Operating by John Brewton

Pick Your Business Model Like Your Life Depends On It.

John Brewton — Fri, 18 Sep 2026 10:24:55 GMT

Hello Operators!

Most founders do not really choose a business model. They copy one.

They watch someone build a course, so they start outlining a course. They see a consultant with five retainers, so they decide they need retainers. Someone launches a cohort. Someone else sells a $10,000 advisory package. Someone licenses their IP.

What was visible becomes what feels possible.

The problem is that a business model is not a trend, a personality type, or an identity. It is the delivery system for your expertise. And the right delivery system has to fit two things at the same time: the shape of what you know and the shape of the life you actually want to live.

Today’s free Oper(AI)te drop is built to sort those two things.

FREE DROP 05: The Model Sorter

Download Today's Drop

This is not really a workbook you read. Upload the PDF into Claude or ChatGPT. Paste one sentence describing what you do. The workbook takes over from there.

You give it one capability and answer a short interview. It scores six business models against your constraints, shows you the grid before it sorts, lets you correct anything you disagree with, and then produces a one-page PDF with the result.

The workbook estimates the conversation at about seven minutes. Go get to work.

Subscribe now

Your Expertise Is Only Half the Business Model

The first four Oper(AI)te drops were largely about evidence.

The Calendar Autopsy looked at what your work has actually required you to do.

The Receipt Finder turned an accomplishment into a claim you can defend.

The Compliment Decoder looked for the language other people already use to describe your value.

The Free Advice Audit looked for expertise you may be giving away because it feels too easy to you.

If you have been running them, you are beginning to accumulate something useful: a picture of what you are unusually good at and evidence that you have actually done it.

But knowing what you could sell does not tell you how you should sell it.

That is a different decision.

And founders routinely make it without considering the constraints that will eventually determine whether they love or hate the company they built.

The Workbook Output

The final output is deliberately small, clear, crisp and clean.

Your top two models appear with their scores out of four and the constraint that separated them. You get the one model the Sorter ruled out and why it made that choice.

Then you get something I find more useful than another list of advantages and disadvantages:

The characteristic failure mode of your winning model.

Productized service fails when scope creeps until every engagement becomes custom again, but the price never moves with it.
Retainer or advisory fails when the founder slowly becomes the client’s employee without the salary, equity, or exit.
Licensing fails when nobody wants to buy a method they have never watched the founder run, so the founder keeps running it.
A cohort can turn your calendar into a permanent launch treadmill.
A self-serve course can quietly turn the founder into a full-time marketer.
The diagnostic can get given away in an attempt to win an implementation that never closes.

And then the workbook ends with one clean, clear, crisp line:

Your model is X. The one you were about to build is Y.

Download Today's Drop

Where the Free Drops End and Oper(AI)te: The Hidden Offer Begins

The Model Sorter can help you answer one important question:

What shape should this business take?

It cannot build the rest of the business for you.

That is the distinction between these free drops and the Oper(AI)te: The Hidden Offer cohort Wessal Khader and I are building.

The drops isolate one problem and give you one useful output. The October program connects the evidence, expertise, model, market, pricing, offer, sales page, and selling system into one operating system.

Most experienced founders, operators, executives, consultants, and advisors do not have an expertise problem. They have a problem seeing, packaging, pricing, and selling what they already know.

That is our entire premise behind Oper(AI)te: The Hidden Offer.

Take Your Seat

The offer someone will pay you for is probably not something you need to invent from scratch. It is much more likely to be sitting inside the work you have already done, the results you have already produced, the problems people already bring you, and the expertise you have accumulated so gradually that you no longer recognize how unusual it is.

From October 1 through October 29, we are building that system live across six sessions. You build nine AI agents on our IP and keep them: the Everything File, Superpower Scan, Fit Test, Client Evidence Pull, Market Map, Intersection Engine, Price Evidence Sheet, Page Builder and Language Audit, and the Selling Agent with its Daily 15 rhythm.

The goal is to exit October with your offer ready to launch as this year ends and 2027 awaits!

Here’s what our cohort members will leave with:

Your evidence gathered.
Your expertise named.
Your model selected.
Your market researched.
Your price supported.
Your sales page published.
Your first buyer conversations underway.
And a 90-day operating rhythm for continuing the work after the cohort ends.

See everything inside Oper(AI)te: The Hidden Offer →

Doors close September 30.

The live cohort runs October 1–29.

I am keeping all 16 free Oper(AI)te drops together here and updating the library as each one goes live.

Because your ideal offer is probably not something you need to dream up. And the business model probably should not be something you copy.

Both should come from the evidence of the work you’ve already done, been paid for, and delivered immense value to your companies, teams, colleagues, and customers with already.

Take Your Seat

Build Your Substack to Sell High Ticket Consulting, Coaching, Cohort and Community Offers, Not $10 Subscriptions

John Brewton — Wed, 16 Sep 2026 16:09:38 GMT

Available for iOS and Android

My Biggest Give Away Yet: The Oper(AI)te (Hidden Offer) Drop Library

John Brewton — Wed, 16 Sep 2026 11:17:31 GMT

Subscribe now

For the next two weeks, I am giving away one of the operating tools Wessal Khader and I have been building around Oper(AI)te: The Hidden Offer, every morning.

Each drop is a small AI-powered operating system built to take something you already have (your calendar, an old client result, emails people have sent you, your pricing, a competitor’s website) and turn it into a meaningful insight or asset that will help you to build a better offer and a better business to operate.

A piece of evidence.
A clearer understanding of what you are unusually good at.
A pricing insight.
A case study.
A list of real prospects.
A better way to explain what you sell.

My goal is to give you something valuable enough to change how you see the expertise you already own. I created this article to keep every drop in one place. I will update it each morning as the next one goes live.

Bookmark it. Save it. Send it to somebody who has spent a career becoming excellent at something and still has not figured out how to turn that expertise into an offer.

How to Use the Drops

1. Download the workbook.

2. Upload it into Claude or ChatGPT.

3. Tell the model to begin the workbook.

Then follow the conversation.

The workbook will ask the questions, analyze the information, and produce the output. Some drops work entirely from information you provide. Others use your files, connected tools, live research, or material you have already created.

Do not overthink them.

Run them on your own intuition and instinct.

The value is not in understanding what the workbook does. The value is in seeing what it discovers about you.

What These Drops Are Actually Building

There is a larger reason Wessal and I built these 16 workbooks.

Most experienced founders, operators, executives, consultants, and advisors do not have an expertise problem. They have a problem seeing, packaging, pricing, and selling what they already know.

That is our entire premise behind Oper(AI)te: The Hidden Offer.

Take Your Seat

These 16 drops are designed to help you start finding it.

Over the next two weeks, we are going to move through a very deliberate sequence:

Find the proof.
Identify the expertise.
Understand the buyer.
Check the market.
Establish the price.
Articulate the offer.
Build the sales assets.
Start selling.

But there is an important distinction.

The drops give you pieces. The course builds the system.

The free drops take one problem, one input, and one pass and give you something useful back. Inside Oper(AI)te, we connect those discoveries.

Instead of examining one quarter of your calendar, you build an Everything File that lets AI understand the body of work you have built across your career.
Instead of identifying a single piece of valuable expertise, the Superpower Scan and Fit Test help determine what you should actually build a business around and which business model fits it.
Instead of focusing on a single competitor or a single pricing question, the Market Map and Price Evidence Sheet examine what buyers are paying right now and what your evidence can support.
Instead of writing a better sentence, the Intersection Engine, Page Builder, and Language Audit turn the evidence into an offer and put it onto an actual sales page.
And instead of finding five prospects once, the Selling Agent and Daily 15 are built to help make selling part of how the business operates every day.

There are 9 AI agents in the complete system. You build with them live, and you keep them. By the end of the October program, the goal is not for you to simply understand more about offers.

The goal is for you to leave holding one.

Your evidence gathered. Your superpower named. Your business model selected. Your market understood. Your offer stated clearly. Your price supported by evidence. Your sales page live. Your selling system operating. Your first buyer conversations underway.

But you do not need to take my word for any of this yet.

Run the free tools first.

If they show you nothing useful, keep the workbooks. If they begin uncovering things you have spent years overlooking, pay attention.

Those discoveries are not random.

They are your evidence.

Eventually the question becomes:

What happens if you connect all of it?

For now, let’s start with the work.

AVAILABLE NOW

FREE DROP 1

The Calendar Autopsy

Your title tells people what your job was called. Your calendar shows what you were actually paid to do.

Your résumé is aspirational. Your LinkedIn profile is curated.

Your calendar is harder to fake.

Open 90 days of it and you will find a surprisingly honest record of what an organization actually depended on you to do.

