Subscribe now

On Wednesday, September 9, Wessal Khader (THE GO-TO-MARKET GENIUS) and I are teaching a free 90-minute masterclass where we will walk through exactly how to uncover, build, and price the offer you need to make for your business right now.

Register for Oper(AI)ting Live here: Even if you cannot attend live, register anyway, and we’ll send you the recording.

Grab Your Free Seat Here!

In 2019, investor Li Jin published an influential essay about what she called the “Passion Economy.” The promise was compelling: the internet would allow individuals to monetize their unique skills, knowledge, and personality without waiting for an employer, publisher, media company, or other gatekeeper to give them permission.

A year later, Jin was writing in Harvard Business Review that the creator economy needed a middle class. The same ecosystem that promised economic independence had already begun producing a familiar pattern: a small number of enormous winners and a much larger group working constantly to make the economics work.

The problem was not that the creator economy failed.

The problem was that, somewhere along the way, we confused the value as being the distribution system, rather than what was being distributed.

Mistake Number One: Building the Channel Before the Offer

Audience building solved a real problem. Distribution used to be scarce. Publishers owned it. Networks owned it. Employers and institutions often decided whose ideas, expertise, and work reached the market.

Social platforms changed that. They gave almost anyone the ability to publish, build an audience, create trust, and eventually transact directly with the people they reached.

But an industry inevitably formed around getting better at the mechanism itself.

• Content about making content.

• Newsletters about growing newsletters.

• Courses about launching courses.

• Creators teaching people how to become creators.

Nadia Asparouhova described this years ago as a “creator industrial complex”: An ecosystem that risks becoming increasingly dedicated to perpetuating creation itself rather than helping people accomplish something more valuable through it.

Media scholar Brooke Erin Duffy has another useful concept for what this produces: “aspirational labor.” People perform enormous amounts of work today (publishing, networking, promoting, building) in anticipation of income, opportunity, or status arriving later.

There are people who teach these skills extraordinarily well. I have learned from them myself. Justin Welsh, Chris Donley, Will McTye, and others have built real expertise around distribution and deliver ridiculous value to the people who buy from them.

They are not the problem.

The problem is what the rest of us inferred from their success.

We began teaching experienced professionals that before they could build a business, they first needed to become creators. Learn the algorithm. Post every day. Find three content pillars. Grow the newsletter. Reach 10,000, 20,000, 30,000 followers. Then, once enough people were paying attention, figure out what to sell them.

That sequence is backwards for a huge number of people.

The creator economy taught us to overvalue what we learned last year and undervalue what took us twenty years to learn.

My Story: How I Made All These Mistakes

I know this because I made almost every mistake I just described. Two and a half years ago, I had a little over 500 LinkedIn connections and decided I was going to post every day. I studied content, built relationships with other creators, took Justin Welsh’s LinkedIn MBA and Ship 30 for 30, and later invested $5,000 in Chris Donley’s Creator Accelerator, one of the best investments I made during that period.

I learned how to make content. I learned how to grow. Over the next two and a half years, my work generated more than 30 million LinkedIn impressions and helped me build a bestselling Substack newsletter, Operating by John Brewton. But I made one enormous mistake: I assumed that if I built the audience, the clients would come. For the first 18 months, I had neither a clear offer nor even a clear enough purpose for the newsletter.

The frustrating part is that the most valuable offer I make today was available to me before any of that growth happened. I spent my twenties studying economics at Harvard and the University of Chicago, my thirties helping build, scale, optimize, and eventually sell an industrial distribution business with my father, and the last six years consulting with manufacturing companies, B2B businesses, and technology firms. My real expertise is operational optimization as a means of improving financial performance, not content creation. I love this creator ecosystem, and coaching solopreneurs inside it has become some of the most rewarding work I do, but the thing I am still paid best to do is the thing I spent twenty years learning. The money I left on the table came from confusing the skill I had recently learned to distribute with the expertise I had already earned.

The economics were never as democratic as the distribution

Goldman Sachs estimated that there were roughly 50 million creators globally in 2023 and that only about 4% earned $100,000 or more annually. More importantly, Goldman expected that professional share to remain roughly stable even as the overall creator economy grew dramatically.

That distribution should not surprise us.

In 1981, economist Sherwin Rosen described the “economics of superstars.” When technology allows one person’s output to be distributed to an enormous market at almost no marginal cost, relatively small differences in performance can produce enormous differences in income.

Recorded music did it.

Television did it.

Social platforms amplified it again.

The pie can grow while the odds for the median participant barely move. That does not mean audience building is irrational. CreatorIQ’s 2026 research on more than 5,000 creators found follower and subscriber counts remain strongly related to income. Distribution still compounds, and a large audience can be an extraordinarily valuable asset.

But for an experienced professional, there is a different question worth asking:

The Game Has Changed

The creator economy was built during a period when distribution, production, business infrastructure, and access to information were all expensive.

Every one of those constraints has weakened.

Recommendation systems increasingly distribute individual ideas beyond an existing follower graph. A sufficiently relevant piece of content can reach people who have never heard of you. Software has collapsed much of the infrastructure once required to operate independently: billing, CRM, scheduling, research, publishing, payments, delivery, and customer communication.

Andreessen Horowitz was already writing in 2021 about lawyers, recruiters, consultants, and other professionals “going solo,” with software beginning to replace much of the infrastructure that a traditional firm once provided.

That should have changed the playbook. Instead, we kept handing professionals the entertainer’s model. Become visible first. Become valuable later.

But a 45-year-old operator with 22 years of experience does not begin at zero because she has 400 LinkedIn followers. She may have spent two decades solving problems companies were willing to pay millions of dollars to address.

Her audience might be small. But her economic value is not.

1. Why Enterprise AI Pilots Stall, and the 51 That Didn’t

2. Operating Stories: The $20 Billion Swing - How Uber turned losses into a cash machine.

3. Porter’s Five Forces, Reread for 2026

4. AI Was Supposed to Kill Consulting. Instead It Has Started a Golden Age.

5. The Quiet Builders Are Coming. Great Companies Will Build Around Them.

Subscribe now

The AI Of It All…

There is an easy version of the AI argument that says expertise is becoming more valuable. The research is more complicated.

Harvard Business School professor Karim Lakhani and his coauthors argue that AI is making many forms of expertise abundant. Economist David Autor has similarly argued that AI may allow more workers to perform tasks that previously required highly paid experts. They are probably right.

AI is rapidly collapsing the value of expertise that can simply be written down.

• Facts.

• Procedures.

• Frameworks.

• Reference knowledge.

• Generic analysis.

• Generic advice.

• Increasingly, even technically competent content.

MIT Sloan Management Review describes the shift well: When knowledge becomes commoditized, value moves from the content to the context. There is expertise you can retrieve. But more valuable than that is the expertise you had to live.

The second kind is built by making decisions with incomplete information, being responsible for the outcome, watching projects fail, learning which variable actually mattered, seeing the same problem in five different disguises, and developing judgment because the consequences were real. Judgment under consequence.

AI can make codified expertise cheaper. It can put sophisticated tools into far more hands. But that makes it even more important to distinguish between someone who can produce an answer and someone who knows whether the answer is any good.

The latter is often what a career has been quietly building.

Your Experience as Inventory

This is the part I think the creator economy caused many experienced people to miss. We have been trained to view our careers as credentials: titles, employers, promotions, accomplishments, and years of service.

Look at the same career commercially instead:

• What problems have you repeatedly been trusted to solve?

• What became easier because you entered the room?

• Where have companies already paid for your judgment?

• What do you recognize after five minutes because you have seen the problem 50 times?

• What is expensive when somebody gets it wrong?

The better question is not: “What niche should I create content about?” It is: “Where does my evidence meet an expensive problem someone already wants solved?”

That intersection can become an advisory offer, a diagnostic, a workshop, an implementation, a productized service, software, research, a membership, or yes, sometimes a course.

The delivery model should follow the value of the problem. Not the conventional product ladder of the creator economy. And then content becomes incredibly important again:

• Content becomes proof.

• Content becomes trust.

• Content becomes discovery.

• Content becomes distribution.

Don’t do this: Content → Audience → Offer

Do this instead: Evidence → Judgment → Offer → Content → Distribution

That is a subtle change in order with enormous economic consequences. Before spending another year trying to grow an audience, ask yourself three questions:

• If my audience disappeared tomorrow, would the valuable thing still exist?

• Does someone already pay to have this problem solved well?

• Did my advantage come from information somebody can download, or judgment I had to earn?

If the asset survives those questions, you probably do not need to become more of a creator before you become a business owner.

The creator economy accomplished something extraordinary. It democratized distribution. The mistake I made, and many of us out here have made, was confusing distribution with the asset being distributed.

Do not throw away twenty years of accumulated judgment just to become a beginner at being a creator. Build around what you have already earned the right to be exceptional at delivering and selling.

Then become extraordinarily good at distributing it.

Become an expert with distribution.

Not a creator looking for something to monetize.

Companies are becoming tech stacks.

We are all becoming companies.

- j -

On Wednesday, September 9, Wessal Khader (THE GO-TO-MARKET GENIUS) and I are teaching a free 90-minute masterclass where we will walk through exactly how to uncover, build, and price the offer you need to make for your business right now.

Register for Oper(AI)ting Live here: Even if you cannot attend live, register anyway, and we’ll send you the recording.

Grab Your Free Seat Here!

John Brewton documents the history and future of operating companies at Operating by John Brewton. He is a graduate of Harvard University and began his career as a PhD student in economics at the University of Chicago. Since selling his family’s B2B industrial distribution company in 2021, he has been helping business owners, founders, and investors optimize their operations.

Connect with John on LinkedIn

Every company is racing toward the same destination. They are buying the same AI models, installing the same copilots, building the same agents and automating the same workflows. That will make companies more productive, but it will also make more of what they do easier for competitors to reproduce.

A study of 5,179 customer-support agents found that an AI assistant raised productivity most among novice and lower-skilled workers; other experiments with professional writing found faster, higher-quality output, while research into creative writing found that AI-assisted stories became better individually but more similar collectively. The floor rises, the range compresses, and competent work becomes more abundant.

At the same time, something is happening in the opposite direction. For more than a century, companies have manufactured humanity around an institutional container. Betty Crocker gave a flour company a woman customers could relate to; KFC turned its founder into an enduring character; corporate brands built voices and personalities because a legal entity has no childhood, scars, convictions or lived experience.

The company needed humanity because the actual human did not scale very well. Now the human scales. One founder can increasingly research, analyze, design, build, publish, distribute and operate with capabilities that once required teams.

Companies are becoming more machine-like while people are acquiring the capabilities of companies.

We are all becoming companies.

1. How I Built a $200,000 Newsletter Business From a Substack Nobody Was Reading

2. The Five Moves That Turned My Substack Into a $200,000 Business

3. The Operating Project: Most Coaching Ends With Notes. This Ends With a Machine.

4. How to Produce World-Class Research with Perplexity Computer & Claude -> Fable 5

Subscribe now

The Founder Becomes the Human Interface

A founder-led brand makes the company’s judgment, worldview and reason for existing more legible. Customers, employees, partners and investors are not only evaluating what a company sells; they are deciding whether they trust the people and ideas behind it.

