This is a nice frame for thinking about the divergence in attitudes towards coding with AI assistance in different environments; thanks for sharing. As someone who’s spent decades in brown field environments, my starting assumption when starting in a new environment or project is that the code will be anything but ideal, with historical layers of sediment from different eras of trends evident. I expect it to be poorly and sparsely tested, inconsistently patterned, and unpredictable to change. Most old coding curmudgeons I speak to share these expectations. What I don’t understand is why more people don’t see AI as the greatest assistant in establishing your understanding of an unfamiliar system, and why they think the code AI is writing is going to be worse than all the code we have to deal with that was written by humans in the decades up to now. Because AI is also the best assistant I’ve ever had in both establishing understanding and making leveraged changes to such code bases. But you can’t just prompt your way there expecting code changes from every prompt; you have to pants your way to a minimum viable understanding, then plot your way to well planned changes using intermediate steps such as those encouraged by the Matt Pocock skills.