2000$ on ai subscriptions in that time. 200$ claude for a while down to 100$ a month now. 20$ to openai which is very important for architecture and reviews. 20$ on tests with other ai's, but i rarely use them in the works. I also spent 1500$ on 2 * 3090's to hopefully have a local ai agent in the future. I spend about 2 to 4 hours each day (including weekends) to check that app and write prompts. I would never have been able to create such a large and complex project next to my other tasks and i am very confident that the final product will be good enough for productive work.
  • giancarlostoro 5mo ago
    > None of the advanced features were an idea of the llm's, but a vision i had in my mind or requirements that came up when encountered with problems. This is the correct way to code with AI. If you don't understand the code, we're not yet in a point where the model can do it all, well it can, but where you can confidently move forward knowing its been thoroughly built and reviewed by a model up to par. Some day maybe, but not currently.
• techblueberry 5mo ago
  I had this observation talking to Claude one time. I forget the context exactly but I said something like: Me: Isn’t it crazy that X is better than Y. Claude: what an insightful critique, Y is better than X because of x, y, and z reasons. - And this answer from Claude was good. Thoughtful, well-reasoned. But it was opposite the point I wanted to make so I said : Me: “oh, you heard me say Y is better than X, I actually was being counter/intuitive and said X is better than Y” And Claude responded: Claude: oh you’re absolutely right, X is better than Y for the following reasons (and Claude again provided a well reasoned response here) And this is sort of that dumb smart genius meme. “It’s just autocomplete” “No it’s way more than that it has a model in its mind” “It’s just autocomplete” I liken it to the library of babel. All the genius in the world, but only if you have the right index keys.
  • zephen 5mo ago
    It is a well-known trope that LLMs are prediction engines. But, to get the best out of them, you really have to consider what that means in the small. They are predicting, to a first order of approximation, what you want or expect them to answer. Its response to your first prompt is hilarious, because the LLM completely misunderstood you and based its prediction on what it thought you wrote. Its response to your second prompt further cements that its goal is to predict what you want or expect to see. It's also well known that LLMs are prone to hallucinations. One of the biggest triggers for hallucinations is when the LLM's interpretation of your expectations doesn't match reality. Because the LLM will try to make reality match what it perceives to be your expectations. One of the best ways to reduce hallucinations is to work hard to remove any assertions from your prompts. For example "Isn’t it crazy that X is better than Y." contains an explicit assertion. The LLM misunderstood the direction of the assertion, but certainly understood that an assertion was there, and so it gave you reasons why reality matched its understanding of your assertion. When you clarified the assertion, it switched, and again gave you reasons why reality matches its understanding of your assertion. Lawyers often get into trouble for made-up citations. "Claude, find me case law that shows X" is a recipe for disaster. Instead "Claude, what is the case law on X?" is probably a better starting point.
    • 5mo ago
• maxothex 5mo ago
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• sandeepkd 5mo ago
  Its an interesting topic and it will take a while to really know how things pan out. One thing is for sure, it allows some one to ramp up in an unknown knowledge based territory fairly quickly(physical work still requires craftsmanship). It can allow a skilled engineer to have multiplied effect of repeating their skills HOWEVER it would take away their ability to question, think and improve themselves. The syntax highlighting by editors is a good example, most engineers cant work without it, however its a static skill which does not needs constant improvement so its an acceptable support risk.
• sailfast 5mo ago
  I don’t know that people think their jobs will go away because AI will be better. I think most understand that their jobs are going away because we will need fewer engineers to build the software their companies currently need. Now - huge opportunity for new companies and markets, but not sure if they will be as profitable.
• croes 5mo ago
  > I think AI tools are more like Iron Man’s suit. It can do incredible things, but not on its own. What can Tony Stark do if all is done by the suit? What can he do after a year, two years, five years?
• Henchman21 5mo ago
  I've heard it said that AI is like that old Daft Punk song: Harder, Better, Faster, Stronger -- choose 2 and that's what AI gives you.
