zlacker

>>martin+(OP)
I've noticed a huge gap between AI use on greenfield projects and brownfield projects. The first day of working on a greenfield project I can accomplish a week of work. But the second day I can accomplish a few days of work. By the end of the first week I'm getting a 20% productivity gain.

I think AI is just allowing everyone to speed-run the innovator's dilemma. Anyone can create a small version of anything, while big orgs will struggle to move quickly as before.

The interesting bit is going to be whether we see AI being used in maturing those small systems into big complex ones that account for the edge cases, meet all the requirements, scale as needed, etc. That's hard for humans to do, and particularly while still moving. I've not see any of this from AI yet outside of either a) very directed small changes to large complex systems, or b) plugins/extensions/etc along a well define set of rails.

>>danpal+vd
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"

>>somat+LC
> 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.

>>jjav+xQ
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.

>>unlike+FR
Take Claude Code itself. It's got access to an endless amount of tokens and many (hopefully smart) engineers working on it and they can't build a fucking TUI with it.

So, my answer would be no. Tech debt shows up even if every single change made the right decisions and this type of holistic view of projects is something AIs absolutely suck at. They can't keep all that context in their heads so they are forever stuck in the local maxima. That has been my experience at least. Maybe it'll get better... any day now!