Andreessen Horowitz has been building an explicit New Media strategy around a similar shift. Its recent New Media Playbook argues that individuals increasingly matter more than corporate brands, while its Lighthouse Playbook says founders should define their worldview before growth tactics and create preferential attachment: enough signal and trust that scarce customers, employees, partners and other resources are more likely to choose your company.

You can see extreme versions in Alex Karp, Palmer Luckey and Elon Musk. They are polarizing, but structurally useful examples: their names carry recognizable worldviews, and their companies are understood partly through the founder’s public decisions and personality. a16z has itself highlighted Musk and Karp as examples of technical thinkers newly advantaged by long-form and clip-based media, while giving founders like Palmer Luckey extensive space to communicate their ideas directly.

You do not need their fame or controversy to learn from the structure. If your name is going to become one of your company’s important interfaces with the world, define what that name means before you spend years amplifying it.

Subscribe now

I Learned the Distribution Playbook First

I know this partly because I built my own audience in almost the opposite order. When I started creating content, I became obsessed with distribution: I committed to publishing every day for a year, learned LinkedIn deeply, invested in creator communities, and held more than 150 unpaid Zoom conversations with other creators.

It worked. I eventually documented the process in 0 to 55,000: The First 90 Days Playbook, and I still believe in much of it: show up, publish, build relationships, track what works, and get your reps.

One early breakthrough came from writing about Andy Grove’s High Output Management. Instead of summarizing the book, I wrote about mistakes I had made managing people myself, and the post took off. I came away with a formula I still believe in:

But another lesson matters more now. For 12 to 15 months, I obsessed over follower growth and brand awareness while assuming the business model would eventually emerge. Viral posts sent large amounts of attention toward me before I had a sufficiently clear offer, owned audience or system for turning that attention into a business.

My conclusion at the time was: build the business while you build the audience. I still believe that, but today I would put another instruction before it: build the brand before you amplify the brand.

A lot of creator education starts downstream. It teaches hooks, carousels, social growth and distribution, all useful skills. But distribution is an accelerant; it cannot tell you what should be accelerated.

The creator playbook teaches us how to become known. The founder-led brand requires deciding what we want to become known for.

A Founder-Led Brand Is Not a Following

A founder-led brand is not your follower count, logo, headshot, or chosen family of fonts. It is not even your content; those are expressions and distribution layers targeted to your ICP. Your brand is the set of associations that forms when someone repeatedly encounters your name.

What do you believe?

What have you done?

What problem do you understand unusually well, what are you building because of it, and why should somebody trust you?

Eventually the question becomes more commercial: why should I attach myself to what you are building? The best founder-led brands do not simply create awareness; they make the right people more likely to move toward the company.

That is why I like a16z’s preferential attachment framing. Brand becomes more than communications.

Brand becomes a resource-allocation mechanism.

Seven Questions to Build Yours

I think every founder should answer seven questions before obsessing over distribution.

1. What is your journey?

Your résumé compresses your life into titles, companies and dates. Your founder brand should recover the experiences that explain how you became the person making today’s decisions: the terrible boss, the company you failed to build, the customer who changed your mind, or the decade you spent learning something that only recently became useful.

The journey is the moat. A model can acquire much of the information you learned, but it did not live the sequence that formed your judgment.

2. What do you believe?

This is your worldview. Not “integrity matters” or “AI is changing everything,” but the things you see differently enough to take a position on.

What does your industry get wrong? What advice has become outdated? What future are you trying to create? A memorable founder-led brand comes from conviction, not manufactured controversy.

3. What company does that worldview lead you to build?

The founder brand has to connect to an actual business. Otherwise, you risk becoming an interesting creator without becoming a stronger founder.

The bridge should be simple:

I believe X. Therefore I am building Y. For Z. To produce this outcome.

If you cannot complete that clearly, the brand and company are still too disconnected.

4. What should your name mean?

Force yourself to choose three associations, not seventeen. What do you want somebody to think after encountering your work 20 times?

Then choose what you do not want to be known for. Brands are shaped by exclusion as much as addition.

5. Who do you want to attach to you?

Not all attention is equal. Whose attention could actually change the trajectory of your company: customers, employees, investors, partners or collaborators?

For each group, ask: what must they believe about me before they choose to move toward me? That is a better question than simply asking how to get more followers.

6. What should you repeatedly give away?

Only now do we reach content. Teach, explain, document, show your work and make your thinking useful enough that people understand your worldview through what you give them.

AI should make this easier. Use it for research, organization, editing and derivative content, but keep the scarce part human: the ideas, judgment, story and final selection. As a16z’s Olivia Moore argues in Humans Are for Ideas, AI Is for Execution, the increasing abundance of execution raises the relative value of distinctive ideas, taste and creative direction.

7. How will the signal compound?

Now design distribution. Choose an owned surface, a primary discovery channel, a long-form format, and a cadence you can sustain.

Founder-led should not mean founder-only. a16z’s Lighthouse Playbook extends the idea to executives, engineers, customers and advisors whose own expertise can become additional human nodes of trust around the company.

The Workbook I Wish I Had When I Started

Subscribe now

I still believe in publishing before you feel ready, but before telling somebody to publish every day, I would now spend a few hours answering more fundamental questions. Who are you? What shaped how you see the world? What are you building because of what you believe, and what should your name mean to the people who matter?

So I built the Founder-Led Brand Builder for exactly that. Upload it to ChatGPT or Claude and the model interviews you one question at a time, pushes past generic answers, looks for patterns in your experiences and beliefs, and turns the conversation into a Founder-Led Brand Blueprint: your origin story, worldview, positioning, desired associations, target audiences, signature stories, content territories and a 90-day distribution plan.

The workbook cannot manufacture your brand. The raw material is your life; it simply helps you see that material more clearly before you amplify it.

Download The Founder-Led Brand Builder

If building this kind of business and capability is especially appealing to you, and you want to learn everything you can in the year ahead, become an Operating Founder and join my 52-week-per-year Friday live Q&A sessions with The Operating Founder Community. We are a +100-strong group of Operators and Builders choosing a new vision for our futures.

Become an Operating Founder

The Human Interface to the Company

Companies are going to become extraordinarily capable machines. We should automate workflows, deploy agents, standardize what should be standardized and use AI everywhere it creates leverage.

But something else should happen at the same time: the humans should become more visible. The judgment, taste, history, conviction, and people willing to attach their names to decisions should become easier to see.

AI is giving more of us the operational capabilities of companies. That means more of us need to start thinking like companies about what our names stand for. The creator playbook taught us how to become known; the founder-led brand starts with a different question:

What do you want to become known for?

Define that first, then build the business and the audience around it. Because when everyone has access to increasingly similar machines, who is operating the machine matters more.

Companies are becoming machines.

We are all becoming companies.

- j -

Want to Build This With Me?

If the hard part is not understanding this framework but actually turning your brand, offer or content strategy into a working system, that is what I built The Operating Project to do.

Across four one-to-one working sessions, we take one important problem in your business from diagnosis to a finished result. We build the strategy, create the artifact, and then install the ChatGPT or Claude system that keeps doing the work after our sessions are over.

If your problem is your founder-led brand, we can turn your worldview, positioning, offers and content into a system you actually own.

Learn Everything About The Operating Project and grab your seat →

Start Working With John Today

Frequently Asked Questions

Is a founder-led brand the same thing as a personal brand?

Not quite. A personal brand can exist independently of a business; a founder-led brand is deliberately connected to the company you are building. Your worldview, reputation and identity create trust, but that trust should eventually make the company itself stronger.

Do I need to become an influencer?

No. The objective is not maximum visibility; it is useful visibility among the people whose decisions matter to your business. A founder with 5,000 highly relevant followers can have a far more economically valuable brand than someone with 500,000 people who have little connection to what they sell.

Do I need to become controversial?

No. Musk, Karp and Luckey are useful because their brands make the underlying structure obvious, not because founders should imitate their personalities. Distinctiveness requires conviction, not performative controversy.

Should AI write my content?

AI should absolutely help you research, organize, edit, repurpose and execute. I would be much more cautious about outsourcing your worldview, consequential opinions, personal experiences and final judgment; those are precisely the parts becoming more valuable as execution gets cheaper.

Does founder-led mean the company can never outgrow the founder?

No. In fact, the stronger model may eventually become a network of trusted humans around the company: founders, executives, technical leaders, employees, customers and advisors. The founder begins the signal; the organization eventually creates many credible sources of it.

What should I do first if I have no founder brand today?

Do not begin by choosing platforms or designing a content calendar. Start by answering three questions: What do I believe? What company does that belief cause me to build? What three things do I want my name to mean? Then use the Founder-Led Brand Builder to turn those answers into the rest of the system.

Appendix: Research and Sources

A. AI, Performance and the Compression of Competence

Erik Brynjolfsson, Danielle Li and Lindsey Raymond — “Generative AI at Work.”
NBER working paper studying 5,179 customer-support agents. AI assistance increased productivity by 14% on average, with substantially larger benefits for novice and lower-skilled workers. Read the research at NBER. (National Bureau of Economic Research)

Shakked Noy and Whitney Zhang — “Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence.”
Published in Science. Participants using ChatGPT completed professional writing tasks substantially faster while average output quality increased. Read the paper in Science. (Science)

Anil Doshi and Oliver Hauser — “Generative AI Enhances Individual Creativity but Reduces the Collective Diversity of Novel Content.”
Published in Science Advances. AI-assisted stories were rated more creative and better written, while the collective set of outputs became more similar. Read the paper. (Science)

Andreessen Horowitz — “From Copilots to Agents: Rebuilding the Company Around AI.”
A current company-building example of AI moving beyond isolated assistance toward systems that perform increasingly large shares of business workflows. Listen/read at a16z. (Andreessen Horowitz)

B. Companies Manufacturing Human Identity

General Mills — “How Betty Crocker Got Its Start.”
The corporate history of the 1921 Gold Medal Flour promotion that led Washburn-Crosby to create Betty Crocker as a human persona for answering consumer questions. Read the history. (General Mills)

General Mills — “The Many Portraits of Betty Crocker.”
Additional history of how the fictional persona became a recognizable human face and trusted voice for the company. Read the history. (General Mills)

C. Andreessen Horowitz on New Media and Founder-Led Brands

Erik Torenberg, Ben Horowitz and Marc Andreessen — “a16z’s New Media Playbook.”
The February 2026 discussion in which a16z explicitly argues that individuals increasingly matter more than corporate brands and lays out its evolving approach to direct media. Listen at a16z. (Andreessen Horowitz)

David Booth — “The Lighthouse Playbook.”
The most directly relevant a16z source for this article. It argues that founders should define their worldview before growth tactics, develops the idea of preferential attachment, and describes how employees, customers and advisors can become trusted “lighthouses” around a company. Read the playbook. (Andreessen Horowitz)

