For seven days, I stopped doing the thing that had grown my Substack the fastest.

No more:

• “Subscribe and I’ll subscribe back.”

• “Follow me and I’ll follow you.”

• Transactional support Notes.

• Subscribing to publications only because they subscribed to mine.

I expected my subscriber growth to slow down.

What I did not expect was this:

I published 54% more Notes, but my total reach fell by 28%.

And the average measurable reach of each Note was cut roughly in half.

That result was uncomfortable.

It was also exactly what I needed to see.

Why I stopped a strategy that was clearly working

When I started this Substack, I wanted to understand how a completely new publication could grow from zero.

Subscribe-for-subscribe worked.

At least, it worked according to the most visible metric: subscriber count.

Two reciprocal-support Notes alone brought me:

• 867 combined impressions

• 55 combined replies

• 63 subscribers directly attributed by Substack

One Note generated 50 attributed subscribers.

The other generated 13.

For a new account, those numbers were difficult to ignore.

My subscriber count grew quickly. I eventually reached 253 subscribers after publishing 130 Notes and 12 articles.

But another problem appeared.

I could see how many people had subscribed.

I could not tell how many had subscribed because they genuinely wanted to read my work.

Would they open my next article?

Would they return?

Would they interact when there was no subscription expected in exchange?

Was I building an audience, or simply increasing a number?

The only way to find out was to remove the tactic responsible for the fastest growth.

The rules of the experiment

For the following period, I stopped all explicit reciprocal-growth activity.

What I stopped

I did not publish any subscribe-for-subscribe Notes.

I did not promise to follow people back.

I did not subscribe to publications simply because their writers had subscribed to mine.

I did not ask people to comment “done” so that I could return the action.

What I continued

I kept publishing original Notes.

I replied to other creators when I had something relevant to add.

I published articles.

I participated in conversations.

I promoted my work selectively.

I continued improving my profile, content and tools.

The goal was not to stop growing.

The goal was to see what remained when the transactional activity disappeared.

What happened

The following comparison comes from my latest seven-day analytics window, synced through August 23, 2026.

• Notes published — 13 → 20 (+54%)

• Total impressions — 587 → 420 (–28%)

• Likes — 62 → 54 (–13%)

• Replies — 5 → 5 (no change)

• Restacks — 3 → 1 (–67%)

• Discovery share — 40% → 36% (–4 points)

• Attributed subscribers — 0 → 0 (no change)

One of the 20 recent Notes did not have impression data available, so it should not be treated as receiving zero impressions.

Across the 19 Notes with measurable data, the average was approximately 22 impressions per Note.

During the previous period, the average was approximately 45 impressions per Note.

That means the measurable reach per Note fell by roughly half.

I created more content.

I generated less total distribution.

And each individual Note received significantly less attention.

Publishing more did not solve the problem

The obvious assumption was that more Notes would create more opportunities for discovery.

That did not happen.

I published seven additional Notes, but received 167 fewer impressions.

This does not necessarily mean that publishing more directly caused the decline.

This was not a controlled scientific experiment. Timing, topics, audience activity and Substack distribution could all have influenced the results.

But it does show something important:

Publishing more content does not guarantee more distribution.

My problem was not a lack of output.

I had already published 130 Notes.

The problem was that most of those Notes did not have a strong distribution mechanism behind them.

The reciprocal Notes had one.

They gave readers a direct reason to respond:

Take this action, and I will return it.

Original Notes have to compete differently.

They need to be interesting enough to stop someone in the feed.

Useful enough to make them remember you.

Strong enough to earn a restack.

Relevant enough to attract the right reader.

That is much harder than offering an immediate transaction.

Zero attributed subscribers does not necessarily mean zero impact

Substack attributed no new subscribers directly to Notes in either seven-day period.

That does not prove that none of the Notes influenced a subscription.

Someone can:

1. See a Note.

2. Visit the profile later.

3. Read an article.

4. Return through another source.

5. Subscribe without the original Note receiving credit.

Attribution is incomplete.

That is why I do not want to claim: “My original content generated zero subscribers.”

The more accurate conclusion is:

Substack directly attributed zero subscribers to these Notes during the measured period.

That distinction matters.

It is easy to turn incomplete attribution into a confident but false growth lesson.

I have already made that mistake before.

The real benefit of stopping subscribe-for-subscribe

Stopping reciprocal growth did not immediately improve my numbers.

It improved the quality of the information behind them.

When transactional Notes dominated my dashboard, the obvious recommendation was: publish more reciprocal Notes, because they generate the most subscribers.

That recommendation was technically correct.

It was also strategically dangerous.

If I repeated it indefinitely, I could continue growing the subscriber count while making it increasingly difficult to understand whether anyone actually wanted the content.

Removing the tactic exposed the baseline.

It showed me what my publication could generate without an explicit exchange attached.

The baseline was weaker than I wanted.

But weak, honest data is more useful than impressive data I cannot interpret.

The experiment exposed my actual growth problem

I initially thought the question was: can original content replace subscribe-for-subscribe?

That was not the most useful question.

The real question was: can I build a repeatable distribution system for original content?

Right now, the answer is not yet.

Creating more Notes is not the system.

Publishing another article is not the system.

Posting a link repeatedly is not the system.

A real distribution system needs to answer:

• Which topics attract the right people?

• Which Notes generate profile visits?

• Which readers return more than once?

• Which creators amplify the content?

• Which interactions lead to subscriptions?

• Which subscribers become active readers?

• Which growth is organic, community-driven or reciprocal?

Substack gives me the totals.

I need to understand the mechanism behind them.

Why I started building Subshack

This experiment is also the reason I started building Subshack.

I did not need another dashboard telling me that one Note had more impressions than another.

I needed to know why.

Was the Note successful because the idea was strong?

Because another creator amplified it?

Because it triggered reciprocal activity?

Because the same people repeatedly interacted with my content?

Or because it genuinely reached new readers?

Subshack now helps me separate those signals and identify:

• Organic and reciprocal activity

• Repeat engagers

• Potential relationships

• People amplifying my work

• Subscribers becoming inactive

• Content generating reach without conversion

• Content generating conversion without obvious engagement

The goal is not to automate relationships.

The goal is to stop treating every impression, like and subscriber as if they meant the same thing.

They do not.

What I am changing now

I am not returning to mass subscribe-for-subscribe.

I will still subscribe to publications I genuinely want to read.

I will still support creators whose work interests me.

But that is a relationship, not a transaction.

For the next phase, I am changing five things.

1. Fewer repetitive Notes

Publishing more did not increase total reach.

I will focus on fewer Notes with stronger hooks, clearer opinions and more useful evidence.

2. More distribution around each idea

Instead of publishing an idea once and immediately replacing it, I will reuse strong ideas through different formats:

• A data point

• A contrarian observation

• A practical lesson

• A question

• A personal example

3. More targeted interaction

I will stop interacting randomly.

I want to focus on people who are relevant to my topic, repeatedly engage with similar work and are likely to become genuine readers.

4. Separate acquisition from audience quality

Subscriber growth will remain useful.

But I will also measure:

• Returning engagement

• Article opens

• Repeat interactions

• Restacks

• Replies

• Activity after subscribing

5. Judge content by its job

Not every piece of content needs to generate subscribers.

Some content creates discovery.

Some builds trust.

Some starts conversations.

Some converts.

The mistake is judging all of them with a single number.

The next experiment

My next test will be more targeted.

For seven days, I will use Subshack to identify the people most relevant to my publication.

Not simply the people most likely to follow back.

I will focus on:

• Repeat engagers

• Relevant creators

• People already interacting with Substack-growth content

• Readers who have shown interest but have not subscribed

• Existing subscribers who appear likely to become active readers

I will engage manually and compare the results with my previous random interaction.

