Substack Just Built a Matchmaking Algorithm for Creators. It Could Change How Newsletters Grow
Creator Match now recommends collaborators to every Substack publisher. The more important shift is what it says about Substack’s growth model: the platform is trying to make creator-to-creator trust programmable without taking the human decision away.
Muhammad Ali Akbar
8/26/2026
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On August 25, Substack made Creator Match available to all publishers. It suggests writers and creators with similar audience sizes and interests, ranks the matches, and lets publishers message potential collaborators from the dashboard. The feature does not automatically create partnerships or guarantee subscriber growth; it is better understood as a discovery layer for creator-to-creator collaboration.
A growth algorithm aimed at the people making the content
Most recommendation systems are built to answer a familiar question: what should a user watch, read or listen to next? Substack has started asking a different one: who should the person making the work collaborate with next?
That is the premise behind Creator Match, a feature Substack opened to all publishers on August 25. According to the company, Creator Match identifies writers and creators with similar audience sizes and interests, then ranks those people by how promising a collaboration could be for both sides. A publisher can contact a suggested collaborator directly from the dashboard.[1]
The mechanics sound modest. The strategic move is not. Substack is taking a behavior that has always been informal - writers finding adjacent writers, appearing on each other’s podcasts, swapping recommendations, cross-posting, or simply introducing one another to readers - and building an algorithmic prospecting tool around it.
That matters because collaboration is already part of Substack’s growth architecture. The company has spent years arguing that a trusted recommendation from one publisher can move readers more effectively than a cold piece of promotion. In 2024, Substack said its app and Recommendations network together were driving half of new subscriptions and a quarter of new paid subscriptions. In a separate 2024 update, it said Recommendations alone had generated more than 34 million subscriptions since launch.[2][3] Those figures are platform-reported and are not an independent measure of causation, but they explain why Substack is investing in the collaboration layer rather than treating it as a side feature.
What Creator Match actually changes
Until now, the difficult part of collaboration was often not the collaboration itself. It was knowing whom to approach. A creator could search manually, watch who appeared in Notes, study recommendation pages, or ask around. That works when a niche is small. It becomes less useful as the network expands and the most visible accounts receive more inbound requests than they can realistically consider.
Creator Match turns that messy search into a ranked list. Substack says the ranking considers audience size and interests. It does not publicly describe the full weighting, the precise definition of a 'promising' collaboration, or how much audience overlap is desirable.[1] That missing detail is important. Two publishers can be similar enough to make sense together while still needing enough difference that each introduces the other to genuinely new readers.
So the useful way to read a high match score is not 'Substack says this person will grow my newsletter.' It is 'this is someone worth investigating.' The actual editorial fit still has to be established by the people involved.
That distinction protects the strongest part of creator collaboration: trust. A recommendation has value because a publisher is putting a little of their own reputation behind it. If matching becomes a volume game - hundreds of interchangeable outreach messages to anyone the dashboard surfaces - the feature could reproduce the spam dynamics that creators already dislike on other platforms.
The bigger update is not only Creator Match
The August product release also makes Substack look more like a lightweight audience operating system than a simple newsletter tool. Publishers can now use segments and tags to communicate with subsets of their lists, and can insert audience-specific blocks into written, podcast or video posts for free, paid or founding subscribers.[1]
That changes the economics of a collaboration in a practical way. Finding a new reader is only one half of growth; deciding what that reader sees after arriving is the other. A publisher who receives a wave of subscribers from a joint live show or cross-post can now treat those readers differently from the rest of the list instead of sending everyone the same message.
Substack also added per-post comment controls, expanded Reply Rules to non-English publications, improved multi-speaker podcast transcript editing, and announced a Typefully integration for distributing work across X, Threads, LinkedIn and Bluesky.[1] None of those features is as headlinefriendly as Creator Match. Together, however, they reveal a consistent product direction: acquire readers through a network, learn who those readers are, give different groups different experiences, and make it easier to carry the work back out to social platforms.
This is different from a normal social recommendation algorithm
TikTok, Instagram and YouTube optimize primarily around content consumption. Their recommendation systems decide what gets placed in front of an audience. Creator Match is one layer removed: it helps decide which relationships might generate the next piece of content, introduction or recommendation.
The difference sounds semantic until you consider who retains the final decision. Substack is not automatically pairing two publications, publishing a cross-promotion, or forcing a recommendation into a reader’s feed. It is narrowing the field and giving the publishers a messaging path. The relationship still has to be negotiated.
That is a meaningful design choice for a platform that sells itself on creator ownership. It also creates a new kind of platform power. Once a dashboard starts ranking the people a creator should know, the platform is no longer merely hosting relationships; it is shaping which relationships are easiest to form.
The open question is whether the ranking broadens a creator’s network or quietly makes it more homogeneous. Matching people who are too similar can create efficient collaborations but repetitive audiences. Matching people who are too different can produce weak editorial chemistry. Substack has not disclosed enough about the ranking system to know how it resolves that tradeoff.[1]
How creators should use the feature without outsourcing judgment
The smartest first use of Creator Match is research, not outreach. A publisher should look at a suggested creator’s recent work, the problems their readers seem to care about, the formats they use, and whether there is an obvious idea that becomes better when both people are involved. 'We have similar audiences' is not a collaboration concept.
A strong proposal is specific enough that the other creator can see the benefit immediately: a joint breakdown of a disputed topic, a two-person podcast where each side brings a different expertise, a cross-post built around contrasting views, or a recommendation tied to a genuinely useful resource. The match score can start the conversation. It cannot supply the reason for the conversation.
Creators should also resist treating audience overlap as a single good-or-bad number. Some overlap can be evidence that the audiences share interests. Too much can reduce incremental reach. Very little may signal a valuable adjacent audience - or simply that the publications have little in common. The right amount depends on the format and objective.
The real bet: growth through relationships, with software in the middle
Substack’s most interesting product decisions increasingly sit in the space between an email list and a social network. Creator Match is a clean example. The company is not abandoning the idea that creators should own their audience relationship. It is trying to make the network around those owned audiences more useful.
For creators, that can be an attractive proposition because the growth mechanism is not simply 'post more and hope the feed rewards you.' It is closer to: find a credible peer, make something worth sharing together, and borrow a little trust from each other’s audiences.
But the algorithm should remain the introduction, not the editor. If Creator Match works, its best collaborations may eventually look obvious in hindsight. They should not feel like two accounts were paired because a dashboard decided they had the same keywords.
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