[rustyboy]

I notice a lot of XY problems with GenAI if you're not careful. Thus I can picture more senior engineers writing (with GenAI) really specific business requirements as unit tests with intermediate and junior engineers (with GenAI) writing the actual code.

A lot more work on building these internally (ex: Azure OpenAI & huggingface) and business analysts or others vetting the outputs or sentiment of output.

I can also picture a lot of work on designing more complex LLMs architectures. Like if you have the money, could you have a rough loop that reviews issues, roadmaps, and/or todos and proposes changes to the code itself? If so - see the importance of writing good tests and solid devops practices.

Conversely, integrate GenAI into PRs. Think "making AI" a team member. And even more so if you think about mythical man month problems. Software engineering makes you a manager for AIs.