Erik Torenberg, Alex Danco, Elena Burger, Henry Williams, Tom Hollands, Brent Liang and Gaby Goldberg — “New Media, One Year In.”
a16z’s June 2026 retrospective describes New Media as effectively “go-direct as a service” for startups and treats creative capabilities, owned distribution and founder communication as company-building infrastructure. Read the retrospective. (Andreessen Horowitz)

Olivia Moore — “Humans Are for Ideas, AI Is for Execution.”
A February 2026 argument that AI increasingly commoditizes execution while distinctive ideas, taste, identity and creative direction remain disproportionately human sources of value. Read the essay. (Andreessen Horowitz)

Alex Danco — “Why Nerds Are More Clippable.”
Explores how the shift from short, tightly controlled media appearances toward long-form source material and short-form clips creates new advantages for technical thinkers with distinctive personalities. The essay specifically cites Elon Musk and Alex Karp as examples. Read the essay. (Andreessen Horowitz)

Chris Dixon and Palmer Luckey — “Palmer Luckey on Hardware, Building, and the Next Frontiers of Innovation.”
A long-form founder conversation recorded at a16z’s Founders Summit and a useful example of the direct founder-media model discussed in the article. Watch/listen at a16z. (Andreessen Horowitz)

D. My Own Audience-Building Lessons

John Brewton — “0 to 55,000: The First 90 Days Playbook — What I’d Do Differently Knowing What I Know Now.”
My earlier breakdown of daily publishing, platform mastery, creator relationships, tracking and distribution—and, more importantly for this article, my realization that I spent too long optimizing follower growth before building the offers and business infrastructure beneath the audience. Read the full Operating article.

The same piece documents the early Andy Grove post that led me to the “credible source material + personal vulnerability + external amplification” formula and the 150+ unpaid creator conversations that became an important part of my education.

E. Work With Me

John Brewton — The Operating Project.
Four working sessions focused on one defined business problem and one finished outcome. The engagement moves from diagnosis to design, then builds a working system inside ChatGPT or Claude and installs it on a live piece of the client’s business. Learn about The Operating Project. (Operating by John Brewton)

Subscribe now

This year, I ran a $99 offer: four coaching sessions for $99.

More than 100 people took me up on it in twelve days.

I ended up delivering more than 400 hours of coaching.

During those same months, this newsletter became a Substack bestseller. I launched two course cohorts. I opened a community on Skool. I started building my own software to help me run my business. And underneath all of it, I maintained a full-time commitment to my consulting work.

None of these endeavors failed.

That is what makes the next part important.

I simply built across too many surfaces:

• Too many places where the business needed me to show up.

• Too many products.

• Too many platforms.

• Too many workflows.

• Too many pieces of software.

• Too many things that were each perfectly reasonable on their own.

That is the dangerous thing about complexity.

You rarely wake up one morning and decide to make your business harder to run.

You add one good idea.

Then another.

Then another.

Eventually, the work of operating all the good ideas becomes the bad idea.

So going into the next 12 months, I am concentrating this side of the business around three things:

1. Teach many people at once.

2. Build working tools that people can operate without me.

3. Publish useful work that compounds and people are willing to pay to receive.

Everything else has to justify why it deserves another piece of my time.

And when I stepped back from all of those decisions, I realized something else.

I had spent the entire year using one framework.

Do. Fail. Learn. Grow. Win.

I wrote the full Do. Fail. Learn. Grow. Win. framework here.

The short version is:

Do: Put something real into the world.

Fail: Pay attention to where reality disagrees with your plan.

Learn: Figure out why.

Grow: Change the next attempt.

Win: Keep what works and use it to create the next opportunity.

Then go back to the start and Do More.

That is basically the story of my year.

And I think it explains much more about where this business is going than any five-year plan I could have written in January. It is also my sincere hope that my experience, the challenges I have faced, and the decisions I’ve made can help you in building your newsletter or solopreneur business.

1. How I Built a $200,000 Newsletter Business From a Substack Nobody Was Reading

2. The Five Moves That Turned My Substack Into a $200,000 Business

3. The Operating Project: Most Coaching Ends With Notes. This Ends With a Machine.

4. How to Produce World-Class Research with Perplexity Computer & Claude -> Fable 5

Subscribe now

1. Do

I did a lot this year.

Probably too much.

But I am increasingly convinced that the “too much” part was necessary to learn what I know now.

I launched the $99 coaching offer.

More than 100 people bought it in twelve days.

I spent more than 400 hours sitting directly with business owners, founders, creators, and operators.

I opened the Skool community.

I started the Friday Q&A calls.

I built the Compound Content Studio, where I taught people how to scale their content operations with Claude. The idea came from my now business partner, Wessal Khader, and man am I happy I said ‘Yes’ to her idea.

We launched the Oper(AI)te Summer of AI Fluency immediately afterward.

I started building internal software with Claude Code.

I experimented with AI agents.

I changed workflows.

I added tools.

I removed tools.

I tested different ways of teaching, consulting, coaching, building, and publishing.

There was no shortage of doing.

And that matters because almost everything I now understand about this business came after I put something in front of a customer.

That is very close to the argument Steve Blank made in his classic Harvard Business Review piece on the Lean Startup.

A new business model is not something you reason your way to perfectly in a conference room. It is a collection of assumptions.

So you put those assumptions into the world.

You watch what happens.

You change them.

Then you try again.

I could have spent six months designing the perfect coaching offer. I still would not have known what 100 actual clients were about to teach me.

I could have spent three months building the curriculum for Compound Content Studio. Instead, we designed it in three days, sold it for one week, and filled all 32 seats.

I could have spent months planning the perfect community. Instead, I built one and discovered that the part people really valued was the Friday call.

Reality is an excellent editor.

You just have to ship something before it can edit you.

There is also a much bigger economic change making this kind of experimentation possible for more people.

The U.S. Census Bureau counted more than 30 million American businesses with no paid employees in 2023. Together, those businesses generated nearly $1.8 trillion in revenue.

The creator economy has added another layer.

An IAB study led by Harvard Business School Professor Emeritus John Deighton estimates that the equivalent of more than 1.5 million Americans now work full-time as digital creators, nearly eight times the 2020 number.

And AI appears to be lowering the barrier to starting even further.

A 2026 study of more than 160,000 Product Hunt launches found a jump in entrepreneurial entry after ChatGPT arrived, disproportionately driven by solo founders.

The same study found an important limit:

Teams still dominated the highest-performing products.

That feels about right.

AI makes it much easier for one person to attempt things that once required a team.

It has not repealed the laws of time.

And time was the problem I eventually ran into.

I did not need more things I could do.

I needed fewer things I had to operate.

If you’re enjoying this article, you’ll probably love this article!

Quiet Quitting or Quiet Building? Our Changing Relationship With Professional Ambition

2. Fail

This is where I want to be careful with the word fail.

My community did not fail because nobody joined.

My coaching offer did not fail because nobody wanted it.

The Operating Project did not fail because nobody bought it.

In some ways, those are harder failures to notice.

Demand can hide a bad operating model.

You can have customers and still have the wrong business.

You can have engagement and still have the wrong product.

You can have revenue and still have terrible economics.

You can be growing and still be building something you do not actually want to operate.

That was the kind of failure I had this year.

The community worked. The container did not.

The Skool community is shutting down.

The Friday calls are staying.

I am moving those to Zoom.

Running the community cost me roughly three to four hours every week.

• There was platform-specific content.

• Engagement.

• Moderation.

• Administration.

And all the little things that come with maintaining another place where people expect you to show up.

When I looked at what members actually valued most, the answer was obvious.

The Friday calls.

Which, inconveniently, were the reason I built the community in the first place. Everything around them had become a second publication I had accidentally agreed to run.

Building a community is not a bad idea.

It just turned out not to be my good idea.

Bain has studied a much larger version of this problem for years.

In Killing Complexity Before Complexity Kills Growth, the firm describes complexity as a silent killer of growth.

Companies add products.

Processes.

Business units.

Layers.

Systems.

Each addition can make sense.

Eventually, more and more of the organization gets consumed by managing the organization itself.

I somehow recreated a tiny solopreneur version of the same problem.

I had a surface-area problem.

The lesson was not:Communities do not work.

The lesson was: The calls were the product. The community was the container.

So I am keeping the product.

And deleting the container.

Coaching worked. My calendar did not.

Coaching was probably the most enjoyable work I did this year.

More than 100 people.

More than 400 hours.

I learned more about how small companies actually operate than I would have learned from almost any dataset.

I am not walking away from coaching.

I am walking away from trying to build a large coaching practice.

One-to-one services scale almost perfectly with one thing I do not have more of:

My calendar.

That is an old professional-services problem.

Harvard Business Review has written about “putting products into services”: turning the repeatable parts of expertise into systems, technology, or products while keeping humans involved where judgment really matters.

That is increasingly what I want.

I do not want someone to need another hour with me every time they want to use something I know.

I want some of the knowledge to remain behind after the call ends.

That leads directly to the Operating Project.

We pick one problem in your business.

We develop the strategy.

Then we build the actual piece of technology you need to run it.

Usually that takes around four sessions, although some projects go longer.

The important difference is the output.

You do not leave with a plan.

You leave with a tool.

That distinction keeps getting more important to me.

• A strategy document creates work.

• A functioning system absorbs work.

• A checklist helps someone remember what to do.

• A tool can actually do some of it.

Career coach Julia Starr ran into a similar constraint after years of selling her time.

In a Business Insider account of how she used AI to extend her coaching methodology, she describes moving from coaching and prompts toward building an actual application around her framework.

That is where I think a lot of expertise businesses are going.

The expert does not disappear.

The expertise becomes more transferable.

3. Learn

This is the most important part of the framework.

Failure itself has almost no value.

People like to say:

“I learned from failure.”

Sometimes.

But plenty of people fail the same way for 15 years.

The value comes from identifying why something missed and changing what you do next.

That is Learn.

And by the end of this year, I had a few lessons I trust.

If building this kind of business and capability is especially appealing to you, and you want to learn everything you can in the year ahead, become an Operating Founder and join my 52-week-per-year Friday live Q&A sessions with The Operating Founder Community. We are a +200-strong group of Operators and Builders choosing a new vision for our futures.

Become an Operating Founder

Lesson 1: The thing customers value may not be the thing you built around it.

The Friday calls were the product.

Skool was the container.

That sounds incredibly obvious now.

It was not obvious when I was building it.

This is why testing behavior before building infrastructure matters.

I should have run four Friday Zoom calls first.

If people loved them, I could decide afterward whether they needed a community around them.

Instead, I built the container and then discovered which part people actually wanted.

Next time, I test the behavior first.

Lesson 2: Demand does not automatically mean scale.

I had plenty of coaching demand.

That was not the problem.

The problem was that every new client meant more of my calendar.

That is fine if your ambition is to build a great coaching practice.

Mine is not.

I want to teach.

I want to work directly with people.