• boesboes 5mo ago
  Multiplying by 0.5 maybe
  • cobbal 5mo ago
    It makes up for that by also multiplying the productivity and happiness of the people who have to review your code by 0.5 too.
• earcar 5mo ago
  I think the multiplier framing is right. The thing it multiplies most is not typing speed, though, it is judgment. If you know what good looks like, the tools are incredible. If you don't, they help you produce plausible wrong things faster.
• wg0 5mo ago
  I'm a severe AI critic. Bitterly so. But this much I would agree that AI has a multiplying effect for the expert who can still do the job by hand albeit slower no matter how much slower but a good job nevertheless. And still worth repeating that AI has net negative gain in team settings but is a booster for lone wolves like me.
  • anonyfox 5mo ago
    lone wolf here too, can confirm. true fullstack (not only tech parts - from marketing to product to boring IT and financials) makers now have insane speedups, IF they previously did everything by hand properly and found ways to be good at it solo. Often the only scarce resource was your own time (or more correct: attention/focus hours per day) for execution and research. Now offloading stuff to agents, especially since you know the domain and the shortcomings by heart and when to (not) not trust AI is supercharged. People fail to reap benefits for organizational scale and mostly fail completely, they try to make AI fit a human process spaghetti theater somehow, but a lone wolf can just change himself entirely in days and be adapted and leverage AI-first if he wants to. just like that. no coordination needed beyond rewriting personal scripts, which is fast with LLMs too.
• siliconc0w 5mo ago
  I agree that every so often you have to clean up a mess and the illusion breaks. Even with a super detailed spec, even with AGENTS and SKILLs specifying certain patterns or practices, even with 'fresh eyes' reviews from other agents, etc there are still these long tail of issues where I have to either hand hold the agent or just manually rework the code. Some examples: * it cheats at verification. Even with specific instructions how to verify, it still cheats. * generating UX(CLI tool) that is absolute garbage and inconsistent, even with specific instructions to minimize unnecessary flags, use convention over configuration ,etc. * it absolutely will not go 'above and beyond' to solve problems - if task is hitting a permission or dependency barrier, it'll likely cheat or handwave the problem away. (gpt 5.5 xhigh) There is maybe this hope/hubris that we can figure out just the right incantations or agent workflows to eliminate these issues - I was optimistic about this too but after trying for awhile and seeing them not only not go away but in some cases regress with newer models, I am less sure.
  • zephen 5mo ago
    > it cheats at verification. Even with specific instructions how to verify, it still cheats. As I responded to another commenter, as a prediction engine, the LLM is trying to predict what you want. It, at one level, correctly predicts that you want tests to pass. Maybe try telling the LLM that you're a verification engineer, and you get bonuses for finding bugs? Think about it. All those security researchers wouldn't be finding real bugs in real programs using LLMs if this were an insurmountable problem.
• ChicagoDave 5mo ago
  Finally, a post that explains the difference between skill+GenAI > anyone+GenAI. One is augmentation and the other is whack-a-mole. Excellent read.
• irchans 5mo ago
  I also "had an Iron Man moment last week" as a mathematician. I've been doing joint math research with two friends (professors) on a project for several years. Last week, I decided to explore part of our research using chat GPT. 1) I would have a thought. 2) Present it to GPT. 3) Ask GPT to write theorems that were easy to prove and put those proofs into LaTeX. (I always have to check the proofs carefully.) 4) Then I would ask it to generate code (I mostly use Mathematica the language.) 5) Running the code would help verify the proofs. It would also inspire more thoughts and I would go back to step 2). This worked very well, but at one point I could not bound a particular expression and I was not understanding it well, so I got out the pencil and paper and redid the derivation myself which helped a lot. This whole process worked about 10 times faster than doing it without GPT. At the end of a couple of hours I had around 20 pages of correct proofs and all the code needed to do numerical simulations related to the proofs.
  • ieieie 5mo ago
    So why are you using ChatGPT?
