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I also use them per-token (and strongly prefer that due to a lack of lock-in).

However, from a game theory perspective, when there's a subscription, the model makers are incentivized to maximize problem solving in the minimum amount of tokens. With per-token pricing, the incentive is to maximize problem solving while increasing token usage.

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I don't think this is quite right because it's the same model underneath. This problem can manifest more through the tooling on top, but still largely hard to separate without people catching you.

I do agree that Big Ai has misaligned incentives with users, generally speaking. This is why I per-token with a custom agent stack.

I suspect the game theoretic aspects come into play more with the quantizing. I have not (anecdotally) experienced this in my API based, per-token usage. I.e. I'm getting what I pay for.

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