Google's large model Gemini's application in consumer-grade products has actually formed a pretty good self-reinforcing mechanism. User behaviors on platforms like Gmail, Google Search, YouTube, and others continuously generate data. This data, in turn, trains and optimizes Gemini's capabilities, and the updated model is then integrated back into these To C products, improving user experience and increasing usage frequency — this is a typical flywheel effect.

From actual results, this logic indeed has a closed-loop feel. With a large user base and diverse usage scenarios, the quality and diversity of data are guaranteed. Improvements in Gemini are directly reflected in the features users interact with daily — more accurate search, smarter email assistants, more relevant recommendations. As it becomes more useful, user stickiness increases, and data feedback becomes even more sufficient. For large model vendors, such ecosystem-level applications are the most imaginative.