I’d be curious to see the breakdown on spending by use case. I’ve heard it said that the majority of tokenmaxing comes from none technical uses like reading PDFs, creating PowerPoints, generating graphics/images… ect. But I’ve never heard any actual proof to that.
One thing I find fascinating as a software engineer who talks to non software engineers who use AI tools is how "reading PDFs" is not more of a solved problem. What I mean is that uploading a PDF into a chatbot tool seems to be an extraordinarily obvious use case that non technical (and technical) users would want to do. IMO claude, chatgpt/codex, etc should be able to optimize the PDF use case to be extremely token…
This discussion was about measures, goals and incentives. Follow the incentives.