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- Hacker News
- There was a reddit thread earlier very similar some interesting comments there too:
https://www.reddit.com/r/technology/comments/1ueidyv/softwar...
> I had an interview where I was asked the obligatory “what’s your Al workflow” and I said I use it for searching documentation and writing small functions or boilerplate that are tedious. Then I was asked whether I use Cursor. I said no, and immediately was told that “I’d be a better programmer if I used Cursor”. I have 13 years of software engineering experience, and was talked down by an Al startup with no minimal viable prototype. Then I was told I did not have the experience for the role. I love this timeline so much
by lyu07282 - This is what I'm seeing at my company as well, "software development principles" are out the window. People have forgotten all about "Second System Syndrome" too, docs, tests, code, design - all LLM generated.
I don't like, I prefer to use Claude/Codex as an aide, not to let it take the wheel entirely but I don't really feel like I have much choice in the matter.
by ventsys - I think it’s becoming more and more performative. We’ve never been less focused on the actual product — the software — than we are now. The focus is increasingly on how someone generates code, despite that not having much to do with the product.
The software is not getting better. Generating code faster does not result in better software. Software continues to be slow, unreliable, complex, and counter-intuitive. AI has not improved on the metrics that matter.
AI, as amazing as it is, has not actually changed the process of software development and as a consequence, the resulting software has not changed. The job hasn’t either, except insofar as you’re judged for tokenmaxxing.
by 4lx87 - The profession has already changed. For the past eight months, AI has been competent enough to code like the best human programmer, but strangely, the software isn't any better yet. Everyone has lost sight of what the profession truly is. It's not just about coding; it's about software engineering. Our role is no longer that of programmers, AI has taken over that role. Our role is that of engineers who manage programming agents. Every attempt to have AI develop a medium-to-large project fails because the goal is to solve everything with a magic four-line prompt. We're forgetting the structural aspect, the engineering side. We must treat the tool as just that: a tool. The direction and responsibility remain in our hands. It's not about reviewing the code line by line; it's about ensuring that the product faithfully represents a well-planned engineering intent. That's why the concept of AI-augmented Software Engineering is so important.by montfort
- > AI has been competent enough to code like the best human programmer
It’s really not. Opus 4.8 can’t produce good software design and it still makes straightforward implementation mistakes. Two errors it made in one day for me recently: it built the Cookie class I asked for without a name field—cookies have a name and a value—and it neglected to handle a case where a database could have multiple rows with the same id, just returning whatever came back first.
The “best human programmers” absolutely would not have made those mistakes. At worst, they would have asked if I really meant what they thought I meant.
by jdlshore - What are you writing that Claude is actually writing all of it? Every time I get past the green field stage, I just end up throwing out what it writes half the time since its trash. Claude seems really great at fix this unit test, generate this boiler plate, take this uml and build this framework out. But when I am doing refactorings, or implementing things that are beyond monotonous, I end up writing it all by hand. My best luck is still do the design, query AI for possible choices, sketch out the framework of what I am writing, have AI critique my plan, and then have AI design individual methods, then fix what it writes.by ecshafer
- I mean this with no disrespect, but
> Every time I get past the green field stage, I just end up throwing out what it writes half the time since its trash.
Is a skill/PEBKAC issue. You still need to exercise engineering best-practices like decomposing work to the smallest unit before taking a task on, brainstorming design first and implementation last, clearly defining your success criteria and requirements before beginning any work, etc.
I'm on a >10yr old codebase and have been able to get my org to orchestrate entire features, fully unit tested, e2e tested, storybooked, from scratch without touching an IDE. Refactorings and the endless mountain of 80% completed migrations from one pattern to another are now trivially able to offload.
Point your SOTA de jeur at the original docs, a few of the original examples/PRs and have it draft a skill describing the work, the scope, and the success metrics. Iterate on the skill with the main agent by subagenting to test the skill until you are happy with the result and it mostly gets it right with the guardrails you've defined. Again - keep the scope extremely small. It gives much less rope for the agents to hang themselves with and it is less cognitive load when you have to review/test the PR.
Then set up a reasonable cadence for it to execute an autonomous thread on and review when you get comfortable.
