done.

Meter anxiety is killing AI adoption. When AI feels like a black box of costs, people opt out. @HubSpot almost nails it: predictable packaging + cost transparency beats pure usage billing. blog.hubspot.com/website/why-ai…

The operational trap no one talks about when switching to outcome-based pricing is that visibility ends at the meter. Customers and RevOps inherit the charge, but not the context.

@cursor_ai Nice step toward usage-based pricing. The bigger unlock for code review might be value-based validation: which findings were accepted, actionable and actually helped prevent issues.

Effort levels are now available to all users on usage-based Bugbot and can be configured from your Bugbot dashboard. Learn more: #effort-levels" target="_blank" rel="nofollow noopener">cursor.com/docs/bugbot#ef…

You can now customize how deeply Bugbot thinks during a PR review. At Cursor, we use high effort for changes to our infrastructure and backend so Bugbot detects more issues. Other PRs get default effort.

It is hard to picture how outcome-based pricing would actually play out for your customers and your business. A Claude Code skill that makes it easy. github.com/done-billing/o…

If you are pricing AI on tokens, you are measuring the wrong thing. Real value lives at the outcome and business impact layer. 👉 thesaascfo.com/the-four-layer…

YC is pushing for outcomes. Next comes making them measurable, reliable and monetizable.

From Customer Support teams building AI: Pricing isn't the hard part. Defining "done" is. Once "resolved" varies by team, your monetization model starts to fall apart.

Many AI-native teams are going up against legacy tools. Matching their pricing model makes switching easier for buyers. Seat pricing is the on-ramp, while outcome pricing is the destination.

Talking to coding agent teams: Enterprise wants outcomes SMBs still buy seats But here is the catch: $20–30/seat doesn't cover token costs Seat pricing ≠ compute reality This only works while usage is low