I want to help solve real problems.

I just do not want every additional dollar of revenue to require another hour from me.

That is why the cohorts matter.

And it is why the newsletter matters even more now.

Lesson 3: I am better at one-to-many than one-to-one.

The first cohort was Compound Content Studio, which I built with a business partner I met on Substack. It was her idea. We taught people how to create social content with Claude.

We designed it in three days.

Sold it for one week.

Closed the cohort at 32 people.

It sold out.

Immediately afterward, we launched Oper(AI)te Summer of AI Fluency.

That program teaches people how to build teams of AI agents that perform real work inside their businesses:

• A branding, visual, and voice team that creates proposals, decks, and social content

• A research team that conducts market and company research at institutional quality

• An operations team that absorbs administrative work

• One custom agent team for each member, designed around the work their specific business needs done

The cohort model itself is not new.

Andreessen Horowitz wrote about the rise of cohort-based courses years ago: live instruction, deadlines, peers, accountability, and interaction without requiring the instructor to teach every student separately.

Creator businesses have arrived at similar models.

Former Big Tech program manager Jean Kang described to Business Insider how she moved from individual coaching into a six-week group cohort while building her independent education business.

Justin Welsh’s self-reported account of building a solo business past $10 million in cumulative revenue shows a business evolving across consulting, products, subscriptions, sponsorships, and community rather than relying forever on one-to-one work.

The point is not that cohorts are magic.

They are simply a much better match for how I want to work.

I want interaction.

I want questions.

I want to see people build.

I want to watch someone change what they are doing while we are working together.

I just do not need to explain the same thing 100 times in 100 separate Zoom windows.

Lesson 4: Publishing may be the highest-leverage teaching I do.

This one took me longer to see.

Operating already generates about $17,000 in annual recurring revenue from paid subscriptions.

I have tended to think about Substack in two ways:

As a publishing business.

And as the top of the funnel for everything else.

Someone reads an article.

Maybe they eventually join a cohort.

Hire me.

Buy an Operating Project.

Attend a workshop.

That will all continue.

But I think I have been underestimating the publication itself.

A newsletter is one-to-many teaching too.

Maybe the purest form of it.

I can learn something once.

Write it once.

Teach it to thousands of people.

And unlike a call, the work does not disappear when the hour ends.

It becomes part of an archive.

Someone can discover it six months later.

Or two years later.

A useful article can keep teaching after I have moved on to the next one.

That is exactly the kind of leverage I am trying to create everywhere else in the business.

So the newsletter cannot just promote the work.

The newsletter needs to become more of the work.

Lesson 5: Attempts compound.

This may be the biggest one.

The speed at which we designed Compound Content Studio was not luck.

It was the 400 coaching hours.

By then, I had heard the problems.

Again.

And again.

And again.

I knew what people struggled with.

I knew which explanations landed.

I knew where they got stuck.

I knew what they were willing to pay to learn.

That made the next thing faster.

This is where I think people sometimes misunderstand experimentation.

The failed attempt is not wasted if it makes the next attempt smarter.

Pieter Levels may be one of the clearest creator examples.

His public list of projects contains plenty of recognizable winners: Photo AI, Remote OK, Nomads.com, Interior AI.

It also contains a very large graveyard.

That is the important part.

He built a lot.

Most did not become huge businesses.

A few worked extremely well.

The failures were not an embarrassing detour around the process.

They were the process.

That is also why I like the Do. Fail. Learn. Grow. Win. framework.

It gives failure a job.

Failure creates information.

Learning turns the information into a change.

Growth proves that you actually made the change.

Then you get another chance to win.

Lesson 6: The work should leave something behind.

This is probably the most important lesson for how I think about client work now.

I want the work to end in something the other person can operate without me.

Not just advice.

Not just a deck.

Not just a call recording.

Not just “here is what you should do.”

Something that helps do the thing.

A workflow.

An agent team.

A research system.

A dashboard.

An application.

A tool.

That is where what I learned from coaching intersects with what is happening in AI.

Because for the first time, building the tool is becoming cheap and fast enough that it can be part of the engagement.

And that changes what a consultant can sell.

4. Grow

Growth is where you prove you learned something.

You change the next attempt.

That is what the next 12 months are about.

Not adding more.

Changing the shape of what is already working.

What I am closing

Skool goes away.

The Friday call stays.

Those three to four hours a week move toward making one thing better instead of keeping an entire platform alive.

What I am changing

Large-scale one-to-one coaching goes away.

The Operating Project stays.

It moves from $499 to $999.

The engagement includes more build time.

And the deliverable becomes much clearer:

The output is a working tool, not a plan.

What I am scaling: cohorts

The cohorts worked.

So the plan for the next 12 months is one to two new cohorts per quarter, plus workshops in between.

That becomes one of the centers of this side of the business.

But it is not the only one.

What I am scaling even harder: Operating

The other major bet is this newsletter.

Today, paid subscriptions to Operating generate about $17,000 in annual recurring revenue.

That is meaningful.

But I think it can become much more meaningful.

My goal over the next 12 months is to turn that into a $100,000-plus recurring-revenue business.

That is roughly a sixfold increase.

Which means I cannot get there by simply asking more people to pay for the same thing.

The product has to get better.

A lot better.

So I want to triple down on the thing I discovered I enjoy most this year:

Teaching:

My target is four to five articles every week.

Not four or five essays summarizing AI news.

There are already plenty of places to find AI news.

I want Operating to help people do something.

I want to show you what I am building.

How I built it.

Why I built it.

Where it failed.

What I changed.

What happened when a better model arrived.

What manual workflow became an agent.

What prompt became a Skill.

What piece of software I stopped buying because I built the part I actually needed.

What I learned from solving a real operating problem inside my consulting work.

And what you can take from all of it and use yourself.

I want Operating to become a running field guide for building a more capable business with AI.

There are three things I want to teach here.

1. Build with the models

AI is moving too quickly for this publication to become a static library of prompt tricks.

I want to teach against the frontier as it moves.

When Claude gets better at software development, I want to show you what suddenly becomes buildable.

When ChatGPT gets better at multi-step work, I want to test it against something real.

When agents become more reliable, I want to put them inside actual workflows and show you where they work and where they break.

When a model crosses a capability threshold, I want to go back to something that failed six months ago and try again.

The useful question is not:

What model launched this week?

It is:

What can we build now that we could not build before?

That is the question I want Operating to answer again and again.

2. Build a better business

The second source of material is my consulting work.

I get to sit inside companies and see how work actually happens.

The workaround someone created five years ago.

The spreadsheet everyone quietly depends on.

The process nobody has written down.

The piece of software everybody pays for but nobody enjoys using.

The meeting that creates six follow-up tasks nobody wants to do.

The handoff that only works because Susan remembers it every Thursday.

Increasingly, I can look at those problems and ask:

What would this look like if we rebuilt it for an AI-first company?

Some of those solutions will remain client-specific.

But many of the lessons will not.

I want to bring those lessons back here.

Not confidential client information.

The operating insight.

The pattern.

What failed.

What worked.

What somebody else can use.

That creates a loop I like a lot.

Consulting gives me real problems.

Building gives me real experiments.

Operating gives me a place to teach what I learn.

And the questions readers ask create the next problems worth solving.

3. Build something you own

The third subject may ultimately become the biggest.

I want to spend much more time studying how the best solopreneurs, newsletter writers, creators, and small internet businesses in the world actually operate.

Not just how they create content.

How they build businesses.

What they sell.

How they price.

Where recurring revenue comes from.

How they turn expertise into products.

How they move beyond one-to-one services.

How they build newsletters people pay to read.

How they create distribution they own.

How they use AI.

How small their teams are.

What they automate.

What they refuse to do.

What their technology stacks look like.

Where the margins come from.

And what we can learn from them.

Because I think there is a much bigger story happening underneath all of this.

For most of modern business history, if you were highly ambitious, one of the obvious paths toward creating financial security for yourself and your family was to build a career inside somebody else’s organization.

It looked like this:

• Get promoted.

• Manage more people.

• Earn more.

• Accumulate equity.

There is nothing wrong with that route.

It remains an excellent one.

But technology has opened another path.

A person with expertise, an audience, distribution, AI, and the ability to build products now has access to operating leverage that not long ago would have required employees, developers, designers, researchers, and meaningful capital.

That does not make building a one-person business easy.

It makes a different kind of business possible.

I want Operating to study that possibility seriously.

• Who is actually making it work?

• How are they doing it?

• Which parts are real and which parts are internet mythology?

• What do the best newsletter businesses understand about recurring revenue?

• What do the best creators understand about distribution?

• What do the best solopreneurs understand about focus?

• And how do you turn those lessons into something exceptional that you own?

Something that creates income.

Something that gives you options.

Something that can create real financial security for you and your family.

That is a subject I can imagine exploring here for years.

The newsletter itself becomes part of the experiment

Appropriately, I am going to use my own newsletter to test the thesis.

Today:

About $17,000 in annual recurring subscription revenue.

The next milestone:

$100,000+.

That is not a small increase.

It means I need to make the paid product dramatically more valuable.

• More useful articles.

• More original research.

• More builds.

• More templates.

• More frameworks.

• More case studies.

• More things readers can actually take and use.

But there is an important constraint.

I do not want to reach $100,000 by creating another giant surface-area problem.

I do not want five newsletters.

Seven communities.

Three membership tiers.

A Discord server.

A course vault with 400 videos nobody finishes.

The experiment is whether I can grow the revenue and value of one publication by making the publication itself dramatically better.

That is very consistent with everything this year taught me.

Do not automatically add another surface because you want more growth.

Make the best surface more valuable.

That is what I want to do with Operating.

The three engines

The shape of the business is becoming much clearer.

There are three engines.

1. Paid publishing

Operating.

Teach at scale.

Build an archive.

Own the relationship with the audience.

Create recurring revenue.

Turn the things I am already learning into something thousands of people can use.

2. One-to-many education

Cohorts.

Workshops.

Friday calls.

Teach people live.

Help them build.

Answer questions.

Watch where they get stuck.

Then feed those lessons back into the publication and the next cohort.

3. Tool-building

Operating Projects.

Consulting builds.

Internal applications.

Agent teams.

Take what I know and turn more of it into something another person can actually operate.

These three things feed one another.

The consulting work gives me problems worth solving.

The builds give me things worth teaching.

The cohorts tell me where people struggle.

The newsletter turns those lessons into reusable intellectual property.

The audience creates more questions.

Those questions create new experiments.

And the experiments give me more to write about.

That is the flywheel I want.

Not more surfaces.

More value moving through fewer surfaces.

The stack

The technology underneath all of this is changing too.

Here is what actually runs the business now.

Claude is the operating layer.

Writing, research, analysis, and most of the agent teams start there.

Claude Skills are how Claude learns the business.

Brand systems.

Voice profiles.

Meeting workflows.

Inbox workflows.

Research standards.

Each one encodes a way of working so I do not have to teach the model the business from zero every time.

