17 ms·

The future of software development is software developers

421 points by cdrnsf 10mo ago

simonw 10mo ago
I nodded furiously at this bit: > The hard part of computer programming isn't expressing what we want the machine to do in code. The hard part is turning human thinking -- with all its wooliness and ambiguity and contradictions -- into computational thinking that is logically precise and unambiguous, and that can then be expressed formally in the syntax of a programming language. > That was the hard part when programmers were punching holes in cards. It was the hard part when they were typing COBOL code. It was the hard part when they were bringing Visual Basic GUIs to life (presumably to track the killer's IP address). And it's the hard part when they're prompting language models to predict plausible-looking Python. > The hard part has always been – and likely will continue to be for many years to come – knowing exactly what to ask for. I don't agree with this: > To folks who say this technology isn’t going anywhere, I would remind them of just how expensive these models are to build and what massive losses they’re incurring. Yes, you could carry on using your local instance of some small model distilled from a hyper-scale model trained today. But as the years roll by, you may find not being able to move on from the programming language and library versions it was trained on a tad constraining. Some of the best Chinese models (which are genuinely competitive with the frontier models from OpenAI / Anthropic / Gemini) claim to have been trained for single-digit millions of dollars. I'm not at all worried that the bubble will burst and new models will stop being trained and the existing ones will lose their utility - I think what we have now is a permanent baseline for what will be available in the future.
- thisoneisreal 10mo ago
  The first part is surely true if you change it to "the hardEST part," (I'm a huge believer in "Programming as Theory Building"), but there are plenty of other hard or just downright tedious/expensive aspects of software development. I'm still not fully bought in on some of the AI stuff—I haven't had a chance to really apply an agentic flow to anything professional, I pretty much always get errors even when one-shotting, and who knows if even the productive stuff is big-picture economical—but I've already done some professional "mini projects" that just would not have gotten done without an AI. Simple example is I converted a C# UI to Java Swing in less than a day, few thousand lines of code, simple utility but important to my current project for <reasons>. Assuming tasks like these can be done economically over time, I don't see any reason why small and medium difficulty programming tasks can't be achieved efficiently with these tools.
- underdeserver 10mo ago
  Aren't they also losing money on the marginal inference job?
  - simonw 10mo ago
    I think it is very unlikely that they are charging less money for tokens than it costs them to serve those tokens. If they are then they're in trouble, because the more paying customers they get the more money they lose!
    - anonzzzies 10mo ago
      I guess that depends on the user; most people are not getting most out of flat priced subscriptions. Over all they probably make a profit, and definitely on API use, but some will just spend a lot more. It'll get cheaper though; they are still acquiring as long as there is VC money.
    - mslt 10mo ago
      Operating at a loss to buy market share is pretty much the norm at this point. Look behind the curtain at any “unicorn” for the past 3 decades and you’ll see VCs propping up losses until the general population has grown too dependent on the service to walk away when the pricing catches up to reality.
- nrhrjrjrjtntbt 10mo ago
  Hardest part of programming is knowing wtf all the existing code does and why.
  - doug_durham 10mo ago
    And that is the super power of LLMs. In my experience LLMs are better a reading code than writing it. Have it annotate some code for you.
    - nrhrjrjrjtntbt 10mo ago
      I do!
    - elboru 10mo ago
      Still, code describes what and how, but not why.
- cmrdporcupine 10mo ago
  Indeed, while DeepSeek 3.2 or GLM 4.7 are not Opus 4.5 quality, they are close enough that I could _get by_ because they're not that far off, and are about where I was with Sonnet 3.5 or Sonnet 4 a few months ago. I'm not convinced DeepSeek is making money hosting these, but it's not that far off from it I suspect. They could triple their prices and still be cheaper than Anthropic is now.
- boogieknite 10mo ago
  maybe not the MOST valuable part of prompting an LLM during a task, but one of them, is defining the exact problem in precise language. i dont just blindly turn to an LLM without understanding the problem first, but i do find Claude is better than a cardboard cutout of a dog
- omnicognate 10mo ago
  > claim to have been trained for single-digit millions of dollars Weren't these smaller models trained by distillation from larger ones, which therefore have to exist in order to do it? Are there examples of near state of the art foundation models being trained from scratch in low millions of dollars? (This is a genuine question, not arguing. I'm not knowledgeable in this area.)
  - simonw 10mo ago
    The DeepSeek v3 paper claims to have trained from scratch for ~$5.5m: https://arxiv.org/pdf/2412.19437 https://arxiv.org/pdf/2412.19437 Kimi K2 Thinking was reportedly trained for $4.6m: https://www.cnbc.com/2025/11/06/alibaba-backed-moonshot-releases-new-ai-model-kimi-k2-thinking.html https://www.cnbc.com/2025/11/06/alibaba-backed-moonshot-rele... Both of those were frontier models at the time of their release. Another interesting number here is Claude 3.7 Sonnet, which may people (myself included) considered the best model for several months after its release and was apparently trained for "a few tens of millions of dollars": https://www.oneusefulthing.org/p/a-new-generation-of-ais-claude-37 https://www.oneusefulthing.org/p/a-new-generation-of-ais-cla...
mohsen1 10mo ago
I really really want this to be true. I want to be relevant. I don’t know what to do if all those predictions are true and there is no need (or very little need) for programmers anymore. But something tells me “this time is different” is different this time for real. Coding AIs design software better than me, review code better than me, find hard-to-find bugs better than me, plan long-running projects better than me, make decisions based on research, literature, and also the state of our projects better than me. I’m basically just the conductor of all those processes. Oh, and don't ask about coding. If you use AI for tasks above, as a result you'll get very well defined coding task definitions which an AI would ace. I’m still hired, but I feel like I’m doing the work of an entire org that used to need twenty engineers. From where I’m standing, it’s scary.
- khalic 10mo ago
  I feel you, it's scary. But the possibilities we're presented with are incredible. I'm revisiting all these projects that I put aside because they were "too big" or "too much for a machine". It's quite exciting
- belter 10mo ago
  >> From where I’m standing, it’s scary. You are being fooled by randomness [1] Not because the models are random, but because you are mistaking a massive combinatorial search over seen patterns for genuine reasoning. Taleb point was about confusing luck for skill. Dont confuse interpolation for understanding. You can read a Rust book after years of Java, then go build software for an industry that did not exist when you started. Ask any LLM to write a driver for hardware that shipped last month, or model a regulatory framework that just passed... It will confidently hallucinate. You will figure it out. That is the difference between pattern matching and understanding. [1] https://en.wikipedia.org/wiki/Fooled_by_Randomness https://en.wikipedia.org/wiki/Fooled_by_Randomness
  - joefourier 10mo ago
    Have you used an LLM specifically trained for tool calling, in Claude Code, Cursor or Aider? They’re capable of looking up documentation, correcting their errors by compiling and running tests, and when coupled with a linter, hallucinations are a non issue. I don’t really think it’s possible to dismiss a model that’s been trained with reinforcement learning for both reasoning and tool usage as only doing pattern matching. They’re not at all the same beasts as the old style of LLMs based purely on next token prediction of massive scrapes of web data (with some fine tuning on Q&A pairs and RLHF to pick the best answers).
d_silin 10mo ago
In aviation safety, there is a concept of "Swiss cheese" model, where each successful layer of safety may not be 100% perfect, but has a different set of holes, so overlapping layers create a net gain in safety metrics. One can treat current LLMs as a layer of "cheese" for any software development or deployment pipeline, so the goal of adding them should be an improvement for a measurable metric (code quality, uptime, development cost, successful transactions, etc). Of course, one has to understand the chosen LLM behaviour for each specific scenario - are they like Swiss cheese (small numbers of large holes) or more like Havarti cheese (large number of small holes), and treat them accordingly.
- heliumtera 10mo ago
  Interesting concept, but as of now we don't apply this technologies as a new compounding layer. We are not using them after the fact we constructed the initial solution. We are not ingesting the code to compare against specs. We are not using them to curate and analyze current hand written tests(prompt: is this test any good? assistant: it is hot garbage, you are inferring that expected result equals your mocked result). We are not really at this phase yet. Not in general, not intelligently. But when the "safe and effective" crowd leave technology we will find good use cases for it, I am certain (unlike uml, VB and Delphi)
- kgwxd 10mo ago
  LLMs are Kraft Singles. Stuff that only kind of looks like cheese. Once you know it's in there, someone has to inspect, and sign-off on, the entire wheel for any credible semblance of safety.
  - tomlue 10mo ago
    how sure are you that an llm won't be better at reviewing code for safety than most humans, and eventually, most experts?
    - hansmayer 10mo ago
      It will only get better at generating random slop and other crap. Maybe helping morons who are unable to eat and breathe without consulting the "helpful assistant".
berdon 10mo ago
There is a guaranteed cap on how far LLM based AI models can go. Models improve by being trained on better data. LLMs being used to generate millions of lines of sloppy code will substantially dilute the pool of good training data. Developers moving over to AI based development will cease to grow and learn - producing less novel code. The massive increase in slop code and loss of innovation in code will establish an unavoidable limit on LLMs.
- 9dev 10mo ago
  That is a naive assumption. Or rather multiple naive assumptions: Developers mostly don’t move over to AI development, but integrate it into their workflow. Many of them will stay intellectually curious and thus focus their attention elsewhere; I’m not convinced they will just suddenly all stagnate. Also, training data isn’t just crawled text from the internet anymore, but also sourced from interactions of millions of developers with coding agents, manually provided sample sessions, deliberately generated code, and more—there is a massive amount of money and research involved here, so that’s another bet I wouldn’t be willing to make.
- AlexCoventry 10mo ago
  I think most of the progress is training by reinforcement learning on automated assessments of the code produced. So data is not really an issue.
- cmrdporcupine 10mo ago
  But they're not just training off code and its use, but off a corpus general human knowledge in written form. I mean, in general not only do they have all of the crappy PHP code in existence in their corpus but they also have Principia Mathematica, or probably The Art of Computer Programming. And it has become increasingly clear to me that the models have bridged the gap between "autocomplete based on code I've seen" to some sort of distillation of first order logic based on them just reading a lot of language... and some fuzzy attempt at reasoning that came out of it. Plus the agentic tools driving them are increasingly ruthless at wringing out good results. That said -- I think there is a natural cap on what they can get at as pure coding machines. They're pretty much there IMHO. The results are usually -- I get what I asked for, almost 100%, and it tends to "just do the right thing." I think the next step is actually to actually make it scale and make it profitable but also... fix the tools -- they're not what I want as an engineer. They try to take over, and they don't put me in control, and they create a very difficult review and maintenance problem. Not because they make bad code but because they make code that nobody feels responsible for.
pointbob 10mo ago
[dead]
aizk 10mo ago
This time it actually is different. HN might not think so, but HN is really skewed towards more senior devs, so I think they're out of touch with what new grads are going through. It's awful.
- ehnto 10mo ago
  What is it that new grads are going through? If you are referring to difficulty finding a job, keep in mind that there is both an economic downturn and an over-hiring correction happening in the industry right now. I imagine the AI industry is indeed having an impact in how management is behaving, but I would not yet bet on AI actually replacing developers jobs holistically.
  - aizk 10mo ago
    I can talk about my own experiences. About a year and half ago I dropped everything to get into tech, a couple years out from completing my civil engineering degree because I hated it. I worked very intensely day in day out building many projects, and I had one project go extremely viral, changing my life. That led to me getting hired at a small AI start up, where I learned an enormous amount, but 9 months later, I still got let go as work had dried up for the product I was working on. Not much I was able to do there unfortunately. Still, I really do believe that due to AI there was just far less work that I could meaningful contribute to, speaking as someone who is more junior in the field. Little bug fixes here and there, organization, grunt work tasks, etc - those are completely automated now. The rungs on the ladder of meaningful progression are growing increasingly wider as eac day goes by. A decade ago I think big tech companies would be fighting to get me on board, but now it's different, That said, to end this comment on some silver lining - I know someone who currently works at a big lab who got an enormous amount of work xp by contributing on OSS projects till he became more senior, so maybe there's more to it. Also one other thought - companies will still have job postings for juniors because a "junior" can be an MIT IOI gold medalist.
mikewarot 10mo ago
>WYSIWYG, drag-and-drop editors like Visual Basic and Delphi were going to end the need for programmers. VB6 and Delphi were the best possible cognitive impedance match available for domain experts to be able to whip up something that could get a job done. We haven't had anything nearly as productive in the decades since, as far as just letting a normie get something done with a computer. You'd then hire an actual programmer to come in and take care of corner cases, and make things actually reliable, and usable by others. We're facing a very similar situation now, the AI might be able to generate a brittle and barely functional program, but you're still going to have to have real programmers make it stable and usable.
EagnaIonat 10mo ago
I read a book called "Blood in the machine". It's the history of the Luddites. It really put everything into perspective to where we are now. Pre-industrial revolution whole towns and families built clothing and had techniques to make quality clothes. When the machines came out it wasn't overnight but it wiped out nearly all cottage industries. The clothing it made wasn't to the same level of quality, but you could churn it out faster and cheaper. There was also the novelty of having clothes from a machine which later normalised it. We are at the beginning of the end of the cottage industry for developers.
- utopiah 10mo ago
  Does the analogy hold though? We had "free clothes" for years, decades now. I don't mean cheap I mean literally free, as in $0.0 software. Cheaper software isn't new. Also there are still clothe designers, fashion runways, and expensive Patagonia vests today. The clothing industry is radically different from back then but it's definitely not gone.
  - EagnaIonat 10mo ago
    It didn't kill everything. Some survived but not to the extent that it was. > The clothing industry is radically different from back then but it's definitely not gone. Small towns had generations of people who had learned skills in making clothing / yarn. To do the work you needed years of experience and that's all you knew. Once the industrial revolution hit they hired low skilled workers that could be dumped at a moments notice. It made whole villages destitute. Some survived, but the far majority became poor. That was one industry. We now have AI at a point to wipe out multiple industries to a similar scale.
  - aidenn0 10mo ago
    I posted elsewhere, but you are looking at the wrong part of the chain. We have cheap (or free) software for large markets, and certain small markets where software developers with hobbies have made something. If every niche that will never be able to afford a large 6-figure custom software could get slop software for an affordable price, then that establishes a foot-hold for working its way up the quality ladder.
aidenn0 10mo ago
In past cases of automation, quantity was the foot-in-the-door and quality followed. Early manufactured items were in many cases inferior to hand-built items, but one was affordable and the other not. Software is incredibly expensive and has made up for it with low marginal costs. Many small markets could potentially be served by slop software, and it's better than what they would have otherwise gotten (which is nothing).
brap 10mo ago
>AGI seem as far away as it’s always been This blurb is the whole axiom on which the author built their theory. In my opinion it is not accurate, to say the least. And I say this as someone who is still underwhelmed by current AI for coding.
john61 10mo ago
It is just the Eliza effect on a massive scale: https://en.wikipedia.org/wiki/ELIZA_effect https://en.wikipedia.org/wiki/ELIZA_effect Reading Weizenbaum today is eye opening: https://en.wikipedia.org/wiki/Computer_Power_and_Human_Reason https://en.wikipedia.org/wiki/Computer_Power_and_Human_Reaso...
- trashb 10mo ago
  I agree the ELIZA effect is strong, additionally I think it is some kind of natural selection. I feel like LLM's are specifically selected to impress people that have a lot of influence. People like investors and CEO's. Because a "AI" that does not impress this section of the population does not get adopted widely. This is one of the reasons I think AI will never really be an expert as it does not need to be. It only needs to adopt a skill (for example coding) to pass the examination of the groups that decide if it is to be used. It needs to be "good enough to pass".
