The opening sections including graphs do not match my experience. I think they only apply to certain workflows which can be described as "work we have to do because software has a history of poor integration". I.e. repeating solved problems.

Claude will, when given a task off the beaten track, churn through tokens for a while, then produce a completely incorrect answer. (Most recent anecdote: fixing a barostat in an MD sim)

Specifically: How does Spotify, a music streaming service, improve due to AI agents producing code all night? What is improving or being fixed which needs that much abstract code and problem solving? I am guessing the AI code is just building more messy architecture on top of the messy architecture which is causing so much work to be generated.