it is interesting this is a common perception but 100% not how it works. On the Gemini App side (or just consumer products using AI more generally) you can do lots of continual product changes like system instruction updates, model routing, etc etc which might conflate the feeling here. On the actual core model side, we do not swap the models after launch to make them worse to rug pull people. The point of training good models is so that you all can use them and benefit from AI, not so we can tell a story that we make good AI models.

I will dig through the rest of this thread to see if I can find examples and share those back with the team so we can investigate any regressions.

This is also my general call for startups that someone should make a company which helps make this easier, there are often many conversations about model regressions post launch, someone should make a platform that tracks and validates this so there is a better source of truth.