  - molteanu 10mo ago
    I got this wild idea a short while ago and your comment helped cement it: probably one of the reasons why languages like Lisp are not "successful" has something to do with the impressability factor? If the people with money (and the decision) do not understand the tech or are not able to even fake that understanding, will they bet their money on it?
    - andrekandre 10mo ago
      > If the people with money (and the decision) do not understand the tech or are not able to even fake that understanding, will they bet their money on it? aka marketing
zkmon 10mo ago
I see it as pure deterministic logic being contaminated by probabilistic logic at higher layers where human interaction happens. Seeking for human comfort by forcing computers to adapt to the human languages. Building adapters that can allow humans to stay in their comfort zone instead of dealing with the sharp-edged computer interfaces. At the end, I don't see it going beyond being a glorified form-assistant who can search internet for answers and summarize. That boils down to chat bots that will remain and become part of every software component that ever need to interface with humans. Agent stuff is just a fluff that is providing hype-cushion around chat bots and will go away with hype cycle.
pjmlp 10mo ago
As someone having watched AI systems being good enough to replace jobs like content creation on CMS, this is being in denial. Yes software developer are still going to be need, except much fewer of us, exactly like fully automated factories still need a few humans around, to control and build the factory in first place.
- encyclopedism 10mo ago
  I concur with your sentiments. Am puzzled why so many on HN cannot see this. I guess most users on HN are employed? Your employers - let me tell you - are positively salivating at the prospect of firing you. The better LLM's get the fewer of you will be needed.
  - pjmlp 10mo ago
    Denial, like those factory workers at the first visit from the automation company, each one hoping they are the ones elected to stay and overwatch the robots. I have seen projects where translator teams got reduced, asset creation teams, devops head count, support teams on phone lines,... It is all about how to do more with less, now with AI help as well.
laszlojamf 10mo ago
The way I see it, the problem with LLMs is the same as with self-driving cars: trust. You can ask an LLM to implement a feature, but unless you're pretty technical yourself, how will you know that it actually did what you wanted? How will you know that it didn't catastrophically misunderstand what you wanted, making something that works for your manual test cases, but then doesn't generalize to what you _actually_ want to do? People have been saying we'll have self-driving cars in five years for fifteen years now. And even if it looks like it might be finally happening now, it's going glacially slow, and it's one run-over baby away from being pushed back another ten years.
- lynx97 10mo ago
  People used to brush away this argument with plain statistics. Supposedly, if the death statistics is below the average human, you are supposed to lean back and relax. I never bought this one. Its like saying LLMs write better texts then the average huamn can, so you are supposed to use it, no matter how much you bring to the table.
- nevercat 10mo ago
  The self driving car analogy is a good one, because what happens when you trust the car enough to do most of your driving but it suddenly thrusts the controls upon you when it shits the bed and can't figure out what to do? You suddenly realise you've become a very rusty driver in a moment that requires fast recall of skill, but your car is already careening off a cliff while you have this realisation. [The "children of the magenta line"](https://www.computer.org/csdl/magazine/sp/2015/05/msp2015050104/13rRUxASutL https://www.computer.org/csdl/magazine/sp/2015/05/msp2015050...) is a god explanation of this, and is partly why I often dissuade junior devs from pretty user friendly using tools that abstract away the logic beneath them.
anoplus 10mo ago
The future of problem solving is problem solvers
- beeboop0 10mo ago
  [dead]
postexitus 10mo ago
"We really could produce working software faster with VB or with Microsoft Access" Press X to doubt.
TheOtherHobbes 10mo ago
The future of weaving is automated looms, but sheep will still be needed to provide the raw materials.
frankie_t 10mo ago
Just like the pro-AI articles, it reads to me like a sales pitch. And the ending only adds to it: the author invites to hire companies to contract him for training. I would only be happy if in the end the author turns out to be right. But as the things stand right now, I can see a significant boost to my own productivity, which leads me to believe that fewer people are going to be needed.
- sokoloff 10mo ago
  When coal powered engines became more efficient, demand for coal went UP. It went up because vastly more things could now be cost effectively be coal-powered. I can see a future where software development goes the same way. My wife works in science and I see all kinds of things in a casual observation of her work that could be made more efficient with good software support. But not by enough to pay six-figures per year for multiple devs to create it. So it doesn’t get done and her work and the work of tens of thousands like her around the world is less efficient as a result. In a world where development is even half as expensive, many such tasks become approachable. If it becomes a third or quarter as expensive, even more applications are now profitable. I think far more people will be doing something that creates the outcomes that are today created by SWEs manually coding. I doubt it will be quite as lucrative for the median person doing it, but I think it will still be well above the median wage and there will be a lot of it. See: https://en.wikipedia.org/wiki/Jevons_paradox https://en.wikipedia.org/wiki/Jevons_paradox
  - encyclopedism 10mo ago
    Many HN users may point to Jevons paradox, I would like to point out that it may very well work up until the point that it doesn't. After all a chicken has always seen the farmer as benevolent provider of food, shelter and safety, that is until of course THAT day when he decides he doesn't.
    - sokoloff 10mo ago
      It is certainly possible that AI is the one great disruptor that we can’t adapt to. History over millenia has me taking the other side of that bet, seeing the disruptions and adaptations from factory farming, internal combustion engines, moving assembly lines, electrification, the transistor, ICs, wired then wireless telecommunications, the internet, personal computing, and countless other major disruptions.
ChicagoDave 10mo ago
Here’s how I see it. Writing code or building software well requires knowledge of logic, data structures, reliable messaging, and separation of concerns. You can learn a foreign language just fine, but if you mangle the pronunciation, no one will talk to you. Same thing with hacking at software without understanding the above elements. Your software will be mangled and no one will use it.
- chii 10mo ago
  > Your software will be mangled the quality of how maintainable the source code is has no bearing on how a user perceives the software's usefulness. If the software serves a good function for the user, they will use it, regardless of how badly the datastructures are. Of course, good function also means reliability from the POV of the user. If your software is so bad that you lose data, obviously no one will use it. But you are conflating the maintainability and sensibilities of clean tools, clean code and clean workspaces, with output. A messy carpentry workshop can still produce great furniture.
  - ozgrakkurt 10mo ago
    This is bean counter mentality. Personally just don’t believe this is how it works. The intention/perspective of development is something on its own and doesn’t correspond to the end result directly. This is such a complex issue that everything comes down to what someone believes
- encyclopedism 10mo ago
  Software doesn't have to be good academically speaking. It just needs to furnish a useful function to be economically viable. LLM's may not generate the best code but they need only to generate useful code to warrant their use.
snickerer 10mo ago
After working with agent-LLMs for some years now, I can confirm that they are completely useless for real programming. They never helped me solve complex problems with low-level libraries. They can not find nontrivial bugs. They don't get the logic of interwoven layers of abstractions. LLMs pretend to do this with big confidence and fail miserably. For every problem I need to turn my brain to ON MODE and wake up, the LLM doesn't wake up. It surprised me how well it solved another task: I told it to set up a website with some SQL database and scripts behind it. When you click here, show some filtered list there. Worked like a charm. A very solved problem and very simple logic, done a zillion times before. But this saved me a day of writing boilerplate. I agree that there is no indication that LLMs will ever cross the border from simple-boilerplate-land to understanding-complex-problems-land.
- constantcrying 10mo ago
  >After working with agent-LLMs for some years now, I can confirm that they are completely useless for real programming. >They never helped me solve complex problems with low-level libraries. They can not find nontrivial bugs. They don't get the logic of interwoven layers of abstractions. >LLMs pretend to do this with big confidence and fail miserably. This is true for most developers as well. The mean software developer, especially if you outsource, has failure modes worse than any LLM and round-trip time is not seconds but days. The promise of LLMs is not that they solve the single most difficult tasks for you instantly, but that they do the easy stuff well enough that they replace offshore teams.
  - shafyy 10mo ago
    > The promise of LLMs is not that they solve the single most difficult tasks for you instantly, but that they do the easy stuff well enough that they replace offshore teams. But that's exactly the *promise* of LLMs by the hypepeople behind it.
    - constantcrying 10mo ago
      >But that's exactly the promise of LLMs by the hypepeople behind it. I do not know and do not care what the "hypepeople" say. I can tell you that, by pure logic alone, LLMs will be superior at simple and routine tasks sooner, which means they will compete with outsourced labor first. LLMs need to be measured against their competition and their competition right now is outsourced labor. If an LLM can outperform an offshore team at a fraction of the cost, why would any company choose the offshore team? Especially when the LLM eliminates some of the biggest problems with offshore teams (communication barriers, round trip times). If LLMs take any programmer jobs they will at the very beginning make those outsourced jobs obsolete, so the only relevant question is whether they have done that or are in the process of doing so. If they don't, then their impact will be minimal, if they do, then their impact will be massive. I think that this line of thinking is a far better benchmark then asking whether an LLM gets X or Y question wrong Z% of the time.
deleted 10mo ago
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trashb 10mo ago
> Edgar Dijkstra called it nearly 50 years ago: we will never be programming in English, or French, or Spanish. Natural languages have not evolved to be precise enough and unambiguous enough. Semantic ambiguity and language entropy will always defeat this ambition. This is the most important quote for any AI coding discussion. Anyone that doesn't understand how the tools they use came to be is doomed to reinvent them. > The folly of many people now claiming that “prompts are the new source code”, These are the same people that create applications in MS Excel.
- AnimalMuppet 10mo ago
  Further evidence: After all these years (far longer than we have been doing programming), we still don't do math in English or French or Spanish. We do it in carefully defined formal notations. But so many problems (programming, but also physics and math) start as informal word problems. A major skill is being able to turn the informal into something formal and precise.
- bdangubic 10mo ago
  > These are the same people that create applications in MS Excel. Ones that want their application to work? :) the last piece of software you should be knocking is MS Excel, in my 30+ year career the one common thread just about everywhere I worked (or contracted at) has used Excel to run some amazing sh*t
  - newsoftheday 10mo ago
    Everywhere I've worked as a software engineer the past 30 years I've seen Excel spreadsheets but rarely anything amazing, maybe once back in the 1990's at one place by an Excel guru but those are rare. 00% of the time Excel is used to make essentially table layouts of data maybe with some simple cell calculations.
    - bdangubic 10mo ago
      I dunno much but I do know that if you can start a business that replaces Excel spreadsheets with an application(s) that your business builds you'd be the World's first trillionaire (many "tri" over) :)
constantcrying 10mo ago
Totally delusional. The article does not even try to figure out why any of this happened. In all of the cases the main prediction that was made came true. The cost, especially the human cost, of developing some piece of software dramatically decreased. The only reason why the amount of programmers needed still rose was because the amount of software needed rose faster. Clearly that trend will not hold forever. >The hard part of computer programming isn’t expressing what we want the machine to do in code. The hard part is turning human thinking – with all its wooliness and ambiguity and contradictions – into computational thinking that is logically precise and unambiguous, and that can then be expressed formally in the syntax of a programming language. And there is exactly one single technology which has ever been able to do this task, which is LLMs. Not addressing the elephant in the room, which is that an LLM can actually take such instructions and produce something meaningful with it, just makes the whole article worthless. Everything in this article is just inverse special pleading. Since the last N times, the most enthusiastic predictions did not come true, this time only minor changes can happen. If LLMs are only a revolution on the scale of fast interpreted languages (which have significantly impacted what a small team is capable of delivering in terms of complexity), then they will drastically impact most of the software industry. If these changes happen, and simultaneously the rate at which software is demanded does not also increase (why would it?), then the implications will be extremely serious. Especially if you are not a developer in an established position.
rr808 10mo ago
The biggest threat to American software engineers is outsourcing, AI is just a distraction. I am an immigrant, I work at a prestigious financial corporation in NYC. Pretty much 95% of the staff were born and did undergraduate degree in other countries. We hire a few grads but they usually quit or get laid off after a few years - most new hires are H1Bs or contractors on H1Bs. Just about to open another big office in a developing country.
- bdcravens 10mo ago
  Perhaps, but that's been going on for decades.
tonnydourado 10mo ago
I have been programming professionally (i.e., getting paid for it) for a much more modest 13 years, but unlike a quite large portion of my peers, I am actually interested in the history of our field. So, yeah, I agree.
twoodfin 10mo ago
I don’t recall seeing entire economies betting on 4GLs. Not even a nitpick because the scale is indeed orders of magnitude different, but… There was a lot of chatter, government attention, and both public and private money chasing 4GL’s in the 1980’s, due to what turned out to be (for the US, at least) a phantom “software crisis”. It was mostly for naught? Same in Japan, where you can argue about the motivations of MITI’s “Fourth Generation Project”, but at its software core was a primeval 4GL in the form of Prolog. Their perceptions of a Japanese software crisis were ultimately more well-founded.
csomar 10mo ago
I really wonder why people aren't drawing comparisons with crypto/blockchain. In the beginning, there were also two camps: people who thought crypto would replace our entire financial system and everything would be tokenized on the blockchain, and another camp who predicted that within a few years bitcoin would be worth exactly zero and all crypto/blockchain companies would disappear. The reality turned out to be somewhere in the middle. Crypto didn't replace our financial system, but it exists as a 1-2 trillion dollar segment serving a particular (though controversial) niche of the global economy. It's not going to zero anytime soon. I think AI/LLMs will follow the same path. There's definitely a level of usefulness there. But we've clearly hit a ceiling since 3.5/4.0. Advancement has only happened in benchmarks and open source models. Also, the idea that a neural net that accepts a fixed amount of padded tokens and returns a list of probabilities will replace the complexities of the human brain is delusional at best. The only real issue I see is that certain actors in the US have taken such large positions that unwinding them could potentially destroy the US economy at worst, or trigger a recession at best. But this mainly concerns the US, which is on an AI high at the moment.
farazbabar 10mo ago
I am good at software. It turns out that isn’t sufficient, or alternatively stated, you have to be good at a number of other things than just churning code, even good code. So to me, the combination of being good at software, understanding complexity and ability articulate it concisely and precisely, when combined with the latest and greatest LLMs, is magic. I know people want to examples of success, I wish I could share what we are working on, but it is unbelievable how much more productive our team is, and I promise, we are solving novel problems, some that have not been tackled yet, at least not in any meaningful way. And I am having time of my life doing what I love, coding. This is not to downplay folks who are having a hard time with LLMs or agents, I think, it’s a skill that you can learn, if you are already good at software and the adjacencies.
- newsoftheday 10mo ago
  > This is not to downplay folks who are having a hard time with LLMs or agents, I think, it’s a skill that you can learn, if you are already good at software and the adjacencies. Someone on the page already quoted Dijkstra, recommend reading that, he was correct. Prompt engineering isn't engineering at all, it's throwing words at a wall to see which ones stick then declaring success if the outcome looks at all recognizable. That isn't actually success.
alexwhb 10mo ago