The Calendar Autopsy analyzes the problems you repeatedly spent time solving, the capabilities that kept pulling you into rooms, the work consuming the largest share of your time, and which of those capabilities sat closest to revenue.

It then asks a much more interesting question:

Does the work your title says you do match the work your calendar says people actually needed you to do?

For many experienced operators, the answer is no.

That gap matters because the work people repeatedly pull you into is often much closer to your monetizable expertise than the generic description sitting at the top of your LinkedIn profile.

What this drop finds

The capabilities people repeatedly relied on you to provide and the evidence of what your real job actually became.

What it doesn’t know yet

Which of those capabilities a market would pay for, what form the offer should take, or which piece of your experience should become the business.

Download The Calendar Autopsy →

One note: this workbook works best when the model can access your calendar or when you provide the last 90 days of calendar data.

FREE DROP 2

The Receipt Finder

Turn “I helped them improve the business” into a claim a buyer can actually evaluate.

Experienced people are often surprisingly bad at describing what they have accomplished.

We say things like:

I helped grow the company.

I improved operations.

I helped the team scale.

I worked on the transformation.

All of those statements may be true. Almost none gives a buyer enough information to understand what actually happened.

The Receipt Finder starts with one accomplishment and forces you to establish four things:

What was true before you arrived?

What was true afterward?

Over what period did the change happen?

What role did you actually play in creating it?

Then it goes one step further. The workbook builds multiple versions of your claim based on what your evidence actually supports.

A conservative version. An accurate version. And the strongest version you can currently defend.

That distinction matters.

Because I do not think the answer to selling expertise is making enormous claims. I think the answer is becoming unusually precise about the value you have actually created.

You leave with language you can use in a buyer conversation and a clear list of the evidence you would need before making a stronger claim.

What this drop finds

The measurable result hiding underneath a vague accomplishment and the strongest version of that result your current evidence supports.

What it doesn’t know yet

Which of your dozens of career accomplishments matters most to the buyer you eventually decide to serve.

Download The Receipt Finder →

FREE DROP 3

The Compliment Decoder

Other people may already have written your positioning for you.

One of the hardest transitions in building a coaching, consulting, advisory, or expertise-based business is becoming comfortable selling yourself as the service.

We tend to believe we need a library of polished testimonials and professionally produced case studies before we have enough proof. But much of the proof already exists.

It is sitting inside old emails from clients, thank-you notes from colleagues, LinkedIn recommendations, performance reviews, referral introductions, Slack messages, texts, and sentences people have written for years about what you do unusually well.

The Compliment Decoder turns that unstructured feedback into evidence.

Give it 10–20 pieces of real feedback and it begins looking for repetition. Which capabilities keep appearing? What do people actually rely on you for? What language do they naturally use when describing the value you create?

And there is an important distinction buried inside that analysis:

What people admire about you is not always what they rely on you for.

The workbook separates those two things. Then it looks for something even more useful: the blind spot.

The thing other people consistently recognize in you that barely appears in the way you describe yourself.

That can be uncomfortable. It can also be incredibly useful.

Because suddenly your positioning is no longer based entirely on what you hope you are good at. It is grounded in what other people have been telling you for years.

What this drop finds

The recurring themes and exact language other people already use when they describe the value you create.

What it doesn’t know yet

Whether that valued capability maps to a real market, a specific buyer, a viable business model, or a problem somebody will pay to solve.

Download The Compliment Decoder →

FREE DROP 4

The Free Advice Audit

The thing you give away in 20 minutes may take somebody else six months to learn.

There are probably a handful of questions people ask you constantly.

“Can you take a quick look at this?”

“What would you do here?”

“Can I get your opinion on my pricing?”

“Who would you hire?”

“Can I pick your brain for 20 minutes?”

And because answering feels easy, you answer. Usually for free.

That ease is exactly the problem The Free Advice Audit is designed to expose.

Something that takes you 20 minutes may take the person asking you weeks or months to solve without you. That difference is not trivial.

It is the accumulated value of your experience, pattern recognition, mistakes, judgment, relationships, and expertise. And in some cases, it may be the beginning of an offer.

The workbook asks you to identify the five things people most frequently come to you for and then compares them across three dimensions:

Effort asymmetry: How long does solving this take you compared with them?

Consequence: What happens if they make the wrong decision?

Frequency: How often does this problem occur?

The workbook ranks all five. Then it identifies the strongest candidate and gives it a very specific label:

This is a product, not a favor.

But my favorite part is the final number.

The workbook looks at your own hourly value and estimates how much of this expertise you gave away during the last 12 months. It puts a dollar figure on the advice.

That number may sting a little.

I hope it does.

Because one of the easiest things in the world to undervalue is knowledge you acquired so slowly that you forgot how difficult it was to learn.

What this drop finds

The piece of expertise with the greatest gap between how easy it feels to you and how valuable it may be to somebody else.

What it doesn’t know yet

Whether anyone will buy it, which buyer needs it most, what form it should take, how it should be positioned, or what that buyer will pay.

Download The Free Advice Audit →

Your Operating Inventory After Four Drops

This is the part I want you to notice.

If you have actually run the first four workbooks, you no longer have four PDFs. You have real, unique evidence.

You have a picture of what your work has actually required you to do. You have at least one career result translated into a defensible claim.

You have language other people repeatedly use to describe the value you create. And you have a ranked list of expertise people already seek from you, including s omething you may be giving away that deserves to become a product.

Four days ago, much of that information already existed. But it was scattered between your memory, calendar, inbox, client work, and other people’s words.

Now you can see it.

And this is where the next phase begins, because identifying valuable expertise is not enough. You still have to decide what to build around it.

Take Your Seat

COMING NEXT…

FREE DROP 5 · SEPTEMBER 17

The Model Sorter

Most people choose a business model by imitation. They see somebody selling a course, coaching program, retainer, advisory engagement, or productized service and decide that must be the model they need too.

The Model Sorter starts somewhere else: with the shape of your expertise and the shape of your life.

It looks at your available time, appetite for synchronous work, how dependent the value is on your personal judgment, whether the customer problem repeats, and other constraints. Then it compares different business models and tells you not only which models fit, but which one you should probably rule out.

FREE DROP 6 · SEPTEMBER 18

The Buyer’s Real Words

You describe the customer’s problem using your vocabulary. Your buyer describes it using theirs.

Those two languages often barely overlap.

The Buyer’s Real Words researches the places your customer talks naturally and pulls out the language they use to describe the problem, what they have already tried, and why those attempts failed.

You leave with language you can use in positioning and sales because it came from the market instead of your imagination.

FREE DROP 7 · SEPTEMBER 19

The 90-Second Teardown

You have probably looked at competitor websites. You may never have actually read one.

Give this workbook a competitor URL and it breaks the page into the promise, buyer, proof, pricing logic, objection handling, and something most competitive analyses ignore:

What the competitor never talks about.

That silence may be where your differentiation is hiding.

FREE DROP 8 · SEPTEMBER 20

The Price Spread

Three people can appear to sell roughly the same work for $2,000, $15,000, and $80,000. The work is rarely 40 times different.

Something else is creating the spread.

The Price Spread researches real providers across different pricing tiers and begins identifying what the buyer is actually paying more for: risk transfer, access, proof, buyer type, outcome ownership, contract structure, category framing, or some combination of them.

The question changes from “How can they charge that?” to “What would have to become true for me to occupy that tier?”

FREE DROP 9 · SEPTEMBER 21

The Underpricing Calculator

Most experts price the work. Buyers value the outcome.

Those are two entirely different numbers.

The Underpricing Calculator puts your current price beside the financial consequence of the outcome you create and calculates the relationship between the two. Not to manufacture the biggest number possible, but to find the number your existing evidence can actually support.

FREE DROP 10 · SEPTEMBER 22

The Generic Detector

You cannot hear your own filler. Most of us have bios, About pages, and LinkedIn profiles full of phrases thousands of competitors could use word for word.

The Generic Detector applies a simple test:

Could a direct competitor say this exact sentence?

If the answer is yes, the sentence is carrying almost no information. The workbook flags the generic language, calculates how much of your writing is truly specific to you, and begins replacing the filler with evidence from your actual work.

FREE DROP 11 · SEPTEMBER 23

The One-Sentence Compressor

If it takes three paragraphs to explain what you sell, you probably do not have an offer yet. You have a description.

The One-Sentence Compressor takes your current explanation and creates several structurally different versions, then tests them for specificity, buyer recognition, clarity, and one criterion I especially like:

Could somebody else accurately explain what you do after hearing it once?

Because an offer that cannot travel without you is much harder to sell.

FREE DROP 12 · SEPTEMBER 24

The Proof Page

Most experienced operators already have work that could help close their next customer. Nobody can see it.

