Ai-Measurement

AI can create drafts faster than one person can responsibly review them. Generation and evaluation scale differently, and the gap between them is where the approval pile comes from.

TypeSafe's Jev returns a calibrated probability with every decision. Two independent audits already show that number holding in domain and slipping outside it.

The frontier premium got priced this week: 4.4 months of head start at five times the cost. For a one-person business, that turns model choice into a per-task decision.

Meta's own numbers: code changes up 220 percent, features reaching users up 36 percent, incidents up 40. Plus the platform under your AI just changed owners.

Run a 90-day measurement on the workflow you use most and find out which shape it draws. A protocol, a column of numbers, and an honest account of what nobody has measured yet.

Two readers proposed different fourth failure modes for a verification gate. Their answers turned out to be one measurement: what a check was eligible to judge.

The Agent Audit is a two-week measurement engagement that shows, from your own git-log evidence, whether your AI gains compound or vanish by Wednesday.

Engineering-grade AI keeps an audit trail: the sources it used, the checks it ran, the verdicts they returned, and who approved the result.

The Operator Tax is the recurring cost of re-teaching your AI the same standards every week — work that should compound but instead keeps resetting.

Score AI output against a written standard on a fixed cadence. A baseline tells you where you are. What happens next is something you observe, not something the method promises.

Open the AI tool you bought six months ago. Sharper than day one, or exactly the same? That one answer tells you whether you bought a system or a pile.

Your AI gets worse over time because of drift: unmeasured output that slowly decays. No new tool fixes drift on its own. Here is why, and what does.