The numbers, and where they come from.
Every figure on this site carries a footnote. This page is the footnotes.
F1Review reality
Global industry survey (McKinsey, State of AI): 27% of organizations using generative AI review all AI-created content before use; roughly as many review at most a fifth. Full review is documented standard in healthcare (the clinician reviews and signs every AI-drafted note before it enters the record) and in US securities (a registered principal pre-approves retail communications; the approver’s name and date are archived; firms are fully responsible for AI-generated content). In July 2026 the regulator, FINRA, proposed risk-based selection instead of pre-review of everything — explicitly because AI volumes have made full review unmanageable.
F2The middle lane and the error-cap span
5–10x (the middle lane’s escalation saving) and the twenty-fold error-cap span: measured in replayed tests on real model data (July 2026), at the same measured error cap.
F3Model-swap detection
Model swaps were detected in testing within a median of 36–59 responses; the step-down covers detected swaps and gross shifts — silent drift without ground truth is the meter’s job (see The Insurance Bridge).
F4The guard caught our own error
One of our calibrated limits let through 1.6x more alarms than promised; the guard’s alarm was correct, and the correction is computed and confirmed in a follow-up control study.
F585–94% automation
85–94%: measured automation share at error caps of 1% and 5% respectively, in replayed tests (July 2026) on two open language models (70–72 billion parameters) and public test data. Your number depends on your case mix and is measured in a pilot.
F64.0–4.4% against a promised 5.0%
4.0–4.4% against a promised maximum of 5.0%: measured share of false alarms in four separate test flows on real models, with statistical margins reported.
F7The missing record
Industry analyses and the reading of the EU’s automatic event-logging requirements: operator-mutable application logs do not hold up as strong evidence; NIST’s framework for AI agents defines the record required instead — who approved an action, under which policy, when, with what outcome.
F8EU AI Act timeline
The EU AI Act after the June 2026 decision: transparency duties from 2 August 2026; full high-risk duties by 2 December 2027 at the latest, with the possibility of moving the date earlier.
F9The insurance market
Specialist programs at Lloyd’s write up to $25M per organization (early 2026); insurers already back AI performance warranties for vendors; the largest European insurers do not yet have their own AI liability products.
F10The meter
Two studies in simulation on frozen real model data — 60,000 runs across 72 conditions, then confirmed in an independent repetition with corrected reference flows (24 control conditions). Medians 141–157 against a computed information-theoretic floor of 145; false alarms 1.7–4.0% against a 5% budget, versus 57% for the check-every-time method.
Questions about a number?
We're happy to walk through any figure, method or margin in detail.