The Proof Page takes one past project and turns it into a clean, client-facing case study built around the situation, the constraint, what you did, the defensible result, and what that result demonstrates you can do again.

Unlike most of the drops, this one does not only give you an insight.

It gives you something you can send to a buyer that day.

FREE DROP 13 · SEPTEMBER 25

The Objection Pre-Mortem

You are going to hear many of the same objections repeatedly once you start selling an offer. You can wait for the next 20 conversations to discover them, or you can prepare now.

The Objection Pre-Mortem starts with your actual offer and price, identifies the objections most likely to appear, separates the surface objection from the underlying concern, and determines what evidence would answer it.

You leave with something even more useful than an objection list:

An inventory of the proof your business still does not have.

FREE DROP 14 · SEPTEMBER 26

The First Five

“Find me leads” is a terrible instruction.

“Who specifically appears to have this problem right now, and what is the evidence?” is much better.

The First Five starts with your offer and buyer and looks for five real companies or people with a reason the problem may be live now: a new hire, funding round, leadership change, job posting, launch, complaint, regulatory shift, or another observable trigger.

Five prospects. Five reasons why now.

FREE DROP 15 · SEPTEMBER 27

One Day of the Daily 15

This drop is not really another diagnostic. It is a protocol.

One post. One new conversation. One follow-up. Fifteen minutes.

You use the work produced by the previous drops to execute one complete day of a simple selling rhythm.

The interesting part is not whether you can do it once.

The interesting question is what happens when the business can help you do it every day.

FREE DROP 16 · SEPTEMBER 28

The Headline Stress Test

You can spend months developing an offer and four minutes writing the headline that introduces it.

The final numbered drop takes your offer and produces 12 different headline structures, then scores them for specificity, buyer recognition, clarity, and how much meaningful information appears in the first few words.

You finish with page-ready language.

But by then, the headline will almost be beside the point.

Because look at what you will have accumulated.

What You Will Hold After 16 Drops

You will have a much clearer description of the work people have actually relied on you to do, along with a defensible career result and a bank of language other people use to describe your value. You will also have evidence of expertise you may have been giving away for years without realizing how commercially valuable it could be.

You will have a business model that better matches how you actually want to work. You will understand more of your buyer’s language, your competitive environment, and what creates pricing power in your category.

You will have a better view of the relationship between your price and the value you create, more specific positioning, and a one-sentence offer. You will also have a finished case study, a clearer view of your likely objections, and an inventory of the evidence required to answer them.

You will have five real prospects with reasons to contact them now. You will have a simple selling rhythm and page-ready language to put around the offer.

Sixteen useful outputs, all built from your career, your evidence, your market, and your buyers.

And then we arrive at the most important part.

What you still will not have is anything that runs.

You will have performed the analysis and produced the individual pieces. But you will not yet have one AI business brain that holds that evidence together, updates it, and uses it across the rest of the business.

You will not have agents continually reading that evidence or a Market Map designed to be rerun as the category changes. You will not have a permanent Price Evidence Sheet connecting your market, proof, and pricing decisions.

You may have the pieces of an offer, but not yet the complete offer assembled from the intersection of your proof and actual market demand. You may have stronger copy, but not necessarily the sales page published and working as a conversion surface.

And you will not have a Selling Agent waking up with you every morning and turning all of that thinking into activity.

That difference is Oper(AI)te: The Hidden Offer.

The drops are designed to let you experience pieces of what Wessal and I have been building. The October cohort is where we assemble those pieces into the operating system.

From October 1–29, we build it live.

You will build the Everything File, the Superpower Scan, the Fit Test, the Client Evidence Pull, the Market Map, the Intersection Engine, the Price Evidence Sheet, the Page Builder and Language Audit, and the Selling Agent with its Daily 15 rhythm.

That is 9 AI agents built on our IP, yours to keep.

Across six live sessions, you will build against your actual career, business, expertise, market, and buyers rather than watching us demonstrate the process against a hypothetical company. The objective is for the work to leave the classroom and become something operating inside your business.

Your sales page gets published. Your first real buyer conversations get opened and coached, and you leave with a 90-day rhythm for continuing to operate the system after the cohort ends.

The finish line is not, “I learned how to build an offer.”

It is being able to say: I have an offer. I know who it is for and why they need it. I know what evidence supports it and what I can defend charging for it.

You can send somebody the page.

And you have a system helping you sell it.

If these free drops are already showing you things about your career and expertise you had not seen before, that is the best explanation I can give you for why we built the full program. The question is no longer whether the expertise exists.

The question is whether you want to turn it into something that operates.

See everything inside Oper(AI)te: The Hidden Offer →

Doors close September 30, and the live cohort runs October 1–29. Until then, keep running the drops and keep paying attention to what the evidence is telling you.

Drop 5 goes live tomorrow morning.

This page will keep getting better every day.

Harvard Taught Me How to Negotiate in Theory. $250 Million in Deals and 20 Years of Work Taught Me the Rest.

John Brewton — Mon, 14 Sep 2026 11:42:05 GMT

Oper(AI)te: The Hidden Offer

Oper(AI)te™: The Hidden Offer is a four-week live cohort that helps experienced founders and operators turn the expertise they’ve already built into one clear, market-validated offer with a defensible price. You leave with the offer named, priced, published on a live sales page, and supported by AI tools designed to help you start buyer conversations.

Take Your Seat

The Beginning

While I was an undergraduate at Harvard studying economics, one of the most useful things I studied had surprisingly little to do with economics. I had the opportunity to study negotiation through Harvard Law School and the intellectual tradition around the Harvard Negotiation Project, the work that produced ideas like Getting to Yes, BATNA, interests versus positions, objective criteria, and creating options for mutual gain.

At 20-something years old, these ideas felt elegant. Twenty years later, they feel expensive, because I got to spend much of the next two decades testing them with real companies, real contracts, real jobs, real inventory, real payroll, real margins, and hundreds of millions of dollars at stake.

I eventually became CEO of an industrial distribution business. Over 12 years, we grew the company from roughly $27.5 million to more than $75 million in annual revenue before selling it in 2021. Negotiation sat underneath almost everything we did.

I negotiated with companies like Johnson Controls, All-Clad Metalcrafters, and more automative, aerospace and defense OEMs than I can count. Over time, I negotiated and structured more than $250 million in enterprise contracts, including customer relationships that grew from roughly $750,000 to $13 million in annual revenue for the company. It was an extraordinary learning experience not only in negotiating deals, but also in understanding how offers for services and products are built and optimized.

These were rarely simple negotiations over a number on a purchase order. They involved engineering requirements, sourcing, inventory, quality, capacity, tooling, lead times, service levels, payment terms, logistics, forecasting, capital, risk, contractual commitments, and relationships that could last for years.

There was another important difference: I often had to operate the deal after I negotiated it. That may be the single biggest thing that changed how I think about negotiation. A salesperson can celebrate the signed agreement. An operator eventually discovers whether the agreement was any good.

Harvard Gave Me the Language

The Harvard approach to negotiation begins with several deceptively simple principles: separate the people from the problem, focus on interests rather than positions, create options for mutual gain, rely on objective criteria rather than arguing over whose number is right, and understand your BATNA—your best alternative if an agreement cannot be reached.

I still believe all of those things. What operating taught me was what they look like after they collide with reality.

Take the classic distinction between a position and an interest. A customer says, “We need the price reduced by 10 percent.” That is a position. The more important question is why.

Maybe procurement has a savings target. Maybe another supplier quoted less. Maybe the customer’s budget changed. Maybe somebody internally needs to demonstrate a concession before approving the contract. Maybe total price matters less than cash flow. Maybe the real issue isn’t price at all but inventory, lead time, quality, capacity, service, or uncertainty about whether you can deliver.

If you negotiate only the position, you have one variable: price. Understand the interest and you may suddenly have ten.

That distinction probably saved me millions of dollars during my career. It also led me toward one of the first additions I made to what I had learned academically: before you concede on one variable, create more variables.

A customer wants a lower price. Fine. What changes with it? Volume? Payment terms? Length of commitment? Inventory? Freight? Implementation? Service levels? Scope? Forecast guarantees? Exclusivity?

Sophisticated enterprise agreements tend to make these variables explicit. Independent professionals frequently do the opposite and reduce their entire negotiation to a single number.

A client says, “I can’t do $25,000. Can you do $15,000?” The consultant hears two possible agreements: $25,000 or $15,000. In reality, there may be twenty. Perhaps $15,000 buys a smaller scope, fewer deliverables, less access, a different timeline, no implementation, different payment terms, or a group format rather than one-to-one support.

The question does not always have to be whether you will accept less. A better question is: What does less buy?

That is an entirely different negotiation.

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Never Win a Deal You Cannot Afford to Deliver

BATNA taught me to understand what happened if the deal did not occur. Operating taught me to add another test: never negotiate a deal you cannot afford to lose, and never win a deal you cannot afford to deliver.

