Why is a TikTok-like algorithm for giving new users a boost hard?

1. Social network vs. social media — we started as a follow-based social *network*. The core audience on the network uses a follow graph to express interests. It's also a social network vs. social media (i.e. television with an algo on phone). TikTok was a broadcast / interest-based algo from the start.

2. Narrow focus with a novel primitive — TikTok was extremely successful with a single category — young people (mostly women) dancing / singing to music with high remix-ability — and it took them several years to expand to their next category (gaming-oriented content for younger men). This existed for multiple years before Meta made the big shift to video on Instagram in August 2020.

3. User acquisition — They spent billions of dollars per year on user acquisition (infamously re-acquiring the same users multiple times). So there was a massive audience (even if not retained) to distribute the videos to (with an at-scale AI algorithm from their parent company Bytedance). So if you boost without the audience, you don't get the same result.

4. Text limitations — videos have a longer shelf life vs. text tends to feel stale after a day. There's also a high degree of unsaid context and in-group value (a typical cast from @six or @gwart has 3 layers to the onion). It's also harder to make a widely interesting text post vs. a video (or image).

5. Text is linked to who you are — a video is more likely to stand on its own (assuming it's visually stimulating) whereas what makes text interesting (on a relative basis) depends on who is saying it. Imagine the average new user coming into the network saying "gm happy to be here" vs. someone with 500K+ followers on Twitter saying the same thing. The engagement is as much about the person as what's being said. (Ironically, this is why anons have a lot of success on text-based social networks -- a lot of work to breakthrough of course -- but once you do no one is judging your opinion based on your credentials but on the merits of how smart / funny you've been).

This really breaks it down well. The contrast between text-based networks and media-driven platforms like TikTok is massive — both in infrastructure and human psychology. Discovery in text networks isn’t just about what’s said, it’s who says it and how it’s embedded in the community. Maybe we need less TikTok-style boosts and more ways to help people earn context faster.