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Outcome-based pricing is a trust contract enforced in code. A guide for engineering leaders on building it right thewitn.com/blog/an-engine…

Cut average call resolution from six minutes to three and you have made your product twice as good while cutting your revenue in half thewitn.com/blog/how-voice…

Interesting framework from @jakesaper Emergence Capital emcap.com/ai-native-serv…

@sudiprokaya Agree on receipt as the system of record. That pulls the layer toward billing, not compute, since done is a contractual term and not an inference call.

MIT: 95% of AI pilots deliver zero measurable P&L impact. Buyers are not buying a vision anymore. thewitn.com/blog/why-selli…

Deloitte's Technology Spotlight provides a legitimate accounting framework for outcome-based pricing under ASC 606. The core challenge is that most billing systems cannot produce the specific evidence this guidance assumes you have. thewitn.com/blog/implement…

The math on how AI gets priced is wild. Usage and credits both bill the input, so when the product gets faster and smarter, the customer consumes less. Team ships a better product and gets punished for it. With outcome pricing each product improvement grows margin and revenue.

@johniosifov Agree on the gap, but the counterfactual is the trap. The fix is picking the smallest concrete outcome both sides can point at and agree happened. Successful teams pick a boring definitions like ticket resolved, appointment booked. Observable, repeatable, hard to dispute.

$300 billion poured into AI startups in Q1 2026 alone. 6,000 startups funded. 88% of all global venture capital now flowing to AI-adjacent companies. And yet — a company raised $10 million specifically to help CFOs answer the question: "Is our AI actually working?" The ROI gap is real. Money is flooding in at the macro level while enterprises struggle to prove value at the micro level. VCs believe. Boards believe. But the data from deployed systems often doesn't close the loop. The problem isn't belief in AI. The problem is measurement. Most AI deployments don't have proper instrumentation. You can see the output (an agent generated a response, an automation ran). You can't easily see the counterfactual (what would have happened without it), the error rate, or the dollar value of the outcome vs the inference cost. So you get this odd pattern: companies are investing $10M, $50M, $100M in AI transformation while simultaneously not being able to tell their investors whether those investments compound or decay. VCs fund based on category growth and team signal. CFOs need different evidence — they need unit economics at the workflow level. Revenue per agent-hour. Cost per resolved ticket. Yield improvement per automated step. That measurement infrastructure doesn't exist at most companies. It's being built right now, after the spend. Investment thesis: the "AI ROI stack" — measurement, attribution, cost tracking for agentic workflows — is the unsexy infrastructure layer that becomes mandatory once the first AI budget cycle needs to justify its renewal. What's being measured at your org?

Sharpest framing on why outcome-based pricing is easy to pitch and hard to ship 👇 thesaascfo.com/how-to-build-o…

Everyone talks about "outcome pricing" like the outcome is one fixed unit. It is a package of risk. Appointment booked = $1 Appointment booked AND happened = $5 More vendor skin in the game should mean more upside.

@DataAgentsAI 100%. Though it is really two audiences. Product & Engineering can usually trace the spike. RevOps and the customer can't, even though they are the ones holding the bill. Outcome pricing fails at the handoff.

@done___hq Outcome pricing is great until you can't trace what drove the spike. The gap between "we billed $ X" and "here's why" is where most teams lose control.

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.

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…

@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.

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