Claims about LLMs increasing programmer "productivity" should start with a measurable definition of productivity. I have 40+ years experience programming and I

Claims about LLMs increasing programmer "productivity" should start with a measurable definition of productivity. I have 40+ years experience programming and I have yet to find a useful metric of "programmer productivity," so I am going to assume that all references to "productivity" come from subjective feelings and opinions -- like saying "I feel healthier" after a colon cleanse. Junior programmers will likely find LLM coding tools magical because they don't have the skills and experience to outperform the "AI." I liken that to my parents thinking I have a special genius with computers when I get their wi-fi to work again or show them how to search for an email message -- let's call it a skills issue. More senior programmers may find LLMs handy for filling in boilerplate or dropping in a well-known implementation of an algorithm or code snippet. Previously we would have to break stride and search StackOverflow and other places, or a book, or figure it out ourselves, so maybe we gain some time getting a chunk of code regurgitated for us, and lose some of that time carefully reviewing and testing what we got. A significant part of programming involves rote tasks, and in the most common domains (like web development) the code solutions tend to look the same. If GPT or Copilot can save me a few seconds of looking up how to reduce an array in Javascript, great, but I'm not sure I'll call that a big productivity boost because I don't measure my productivity in lines of code per minute. To put it less charitably, the closer your skills approach 0x productivity (relative to the 1x - 10x programmers) the more an "AI assistant" will increase your output. But since we don't have a good way to measure productivity or compare it to past performance, much less to other programmers, we just talk about subjective experience made worse by Dunning-Kruger effects.