As much as my first gut reaction to this article was to be excited about its conclusion, I can’t say my experience matches up. Are LLMs perfect? Absolutely not, but I can point to many examples in my own work where using Codex has saved me easily a week or more—especially when it comes to tedious refactors. I don’t agree with the conclusion; the real-world improvement and progress I’ve seen in the last year in the problem-solving quality of these agents has been pretty astounding. The reason for my excitement about the conclusion is obvious: I want programmers and people to stay in demand. I find the AI future to be quite bleak if this tech really does lead to AGI, but maybe that’s just me. I think we’re at a pretty cool spot with this technology right now—where it’s a powerful tool—but at some point, if or when it’s way smarter than you or me… I'm not sure that's a fun happy future. I think work is pretty tied to our self worth as humans.
- jimbo808 10mo ago
  > but I can point to many examples in my own work where using Codex has saved me easily a week or more Care to share a few of these examples?
  - alexwhb 10mo ago
    Sure thing. I work on a few projects for my company. The main one is an Android and iOS audiobook-media-player app. I had to update the Android side to use the Google Media3 library instead of the legacy ExoPlayer library. In a typical app this would be pretty straightforward, but we’ve mixed in a lot of custom elements that made the transition quite challenging. I actually attempted it manually back in the day and probably spent three or four days on it, but I simply didn’t have time to finish, so I shelved it. A few months ago I tried again in Codex; within two prompts it was done flawlessly and starting from the beginning. Another example: I also maintain the back-end API for these apps. There was a lot of old utility code that was messy, unorganized, and frankly gross. I had Codex completely reorganize the code into a sensible folder structure and strip out any functions that weren’t being used anymore—something like 300-plus functions. Doing that by hand would easily have taken a day or more I could go on, but those were the two initial “aha” moments that showed me how powerful this tool can be when it’s used for the right tasks.
jader201 10mo ago
I feel like the comments/articles that continue to point out how LLMs cannot solve complex problems are missing a few important points: 1. LLMs are only getting better from here. With each release, we continue to see improvements in their capabilities. And strong competition is driving this innovation, and will probably not stop anytime soon. Much of the world is focused on this right now, and therefore much of the world’s investments are being poured into solving this problem. 2. They’re using the wrong models for the wrong job. Some models are better than others at some tasks. And this gap is only shrinking (see point 1). 3. Even if LLMs can’t solve complex problems (and I believe they can/will, see points 1 and 2), much of our jobs is refactoring, writing tests, and hand coding simpler tasks, which LLMs are undeniably good at. 4. It’s natural to deny LLMs can eventually replace much/all of what we do. Our careers depend on LLMs not being able to solve complex problems so that we don’t risk/fear losing our careers. Not to mention the overall impact to the general way our lives are impacted if AGI becomes a reality. I’ve been doing this a while, and I’ve never seen the boost to productivity that LLMs bring. Yes, I’ve seen them make a mess of things and get things wrong. But see points 1-3.
- theshrike79 10mo ago
  Also 5: "But LLMs produce a bunch of code I need to read and review". Yes, but so do your coworkers? Do you complain about every PR you need to read? Are they all compressed diamonds of pure genious, not a single missed character or unoptimised function? Not a bad or suboptimal decision in sight? If so, shoot me a mail, I want to work where you're working =)
  - adonovan 10mo ago
    My coworkers learn, and an important part of my job is teaching them. LLM-based tools don't.
    - theshrike79 10mo ago
      A circular saw doesn't learn either. It's a tool, just like an LLM. The LLM isn't replacing your coworkers, it's a tool they can (and IMO should) learn to use, just like an IDE or a debugger.
Twey 10mo ago
‘Obviously’ AI and programming are antonyms. AI is defined as the ability for computers to do whatever it is that humans can do that (most) computers can't currently do. Conversely, ‘programming’ is whatever activity is required to fully take advantage of computer hardware that (most) humans can't currently do. Both sets of goalposts are firmly affixed to their wheels :)
solaire_oa 10mo ago
Most people in this thread are quibbling about the exact degree of utility LLMs provide, which a tedious argument. What's more interesting to me is, per the article, the concern regarding everyone who is leaning into LLMs without realizing (or downplaying) the exorbitant, externalized cost. Our current LLM usage is being subsidized to the point of being free by outside investment. One day when the well runs dry, you must be able to either pay the actual cost (barring grand technology breakthroughs), or switch back to non-LLM workflows. I run local LLMs infrequently, and every single prompt makes my beefy PC sounds like a jet engine taking off. It's a great reminder to not become codependent.
- bogzz 10mo ago
  I am sorry, but this kind of level-headed and realistic take is completely unacceptable on hackernews, and you should be ashamed of yourself. This is not a place for rational discussion when it comes to LLMs. LLMs are amazing and they will change everything, and then everything will be changed.
- JeremyNT 10mo ago
  This is a good point but you can see the price "ceiling" by examining the prices for PCs that can effectively run local models. A DGX Spark is ~$4k (plus power) for example. That's not nothing, but it's still not very much to pay compared to e.g. the cost of a FTE.
  - __float 10mo ago
    You're counting the cost of running the model, but what about training it? You can't count the compute and data costs at $0.
    - philipkglass 10mo ago
      You can assume that already-published open weights models are available at $0, regardless of how much money was sunk into their original development. These models will look increasingly stale over time but most software development doesn't change quickly. If a model can generate capable and up-to-date Python, C++, Java, or Javascript code in 2025 then you can expect it to still be a useful model in 2035 (based on the observation that then-modern code in these languages from 2015 works fine today, even if styles have shifted).
dfilppi 10mo ago
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dilly-dally 10mo ago
The more articles written like this only reflect the inevitable. This is not an era of slow progress which may have supported the authors opinion. This era is a rapid replacement of what was once dominated by masters of one who edged out others and arrogantly tried to hold onto their perceived positions of all knowing. There will always be those types unfortunately, but when the masters of one realize theyve wasted time stalling the inevitable, instead of accepting and guiding the path forward and opening the door to a broader audience, you will see a lot more articles like this which are a clear signal to many of us that theyre simply doing and saying too much trying to hold on to their perceived worth.
harrisreynolds 10mo ago
I think "The Bitter Lesson" is relevant here [1]. This wave IS different. It isn't a matter of "if" but "when". I am a long-time software developer too... but I am strongly encouraging people to embrace the future now and get creative in finding ways to adapt. There will always be demand for smart and creative people. BUT ... if you don't look up and look around you right now at some point you will be irrelevant. Also see Ray Dalio's "Principles" book on embracing reality. A critical principle in the modern age of AI. Nothing but love for my fellow developers!! [1] http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas.net/IncIdeas/BitterLesson.html
- robofanatic 10mo ago
  why didn't ready to eat meals available so cheaply at any grocery store make the local mom and pop restaurants irrelevant?
  - harrisreynolds 10mo ago
    People like experiences! Plus those meals are disgusting! :-) Plus plus… those meals are actually expensive!!
QuiEgo 10mo ago
I think the only thing we can say about the future of LLMs is “we don’t know.” Everything is changing too fast. Models from a year ago are already obsolete. We seem to be hitting the top of an asymptote on improvements, but things have not been steady state for long enough to know. I also agree with the author that the VC money is driving all of this, and at some point the check is going to come due. This thread is full of antidotes from “AI is useless for me” to “AI changes everythingfor me” I believe each and every one of them. In firm wait and see mode.
kenforthewin 10mo ago
Just a reminder that everyone copes with change differently.
FloorEgg 10mo ago
AI is like electricity. Agentic IDEs are like power tools. While most construction work today uses power tools, there are still lots of artisans productively using hand tools. LLMs can be useful and developers can still be productive without them. Things get weird when we confuse the developers with the power tools.
beeboop0 10mo ago
there's a huge flaw in the logic here but I can't pinpoint it we've never had machines that can reason. today, they reason badly. tomorrow, they'll reason better. not all ai research white papers will pan out, but some will. the ai research community is acutely aware of the limitations in reasoning; it's their whole mission right now. 100% chance someone will make progress. Analogous to the chip industry or self driving, in that regard incremental improvements in reasoning will broaden the dead zone for human developers what difference does it make if in 20 years NASA still needs a few human developers but the rest of us are unemployed? ai agents still can't get you to the moon with one shot coding session in 2045? who cares? the "still can't" we really need to be worried about is "politicians still can't consider UBI"
deleted 10mo ago
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Havoc 10mo ago
This is the usual „but they can’t do the hard stuff“ argument. It’s not the right lens - at least not for employment. If you need three programmers to deliver a project, one doing boilerplate, one intermediate and one doing hard task. And the AI can’t do the hard task then you’ve still got two unemployed dudes. So yeah you can make the technically correct argument that it still needs a programmer but it glosses over the coming storm in real world job market. The only other option to square that circle is to assume that A) we suddenly need 3x as much projects and B) boilerplate guy can somehow graduate to hard tasks
- performative 10mo ago
  i hope the collective realization that "hard task" guys were once intermediate or boilerplate guys comes sooner than later :(
  - Havoc 10mo ago
    I think the realisation is already there but nobody knows what to do about it. Companies won’t hire people just to train.
badcryptobitch 10mo ago
One thing that many of these kinds of articles don't really talk about but that also emphasizes their point is that there will be an increase in interfaces/languages for software developers to do increasingly specialized for work. As such, there will be more compilers, vms, dsls, etc which means that there will be more of a demand for developers who have this skillset. Ideally, this should lead to more folks becoming developers or at least acquiring skills that professional developers have in order to make effective use of these tools.
- XenophileJKO 10mo ago
  I would agrue as LLMs increase in capability, the demand for new languages will collapse.
  - badcryptobitch 10mo ago
    My counterargument for that is that LLMs need to communicate not only between themselves and humans but LLM to LLM communication. We will get more niche and domain specific languages designed for LLMs as a result.
bitwize 10mo ago
The future of software development is systems analysts. It was discovered in the 1970s (at the latest) that the hard part of software is figuring out WHAT to build not HOW to build it—and the two should be separate responsibilities with separate personnel bearing separate talents. (Do NOT let your programmers do systems analysis!) Furthermore, it was discovered that without a clear, precise description of what to build, even the most talented programmers will happily use industry best practice to build the wrong thing, and that is worse than useless: it's a net cost to the business. This is something that programmers have in common with LLMs. So it turns out, in order to build software correctly and cost-effectively, you need to perform lots of systems analysis up front, then through a stepwise refinement process design and document your solution, yielding a detailed spec. It also turns out that LLMs, like human programmers, do just fine if you give them a detailed spec and hold them accountable to that spec! So much of the "grunt work" of programming can be handed to the LLM—IF you perform the necessary systems analysis up front to determine what needs to be built! (Bryce's Law: Programming is a translation function, taking human-readable requirements to machine-executable instructions.) As the AI era matures we are either going to see a revival of PRIDE—the original and still most complete software development methodology, but minus the programmers Milt and Tim Bryce loathed so much—or the entire collapse of software as a field.
jollyllama 10mo ago
The bit about drag-and-drop and Visual Studio hides a key insight: insofar as those tools allowed non-software engineers to build applications that were key to certain business processes, they were tech-debt accelerators. It is true to a very large degree; there are still businesses out there depending on shoddy VB applications that were built by someone that didn't know what they were doing, and they are hobbled by it. LLM-generated applications are the turbocharged equivalent of this, and I anticipate that giant spagehetti codebases will be made out of them, the likes of which have never been seen before, and the workflows that are wedded to them will be doomed.
alex_young 10mo ago
Edgar Dijkstra called it nearly 50 years ago: we will never be programming in English, or French, or Spanish. Natural languages have not evolved to be precise enough and unambiguous enough. Semantic ambiguity and language entropy will always defeat this ambition. I don’t think that’s actually what Dijkstra was saying, nor do I think it stands to reason. Assuming that product designers articulate their designs in English to software engineers, who subsequently turn those ideas into products, doesn’t it stand to reason that this process is to some degree approachable? I’m not saying that we are at that point or that we will be soon, but it just seems like flawed reasoning.
Imustaskforhelp 10mo ago
> The foreseeable future of software development is one where perhaps “AI” – in a much more modest form (e.g., a Java coding assistant built atop a basic language model) – is used to generate prototypes, and maybe for inline completion on production code and those sorts of minor things. This... I have said this in multiple other comments and I 100% agree with the author's take. I feel like AI can be good for prototyping sometimes but that's about it. The reason is simple but I just don't trust AI system from not breaking given their volatile nature and I wouldn't expect to be paying for AI generated code that much/zero to be honest And I will treat other people the same way I wish to be treated (golden rule) so why would I use AI when I can do things by my hand? (There are also 1000's of other discussions I can point in the moral,financial,even technological impacts of the whole situation) That being said, prototyping as author pointed out feels the only good use to me because at that point you are building it for yourself or a small community. I think a good use of AI can be to build open source prototypes of softwares and if you really like something, one can rebuild it or build it the way they like it as an example. Prototyping can be similar to the drag n drop and other things and honestly wordpress did reduce the number of programmers needed to maintain a website and for good measure, I think AI is similar to this then anything else and even then small models will win as author states and so the financial point of these companies training these usually becomes moot. So till the time being, I use these free/subsidized websites to get prototypes to be generated but I feel as if long term, I can find projects which heavily resonate with me and I can build them/learn languages catered to them and build my mental model of thinking around them Although I must admit, prototyping using AI models definitely gives me Imposter syndrome and I sometimes feel that perhaps its a learning opportunity and that I can perhaps instead spend hours but I don't really know too much about it so its something that I am figuring out as we speak Like there is this feeling that I can spend more hours and feel more "proud" of what I did if I did it without AI than if I did with AI since perhaps the journey matters too. I really don't know but just weighing all my thoughts on the matter as I thought about it really closely once. Edit: although I am not sure about calling AI wordpress-like since then it would mean that I am giving significance to AI similar to wordpress, honestly I respect wordpress more but not sure, perhaps they are similar, perhaps they are not, I don't really know perhaps but what I do know is that the financial impact of the AI bubble is gonna impact all of us negatively.
nurettin 10mo ago
The future of IT is going back to basics. People who know their karnough diagrams, their algorithm flow charts, memory models, state machines, algorithms, their sql CTEs, window functions, their obscure cpu/gpu instructions, people who can build a VM and a language on top from ground up will become even more valuable, because they can drive LLMs more effectively. Your run of the mill bootcamp webdev/data scientists will have to educate themselves or find manual jobs.
bossyTeacher 10mo ago
Playing devil's advocate here. My main take with the responses here is that the naysayers are assuming that the current capabilities of models will remain fixed for the next 1-2 decades which is questionable. They are saying that AI (now or in the future) won't eat a good chunk of my career opportunities because AI now isn't good now. That's the wrong viewpoint. My views: - Developers are expensive - Most development isn't that hard or that creative - LLMs can already do some of the sub tasks that the above kind of development entails - If the above kind of development disappears, then there is only the harder, creative kind of development left - Less development jobs and more developers will reduce dev salaries and increase dev competition regardless of the kind of development that you do. Any such change will be fairly slow and gradual - The 2010s Everyone Can Code era is gone and is not going to come back as a result of the above
wedesoft 10mo ago
I did a bit of scripting trying to automate the TDD cycle given a task description. The problem is, that the LLM tends to jump forward and submit a full implementation instead of a minimal change. I guess the problem is, that LLMs are trained on complete solutions instead of minimal steps.
ishashankmi 10mo ago
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421 points by cdrnsf 10mo ago

