Engineers who dismiss AI

You can always ask it to nudge you in the right direction instead of giving the solution right away. I suspect this way of using it is not very popular though.

This is not a new problem I think. How do you use Google, translator, (even dictionaries!), etc without "degenerating" your own abilities?

If you're not careful and always rely on them as a crutch, they'll remain just that; without actually "incrementing" you.

I think this is a very good question. How should we actually be using our tools such that we're not degenerating, but growing instead?

The nondeterminism is what makes LLMs different.

You download a tool written by a human, you can reasonably expect that it does what the author claims it does. And more, you can reasonably expect that if it fails it will fail in the same way in the same conditions.

If only you put half as much effort into learning ai as you do trolling people who are getting gains from it...

Do you save time by using a calculator / spreadsheet or try to do all calculations in your head, because your ability to do quick calculations degrades the more you rely on tools to do it.

I'm not too worried about degrading abilities since my fundamentals are sound and if I get rusty due to lack of practice, I'm only a prompt away from asking my expert assistant to throw down some knowledge to bring me back up to speed.

Whilst my hands on programming has reduced, the variety of Software I create has increased. I used to avoid writing complex automation scripts in bash because I kept getting blocked trying to remember its archaic syntax, so I'd typically use bun/node for complex scripts, but with AI I've switched back to writing most of my scripts in bash (it's surprising at what's capable in bash), and have automated a lot more of my manual workflows since it's so easy to do.

I also avoided Python because the lack of typing and api discovery slowed me down a lot, but with AI autocomplete whenever I need to know how to do something I'll just write a method stub with comments and AI will complete it for me. I', now spending lots of time writing Python, to create AI Tools and Agents, ComfyUI Custom Nodes, Image and Audio Classifiers, PIL/ffmpeg transformations, etc. Things I'd never consider before AI.

I also don't worry about its effects as I view it as inevitable, with the pendulum having swung towards code now being dispensable/cheap to create, what's more important is velocity and being able to execute your ideas quickly, for me that's using AI where I can.

We’ve been “losing skills” to better tools forever, and it’s usually been a net positive. Nobody hand-writes a sorting algorithm in production to “stay sharp”, most of us don’t do long division because calculators exist, and plenty of great engineers today couldn’t write assembly (or even manage memory in C) comfortably. That didn’t make the industry worse; it let us build bigger things by working at higher abstraction.

LLM-assisted coding feels like the next step in that same pattern. The difference is that this abstraction layer can confidently make stuff up: hallucinated APIs, wrong assumptions, edge cases it didn’t consider. So the work doesn’t disappear, it shifts. The valuable skill becomes guiding it: specifying the task clearly, constraining the solution, reviewing diffs, insisting on tests, and catching the “looks right but isn’t” failures. In practice it’s like having a very fast junior dev who never gets tired and also never says “I’m not sure”.

That’s why I don’t buy the extremes on either side. It’s not magic, and it’s not useless. Used carelessly, it absolutely accelerates tech debt and produces bloated code. Used well, it can take a lot of the grunt work off your plate (refactors, migrations, scaffolding tests, boilerplate, docs drafts) and leave you with more time for the parts that actually require engineering judgement.

On the “will it make me dumber” worry: only if you outsource judgement. If you treat it as a typing/lookup/refactor accelerator and keep ownership of architecture, correctness, and debugging, you’re not getting worse—you’re just moving your attention up the stack. And if you really care about maintaining raw coding chops, you can do what we already do in other areas: occasionally turn it off and do reps, the same way people still practice mental math even though Excel exists.

Privacy/ethics are real concerns, but that’s a separate discussion; there are mitigations and alternatives depending on your threat model.

At the end of the day, the job title might stay “software engineer”, but the day-to-day shifts toward “AI guide + reviewer + responsible adult.” And like every other tooling jump, you don’t have to love it, but you probably do have to learn it—because you’ll end up maintaining and reviewing AI-shaped code either way.

Basically, I think the author hit just in the point.