The framing of craft vs. slop misses something important: most production software quality problems aren't about aesthetics or elegance, they're about correctness under real-world conditions.

I've been using AI coding tools heavily for the past year. They're genuinely useful for the "plumbing" - glue code, boilerplate, test scaffolding. But where they consistently fail is reasoning about system-level concerns: authorization boundaries, failure modes, state consistency across services.

The article mentions AI works best on "well-defined prompts for already often-solved problems." This is accurate. The challenge is that in production, the hard problems are rarely well-defined - they emerge from the interaction between your code and reality: rate limits you didn't anticipate, edge cases in user behavior, security assumptions that don't hold.

Craft isn't about writing beautiful code. It's about having developed judgment for which corners you can't cut - something that comes from having been burned by the consequences.

> Craft isn't about writing beautiful code. It's about having developed judgment for which corners you can't cut - something that comes from having been burned by the consequences.

That's why I'm of the opinion that for senior developers/architects, these coding agents are awesome tools.

For a junior developer? Unless they are of the curious type and develop the systems-level understanding on their own... I'd say there's a big chance the machine is going to replace their job.

Most people using LLMs dont have this craft...... which begs the question. Should they be using LLMs in the first place? Nope. But given that its rammed down their throat by folks internally and externally, they will.