That sounds obvious until a very large revenue number is sitting in front of you.

A $5 million contract can be a terrible contract. Can you source it profitably? Can you finance the inventory? Can you meet the quality requirements? Do you have the engineering capacity? What does the working-capital requirement look like? What happens if the forecast is wrong, freight costs rise, or the customer suddenly needs twice as much product? What happens if the agreement consumes the people and capital you need for a better opportunity?

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Revenue is seductive. Bad revenue is still bad.

As an operator, I learned to view the agreement and the operating system required to support it as the same decision. That principle applies just as readily to a one-person consulting company. If you sell unlimited access for a fixed fee, for example, you have not merely made a pricing concession. You have designed an operating model, and it may be a terrible one.

The same experience changed my definition of what it means to win a negotiation. When you negotiate with the same customers and suppliers for years, you stop believing every transaction should be maximized independently. Some of the most valuable customer relationships we built at R.G. Brewton became many times larger than where they began. That happens only when both sides keep wanting to return to the table.

Sometimes that meant making a concession. Sometimes we invested more in engineering, inventory, service, or some new capability because we believed the relationship could become much larger. Sometimes we simply did more than the agreement technically required.

That isn’t weakness. It’s strategy.

The important distinction is whether the concession is intentional. There is an enormous difference between giving something because you believe it creates more long-term value and giving something because you are afraid the customer will say no. One is an investment. The other is fear dressed up as customer service.

My definition of a great negotiation today is therefore different from what it was when I first encountered these ideas in a classroom:

A great negotiation improves the economics, protects your ability to deliver, and increases the probability that both sides want to do the next deal.

Winning the room and losing the relationship isn’t winning. Neither is winning the contract and losing money fulfilling it.

The Operating Negotiation Framework

After enough repetitions, I found myself reducing much of what I had learned into six questions. Together, they form what I now think of as the Operating Negotiation Framework: Proof → Market → Value → Price → Terms → Walk.

It incorporates ideas I first encountered while a student at Harvard College, but the sequence reflects two decades of actually having to live with the agreements I made.

Proof asks: What can I demonstrate? Before I tell someone what something is worth, I want evidence. What have we delivered before? What outcomes have we produced? What capabilities actually exist? Why should the other person believe the promise we are making?

This has become particularly important to me now that so much of my work involves selling expertise. Your career is not merely background information. Your career is evidence. The projects matter. The promotions matter. The customers matter. The P&L responsibility matters. The failures matter. The problems people repeatedly trusted you to solve matter. You should know your proof before you ever defend your price.

Market asks: What alternatives exist? What does the market already pay? What other suppliers are available? What would it cost to hire someone, build the capability internally, buy software, engage another advisor, or simply live with the problem?

This is where Harvard’s emphasis on objective criteria and alternatives becomes particularly useful. Pricing should not begin with how confident you feel that morning. It should begin with evidence about the market surrounding the decision.

Value asks: Why does this problem matter to this buyer? A company is rarely buying the literal thing printed on the proposal. It isn’t merely buying a component, a consulting engagement, a software implementation, or an hour of somebody’s time. It is buying what happens afterward: more throughput, less downtime, lower risk, faster growth, a solved problem, an avoided mistake, or a capability the company did not previously possess.

Understanding value means understanding why the buyer is in the room at all.

Only then do I want to arrive at price. What number can the evidence actually support? Not, “What would I feel comfortable asking?” Not, “What did somebody on LinkedIn tell me consultants are supposed to charge?” And certainly not, “What number seems unlikely to scare the customer away?”

Proof. Market. Value. Then price.

For my own work today, I also want to know three prices before any meaningful negotiation begins.

There is the market price: what does this problem normally cost to solve?
There is a strategic price: is there a legitimate reason this particular relationship deserves different economics?
There is a walk-away price: below what number does the work stop making sense?

Those numbers are much easier to determine before somebody is sitting across the table waiting for your answer.

Then come terms. What else can move besides price? Scope, access, volume, timing, implementation, payment, service, commitment, deliverables, risk. Never assume that a disagreement over price has to be solved with price. Often the best agreement is hiding inside a different combination of terms.

Finally comes walk. At what point is no deal better than this deal? Know what you will not do. Know the economics that no longer work. Know the terms that create more risk than reward, and know when your capacity is worth more somewhere else.

The important thing is to determine that boundary before emotion enters the room. You should not have to invent your principles while somebody is asking you to violate them.

THE OFFER PROBLEM
For most of your career, an employer did four incredibly valuable things for you: it packaged your expertise, found the customer, established an economic model around your work, and gave you permission to defend that model.
When you become independent, all four responsibilities become yours.
Your expertise did not suddenly become less valuable. The commercial infrastructure around it disappeared.

Oper(AI)te: The Hidden Offer

Take Your Seat

Then I Put My Own Name on the Proposal

This is the part of my negotiation history I find most interesting now.

After Harvard, after running a company, after negotiating with sophisticated procurement organizations and multinational manufacturers, after more than $250 million in enterprise agreements, and after selling the company, I started working for myself.

And I became worse at negotiating.

My first coaching offer was four hours of my time for $99. One hundred people bought it in 12 days. There was obviously demand. There was also an obvious pricing problem.

The person who had spent years protecting millions of dollars of company economics had put his own name on the proposal and begun behaving completely differently.

That became more interesting as I spent hundreds of hours coaching founders, consultants, executives, and operators and watched the same pattern repeat. People who had spent 15 or 20 years confidently defending budgets, margins, compensation, contracts, and corporate pricing suddenly started apologizing for their own invoices.

They discount before anyone asks. They add another deliverable and then another. They take another call, expand the scope, and turn a defined project into unlimited access. Because they genuinely care about doing great work, they describe all of this as overdelivering.

Sometimes it is. Sometimes it is a permission problem.

The corporation gave you permission to protect its economics. Once your own name replaces the logo on the proposal, you have to give yourself the same permission.

This is also why I no longer believe pricing an offer should primarily be an act of confidence. It should be an act of research.

Instead of beginning with, “What should I charge?” start somewhere else. What have I repeatedly proven I can do? Who has an expensive reason to need it? What alternatives are they using today? What do those alternatives cost? What changes economically if the problem gets solved? Where does my experience make me unusually credible?

Then price it.

The number becomes easier to say because you are no longer asking another person to validate your self-worth. You are presenting a conclusion supported by evidence.

BUILD THE OFFER BEFORE YOU NEGOTIATE IT

This is much of what Wessal Khader and I have built into Oper(AI)te™: The Hidden Offer.
We don’t begin by inventing a product or asking what someone wants to charge. We begin with the evidence accumulated across a career. We use that evidence to identify the expertise with the greatest commercial value, map it against what buyers are paying for now, turn the intersection into one clear offer, and establish a defensible price.
Proof → Market → Value → Price.
The objective isn’t to teach an experienced operator another random skill. It is to identify the valuable skill they have already spent 15 or 20 years proving and finally build the offer around it.

Take Your Seat

The Hardest Person I Have Ever Negotiated For Is Myself

I’m grateful I got to study negotiation when I did. Harvard gave me a vocabulary for ideas I would encounter thousands of times afterward: interests, positions, options, objective criteria, alternatives, relationships, commitments.

Running companies gave those ideas consequences.

Operating taught me that price is only one part of an agreement and that a signed contract can still be a terrible deal. It taught me to create variables before giving one away, to consider the economics of fulfilling an agreement as carefully as the economics of signing it, and to recognize that the biggest opportunity is often not the first transaction but the relationship that follows it.

Entrepreneurship taught me something different.

You can spend 20 years learning to defend the value of companies, products, contracts, employees, inventory, engineering, and entire operating systems and still hesitate when the thing being valued is you.

So today I use the same framework.

Know the proof your career has accumulated. Know the market surrounding the problem. Understand the value of solving it. Let the price follow from the evidence. Create enough terms that price isn’t your only lever. Decide in advance where the agreement stops making sense.

Then be generous. Overdeliver when overdelivering is a strategic decision. Build relationships worth protecting, and create enough value that people want to sit across from you and negotiate the next deal.

And when your own name is finally on the proposal, negotiate for it with the same discipline you spent your career applying to everyone else’s.

Then send the invoice.

- j -

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The Creator Economy’s Original Sin: Building An Audience Without An Offer

John Brewton — Fri, 11 Sep 2026 14:05:14 GMT

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My Story

Two and a half years ago, I had a little over 500 LinkedIn connections and decided to post every day with the aim of finally building the audience had been telling me I should build for over a decade.

I studied content and built relationships with other creators. I took Justin Welsh’s LinkedIn MBA and Ship 30 for 30, then invested $5,000 in Chris Donley’s Creator Accelerator, one of the best investments I made during that period.

