Jev vs Human Review: The Real Math

Every "AI vs human review cost" spreadsheet makes the same mistake: it prices the humans doing today's job and the model doing today's job, then declares a winner. But today's job was shaped by what humans could afford to review. Price the job you actually want, every item judged by a decision model like Jev from TypeSafe AI, and the comparison turns into a different question with a much more useful answer.

This page owns the labor math. The broader thesis, that cheap judgment flips sampling into coverage everywhere, is why decisions became free; here we apply it with a calculator.

The formula both sides share

Strip review to its units and both options reduce to the same line.

Human review cost = items reviewed × minutes per item × loaded hourly rate ÷ 60.

Decision-model cost = items × questions per item × cost per verdict, plus the human time spent on whatever the model escalates.

Plug in your own numbers; they're the only ones that matter. On the model side, builder-reported receipts give the scale. Ian Nuttall ran 3,282 posts through eight questions each for $0.1282 in 8 minutes 34 seconds, roughly 26,000 verdicts, as reported. Hassan's fraud cascade judged 100 emails in 1.42 seconds and sent only the uncertain ones to a bigger model, 96 of 100 correct for about $0.07, as reported. There are no official Jev benchmarks; these are receipts, and they are consistent.

The human side you know better than we do. The point is the ratio: per-item model cost rounds to fractions of a cent, and per-item human cost is measured in minutes of a salaried person.

Review team economics: why "replace the team" is the wrong frame

The tempting conclusion is "fire the reviewers." It's also the wrong one, for two reasons that show up in the formula.

First, the human term never goes to zero. Escalated items still need people. Low-confidence verdicts, contested calls, and anything with real consequences land in a human queue. A well-tuned cascade shrinks that queue to the genuinely hard slice, but someone owns the slice.

Second, most review teams were never reviewing everything. Contact-center QA, the canonical example, typically scores one or two percent of calls by hand, per the pattern covered in AI call QA scoring. If a team reviews two percent, "replacing" it with a model that reviews two percent saves a rounding error. The math only gets interesting when the model covers the other 98.

So run the formula twice. Once as a swap (same coverage, model instead of humans) and once as a flip (100 percent coverage by model, humans on escalations and audit). The swap usually saves modest money. The flip usually costs about the same as today's team and delivers fifty times the coverage.

What the flip buys that the swap can't

Sampling answers "roughly how are we doing?" Full coverage answers questions sampling structurally cannot: which reviewer, agent, vendor, or queue drifts on which criterion, whether a policy change actually changed behavior, and where the rare-but-serious failures cluster. A two percent sample of a rare failure is usually zero observations.

The hiring receipt shows the shape at its most consequential. The Alan AI application review console screened 3,518 internship applications in 4 minutes 54 seconds and surfaced 20 for human review, as reported. Read that correctly: the model did not hire anyone. It narrowed a pile no team would have read carefully, and humans made every decision that mattered.

That's the posture for any high-stakes domain (hiring, grading, medical, money): the model triages and flags, a named human decides, and the question set, threshold, and every verdict are logged so the decision procedure can be audited later. Irreversible outcomes never ride on a lone verdict. None of this is legal advice; if your domain is regulated, your counsel owns the final design.

Where human review still wins outright

Honesty cuts both ways. Humans win on novel judgment (the case your rubric never anticipated), on context the text doesn't carry (the customer's history, the tone of the actual call), and on accountability, where a regulator or a court wants a person's name next to the decision. They also win when volume is small: if you review forty items a week, the engineering to automate it costs more than the reviewing.

And the model needs its own reviewers. Calibration against human labels, a rotating audit sample, and question maintenance are real, recurring work. Budget them in the human term, or the flip will look cheaper than it is. The 100-case calibration method is where that budget starts.

Frequently asked questions

Is AI review cheaper than human review?

Per item, dramatically, at builder-reported fractions of a cent per verdict. Per team, the honest comparison is full coverage plus a smaller human escalation-and-audit role against today's sampled review, which usually costs about the same and covers far more.

Can Jev replace my QA or moderation team?

It replaces sampling, not judgment: the model covers every item and humans handle flags, escalations, and audits. Teams that cut the humans entirely lose the calibration that keeps the model honest.

How do I estimate the cost for my workload?

Multiply items by questions per item by a per-verdict cost from real receipts, then add human minutes for the escalated slice. The itemized receipts live in what builds actually cost.

Is automated review allowed in hiring or lending?

Rules vary by jurisdiction and this is not legal advice. The defensible posture everywhere is model-as-triage, human-as-decider, with versioned questions and logged verdicts as your documented procedure.