Then I will measure:

• Profile visits

• Replies

• New relationships

• Attributed subscriptions

• Returning readers

• Time spent per result

That should answer a more valuable question: can targeted relationships outperform transactional growth?

The conclusion

Subscribe-for-subscribe gave me something every new creator wants: momentum.

It helped me leave zero.

It introduced me to other writers.

And two Notes generated 63 directly attributed subscribers.

Pretending it did not work would be dishonest.

But the experiment also revealed its limit.

Once I removed the transaction, publishing more content did not preserve the same distribution.

I published 54% more Notes.

My impressions fell by 28%.

My measurable reach per Note was roughly cut in half.

That does not mean I should return to the old strategy.

It means I now know what needs to be built next.

Not a faster way to increase the subscriber count.

A better system for reaching the right people, turning attention into genuine interest and identifying who actually comes back.

That is a much harder problem.

It is also a much more valuable one.

Want to see which parts of your own Substack growth are actually working?

Try Subshack — the Chrome extension for understanding your Notes, audience and growth.

Subscribe now

The Truth About My Substack Growth — Part 4 of 5

Data snapshot: 23 August 2026, 16:43 UTC — 130 Notes, 253 current subscribers.

The cleanest comparison in my Substack experiment happened by accident.

On 12 August, I stopped using explicit subscribe-for-subscribe Notes. By my latest synced snapshot I had published exactly 65 Notes before that cutoff and 65 after it — two groups almost perfectly matched in size.

The result looked brutal.

My first 65 Notes were credited with 69 subscribers. Among the next 65, Substack returned usable attribution statistics for 64 — and those 64 were credited with zero.

If I had stopped at the Note-level dashboard, the conclusion would have been obvious:

I removed the tactic that worked, and my Notes stopped generating subscribers.

Then I opened the subscriber-level data. 29 current subscribers had joined after the cutoff, and 19 of them were marked as having come through Substack Notes.

Zero attributed to an exact Note. Nineteen acquired through the Notes channel. Both numbers are correct — I had been asking two different questions as if they were the same one.

The 65-versus-65 comparison

Here is the complete split from the synced data:

The shift was not limited to subscriber attribution. Impressions fell by roughly 54%, replies from 62 to 14, likes more moderately from 207 to 170. Restacks moved the other way, from 2 to 6, though the numbers are too small to treat as a stable pattern.

The reciprocal Notes in the first group gave people an immediate reason to act. Remove that incentive and the visible activity becomes much less dramatic.

But “less dramatic” is not the same as “zero growth”. That distinction was hidden one level deeper.

Exact-content attribution is not channel attribution

The Note-level metric answers a narrow question:

Which exact Note received credit for this subscription?

The subscriber record answers a broader one:

Through which Substack surface did this subscriber arrive?

After the cutoff, the 29 subscribers still on the list at the time of the sync broke down like this:

• 19 — Substack Notes

• 4 — reader profile discovery

• 3 — Substack app

• 1 — direct-to-app

• 2 — unknown

So roughly 66% of that cohort was associated with the Notes channel.

That does not identify the Note that persuaded each person, or prove that one of my own Notes was the decisive touchpoint. Someone could have found me through a reply, visited my profile after several interactions, or subscribed after seeing me repeatedly across the Notes ecosystem. The available data cannot isolate those paths.

The Note-level zero was not fake either: for the 64 post-cutoff Notes with returned attribution data, no individual Note received subscriber credit. The mistake would be translating that into:

Notes produced no subscribers.

That goes beyond what the metric can support. It is the same problem my classifier exposed in Part 3: the raw number is correct while the lesson extracted from it is wrong. There the missing context was intent; here it is grain — one table measures an exact piece of content, the other measures the wider acquisition surface.

The fix is not to pick whichever number makes the better story. It is to keep both, and label them correctly.

What the comparison actually supports

1. Removing the reciprocal incentive reduced visible activity. The second group received fewer impressions and far fewer replies — consistent with reciprocal Notes being unusually effective at creating immediate action. It is not a controlled causal experiment: topics, timing, audience size and content mix also changed.

2. Exact Note attribution disappeared. No post-cutoff Note with returned attribution data received direct subscriber credit. That is a real weakness in the second group.

3. The Notes channel still participated in acquisition. Nineteen current subscribers who joined after the cutoff were associated with Substack Notes at the channel level. The data cannot identify the decisive interaction.

4. Acquisition is still not the final outcome. What matters is what those people did after joining — whether they received the next article, opened it, came back, replied or restacked without being offered anything in exchange. Part 2 showed how wide that gap can get: my subscriber count grew while my open rate fell. That is the measurement layer I still need to improve.

The experiment I am redesigning

My first growth experiment optimised for speed — I described that version in Part 1. The next one needs to preserve the full path:

• Distribution — how many people saw the Note, and how many were not already connected to me?

• Channel acquisition — did the subscriber arrive through Notes, the app, profile discovery, search or another surface?

• Exact-content attribution — when Substack can connect a subscription to a specific Note or article, which content receives credit? When it cannot, the answer should remain unknown, not silently become zero.

• Reader behaviour — after joining, did they receive an email, open an article, return and interact again?

• Editorial signal — which useful ideas generated replies, restacks and repeat attention without depending on a reciprocal promise?

I still want to publish useful Notes, write substantive replies and produce deeper articles. I also want to test one external community at a time instead of dropping links everywhere and calling that distribution.

But subscriber speed is no longer the score that decides whether the experiment worked. The system needs to distinguish:

Who arrived, where they arrived from, what received credit — and whether they came back.

What I am changing in Subshack

I am adding this distinction to Subshack. A zero in the content table should not silently become a zero in the growth story.

The product should show exact attribution when it exists, channel-level attribution when that is all the data supports, unavailable data as unavailable, and post-subscription behaviour separately.

No invented precision. No automatic causal story. Just the strongest conclusion the available data can actually support.

Install Subshack — Free on Chrome

If you want to see the same distinction on your own account before I ship it, run the free Substack profile audit — it reads your public profile and scores it without asking for a login.

The uncomfortable conclusion

My first 65 Notes told a simple story: attention, replies and attributed subscribers, quickly. My next 65 looked much worse in the Note-level dashboard.

But the subscriber records showed that growth had not stopped. It had become harder to attribute.

That is less satisfying than a clean before-and-after victory. It is also more useful. Because the real question is no longer whether Notes generated zero subscribers or nineteen. It is:

Which exact content can be credited, which channel participated, and did those subscribers become readers?

Until a dashboard separates those questions, the cleanest number may produce the weakest lesson.

Have you ever seen two Substack screens appear to tell different growth stories? Which number did you trust?

Tell me in the comments — especially if you have hit a similar attribution gap.

Next in Part 5: I compare what happened when I published on X and Bluesky, contributed on Reddit, and waited for Google — because publishing everywhere is not the same as building distribution.

Method. Subshack snapshot of 23 August 2026, 16:43 UTC. The post-cutoff cohort counts people who joined on or after 12 August and were still subscribed at the sync. One of the 65 post-cutoff Notes returned no impression or attribution data. Channel source does not identify the exact content that caused a subscription. Observational, not a controlled experiment.

The first version of my growth classifier made a reasonable mistake.

It labelled my own article about subscribe-for-subscribe as reciprocal content.

The article was not asking anyone to subscribe back.

It was analysing why reciprocal growth had distorted my early results.

But the classifier saw the language inside it:

• subscribe-for-subscribe

• follow-for-follow

• reciprocal support

• subscribing back

It recognised the subject.

It completely missed the intent.

That false positive exposed a larger problem in the way growth tools interpret performance.

A metric can be correct while the lesson drawn from it is wrong.

The problem I was actually trying to solve

In Part 1, I analysed 105 Notes.

