11–20 of 162 posts
"The leaderboard, which ranked employees and teams by token consumption, inadvertently incentivized usage volume over productive output." Who could possibly have predicted that happening?
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.
You can rack up token consumption extremely quickly when you embed LLMs into automated processes or products.
I'd be very surprised if these numbers are just typical coding usage with no scripting/pipeline/automation stuff
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.
IMO claude, chatgpt/codex, etc should be able to optimize the PDF use case to be extremely token efficient as it's a very obvious use case. But when I start to explain to my wife/friends why it burns through so much quota, I find myself thinking "why should they have to understand this aspect of it". to me, that the details of PDF parsing and extracting are relevant to users (instead of solved such that you don't have to pay attention to it) shows how these tools are not nearly as "ready" as they are made out to be. I may be preaching to the choir on this one, but just my 2c
"The leaderboard, which ranked employees and teams by token consumption, inadvertently incentivized usage volume over productive output." Who could possibly have predicted that happening?
Just wonder what happens when more and more companies introduce similar restrictions. Will that lead to devaluations of the LLM companies?
measure outcomes (impact), not effort (token usage, lines of code, code coverage, hours worked, etc.)
It wants to see faster R&D, higher revenues from existing assets, greater operating margins, higher sales to invested capital ratio and so on…
The best way to measure that for a software firm is up-time of services, usage and project completion duration
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…