simonw 10mo ago

I nodded furiously at this bit: > The hard part of computer programming isn't expressing what we want the machine to do in code. The hard part is turning human thinking -- with all its wooliness and ambiguity and contradictions -- into computational thinking that is logically precise and unambiguous, and that can then be expressed formally in the syntax of a programming language. > That was the hard part when programmers were punching holes in cards. It was the hard part when they were typing COBOL code. It was the hard part when they were bringing Visual Basic GUIs to life (presumably to track the killer's IP address). And it's the hard part when they're prompting language models to predict plausible-looking Python. > The hard part has always been – and likely will continue to be for many years to come – knowing exactly what to ask for. I don't agree with this: > To folks who say this technology isn’t going anywhere, I would remind them of just how expensive these models are to build and what massive losses they’re incurring. Yes, you could carry on using your local instance of some small model distilled from a hyper-scale model trained today. But as the years roll by, you may find not being able to move on from the programming language and library versions it was trained on a tad constraining. Some of the best Chinese models (which are genuinely competitive with the frontier models from OpenAI / Anthropic / Gemini) claim to have been trained for single-digit millions of dollars. I'm not at all worried that the bubble will burst and new models will stop being trained and the existing ones will lose their utility - I think what we have now is a permanent baseline for what will be available in the future.

