That time dilation feels a bit like what METR reported:
https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...
Developers believe they complete tasks 25% faster with AI but when measured they are 19% slower when using AI.
It depends on how the AI is used; there's a huge difference in productivity between a structured workflow with repeated automated feedback to the AI, and just ad-hoc prompting. For instance, Gemini 2.5 coding up a spec-compliant HTTP2.0 server in two weeks:
https://outervationai.substack.com/p/building-a-100-llm-writ... . 15k lines of code and 30k lines of tests; no human coder could produce something like that so fast.
Would that application not exist in the training data already?
The are around three Golang HTTP2.0 servers on Github, but it wasn't just regurgitating the application from memory, as if it was then it would have been mostly correct first try, and it wouldn't have needed to spend 80%+ of the development time in a code-compile-test cycle of fixing bugs identified by integration tests and spec conformance tests.
So you are saying that LLMs have no capacity to re-use existing work, and will just burn hours and money re-inventing wheels?