I learned how to grow.

My work has since generated more than 40 million LinkedIn impressions. My audience across platforms now exceeds 75,000. That includes 50,000 on my LinkedIn pages and 10,000 on TikTok. Here on Substack, Operating has 12,000 followers and 6,000 subscribers, and has become a bestselling publication.

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For the first 18 months, I had neither a clear offer nor a clear enough purpose for the newsletter. I assumed that if I built the audience, the clients would come.

The expertise behind my best-paid work was available before any of that growth, but it was hidden within the consulting engagements that had long been my bread and butter.

My first offer was 4 hours of my coaching time for $99.

It sold +100 seats in 12 days.

I wanted to learn how to build a coaching offering. I wanted to do something crazy and unscalable that people would talk about if it succeeded. During those hours, I listened to the work and business challenges each member was facing and helped them solve the problems they brought.

Many wanted help scaling a newsletter, improving their content, or growing an audience. Most were trying to figure out what to sell and how to make money as solopreneurs. The conversation would get into the business underneath the content.

Then there were the people who already owned larger businesses. They brought 4 kinds of problems:

AI transformation.
Enterprise technology and software that needed improvement or optimization.
Sales and inventory operations strategy.
Growing EBITDA ahead of a business exit.

These were the kinds of challenges I had spent years solving in businesses I operated and companies I advised. When someone needed further help, I offered a larger coaching engagement. From there, I moved into the work I had been doing throughout my consulting career: understanding the situation, scoping the work, and building a proposal. We negotiated the engagement and decided whether to proceed.

That part was familiar. I knew how to understand a business problem and propose work to address it. I had been doing it for years.

A number of those coaching members became consulting or advisory clients. That work has brought in hundreds of thousands of dollars this year. I expect it to continue into next year.

The coaching offer itself was massively unsustainable. Four hours for $99 works out to $24.75 per coaching hour, before preparation or follow-up. More sales meant more time committed at that rate.

Those first 100 sales brought in roughly $15,000 in gross revenue. I was getting paid to learn how to build a coaching offering and whether I wanted to keep doing it. I eventually decided I did not want to build that coaching business.

The experiment also became a paid way to meet people who needed larger consulting and advisory engagements. Judging it only by the hourly coaching rate would miss most of what it produced.

Oper(AI)te™: The Hidden Offer is a four-week live build designed to turn the expertise, results, and experience you already have into an offer people will pay for. We’ll help you find it, validate the market, price it with evidence, publish the sales page, and build the AI systems that help sell it. You already did the hard part: becoming exceptionally good at something. Now we’re going to make it buyable.

Take Your Seat Today

Some people wanted advice on building an online business. Others needed the operating judgment I had developed long before I started publishing online. The conversations helped me understand the difference and decide where to put my work.

I spent my twenties studying economics at Harvard and the University of Chicago. I spent my thirties helping my father build, scale, optimize, and eventually sell an industrial distribution business. Over the last 6 years, I have consulted with manufacturing companies, B2B businesses, and technology firms.

The work I am paid best to do is operational optimization that improves financial performance. I spent 20 years learning it.

I love the creator community. Coaching solopreneurs has become some of the most rewarding work I do. I value the audience, the training, and the relationships I have built. 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.

I could have tested an offer when I had 500 connections. I could have taken a specific problem and a proposed piece of work to people in my professional network. Their responses would have given me something to examine while I continued learning to publish.

Instead, I spent 18 months leaving the offer unresolved.

For someone with a long career, the work worth examining may be the work that feels most ordinary. Scoping a consulting engagement was familiar to me because I had done it repeatedly. Understanding a company’s operating problems was familiar for the same reason.

That familiarity made it easier to overlook the work when I was thinking about what to sell online.

Start With The Work You Have Already Done and Been Paid For

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The Problem With The Creator Economy

John Brewton — Tue, 08 Sep 2026 13:51:39 GMT

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The Problem With the Creator Economy

John Brewton — Mon, 07 Sep 2026 10:18:27 GMT

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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.

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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:

Why enter a superstar lottery before attempting to monetize an economic asset you already possess?

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.

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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 -

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Enterprise Software Was Built for Someone Else

John Brewton — Mon, 31 Aug 2026 11:17:11 GMT

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What do I need to understand about the future of enterprise software in 60 seconds?

TL → DR

Enterprise software has historically been designed around the complexity of large organizations and sold downward. AI changes that because the cost of creating company-specific software, workflows, intelligence, and interfaces is falling dramatically.

That does not mean small companies are ahead in AI adoption. They aren’t. The opportunity is that an established SMB can combine assets a startup does not have—customers, reputation, industry knowledge, distribution, operating experience—with more freedom to redesign than a large enterprise has.

The winning architecture is unlikely to be “build everything yourself.” It is more likely to be buy the durable core, own the differentiated logic, and build a governed layer of data, context, intelligence, and workflows around the way your company actually operates.

That creates a second possibility: some of tomorrow’s enterprise software companies may begin as operating companies that build software for themselves first.

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Introduction

I have spent the last 20 years of my career learning how to operate companies as efficiently as possible.

That has meant leading an unusually large number of projects focused on:

Migrating companies to new enterprise software
Implementing and integrating those systems
Optimizing existing enterprise applications
Building the databases, data warehouses, and now data lakes required to support them
Creating the data infrastructure that allows operators to run critical workflows, analyze performance, and understand what is actually happening inside their businesses

Along the way, I have developed deep experience with ERP systems, CRMs, inventory management platforms, financial management software, and the analytics layers that sit above them.

The last 24 months have fundamentally changed how I think about this entire category of work.

Large language models, combined with code-generation tools such as Claude Code, Codex, and Cursor, have dramatically expanded what companies can build for themselves. Custom enterprise applications, and, perhaps more importantly, highly specific modules that extend or replace pieces of larger enterprise systems—can now be built faster, more affordably, and with far greater precision than was previously practical.

That changes what operators should expect from their software.

Companies no longer need to accept quite as many compromises between what their business actually requires and what an off-the-shelf enterprise platform allows them to do. The potential for customization is increasing, while the cost and difficulty of building it are falling.

The same transformation is happening underneath the applications themselves.

Large language models can now assist with preparing, cleaning, structuring, and transforming enterprise data. Modern database platforms such as Supabase have made sophisticated data infrastructure significantly more accessible. Data warehouses and data lakes can be built and managed in ways that would have required substantially more time, expertise, and capital only a few years ago.

Together, these changes are creating a new set of possibilities for how companies architect their systems, manage their data, automate their workflows, and build the software they depend on every day.

This article is written for operators, founders, and CEOs who should be thinking more ambitiously about those possibilities.

My goal is to help you understand what is changing, what is now realistically achievable, and what these shifts could mean for the accuracy of your systems, the quality of your data, the competitiveness of your company, and ultimately its profitability.

The expectations we place on enterprise software should be changing. So should the way we build it and make the associated purchasing decisions.

This article is the beginning of a much larger body of work I plan to write on the subject. If you are an operator, founder, or CEO wrestling with these questions inside your own company, I hope it helps, and if there is a specific problem you would like me to explore, please message me.

Why was enterprise software built around companies much larger than yours?

For most of the last 30 years, enterprise software moved in one direction…

Large organizations needed systems capable of handling enormous complexity. ERP, CRM, finance, inventory, HR, procurement, and analytics platforms were built around those requirements and gradually packaged into products for smaller companies. Panorama Consulting’s 2026 ERP research still segments the market into revenue tiers, from companies under $10 million to those above $750 million.

The smaller company ends up adapting itself to software designed for somebody else.

A 75-person manufacturer, distributor, healthcare business, or professional-services company does not necessarily need a smaller version of a system built for a multinational corporation. It needs an operating system shaped around the way its own company creates value.

A16z’s research on next-generation ERP makes a similar argument from the software-founder side: platform shifts create opportunities to rethink ERP around specific verticals, company sizes, and transition points rather than recreating the same monolithic architecture.

AI makes that alternative increasingly possible.

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What happens when an established SMB gets a second startup phase?

An NBER analysis built from Census Bureau microdata finds AI usage increases substantially with company size, particularly in information, professional services, and finance. Even more interesting, however, is how shallow adoption still is: among firms already using AI, 57% use it in three or fewer business functions.

Most companies have adopted AI.

Very few have rearchitected themselves around it.

That creates the opportunity.

A 20-year-old, $30 million business ordinarily never gets to become a startup again. It already has customers, employees, suppliers, habits, systems, processes, and an established economic model.

AI offers a rare second startup phase.

The business gets to keep the valuable things it spent years building (customers, reputation, industry expertise, distribution, relationships, institutional knowledge) while reconsidering how work gets done.