thisoneisreal 10mo ago
The first part is surely true if you change it to "the hardEST part," (I'm a huge believer in "Programming as Theory Building"), but there are plenty of other hard or just downright tedious/expensive aspects of software development. I'm still not fully bought in on some of the AI stuff—I haven't had a chance to really apply an agentic flow to anything professional, I pretty much always get errors even when one-shotting, and who knows if even the productive stuff is big-picture economical—but I've already done some professional "mini projects" that just would not have gotten done without an AI. Simple example is I converted a C# UI to Java Swing in less than a day, few thousand lines of code, simple utility but important to my current project for <reasons>. Assuming tasks like these can be done economically over time, I don't see any reason why small and medium difficulty programming tasks can't be achieved efficiently with these tools.
underdeserver 10mo ago
Aren't they also losing money on the marginal inference job?
- simonw 10mo ago
  I think it is very unlikely that they are charging less money for tokens than it costs them to serve those tokens. If they are then they're in trouble, because the more paying customers they get the more money they lose!
  - anonzzzies 10mo ago
    I guess that depends on the user; most people are not getting most out of flat priced subscriptions. Over all they probably make a profit, and definitely on API use, but some will just spend a lot more. It'll get cheaper though; they are still acquiring as long as there is VC money.
  - mslt 10mo ago
    Operating at a loss to buy market share is pretty much the norm at this point. Look behind the curtain at any “unicorn” for the past 3 decades and you’ll see VCs propping up losses until the general population has grown too dependent on the service to walk away when the pricing catches up to reality.
nrhrjrjrjtntbt 10mo ago
Hardest part of programming is knowing wtf all the existing code does and why.
- doug_durham 10mo ago
  And that is the super power of LLMs. In my experience LLMs are better a reading code than writing it. Have it annotate some code for you.
  - nrhrjrjrjtntbt 10mo ago
    I do!
  - elboru 10mo ago
    Still, code describes what and how, but not why.
cmrdporcupine 10mo ago
Indeed, while DeepSeek 3.2 or GLM 4.7 are not Opus 4.5 quality, they are close enough that I could _get by_ because they're not that far off, and are about where I was with Sonnet 3.5 or Sonnet 4 a few months ago. I'm not convinced DeepSeek is making money hosting these, but it's not that far off from it I suspect. They could triple their prices and still be cheaper than Anthropic is now.
boogieknite 10mo ago
maybe not the MOST valuable part of prompting an LLM during a task, but one of them, is defining the exact problem in precise language. i dont just blindly turn to an LLM without understanding the problem first, but i do find Claude is better than a cardboard cutout of a dog
omnicognate 10mo ago
> claim to have been trained for single-digit millions of dollars Weren't these smaller models trained by distillation from larger ones, which therefore have to exist in order to do it? Are there examples of near state of the art foundation models being trained from scratch in low millions of dollars? (This is a genuine question, not arguing. I'm not knowledgeable in this area.)
- simonw 10mo ago
  The DeepSeek v3 paper claims to have trained from scratch for ~$5.5m: https://arxiv.org/pdf/2412.19437 https://arxiv.org/pdf/2412.19437 Kimi K2 Thinking was reportedly trained for $4.6m: https://www.cnbc.com/2025/11/06/alibaba-backed-moonshot-releases-new-ai-model-kimi-k2-thinking.html https://www.cnbc.com/2025/11/06/alibaba-backed-moonshot-rele... Both of those were frontier models at the time of their release. Another interesting number here is Claude 3.7 Sonnet, which may people (myself included) considered the best model for several months after its release and was apparently trained for "a few tens of millions of dollars": https://www.oneusefulthing.org/p/a-new-generation-of-ais-claude-37 https://www.oneusefulthing.org/p/a-new-generation-of-ais-cla...

mohsen1 10mo ago

I really really want this to be true. I want to be relevant. I don’t know what to do if all those predictions are true and there is no need (or very little need) for programmers anymore. But something tells me “this time is different” is different this time for real. Coding AIs design software better than me, review code better than me, find hard-to-find bugs better than me, plan long-running projects better than me, make decisions based on research, literature, and also the state of our projects better than me. I’m basically just the conductor of all those processes. Oh, and don't ask about coding. If you use AI for tasks above, as a result you'll get very well defined coding task definitions which an AI would ace. I’m still hired, but I feel like I’m doing the work of an entire org that used to need twenty engineers. From where I’m standing, it’s scary.

khalic 10mo ago
I feel you, it's scary. But the possibilities we're presented with are incredible. I'm revisiting all these projects that I put aside because they were "too big" or "too much for a machine". It's quite exciting
belter 10mo ago
>> From where I’m standing, it’s scary. You are being fooled by randomness [1] Not because the models are random, but because you are mistaking a massive combinatorial search over seen patterns for genuine reasoning. Taleb point was about confusing luck for skill. Dont confuse interpolation for understanding. You can read a Rust book after years of Java, then go build software for an industry that did not exist when you started. Ask any LLM to write a driver for hardware that shipped last month, or model a regulatory framework that just passed... It will confidently hallucinate. You will figure it out. That is the difference between pattern matching and understanding. [1] https://en.wikipedia.org/wiki/Fooled_by_Randomness https://en.wikipedia.org/wiki/Fooled_by_Randomness
- joefourier 10mo ago
  Have you used an LLM specifically trained for tool calling, in Claude Code, Cursor or Aider? They’re capable of looking up documentation, correcting their errors by compiling and running tests, and when coupled with a linter, hallucinations are a non issue. I don’t really think it’s possible to dismiss a model that’s been trained with reinforcement learning for both reasoning and tool usage as only doing pattern matching. They’re not at all the same beasts as the old style of LLMs based purely on next token prediction of massive scrapes of web data (with some fine tuning on Q&A pairs and RLHF to pick the best answers).

d_silin 10mo ago

In aviation safety, there is a concept of "Swiss cheese" model, where each successful layer of safety may not be 100% perfect, but has a different set of holes, so overlapping layers create a net gain in safety metrics. One can treat current LLMs as a layer of "cheese" for any software development or deployment pipeline, so the goal of adding them should be an improvement for a measurable metric (code quality, uptime, development cost, successful transactions, etc). Of course, one has to understand the chosen LLM behaviour for each specific scenario - are they like Swiss cheese (small numbers of large holes) or more like Havarti cheese (large number of small holes), and treat them accordingly.

heliumtera 10mo ago
Interesting concept, but as of now we don't apply this technologies as a new compounding layer. We are not using them after the fact we constructed the initial solution. We are not ingesting the code to compare against specs. We are not using them to curate and analyze current hand written tests(prompt: is this test any good? assistant: it is hot garbage, you are inferring that expected result equals your mocked result). We are not really at this phase yet. Not in general, not intelligently. But when the "safe and effective" crowd leave technology we will find good use cases for it, I am certain (unlike uml, VB and Delphi)
kgwxd 10mo ago
LLMs are Kraft Singles. Stuff that only kind of looks like cheese. Once you know it's in there, someone has to inspect, and sign-off on, the entire wheel for any credible semblance of safety.
- tomlue 10mo ago
  how sure are you that an llm won't be better at reviewing code for safety than most humans, and eventually, most experts?
  - hansmayer 10mo ago
    It will only get better at generating random slop and other crap. Maybe helping morons who are unable to eat and breathe without consulting the "helpful assistant".

berdon 10mo ago

There is a guaranteed cap on how far LLM based AI models can go. Models improve by being trained on better data. LLMs being used to generate millions of lines of sloppy code will substantially dilute the pool of good training data. Developers moving over to AI based development will cease to grow and learn - producing less novel code. The massive increase in slop code and loss of innovation in code will establish an unavoidable limit on LLMs.

9dev 10mo ago
That is a naive assumption. Or rather multiple naive assumptions: Developers mostly don’t move over to AI development, but integrate it into their workflow. Many of them will stay intellectually curious and thus focus their attention elsewhere; I’m not convinced they will just suddenly all stagnate. Also, training data isn’t just crawled text from the internet anymore, but also sourced from interactions of millions of developers with coding agents, manually provided sample sessions, deliberately generated code, and more—there is a massive amount of money and research involved here, so that’s another bet I wouldn’t be willing to make.
AlexCoventry 10mo ago
I think most of the progress is training by reinforcement learning on automated assessments of the code produced. So data is not really an issue.
cmrdporcupine 10mo ago
But they're not just training off code and its use, but off a corpus general human knowledge in written form. I mean, in general not only do they have all of the crappy PHP code in existence in their corpus but they also have Principia Mathematica, or probably The Art of Computer Programming. And it has become increasingly clear to me that the models have bridged the gap between "autocomplete based on code I've seen" to some sort of distillation of first order logic based on them just reading a lot of language... and some fuzzy attempt at reasoning that came out of it. Plus the agentic tools driving them are increasingly ruthless at wringing out good results. That said -- I think there is a natural cap on what they can get at as pure coding machines. They're pretty much there IMHO. The results are usually -- I get what I asked for, almost 100%, and it tends to "just do the right thing." I think the next step is actually to actually make it scale and make it profitable but also... fix the tools -- they're not what I want as an engineer. They try to take over, and they don't put me in control, and they create a very difficult review and maintenance problem. Not because they make bad code but because they make code that nobody feels responsible for.

pointbob 10mo ago

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aizk 10mo ago

This time it actually is different. HN might not think so, but HN is really skewed towards more senior devs, so I think they're out of touch with what new grads are going through. It's awful.

ehnto 10mo ago
What is it that new grads are going through? If you are referring to difficulty finding a job, keep in mind that there is both an economic downturn and an over-hiring correction happening in the industry right now. I imagine the AI industry is indeed having an impact in how management is behaving, but I would not yet bet on AI actually replacing developers jobs holistically.
- aizk 10mo ago
  I can talk about my own experiences. About a year and half ago I dropped everything to get into tech, a couple years out from completing my civil engineering degree because I hated it. I worked very intensely day in day out building many projects, and I had one project go extremely viral, changing my life. That led to me getting hired at a small AI start up, where I learned an enormous amount, but 9 months later, I still got let go as work had dried up for the product I was working on. Not much I was able to do there unfortunately. Still, I really do believe that due to AI there was just far less work that I could meaningful contribute to, speaking as someone who is more junior in the field. Little bug fixes here and there, organization, grunt work tasks, etc - those are completely automated now. The rungs on the ladder of meaningful progression are growing increasingly wider as eac day goes by. A decade ago I think big tech companies would be fighting to get me on board, but now it's different, That said, to end this comment on some silver lining - I know someone who currently works at a big lab who got an enormous amount of work xp by contributing on OSS projects till he became more senior, so maybe there's more to it. Also one other thought - companies will still have job postings for juniors because a "junior" can be an MIT IOI gold medalist.

mikewarot 10mo ago

>WYSIWYG, drag-and-drop editors like Visual Basic and Delphi were going to end the need for programmers. VB6 and Delphi were the best possible cognitive impedance match available for domain experts to be able to whip up something that could get a job done. We haven't had anything nearly as productive in the decades since, as far as just letting a normie get something done with a computer. You'd then hire an actual programmer to come in and take care of corner cases, and make things actually reliable, and usable by others. We're facing a very similar situation now, the AI might be able to generate a brittle and barely functional program, but you're still going to have to have real programmers make it stable and usable.

