No I think you're right that the amount of compute spent on office work is lower than coding - although I don't have any sense for the right share. The best source I could find was an OpenAI report [1] which mentions that ~64% of enterprise token generation is via Codex, which I would expect to skew entirely towards coding. But it's hard to say how the remainder is split, what proportion is 'frontier', or whether it's representative for Anthropic.

On your questions - I've spoken to a number of execs and seniors behind closed doors but nothing public I can point to. Anecdotally, I've spoken to senior leaders at banks spending billions of tokens on one-off tasks like prepping execs for earnings calls or piloting end-to-end agent workflows for specific use cases (but mostly piecemeal/one-off).

On Fable, financial analysts I know are using it to produce research docs, models and decks - I hear that it's a big improvement for these tasks. This lot have been blindsided by the spend growth [2], the same as for coders in enterprise (e.g. Uber blowing annual budget in 4 months [3]), so I do think there's appetite and budget for a capable, cheaper open model - but, due to the price, Kimi does not obviously fill that role the way it might for coding. That said, I still largely agree with you on share - where coding has seen a broad deployment across software development, most of the office work stuff is still fairly piecemeal and certainly lower compute-spend.

I think it's fair to say I could've focussed on coding more rather than taking AA's benchmark distribution as representative - perhaps a more balanced title would be "Kimi K3 is not cheap across the board"? I guess there's also some ambiguity about what 'cheap' means - as I said elsewhere in this thread, I think when some people talk about the price of Chinese models, they imagine Deepseek competing with o1 for 1/20th of the price. Even though it is better priced for coding, Kimi isn't Deepseek-level cheap.

I do, however, think you could debate whether coding will remain at >50% total token usage going forwards - big enterprises are hunting for ways to get value out of LLMs, and the labs are investing a correspondingly large amount in generating demonstrations and RL environments to get the models up to par. At the end of the day, programmers make up ~5% of all white collar work. Of course, it's also possible that Chinese labs will shift focus to white collar applications now they've demonstrated a lead on coding cost efficiency, so, I mean who knows - it'll be interesting to get some detail when Anthropic IPOs.

Sorry for the long reply! Appreciate it's quite meandering...

[1] https://cdn.openai.com/pdf/5d1e1489-21c0-43e4-9d42-f87efdbf0...

[2] https://www.reuters.com/business/finance/australias-cba-flag...

[3] https://fortune.com/2026/05/26/uber-coo-ai-spending-tokens-c...