Set the person. Watch the month.
Four sliders describe who is deciding. One control sets how fast a wrong decision comes to light. The grid is thirty days of decisions — white ones right, red ones wrong, orange ones built on a wrong one before anyone noticed. The balance at the bottom prices the difference between this person and an average one.
The person deciding
Decisions a day
Every prompt, every accept or reject, every "ship it". Before AI a developer made a few dozen a day. Now the ceiling is gone.
How fast do you find out you were wrong?
Until you find out, the next 4 decisions are built on the wrong one. Fixing it then costs about 40 min.
Thirty days · 1,800 decisions
rightwrongbuilt on a wrong one
Showing one in 6 decisions. Same inputs always draw the same picture.
Wrong, and what it dragged along
What that costs
01 · The rule
Quality is a curve, not a switch
The four traits average to a profile. All ones decide right 20% of the time, an average profile about 77%, all tens about 99%. Multiply that by two thousand decisions a month and the gap is a team's month. The Pirxey preset is a translation of a test result into sliders, not a measurement — the CCAT covers speed and reading, not your domain.
02 · The lag
Wrong decisions have children
Until a wrong decision is caught, the next ones are built on it. Same hour: one. Next sprint: fifteen. In production: forty. Cost of the fix grows the same way — the old 1-10-100 rule, with AI's volume behind it.
03 · What it doesn't say
AI is not the decider
The model writes the option. A person picks it, on the strength of what they know, how fast they think, how carefully they read and how clearly they asked. That is what the sliders weigh.
To decide well, a person has to already be four things.
None of these is a prompt technique. They are who the person is on the day the question arrives — and AI made the questions arrive faster than ever.
Knowledge — wide, not just deep
The model knows the framework. The person has to know that the client's invoices run on the 25th, that the last migration broke reporting, and that 'customer' means three different tables. A decision made without that context is a guess with confidence.
Speed of thought
A factory that plans, builds and verifies in minutes gives the person minutes to decide. Slow deciders do not make worse decisions — they make fewer, and the line waits. Speed is part of quality now. We test for it: our people sit the CCAT, a cognitive aptitude test of problem solving and learning speed, and 80% of the crew score above the 90th percentile.
Reading with comprehension
The output is fluent, long and confident. The one wrong line looks exactly like the fifty right ones. Reading it properly is the whole job of the inspection gate — and it is the skill that separates 'looks fine' from 'is fine'.
Clarity of expectations
'Make it better' produces a random improvement. 'Keep the API stable, cut p95 under 200 ms, add a test for the midnight case' produces the improvement you meant. The person who can say the second sentence is worth several who can only say the first.
The compounding
Decisions stack. If each one is right 95% of the time, a chain of twenty holds together 36% of the time. At 99%, 82%. Small differences in the person become large differences in the product — which is what the ledger shows in hours.
The lag
The cost of a wrong decision grows with how long it survives — the oldest rule in software economics. AI did not change the rule. It changed the volume flowing through it: more decisions, less time between them, more built on each one.
Why we think this is the constraint now.
The ledger is a model. The pressure it models is measured — by other people, on other teams, and by us on our own product.