----
The issue I've been running into lately is simply that we've got so many PRs coming in that actually doing thorough human reviews on them is not sustainable relative to the rate the team is creating agents to open them and people (especially juniors and mid level) are getting burned out by essentially having entire days where they are just doing code reviews.
by clates - What you say could be theoretically possible, but it's probably an issue with your usage of if. For eg: if any of this hard non-promptable project is available on github, or you've seen this problem in any large scale github project, you can share that. I've rarely seen a repo and a problem that claude can't chew through with the right prompt.
- > What are you writing that Claude is actually writing all of it? Every time I get past the green field stage, I just end up throwing out what it writes half the time since its trash.
For the current state of frontier models, you need to break the steps down so that the LLM understands a process like what you might go through as you expect it (which is often different for everyone).
i.e., get it to agree to a spec, then get it to agree to a build plan, agree on unit test signatures, UI etc as needed, then let it build, ...
"Prompt engineering"
by tedmiston - "Computer" use to be a job title. So no, I am not optimistic about the future of most programmers, maybe even all programmers.
One possibility is that software starts to look more like traditional manufacturing.
The machine is the company’s core asset. The engineer only needs to know how to operate the machine well. Once that happens, the barrier gets much lower, need much less people, and the job naturally become much less valuable. Some parts will still need to be done by hand, of course. But only a very small part. It is like old factories. They used to need lots of fitters, at all levels of skill. Now you only need a few of the elite ones.
AI is the CNC machine of the software industry.
The more pessimistic future is that, maybe five years from now, the best programmers will look at AI the same way the best Go or chess players look at AI today: Like KeJie said, "I don't even know what I am trying so hard against." We now have a new SOTA every two months. It just took 18 months for LLMs from reasoning models to disproving the unit distance conjecture. ChatGPT itself has not even existed for as long as a college student spends in university.
In any case, we have already passed the point where this can be rolled back.
Maybe ten years from now I will be leaving a comment saying that "programmer" used to be a job too :-/
Programming is the low-hanging fruit for AI. Open source and knowledge sharing have given it huge amount of public, high-quality training data at a level other industries can hardly imagine. And almost everything in programming can be tested and verified inside the computer quickly in a closed loop. No robot arm is needed.
The main weakness of current LLMs is still that they are static: They do not really change themselves through use. Harness tools are just elaborate ornamentation on top of prompts. LLMs are frozen at the moment training stops. Once we get models that can change their own weights through self-feedback, then maybe AGI really is on the horizon.
Thinking optimistically: I may be lucky enough to see it in my lifetime. Maybe by then, people will be able to live more like human beings, instead of organizing their whole lives around work :-)
by linzhangrun - Thank you for your comment. I enjoyed it a lot. Good food for thought.
Your analogon is a bit leaky abstraction in the sense that it misses out on the broad stastical nature of LLMs. However, I find it is a good way to illustrate the potential industrial transformation.
It is hard to say what the future will bring. The original AsK HN post is definitly an omen for things to come.
by wuschel - > AI is the CNC machine of the software industry.
CNC and other factory automation have eliminated innumerable jobs.
by zrn900 - A well-equipped local-AI-capable machine is already much cheaper than a CNC machine -- and will presumably get cheaper over time, RAM prices notwithstanding.
I'm not sure the comparison works if individuals can afford these machines themselves and don't have to commute to a factory to use them; I doubt employers will serve as either gatekeepers or sole providers of access to AI.
by reverius42 - Low-skill work that used to be outsourced will go to cheaper LLMs, unless wages are depressed enough / running costs are high enough to keep using humans as cogs in the machine. This will also consume a ton of small-scale things, like personal-sized automation and small-business customization of better-crafted things (stuff that normally wouldn't be paid for in the first place, or only extremely rarely). Some will obviously exist, because paying someone else to farm out a ton of mediocre output with LLMs is still worthwhile sometimes, but it's going to be gutted as a general statement.
Especially with prototyping-style work, LLMs are clearly good enough for a ton of business-oriented proof-of-concepts, and that line of work is essentially dead. Unfortunately a lot of mid-tier art falls into this category as well, particularly because execs very clearly can't tell good art from bad (on a "customers like this" scale, with functionality being the judge, which is fairly objective. not a subjective "this is good art").
