To understand the token count thing - spending tokens is necessary and not sufficient to demonstrate that you are adopting AI.
Where we were 6mo ago is that a lot of big orgs realized they were behind, and needed some way of measuring if the tools were usable at all.
No sawdust at all on your job site, and you can tell nobody is cutting wood.
Now that tooling is more mature, you can measure things like % of diffs AI-generated, % of AI suggestions accepted vs edited, % of KB queries successful etc - all more useful than raw token count for quantifying how your org is using the tool.
So it’s a pragmatic metric that got a bit Goodhearted.
My feeling is it's not as bad of a metric as people think. Companies don't fully know the best way to use AI and things are changing rapidly, so you want people using a lot of tokens even on stuff that seems maybe kind of dumb on the surface, because if you find one useful thing and share it in the org that makes up for a lot of failures.
But I do think you also need to say, "To be clear, don't game the system. Any token usage that is even remotely justifiable as useful for the business is fine, and we will give you a lot of latitude. But if you're in the top 10% of token users, we are going to review your token usage, and if we find that you have a dozen agents perpetually running writing slam poetry, you're going to get fired."
No sawdust is bad. But it's also bad if you cut all your boards into sawdust. Completely. Obliterated. No useful output, only sawdust.
% of AI suggestions accepted vs. edited is also a BS metric that Anthropic et. al. like to push, similar to LoC, because they're large numbers and large numbers must be good, right?
Well guess what, I have auto-accept on and then adjust after it's "done". And I do it by telling it what changes to make and those have auto-accept on as well. That's quite a high "accept" rate, by definition. But in reality it may have churned on 50% of the lines it generated and auto-accepted first.