Fireside Alpha: Wang says Meta CFO Susan Li warned him ‘if this doesn’t work, this is going to be really, really bad’

Alexandr Wang reveals Meta CFO Susan Li pulled him aside “many times” at an exec offsite in Hawaii to warn that if the model didn’t work, it would be very, very bad in financial terms

ZUCKERBERG: “So I think this is part of the excitement of booting up a frontier lab now, where it is basically like the ante is extremely high.”

WANG: “And I think this is, and I kind of viscerally remember this. We were in Hawaii at the end of last year. It was kind of like the whole exec team got together.”

“And yeah, Susan pulled me aside many times and was like, you know, I just want to tell you if this doesn’t work, this is going to be really, really bad. It’s going to be very, very bad in financial terms.”

“It’s like, it has to be good. If it’s good, it’s all good. We’re great. But if it’s bad, it’s really, really, really bad.”

Quoting @firesidealpha: $META Devansh Tandon reveals the tokens-in, engagement-out flywheel behind Meta’s recommendation system, where better models lift Reels watch time 30% and the monetization pays for the next training run

“These scaling curves aren’t just academic research. They’re driving real product impact at scale for some of the biggest consumer businesses in the world.”

“Here’s a couple of examples I have from Meta’s recent earnings reports. Instagram Reels had a strong quarter, 30% year on year watch time.”

“And the optimizations we made to improve the quality of recommendations included simplifying our ranking architecture to enable efficient model scaling and longer interaction histories to identify a person’s interests.”

“We doubled the length of user interaction sequences used for training Instagram and increased the richness of each user interaction.”

“So these are direct parallels to the power law scaling curves for LLMs.”

“And I want to introduce this idea of a flywheel of tokens in engagement out, which is what’s powering all of these recs models scaling.”

“You train a model, you then run inference on it, which is the tokens in, that recommendation model results in better content recommendations, it drives consumer engagement, daily active users’ time spent, it translates to monetization and ads or subscription, which pays for the next model training run.”

“And so every step on the scaling curve is one loop around this flywheel, and a lot of consumer apps are spinning this core flywheel at the heart of their business. So we have a long way to scale these recommender systems.”

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For the full breakdown of the lecture on Meta’s ads recommendation system: https://firesidealpha.substack.com/p/metas-ad-recommendation-system-and