Isn't this true of any greenfield project? with or without generative models. The first few days are amazingly productive. and then features and fixes get slower and slower. And you get to see how good an engineer you really are, as your initial architecture starts straining under the demands of changing real world requirements and you hope it holds together long enough to ship something.

"I could make that in a weekend"

"The first 80% of a project takes 80% of the time, the remaining 20% takes the other 80% of the time"

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jjav 36 minutes ago | parent | next [-]

> Isn't this true of any greenfield project?

That is a good point and true to some extent. But IME with AI, both the initial speedup and the eventual slowdown are accelerated vs. a human.

I've been thinking that one reason is that while AI coding generates code far faster (on a greenfield project I estimate about 50x), it also generates tech-debt at a hyperastonishing rate.

It used to be that tech debt started to catch up with teams in a few years, but with AI coded software it's only a few months into it that tech debt is so massive that it is slowing progress down.

I also find that I can keep the tech debt in check by using the bot only as a junior engineer, where I specify precisely the architecture and the design down to object and function definitions and I only let the bot write individual functions at a time.

That is much slower, but also much more sustainable. I'd estimate my productivity gains are "only" 2x to 3x (instead of ~50x) but tech debt accumulates no faster than a purely human-coded project.

This is based on various projects only about one year into it, so time will tell how it evolves longer term.

	▲	unlikelymordant 22 minutes ago \| parent [-]
		In your experience, can you take the tech debt riddled code, and ask claude to come up with an entirely new version that fixes the tech debt/design issues you've identified? Presumably there's a set of tests that you'd keep the same, but you could leverage the power of ai in greenfield scenarios to just do a rewrite (while letting it see the old code). I dont know how well this would work, i havn't got to the heavy tech debt stage in any of my projects as I do mostly prototyping. I'd be interested in others thoughts.

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michaelt an hour ago | parent | prev | next [-]

> Isn't this true of any greenfield project?

Sometimes the start of a greenfield project has a lot of questions along the lines of "what graph plotting library are we going to use? we don't want two competing libraries in the same codebase so we should check it meets all our future needs"

LLMs can select a library and produce a basic implementation while a human is still reading reddit posts arguing about the distinction between 'graphs' and 'charts'.

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dust42 2 hours ago | parent | prev [-]

From personal experience I'd like to add the last 5% take 95% of the time - at least if you are working on a make over of an old legacy system.