Anthropic’s explanation of Agent Skills describes them as reusable instructions, resources, and code that give an agent specialized ways of performing recurring work.

That is exactly how I think about them.

I do not want to prompt better forever.

I want the standard built into the system.

Claude Cowork is where more of that work runs against my files and connected systems.

Anthropic’s Claude for Small Business points directly toward this model: agents, skills, connected information, and workflows built around functions such as operations, sales, marketing, finance, and customer service.

Claude Code is where I build.

Internal applications.

Workflows.

Small pieces of software.

Tools for my own company.

Tools inside client engagements.

ChatGPT Work is the second operating environment.

I want two frontier AI systems.

They have different strengths.

Different weaknesses.

Different failure modes.

Anything important gets a second opinion.

OpenAI’s current direction with ChatGPT Work points in the same direction as Claude: persistent projects, reference files, reusable templates, tools, and multi-step work rather than a blank chat box that starts from zero every time.

Perplexity Comet and Computer handles advanced web research.

Perplexity describes Comet as an AI-powered browser capable of researching, navigating, summarizing, and working across the web.

Zoom runs every call.

More interestingly, it is starting to become part of the connective tissue between systems.

Zoom’s MCP integration with Claude can expose meeting intelligence to Claude Cowork and Claude Code.

That matters to me much more than having another AI meeting summary.

A meeting should not disappear inside the meeting app.

It should become operating context.

Plaud captures meeting transcripts.

Attio is the CRM.

And increasingly, Attio is the spine of the operation.

It holds client context.

Transcripts.

Progress.

Relationships.

And through Attio’s MCP support, AI systems such as Claude and ChatGPT can work directly against that CRM information.

That is the direction I want the entire stack moving.

Fewer isolated apps.

More shared context.

Calendly handles scheduling and intake.

Superhuman on Gmail runs email, with Claude handling much of the triage and drafting against a written standard.

Google Drive holds documents and course material.

Notion holds documentation.

The tables are on their way out.

Substack is now one of the core products of the business.

It is the publishing platform.

The archive.

The distribution engine.

And increasingly, a recurring-revenue business of its own.

Today, paid subscriptions generate about $17,000 in annual recurring revenue.

The goal for the next 12 months is $100,000-plus.

That means the newsletter is more than top-of-funnel.

It is one of the things I am building.

Why I think this stack gets much more powerful over the next 12 months

This is where I am making a bet.

I think the stack gets substantially more capable without becoming substantially larger.

There are four reasons.

1. AI models are crossing usefulness thresholds very quickly

My experience this year has been strange.

There were things I tried to build in March that did not work.

I tried them again in June.

They worked.

I was not a materially better software developer in June.

The models were better.

That sounds anecdotal until you look at the broader trend.

The Stanford AI Index has documented enormous gains on software-engineering benchmarks, including rapid improvements on increasingly realistic coding tasks.

METR has tried to measure the same phenomenon differently.

Instead of asking only whether an AI gets a benchmark question right, it studies the length and complexity of software tasks frontier AI systems can complete at a given reliability level.

Its research on AI task-completion horizons found that historically, the length of software tasks frontier systems can reliably perform has been increasing very quickly.

METR is also careful about what that means.

Its researchers explain in their limitations discussion that a model’s measured task horizon does not mean you can simply replace a person doing an equivalent number of hours of real work.

Real work is messier.

People collaborate.

Requirements change.

Information is missing.

Judgment matters.

But the direction still matters.

If a task sits just outside the capability line today, I increasingly think it is worth trying again later.

“AI cannot do this” has a much shorter shelf life than it did two years ago.

2. Yesterday’s frontier capability keeps getting cheaper

Better models are only half of the story.

The other half is cost.

The 2025 Stanford AI Index documented a dramatic collapse in inference costs for a fixed level of capability.

That may be even more important for businesses than the benchmark race.

The interesting question is not:

Can the most expensive model in the world do this once?

The useful question is:

Can a sufficiently capable model do this cheaply enough that I can put it inside a workflow and run it 500 times?

That is the threshold that matters.

It is one thing to have AI write one email.

It is another thing to have a reliable system process every meeting, every transcript, every follow-up, every piece of research, and every client update.

As capability gets cheaper, more of those workflows become economically sensible.

3. The model is becoming the layer that operates the software

The first version of generative AI gave us a text box.

Ask a question.

Get an answer.

Copy it somewhere else.

That is not where the products are going.

Claude now has Skills, Cowork, Code, connectors, and MCP.

ChatGPT Work combines models with files, reusable systems, connected tools, and multi-step execution.

Zoom can expose meeting intelligence to an agent.

Attio can expose CRM information to the same agent.

The model is slowly becoming less like another application in the software stack and more like the layer that operates the stack.

That is a much bigger change than better chat.

Consider one client meeting:

• The meeting happens in Zoom.

• Plaud records the transcript.

• The transcript goes into Attio.

• An agent can compare the conversation with prior meetings.

• Extract commitments.

• Update the CRM.

• Generate the follow-up.

• Identify the next action.

• Prepare the next meeting.

• And carry what happened into the next piece of work.

Those used to be separate activities spread across separate applications.

Increasingly, they can become one workflow.

This is why protocols like MCP matter.

The protocol itself is not particularly interesting to most business owners.

Shared context is interesting.

The less often I have to manually move information from one place to another, the more useful the entire stack becomes.

4. The cost of creating narrow software is collapsing

This is probably the biggest bet I am making.

For most of my career, if I wanted custom software, I had two options.

Buy something close enough.

Or hire someone to build it.

There is now a third option.

Describe it.

Build it with an AI coding system.

Test it.

Change it.

Keep what works.

The Financial Times described vibe coding as a new kind of DIY because natural-language development tools are making it possible for nontraditional developers to create applications that would never have justified the cost of a conventional software project.

Pieter Levels argues that this produces another consequence.

When everyone can build apps, distribution becomes the scarce thing.

That is an important inversion.

Software creation gets cheaper.

Understanding the customer becomes more valuable.

Having an audience becomes more valuable.

Owning distribution becomes more valuable.

Knowing the workflow becomes more valuable.

Knowing what should be built becomes more valuable.

Those are all good developments for somebody who just spent more than 400 hours talking directly to customers—and who publishes to an audience every week.

This is another reason the newsletter matters.

If building gets cheaper while distribution becomes more valuable, then owning the relationship with the audience becomes an increasingly important asset.

What I am replacing

The larger technology project underneath the stack is subtraction.

Gamma is gone.

Canva is gone.

The Notion tables are going.

In each case, I have replaced or am replacing parts of the software with something I built using Claude Code, sized around the way my business actually works instead of around the median customer of a software company.

This is the part I did not see coming.

The models became good enough to build the tool.

That changed what I needed to buy.

And that is beginning to change how I think about software categories altogether.

A surprising amount of software is an interface wrapped around a workflow.

For years, the hard part was turning a description of that workflow into functioning software.

That part is getting cheaper.

But I want to be precise here.

I do not think SaaS is going away.

And I definitely do not think you should cancel every subscription and vibe-code your accounting system this weekend.

The Financial Times has made a useful counterargument to the idea that AI coding simply kills SaaS.

Mature software companies provide things that homemade applications do not automatically give you:

• Reliability.

• Security.

• Collaboration.

• Maintenance.

• Support.

• Integrations.

• Compliance.

• Years of accumulated domain knowledge.

There are also very real risks in AI-built software.

WIRED reported on research into thousands of publicly accessible vibe-coded applications that found serious authentication and data-exposure problems.

MIT researchers studying the roadblocks to autonomous software engineering make a similar point from the engineering side.

Writing code is only one part of software engineering.

There is also:

• Architecture.

• Testing.

• Security.

• Maintenance.

• Requirements.

• Debugging.

• Reliability.

• System design.

So my thesis is narrower than:

Build everything.

It is this:

For a small, internal, well-bounded workflow, the default question is changing from “Which software category should I buy?” to “Could I build the 20% of this software I actually use?”

That is a much more interesting question.

I am not rebuilding payroll.

I am not vibe-coding authentication for sensitive customer data.

I am not rebuilding accounting controls because QuickBooks annoys me.

I am looking around the edges.

The internal dashboard.

The research organizer.

The transcript system.

The intake processor.

The content workflow.

The little application that turns 17 steps into three buttons.

The software where the output is easy to check.

The software where failure is annoying rather than catastrophic.

The software that exists because my business works a particular way.

I think that category is about to get very interesting.

The productivity research points in the same direction

There is another reason I think these systems will absorb more operating work.

We already have strong evidence that generative AI can improve performance on bounded knowledge tasks.

In a controlled experiment published in Science, people using generative AI on professional writing tasks completed the work substantially faster while independent evaluators also rated the output higher.

A separate NBER study of customer-support workers found meaningful average productivity gains, with particularly large improvements among less-experienced workers.

Those studies do not prove that AI can run a company by itself.

That is not my argument.

They point toward something much more practical.

There are pieces of knowledge work where the cost of producing a useful first draft, analysis, recommendation, or piece of code has fallen dramatically.

A one-person company has a lot of those pieces.

And that is why this technology may matter disproportionately to very small businesses.

A large company can hire another analyst.

A solopreneur cannot.

But a solopreneur can increasingly acquire some of the capability of another analyst without acquiring another payroll line.

There are already field reports of people trying to push that much further.

One founder told Business Insider that AI agents handle work ranging from proposals and scheduling to invoicing.

Another described building a collection of specialized AI agents across legal, finance, HR, and operations.

Those are individual accounts.

Not controlled studies.

But that is why they are interesting.

They are field reports from people trying to figure out what the operating model looks like before anyone has written the textbook.

That is the experiment I am running too.

How small can the stack get while the capability of the business goes up?

5. Win

The wins are easier to see now.

This newsletter became a Substack bestseller.

More than 100 people trusted me enough to work with me.

I spent more than 400 hours learning directly from them.

Compound Content Studio sold out.

Oper8 showed us another way to teach AI fluency in a group.

The Operating Project became a much better product because I finally understood where the real value was.

Operating became a real recurring-revenue business.

I built software I could not have imagined building myself two years ago.

And maybe most importantly, I now have a much clearer picture of the business I actually want to operate.

But this is where the final part of the Do. Fail. Learn. Grow. Win. framework matters.

Win is not the end.

Win sends you back to Do.

I do not think I “figured out” my business this year.

That would be a dangerous conclusion.

I think I earned a better set of experiments for next year.

That is different.

And it is enough.

The next loop

The next 12 months are another experiment.

• Can I teach more people without adding more calendar?

• Can I turn roughly $17,000 of recurring newsletter revenue into $100,000-plus by making one publication dramatically more useful instead of creating five more products?

• Can I publish four to five genuinely useful pieces every week without turning the publication itself into another surface-area problem?

• Can the Operating Project turn more of my expertise into tools clients keep using without me?

• Can I reduce the number of software products I buy while increasing what the business can actually do?

• Can AI agents absorb more administrative work without creating an even bigger mess underneath them?

