RecSys is the crux of the bull/bear case on Meta, I wouldn't get too distracted by much else: 1. Meta CFO Susan Li saying on the follow-up call that the Meta RecSys still has gaps and can't fully reason user interests, which is interesting. - "Our current recommendation systems are very, very good at leveraging user interaction histories, but unlike LLMs, they don’t have the ability to reason about content or user interest from first principles." 2. IG Head Adam Mosseri had a similar point on Lenny's pod earlier this quarter that the RecSys is not as sophisticated as people assume but critically he said it's "only now getting as sophisticated as people assumed for many years" ie some form of inflection. - "I think a misconception historically is, until recently, we don't really know as much about you as you think. We were just like, oh, you liked these photos, these people also liked those same photos, and they like these other photos, so you might like those other photos. That's kind of how -- I'm oversimplifying, but that's kind of how it worked. Only now are we actually getting as sophisticated as I think people have assumed we've been for many years." 3. They also talked about the RecSys upgrade at their @Scale conference in a session. - "Recommendation systems are still very limited as it... relies on something called collaborative filtering which means it recommends content based on what others could engage in our platform. So it doesn't address dynamic and personalized motivations and needs for the users." - Basically, they log what you're clicking or watching but they never understood the "why" which is where they're headed next. All in, how fast or slow Meta's RecSys improves or not is the real determinant of whether the infra spend has legit ROI or not. Plus, moving over to a new system probably causes stop gaps on topline too. Talk to any advertiser and they'll tell you every system upgrade is always a cluster.