Comment by delichon
1 day ago
There is little new under the big fusion reactor in the sky. I just read a chapter in James Glieck's "The Information" about tokenmaxxing in the telegraphy industry. There used to be a big market for code books to reduce the per-character charges for sending telegrams. Compression was cash in the pocket. The telegraph companies discouraged the practice but were forced to accept it. The telegraph code industry started with the initial commercialization of telegraphy and didn't end until the 1920s.
There was a cost to it though. Codes greatly reduced redundancy, and caused large miscommunications from very small errors. As Glieck explains it, this was the opposite of the African drumming practice of adding redundancy to strengthen the relationship between the rhythm and the language that the drums mimic.
Isn't that the exact opposite of tokenmaxxing - instead, the telegraphy analogy would be if telegraph operators were ranked by how many hours per day they tied up telegraph lines (highest number of tokens burned/highest $ spend wins) instead of by customers served (programmers delivering features).
What you were describing would be token-minimizing, not maxxing.
That is interesting but tokenmaxxing is not maximizing token usage _efficiency_. It is maximizing its usage.
Thanks, that's so odd that I assumed it was about efficiency, which is how I treat tokens. It's hard to imagine a 19th century business man ordering his staff to send as many long winded telegrams as they can.
Think of it like this: telegraphs are the hot new thing. The more you send, the more modern and relevant your company. No more Pony Express. You can either have employees sending 1-2 a day. Or, 100 per day. Wow so advanced, so modern, invest now.
I don’t think the analogy works too well for one specific reason: you can increase your number of used tokens without ever „sending a telegram“! Run a bunch of Claude sessions, ask them to review various docs sites, create random prototypes, they just throw all of that away. Congrats, you’re a token maxxer
This will probably lead to some balancing act like ye olden days of big data etc. Companies want AI native engineers who will use AI to do their work, but don't want AI quality outputs and don't want to drop 200k per year per employee.
AI quality outputs are fine for backoffice work now, but they are awful to read and reason about. Hallucinated features are also difficult to work with.
What you describe is practically the opposite of tokenmaxxing.