• Can shared context remove more of the handoffs between systems?

• Can I learn from the best solopreneurs and creator businesses in the world and turn those lessons into something useful for people building businesses of their own?

• Can I operate fewer surfaces and produce more value?

• Can I make this business simpler while making it more capable?

I do not know yet.

That is the point.

Do.

Fail.

Learn.

Grow.

Win.

Then go do it again.

- j -

If building this kind of business and capability is especially appealing to you, and you want to learn everything you can in the year ahead, become an Operating Founder and join my 52-week-per-year Friday live Q&A sessions with The Operating Founder Community. We are a +200-strong group of Operators and Builders choosing a new vision for our futures.

Become an Operating Founder

What I would do differently

What to do this week

Research Appendix

I have grouped the research and practitioner examples by topic so this can also serve as a reading list for anyone thinking through the same questions.

1. Do. Fail. Learn. Grow. Win.

Operating by John Brewton

Do. Fail. Learn. Grow. Win.: My 5-Step Framework

The framework behind this article.

The core idea is that failure only becomes useful when you diagnose it, change your next attempt, and convert the lesson into growth.

Why read it: It is the operating model I use for experiments, mistakes, decisions, and iteration.

2. The rise of tiny businesses, creators, and solo entrepreneurship

U.S. Census Bureau

Census Bureau Data Tell the Small Business Story

The Census data provides useful scale for the one-person-company conversation. Tens of millions of American businesses operate without paid employees and collectively produce enormous economic output.

Why read it: Solopreneurship is not an internet niche. It is a substantial part of the economy.

IAB / John Deighton

Measuring the Digital Economy 2025

Research into the scale and growth of digital creators in the United States.

Why read it: Useful context for understanding independent creators as an increasingly important business and labor category.

Kim, Kang & Song

Generative AI Fuels Solo Entrepreneurship, but Teams Still Lead at the Top

Research using more than 160,000 Product Hunt launches to examine changes in entrepreneurial entry after the arrival of generative AI.

Why read it: It is one of the most directly relevant academic attempts to measure whether AI is lowering barriers to entrepreneurship.

Important caveat: The researchers also find that teams continue to dominate the highest-performing products.

3. Complexity, focus, and surface area

Bain

Killing Complexity Before Complexity Kills Growth

Bain’s argument that successful organizations often accumulate products, processes, systems, and layers until managing the complexity starts eating the benefits of growth.

Why read it: This is the large-company version of the surface-area problem.

Bain

The Founder’s Mentality

Research and thinking around how companies lose speed and customer focus as growth introduces complexity.

Why read it: Particularly useful if your company is getting bigger while somehow becoming harder to operate.

4. Experimentation and iteration

Steve Blank / Harvard Business Review

Why the Lean Start-Up Changes Everything

The classic argument for treating a new business model as a set of hypotheses to test against customers rather than a plan to perfect in advance.

Why read it: It provides a research-backed business explanation for the “Do” portion of Do. Fail. Learn. Grow. Win.

5. Turning expertise into scalable products

Harvard Business Review

Putting Products into Services

An examination of how professional-services businesses can standardize, automate, and productize repeatable pieces of expert work.

Why read it: This is one of the closest established business concepts to what I am trying to do with the Operating Project.

MIT Sloan Management Review

How to Turn Professional Services Into Products

A more recent treatment of productizing expert services.

Why read it: Especially relevant for consultants, agencies, coaches, accountants, and other businesses whose core product has traditionally been expert time.

6. Cohorts and one-to-many education

Andreessen Horowitz

Investing in Maven

A useful early explanation of cohort-based courses and the value of combining live instruction, accountability, peers, and deadlines.

Why read it: Helpful for anyone choosing between prerecorded courses, cohorts, communities, and individual teaching.

Jean Kang / Business Insider

I Quit My Big Tech Job and Made $100,000 in Revenue Within Five Months

Kang describes moving from individual coaching toward a six-week cohort as part of building an independent education business.

Why read it: A practitioner example of changing the economics of teaching without removing live interaction.

7. Creators and solopreneurs worth studying

These examples are field reports, not academic proof. I find them useful because they show people actively testing these operating models.

Pieter Levels

List of All My Projects Ever

A public record of Levels’ many launches, including both major successes and many projects that went nowhere.

Why read it: The sheer number of attempts may be more instructive than any individual success.

Also useful:

Everyone Can Now Build Apps With AI, So Distribution Is the Real Challenge

Why read it: A useful argument for why cheaper software creation makes audience, customer insight, and distribution more valuable.

Justin Welsh

My Complete $10M Journey

Welsh’s self-reported retrospective on building a solo business across consulting, digital products, subscriptions, sponsorships, and community.

Why read it: A good example of a creator business evolving through repeated experiments rather than emerging with one perfect model.

Julia Starr / Business Insider

I Built an AI Tool to Mimic My Work

A coach’s account of turning part of her expertise into an AI-enabled application.

Why read it: Probably the practitioner example closest to the Operating Project thesis.

Justin Parnell / Business Insider

AI Agents Handle Everything From Invoices to Proposals

A firsthand account of putting AI agents into several operating functions of a small business.

Why read it: Useful for seeing what an AI operating layer looks like when someone actually attempts one.

Aaron Sneed / Business Insider

A Solo Founder Runs His Company With Specialized AI Agents

A founder’s account of building specialized AI roles across finance, HR, legal, and operations.

Why read it: The interesting idea is organizational. Instead of thinking only in prompts, start thinking in roles, standards, responsibilities, and handoffs.

8. What AI capability is actually doing

Stanford HAI

The 2026 AI Index Report

Stanford’s annual AI Index is one of the best broad sources for tracking model capabilities, adoption, costs, investment, research, and benchmarks.

Why read it: If you want one source for separating real AI progress from daily hype, this is a strong place to start.

METR

Measuring AI Ability to Complete Long Software Tasks

METR tries to measure AI progress by looking at the length and complexity of tasks agents can successfully complete.

Also read:

Clarifying Limitations of Time Horizon

Why read both: The first illustrates how quickly capabilities are changing. The second prevents you from over-interpreting the metric.

Stanford HAI

2025 AI Index: Research and Development

Research into falling inference costs and improvements in model efficiency.

Why read it: Capability getting cheaper is just as important to business adoption as capability getting better.

9. The emerging AI operating layer

Anthropic

Equipping Agents for the Real World With Agent Skills

Anthropic’s explanation of reusable skills, instructions, resources, and code.

Why read it: This is one of the most important concepts in my stack. The model becomes dramatically more useful when your operating standards are encoded rather than repeatedly prompted.

Anthropic

Claude for Small Business

Anthropic’s direction for applying Claude, Skills, Cowork, and connected systems to functions across a small business.

Why read it: It points directly toward the kind of AI operating layer I am trying to build.

OpenAI

ChatGPT for Your Most Ambitious Work

OpenAI’s move toward persistent work environments built around files, projects, tools, reusable templates, and multi-step execution.

Why read it: Another sign that AI products are moving from question-answering toward completing bodies of work.

Perplexity

Comet

Perplexity’s AI-powered browser.

Why read it: An example of the browser itself becoming an AI operating surface for research and web-based work.

10. The connective layer: meetings, CRM, and shared context

Zoom

Zoom Meeting Intelligence in Claude

An example of Zoom exposing meeting intelligence to AI systems through MCP.

Why read it: The interesting idea is not another meeting summary. It is the meeting becoming usable context elsewhere in the business.

Attio

Attio MCP

Attio’s integration for allowing AI systems to work against CRM information.

Why read it: A strong example of why the CRM can become the spine of an AI-enabled operating system.

11. What the productivity research says

Noy & Zhang / Science

Experimental Evidence on the Productivity Effects of Generative AI

A controlled experiment examining generative AI’s impact on professional writing tasks.

Why read it: One of the clearest early randomized experiments showing meaningful improvements in speed and judged work quality.

Brynjolfsson, Li & Raymond / NBER

Generative AI at Work

A field study of customer-support workers using generative AI.

Why read it: Particularly relevant for understanding AI as a way of distributing expertise, standards, and best practices.

12. Build vs. buy and the rise of AI-built software

Financial Times

“Vibe Coding” Is the New DIY

A look at natural-language software development and what happens when people without traditional development backgrounds can build useful applications.

Why read it: Good context for the falling cost of creating narrow software.

Financial Times

Why Vibe Coding Isn’t Simply Going to Kill Business Software

The other side of the argument.

Established software vendors still provide security, reliability, support, domain expertise, maintenance, integrations, and infrastructure.

Why read it: Read this next to the piece above. The interesting future is probably somewhere between “buy everything” and “build everything.”

WIRED

Thousands of Vibe-Coded Apps Expose Corporate and Personal Data

Reporting on security and authentication problems found in AI-built applications.

Why read it: An important warning that easier software creation does not automatically produce safe software.

MIT

Can AI Really Code? Study Maps Roadblocks to Autonomous Software Engineering

MIT researchers examine the engineering work that remains difficult even as code generation improves.

Why read it: A useful antidote to both “AI cannot build software” and “software engineering has been solved.”

Subscribe now

A growing number of Substack creators believe the platform is losing its way.

Paid subscriptions are getting harder to grow.

Notes rewards people who post constantly.

Followers do not feel as valuable as email subscribers.

Video, Lives, clips, recommendations, and algorithmic feeds are becoming more prominent, while also adding to the overwhelm some creators feel about the work required to grow and maintain their audiences across the platform.

Scott Carney recently argued that “the subscription model has maxed out.” Taylor Lorenz wrote that she has more than 200,000 followers while her paid subscriptions have “fallen off a cliff,” making Substack feel increasingly like another social media feed.

Their experiences should be taken seriously.

Their conclusion should not.

My response to the concern that Substack is becoming another giant social platform is simple:

Good. It should.

Substack’s transformation into a large social platform is not a betrayal of the original value proposition that the paid newsletter model offered, nor is it an inevitable path to declining quality of content, community, and creator revenues.

It is an opportunity for all involved to grow.

The real danger is not that Substack becomes larger, more algorithmic, more social, or less about writing and more about multimedia.

The danger is that it becomes the wrong kind of social platform.

1. How I Built a $200,000 Newsletter Business From a Substack Nobody Was Reading

2. The Five Moves That Turned My Substack Into a $200,000 Business

3. The Operating Project: Most Coaching Ends With Notes. This Ends With a Machine.

4. How to Produce World-Class Research with Perplexity Computer & Claude -> Fable 5

Subscribe now

Harder does not mean worse

Every valuable market becomes more competitive as more people discover its value.

Early Substack writers entered a much emptier marketplace.

There were fewer publications, fewer professional creators, fewer competing Notes, and fewer sophisticated subscription businesses. Category growth lifted many writers at once.

That was never going to last forever.

Companies, especially the venture-backed kind, have a requirement to grow at all costs.

People frequently describe a platform as “better” when what they really mean is that there were fewer people competing with them on it, so they personally felt more successful.

The loss of early-mover ease is not the same thing as platform decline.

