I think Baldur is onto something here with his categorisation of software. There’s the software based on innovation, something truly novel:
Innovation’s the word. Pushing the boundaries. You know the phrases. Usually spouted by that dude at the party.
Then there’s the software based on itertion, making a better version of a proven tool:
We are now in a place where we have entire genres of software that have decades of history, are backed by stacks of new and old research, have dozens of successful, well-made exemplar apps, and a broad enough conceptual space to allow for new variations on the theme.
In short, we have genre software and we have avant-garde software, and I’ve always been more interested in genre fiction than literary fiction.
You can’t write a compelling narrative about the thing you didn’t build. Nobody gets promoted for the complexity they avoided.
Complexity looks smart. Not because it is, but because our systems are set up to reward it.
Anyone can add complexity. It takes experience and confidence to leave it out.
Generated code is rather a lot like fast fashion: it looks all right at first glance but it doesn’t hold up over time, and when you look closer it’s full of holes. Just like fast fashion, it’s often ripped off other people’s designs. And it’s a scourge on the environment.
I’ve seen so many times how 10 lines of code can end up being worth £millions, and 10,000 ends up being worthless.
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.
Can you ship AI-generated code without creating a maintenance nightmare six months from now? Can you debug it when it breaks? Can you modify it when requirements change? Can you onboard new engineers to a codebase they didn’t write and the AI barely explained?
Most teams haven’t realized this shift yet. They’re optimizing for code generation speed while comprehension debt silently accumulates in their repos.
One team I talked to spent 3 days fixing what should have been a 2-hour problem. They had “saved” time by having AI generate the initial implementation. But when it broke, they lost 70 hours trying to understand code they had never built themselves.
That’s comprehension debt compounding. The time you save upfront gets charged back with interest later.
Whether you’re generating slop or code, underneath it’s the same shoggoth with a smiley face.