EagnaIonat 10mo ago

I read a book called "Blood in the machine". It's the history of the Luddites. It really put everything into perspective to where we are now. Pre-industrial revolution whole towns and families built clothing and had techniques to make quality clothes. When the machines came out it wasn't overnight but it wiped out nearly all cottage industries. The clothing it made wasn't to the same level of quality, but you could churn it out faster and cheaper. There was also the novelty of having clothes from a machine which later normalised it. We are at the beginning of the end of the cottage industry for developers.

utopiah 10mo ago
Does the analogy hold though? We had "free clothes" for years, decades now. I don't mean cheap I mean literally free, as in $0.0 software. Cheaper software isn't new. Also there are still clothe designers, fashion runways, and expensive Patagonia vests today. The clothing industry is radically different from back then but it's definitely not gone.
- EagnaIonat 10mo ago
  It didn't kill everything. Some survived but not to the extent that it was. > The clothing industry is radically different from back then but it's definitely not gone. Small towns had generations of people who had learned skills in making clothing / yarn. To do the work you needed years of experience and that's all you knew. Once the industrial revolution hit they hired low skilled workers that could be dumped at a moments notice. It made whole villages destitute. Some survived, but the far majority became poor. That was one industry. We now have AI at a point to wipe out multiple industries to a similar scale.
- aidenn0 10mo ago
  I posted elsewhere, but you are looking at the wrong part of the chain. We have cheap (or free) software for large markets, and certain small markets where software developers with hobbies have made something. If every niche that will never be able to afford a large 6-figure custom software could get slop software for an affordable price, then that establishes a foot-hold for working its way up the quality ladder.

aidenn0 10mo ago

In past cases of automation, quantity was the foot-in-the-door and quality followed. Early manufactured items were in many cases inferior to hand-built items, but one was affordable and the other not. Software is incredibly expensive and has made up for it with low marginal costs. Many small markets could potentially be served by slop software, and it's better than what they would have otherwise gotten (which is nothing).

brap 10mo ago

>AGI seem as far away as it’s always been This blurb is the whole axiom on which the author built their theory. In my opinion it is not accurate, to say the least. And I say this as someone who is still underwhelmed by current AI for coding.

john61 10mo ago

It is just the Eliza effect on a massive scale: https://en.wikipedia.org/wiki/ELIZA_effect https://en.wikipedia.org/wiki/ELIZA_effect Reading Weizenbaum today is eye opening: https://en.wikipedia.org/wiki/Computer_Power_and_Human_Reason https://en.wikipedia.org/wiki/Computer_Power_and_Human_Reaso...

trashb 10mo ago
I agree the ELIZA effect is strong, additionally I think it is some kind of natural selection. I feel like LLM's are specifically selected to impress people that have a lot of influence. People like investors and CEO's. Because a "AI" that does not impress this section of the population does not get adopted widely. This is one of the reasons I think AI will never really be an expert as it does not need to be. It only needs to adopt a skill (for example coding) to pass the examination of the groups that decide if it is to be used. It needs to be "good enough to pass".
- molteanu 10mo ago
  I got this wild idea a short while ago and your comment helped cement it: probably one of the reasons why languages like Lisp are not "successful" has something to do with the impressability factor? If the people with money (and the decision) do not understand the tech or are not able to even fake that understanding, will they bet their money on it?
  - andrekandre 10mo ago
    > If the people with money (and the decision) do not understand the tech or are not able to even fake that understanding, will they bet their money on it? aka marketing

zkmon 10mo ago

I see it as pure deterministic logic being contaminated by probabilistic logic at higher layers where human interaction happens. Seeking for human comfort by forcing computers to adapt to the human languages. Building adapters that can allow humans to stay in their comfort zone instead of dealing with the sharp-edged computer interfaces. At the end, I don't see it going beyond being a glorified form-assistant who can search internet for answers and summarize. That boils down to chat bots that will remain and become part of every software component that ever need to interface with humans. Agent stuff is just a fluff that is providing hype-cushion around chat bots and will go away with hype cycle.

pjmlp 10mo ago

As someone having watched AI systems being good enough to replace jobs like content creation on CMS, this is being in denial. Yes software developer are still going to be need, except much fewer of us, exactly like fully automated factories still need a few humans around, to control and build the factory in first place.

encyclopedism 10mo ago
I concur with your sentiments. Am puzzled why so many on HN cannot see this. I guess most users on HN are employed? Your employers - let me tell you - are positively salivating at the prospect of firing you. The better LLM's get the fewer of you will be needed.
- pjmlp 10mo ago
  Denial, like those factory workers at the first visit from the automation company, each one hoping they are the ones elected to stay and overwatch the robots. I have seen projects where translator teams got reduced, asset creation teams, devops head count, support teams on phone lines,... It is all about how to do more with less, now with AI help as well.

laszlojamf 10mo ago

The way I see it, the problem with LLMs is the same as with self-driving cars: trust. You can ask an LLM to implement a feature, but unless you're pretty technical yourself, how will you know that it actually did what you wanted? How will you know that it didn't catastrophically misunderstand what you wanted, making something that works for your manual test cases, but then doesn't generalize to what you _actually_ want to do? People have been saying we'll have self-driving cars in five years for fifteen years now. And even if it looks like it might be finally happening now, it's going glacially slow, and it's one run-over baby away from being pushed back another ten years.

lynx97 10mo ago
People used to brush away this argument with plain statistics. Supposedly, if the death statistics is below the average human, you are supposed to lean back and relax. I never bought this one. Its like saying LLMs write better texts then the average huamn can, so you are supposed to use it, no matter how much you bring to the table.
nevercat 10mo ago
The self driving car analogy is a good one, because what happens when you trust the car enough to do most of your driving but it suddenly thrusts the controls upon you when it shits the bed and can't figure out what to do? You suddenly realise you've become a very rusty driver in a moment that requires fast recall of skill, but your car is already careening off a cliff while you have this realisation. [The "children of the magenta line"](https://www.computer.org/csdl/magazine/sp/2015/05/msp2015050104/13rRUxASutL https://www.computer.org/csdl/magazine/sp/2015/05/msp2015050...) is a god explanation of this, and is partly why I often dissuade junior devs from pretty user friendly using tools that abstract away the logic beneath them.

anoplus 10mo ago

The future of problem solving is problem solvers

beeboop0 10mo ago
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postexitus 10mo ago

"We really could produce working software faster with VB or with Microsoft Access" Press X to doubt.

TheOtherHobbes 10mo ago

The future of weaving is automated looms, but sheep will still be needed to provide the raw materials.

frankie_t 10mo ago

Just like the pro-AI articles, it reads to me like a sales pitch. And the ending only adds to it: the author invites to hire companies to contract him for training. I would only be happy if in the end the author turns out to be right. But as the things stand right now, I can see a significant boost to my own productivity, which leads me to believe that fewer people are going to be needed.

sokoloff 10mo ago
When coal powered engines became more efficient, demand for coal went UP. It went up because vastly more things could now be cost effectively be coal-powered. I can see a future where software development goes the same way. My wife works in science and I see all kinds of things in a casual observation of her work that could be made more efficient with good software support. But not by enough to pay six-figures per year for multiple devs to create it. So it doesn’t get done and her work and the work of tens of thousands like her around the world is less efficient as a result. In a world where development is even half as expensive, many such tasks become approachable. If it becomes a third or quarter as expensive, even more applications are now profitable. I think far more people will be doing something that creates the outcomes that are today created by SWEs manually coding. I doubt it will be quite as lucrative for the median person doing it, but I think it will still be well above the median wage and there will be a lot of it. See: https://en.wikipedia.org/wiki/Jevons_paradox https://en.wikipedia.org/wiki/Jevons_paradox
- encyclopedism 10mo ago
  Many HN users may point to Jevons paradox, I would like to point out that it may very well work up until the point that it doesn't. After all a chicken has always seen the farmer as benevolent provider of food, shelter and safety, that is until of course THAT day when he decides he doesn't.
  - sokoloff 10mo ago
    It is certainly possible that AI is the one great disruptor that we can’t adapt to. History over millenia has me taking the other side of that bet, seeing the disruptions and adaptations from factory farming, internal combustion engines, moving assembly lines, electrification, the transistor, ICs, wired then wireless telecommunications, the internet, personal computing, and countless other major disruptions.

ChicagoDave 10mo ago

Here’s how I see it. Writing code or building software well requires knowledge of logic, data structures, reliable messaging, and separation of concerns. You can learn a foreign language just fine, but if you mangle the pronunciation, no one will talk to you. Same thing with hacking at software without understanding the above elements. Your software will be mangled and no one will use it.

chii 10mo ago
> Your software will be mangled the quality of how maintainable the source code is has no bearing on how a user perceives the software's usefulness. If the software serves a good function for the user, they will use it, regardless of how badly the datastructures are. Of course, good function also means reliability from the POV of the user. If your software is so bad that you lose data, obviously no one will use it. But you are conflating the maintainability and sensibilities of clean tools, clean code and clean workspaces, with output. A messy carpentry workshop can still produce great furniture.
- ozgrakkurt 10mo ago
  This is bean counter mentality. Personally just don’t believe this is how it works. The intention/perspective of development is something on its own and doesn’t correspond to the end result directly. This is such a complex issue that everything comes down to what someone believes
encyclopedism 10mo ago
Software doesn't have to be good academically speaking. It just needs to furnish a useful function to be economically viable. LLM's may not generate the best code but they need only to generate useful code to warrant their use.

snickerer 10mo ago

After working with agent-LLMs for some years now, I can confirm that they are completely useless for real programming. They never helped me solve complex problems with low-level libraries. They can not find nontrivial bugs. They don't get the logic of interwoven layers of abstractions. LLMs pretend to do this with big confidence and fail miserably. For every problem I need to turn my brain to ON MODE and wake up, the LLM doesn't wake up. It surprised me how well it solved another task: I told it to set up a website with some SQL database and scripts behind it. When you click here, show some filtered list there. Worked like a charm. A very solved problem and very simple logic, done a zillion times before. But this saved me a day of writing boilerplate. I agree that there is no indication that LLMs will ever cross the border from simple-boilerplate-land to understanding-complex-problems-land.

