I wonder if horse carriage drivers said the same thing about the advent of cars. Telling the LLM build me a login page instead of laboriously looking up example code in docs and retyping stack overflow snippets is definitely a different way of working, but thinking that makes someone a politician seems like a bit of a stretch.

At first, there was some skepticism, and even a bit of anxiety. When we said, “We’re going full AI-assisted development,” the natural reaction was: “What does that mean for my skillset?”

But here’s what happened in practice:

Most of the repetitive tasks — CRUD, glue logic, API boilerplate — disappeared. Instead, devs started focusing on system design, agent orchestration, prompt engineering, constraint writing, testing strategy, and overall architecture.

And they’re thriving.

Nobody’s DSA muscles are atrophying — they’re just being used differently. If anything, they’ve gained new skills that aren’t widely available yet: how to design workflows with stochastic tools, how to debug agent behavior, how to build structured memory into LLM stacks. These are things you won’t find in textbooks yet, but they’re very real problems — and deeply technical.

And let’s be real: you don’t forget how to reverse a linked list just because you stopped manually writing route handlers for user creation.

In short: the devs that leaned into it have grown faster, not slower. And the ones who felt it wasn’t for them — they moved on. Which is fine.

Every shift in tooling brings a kind of Darwinian filtering. It’s not about better or worse, just about who’s willing to adapt to a new abstraction layer. And that’s always been part of how tech evolves.