Two networking Notes accounted for 56 of the 62 subscribers Substack attributed to individual Notes.

One was explicit:

Follow = Follow
Subscribe = Subscribe
Let’s start.

The other told writers I was reading, subscribing and supporting them, then invited them to connect.

The results were real.

The first Note recorded 479 impressions, 39 replies and 44 attributed subscribers.

The second recorded 355 impressions, 14 replies and 12 attributed subscribers.

But neither result proved that readers wanted my next article.

Then, in Part 2, I found that my subscriber count continued rising while later article open rates clustered between 18% and 23%.

That still did not prove that reciprocal subscribers caused the decline.

It showed something narrower—and more useful:

Subscriber acquisition and reader attention were no longer telling the same story.

I’m building this classifier into Subshack, the Chrome extension I’m creating for Substack writers.

So I wanted Subshack to answer a harder question than:

Which Notes produced the biggest numbers?

I wanted it to ask:

What mechanism produced those numbers—and should that mechanism influence my editorial recommendations?

The first version failed for an important reason

A basic classifier can look for obvious exchange language:

• “subscribe and I’ll subscribe back”

• “follow for follow”

• “drop your Substack”

• “support me and I’ll support you”

Those patterns are useful.

They are also insufficient.

An article criticising subscribe-for-subscribe may contain exactly the same words as a Note promoting it.

One describes the mechanism.

The other activates it.

My first classifier treated both as equivalent.

That meant it could make two opposite mistakes:

1. Exclude genuinely editorial analysis because it mentioned reciprocal growth.

2. Treat reciprocal performance as evidence that the underlying topic created genuine demand.

The problem was not the raw data.

The problem was allowing a weak interpretation of that data to control the recommendations.

I stopped treating classification as a moral judgement

“Real growth” and “fake growth” make dramatic labels.

They are not precise enough for an analytics product.

A reciprocal subscriber is still a person.

A community interaction can become a genuine relationship.

A subscriber acquired through an editorial Note may still never open another article.

So the purpose of classification is not to declare one person valuable and another worthless.

It is to separate how an outcome happened from the outcome itself.

I now use four categories:

Organic content

The Note primarily shares an idea, observation, story, argument or useful resource without requesting an exchange.

Community or networking content

The Note is designed to meet people, participate in a creator community or start a conversation, but it does not explicitly promise reciprocal action.

Reciprocal content

The Note contains an explicit or strongly implied exchange: subscribe back, follow back, support in return or another action tied to reciprocity.

Unknown

The available evidence is not strong enough to classify the mechanism honestly.

Unknown is not a failure.

It is better than forcing certainty into ambiguous data.

How I changed the classifier

I did not solve the problem by asking an AI model to guess harder.

I changed the order of decisions.

1. Explicit exchange rules come first

Clear promises such as “subscribe and I’ll subscribe back” can be classified deterministically.

The same input should produce the same result every time.

2. Analytical context acts as a safeguard

If the content is reviewing, criticising or reporting on reciprocal growth, the presence of those keywords is not enough to classify it as reciprocal.

The system must distinguish participation from analysis.

3. Ambiguous content stays uncertain

Semantic classification is used only when the deterministic evidence is incomplete.

If the signals still conflict, the result remains unknown instead of being forced into the nearest category.

4. Manual correction always wins

No automated system understands every context.

If I correct a classification, that override persists. The same Note should not be misclassified again during the next analysis.

5. Recommendations require enough organic evidence

Subshack does not make a strong editorial recommendation from one lucky Note.

It requires at least five organic Notes before presenting a pattern as something worth repeating.

Below that threshold, the honest answer is:

There is not enough evidence yet.

The rule that changed the product

The simplest version of the rule is:

Raw truth stays intact. Recommendations get filtered.

Subshack never deletes the impressions, replies, subscribers or other activity produced by reciprocal Notes.

Doing that would rewrite history.

Instead, the dashboard shows two different views:

• All activity answers what happened.

• Organic analysis helps decide what editorial content to repeat.

In the later dashboard snapshot, 106 Notes had synced—one more than the original 105-Note audit.

The classifier separated them into:

• 92 organic Notes

• 8 community or networking Notes

• 6 reciprocal Notes

The 14 community and reciprocal Notes still remained in the raw analytics.

They were simply excluded from recommendations about what editorial content worked.

That distinction produced a less flattering but more useful organic view:

• 1.3 subscribers per 1,000 impressions

• 4 subscribers gained

• 3,072 impressions

• 45% discovery rate

Those numbers do not erase my early growth.

They stop two exceptional reciprocal Notes from teaching the software that I should publish more reciprocal Notes.

What the classifier still cannot know

Classification adds context.

It does not create perfect attribution.

Subshack still cannot know with certainty:

• why every person subscribed

• whether a community interaction was sincere

• whether an organic subscriber will become a regular reader

• whether a reciprocal connection will eventually become genuine

• how much algorithmic distribution affected a Note

The categories describe the most likely mechanism visible in the content.

They do not read people’s minds.

That limitation matters because clean labels can create false confidence.

The purpose of the system is not to remove uncertainty.

It is to stop obvious distortions from silently becoming advice.

A small classification test

Consider these three Notes:

“Subscribe and I’ll subscribe back. Drop your link below.”

“I tested subscribe-for-subscribe for seven days. Here is what happened to my open rate.”

“Looking for other SaaS writers to exchange feedback and learn from.”

The first is reciprocal.

The second is editorial analysis.

The third is harder.

It may be community networking.

It may become reciprocal depending on what “exchange” means in practice.

That is exactly why I need an unknown state, reviewable evidence and manual correction—not just a confident label.

How would you classify the third Note?

I would genuinely like to see where other writers draw the line.

The uncomfortable product lesson

I originally built analytics to find what worked so I could repeat it.

That sounds sensible.

But “repeat what worked” becomes dangerous when the software cannot distinguish:

• attention from intent

• transactions from amplification

• community participation from editorial demand

• acquiring a subscriber from creating a reader

The biggest number is not automatically the strongest signal.

Sometimes it is simply the easiest mechanism to activate.

My first classifier made the same mistake I had made personally.

It saw the surface result and moved too quickly toward a conclusion.

Correcting that mistake changed Subshack from a performance dashboard into something more useful:

a system that preserves what happened while remaining sceptical about what it means.

If your analytics showed that your best-performing content was driven by reciprocity, would you still want it recommending more of the same?

I’m building this classification system into Subshack. If you want to test it as I build it, join the Founding Beta.

Next in Part 4: I tested four distribution channels outside Substack. Together, they produced one subscriber.

Subscriber count told me I was growing.

Open rate told me to slow down.

Neither number told the whole truth.

I went from zero to more than 150 subscribers during my first week on Substack.

By the time of this audit, the publication had reached 241 subscribers.

From the outside, the direction was obvious:

More subscribers meant more growth.

But after auditing the Notes responsible for that growth, I looked at what happened after people subscribed.

The result was less flattering.

On August 3, one of my articles recorded a 34% open rate.

Ten days later, my newest article recorded 18%.

That is a decline of 16 percentage points—or roughly 47% relative to the earlier rate.

My list was getting bigger.

The percentage of it opening my new articles was getting smaller.

The pattern across my first nine articles

The decline did not happen in a perfectly straight line.

These were the recorded open rates:

The 34% result was the high point, not the permanent baseline.

But the later pattern is still difficult to ignore.

Every article published after August 5 recorded an open rate between 18% and 21%.

Meanwhile, the subscriber count continued rising.

The list was expanding faster than reader attention.

A falling open rate does not automatically mean fewer readers

This distinction matters.

A percentage can fall while the absolute number of readers still rises.

For example:

• 100 subscribers at a 34% open rate means 34 opens.

