World Model Readiness Or just the convincing ones

Module · Or just the convincing ones

The Verification Discipline Check

AI is fluent whether or not it is right, so the convincing answer and the correct answer feel identical from the inside. Verification is the discipline that closes that gap, and it is the first thing to slip once the tool has earned your trust. This check looks at five parts of it: how strong the habit is, whether you check sources, whether your effort matches the stakes, whether you notice your own drift into trusting it, and whether you have a record of what you have caught.

Question 1 of 5 · The habit is real

Before you use an AI answer, do you check it, or does that depend on your mood?

A verification habit is a rule, not a feeling. If you check when you happen to feel uncertain, you will skip exactly the confident-sounding answers that most deserve a look. The habit has to fire on its own.

Question 2 of 5 · You check the sources

When AI gives you a fact or a citation, do you confirm it exists?

These tools invent sources that look real: plausible titles, plausible authors, plausible page numbers, none of it there. A citation you have not opened is not evidence, it is a guess wearing the costume of one.

Question 3 of 5 · Effort matches stakes

Does how hard you check scale with what it would cost to be wrong?

Verifying a throwaway draft as hard as a board number wastes your time; verifying them the same light way risks your reputation. Calibration is spending your checking effort where a mistake actually hurts.

Question 4 of 5 · You watch your own bias

Do you notice yourself trusting AI more the longer it has been right?

Automation bias is the quiet drift: the tool earns your trust on a hundred easy tasks, and you stop checking just before the one it gets wrong. The danger is not the model, it is your own relaxing vigilance.

Question 5 of 5 · You have caught real errors

Can you point to specific AI mistakes your checking has actually caught?

A verification habit that has never caught anything is either unnecessary or not really running. A short mental list of real catches is proof the discipline works, and it keeps you honest about why you bother.

For the statistics · one click each

Three questions for the public picture

These do not affect your score. They feed the anonymised, aggregated statistics; groups under 8 respondents are never shown.

What share of your AI outputs do you actually verify before using?

Almost none
The important ones
Around half
Most of them
Effectively all

How costly would a wrong AI answer typically be in your work?

Low, easily caught later
Moderate, some rework
High, reaches customers or records
Severe, money or safety or legal
It varies widely

Has an unchecked AI answer ever caused a problem you had to fix?

Not that I know of
A near-miss, caught late
Yes, minor
Yes, serious
I would not necessarily know

Your context

Used to calibrate the report. Company size and sector remain in the anonymized dataset; your email does not.