High-skill work is still necessary, but it's hard to tell if it's actually going to be more important (because skill is obviously still needed for actually-good results, and I honestly see no evidence that this will change with current tech) or less (primarily due to less demand, and it being significantly harder for non-skilled to judge skill when everyone can prototype something seemingly-impressive in a weekend). Some will very obviously continue to exist though.
Whether this means "high-skill people are going to be fine, stay the course" or "<10% of high-skill people will be fine, you had better be scrambling right now or looking for a new line of work" is... much less clear.
by Groxx - It’s like how google translate replaced the low end but we still have human translators for high end stuffby ex-aws-dude
- These are people who don't understand the job of SWE.
Look, the job of a medicine doctor, or a plane pilot, have largely been automated. The doctor could rely on google searches, or even AI, to answer your questions. The pilot can trust the auto pilot. Yet, the doctor who does advanced stuff (surgery) has no shortcut to mastering his skill : he still has to go through 10 years of studies + years of experience under the supervision of a senior with 20y of XP. Similar path for the plane pilot.
And the same reasoning applies to the SWE. AI allows to : delegate the repetitive tasks, generate the boilerplate code, fix time consuming bugs, to focus on more meaningful stuff.
AI is a tool. It's an additional layer of abstraction. The winners will be the guys who are passionate, who still put efforts to master SW, who still put efforts to remain up to date with the regular updates (new releases, new libraries, new approaches...), and who understand how to use AI the right way.
All those guys who 100% rely on AI do is help big tech cover their massive costs for the deployment of AI infrastructures. Big tech does need those guys.
Imagine the auto pilot fails for some reason, during a flight. There are 400 passengers behind. This is a minor issue for the trained and experienced pilot.
What do you do, as a SWE, when tomorrow, anthropic/google/whoever, tells you : "we can't keep running at a loss, our prices for AI have to reflect our costs. Your monthly subscription for Claude is now $500/month". Or there is a power shortage and for a couple of days you cannot rely on AI, you can only rely on your brain to deliver this feature you committed to deliver tomorrow ?
by doweneedai47 - >Look, the job of a medicine doctor, or a plane pilot, have largely been automated. The doctor could rely on google searches, or even AI, to answer your questions.
And clearly you are not someone who understands the job of a physician.
by amoxichillin - I have had some truly spectacular results that still kind of stagger me in the last few months using Claude in my hobby projects -- but even though Claude insists on trying to slip its name into the git history as credit it's not Claude -- it's me. Someone who has studied CS and software engineering for decades will craft different prompts from someone without that background. A suggested axiom: there is nothing I can build with Claude that I could not build myself with my current level of CS knowledge, assuming I had infinite focus and time. In my hands it can go as far I could anyway, and no further. (But it is faster!) My experience bears that out so far.by retrac
- > Someone who has studied CS and software engineering for decades will craft different prompts from someone without that background.
This, to me, is the biggest differentiator. In terms of results, there's a huge yawning chasm between the person who says "Claude make me a $thing" versus the person who puts in the effort to lay down the overall architecture, gives some thoughts to libraries and dependencies, performance trade-offs etc, and only then begins prompting.
Knowing how to implement Djikstra or a linked list by heart is no longer important. Actual software engineering skills are more important than ever.
by stackghost - > hobby projects
Unfortunately despite being impressive for solo stuff, such results don’t scale to software you’d give to others.
by cadamsdotcom - Fair enough but speed, especially the kind that comes with LLMs, is fast enough to open new ways of working and doing things. We don't have infinite time and if there's something that can give me multiple, for example, UI suggestions in a minute which I can pick from, it's a different way of working than sitting with a UI designer for several hours have discussions. So, while I agree with you in theory, I don't fully agree with you in, what I think you're implying, when it comes to practice.
- I think a lot of developers probably FEEL like they are in super mode, but in reality they're just letting Claude drive the boat and they get to wear the captain's hat.
Maybe I'm wrong. Maybe AI Natives will be faster in the end and can build / do more, or building software really is a dead field - but I noticed that I was losing my brain and had to get back into the seat.
