The idea, as far as I was able to understand it, is that you want your core models as dependency-free as possible. If you, for whatever reason, were to drop Pydantic, that would only affect the way you validate inputs from API, and nothing deeper.

This wasn't mentioned, but the constant validation on construction also costs something. Sometimes it's a cost you're willing to pay (again, dealing with external inputs), sometimes it's extraneous because e.g. a typechecker would suffice to catch discrepancies at build time.

Exactly. I love the comments by the way! I never expected this would take off like this. The fact that this isn’t clear in the article is excellent feedback, and I'll take it into account when revising it. After a few hours of writing, it's easy to forget to convey the real message clearly.

But you are absolutely right. To add a little: In practice, if a third-party library hardly ever changes and makes life dramatically easier, you can consciously decide to accept the coupling in your domain, but that should be the exception, not the rule.

Pydantic is great at turning large, nested dictionaries into validated objects, yet none of that power solves a domain problem. Inside the domain you only need plain data and behaviour: pure dataclasses already give you that without extra baggage. And that's the main reason to leave it out.

The less your domain knows about the outside world, the less often you have to touch it when the outside world moves. And the easier it becomes for any new team member to adopt that logic: no extra mental model, no hidden framework magic, just the business concepts in plain Python. And exactly what you mentioned: if you ever want to drop Pydantic, you don't need to touch the domain. The less you have to touch, the easier it's to replace.

So the guideline is simple: dependencies point inward. Keep the domain free of third-party imports, and let Pydantic stay where it belongs, in the outside layers.