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.

Enterprise IT dinosaur here, seconding this perspective and the author’s.

When I needed to bash out a quick Hashicorp Packer buildfile without prior experience beyond a bit of Vault and Terraform, local AI was a godsend at getting me 80% of the way there in seconds. I could read it, edit it, test it, and move much faster than Packer’s own thin “getting started” guide offered. The net result was zero prior knowledge to a hardened OS image and repeatable pipeline in under a week.

On the flip side, asking a chatbot about my GPOs? Or trusting it to change network firewalls and segmentation rules? Letting it run wild in the existing house of cards at the core of most enterprises? Absolutely hell no the fuck not. The longer something exists, the more likely a chatbot is to fuck it up by simple virtue of how they’re trained (pattern matching and prediction) versus how infrastructure ages (the older it is or the more often it changes, the less likely it is to be predictable), and I don’t see that changing with LLMs.

LLMs really are a game changer for my personal sales pitch of being a single dinosaur army for IT in small to medium-sized enterprises.

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"

I find AI great for just greasing the wheels, like if I’m overthinking on a problem or just feel too tired to start on something I know needs doing.

The solutions also help me combat my natural tendency to over-engineer.

It’s also fun getting ChatGPT to quiz me on topics.

> Anyone can create a small version of anything

Yup. My biggest issue with designing software is usually designing the system architecture/infra. I am very opposed to just shove everything to AWS and call it a day, you dont learn anything from that, cloud performance stinks for many things and I dont want to get random 30k bills because I let some instance of something run accidentally.

AI sucks at determining what kinda infrastructure would be great for scenario x due to Cloud being to go to solution for the lazy dev. Tried to get it to recommend a way to self host stuff, but thats just a general security hazard.

It seems to be fantastic up to about 5k loc and then it starts to need a lot more guidance, careful supervision, skepticism, and aggressive context management. If you’re careful, it only goes completely off the rails once in a while and the damage is only a lost hour or two.

Overall, still a 4x production gain overall though, so I’m not complaining for $20 a month. It’s especially good at managing complicated aspects of c so I can focus on the bigger picture rather than the symbol contortions.

Yeah, my observation is that for my usual work, I can maybe get a 20% productivity boot, probably closer to 10% tbh, and for the whole team overall productivity it feels like it has done nothing, as senior use their small productivity gains to fix the tons of issues in PR (or in prod when we miss something).

But last week I had two days where I had no real work to do, so I created cli tools to help with organisation, and cleaning up, I think AI boosted my productivity at least 200%, if not 500.

Similar experience. I love using Gemini to set up my home server, it can debug issues and generate simple docker compose files faster than I could have done myself. But at work on the 10 year old Rails app, I find it so much easier to just write all the code myself than to work out what prompt would work and then review/modify the results.

It’s fantastic to be able to prototype small to medium complexity projects, figure what architects work and don’t, then build on a stable foundation.

That’s what I’ve been doing lately, and it really helps get a clean architecture at the end.

I find that setting up proper structure while everything still fits in a single context window of Claude code, as well as splittjng as much as possible into libraries works pretty well for staving off that moment.

I have experienced much of the opposite. With an established code base to copy patterns from, AI can generate code that needs a lot less iteration to clean up than on green fields projects.

My observations match this. I can get fresh things done very quickly, but when I start getting into the weeds I eventually get too frustrated with babysitting the LLM to keep using it.