Data: where the real advantage sits
Models are becoming interchangeable. Several providers, similar designs, similar quality, and the gap narrows every year. What is not interchangeable is your data: the material only you hold, and the expert judgement only your people can supply. Understand this layer and it becomes obvious why a few AI products succeed and most quietly do not.
Explain why one AI product has a durable advantage and another does not, without mentioning which model either of them uses.
Lessons 1 to 4. The training stages from lesson 1 and the test sets from lesson 4 both come back here.
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Each section opens with a short summary before the detail. Turn this off once the vocabulary feels familiar.
Anyone can rent the same model you are using. Nobody else has your documents, your experts, or your record of what good looks like in your field.
Models are becoming a commodity. Several providers, broadly similar designs, broadly similar quality at any given price point, and the differences shrink with every release. Which means the thing that makes an AI product hard to copy is not the model. Anybody can rent the same one tomorrow.
Here is the line worth carrying into meetings: for any serious build, the advantage is the data you own plus the expert judgement you can apply to it. That is defensible. Access to a model API is not, because your competitor has the same access.
This is also the honest answer to the accusation that every AI product is just a thin wrapper around somebody else's model. Sometimes that is exactly what it is. But the model is the commodity layer, and the data, the evaluations, and the understanding of how the work actually gets done are the layer that takes years to build and cannot be bought with a bigger budget.
The rest of this lesson is part of the paid course.
Answer in your head first, then reveal. Getting it wrong is the part that makes it stick.
Two companies build a legal research assistant on the same model. What would make one of them defensible?
The rest of this lesson
- The six kinds of data work
- How much human oversight, and where
- One worked example: the data behind an AI tutor
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