Some solid Q&A on seat vs usage vs outcome pricing for AI. Clearly, no perfect answers but it def made me fine-tune my POV. Views: > marginal cost of s/w = 0; marginal cost of AI is not. That alone implies you HAVE to find SOME way to correlate price to usage/COGS. > "but AI labs all do tiered pricing" - actually all AI-lab rev is token metered which is why every tier has rate limits, caps, model gating, etc. Labs are actually the strongest case _for_ token/usage pricing. > variable cost / task, but customers want predictability; agree - but true only for tasks that have median & mode usage within striking distance; in AI, power users can use 1000x median and seat pricing on variable COGS is a disaster. Needs to be _very_ carefully planned. > I recall "runaway" usage as a huge issue when we launch bigQuery at GCP; knee-jerk reaction was to limit use; what made BQ a $10B biz was still usage pricing but paired with account budgets, threshold alerts, dry-run estimates. That's a better approach (but harder for long-horizon / unbounded tasks I'd admit) > "why not do outcome based pricing" - imho, this _is_ usage/token based pricing, just metered at a higher order unit. It works best, again, when median vs mode are similar. e.g. say sierra charges 2$ / resolution, and resolution takes 2min (cost = 0.2$) or 2hrs ($12), the lose on the 2hr call (v low vol) and make money on the 2min call (v high vol). Unbounded agentic tasks need to be metered on usage. My best guess is that the real uncapped promise & value in AI is on agentic tasks. There is this idea of agentic frontier where if you are more human-assist / co-pilot, you'd end up preferring seat-based but over time as you start solving agentic problems, which is the true value of AI lies, you'd end up above the frontier and shift to usage based. The more unbounded & agentic the problem, the higher the preference for usage pricing. Harvey may be sitting below the frontier today, but will eventually move above.