I've been using AI to write basically 100% of my code since late 2024, back in the Sonnet 3.5 days, when it barely worked and there were few people doing this and very little support for this way of developing. I stubbornly thought this was going to be the future and came up with a methodology for making it work, before "AI skills" were even a thing. Since then I've continued to refine the method and I just recently published it as open source. The method focuses on requirements (PRD, TDD) up front, and has skills that autonomously refine the requirements, declaring assumptions that should be validated by stakeholders. It doesn't expect you to be a solo founder or that whoever is at the keyboard directing Claude or Codex to be the product owner and business decider in addition to being the developer. I think most of us sit in the seat where we develop for someone else who decides. And if AI is all about velocity, then if we have to slow down to get product owners to review and decide, that puts a serious damper on the efficiency everyone expects from AI development. So a defining attribute of the Enso Method is, when it reviews the requirements prior to implementation, it asks what questions/gaps there are and it tries to answer its own questions. If it can get to 95%+ confidence in the answers, it just rolls with those. If less then it will declare the assumption in an assumptions.md file which gets routed to business stakeholders to review. In the meantime however we still consider this to be a green light, we can move forward and implement with the assumptions, and if we later hear from the business that the assumption was wrong, we update the requirements and re-implement, because implementation is now cheap, and waiting isn't.