• 200 subscribers at an 18% open rate means 36 opens.

The second list has a much weaker rate but slightly more total readers.

So I cannot honestly say that my audience disappeared simply because the percentage declined.

A larger list can still create more reach.

The problem is efficiency.

I needed almost twice as many subscribers to produce roughly the same number of opens.

That means subscriber growth and audience growth are not always the same thing.

One measures how many people joined.

The other measures how many people continue paying attention.

The explanation I cannot prove—but cannot ignore

The most obvious explanation is acquisition quality.

In Part 1, I found that the two strongest networking Notes accounted for 56 of the 62 subscribers Substack attributed to individual Notes.

One explicitly offered reciprocal support.

The other told writers I was reading, subscribing and supporting them.

Both generated subscribers quickly.

But someone who subscribes because they expect support in return is not necessarily subscribing because they want my next article.

When the next email arrives, the original reason for subscribing matters.

Did they join because they wanted:

• Substack growth experiments?

• data and practical tactics?

• updates about building Subshack?

• or simply another subscriber in return?

Those motivations all produce the same +1 in the subscriber count.

They do not produce the same reading behaviour afterward.

The falling open rate is consistent with audience dilution.

But it does not prove that reciprocal growth caused it.

What the data cannot tell me

This audit covers only nine articles from a publication that was a few weeks old.

The articles also had different:

• titles

• subjects

• publication dates

• levels of relevance

• amounts of promotion

The 34% article discussed why subscribe-for-subscribe was not really win-win.

The 18% article documented my first Substack Chat experiment.

The difference in subject alone may explain part of the gap.

I also do not have a clean cohort analysis showing:

• which acquisition source produced each reader

• whether reciprocal subscribers opened less often

• whether organically acquired subscribers returned more frequently

• how engagement changed for the same subscribers over time

So the accurate conclusion is not:

Reciprocal subscribers caused my open rate to fall.

It is:

My subscriber count increased while the open rates on later articles remained materially below my early high points. Reciprocal acquisition is one plausible explanation, but the available data cannot isolate the cause.

That sentence is less dramatic.

It is also more honest.

The subscriber count hides different types of growth

Before this audit, I treated every new subscriber as an identical unit of growth.

Now I see at least three different possibilities.

Transactional growth

Someone subscribes because I subscribed, replied or offered support first.

Community growth

Someone subscribes because we interacted, share the same experience or belong to the same creator network.

Editorial growth

Someone subscribes because the content itself created enough interest for them to want the next piece.

All three subscribers are real people.

All three may eventually become genuine readers.

But their original motivation is different.

A dashboard that combines them into one number cannot explain why the list grows while the open rate weakens.

What I should have measured from the beginning

Subscriber count is still useful.

It just needs context.

I should have tracked it alongside:

• article open rate

• total views

• comments and replies

• restacks

• returning readers

• unsubscribes after each publication

• the mechanism that produced each subscriber

The most important question is no longer:

How many subscribers did this tactic produce?

It is:

What did those subscribers do after joining?

Did they open the next article?

Did they read a second one?

Did they reply without expecting anything in return?

Did they share the work with their own audience?

That behaviour is much closer to audience growth than the initial subscription.

What I am changing now

I am not deleting reciprocal subscribers or pretending the early growth never happened.

The raw numbers remain part of the story.

But I am changing what I allow those numbers to teach me.

A networking Note can be excellent for meeting creators.

It should not automatically become evidence that the subject of the Note created editorial demand.

A reciprocal subscription can begin a real relationship.

It should not automatically carry the same weight as someone who subscribed after reading an article.

And a growing list should not be celebrated without checking whether people continue opening the work.

For the next articles, I am watching both sides:

• acquisition: who subscribes and why

• retention: who returns and continues reading

Growth needs both.

The uncomfortable conclusion

My publication reached 241 subscribers quickly.

That achievement is real.

But the open-rate pattern suggests that the list may have grown faster than the audience’s interest in my work.

I did not necessarily build a smaller audience.

I may have built a larger audience with weaker average intent.

That is not the same failure.

But it requires a different strategy.

The goal is no longer to make the subscriber count rise as quickly as possible.

The goal is to make the next subscriber more likely to become a reader.

That starts before the subscription, on the profile they land on. I built a free Substack profile audit that scores what a first-time visitor sees, out of 100.

If your subscriber count doubled while your open rate nearly halved, would you still call it growth?

Would you prefer a smaller, highly engaged list—or a larger list that produces more total reach but weaker average engagement?

Tell me which one you would choose in the comments.

Next in Part 3: How I taught Subshack to distinguish organic, community-driven and reciprocal growth—after the first version classified my own article as reciprocal content.

Subscribe now

Two weeks ago, I thought I had found a fast way to grow a new Substack.

I started from zero, used Notes heavily, joined conversations, and hit my first 100 subscribers in one week, before crossing 150 shortly afterward

The number looked good.

The method looked repeatable.

So I began sharing what I was learning.

Then I built a tool to analyse my own growth.

The first thing it showed me was that the story I had been telling was incomplete.

I analysed 105 of my Notes.

This audit covers 105 Notes published between July 28 and August 17, 2026.

Only two of them were responsible for 90% of the subscribers I could attribute to individual Notes.

And those two Notes were the exact kind of content I had publicly decided to stop writing.

The number I couldn’t ignore

The first Note was direct:

Follow = Follow
Subscribe = Subscribe
Let’s start.

It generated:

• 479 impressions

• 39 replies

• 44 attributed subscribers

The second was less explicit:

To everyone publishing on Substack — I’m reading. I’m subscribing. I’m supporting you. Let’s connect.

It generated:

• 355 impressions

• 14 replies

• 12 attributed subscribers

Together, these two Notes represented only 1.9% of the 105 Notes in my dataset.

But among the six Notes for which subscriber attribution was recorded, they accounted for 56 of the 62 attributed subscribers.

That is 90%.

The other four Notes carrying attribution data accounted for the remaining six subscribers.

For the other 99 Notes, attribution was unavailable—not zero.

The obvious recommendation from the data would be simple:

Write more networking Notes.

But that recommendation would optimise the number while ignoring the reason behind it.

The tactic didn’t fail. It worked too well.

Subscribe-for-subscribe is often criticised because it creates low-quality subscribers.

That description is too comfortable.

My data suggests a more dangerous problem:

Reciprocal growth can outperform editorial content so dramatically that it becomes difficult to see what your audience genuinely values.

Here is what my data showed:

12.8 times more impressions and 189 times more replies per Note, every normal analytics dashboard will tell you to repeat it.

The metric is not fake.

The activity happened.

The subscribers are real people.

But the recommendation can still be wrong.

Those people may have subscribed because they wanted support in return—not because they wanted to read my next article about Substack growth.

That distinction does not appear in the subscriber count.

The signal that was missing

The two strongest acquisition Notes generated zero restacks.

That matters.

A like is cheap.

A reply can be part of an exchange.

A subscription can be reciprocal.

A restack is different.

When someone restacks a Note, they put it in front of their own audience.

They attach a small part of their credibility to it.

The two Notes that generated most of my attributed subscribers were successful at activating people.

But nobody chose to redistribute them.

They created transactions, not amplification.

That does not make the Notes worthless.

One of them helped me meet other new writers, and some of those relationships may become genuine.

But it changes what I can honestly claim they proved.

They proved that reciprocal and community-driven Notes can grow a subscriber count quickly.

They did not prove that the underlying content created demand.

The limitation I need to state clearly

My attribution data is incomplete.

Subscriber attribution was recorded for only six of the 105 Notes in this audit.

That means I cannot honestly claim that these two Notes generated 90% of every subscriber I acquired.

The accurate claim is narrower:

Among the six Notes for which subscriber attribution was recorded, the two strongest accounted for 56 of the 62 attributed subscribers—90%.