There are definitely great use cases for agents - but I think a lot of us aren't flexing our brains anymore and, even worse, some devs believe they are. I urge every developer to put Claude down for a day/week... see how well you can do in the "old" ways. It'll still be here when you get back, but my guess is it'll be a rude awakening.
- i understand your concern about relaying too much on ai tools like claude. while they can enhance productivity, they shouldnt replace critical thinking and problem solving skills that define good software developer so yea taking break from ai and working on "old" way can help sharpen those skills. i think its important to strike balance between working on new/old way but i also have feeling that everyone is addicted and trying to develop 24/7 cos someone might "steal" their idea. like theres some kind of imaginary race and if u dont code for 1 day with claude (even if it is nonsense) u wont end up first...by shinobi-apps
- I'm a Senior Freelance Programmer, I can see many of my past and present clients moving towards the exact path you described. I keep warning them during meetings that Claude model isn't sustainable for long, eventually the VCs will come for their revenues and Claude will be forced to close their access to all but the most enterprisey ones with deep pockets. The mere electricity cost for that kind of high level reasoning and abstraction can't be subsidized forever. However, there are other forces which pull them towards Claude and AI workflows. Most of the clients are in a "wait and watch" mode right now, using LLM assistance for code generation but not fully depending on them.
Before LLMs came, there used to be the technical debt to deal with in a project, now there is also the added cognitive debt which is way more subtle and impactful long-term. If your source of truth isn't source code but a prompt (or even a series of prompts with branches) and the executor of prompts is a non-deterministic agent, I think you've already lost the battle there.
by pyeri - As a Freelance Programmer, are you even getting consistent clients at decent rates? If so, how are you getting clients consistently and how do you convince businesses that you are better than AI?by karakoram
- > Claude model isn't sustainable for long, eventually the VCs will come for their revenues
This is cope. There are multiple open models that are already good enough and cheap enough at API rates to sustain this.
by jeremyjh - The electricity cost per unit of machine “reasoning” is vastly less than the cost of salary for human reasoning. That’s a weak argument. You should focus on the second part… LLMs (at least today’s) don’t build simple solutions, and the complexity they introduce has a cost.by titanomachy
- You ignore that Claude are not alone, tech progresses and reduce costs, and there are always the Chinese alternatives which are becoming sufficiently better over time.by krembo
- Using today's model prices as a rebuttal is a very weak argument.
Two years ago, SOTA was gpt-o1, and it was much more expensive than Fable. Now, for $4,699, you can easily run a much smarter Qwen3.6-35B locally with DGX Spark.
Think about where we are. This is an era where a new SOTA arrives every two months. It took LLMs only about 18 months to go from chain-of-thought reasoning to disproving the unit-distance conjecture. chatGPT itself is only three and a half years old.
DeepSeek V4, released two months ago, is almost as cheap as the electricity costed, has the ability to being absolutely a top-tier model in 2025 standards.
by linzhangrun - I fully agree with that. Well said.
You're standing on the shore, and your clients are having fun in the water. The tide is going up, and you're screaming at your clients "come back! it's not safe". And so, they show you the face. You appear to them like the boring guy who's not fun to hang out with. Eventually the tide is high, there is strong current, and they are being swept away further and further from the shore and they are panicking : "pyeri! help us! please!"
People (the non tech people, the MBA people) don't want to hear what you, the tech guy has to say. You're the not fun guy. Stay in touch until they do need you and say : you were right. That's the day you charge them a dear price for the service.
AI is still at the bait stage of rollout. They subsidize it, they want you to get hooked onto it to the point where you cannot do without it. Then only, they start to charge. I used google code assist for around 9 months. It was free. I would ask it questions from time to time, to help to fix bugs, and to avoid to spend an hour browsing SO. Now, it's around $30 per month. They are losing too much too fast atm, they have reached the stage where they have to start to charge. Another one of their strategies is : IPO. Once they (openai/anthropic) are listed on the nasdaq, you will pay whether you want it or not (via your exposure to the nasdaq/S&p500 with your etfs).
by doweneedai47