constantcrying 10mo ago
>After working with agent-LLMs for some years now, I can confirm that they are completely useless for real programming. >They never helped me solve complex problems with low-level libraries. They can not find nontrivial bugs. They don't get the logic of interwoven layers of abstractions. >LLMs pretend to do this with big confidence and fail miserably. This is true for most developers as well. The mean software developer, especially if you outsource, has failure modes worse than any LLM and round-trip time is not seconds but days. The promise of LLMs is not that they solve the single most difficult tasks for you instantly, but that they do the easy stuff well enough that they replace offshore teams.
- shafyy 10mo ago
  > The promise of LLMs is not that they solve the single most difficult tasks for you instantly, but that they do the easy stuff well enough that they replace offshore teams. But that's exactly the *promise* of LLMs by the hypepeople behind it.
  - constantcrying 10mo ago
    >But that's exactly the promise of LLMs by the hypepeople behind it. I do not know and do not care what the "hypepeople" say. I can tell you that, by pure logic alone, LLMs will be superior at simple and routine tasks sooner, which means they will compete with outsourced labor first. LLMs need to be measured against their competition and their competition right now is outsourced labor. If an LLM can outperform an offshore team at a fraction of the cost, why would any company choose the offshore team? Especially when the LLM eliminates some of the biggest problems with offshore teams (communication barriers, round trip times). If LLMs take any programmer jobs they will at the very beginning make those outsourced jobs obsolete, so the only relevant question is whether they have done that or are in the process of doing so. If they don't, then their impact will be minimal, if they do, then their impact will be massive. I think that this line of thinking is a far better benchmark then asking whether an LLM gets X or Y question wrong Z% of the time.

deleted 10mo ago

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trashb 10mo ago

> Edgar Dijkstra called it nearly 50 years ago: we will never be programming in English, or French, or Spanish. Natural languages have not evolved to be precise enough and unambiguous enough. Semantic ambiguity and language entropy will always defeat this ambition. This is the most important quote for any AI coding discussion. Anyone that doesn't understand how the tools they use came to be is doomed to reinvent them. > The folly of many people now claiming that “prompts are the new source code”, These are the same people that create applications in MS Excel.

AnimalMuppet 10mo ago
Further evidence: After all these years (far longer than we have been doing programming), we still don't do math in English or French or Spanish. We do it in carefully defined formal notations. But so many problems (programming, but also physics and math) start as informal word problems. A major skill is being able to turn the informal into something formal and precise.
bdangubic 10mo ago
> These are the same people that create applications in MS Excel. Ones that want their application to work? :) the last piece of software you should be knocking is MS Excel, in my 30+ year career the one common thread just about everywhere I worked (or contracted at) has used Excel to run some amazing sh*t
- newsoftheday 10mo ago
  Everywhere I've worked as a software engineer the past 30 years I've seen Excel spreadsheets but rarely anything amazing, maybe once back in the 1990's at one place by an Excel guru but those are rare. 00% of the time Excel is used to make essentially table layouts of data maybe with some simple cell calculations.
  - bdangubic 10mo ago
    I dunno much but I do know that if you can start a business that replaces Excel spreadsheets with an application(s) that your business builds you'd be the World's first trillionaire (many "tri" over) :)

constantcrying 10mo ago

Totally delusional. The article does not even try to figure out why any of this happened. In all of the cases the main prediction that was made came true. The cost, especially the human cost, of developing some piece of software dramatically decreased. The only reason why the amount of programmers needed still rose was because the amount of software needed rose faster. Clearly that trend will not hold forever. >The hard part of computer programming isn’t expressing what we want the machine to do in code. The hard part is turning human thinking – with all its wooliness and ambiguity and contradictions – into computational thinking that is logically precise and unambiguous, and that can then be expressed formally in the syntax of a programming language. And there is exactly one single technology which has ever been able to do this task, which is LLMs. Not addressing the elephant in the room, which is that an LLM can actually take such instructions and produce something meaningful with it, just makes the whole article worthless. Everything in this article is just inverse special pleading. Since the last N times, the most enthusiastic predictions did not come true, this time only minor changes can happen. If LLMs are only a revolution on the scale of fast interpreted languages (which have significantly impacted what a small team is capable of delivering in terms of complexity), then they will drastically impact most of the software industry. If these changes happen, and simultaneously the rate at which software is demanded does not also increase (why would it?), then the implications will be extremely serious. Especially if you are not a developer in an established position.

rr808 10mo ago

The biggest threat to American software engineers is outsourcing, AI is just a distraction. I am an immigrant, I work at a prestigious financial corporation in NYC. Pretty much 95% of the staff were born and did undergraduate degree in other countries. We hire a few grads but they usually quit or get laid off after a few years - most new hires are H1Bs or contractors on H1Bs. Just about to open another big office in a developing country.

bdcravens 10mo ago
Perhaps, but that's been going on for decades.

tonnydourado 10mo ago

I have been programming professionally (i.e., getting paid for it) for a much more modest 13 years, but unlike a quite large portion of my peers, I am actually interested in the history of our field. So, yeah, I agree.

twoodfin 10mo ago

I don’t recall seeing entire economies betting on 4GLs. Not even a nitpick because the scale is indeed orders of magnitude different, but… There was a lot of chatter, government attention, and both public and private money chasing 4GL’s in the 1980’s, due to what turned out to be (for the US, at least) a phantom “software crisis”. It was mostly for naught? Same in Japan, where you can argue about the motivations of MITI’s “Fourth Generation Project”, but at its software core was a primeval 4GL in the form of Prolog. Their perceptions of a Japanese software crisis were ultimately more well-founded.

csomar 10mo ago

I really wonder why people aren't drawing comparisons with crypto/blockchain. In the beginning, there were also two camps: people who thought crypto would replace our entire financial system and everything would be tokenized on the blockchain, and another camp who predicted that within a few years bitcoin would be worth exactly zero and all crypto/blockchain companies would disappear. The reality turned out to be somewhere in the middle. Crypto didn't replace our financial system, but it exists as a 1-2 trillion dollar segment serving a particular (though controversial) niche of the global economy. It's not going to zero anytime soon. I think AI/LLMs will follow the same path. There's definitely a level of usefulness there. But we've clearly hit a ceiling since 3.5/4.0. Advancement has only happened in benchmarks and open source models. Also, the idea that a neural net that accepts a fixed amount of padded tokens and returns a list of probabilities will replace the complexities of the human brain is delusional at best. The only real issue I see is that certain actors in the US have taken such large positions that unwinding them could potentially destroy the US economy at worst, or trigger a recession at best. But this mainly concerns the US, which is on an AI high at the moment.

farazbabar 10mo ago

I am good at software. It turns out that isn’t sufficient, or alternatively stated, you have to be good at a number of other things than just churning code, even good code. So to me, the combination of being good at software, understanding complexity and ability articulate it concisely and precisely, when combined with the latest and greatest LLMs, is magic. I know people want to examples of success, I wish I could share what we are working on, but it is unbelievable how much more productive our team is, and I promise, we are solving novel problems, some that have not been tackled yet, at least not in any meaningful way. And I am having time of my life doing what I love, coding. This is not to downplay folks who are having a hard time with LLMs or agents, I think, it’s a skill that you can learn, if you are already good at software and the adjacencies.

newsoftheday 10mo ago
> This is not to downplay folks who are having a hard time with LLMs or agents, I think, it’s a skill that you can learn, if you are already good at software and the adjacencies. Someone on the page already quoted Dijkstra, recommend reading that, he was correct. Prompt engineering isn't engineering at all, it's throwing words at a wall to see which ones stick then declaring success if the outcome looks at all recognizable. That isn't actually success.

alexwhb 10mo ago

As much as my first gut reaction to this article was to be excited about its conclusion, I can’t say my experience matches up. Are LLMs perfect? Absolutely not, but I can point to many examples in my own work where using Codex has saved me easily a week or more—especially when it comes to tedious refactors. I don’t agree with the conclusion; the real-world improvement and progress I’ve seen in the last year in the problem-solving quality of these agents has been pretty astounding. The reason for my excitement about the conclusion is obvious: I want programmers and people to stay in demand. I find the AI future to be quite bleak if this tech really does lead to AGI, but maybe that’s just me. I think we’re at a pretty cool spot with this technology right now—where it’s a powerful tool—but at some point, if or when it’s way smarter than you or me… I'm not sure that's a fun happy future. I think work is pretty tied to our self worth as humans.

jimbo808 10mo ago
> but I can point to many examples in my own work where using Codex has saved me easily a week or more Care to share a few of these examples?
- alexwhb 10mo ago
  Sure thing. I work on a few projects for my company. The main one is an Android and iOS audiobook-media-player app. I had to update the Android side to use the Google Media3 library instead of the legacy ExoPlayer library. In a typical app this would be pretty straightforward, but we’ve mixed in a lot of custom elements that made the transition quite challenging. I actually attempted it manually back in the day and probably spent three or four days on it, but I simply didn’t have time to finish, so I shelved it. A few months ago I tried again in Codex; within two prompts it was done flawlessly and starting from the beginning. Another example: I also maintain the back-end API for these apps. There was a lot of old utility code that was messy, unorganized, and frankly gross. I had Codex completely reorganize the code into a sensible folder structure and strip out any functions that weren’t being used anymore—something like 300-plus functions. Doing that by hand would easily have taken a day or more I could go on, but those were the two initial “aha” moments that showed me how powerful this tool can be when it’s used for the right tasks.

jader201 10mo ago

I feel like the comments/articles that continue to point out how LLMs cannot solve complex problems are missing a few important points: 1. LLMs are only getting better from here. With each release, we continue to see improvements in their capabilities. And strong competition is driving this innovation, and will probably not stop anytime soon. Much of the world is focused on this right now, and therefore much of the world’s investments are being poured into solving this problem. 2. They’re using the wrong models for the wrong job. Some models are better than others at some tasks. And this gap is only shrinking (see point 1). 3. Even if LLMs can’t solve complex problems (and I believe they can/will, see points 1 and 2), much of our jobs is refactoring, writing tests, and hand coding simpler tasks, which LLMs are undeniably good at. 4. It’s natural to deny LLMs can eventually replace much/all of what we do. Our careers depend on LLMs not being able to solve complex problems so that we don’t risk/fear losing our careers. Not to mention the overall impact to the general way our lives are impacted if AGI becomes a reality. I’ve been doing this a while, and I’ve never seen the boost to productivity that LLMs bring. Yes, I’ve seen them make a mess of things and get things wrong. But see points 1-3.

theshrike79 10mo ago
Also 5: "But LLMs produce a bunch of code I need to read and review". Yes, but so do your coworkers? Do you complain about every PR you need to read? Are they all compressed diamonds of pure genious, not a single missed character or unoptimised function? Not a bad or suboptimal decision in sight? If so, shoot me a mail, I want to work where you're working =)
- adonovan 10mo ago
  My coworkers learn, and an important part of my job is teaching them. LLM-based tools don't.
  - theshrike79 10mo ago
    A circular saw doesn't learn either. It's a tool, just like an LLM. The LLM isn't replacing your coworkers, it's a tool they can (and IMO should) learn to use, just like an IDE or a debugger.