Small datasets make dramatic headlines easy.

They also make methodological honesty essential.

At the time of this audit, I had:

• 241 subscribers

• 105 analysed Notes

• 9 published articles

• a publication that is only a few weeks old

This is not a universal study of Substack.

It is an audit of my own behaviour.

What this audit changed

I no longer want my analytics to answer only this question:

Which Notes produced the biggest numbers?

I need them to answer a harder one:

Which Notes produced behaviour that is consistent with genuine interest?

That means separating raw performance from the mechanism that produced it.

The raw numbers must remain untouched.

Hiding reciprocal subscribers would be dishonest.

But recommendations should not automatically treat these three things as the same signal:

• a reciprocal interaction

• a community conversation

• an editorial response

They may produce similar numbers for completely different reasons.

That is what I am now building into Subshack.

The uncomfortable conclusion

My early growth was not imaginary.

The subscribers were real. So were the replies.

But I still do not know how much of that growth represented genuine demand for my work.

And if I want to give other writers growth advice, that is the first question I need to answer.

I built a tool to analyse my Substack growth.

One part of it is already free and public: a Substack profile audit that scores what a first-time visitor sees on your profile, out of 100.

The first thing it told me was that my growth story did not survive contact with the data.

I’m publishing the numbers anyway.

If you removed your two best-performing Notes, would your growth story still hold?

Mine didn’t.

Have you ever used reciprocal growth? Did those subscribers continue reading afterward?

Tell me what happened in the comments.

Next in Part 2: My subscriber count kept growing while my article open rate fell from 34% to 18%. I’ll show what happened to engagement while the list got bigger.

Subscribe now

The thread generated 34 replies.

Here’s exactly how I started it—and the unexpected way Substack promoted it in the Notes feed.

Why I created a Chat

During my first week on Substack, subscribe-for-subscribe was my main acquisition strategy.

It helped me grow from zero to more than 150 subscribers quickly.

This week, I stopped completely.

No reciprocal subscriptions.

No “subscribe and I’ll subscribe back.”

I wanted to discover whether I could continue growing by publishing useful content, writing meaningful replies, and building relationships.

My early observation is that organic growth becomes much harder when your publication doesn’t have a community around it.

Publishing brings people to your work.

Community gives them a reason to return.

The exact message I published

I created a Chat called Subshack Growth by Kacoulib and used this welcome message:

👋 Welcome everyone!

I created this chat as a place to talk about growing on Substack—what’s working, what isn’t, and the experiments we’re testing.

Feel free to share your publication, ask questions, or talk about a growth problem you’re currently facing.

To get things started:

What’s the #1 thing you’re struggling with on Substack right now?

I’ll try to help whenever I can—and I’ll also share what I learn from my own experiments here. 🚀

The thread received four likes and 34 replies.

The likes weren’t the most interesting result.

The replies were.

Why this question generated conversations

I think the message worked for four reasons.

1. The purpose was immediately clear

The Chat wasn’t presented as a general networking group.

It had one specific purpose: helping people discuss and experiment with Substack growth.

People could quickly decide whether the conversation was relevant to them.

2. People knew what they were allowed to share

I explicitly invited members to:

• Share their publication

• Ask questions

• Describe a growth problem

• Discuss their own experiments

They didn’t have to guess what was acceptable.

3. I asked only one question

I didn’t ask people to introduce themselves, describe their publication, share their goals, and explain their entire strategy.

I asked:

What’s the #1 thing you’re struggling with on Substack right now?

It was specific, relevant, and easy to answer.

4. The replies created more replies

Once people started sharing their problems, the thread no longer looked empty.

Each response gave someone else an example of what they could discuss.

The first participants helped make the conversation easier for everyone who arrived afterward.

Substack promoted the Chat automatically

After publishing the thread, I noticed something unexpected.

Substack displayed a native card in the Notes feed with the label:

From the Subshack Growth by Kacoulib chat

The card showed part of my message, the participants, and the number of replies.

Naturally, I tried to reproduce it myself.

I copied the thread link and shared it inside a regular Note.

It didn’t work. 😂

The Note contained the same destination, but Substack didn’t display it as the same native Chat card.

This appears to be an internal distribution mechanism that creators cannot manually trigger by simply sharing a link.

I don’t know exactly when Substack decides to show these cards or who receives them, so I’m not calling it an algorithm hack.

It’s only an observation from my experiment.

But it shows that Substack Chat may provide an additional native way to reach your existing audience inside the Notes feed.

Subscribers are not the same as a community

Subscribe-for-subscribe can increase a subscriber count.

But it doesn’t automatically create an audience that remembers you, reads your work, or participates in what you’re building.

A community is visible through behavior:

• People return

• People ask questions

• People help one another

• People share what they’re testing

• People participate without expecting something in exchange

That is why the difference between four likes and 34 replies matters.

A like is a quick reaction.

A reply starts a relationship.

Chat can also become a content engine

The replies revealed the actual problems people face while growing on Substack.

Those conversations can become:

• New Notes

• Detailed articles

• Growth experiments

• Tutorials

• Tools

• Future Chat discussions

This creates a useful loop:

Ask a question → understand the problem → publish an answer → attract new readers → invite them into the next conversation.

Instead of guessing what my audience wants, I can listen to what members are already asking.

What this result does not prove

Thirty-four replies do not mean the Chat generated 34 subscribers.

It also doesn’t prove that every new Substack publication should create a Chat.

This experiment only shows that one focused question generated significantly more conversation than I expected.

The next step is measuring whether those conversations lead to:

• Returning participants

• More profile visits

• New subscribers

• Better-performing Notes and articles

• Recommendations from members

That is what I’ll be tracking next.

Profile visits only count if the profile answers who you are and what you publish. I later built a free Substack profile audit that scores what a visitor sees, out of 100.

The simple setup you can reproduce

If you want to test Substack Chat for your publication:

1. Give the Chat one clear topic.

2. Explain what members can discuss.

3. Start with one easy, specific question.

4. Respond to early participants.

5. Use their problems to create future content.

6. Continue the conversation with focused follow-up threads.

You don’t need hundreds of active members.

Ten people who regularly return and participate can be more valuable than hundreds of silent subscribers.

My current hypothesis is simple:

Sub-for-sub can help you escape zero. Community may be what helps you grow after that.

I’ll continue sharing the real results as I test it.

If you’re trying to grow on Substack, join the Subshack Growth by Kacoulib Chat and tell us the biggest challenge you’re currently facing.

Subscribe now

It helped me move from zero to more than 150 subscribers quickly.

That result was real.

But it left one important question unanswered:

Can I continue growing without reciprocal subscriptions?

Getting someone to subscribe because you subscribed to them is not the same as attracting someone who genuinely wants to read your work.

So this week, I’m stopping subscribe-for-subscribe completely.

No “subscribe and I’ll subscribe back.”

No reciprocal-growth threads.

No promising a follow in exchange for a follow.

For seven days, I’ll rely on original Notes, meaningful replies, useful articles, a custom domain, and improved SEO settings.

This is day two of the experiment.

Why I stopped subscribe-for-subscribe

Subscribe-for-subscribe solved one immediate problem:

It helped me escape zero.

When a publication is new, almost nobody knows it exists.

Reciprocal-support threads put my profile in front of other creators who were also trying to grow.

That created momentum quickly.

But acquisition speed is not the only thing that matters.

A subscriber gained through reciprocity may have subscribed because of the exchange—not because they were interested in the publication.

That does not make the subscriber worthless.

It simply means the original motivation may be different.

The real test comes afterward:

• Will they open the next article?

• Will they read again?

• Will they reply?

• Will they stay subscribed?

• Will they care about the topic when reciprocity is no longer involved?

