The million dollar (perhaps literally) question is – could @kentonv have written this library quicker by himself without any AI help?
I *think* the answer to this is clearly no: or at least, given what we can accomplish today with the tools we have now, and that we are still collectively learning how to effectively use this, there's no way it won't be faster (with effective use) in another 3-6 months to fully-code new solutions with AI. I think it requires a lot of work: well-documented, well-structured codebases with fast built-in feedback loops (good linting/unit tests etc.), but we're heading there no
I think these discussions need to start from another point. The techniques changed radically, and so did the way problems are tackled. It's not that a software engineer is/was unable to deliver a project with/without LLMs. That's a red herring. The key aspects are things like the overall quality of the work being delivered vs how much time it took to reach that level of quality.
For example, one of the primary ways a LLM is used is not to write code at all: it's to explain to you what you are looking at. Whether it's used as a Google substitute or a rubber duck, developers are able to reason with existing projects and even explore approaches and strategies to tackle problem like they were never able to do so. You no longer need to book meetings with a principal engineer to as questions: you just drop a line in Copilot Chat and ask away.
Another critical aspect is that LLMs help you explore options faster, and iterate over them. This allows you to figure out what approach works best for your scenario and adapt to emerging requirements without having to even chat with anyone. This means that, within the timeframe you would deliver the first iteration of a MVP, you can very easily deliver a much more stable project.