The Algorithm Nobody Talks About
Substack quietly rolled out its recommendation engine a couple of years ago, and most creators barely noticed. Now it’s one of the most reliable sources of organic subscriber growth on the platform – and it works differently from almost every other discovery system in the social media space.
How the Engine Actually Works
When a reader subscribes to a Substack newsletter, the platform immediately surfaces a screen asking if they’d like to follow a handful of related publications. Those suggestions aren’t random. Substack’s system maps the overlap between subscriber lists across publications and surfaces newsletters that share meaningful audience segments. The more overlap there is between your readership and another writer’s readership, the more likely the algorithm is to recommend you to their new subscribers.
This is fundamentally different from SEO-driven or hashtag-driven discovery. There’s no keyword stuffing, no trending topic chasing. The signal Substack is reading is who your readers already are – their other subscriptions, their engagement patterns, their demonstrated interests. A newsletter about independent cinema might find itself recommended alongside a publication about film scores or a cultural criticism digest, purely because the same people tend to read all three.
The recommendation system also runs in the other direction. Writers can manually recommend other Substack publications from their own settings panel. When one creator recommends another, that recommendation becomes visible to all their subscribers and can actively drive sign-ups. It’s a reciprocal economy – you send your readers toward someone else’s work, they do the same for you, and the algorithm learns from the resulting subscription patterns to calibrate its automated suggestions over time.
What makes this model interesting is that it creates compounding returns. A newsletter with 2,000 subscribers that gets recommended by a larger publication with 40,000 readers can pick up hundreds of new subscribers in a single week without spending a dollar on promotion. Those new subscribers then shift the algorithm’s understanding of that newsletter’s audience profile, potentially triggering more automated recommendations down the line. The flywheel is slow to start and fast once it’s spinning.
Why This Is Actually a Bigger Deal Than It Looks
Most content discovery platforms optimize for time-on-platform. TikTok wants you scrolling. YouTube wants you watching. The engagement metrics those platforms reward are designed to keep attention inside the app. Substack’s recommendation engine optimizes for something different: subscriber acquisition. The platform makes money when writers make money, and writers make money when they have paying subscribers. So the recommendation system is structurally aligned with helping creators grow real audiences, not just impressions.
This alignment changes the incentive structure for creators in a meaningful way. On platforms where reach is driven by virality, there’s constant pressure to produce content that performs well in the moment – controversial takes, emotionally charged hooks, trend-jacking. On Substack, the recommendation engine rewards editorial consistency and niche depth. A newsletter that publishes reliably about a specific topic for a specific kind of reader will accumulate the subscriber overlap signals that make the algorithm want to recommend it. Chasing broad appeal actually works against you here.
There’s also a quality filtering effect built into the system. Because recommendations are tied to subscriber retention – not just initial clicks – publications that attract subscribers who immediately churn don’t accumulate the same recommendation weight as publications that hold onto readers. A newsletter that gets 500 new subscribers from a recommendation but loses 400 of them within a month is telling the algorithm something important. Substack’s system, over time, should theoretically surface writers who actually deliver on their promise to readers.
For writers coming from ad-supported blogging or social media, this represents a genuine shift in what “growth” means. Getting 10,000 page views from a viral post on X feels like growth. Getting 300 new paying subscribers from Substack’s recommendation engine is growth. The numbers look smaller. The business impact is not.
The practical implication for new creators is that the entry strategy matters enormously. Writers who launch by aggressively recommending complementary publications – and actively cultivating reciprocal recommendations from others in their niche – tend to seed the algorithm with the data it needs to work on their behalf. Launching cold, without any connections to the existing Substack ecosystem, means waiting much longer for the recommendation system to develop an accurate picture of where your newsletter belongs.
Building Into the System, Not Around It
The writers getting the most out of this engine aren’t passive about it. They treat recommendations like editorial relationships – reaching out to writers whose audiences genuinely overlap with theirs, reading each other’s work, making introductions, and building the kind of cross-pollination that the algorithm eventually formalizes into automated suggestions. It’s networking, but with measurable downstream effects. A recommendation from the right publication in your niche can be worth more than months of social media posting.
One real tension in this model is that it advantages established creators. Writers who already have large subscriber lists can offer meaningful traffic when they recommend a smaller publication. Writers just starting out have little to offer in return, which can make the early reciprocal relationship harder to establish. Substack has acknowledged this dynamic but hasn’t fully resolved it – the recommendation engine is powerful, and it still tilts toward those who are already known.
