Ask HN: Where is the programming profession going?

I think of AI as an intelligent partner that took away the boring stuff and give me the real design decisions. The expectations of clients have changed but most of the code I have seen written by AI does the work but AI do ignore somethings that matter in scaling. In my company actually boss thinks he is a programmer now with claude but writes too much error prone code which I think mainly not because of claude because non technical person giving generic commands. For me in near future the need for proper engineers is still very high.

My personal experience: writing code has always been the easy part. AI does most of that now. Understanding the problem and the existing system well enough to design the right solution, even with AI assistance, is a higher cognitive load. I’m doing a lot more of that lately. I’m more productive, but also more tired. This may be due in part to the breadth of what my team owns, which makes my day a bit more context-switchy than other teams. As others in this thread have noted, the situation is still evolving. However, I worry less each day about being replaced by AI. There has always been more work than available bandwidth in my experience. What seems clear to me is that expectations around velocity and throughput will increase (are increasing). AI use will be required to meet those expectations. Learning to use this new tool effectively will be essential for career progression (and preservation).

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.

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 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.

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 ?

Hmm my personal experience has been that my team has been overloaded with a ton of work and expectation to run more experiments and show impact than ever before. I feel like I've been working much more now with AI than ever before.

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.

"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 :-)

Programming models will continue to improve; in the foreseeable future, enterprises will rely less and less on human programmers to write code. If you are still interested in programming, do not waste time delving deep into the code itself; instead, spend time understanding requirements and the market. The combination of an individual and Codex will be the optimal approach to programming.

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.

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.

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.

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.

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

compare this to Electronic engineering, we are not making chips by hand, It is impossible to squeeze too many transistors in such a small space, we just create the circuit designs. I believe writing the code is heading in the same direction as well, you don't write too much code now, but you have to design the architecture and connect things. But it does not mean that you don't need to understand the code, now of all the times people who understand code are more important. Many below par devs will loose jobs for sure.

I've posted a recent article about the future of software development https://saturnino.substack.com/p/out-of-the-loop?r=7eqhw&utm... Basically, in a decade or so, we'll be completely out of the loop in software development; even this title won't exist anymore (like the 2000's webmaster). We'll still be around, but with different roles.

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My impression is that smaller companies, that depend on rapid prototyping to gain clients, exert a lot of pressure onto their devs to use LLMs. At least that's the situation in the companies some friends of mine are working at. I'm in a slow-moving, much bigger company. Lot's of talk about "AI" here and we can use copilot if we want to, but there is 0 pressure. I'm in a small team and one colleague uses copilot often. In the beginning there was a minor conflict between him and me, because I found the quality of the LLM code unacceptable and had to ask him to review it more carefully. I think that's settled now, but it makes me sad how a once motivated colleague now seems to try to cheat his way out of work. I personally find it incredibly boring to write copilot prompts or read its answers full of boiler plate and sycophancy. I don't understand how anyone would want to replace the cognitive work of programming, that I find enjoyable for the most part, with the cognitive work of "talking" to an LLM. Anyway, I think it will be like this at least for a little while longer: only vibe coding allowed in small companies and less vibe coding the bigger the company is. But before vibe coding can take over the slow-moving big companies, all the accumulated technical debt will come back to haunt us and vibe-free software will be the new fad. That's what I hope at least.

I think the genie gets put back in the bottle, at least partly. I don't think the future is massive data centers running at a staggering loss to generate questionable code. The future is rethinking IDEs to have local models work in partnership with the developer to ease tedium and catch mistakes. A model that maintains a visual, zoomable mind-map of the entire project, with two way binding. Code can be created visually or textually, same with data flows. Project structure and architecture are presented in high-level ways, that can be easily altered and refactored with almost zero tedium. I think we start using AI for what it's good for: pattern matching and transformation, and stop trying to make it reason and pretend like it's a human. Once we, as an industry, figure this out we'll unlock a massive boost in quality and productivity, but it looks like there will be some painful times ahead before everyone realizes that the token extrusion machines are only increasing the total cost of ownership, and they are being used incorrectly when we try to outsource our thinking to them. I think there's an enormous opportunity to build these tools right now, and that whoever nails it will win.