World Model Readiness Buying the story or the system

Module · Buying the story or the system

The M&A AI Due Diligence Check

Every target now has an AI story, and the story is priced into the deal. Some of it is a real system with real data and a defensible edge; some of it is a wrapper around a public model, a founder who leaves at closing, and a revenue line that AI touched but did not earn. This module checks whether your diligence can tell the two apart: verifying the technical claims, tracing who owns the training data, testing the dependency on key people, confirming the model and IP actually transfer, and attributing revenue to AI honestly.

Question 1 of 5 · Claims are verified

Can your diligence team actually test the target's AI claims, not just read them?

A deck says the model is proprietary and the accuracy is high. Diligence that only reads the deck is buying the claim at face value. Someone who can look under the hood, at the model, the code, the evaluation, is the difference between verifying an asset and financing a story.

Question 2 of 5 · Training data is owned

Do you know whether the target owns the data its models were trained on?

A model is only as transferable as the data behind it. If that data was scraped, licensed on terms that do not survive a sale, or belongs to customers who can withdraw it, the asset you are buying can evaporate at closing or surface later as a lawsuit.

Question 3 of 5 · Not one person's head

If the key AI people leave at closing, does the capability survive?

In many targets the real AI asset is two or three people, and the model is a thing they know how to keep working. If the capability walks out the door when the earn-out vests, you bought a snapshot, not a system. Retention and documentation decide which one it is.

Question 4 of 5 · The model transfers

Will the models, IP and infrastructure actually transfer with the deal?

The AI may run on a founder's personal cloud account, depend on a licence that is not assignable, or sit on IP the company never properly assigned to itself. What runs today is not automatically what you own tomorrow. The transfer is a legal and technical question, and both answers matter.

Question 5 of 5 · Revenue attribution is honest

Can you tell how much of the target's revenue AI actually earns?

AI-driven revenue is the line that justifies the premium, and the easiest one to inflate. Revenue that AI touched is not revenue AI earned. Diligence has to separate the sales that genuinely depend on the AI from the ones that would have closed anyway with a spreadsheet.

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.

How active are you in acquiring companies with AI capabilities?

Not acquiring
Exploring targets
The occasional deal
Actively acquiring
Prefer not to say

Who verifies the AI claims in a target you are buying?

Nobody specific
The deal or finance team
Internal technical staff
Independent AI experts
No AI deals yet

Has an acquired AI capability ever underdelivered against the deal case?

No
Yes, a little
Yes, significantly
We have not measured
No such deals yet

Your context

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