    • Ey7NFZ3P0nzAe 5mo ago
      > This whole process worked about 10 times faster than doing it without GPT.
• piposlab 5mo ago
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• hoppp 5mo ago
  Yeah I mean I can now program in every language. Before I used to learn them and it took me a week to a month to start working with a new language (talking about similar syntax languages) Now I can just get to it. I know what I want and can organize the codebase, whatever the code is I can generate it.
• alexbufarini 5mo ago
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• wccrawford 5mo ago
  I think that it's not a multiplier on skills. It's a reducer of time. For less experienced developers, it's an immediately reduction at the start of a project. But then they will almost certainly have problems later when their initial decisions come back to haunt them. For senior devs, it's like having a junior or mid-level dev that will instantly do things within their capability, so long as it's explained to them well enough. This junior dev will do things fairly smartly, but any important decisions left to them will be wholly or subtly wrong. And the subtle ones are the worst ones, because they're so hard to detect. But if that senior dev sets the guidelines well enough, and notices the problems, development is so, so, so much faster. It's wild.
  • steve_adams_86 5mo ago
    > I think that it's not a multiplier on skills. I'm not sure. It's a reducer on time to refine and adopt skills, if you're interested. I know that's somewhat pedantic, but there really is a skill multiplier if that's what you want out of it. I'm far, far better at using AWS than I was after years of using it (for better or worse). I use the command line more effectively than ever. These are real skills that have come to me as a result of that reduction of time to get the answers I wanted. This applies to all kinds of things in my work, and it's been quite liberating to have this incredible tightening of temporal scales in order to get where I'd like to be, resulting in actual differences in my own outputs and capabilities. Still, I agree that time really is a key facet. I could have found this information before, too. It just took so long to do it.
  • QuercusMax 5mo ago
    It can spit out a utility bash or python script lightning fast, which is a real game changer. I wanted to run a stupid little webserver on one of my raspis, so I just asked Gemini to write my the code, along with a bash script to set up the proper configs to run as a systemd service. Nothing I couldn't basically do in my sleep, but it takes some amount of time and focus to do. In the time it took me to write this comment, it wrote exactly what I needed. Not that impressive in isolation, but now I've been doing all the little home automation things I never had the energy for with other responsibilities.
• block_dagger 5mo ago
  > You could give me Jimi Hendrix’s exact guitar but it would sound very different if I tried to play it! Guitars do not think. AI does. The analogies that try to paint AI as "just another inanimate tool" are way off base, and so is the conclusions of this article.
  • tj-teej 5mo ago
    I think the better analogy is "you could give me an afternoon in the studio with Jimi Hendrix's sound engineer and the record we create would sound very different from Jimi's albums."
  • deleted 5mo ago
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  • joshwcomeau 5mo ago
    I guess that depends on how you define "think". I wouldn't say that AI thinks. I admit the analogies aren't perfect, but the analogies are mostly used to help explain the empirical stuff I’m seeing in the real world. Are you seeing something different?
• znnajdla 5mo ago
  Bingo. AI is NOT making raw skill/talent obsolete, rather it’s actually making it more valuable. Deep technical knowledge now has even more leverage in the real world, not less, because you then have more “surfaces” to apply the use of AI. And this realization is what actually inspired me to build my own homelab datacenter to host my tech SaaS rather than use cloud services like AWS. The value of learning basic networking, devops, and server hardware is now multiplied because that expertise can be applied faster and farther with AI. Before AI I would have to spend several hours or days learning RouterOS, for example, to be able to configure a datacenter-class Mikrotik router. With Claude that became a 20 minute job and I learned a lot about routing configuration in the process — that gives me unique controls over my product offering that I simply wouldn’t have if I “just used the cloud”. In fact I am actually tempted to build my own OS — something I wouldnt have dared to think of before AI.
