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?

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'.