This experiment is not designed to prove that subscribe-for-subscribe is bad.

It is designed to find out whether I can build a second growth engine that does not depend on it.

What I’m testing instead

For the next seven days, I’m focusing on five things.

1. Original Notes

During my first week, I published more than 40 Notes.

Some promoted articles.

Some shared observations.

Some asked questions.

Some documented what I was learning about Substack growth.

This week, the Notes will have one additional constraint:

They must be useful even if nobody subscribes afterward.

I’ll publish Notes around:

• practical growth observations

• experiments

• mistakes

• content performance

• questions creators are already discussing

• lessons from building a publication from zero

The goal is not simply to publish more.

The goal is to find out which ideas create genuine interest.

2. Meaningful replies

I’ll continue replying to other creators.

But I will not promise a subscription or follow in return.

Each reply should add something useful to the conversation.

That could be:

• a concrete observation

• a relevant question

• a nuance

• a lesson from my own experiment

• a related article when the connection is genuinely useful

The reply should still make sense if the promotional link is removed.

This matters because replies can create discovery without turning every interaction into a transaction.

3. Original articles

During my first week, I published several articles about what I was testing.

The articles were not my largest direct acquisition source.

But they served another purpose.

They gave profile visitors context.

Context only helps if the profile itself is clear. I built a free Substack profile audit that scores what a visitor actually sees, out of 100.

A Note may make someone curious.

An article can help them understand:

• what I write about

• how deeply I explore a topic

• whether the publication is relevant to them

• whether they want to keep reading

This week, I’ll continue turning short observations into deeper articles.

Notes create entry points.

Articles create depth.

4. A custom domain

I also connected a custom domain to the publication.

This will not magically create traffic.

And I do not expect a meaningful SEO result after only seven days.

The reason is longer-term ownership.

I want the publication to become associated with a domain that I control, not only a platform profile.

That should make it easier to build:

• a recognizable brand

• direct traffic

• search visibility

• links that remain connected to my own domain

• a future home for the tools I’m building

The short-term experiment is about Substack growth.

The custom domain is a longer-term asset.

5. SEO improvements

I reviewed the publication and post settings to improve basic SEO.

That includes:

• clearer titles

• stronger descriptions

• relevant topics

• internal links between related articles

• useful image previews

• a more consistent publication position

Again, seven days is too short to properly evaluate search performance.

Google may take longer to discover, crawl, and rank the content.

So I will treat any search traffic this week as an early signal—not proof that the SEO work succeeded.

What I will measure

Subscriber count is still useful, but it will not be the only metric.

I’ll track:

• new subscribers

• article views

• open rates

• replies

• comments

• restacks

• direct traffic

• traffic from Substack Notes

• search traffic

• unsubscribes

• returning readers

I also want to compare the quality of the signals.

For example:

A Note that generates ten replies may reveal more genuine interest than one that brings five silent subscribers.

An article with fewer views may still be valuable if readers return afterward.

A slower week may still be better if the audience is more engaged.

What would count as success?

Success does not require matching the speed of subscribe-for-subscribe.

My first week was optimized for rapid acquisition.

This week is testing a different system.

The experiment will be useful if I can answer three questions:

1. Can I continue acquiring subscribers without reciprocity?

2. Do the new subscribers engage more with the content?

3. Which Notes, replies, and articles create the strongest signals of genuine interest?

Growth may slow down.

That would not automatically mean the experiment failed.

A smaller number of genuinely interested readers may be more valuable than a larger number of inactive subscribers.

The limitation of this experiment

This is not a perfectly controlled test.

I am changing several things at once:

• stopping subscribe-for-subscribe

• changing my Notes strategy

• publishing new articles

• improving internal linking

• using a custom domain

• updating SEO settings

That means I will not be able to attribute every result to one specific change.

If subscriber growth continues, I cannot say that SEO caused it.

If growth slows down, I cannot say that stopping subscribe-for-subscribe was the only reason.

The goal is simpler:

Can this new system sustain growth without reciprocal subscriptions?

If it works, I can isolate individual parts in future experiments.

My hypothesis

My current hypothesis is:

Growth will be slower, but the people acquired may show stronger signs of genuine interest.

I may get fewer subscribers.

But I may see:

• better conversations

• more article engagement

• more returning readers

• fewer transactional subscriptions

• a clearer understanding of which content attracts the right people

That is what I want to test.

Why this experiment matters

My first week answered one question:

Can subscribe-for-subscribe create fast initial momentum?

For me, the answer was yes.

This week is designed to answer a harder question:

Can original content, useful interactions, and long-term discovery channels keep the publication growing without reciprocity?

I’m currently on day two.

At the end of day seven, I’ll publish:

• subscriber growth

• traffic sources

• article performance

• engagement

• unsubscribes

• what appeared to work

• what failed

• what I still cannot prove

The results may be weaker than the first week.

That is part of the experiment.

Subscribe to receive the full seven-day results—whether the numbers are good or bad.

Subscribe now

In my first week, I reached 150+ subscribers.

I didn’t use paid ads.

I tested three things:

1. Subscribe-for-subscribe

2. Substack Notes

3. Original articles

The goal was simple:

Understand what actually creates growth.

The experiment

Period:
First week

Actions:

• 5 published articles

• 40+ Notes

• daily engagement with creators

• subscribe-for-subscribe interactions

Strategy 1: Subscribe-for-subscribe

This was the biggest acquisition driver.

It helped me escape zero quickly.

Why it works:

New creators already have a growth mindset.

They are actively looking for:

• new writers

• communities

• interesting publications

The limitation:

A subscription does not automatically mean a reader.

The next challenge is retention.

Strategy 2: Substack Notes

Notes became my discovery channel.

They helped me:

• start conversations

• share experiments

• bring people to my profile

• test ideas quickly

Short content created repeated visibility.

Notes bring people to your profile. What they see there decides the rest, so I later built a free Substack profile audit that scores it out of 100.

Strategy 3: Original articles

Articles were not the biggest acquisition source.

But they played another role.

They answered:

“Why should I subscribe?”

A Note can get attention.

An article can build trust.

The surprising lesson

I initially thought:

“Better articles = more subscribers.”

The data showed something different.

Growth came from combining:

Distribution + Trust.

Reciprocal interactions created the first contact

Notes created repeated exposure.

Articles created context.

What I will test next

Now that I understand acquisition, the next experiment is retention.

I will measure:

• open rates

• returning readers

• replies

• unsubscribes

• article conversion

Because 150 subscribers is only the beginning.

The real question:

How many become active readers?

I’m documenting the numbers, mistakes and results as I go.

Subscribe to follow the next experiment — when I stop measuring how many people subscribe and start measuring how many actually stay.

Subscribe now

Then I reached 100 newsletter subscribers during my first week.

Before that first week was even over:

100 → 150 subscribers.

The obvious reaction would be to celebrate the number.

But I’m more interested in understanding why the growth continued after 100 — and whether those subscribers become real readers.

I’m documenting these experiments as they happen: the numbers, what I tried, what appeared to work, and what I still cannot prove.

The baseline

I started this publication from zero.

During my first few days, I tested several ways of getting discovered:

• participating in creator and subscribe-for-subscribe Notes

• replying to other writers

• publishing my own Notes

• sharing growth experiments publicly

• writing articles about what I was learning

• linking those articles when they genuinely matched a conversation

That helped me reach my first 100 subscribers.

But the interesting part is that the growth didn’t stop at 100.

Before my first week was finished:

Subscribers: 150

That means another 50 subscribers arrived after the 100-subscriber milestone.

What changed after 100?

I didn’t suddenly discover a new growth hack.

I kept doing largely the same things.

But one thing had changed:

I now had more content for people to discover when they landed on my profile.

