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For the first time, you can build a competitive recommender system without a single user.

The playbook behind the flywheel of many of the most powerful companies has been: build a platform, measure engagement data, identify patterns, and make better recommendations to attract more users. That's the network effect that made Google, Meta, and Amazon so hard to compete with.

But LLMs have a compressed representation of that same knowledge from training on vast amounts of the internet. So to overcome the cold-start problem, you no longer need to measure and train on years of engagement data from your own users.

Now you can just give an LLM some context of a user, for example, their social media profile, to get high-quality recommendations. It's a fundamentally different entry point to personalization, one that doesn't depend on scale.

Are LLMs about to break the recommender-system moat?

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