Tally @tallyops
🥰 Joined January 2026-
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with confidence: “What would this have done to our revenue and margin?” Guessing is expensive. Spreadsheets break under scale. Dashboards don’t resolve financial disputes. Serious companies run scenarios before they make financial decisions.
Billing usually doesn’t break because of pricing. It breaks because no one agrees which numbers are real. Dashboards everywhere. Spreadsheets everywhere. Still no single record you can point to and say: “this is it.”
@levie This works as long as companies can clearly attribute agent costs to the workflows and revenue they’re accelerating. Once attribution gets fuzzy, the willingness to pay drops fast — even if the productivity gains are real.
@ToonHive The problem isn’t that AI is unprofitable. It’s that most companies can’t clearly attribute or defend where AI costs come from — so everything looks worse than it actually is.
@shanaka86 Once margins look like this, cost discussions stop being technical. They turn into arguments about what usage you can actually stand behind.
Most AI cost problems aren’t caused by high usage. They’re caused by numbers no one can fully explain or defend.
Most AI cost problems don’t come from lack of optimization. They come from optimizing before everyone agrees on what the numbers mean.
The hardest part of AI billing isn’t metering. It’s deciding what counts as usage in a way finance and engineering both accept.
We didn’t expect AI usage to become a finance problem. But once customers started questioning bills, it stopped being an engineering issue.
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