  • simulator5g 5mo ago
    [Child]: Moooom can we get Terry Davis? [Mom]: We have Terry Davis at home. [znnajdla]: In fact I am actually tempted to build my own OS — something I wouldnt have dared to think of before AI.
  • AngryData 5mo ago
    Im not so sure, people probably thought the same thing about power tools and nail guns. They allow the house to be built much faster, but wages dropped, work quality went down, and the value of having skills and experience was severly diminished. Plastering walls use to be a great paying skilled job, and when drywall came out and everyone thought that meant less time making boring flat walls and more time doing fancy plasterwork in corners and edges. But the fancy corners and edgings disappeared, it took too long compared to the rest of the wall plane and people who did it still wanted decent pay for maintaining or building that skill. And even for plain drywallers, productuon demands went up while wages stagnated. And now these days most drywall is seamed like trash and most guys doing it are desperate and/or addicts. The only thing that earns money now in drywall and plaster is meth head production speed and a lack of complaints about the work.
• bartread 5mo ago
  > AI has a multiplying effect on existing technical skills It also has a multiplying effect on technical deficits. If you habitually demonstrate poor attention to detail when developing software, AI will amplify that too. LLMs project who we are back at us, amplified, for good or ill, and I’m starting to wonder exactly how deep that runs.
• ath3nd 5mo ago
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• trinsic2 5mo ago
  > Without guidance, LLMs tend to paint themselves into a corner, because they’re generating code to solve individual prompts, not thinking holistically about an application’s architecture. Yes this is what I have been experiencing as well. I kind of live in a bubble around my own technical expertise, but it seemed only natural that you would have to guide the model. I'm new to AI as of February. Someone suggested I use it to upgrade my Hugo site and that was all I needed. No I am converting a CRM to obsidian and Rewriting some Obsidian plug-ins to do what I want them to do. For example, I am using an Obsidian plug-in to do presentations from my markdown files "Advanced Obsidian Slides" but it styles the slides using absolute styling. Every time I tried to the prompt model to improve display elements in that styling I spent a lot of time asking it to go back and try something else to get the results I wanted. Finally I decided to rewrite the plugin with a fluid design with in the fixed boundary of the slides. Instead of using floats I switched to Flexboxes and all my problems were solved in one-shot situations. The moral of the story is don't expect the model to do what you want it to do without the proper framework. And research correct terms, you will save yourself a lot of time prompting/writing instruction docs that the LLM will end up implementing.
• akomtu 5mo ago
  The effect of AI on technical skills is closer to the famous Microsoft's strategy - Embrace, Extend, Extinguish. First you embrace AI because it appears so helpful: it can do boring stuff for you. Next you change the way you work and think to better leverage the AI greatness: now you verify half-assed AI output instead of making your own, you switch between 5-6 parallel projects done by AI instead of focusing on one thing, you find clever ways to prompt AI instead of building a mental model of your work and so on. You are an extension of AI at this point. Eventually you delegate all thinking to AI, because why bother? Your mind atrophies, while AI becomes good enough to replace you and effectively extinguish.
• fathermarz 5mo ago
  100% could not agree more. Was hired to fix a vibe coded app for a company that one of their people whipped up over five months. Tens of thousands of lines of code in a single file and no security whatsoever. The guy uses Claude Code, same as me… it’s like a highly skilled mason with a chisel, against me with a chisel. I’m not going to produce the same masterpiece, because there is SME that underpins the accelerant.
• deleted 5mo ago
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• muralisid 5mo ago
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• effnorwood 5mo ago
  and lack thereof
• mik09 5mo ago
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• HironoOcto 5mo ago
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• sharts 4mo ago
  Do all the folks complaining that the code looks trash, even realize that this is just like having V1 of a feature? Even early stage startups have folks slapping together PoCs / MVPs, etc. that most would consider trash after seeing it years later. The trash creates some technical debt (and job security maybe) but you actually have something that can ship and be validated as being worth further investment sooner than later. One can always go back and do better V2. In fact, surely in a few years models would be good enough to even do that V1->V2 as well.