Losing jobs to AI

There is a lot of talk these days on the topic of jobs lost to AI. I admit I was worried for some time — maybe that would be my last job as a programmer and I should recycle into something an AI can’t replace !

I found comfort in a recent study that, yes, some jobs could be replaced, but AI also presents new opportunities.

But that’s not where I want to go today. I think we should embrace AI to become more productive — and here’s why.

Automating repetitive work

Every project has work that has to happen but doesn’t require your full attention: boilerplate, unit tests, documentation, migration scripts. It’s the kind of work that takes hours and leaves you tired without actually advancing the hard parts.

AI handles this well. I’ve used it to generate test scaffolding, write the first pass at API documentation, and turn a rough sketch of a database schema into a full migration. None of those required judgment — they required time. Getting that time back means I can spend it on the parts of the project that actually need a human.

Creative work

There’s a misconception that AI kills creativity. In my experience it’s the opposite. Having a tool that can quickly prototype an idea — a UI layout, an architecture diagram, a rough draft of a feature — lowers the cost of exploring. You can try three approaches before committing to one, instead of committing because you can only afford to try one.

Creativity isn’t just inspiration. It’s also iteration. The faster you can move from idea to something you can look at, the more creative you actually get to be.

Amplifying your expertise

AI doesn’t replace deep knowledge — it multiplies it. A senior developer who understands the trade-offs can direct an AI like a capable assistant and ship what used to take a small team. A junior developer can reach beyond their current level by asking the right questions and learning from the answers.

What goes up in value is knowing why something works. An AI can write the code; it can’t tell you whether the code solves the right problem, or whether the approach will hold up six months from now when the requirements change. That judgment is yours.

Learning faster

AI is the most patient tutor most of us have ever had. You can explore an unfamiliar codebase, a language you’ve never touched, or a domain that usually takes months to pick up — and get clear explanations at your own pace, without judgment, at two in the morning if that’s when you have time.

I’ve used it to get up to speed on parts of a stack I inherited, to understand papers I wouldn’t have had the patience to read alone, and to work through ideas out loud until they made sense. The barrier to expanding your skills has never been lower. The only question is whether you use it.

Focusing on what actually matters

When the boring parts are handled — the boilerplate, the documentation, the research synthesis — you spend your hours on the decisions only you can make: the architecture, the trade-offs, understanding what the customer actually needs versus what they asked for.

The work gets more interesting, not less. I spend less time on the parts of the job I was doing out of obligation and more on the parts I took this career for in the first place.

The question was never really whether AI would change our jobs. It will, and it already has. The real question is whether you’re the one holding the wheel when it does.

Embracing AI isn’t about keeping up — it’s about getting ahead. The developers who treat it as a threat will spend the next few years defending ground. The ones who treat it as a tool will spend that time building things they couldn’t have built alone.