Ask HN: Where is the programming profession going?

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.

In my experience, AI solves the implementation part well - if you know explicitly what should be implemented. If your engineering understanding is fuzzy, the implementation will be fuzzy too. Engineering and architectural skills matter more now than before. Previously, you had time for rethinking and rearchitecting while coding. That timeline is collapsing - your ideas become real in hours, the bad ones included. Verification matters more than implementation. When you ship 20 PRs to verify in a single day, every day, all month, how can you be sure the ideas are right, and the implementation is correct? You need ways to challenge your own system: integration tests, runtime tests, and load tests that make you confident in what you're building. Three skills become non-negotiable in this AI era (until tomorrow, when the answer probably changes again): 1. System Architecture. Build correct framing for your ideas with room for future extensions. Requirements should be complete, precise, and extensible. Project documentation grows with the code and stays current. 2. Organizing AI agentic teams into verifiable flows. Self-evolving specialized agents that verify each other's output until you're confident the code matches the requirements you actually had. 3. Verification, verification, verification. Integration tests, runtime tests, load tests, experiments. The code an AI produces has to be confirmed correct across every dimension that matters.