I think veteran engineers have always known that the real problems with velocity have always been more organizational than technical. The inability for the business to define a focused, productive roadmap has always been the problem in software engineering. Constantly jumping to the next shiny thing that yields almost no ROI but never allowing systemic tech debt to be addressed has crippled many company's I have worked at in the long-term.
Any competent engineer should understand that engineering is just the assembly line side of product development. Deciding when to release which feature, bug fixes, etc. and the development/management of the product in general has always been the real challenge, and a lot of the strategy involved in doing this relies on feedback loops that AI cannot speed up. Though at the same time I do feel like leaders on the business side often scapegoat engineer's speed as an excuse instead of taking responsibility for poor decisions on their end.
It’s part of the problem but AI also can crush this on pure lines of code and functionality alone. It can put out 100,000 lines of somewhat decent code in a day. That usually takes months or years of manual coding for a team.
> The inability for the business to define a focused, productive roadmap has always been the problem in software engineering.
Agreed, and I also agree that most developers come to this realization with time and experience. When you have a clear understanding of business rationale, scope, inputs, and desired outputs, the data models, system design and the code fall out almost naturally. Or at least are much more obvious.