Substack passed five million paid subscriptions in March 2025, after crossing one million in 2021 and three million in February 2024. By April 2026, Substack said nearly 100,000 publishers were earning money, including almost 30,000 outside the United States.

The latest official paid-subscription milestone I can verify is still the five-million figure announced in March 2025. We should not pretend we know that paid subscriptions are continuing to accelerate at the same rate.

But “growth may be slowing” and “the platform is collapsing” are radically different claims.

An individual writer can lose paid subscribers while the total market grows.

A category can become saturated while the broader platform expands.

A creator’s conversion rate can decline because the platform is bringing a much larger and colder audience into the top of the funnel.

That last distinction matters.

A follower who encounters one 100-word Note is not the same kind of prospect as a reader who intentionally visits a publication, reads three essays, and gives the writer their email address upon subscription sign-up.

When a platform adds a high-volume, low-intent discovery channel, the blended conversion percentage can fall even as the number of opportunities to convert readers rises.

That is not necessarily cannibalization.

It is a different funnel.

Email is not a discovery system

Email is one of the most valuable distribution tools a creator can possess.

It is direct.

It is portable.

It reaches people who have explicitly asked to hear from you.

I have grown a social following of +80,000 people across platforms over the last two and a half years, but the single most valuable assets I have after all of that work are the 2,250 people of the 6,000 total subscribers on my Substack that open every single article I write and every single offer I make.

Substack also allows creators to export their subscriber lists.

But email begins after discovery.

Before someone can subscribe, they must first encounter the creator. Then they must understand the creator’s value. Then they must develop enough interest or trust to invite that creator into their inbox.

A traditional newsletter platform helps you communicate with the people you already have. A media network helps you find the people you do not have yet.

That is the strategic difference between Substack and infrastructure-first alternatives such as Kit or Beehiiv.

Those platforms may offer better economics, customization, advertising products, or operational control for certain publishers. Substack’s differentiated value is the network surrounding the publishing software. The larger that aggregate market becomes, the more differentiated value is created for all Substack creators.

Substack says its network, anchored by recommendations and the app, now produces more than half of all subscriptions and 30% of paid subscriptions on the platform. In February 2024, it reported that the network was already driving 50% of new subscriptions and 25% of new paid subscriptions.

That reality has certainly held for me. While I arrived at Substack with a following of +25,000 on LinkedIn (now +40,000) and an old email list from that effort, I chose to start from zero and not upload any prior accumulation of email addresses into. I came to Substack because I wanted the network effects, and so I wanted to understand what it meant to grow organically. Here’s what that has looked like for me.

The feed is the customer-acquisition engine for the newsletter.

Without Notes, recommendations, follows, profiles, search, Lives, video, clips, and algorithmic distribution, Substack is just an email service with payment processing and fails to deliver the mechanisms that have created so much opportunity for its community of creators.

In order for our collective opportunity to grow, Substack needs a large geography to direct their machinery against. In the absence of more consuming eyeballs and creating platform members, Substack fails, our new businesses fail, and we move on to the next thing.

Now let’s talk about the difference between social media and interesting media…

TikTok’s real innovation was not short video

TikTok changed media distribution because it reduced the importance of the social graph. Older social platforms began by asking:

• Who are your friends?

• Whom do you follow?

• Which accounts have you already chosen?

TikTok began with a different question:

What are you likely to care about?

Its For You system learns from behavior such as what a person watches, finishes, skips, likes, shares, searches for, or marks as uninteresting.

TikTok has said that follower count and a creator’s previous viral success are not direct ranking factors in its recommendation system. The objective is to match an individual item with people likely to value it, including people who have never encountered its creator before. TikTok’s explanation is here.

This is how they invented the Interest Graph that has become the underpinning for what all social algorithms are being engineered to achieve across all the social media platforms.

The old feed asked:

What did the people you follow publish?

The new feed asks:

Of everything published, what are you most likely to care about?

The rest of the industry has followed.

YouTube says recommendations generate more viewing than subscriptions or search.

Meta explicitly describes Facebook’s future as “social discovery” and uses AI to recommend content from accounts people do not follow.

LinkedIn’s researchers describe retrieving out-of-network posts based on a member’s topical interests, narrowing hundreds of millions of possible posts to roughly 2,000 candidates.

This is not an isolated product trend.

It is the migration from social media to interest media.

Substack must find the voice

Substack should not simply copy TikTok. TikTok is primarily trying to identify the next video someone will watch. Substack must answer a more valuable question:

Which creator, publication, perspective, or body of work might this reader value repeatedly?

TikTok finds a single video, a single, often disposable piece of content.

Substack must find the voices that their platform member would most likely want to consume multiple pieces of content from.

That requires an interest graph built for ideas and expertise.

It should understand what a creator writes and speaks about across an entire archive. It should understand the subjects readers identify in their profiles, the publications they return to, the Notes they save, the Lives they attend, the writers they follow, the subscriptions they purchase, and the subscriptions they retain.

It should then connect and direct those signals to drive a more valuable content consumption experience for the user, and it should provide the creator on the other side with as much clarity as possible for what pieces of content drove that new post engagement, follow choice, subscription click, and paid commitment.

A reader who repeatedly engages with organizational psychology, AI, leadership, and company operations should be able to discover a new writer working at that exact intersection, even if the writer has 300 followers rather than 300,000.

That is the promise of the interest graph.

More creators entering the platform does increase competition.

But it also gives the platform more ideas, more expertise, more behavior, and more potential matches from which to learn.

Growth creates crowding, yes, but it also creates richer discovery.

Following should be a signal, not an entitlement

The critics would be right if Substack’s future were simply a larger follower hierarchy. If the biggest accounts automatically receive the most distribution, early incumbents will own the feed.

Follower counts will become status markers.

Frequency will defeat substance.

Outrage will defeat expertise.

New creators will technically be allowed to publish but structurally prevented from being discovered.

That is not the social platform Substack should build.

Substack describes following as a lighter-weight way for readers to get to know a creator before committing to a subscription. That is exactly how following should work. A follow should help the platform understand that a particular reader may value a particular creator.

A creator’s total follower count should not grant permanent ownership of everyone else’s attention.

Following should be an input into relevance, not a deed to the feed.

Each Note, essay, podcast, clip, and Live should still have to earn distribution by being useful to the people receiving it.

I find that many of my most successful notes drive plenty of engagement and new followers, but not any new subscribers. Here’s an illustration:

In terms of engagement, this was my best-performing note from the last seven days. It did not drive a single new subscriber, but it did add quite new followers. I wouldn’t expect this note to have driven new subscribers. It was simply a funny picture, but it did introduce my work to 1,277 new Substackers via the feed. As the platform grows, that 1,277 will become 12,777, the number of followers a note like this drives will be greater, and ultimately the number of conversions from followers to free subscribers, and yes, to paid, will grow. That only happens if Substack continues to mature into a full-fledged social media platform. My biggest growth spurt of new free and paid subscribers (the period that made me a best seller) started with the outperforming success of two notes that led to 27 of my articles 5x-ing their total views over a period of several weeks.

Substack should normalize performance for creator size, test work with appropriately matched audiences, and reserve meaningful discovery capacity for smaller and unfamiliar publications. The point is not to penalize large creators. It is to prevent accumulated popularity from replacing present-tense relevance.

Optimize for relationships, not reactions

Substack also has an economic advantage that TikTok, Instagram, and X do not.

Its subscription-centered business model benefits when a reader establishes a durable financial relationship with a creator. (Substack)

Its recommendation system should therefore optimize beyond likes, comments, restacks, and time spent.

It should ask:

• Did the reader visit the publication?

• Did they follow?

• Did they become a free subscriber?

• Did they become a paid subscriber?

• Did they remain subscribed after 30, 90, or 365 days?

• Did they return to the archive or attend another Live?

A Note that receives 20,000 likes but produces almost no subscribers may be less valuable to the Substack ecosystem than one that receives 300 likes, generates 75 free subscribers, creates 12 paid subscribers, and retains ten of them a year later.

To create the media business all of us are talking about, first, we need platform activity, interest from a growing audience, and a real distribution mechanism underpinning our content.

Notes is customer-acquisition work when it introduces a creator to relevant readers and turns some of that attention into relationships the creator can retain.

It becomes uncompensated platform labor when it merely keeps the feed active, improves Substack’s engagement numbers, and leaves the creator with little more than another vanity metric inside the platform.

This is so critically important. If you expect the Substack platform to deliver new free subscribers, much less paid subscribers, to your publication each week, then all of those users need to have a feed they can’t wait to get back to as their reason for opening the application and potentially discovering your work.

Creators should demand better attribution, not less discovery.

They should be able to see which Notes generate profile visits, which follows become subscriptions, which Lives create paid conversions, which topics introduce them to new interest communities, and which acquisition channels produce durable readers rather than rapid churn.

The quality of the analytics is, in my opinion, maybe the biggest opportunity the Substack team has to increase value for their creators, keep them on their platform long term, and continue to attract the new and best creator names on the planet. Substack needs to show their creators how to better monetize their publications from every possible angle in their sales funnel.

Do not ask Substack to remain small

Substack was never going to remain a quiet neighborhood of early writers, unusually high conversion rates, and limited competition.

Nor should it.

Creators should not ask Substack to freeze the advantages of its earliest years.

They should ask it to become the best interest-based media network in the world.

That means a platform where an unknown writer can reach a reader because the work is relevant, not because the writer is already famous.

It means using Notes, Lives, recommendations, video, and following to create subscriptions rather than replace them.

It means treating follower count as a clue, not a governing currency.

And it means converting algorithmic discovery into direct, durable relationships that creators can actually build businesses around.

Substack becoming a giant social platform is not the threat.

Becoming the wrong kind of giant social platform is.

The feed is not the enemy.

The wrong algorithm is.

- j -

Do you want some help building your newsletter or creator business?

Become an Operating Founder, receive a 60-minute review of your content, business, and strategy, and join my weekly Operating Founder Office Hours, where I answer any and all of your questions and share what I’m working on and learning from building my business.

Here’s everything you need to know about the business I have built so far.

Become a Paid Subscriber Today!

About John

John Brewton documents the history and future of operating companies at Operating by John Brewton. He is a graduate of Harvard University and began his career as a PhD student in economics at the University of Chicago. After selling his family’s B2B industrial distribution company in 2021, he has been helping business owners, founders, and investors optimize their operations ever since. He is the founder of Operating by John Brewton and conducts advisory and consulting work for global companies, asking the question: What is the future of companies?

Subscribe now

Appendix: Sources and evidence

The current creator debate

1. Scott Carney, “The Real Reason that Substack Is Collapsing”

2. Taylor Lorenz on follower growth and falling paid subscriptions

Substack’s scale and discovery network

3. Substack network growth statistics

4. Recommendations and the app driving subscriptions

5. Exporting a Substack email list

6. How following works on Substack

7. Substack’s description of its creator business model

The shift toward interest-based distribution

12. How TikTok recommends videos

13. YouTube’s recommendation system

14. Meta’s vision of Facebook as social discovery

15. LinkedIn’s out-of-network, topical-interest retrieval system

Evidence limitations

Substack has not publicly disclosed comprehensive platform-wide churn data, detailed cohort conversion rates, or the full ranking logic governing Notes and recommended posts. The latest official paid-subscription milestone I could verify is the five-million announcement from March 2025. There is also no public evidence resolving whether Substack’s recommendation system systematically favors or disadvantages paywalled posts.

