Coding Is When We’re Least Productive – Codemanship’s Blog

Deep Blue

My social networks are currently awash with Deep Blue:

…the sense of psychological ennui leading into existential dread that many software developers are feeling thanks to the encroachment of generative AI into their field of work.

I miss thinking hard.

There are two wolves inside you…

My Builder side won’t let me just sit and think about unsolved problems, and my Thinker side is starving while I vibe-code. I am not sure if there will ever be a time again when both needs can be met at once.

The Future of Software Development is Software Developers – Codemanship’s Blog

The hard part of computer programming isn’t expressing what we want the machine to do in code. The hard part is turning human thinking – with all its wooliness and ambiguity and contradictions – into computational thinking that is logically precise and unambiguous, and that can then be expressed formally in the syntax of a programming language.

That was the hard part when programmers were punching holes in cards. It was the hard part when they were typing COBOL code. It was the hard part when they were bringing Visual Basic GUIs to life (presumably to track the killer’s IP address). And it’s the hard part when they’re prompting language models to predict plausible-looking Python.

The hard part has always been – and likely will continue to be for many years to come – knowing exactly what to ask for.

The Recurring Cycle of ‘Developer Replacement’ Hype

Here’s what the “AI will replace developers” crowd fundamentally misunderstands: code is not an asset—it’s a liability. Every line must be maintained, debugged, secured, and eventually replaced. The real asset is the business capability that code enables.

If AI makes writing code faster and cheaper, it’s really making it easier to create liability. When you can generate liability at unprecedented speed, the ability to manage and minimize that liability strategically becomes exponentially more valuable.

This is particularly true because AI excels at local optimization but fails at global design. It can optimize individual functions but can’t determine whether a service should exist in the first place, or how it should interact with the broader system. When implementation speed increases dramatically, architectural mistakes get baked in before you realize they’re mistakes.

The Hidden Cost of AI Coding – Terrible Software

Feels like an emerging trend:

Instead of that deep immersion where I’d craft each function, I’m now more like a curator? I describe what I want, evaluate what the AI gives me, tweak the prompts, and iterate. It’s efficient, yes. Revolutionary, even. But something essential feels missing — that state of flow where time vanishes and you’re completely absorbed in creation. If this becomes the dominant workflow across teams, do we risk an industry full of highly productive yet strangely detached developers?