Is Bidirectional filtering really that bad?

I've been researching and aplying bidirectional filters a lot recently and the core points are:

• As with everything: think before you enable it. It can be bad, but it might just be what you need.
• If you're doing 'straight' kimball there's not really a lot of danger of creating ambiguity. Whenever I model I try and stick to this as much as possible. 
• Propagating filters (aka synced slicers) is 100 % necessary it's stupid that this isn't a baseline feature.
• Your first model has no ambiguity, you can safely use bidirectional filters there. Your second one is extremely dangerous and unpredictable.
• Taking all of the following into consideration: if it's that important you might want to heavily denormalize your data to facilitate using it properly. I obviously do not have insight to your data but the last image you posted has products and suppliers. Are those products delivered by those suppliers? if so just denormalize, make it one dimension and call it day.

I think his 'stance' is a mix of fearmongering, clickbait and plain truth. If you look at the end of this article however you'll see that he takes a more nuanced tone. https://www.sqlbi.com/articles/bidirectional-relationships-and-ambiguity-in-dax/?platform=hootsuite 'There are a few scenarios where the power of bidirectional cross-filter really shines. However, these need to be leveraged carefully making sure that the model does not become ambiguous because of the bidirectional relationship. '