TikTok’s Secret Sauce

I’m not here to question people’s lived experience of TikTok. But there’s no truth to the idea that TikTok’s algorithm is more advanced than its peers. From everything we know—TikTok’s own  description , leaked  documents ,  studies , and  reverse engineering  efforts—it’s a standard recommender system of the kind that ever

A central challenge for any recommendation algorithm is the tradeoff between safe but somewhat boring recommendations that are similar to recommendations that worked well in the past (“exploitation”), and risky recommendations that are unlikely to be good but have a high payoff if they do turn out to be good (“exploration”). Explorat

Even with the best recommendation algorithm, only a fraction of your feed will be content that you truly enjoy. For example, the average ratio of hearts to views on TikTok is  roughly 5% .  People are just not that predictable. On YouTube, every time you select a video but then decide you don’t want to watch it, it’s an annoyin

Despite TikTok’s design innovations being well known, other apps have trouble copying them because they were originally designed for a very different experience, and they are locked into it due to their users’ and creators’ preferences. This is a classic example of the  innovator’s dilemma

Again, the secret sauce is not the algorithm, because it would be trivial for other platforms to increase the preference for exploration. It comes back to the scrolling experience: TikTok is able to take risks because all it takes is a swipe.

TikTok’s algorithm treats each video more or less independently to assess its viral potential, caring relatively little about how many followers the creator has. This would be a trivial algorithm change for its competitors. What’s stopping them? Only the fact that their top creators, who collectively determine the platform’s fate, would rebel,

TikTok’s recommender system is not its secret: rather, it’s the design, which, of course, isn’t secret at all. More generally, in AI applications, the sophistication of the algorithm is rarely the limiting factor. The quality of the design, the data, and the people that make up the system all tend to matter more.