Scaling AI infrastructure to serve 800M weekly users isn't just about throwing more compute at the problem. The real magic happens in model specialization and strategic fine-tuning.

Platform engineering teams are now doubling down on tailored model architectures instead of one-size-fits-all approaches. What's interesting? The shift toward releasing open-weight models signals a major strategy pivot - balancing proprietary advantages with ecosystem growth.

Managing elite ML teams at this scale means rethinking everything from deployment pipelines to developer tooling. The bottleneck isn't raw model performance anymore; it's making that performance accessible and practical for builders across different use cases.