TL → DR

• 400 hours of coaching Substack founders in 2026 surfaced 5 recurring problems: positioning, packaging, pricing, distribution, and AI.

• Underneath all 5 sits 1 cause. A decision the founder will not make. The writing is never the constraint.

• Positioning: pick the 1 problem you own. Companies that define a category capture roughly 76 percent of its value.

• Packaging: productize the expertise. Service businesses trade at around 1 to 2 multiples, product businesses at around 6 to 8.

• Pricing: attention is not revenue. Price to the reader’s outcome and put an end date on free.

• Distribution: the email list is the only audience you own. Grow on 1 channel and convert to the list.

• AI: the machine makes you roughly 40 percent faster and measurably more alike. Decide task by task what you draft and what only you write.

What you get

One year in the Operating Founder Community: A working room of founders, operators, and writers building real businesses. You bring the problem. The room and I will help you decide.

The weekly Q&A Masterclass: 52 live sessions, 60 minutes each, one a week for a year. Bring one problem. Leave with the decision and the next action. That is 52 hours of live coaching over the course of the year.

Three golden tickets: $1,000 off any course you choose in the next 12 months. Three tickets, $3,000 in course credit, yours to spend on the work that moves you.

Become An Operating Founder

What did 400 hours of coaching Substack founders reveal?

I spent 400 hours this year coaching Substack founders. Different niches, different subscriber list sizes, different decades of experience. I expected 400 hours of variety. I uncovered 5 clearly repeating problems.

Underneath the 5 challenges, sat 1 clear decision the founder needed to make. Every founder I worked with had real expertise and 1 unmade decision. There were no talent gaps nor any unwillingness to put forth the requisite effort.

But there was always a clear choice that needed to be made.

Here are the 5 conundrums these problems present.

How do I pick a niche for my Substack when I have broad experience?

The most common problem is broad experience with no chosen niche. When you can write about anything, you tell the reader nothing, and the subscriber who cannot name the 1 thing you own will not remember you exist.

Broad experience and diversity of storytelling belong in the work. Narrowness belongs in the positioning. Companies that define a category capture roughly 76 percent of its value. The narrow choice is where the value lives. Pick the 1 problem you solve better than anyone in your reach, and stop writing around it or expanding into spaces that confuse your market. This is critical when you’re at the starting line.

Go deeper:

• The Difference Between a First Mover and a Category Creator (Harvard Business Review, 2019). The category-king research behind the 76 percent figure. Category creators capture the dominant share of a category’s market value.

• Why It Pays to Be a Category Creator (Harvard Business Review, 2013). Category creators in the study grew revenue and market cap far faster than their industries. The business case for owning 1 problem.

Download The Five Problems Worksheet

How do I turn my expertise into a product instead of selling hours?

A newsletter that only sells hours caps the business at the size of your calendar. A client book concentrated in a handful of names looks like success, but can break when one disengagement email arrives.

Service businesses trade at around 1-to-2 times multiples. Product businesses trade at around 6 to 8. Crossing that chasm is about designing how your business makes money, not talent. Package the expertise once and sell it many times. If you keep the premium with 1-to-1 work, raise the price, or choose to go 1-to-many via communities and cohorts.

Go deeper:

• Putting Products into Services (Harvard Business Review, 2016). The definitive playbook for converting labor-based expertise into productized offerings, with named cases from professional services firms.

• What Professional Service Firms Must Do to Thrive (Harvard Business Review, 2021). A framework for which client work to keep premium and which to systematize.

Download The Five Problems Worksheet

1. How I Built a $200,000 Newsletter Business From a Substack Nobody Was Reading

2. The Five Moves That Turned My Substack Into a $200,000 Business

3. The Operating Project: Most Coaching Ends With Notes. This Ends With a Machine.

4. How to Build Your Own AI Employees: A Simple 2026 Guide

Subscribe now

When should I start charging for my newsletter, and how do I price it?

Subscribers are a leading indicator. Revenue is a separate transaction that occurs only when you build the offer and set the price. My best advice is to begin monetizing the moment you are clear on the offer you are making. Market response, or lack thereof, is a terrific indicator against which to make forward-looking decisions

Free is defensible as a runway and indefensible as a permanent state. Price to the reader’s outcome, not your effort, and put a date on the day the free period ends.

Go deeper:

• A Quick Guide to Value-Based Pricing (Harvard Business Review, 2016). The mechanics of pricing to the buyer’s outcome and next-best alternative instead of your effort.

• The Creator Economy Needs a Middle Class (Li Jin, Harvard Business Review, 2020). The structural analysis of why audience size and creator income diverge, with platform-level data.

Download The Five Problems Worksheet

Should I post on every platform or focus on building my email list?

Founders posting across 4 platforms watch their reach vanish the week they step away, or never see it show up anywhere. Moreover, platform reach is borrowed and can be revoked without notice.

This is why Substack matters. The email list is the asset you own. The post is not the asset. The list is. Grow on 1-to-2 primary channels strictly with the aim of growing your email and subscriber lists, convert a subset to the list. That list of people is the market you sell to.

Go deeper:

• Pipelines, Platforms, and the New Rules of Strategy (Harvard Business Review, 2016). The canonical explanation of who controls demand on a platform and who captures the margin.

• 1,000 True Fans? Try 100 (Li Jin, Andreessen Horowitz, 2020). The economic argument that a small directly-owned audience paying real prices beats a large rented one.

Download The Five Problems Worksheet

Will using AI make my writing sound like everyone else’s?

Founders adopt the machine and fear it flattens their voice. They are right about the default and wrong about the cause. Generative AI made writers roughly 40% faster and 18% better and made their work measurably more alike.

The uniformity comes from how you prompt, not from a law of the tool. Decide task by task what the machine drafts and what only you write.

Protect your judgment. Keep your voice.

Hand over the production speed to the models.

Grabe a free seat in my writing with AI masterclass, happening next Tuesday!

Grab Your Free Masterclass Seat

Go deeper:

• Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence (Noy and Zhang, Science, 2023). The randomized MIT experiment behind the numbers: 40 percent faster, 18 percent higher quality.

• Generative AI Enhances Individual Creativity but Reduces the Collective Diversity of Novel Content (Doshi and Hauser, Science Advances, 2024). The homogenization evidence: AI-assisted work rated more creative individually and measurably more alike collectively.

What is your one problem underneath all five?

Positioning, packaging, pricing, distribution, and the machine are not 5 skills to study. There are 5 decisions to make, and each one takes an afternoon, not a year.

Most of the founders I coached were not overwhelmed.

They were stalling and calling it overwhelm.

It’s time to move beyond overwhelm.

Pick 1 of the 5 and make the choice this week.

What’s your pick?

- j -

Every founder I coach has real expertise and one thing missing. The Operating Founder Community is the room where you make them with me every week for a year.

What you get

One year in the Operating Founder Community. A working room of founders, operators, and writers building real businesses. You bring the problem. The room and I help you decide.

The weekly Q&A Masterclass. 52 live sessions, 60 minutes each, one a week for a year. Bring one problem. Leave with the decision and the next action. That is 52 hours of live coaching over the year.

Three golden tickets. $1,000 off any course you choose in the next 12 months. Three tickets, $3,000 in course credit, yours to spend on the work that moves you.

The math

52 hours of live coaching. $3,000 in course credit. One year to make the five decisions that turn expertise into a business. Most founders spend a year circling those choices alone. You will spend it making them in a room built for it.

Who is this for?

Founders and operators with real expertise and no clear next move. Writers and creators building on Substack who have the audience and not the offer. Anyone tired of studying the problem and ready to move forward.

Here’s a worksheet I put together to help. Become a Founding member of Operating by John Brewton to join our weekly Masterclass Office Hours, where we unpack and solve problems across members’ businesses.

Become An Operating Founder

Download The Five Problems Worksheet

Frequently asked questions

How many subscribers do I need before I start charging?

Fewer than you think, and the count is the wrong variable. Revenue happens when you build an offer and name a price, not when a subscriber threshold trips. 1 creator with 2.6 million followers sold fewer than 36 units because the audience was never converted to a list of buyers.

Will niching down shrink my audience?

It shrinks the topic, not the audience. Companies that define a category capture roughly 76 percent of its value, and the same concentration shows up in newsletters. The reader who can name the 1 thing you own subscribes, refers, and pays. The reader who cannot does none of the 3.

Should I drop my consulting clients to build products?

No. Keep the premium 1-to-1 work and raise its price. The productized offer removes the calendar cap, and the higher consulting rate filters the client book down to engagements that justify the hours.

Which platform should I grow on?

Pick 1. The channel where your reader already spends attention and where you can publish consistently. The platform’s only job is to feed the email list, because the list is the asset you own and the reach you rent can be revoked without notice.

Is it a mistake to use AI to write my newsletter?

The mistake is using it without a decision about what it touches. The research shows roughly 40 percent speed gains, 18 percent quality gains, and measurable convergence toward sameness. Assign the machine the drafts, the summaries, and the formatting. Keep the judgment and the voice.

What should I do first?

Pick the 1 problem of the 5 that is costing you the most right now and make that decision this week. Each of the 5 is a choice, not a curriculum. An afternoon of deciding beats a quarter of studying.

John Brewton documents the history and future of operating companies at Operating by John Brewton. He is a graduate of Harvard University and began his career as a PhD student in economics at the University of Chicago. After selling his family’s B2B industrial distribution company in 2021, he has been helping business owners, founders, and investors optimize their operations ever since. He is the founder of 6A East Partners, a research and advisory firm asking the question: What is the future of companies

TL → DR

• Two and a half years ago, a college intern on a consulting job I was working with zero hours in Excel and a $20 ChatGPT subscription completed work in 24 hours that I had blocked 25 to 35 hours to do myself, work built on skills I spent 20 years sharpening.

• Being made irrelevant took 24 hours, and I never got a vote. Deciding to become relevant a…

Read more

A few days ago, I published the full account of how I built my newsletter business over the last 6 months: How I Built a $200,000 Newsletter Business From a Substack Nobody Was Reading. It reached more readers than anything I have written, and the replies asked one question more than any other: What are the actual steps?

This is the answer.

The full piec…

Read more

TL → DR: How did a newsletter nobody read become a $200,000 business?

• I started 2026 with 1,014 subscribers and about $2,200 a year. By June 1st I had 5,861 subscribers and 266 paying members.

• The business will generate +$200,000 this yeara across six streams: Advisory, Coaching, the Operating Founder program, Courses, Subscriptions, and a Community.

• The i…

Read more