2 min lesson
The dogfood proof point that anchors exec decks
Walk through each part of "The dogfood proof point that anchors exec decks", then explain what each one does.
Step 1 of 2
The ROI maturity journey, with the numbers buyers ask forqualitative → quantitative → dollars
Proving ROIReturn on Investment. The value gained versus what it cost, the language an economic buyer funds deals in. Press Enter for the full definition. is itself a staged journey, and naming the stage you're in keeps you honest. You start with anecdotal/qualitative signal (are devs excited, do they feel more productive), move to quantitative productivity (PR velocityHow quickly pull requests are merged; the easiest delivery metric to measure, though it sits furthest from the customer outcome. Press Enter for the full definition., code quality, bug counts, % of code turned over), then translate to real dollars. Don't skip ahead - a dollars claim with no quantitative spine dies in procurement.
- Velocity
- Cursor reports velocity increases upward of 30% and rising - treat it as a directional benchmark and re-baseline to this org before you quote it
- Automation time saved
- Per-person automations save roughly 30-60 min/day; an automation costs a tiny fraction of a salary but can make an engineer 10-20% more effective
- Pull-forward revenue
- As deployment collapses from weeks/months to days, teams pull roadmap items forward - and the revenue from those products with them
- Incident response
- ~30 minutes of incident-response time matters when an outage costs millions per minute - a small time saving is real dollars in regulated/high-availability shops
Cursor's strongest internal proof: roughly a third - quoted up to ~40% - of PRs merged at Cursor itself are created end-to-end by cloud agents (the agent wrote all the code, returned artifacts and pushed to production), with about 30-40% merged straight from Slack ("@cursor fix it" → video + PR → review → merge). An individual power-user engineer estimates ~70% of their personal PRs come from cloud agents.
Usage skyrocketed after the early-January artifacts release. Frame these as "here's what mature adoption looks like at the vendor that builds it" - aspirational proof, while you measure this account against its own day-zero baseline.
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Full explanation
A named-team adoption case (Airtable SEs)
Use this when a buyer wants proof the ROIReturn on Investment. The value gained versus what it cost, the language an economic buyer funds deals in. Press Enter for the full definition. lands beyond engineering. Airtable's sales engineers lived in a no-code app builder - a dozen-odd preset components plus "interface extensions" that let you write code. Once every SE got a Cursor license, demos went from pre-baked templates to fully bespoke ones writing TypeScript shipped into the demo environment, opening up use cases like financial reporting and dashboarding the presets couldn't cover. A custom demo used to mean pinging an engineer and waiting a week or two; now an SE who could vibe code folded a bespoke demo into the natural cadence, so more calls could feature a custom demo. The same teams build live-data demos - a usage calculator embedded in Salesforce, hosted on Vercel with webhooks and a proxy. That's the leading-indicator depth story in a non-engineering org.
“I think for us at Airtable Cursor became the bridge between this like no code world that most of our SEs lived in and then what customers would actually envision when they asked for a custom demo.”
Onboarding collapses from a month-long process to days or weeks, partly because engineers can pick up unfamiliar languages quickly with AI - which is what makes the time-to-first-commit pain worth translating to dollars per hire.
A University of Chicago study found more experienced developers were more likely to do an intentional planning step before generating code, especially with AI tools - empirical support that plan-first is a senior habit, not a beginner crutch, when you're justifying the enablement curriculum.