Research from Harvard Business School provides an early picture of what the destination may look like. In “AI-Native Firms,” Hyunjin Kim and Rembrand Koning find that AI-native startups are roughly 25% smaller than comparable non-AI startups. They tend to be flatter, with fewer managers and entry-level roles, and much of the difference comes from embedding AI into the product itself rather than merely helping employees complete existing work faster.

That does not mean an established SMB should cut 25% of its workforce. Rather, we should place our emphasis and attention on the truth that we are beginning to see: companies designed around AI look structurally different from companies that merely use AI.

The opportunity for the established SMB is to determine how much of that architecture it can adopt without harming its current assets, thereby placing it in a more competitive position.

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Why hasn’t AI created the giant productivity gains everyone expected yet?

Because installing a new technology and redesigning a company around that technology are not the same thing.

Erik Brynjolfsson, Daniel Rock, and Chad Syverson describe this phenomenon in their research on the Productivity J-Curve. General-purpose technologies often produce disappointing measured productivity at first because businesses still need to build the processes, knowledge, systems, training, and organizational structures required to exploit them.

Electricity offers the classic analogy.

Factories did not fully realize the benefits of electric motors by placing them where steam-powered machinery had previously stood. The breakthrough came when factories redesigned the floor around what distributed electric power made possible.

We are largely still putting AI where the steam engine used to be. We add Copilot. We summarize meetings. We automate an email. We generate a proposal.

Useful. But the much larger question is:

If this technology had existed when we founded the company, would we have designed the company this way at all?

What should become the durable layer when the software interface becomes in question?

Traditional enterprise software organizes work around modules, menus, forms, fields, and reports.

AI increasingly allows the employee to begin with intent.

Instead of navigating Inventory → Reports → Stock Status → Export → Excel, the employee asks:

“Which products are likely to stock out in the next 60 days, what revenue is at risk, and which purchase orders should we accelerate?”

The application determines what analysis, data, visualization, workflow, or action is required. But that future only works if the system understands what the company means.

What counts as revenue?
Which inventory is actually available to promise?
When does a prospect become a customer?
Which discount requires approval?
Who can see which information?

Research on real enterprise querying makes this limitation very clear. The EntSQL benchmark finds that the overwhelming majority of enterprise questions require domain knowledge beyond what is present in the question or the raw database schema.

That is why I no longer think the database alone is the critical asset. The more valuable layer is a governed model of the company: clean data plus canonical definitions, permissions, relationships, business rules, exceptions, and institutional knowledge, all of which can be housed in common databases, regardless of their data type or current location. Thank God for data lakes and Apache Spark.

a16z describes this as a context layer that gives agents the business meaning they cannot recover from raw tables alone. In a related piece on “headless” software, the firm argues that AI may weaken interface-driven stickiness while leaving operational logic and context extremely valuable.

The interface becomes more disposable.

The company’s understanding of itself becomes more valuable.

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Where should the new line between building software and buying software sit?

This is where the “everyone will build their own ERP” argument goes too far.

Aaron Levie at Box has articulated the strongest objection. In Foundation Capital’s discussion with Levie, he argues that companies buy software not simply because software is difficult to write, but because it is difficult to maintain, secure, permission, support, and operate over time.

He is right. AI has not made software ownership free. It has changed the minimum economically rational scope of software ownership.

Morgan Stanley Investment Management’s 2026 analysis of the software market reaches a similar conclusion. AI-driven insourcing is much more credible for lightweight workflows and thin application layers than for complex, regulated systems of record.

The practical strategy is therefore not build versus buy.

It is: Where should the line move?

Curative provides a useful real-world example. The healthcare company built an internal CRM in roughly two months and canceled a $600,000 annual Salesforce contract, while subsequently acknowledging that maintenance was difficult and AI infrastructure costs were growing rapidly, as described in this reporting on its internal software strategy.

A company may continue buying its general ledger, payroll system, banking infrastructure, and commodity communications tools while building the quoting logic, customer intelligence, inventory allocator, contracting workflow, or decision engine that actually makes the business different.

Buy the durable core. Build the differentiated edge.

This model is the one I currently see most in practice and implement across my own consulting engagements. It's important to add, however, that the capacity to build from a durable core to a differentiated edge would not have been possible 12 or 18 months ago (I’m thinking CRM and inventory management systems). Anthropic's release of Fable 5 was a major step forward on this front and it is my expectation, that 12 months from now or sooner, we will be talking about building more robust custom applications, and having them replace enterprise software line items that currently feel untouchable. CRM and ERP being prime candidates

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When does an operating company become a software company?

This may be the most interesting consequence of all.

Operating companies have something most software startups spend years trying to acquire: a real environment in which to discover what the software should do.

They already have customers, workflows, transactions, exceptions, domain experts, feedback loops, and definitions of success.

Travis Kalanick’s Otter is a useful example. The platform grew out of the restaurant technology built around CloudKitchens’ operating needs before becoming a standalone software business. Business Insider reported that Otter had reached roughly 100,000 paying restaurants and was approaching $70 million in annual revenue.

The cycle is worth paying attention to:

Operate → build → prove → productize → sell.

Sequoia reaches a related conclusion in “Services: The New Software,” arguing that AI businesses increasingly have an opportunity to sell the work itself rather than merely sell a tool that helps somebody else perform the work.

Enterprise software is moving from helping people perform work toward increasingly completing the work itself. Sequoia describes this transition as the movement from copilots toward autopilots, and argues that the larger opportunity may ultimately sit in selling outcomes rather than tools.

That means the software created inside an SMB does not necessarily remain internal.

A distributor builds a superior quoting architecture for itself. A healthcare company builds a better contracting workflow. A manufacturer builds an inventory decision engine.

If those systems create real operating advantage, the company may eventually realize it has also created a product. Some of the next important enterprise-software companies may begin inside businesses that never intended to become software companies.

What should you build if you are selling enterprise software into this market?

Do not begin by asking what feature Salesforce, SAP, NetSuite, or Workday is missing. Instead, ask: What work should no longer require someone to manually operate software?

The market is already moving in this direction. Menlo Ventures found that startups captured 63% of the AI application layer in 2025, up from 36% the year before.

Bessemer describes the architectural transition as a move from systems of record toward systems of action, while its work on AI monetization argues that software economics are shifting away from simply licensing access and increasingly toward usage and outcomes, as outlined in its AI Pricing and Monetization Playbook.

The opportunity for enterprise-software founders is therefore not simply to make existing SaaS prettier with an AI assistant.

Design for the growth-oriented, legacy-light company willing to rethink the workflow.
Expose data and actions so agents can operate the software as easily as humans.
Make permissions, context, definitions, auditability, and learning part of the product.
And sell the shortest path from trusted information to completed work.

For decades, enterprise software has asked smaller companies to adapt to software designed elsewhere. AI allows us to reverse the relationship.

The SMB is interesting because it may now be able to combine the accumulated advantages of an incumbent with some of the architectural freedom of a startup.

That makes it one of the most interesting laboratories we have for discovering what the next generation of enterprise software will be.

- j -

Questions For Executives

Is this primarily an AI adoption project or a company redesign project?

If the objective is simply to give employees access to better tools, buy the tools. If the goal is to change the company's economics, capabilities, or operating model, the work needs to go much deeper into workflows, data, roles, and decision-making.

Should we build our own CRM, ERP, or other core system?

Usually not in its entirety. The more useful question is which parts of the operating stack encode proprietary judgment or create competitive advantage and which parts are standardized infrastructure that another company can maintain more effectively.

What data do we need for agents to operate the business reliably?

Clean records are only the beginning. Companies need common definitions, authoritative sources, permissions, entity relationships, business rules, exception handling, and a way to capture institutional knowledge.

Will AI automatically make a smaller company more productive?

No. Research on firm-level AI adoption still shows limited realized productivity effects, and the broader history of general-purpose technologies suggests that significant gains require complementary organizational redesign.

Does this mean SaaS is going away?

No. It means the surface area over which companies rationally build their own software may expand, while the strongest software vendors increasingly compete on context, proprietary data, workflow knowledge, reliability, governance, and outcomes rather than screens and seats alone.

Why might SMBs be particularly important to this transition?

Not because they currently lead AI deployment. The most ambitious SMBs may simply occupy an unusual position: they have enough operational complexity and accumulated assets to produce meaningful lessons, while often having fewer legacy systems, organizational layers, and entrenched workflows preventing them from changing.

