Facebook is working on an AI system for video recommendations across all its services; the model architecture has led to “an 8% to 10% gain in Reels watch time”
signaling more upside to market @thetranscript_ : $META's Head of Facebook App: “Facebook Reels is now about kind of 1/3 of Facebook's video time. Facebook video time is over 50% of our overall time spent....when you see kind of posts in Feed content posts, ~30% of those posts on average are delivered by our recommendation... Rat King / @mikeisaac : head of FB app tom alison speaking at the morgan stanley conf in SF right now and is rattling off some interesting data points fully leaned into recommendational content algo b/c of generational change. 30 percent of what people see in their newsfeed now is recommended content Forums: r/wallstreetbets : Facebook is building a giant AI model to power all video recommendations, exec says
Context & Ripple Effects
Meta's recommendation effort has moved from training AI to understand activity in public Facebook videos to documented ranking systems across Feed, Stories, and Reels. That progression makes a shared video model a product-infrastructure step, not an isolated Reels feature.
The company had already tied Instagram's Reels growth to AI recommendations and expected the format to reach revenue neutrality as recommendation-driven viewing expanded. Facebook is now applying the same engagement logic across a larger share of its in-app time.
First-order effects
- Meta can use one model architecture to improve video ranking across its services; the reported Reels watch-time lift directly strengthens the role of recommended video in Facebook engagement.
- Creators and publishers gain distribution from a system where recommendations already deliver a meaningful share of Feed posts, while performance becomes more dependent on the model's ranking signals.
Second-order effects
- A measurable engagement gain raises the bar for rival short-video and social platforms: recommendation quality, rather than only a social graph or creator roster, becomes a more immediate retention lever.
- More time in recommended video expands the inventory available for monetization, reinforcing Meta's incentive to prioritize ranking and video-serving infrastructure over chronological or follower-led distribution.
Third-order effects
- If cross-service models continue to outperform siloed rankers, large platforms with multiple content surfaces and behavioral signals may compound an AI distribution advantage over smaller networks.
- The shift makes content discovery increasingly model-mediated: transparency work such as Meta's system cards for its recommendation systems becomes more consequential as recommendation systems determine a larger portion of what users encounter.
The trend: Social platforms are consolidating recommendation systems into broader AI models that turn cross-surface engagement data into a distribution advantage.