Early on, someone visiting my profile saw almost nothing.

Now they could see:

• growth experiments

• original Notes

• opinions about Substack growth

• actual results

• mistakes and lessons

• articles explaining what I was learning

The profile itself had become more useful.

If you want to see what your own profile shows a first-time visitor, I built a free Substack profile audit that scores it out of 100.

Instead of asking:

Who is this person?

a visitor could start asking:

Do I want to keep reading this?

Notes created the surface area

One thing is becoming increasingly clear to me:

Notes have been one of my most important discovery surfaces.

Every Note creates another opportunity for someone to:

• see my name

• read an idea

• reply

• visit my profile

• discover an article

• subscribe

That doesn’t mean posting for the sake of posting.

The Note still needs to give someone a reason to stop.

But original Notes continuously create new entry points into the rest of my content.

Reciprocal support helped — but it is not enough

I also participated in reciprocal-support Notes.

Things like:

Subscribe = Subscribe
Follow = Follow

They helped create initial visibility.

For a publication starting from zero, that can be useful.

But someone subscribing because you subscribed to them is not necessarily subscribing because they want your next article.

That’s why I started publishing original Notes alongside those interactions.

Reciprocity can create the introduction.

Your content has to give people a reason to stay.

Different content may have different jobs

Another pattern is starting to appear in my article statistics.

My more opinionated article about subscribe-for-subscribe attracted more attention and discussion than my straightforward growth reports.

But one of my practical articles was better at directly generating a subscriber.

That suggests different content may serve different purposes:

Opinionated content → attention

Practical content → potential conversion

Notes → discovery

Replies → conversations

Growth reports → credibility

I don’t have enough data yet to call this a rule.

But it changes how I think about publishing.

150 subscribers does not mean 150 readers

This is the part I care about next.

150 subscribers sounds good.

But it doesn’t tell me:

• how many will open the next article

• how many will return

• how many will reply

• how many will unsubscribe

• how many joined mainly because of reciprocity

• how many genuinely care about the topic

Subscriber count measures acquisition.

It does not automatically measure audience quality.

What I still cannot prove

I cannot accurately attribute all 150 subscribers to specific actions.

Several things were happening simultaneously:

• Notes

• replies

• reciprocal-support threads

• articles

• profile visits

• Substack discovery

So this is not:

“Do these exact things and you’ll get 150 subscribers.”

It’s an experiment happening in public.

The growth is real.

The attribution is still incomplete.

What I’m testing next

Getting subscribers is no longer the main question.

Now I want to understand what happens after the subscription.

I’ll be tracking:

• article views

• open rates

• replies

• comments

• profile visits

• new subscribers

• returning readers

• retention

I’ll also continue testing different types of Notes:

• practical tactics

• opinions

• questions

• experiments

• progress updates

• warnings about common growth practices

The question becomes:

Which actions attract people who actually keep reading?

Conclusion

Getting from zero to 100 subscribers was the first milestone.

Going from 100 to 150 before the first week was even over was more interesting.

It suggests that early distribution becomes more valuable as your profile builds up more content worth discovering.

Notes create discovery.

Articles provide depth.

Conversations create familiarity.

Now the real experiment begins:

Can those 150 subscribers become an actual audience?

I’m documenting the numbers, mistakes and results as I go.

Subscribe to follow the next experiment — when I stop measuring how many people subscribe and start measuring how many actually stay.

Subscribe now

Since then, I’ve tested several types of content:

• growth reports

• practical acquisition lessons

• opinionated takes

• warnings about common growth tactics

One pattern stood out quickly.

My article warning about subscribe-for-subscribe attracted more views, a higher open rate and more discussion than my straightforward growth reports.

That does not prove contrarian content always wins.

But it is enough to make me test the idea further.

The baseline

Starting point: 0 subscribers

Current publication: 100 subscribers

Time period: less than 7 days

7-day views: 328

Main objective: understand which types of content attract attention, discussion and eventually active readers.

The subscriber growth is useful context, but this experiment is really about content performance, not about claiming that one article caused the growth.

The numbers

Here are the three articles I compared.

Why Subscribe-for-Subscribe Is Not Really Win-Win

• 62 views

• 30% open rate

• 8 likes

• 7 comments

• 0 directly attributed new subscribers

How I Grew From 25 to 67 Substack Subscribers in Four Days

• 43 views

• 19% open rate

• 0 directly attributed new subscribers

The Fastest Way I Found to Get My First 25 Substack Subscribers

• 29 views

• 17% open rate

• 1 directly attributed new subscriber

The warning about subscribe-for-subscribe had:

• the most views

• the highest open rate

• the most visible discussion

But the practical “first 25 subscribers” article is still the only one showing a directly attributed subscriber.

That distinction matters.

The article that got the most attention

The strongest-performing article was not a growth update.

It was a warning.

Why Subscribe-for-Subscribe Is Not Really Win-Win challenged a behavior many new Substack creators are actively using.

That gave the article something my growth reports had less of:

tension.

The reader immediately has something to react to.

They may agree.

They may disagree.

They may be doing subscribe-for-subscribe themselves.

They may have already been burned by it.

Either way, the topic gives them a reason to care before they even open the article.

Why the warning may have worked

I think three things helped.

1. It addressed a recognizable behavior

Subscribe-for-subscribe is easy to understand.

The reader does not need extra context.

They immediately know what the article is about.

2. It contained a conflict

“Grow faster with reciprocal support” sounds positive.

“Subscribe-for-subscribe is not really win-win” introduces a problem.

Problems create curiosity.

3. It invited disagreement

A growth report mostly tells the reader what happened.

A contrarian article gives the reader something to evaluate.

That naturally creates more room for replies and discussion.

But attention is not conversion

This is the part I do not want to ignore.

The contrarian article generated the most attention.

But it generated zero directly attributed subscribers.

Meanwhile, the article about getting my first 25 subscribers had fewer views and a lower open rate, but it generated one directly attributed subscriber.

So there may already be two different jobs happening:

Opinionated content gets attention.

Practical content may convert better.

I do not have enough data yet to claim that as a rule.

But it is a much more useful hypothesis than simply chasing views.

What these numbers do not prove

The data does not prove that more views lead to more subscribers.

It does not prove that a higher open rate creates better retention.

It does not prove that comments turn into long-term readers.

And it definitely does not prove that controversy for the sake of controversy is a good strategy.

The goal is not to become provocative.

The goal is to find real tensions your audience already cares about.

That is a very different thing.

What I’m testing next

For the next 30 days, I’m going to publish more articles built around real Substack tensions.

Not fake hot takes.

Actual questions creators are already dealing with.

For example:

• subscriber count vs active readership

• growth speed vs audience quality

• subscribe-for-subscribe vs genuine discovery

• followers vs newsletter subscribers

• views vs conversion

I’ll compare:

• views

• open rate

• replies

• comments

• new subscribers

• returning readers

The goal is to understand which topics create attention and which ones actually create audience growth.

Conclusion

My early data suggests that tension earns attention, while practical utility may drive conversion.

Three articles are nowhere near enough to call that a rule. But it’s a strong enough signal to test for the next 30 days.

Related reading

The article that triggered this observation:

Why Subscribe-for-Subscribe Is Not Really Win-Win

I’m documenting what actually works as I grow this Substack from zero.

One tool came out of these experiments, and it is free: a Substack profile audit that scores what a first-time visitor sees on your profile, out of 100.

Subscribe to follow the next 30-day experiment and see which topics create views, discussion and real readers.

Subscribe now

Most subscriptions were attributed to the Substack App, but the experiment also exposed the difference between gaining subscribers and building actual readers.

Here is what I tested, what appeared to work, and what I still cannot prove.

I’m documenting real Substack growth experiments, numbers and lessons. Subscribe to see which Notes actually create returning readers.