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

Research

Enterprise Software and ERP Design

Panorama Consulting — 2026 ERP Report
Market segmentation, ERP buyer characteristics, implementation patterns, and historical enterprise-software structure.
Read the report

Andreessen Horowitz — The Opportunity for Next-Gen ERPs
Research on AI-native ERP challengers, company-size and vertical wedges, and the opportunity created by platform transitions.
Read the research

AI Adoption and the Second Startup Phase

Harvard Business School — AI-Native Firms, Hyunjin Kim and Rembrand Koning
Research on organizational structure, headcount, valuation, and product design at AI-native startups.
Read the HBS working paper

NBER — The Microstructure of AI Diffusion
Census-based analysis of AI adoption by firm size, function, and worker task.
Read the paper

NBER — Firm Data on AI
International executive survey examining current and expected employment and productivity effects from AI.
Read the paper

NBER — The Productivity J-Curve
Brynjolfsson, Rock, and Syverson on why general-purpose technologies require complementary intangible investment and organizational redesign before productivity gains appear.
Read the paper

Data, Context, and AI-Native Software Architecture

Andreessen Horowitz — Your Data Agents Need Context
Framework for canonical entities, business definitions, identity resolution, governance, and institutional knowledge surrounding enterprise data.
Read the article

Andreessen Horowitz — Is Software Losing Its Head?
Analysis of headless software, agent-driven interfaces, and why operational logic and context remain defensible.
Read the article

EntSQL — Enterprise Text-to-SQL Benchmark
Technical research showing the importance of enterprise-specific contextual and domain knowledge when querying business data.
Read the paper

dbt Labs — Semantic Layer vs. Text-to-SQL
Supporting benchmark research on the accuracy improvements created by curated semantic layers.
Read the benchmark

Build Versus Buy

Morgan Stanley Investment Management — AI-Related Sell-Off in Software
Institutional analysis distinguishing software categories vulnerable to internal AI-driven development from durable, regulated systems of record.
Read the report

Foundation Capital — The Case for Context Graphs with Aaron Levie
Levie’s argument about maintenance, support, governance, context, and the enduring economics of buying enterprise software.
Read the discussion

Curative — Internal CRM and SaaS Replacement Case
Reporting on Curative’s internal CRM, Salesforce cancellation, AI infrastructure spending, and maintenance experience.
Read the reporting

From Internal Operations to Software Products

Business Insider — Otter and CloudKitchens
Reporting on the restaurant-software platform’s development, customer scale, and evolution from operating infrastructure into a standalone product.
Read the reporting

Otter — Restaurant Operating Platform
Current product architecture and positioning around restaurant operations.
Visit Otter

Sequoia Capital — Services: The New Software
Julien Bek’s thesis on AI companies selling completed work and outcomes rather than simply providing tools.
Read the thesis

Building and Selling AI-Native Enterprise Software

Menlo Ventures — 2025 State of Generative AI in the Enterprise
Research on startup market share, enterprise AI application spending, and bottom-up distribution.
Read the report

Bessemer Venture Partners — Roadmap: AI Systems of Action
Research on AI-driven systems of action, integration economics, and disruption of traditional systems of record.
Read the research

Bessemer Venture Partners — The AI Pricing and Monetization Playbook
Research on consumption, outcome-based pricing, and the changing economic model for AI-native software.
Read the playbook

I Turned 22 Hours of Founder Office Hour Video Recordings Into a Learning Library with Claude Cowork, Opus 5, Zoom and Google Drive Connectors

John Brewton — Mon, 24 Aug 2026 11:18:48 GMT

There is a category of AI work I don’t think we talk about nearly enough. Not asking ChatGPT a question, generating an email, or summarizing a document. I mean the ugly, multi-step projects sitting on your to-do list that you know need to get done but could easily consume half a day or more of your valuable time.

I had one of those projects this week. I had 22 recordings from my Operating Founder Office Hours sessions that I wanted organized, summarized, researched, packaged, branded, linked, and ultimately turned into a learning library for my community. Doing all of that manually would have meant hours and hours of opening files, reading transcripts, finding links, researching topics, formatting documents, and organizing folders.

Instead, I gave the project to Claude Cowork and Opus 5. Across roughly four or five major prompts, my agents worked on it for about three and a half hours. I did not spend three and a half hours doing the work. Claude did the work, while I focused my enery on more important tasks and was able to spend time with my family.

While the agents worked, I kept working on other things. I responded to client emails, wrote the outline for a new article, got some reading done answer, thought through other projects, and spent time playing with my daughter. That distinction is becoming incredibly important: the value of AI isn’t simply that something that used to take three hours can now take 20 minutes, it is increasingly that a task can take an AI agent three hours while taking almost none of yours.

Watch Me Build It Here on Loom

I recorded the build as I was doing it so you could see what this kind of work actually looks like. I give Claude a set of instructions, let it work, come back to review the output, decide what the next layer should be, and then send it back to work again. The cycle was essentially direct, delegate, leave, review, redirect.

That is increasingly what agentic work looks like for me. I am still responsible for the outcome, the judgment, and the quality of the final product. But I no longer need to personally perform every action from beginning to end.

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Become an Operating Founder

The Project Started With 22 Zoom Calls

Every week, I hold Office Hours for the founding members of my Operating Founders community. We discuss how we are building our companies, Operating with AI, sales and go-to-market strategy, creator and content strategy, and whatever problems founders and operators are working through at the time. I record those sessions on Zoom, and over time, I have accumulated 22 recordings filled with useful conversations.

But I didn’t want a folder containing 22 recordings. I wanted someone to be able to arrive months later and immediately understand what happened in each session, watch the relevant recording, and then go deeper through additional research and resources. I wanted to turn an archive into a learning system.

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Prompt One: Retrieve and Organize Everything

The first layer was operational. Using my Zoom connection, I asked Claude to retrieve the recordings and associated material, move everything into Google Drive, create the appropriate folder structure, and apply a consistent naming convention.

Think about the manual alternative: finding recordings, downloading them, creating folders, renaming files, uploading everything, checking dates, and making sure every file is where it belongs. Necessary work, yes. A particularly good use of my time, no.

Prompt Two: Understand 22 Conversations

Next, I asked Claude to work through all 22 transcripts. For every session, I wanted a title, a concise description of what we discussed, the correct date, and a link back to the corresponding recording in Google Drive.

Even if I could manually review, summarize, and link each session in ten minutes, that step alone would have taken more than three and a half hours. Instead, I defined the desired output once, let Claude work through the entire collection, and returned when it was ready for review.

Prompt Three: Make the Existing Work More Valuable

I didn’t simply want summaries. For every session, I wanted my community to have high-quality resources that extended the conversation beyond what we discussed during Office Hours.

So I had Claude use an Elite Research skill I built to analyze the topics in each transcript and identify strong articles, research, media, and reference materials. The output was no longer simply “Here is what John said.” It became, “Here is what we discussed, here are the important ideas, and here are additional experts and resources that can help you explore the subject further.”

Take a look at one of the session note documents here.

Prompt Four: Turn the Work Into Finished Assets

Then I applied another reusable skill I have built around my personal brand. Claude took the transcripts, summaries, and additional research and turned them into 22 professionally formatted research and notes packages—one for every Office Hours session.

This is where the scale of agentic execution becomes easier to appreciate. Formatting documents, structuring sections, inserting research links, applying branding, naming files, exporting PDFs, and organizing the outputs are not one task. They are dozens of small tasks hiding inside a larger project, and those small actions are exactly where so much of our working day disappears.

Prompt Five: Build the Final Experience

The last step was creating a single resource that my community could actually navigate. For each session, I wanted a title, a short description, a link to the recording, a branded visual, and a link to the associated research package, all assembled into a document I could move directly into Substack.

The result was an organized library of 22 sessions rather than a pile of recordings. The overall workflow was straightforward: Capture → Organize → Understand → Enrich → Package → Publish.

Here’s a look at what the final product looks like on Substack:

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Become an Operating Founder

Your Agent’s Time Does Not Have to Be Your Time

This is the biggest lesson I took from the project. If someone hears that the build took three and a half hours, they might reasonably think that doesn’t sound particularly fast. But those were primarily Claude’s three and a half hours, not mine.

My time was concentrated at the places where judgment mattered: What am I trying to build? Is the output good? What should happen next? What needs to change? Once those decisions were made, I could hand the execution back and continue doing something else.

That changes the productivity question. Instead of asking only, “How much faster can AI help me perform this task?” I increasingly want to ask, “How much of this task requires me at all?”

Start Looking for Three-Hour Agent Jobs

Look through your own week for these projects. Maybe it is reviewing 30 customer calls, researching 40 companies, cleaning up a CRM, turning old webinars into training materials, analyzing proposals for common patterns, organizing a document archive, or converting sales conversations into objection-handling material.

These projects are everywhere, and many of us still calculate whether they are worth doing based on how many hours we would need to spend completing them. That calculation is becoming outdated. The better question is: Can I spend 10 or 20 minutes defining this well enough that an agent can spend the next three or five hours doing it?

