The real bottleneck might be in how current AI systems handle personalized continuous learning. Building adaptive models that evolve with individual user data streams sounds simple in theory, but the engineering complexity is substantial. What's fascinating from the technical experiments I've run: training on massive tweet datasets with proper continual learning mechanisms unlocks genuinely powerful insights. The delta between static models and dynamically-learning systems is dramatic. If teams building timeline algorithms could crack this optimization problem, you'd see a qualitative shift in how personalized feeds work.