Your argument hinges on deciding what "works" means.

You're saying it "works" for some people but maybe not in a way or scale that was originally intended for it to "work".

But isn't that how all learning in a complex system takes place? Doesn't mean you need to rush to cancel something?

What's wrong in saying it doesn't do what we thought it would but it does do something useful? Maybe adjust further investment based on results obtained. Ignore sunk cost.