a machine learning algorithm significantly responsive and accurate can pierce the veil of cultural ignorance. Today, sometimes culture can be abstracted.

in the reverse direction, America has been almost as impenetrable to Chinese companies because of what might be thought of as America’s cultural firewall.

while you can’t listen to your customers exclusively, paying attention to them is a dependable way to build a solid SaaS business, and even in the consumer space it provides useful signal.

allowing watermarked videos to easily be downloaded and distributed via other networks like YouTube, Facebook, and Instagram, helped them achieve hockey-stick inflection among their target market.

Bytedance did two things in particular to jumpstart TikTok’s growth.

First, it opened up its wallet and started spending on user acquisition in the U.S.

Rumors abounded that the 30-day retention of all those new users poured into the top of its funnel was sub 10%. They seemed to be lighting ad dollars on fire.

the most important piece of technology Bytedance introduced to TikTok: the updated For You Page feed algorithm.

after they plugged Musical.ly, now TikTok, into Bytedance’s back-end algorithm, they doubled the time spent in the app.

To help a network break out from its early adopter group, you need both to bring lots of new people/subcultures into the app—that’s where the massive marketing spend helps—but also ways to help these disparate groups to 1) find each other quickly and 2) branch off into their own spaces.

the algorithm acts as a rapid, efficient market maker, connecting videos with the audiences they’re destined to delight. The algorithm allows this to happen without an explicit follower graph.

TikTok’s algorithm sorts its users into dozens and dozens of subcultures

Think of how most other social networks have scaled. The usual path is organic. Users are encouraged to follow and friend each other to assemble their own graph one connection at a time. The challenge with that is that it’s almost always a really slow build, and you have to provide some reason for people to hang around and build that graph, often encapsulated by the aphorism “come for the tool, stay for the network.” Today, it’s not as easy to build the “tool” part when so much of that landscape has already been mined and when scaled networks have learned to copy any tool achieving any level of traction.

Recall the three purposes which I used to distinguish among networks in Status as a Service: social capital (status), entertainment, and utility.

TikTok is less a pure social network, the type focused on social capital, than an entertainment network. I don’t socialize with people on TikTok, I barely know any of them.

what matters here is realizing that another way to describe an entertainment network is as an interest network.

The idea of using a social graph to build out an interest-based network has always been a sort of approximation, a hack.

It worked in college for Facebook because a bunch of hormonal college students are really interested in each other.

The problem with approximating an interest graph with a social graph is that social graphs have negative network effects that kick in at scale. Take a social network like Twitter: the one-way follow graph structure is well-suited to interest graph construction, but the problem is that you’re rarely interested in everything from any single person you follow.

TikTok doesn’t bump into the negative network effects of using a social graph at scale because it doesn't really have one. It is more of a pure interest graph, one derived from its short video content, and the beauty is its algorithm is so efficient that its interest graph can be assembled without imposing much of a burden on the user at all.

It is passive personalization, learning through consumption.

TikTok came along and bypassed all of that. In a two-sided entertainment marketplace, they provide creators on one side with unmatched video creation tools coupled with potential super-scaled distribution, and viewers on the other side with an endless stream of entertainment that gets more personalized with time.

TikTok has figured out the hardest piece, the algorithm. With it, a massive team made up mostly by people who’ve never left China, and many who never will, grabbed massive marketshare in cultures and markets they’d never experienced firsthand.