Twey 10mo ago

‘Obviously’ AI and programming are antonyms. AI is defined as the ability for computers to do whatever it is that humans can do that (most) computers can't currently do. Conversely, ‘programming’ is whatever activity is required to fully take advantage of computer hardware that (most) humans can't currently do. Both sets of goalposts are firmly affixed to their wheels :)

solaire_oa 10mo ago

Most people in this thread are quibbling about the exact degree of utility LLMs provide, which a tedious argument. What's more interesting to me is, per the article, the concern regarding everyone who is leaning into LLMs without realizing (or downplaying) the exorbitant, externalized cost. Our current LLM usage is being subsidized to the point of being free by outside investment. One day when the well runs dry, you must be able to either pay the actual cost (barring grand technology breakthroughs), or switch back to non-LLM workflows. I run local LLMs infrequently, and every single prompt makes my beefy PC sounds like a jet engine taking off. It's a great reminder to not become codependent.

bogzz 10mo ago
I am sorry, but this kind of level-headed and realistic take is completely unacceptable on hackernews, and you should be ashamed of yourself. This is not a place for rational discussion when it comes to LLMs. LLMs are amazing and they will change everything, and then everything will be changed.
JeremyNT 10mo ago
This is a good point but you can see the price "ceiling" by examining the prices for PCs that can effectively run local models. A DGX Spark is ~$4k (plus power) for example. That's not nothing, but it's still not very much to pay compared to e.g. the cost of a FTE.
- __float 10mo ago
  You're counting the cost of running the model, but what about training it? You can't count the compute and data costs at $0.
  - philipkglass 10mo ago
    You can assume that already-published open weights models are available at $0, regardless of how much money was sunk into their original development. These models will look increasingly stale over time but most software development doesn't change quickly. If a model can generate capable and up-to-date Python, C++, Java, or Javascript code in 2025 then you can expect it to still be a useful model in 2035 (based on the observation that then-modern code in these languages from 2015 works fine today, even if styles have shifted).

dfilppi 10mo ago

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dilly-dally 10mo ago

The more articles written like this only reflect the inevitable. This is not an era of slow progress which may have supported the authors opinion. This era is a rapid replacement of what was once dominated by masters of one who edged out others and arrogantly tried to hold onto their perceived positions of all knowing. There will always be those types unfortunately, but when the masters of one realize theyve wasted time stalling the inevitable, instead of accepting and guiding the path forward and opening the door to a broader audience, you will see a lot more articles like this which are a clear signal to many of us that theyre simply doing and saying too much trying to hold on to their perceived worth.

harrisreynolds 10mo ago

I think "The Bitter Lesson" is relevant here [1]. This wave IS different. It isn't a matter of "if" but "when". I am a long-time software developer too... but I am strongly encouraging people to embrace the future now and get creative in finding ways to adapt. There will always be demand for smart and creative people. BUT ... if you don't look up and look around you right now at some point you will be irrelevant. Also see Ray Dalio's "Principles" book on embracing reality. A critical principle in the modern age of AI. Nothing but love for my fellow developers!! [1] http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas.net/IncIdeas/BitterLesson.html

robofanatic 10mo ago
why didn't ready to eat meals available so cheaply at any grocery store make the local mom and pop restaurants irrelevant?
- harrisreynolds 10mo ago
  People like experiences! Plus those meals are disgusting! :-) Plus plus… those meals are actually expensive!!

QuiEgo 10mo ago

I think the only thing we can say about the future of LLMs is “we don’t know.” Everything is changing too fast. Models from a year ago are already obsolete. We seem to be hitting the top of an asymptote on improvements, but things have not been steady state for long enough to know. I also agree with the author that the VC money is driving all of this, and at some point the check is going to come due. This thread is full of antidotes from “AI is useless for me” to “AI changes everythingfor me” I believe each and every one of them. In firm wait and see mode.

kenforthewin 10mo ago

Just a reminder that everyone copes with change differently.

FloorEgg 10mo ago

AI is like electricity. Agentic IDEs are like power tools. While most construction work today uses power tools, there are still lots of artisans productively using hand tools. LLMs can be useful and developers can still be productive without them. Things get weird when we confuse the developers with the power tools.

beeboop0 10mo ago

there's a huge flaw in the logic here but I can't pinpoint it we've never had machines that can reason. today, they reason badly. tomorrow, they'll reason better. not all ai research white papers will pan out, but some will. the ai research community is acutely aware of the limitations in reasoning; it's their whole mission right now. 100% chance someone will make progress. Analogous to the chip industry or self driving, in that regard incremental improvements in reasoning will broaden the dead zone for human developers what difference does it make if in 20 years NASA still needs a few human developers but the rest of us are unemployed? ai agents still can't get you to the moon with one shot coding session in 2045? who cares? the "still can't" we really need to be worried about is "politicians still can't consider UBI"

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Havoc 10mo ago

This is the usual „but they can’t do the hard stuff“ argument. It’s not the right lens - at least not for employment. If you need three programmers to deliver a project, one doing boilerplate, one intermediate and one doing hard task. And the AI can’t do the hard task then you’ve still got two unemployed dudes. So yeah you can make the technically correct argument that it still needs a programmer but it glosses over the coming storm in real world job market. The only other option to square that circle is to assume that A) we suddenly need 3x as much projects and B) boilerplate guy can somehow graduate to hard tasks

performative 10mo ago
i hope the collective realization that "hard task" guys were once intermediate or boilerplate guys comes sooner than later :(
- Havoc 10mo ago
  I think the realisation is already there but nobody knows what to do about it. Companies won’t hire people just to train.

badcryptobitch 10mo ago

One thing that many of these kinds of articles don't really talk about but that also emphasizes their point is that there will be an increase in interfaces/languages for software developers to do increasingly specialized for work. As such, there will be more compilers, vms, dsls, etc which means that there will be more of a demand for developers who have this skillset. Ideally, this should lead to more folks becoming developers or at least acquiring skills that professional developers have in order to make effective use of these tools.

XenophileJKO 10mo ago
I would agrue as LLMs increase in capability, the demand for new languages will collapse.
- badcryptobitch 10mo ago
  My counterargument for that is that LLMs need to communicate not only between themselves and humans but LLM to LLM communication. We will get more niche and domain specific languages designed for LLMs as a result.

bitwize 10mo ago

The future of software development is systems analysts. It was discovered in the 1970s (at the latest) that the hard part of software is figuring out WHAT to build not HOW to build it—and the two should be separate responsibilities with separate personnel bearing separate talents. (Do NOT let your programmers do systems analysis!) Furthermore, it was discovered that without a clear, precise description of what to build, even the most talented programmers will happily use industry best practice to build the wrong thing, and that is worse than useless: it's a net cost to the business. This is something that programmers have in common with LLMs. So it turns out, in order to build software correctly and cost-effectively, you need to perform lots of systems analysis up front, then through a stepwise refinement process design and document your solution, yielding a detailed spec. It also turns out that LLMs, like human programmers, do just fine if you give them a detailed spec and hold them accountable to that spec! So much of the "grunt work" of programming can be handed to the LLM—IF you perform the necessary systems analysis up front to determine what needs to be built! (Bryce's Law: Programming is a translation function, taking human-readable requirements to machine-executable instructions.) As the AI era matures we are either going to see a revival of PRIDE—the original and still most complete software development methodology, but minus the programmers Milt and Tim Bryce loathed so much—or the entire collapse of software as a field.

jollyllama 10mo ago

The bit about drag-and-drop and Visual Studio hides a key insight: insofar as those tools allowed non-software engineers to build applications that were key to certain business processes, they were tech-debt accelerators. It is true to a very large degree; there are still businesses out there depending on shoddy VB applications that were built by someone that didn't know what they were doing, and they are hobbled by it. LLM-generated applications are the turbocharged equivalent of this, and I anticipate that giant spagehetti codebases will be made out of them, the likes of which have never been seen before, and the workflows that are wedded to them will be doomed.

alex_young 10mo ago

Edgar Dijkstra called it nearly 50 years ago: we will never be programming in English, or French, or Spanish. Natural languages have not evolved to be precise enough and unambiguous enough. Semantic ambiguity and language entropy will always defeat this ambition. I don’t think that’s actually what Dijkstra was saying, nor do I think it stands to reason. Assuming that product designers articulate their designs in English to software engineers, who subsequently turn those ideas into products, doesn’t it stand to reason that this process is to some degree approachable? I’m not saying that we are at that point or that we will be soon, but it just seems like flawed reasoning.

Imustaskforhelp 10mo ago

> The foreseeable future of software development is one where perhaps “AI” – in a much more modest form (e.g., a Java coding assistant built atop a basic language model) – is used to generate prototypes, and maybe for inline completion on production code and those sorts of minor things. This... I have said this in multiple other comments and I 100% agree with the author's take. I feel like AI can be good for prototyping sometimes but that's about it. The reason is simple but I just don't trust AI system from not breaking given their volatile nature and I wouldn't expect to be paying for AI generated code that much/zero to be honest And I will treat other people the same way I wish to be treated (golden rule) so why would I use AI when I can do things by my hand? (There are also 1000's of other discussions I can point in the moral,financial,even technological impacts of the whole situation) That being said, prototyping as author pointed out feels the only good use to me because at that point you are building it for yourself or a small community. I think a good use of AI can be to build open source prototypes of softwares and if you really like something, one can rebuild it or build it the way they like it as an example. Prototyping can be similar to the drag n drop and other things and honestly wordpress did reduce the number of programmers needed to maintain a website and for good measure, I think AI is similar to this then anything else and even then small models will win as author states and so the financial point of these companies training these usually becomes moot. So till the time being, I use these free/subsidized websites to get prototypes to be generated but I feel as if long term, I can find projects which heavily resonate with me and I can build them/learn languages catered to them and build my mental model of thinking around them Although I must admit, prototyping using AI models definitely gives me Imposter syndrome and I sometimes feel that perhaps its a learning opportunity and that I can perhaps instead spend hours but I don't really know too much about it so its something that I am figuring out as we speak Like there is this feeling that I can spend more hours and feel more "proud" of what I did if I did it without AI than if I did with AI since perhaps the journey matters too. I really don't know but just weighing all my thoughts on the matter as I thought about it really closely once. Edit: although I am not sure about calling AI wordpress-like since then it would mean that I am giving significance to AI similar to wordpress, honestly I respect wordpress more but not sure, perhaps they are similar, perhaps they are not, I don't really know perhaps but what I do know is that the financial impact of the AI bubble is gonna impact all of us negatively.

nurettin 10mo ago

The future of IT is going back to basics. People who know their karnough diagrams, their algorithm flow charts, memory models, state machines, algorithms, their sql CTEs, window functions, their obscure cpu/gpu instructions, people who can build a VM and a language on top from ground up will become even more valuable, because they can drive LLMs more effectively. Your run of the mill bootcamp webdev/data scientists will have to educate themselves or find manual jobs.

bossyTeacher 10mo ago

Playing devil's advocate here. My main take with the responses here is that the naysayers are assuming that the current capabilities of models will remain fixed for the next 1-2 decades which is questionable. They are saying that AI (now or in the future) won't eat a good chunk of my career opportunities because AI now isn't good now. That's the wrong viewpoint. My views: - Developers are expensive - Most development isn't that hard or that creative - LLMs can already do some of the sub tasks that the above kind of development entails - If the above kind of development disappears, then there is only the harder, creative kind of development left - Less development jobs and more developers will reduce dev salaries and increase dev competition regardless of the kind of development that you do. Any such change will be fairly slow and gradual - The 2010s Everyone Can Code era is gone and is not going to come back as a result of the above

wedesoft 10mo ago

I did a bit of scripting trying to automate the TDD cycle given a task description. The problem is, that the LLM tends to jump forward and submit a full implementation instead of a minimal change. I guess the problem is, that LLMs are trained on complete solutions instead of minimal steps.

ishashankmi 10mo ago

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