Subscribe now

The baseline

Start: 25 newsletter subscribers

Now: 67 newsletter subscribers

Net growth: 42 subscribers (+168%)

Period: August 1–5, 2026

Main objective: Understand how reciprocal-support Notes and original Notes contributed to early subscriber growth.

What I tested

• Participated in reciprocal-support Notes such as “subscribe-for-subscribe” and creator-discovery threads.

• Published my own original Notes about Substack growth, experiments, observations and lessons.

• Shared practical tactics, answered comments and referenced relevant articles when they matched the conversation.

What appeared to work

Reciprocal-support Notes appeared to create the initial momentum by placing my profile in front of creators who were actively looking to connect.

Putting your profile in front of people only pays off if the profile converts them. I built a free Substack profile audit that scores yours out of 100.

Publishing my own Notes served a different purpose: it showed visitors what I actually write about and gave them a reason to subscribe beyond reciprocity.

Both tactics were used during the same period, so I cannot accurately separate their individual contribution.

What remains uncertain

I cannot determine which specific Notes, replies or interactions produced each new subscriber.

I also cannot conclude that every reciprocal subscriber became an active reader.

The subscriber count grew, but I still need to measure article views, replies, retention and returning readers.

The dashboard attributes most subscriptions to the Substack App, but it does not identify which specific Note, reply or interaction generated each signup.

What I’m changing next

• Publish different types of original Notes and track which topics generate the most engagement.

• Track article views, replies, profile visits and returning readers—not only subscriber count.

• Run the next experiment for 30 days.

Conclusion

Reciprocal-support Notes helped me gain visibility quickly.

Publishing original Notes gave that visibility context and showed people what they could expect from my publication.

The next question is whether these new subscribers become active, returning readers.

Related reading

I previously explored why reciprocal subscriptions can create early momentum without necessarily building a lasting readership:

I’m testing which Substack growth tactics create active readers—not only larger subscriber counts.

Subscribe to follow the next 30-day experiment and see which Notes generate article views, replies and returning readers.

Subscribe now

But the exchange often attracts people who are interested in reciprocity—not necessarily in your writing.

Why the common advice fails

The pitch is simple:

“Subscribe to me and I’ll subscribe back.”

It can create quick visibility and make a new publication feel less empty.

But it does not give anyone a reason to keep reading.

What happens in real projects

The risk is an inflated subscriber count with very little active readership.

Some creators never reciprocate. Others subscribe, wait for you to return the favor, then unsubscribe shortly afterward.

The problem is not reciprocal support itself.

It is mistaking the exchange for genuine audience demand.

The better approach

• Subscribe only to publications you genuinely want to read.

• Use reciprocal-support threads for discovery, not as your long-term growth strategy.

• Prioritize opens, replies and returning readers over subscriber count.

This is slower than chasing reciprocal subscriptions and quick wins.

But it builds an audience with a reason to stay.

That reason has to be visible before someone subscribes, and the profile is where they look. I later built a free Substack profile audit that scores what a visitor sees, out of 100.

The real test

Would these people still read your work if you never subscribed to them?

That is the difference between collecting subscribers and building an audience.

Use subscribe-for-subscribe to leave zero—not as the foundation of your publication.

If you’re growing a Substack from scratch, subscribe to follow the experiments. No subscription back required.

Today, it has 41.

The fastest part was crossing the first 25.

I did it by joining “Subscribe = Subscribe” Notes: threads where small creators openly agree to support each other.

It worked.

But only for the problem it actually solves: getting out of zero.

The baseline

Start: 0 subscribers

Now: 41 subscribers

Period: less than one week

7-day views: 46

Initial objective: create enough momentum to stop publishing into an empty profile

What I tested

I used a simple process:

• Find active “Subscribe = Subscribe” Notes

• Open profiles instead of subscribing blindly

• Prioritize creators whose topics overlap with mine

• Leave a real reply when I have something useful to add

What actually worked

These Notes remove the hardest part of starting from zero: discovery.

The intent is explicit. New creators are actively looking for other publications to read and support.

That helped my account cross the first 25 subscribers.

The total then climbed to 41 while I was drafting this article.

Reciprocal support created early momentum quickly.

I cannot claim that every one of those 41 subscribers came from this tactic.

Substack shows that subscribers came through its network, but it does not identify the exact Note, reply or interaction behind every subscription.

Where the tactic breaks

A reciprocal subscriber is not automatically a reader.

This tactic optimizes for the subscription action. It does not guarantee that someone will open the next article, reply to a Note or become a customer.

Used for too long, the subscriber count can keep rising while the real audience stays flat.

That is why I see 25 as a useful stopping point—not a strategy for reaching 1,000.

The first 25 solve the cold-start problem. They do not prove that you have built an audience.

What I’m changing next

Now that the account has reached 41 subscribers, the strategy has to change.

My next steps are:

• Publish useful Notes around Substack growth and acquisition

• Reply to creators inside my actual niche

• Publish one practical newsletter article each week

• Track opens, replies, returning readers and subscriber sources

• Use reciprocal-support threads only selectively

The first 25 made the profile feel less empty.

The next 59 need a reason to stay.

That reason starts with what your profile shows a first-time visitor, so I later built a free Substack profile audit that scores it out of 100.

Conclusion

Use “Subscribe = Subscribe” Notes to leave zero.

Do not use them to build your entire publication.

Early momentum is useful. A qualified audience is more valuable.

When this account reaches 100 subscribers, I’ll publish the complete breakdown: what generated growth, what failed and whether this process looks repeatable.

Subscribe to follow the experiment.

The starting point

Six days ago, I opened this Substack.

Before publishing a single newsletter post, my dashboard showed:

26 subscribers

14 views in the last 7 days

0 newsletter posts

That is a strange place to start.

What I actually did

I wasn’t publishing articles, but I wasn’t inactive either.

I kept the process simple:

• Made my profile clearly about SaaS, AI and building products

• Followed people writing about topics I genuinely care about

• Joined relevant conversations

• Replied to one specific detail instead of posting generic comments

No automated following.

No automatic likes.

Every reply was reviewed and published manually.

What the numbers suggest

The first 17 subscribers all came through the Substack network:

• 14 through the Substack app;

• 3 from existing Substack accounts.

The total then climbed to 26 while I was drafting this article.

I can’t tell which exact interaction generated each subscriber. Twenty-six subscribers is not enough to call this a proven system.

But it suggests that Substack can start distributing your profile before you publish your first article.

Notes, replies and profile positioning aren’t just secondary features.

They are discovery surfaces.

If your profile is a discovery surface, it is worth knowing what it actually shows a visitor. That is why I later built a free Substack profile audit that scores it out of 100.

The next experiment

For the next 30 days, I’m testing a simple routine:

• One newsletter post each week

• One useful Note each weekday

• Up to 10 thoughtful replies on active days

• Tracking growth, views, sources and time spent.

I don’t only want to know whether the account grows.

I want to know which actions actually caused the growth.

The tool behind the experiment

The manual process quickly became repetitive.

Finding relevant conversations takes time. Remembering previous interactions is difficult. And writing a specific, natural reply is harder than generating another generic AI comment.

So I started building a small Chrome extension for myself.

It helps me discover relevant Notes, generate different reply angles and keep track of interactions.

It never publishes automatically.

The goal isn’t to create more noise.

It’s to spend less time searching and more time having worthwhile conversations.

Next milestone: 100

When this account reaches 100 subscribers, I’ll publish the complete breakdown:

• What generated growth

• What failed

• How much time I spent

• How the extension evolved

• Whether the process looks repeatable.

I’m not presenting a finished growth playbook. I’m testing one in public.

If you’re building a SaaS or growing an audience from scratch, subscribe to follow the experiment.