You may not be able to delegate 100 percent of the work. That’s fine. Delegate 60 percent, then 80 percent, while keeping yourself at the judgment points and letting the agents own more of the execution.

Find the Work You Should Stop Doing Yourself

The bigger opportunity here is not better prompting. It is developing the ability to look at complicated work, see the complete system, break it into layers, and understand which parts require human judgment and which parts can increasingly be delegated.

Historically, I might not have built this library at all—not because it wasn’t valuable, but because the opportunity cost of spending most of a day doing it would have been too high. AI didn’t simply make the project faster. It made a project that might previously have been uneconomic worth doing.

That is what excites me most about where we are headed. The productivity revolution isn’t going to come from humans simply learning to perform the same work slightly faster with AI sitting beside them. It is going to come from learning how to orchestrate meaningful work that continues while we are doing something else.

My agents worked for three and a half hours on this project.

The important part is that I didn’t have to.

- j -

Join the Operating Founder Office Hours each week to work through the real problems, decisions, and opportunities you are facing alongside other founders and operators. Every session is recorded and added to a growing learning library of summaries, research, and additional resources, so the value of the conversation continues long after the call ends. Come for the live discussion, and leave with a deeper body of knowledge you can keep using.

Become an Operating Founder

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 a career consultant and business operator, asking the question: What is the future of companies?

The Value of “We Don’t Know”

John Brewton — Thu, 20 Aug 2026 18:28:32 GMT

Available for iOS and Android

The Value of We Don’t Know Part Two

John Brewton — Thu, 20 Aug 2026 11:18:30 GMT

The argument I keep making in Operating is that the companies that survive and become greater than their current location this decade will not be the firms that adopted AI. They will be the organizations that rebuild their operating philosophies and structure from AI-native principles. They will redesign every role, every process, every means of production through this new lens, with these new tools.

My good friend Alex Pawlowski has written a warning to leaders and executives that personifies this expectation, and I am thrilled to have published this work in two parts. Part One can be found here and today we are releasing Part Two below.

Alex is a friend and one of the sharpest people I know on agentic systems, orchestration, and the architecture of AI-native work. He spends his time building and investing in the next internet. We have spent much of the past year working on similar problems from varying viewpoints. I come at it through strategy, organizational capability, and judgment. He studies and speaks to the machinery that makes the transformation possible.

His claim in this article is as follows: Organizations have never managed knowledge. They manage distributed uncertainty. The internal engineer sees, develops and manages the architecture. The marketer speaks to the customer. Legal worries about exposure. Finance manages risk. Intelligence gets assembled out of incomplete views colliding before a decision hardens. In that system, “I don’t know” is a routing signal to send the query to another department.

Generative AI collapses the interval between question and answer to seconds, eliminating cross-functional departmental debates and meetings. The explanation that arrives is fluent, internally consistent, and often correct.

Encoding your operation is still the right move. Encode the answers, not the questions, and you have built a company that reaches conclusions faster than it ever could.

I’m sincerely excited about these two pieces and am privileged to be publishing them via Operating by John Brewton.

Hope this helps, friends.

- j -

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IV. Explanation Is Becoming Cheap. Judgment Is Not.

Every major technological shift changes the economics of something. The steam engine reduced the cost of physical labour. Industrial manufacturing reduced the cost of production. Computing reduced the cost of calculation. The internet reduced the cost of accessing information.

Generative AI appears to be changing something altogether different. It dramatically lowers the cost of producing explanations.

That distinction deserves careful attention because explanations occupy an unusual position within organizations. They satisfy a deeply human need to make events appear coherent. A strategy failed because the market shifted. A customer left because pricing was too high. A competitor succeeded because its product was superior. Most organizational life is surrounded by narratives that transform complex, multidimensional events into stories that feel understandable enough to support action.

The problem is not that explanations are inherently misleading. Organizations cannot function without them. Every roadmap, investment proposal, product strategy and board presentation depends on a shared interpretation of reality. The difficulty lies elsewhere. Coherence and accuracy are related, but they are not identical. A persuasive explanation may still overlook critical variables, underestimate uncertainty or mistake correlation for causation. History is filled with decisions that appeared perfectly reasonable because the underlying narrative was internally consistent, only for reality to reveal later that the organization had misunderstood the problem from the very beginning.

Generative AI introduces a fascinating dynamic into this relationship because it produces coherence with remarkable ease. Faced with an unfamiliar question, the system does not experience hesitation, uncertainty or intellectual discomfort. It generates a plausible interpretation almost immediately, drawing together patterns from enormous volumes of text into a response that feels structured, complete and often highly persuasive. Even when the underlying reasoning is imperfect, the explanation itself carries many of the characteristics that humans naturally associate with understanding: fluency, confidence, logical progression and linguistic precision.

From the perspective of organizational psychology, this matters because human beings rarely experience uncertainty as an abstract philosophical concept. We experience it emotionally. Uncertainty creates tension. It interrupts momentum. It delays decisions. It exposes the possibility that our current understanding may prove inadequate. Throughout history, that discomfort encouraged investigation. The effort required to reduce uncertainty often became the mechanism through which better knowledge emerged.

AI changes that emotional sequence.

The question is asked.

An explanation appears.

The feeling of uncertainty recedes.

Whether uncertainty itself has actually diminished remains an entirely separate question.

This difference between objective uncertainty and subjective certainty may become one of the defining management challenges of the coming decade. Organizations have always operated in environments characterised by incomplete information, delayed feedback and complex causal relationships. None of those conditions disappear because explanations become easier to generate. Markets do not become more predictable because summaries become more elegant. Customers do not become more consistent because presentations become more convincing. Competitive advantage does not emerge because every employee can now produce a beautifully written strategic analysis in under five minutes.

Reality has not become simpler.

Only our interface with it has.

Perhaps the most significant consequence is not technological but social. Every organization depends on countless small signals that reveal where understanding remains incomplete. Someone asks another department for help. An engineer escalates an issue to an architect. A consultant admits they need to investigate further before making a recommendation. A physician requests another opinion. These interactions do more than exchange information. They reveal where uncertainty currently resides inside the organization, allowing expertise to flow toward it.

If explanations become permanently available, many of those signals begin to disappear. Employees no longer need to expose uncertainty before receiving a plausible answer. Teams become increasingly capable of operating independently, but independence and understanding are not synonymous. An individual equipped with an excellent language model may appear highly informed while remaining disconnected from colleagues whose experience would have fundamentally altered the interpretation of the problem. Knowledge becomes more accessible at precisely the moment collective sensemaking risks becoming less frequent.

This is an irony worth reflecting upon. Organizations adopted digital collaboration platforms to connect people more effectively across functions and geographies. Generative AI may inadvertently reverse part of that dynamic by allowing individuals to resolve questions privately that previously required conversation. The immediate productivity gains are obvious and, in many cases, entirely justified. The longer-term organizational effects are more ambiguous. Every question answered without conversation is also a conversation that never takes place, an assumption that is never challenged and a perspective that never enters the decision-making process.

The implications extend beyond collaboration into the architecture of organizational learning itself. Much of what organizations know is not stored in documents, databases or process manuals. It exists in the interactions through which people compare interpretations, negotiate meaning and gradually refine their understanding of ambiguous situations. Organizational theorists have long distinguished between explicit knowledge—the things that can be documented—and tacit knowledge—the intuitions, experiences and contextual judgments that resist formalization. AI dramatically improves access to the former. Its influence on the latter remains far less certain.

Perhaps the greatest risk, then, is not that AI produces incorrect answers. Incorrect answers have always existed. Organizations have developed countless mechanisms for discovering and correcting them. The more subtle possibility is that explanations become so immediate, so abundant and so persuasive that they quietly reduce the number of occasions on which people realise they have reached the boundary of their own understanding. If that boundary becomes less visible, organizations may discover that they have become exceptionally efficient at producing explanations while simultaneously becoming less effective at recognising when explanation should give way to further inquiry.

V. Designing Organizations That Preserve Uncertainty

The discussion surrounding agentic AI often begins with capability. How many tasks can an agent perform? How many systems can it connect? How much human intervention can be removed from a workflow? These are sensible engineering questions, but they say surprisingly little about organizations themselves. Enterprises have never struggled because they lacked the ability to execute instructions. They struggle because they repeatedly execute assumptions that later turn out to be incomplete.

Viewed through that lens, the next generation of organizational design may depend less on what AI can accomplish autonomously and more on the role it plays within the organization’s broader process of judgment. Every technology eventually becomes embedded in an institution, and institutions shape technology at least as much as technology reshapes institutions. The real design challenge, therefore, is not simply building more capable agents. It is deciding which organizational functions those agents should strengthen and which human capabilities they should deliberately leave intact.

Current development trends reveal a strong preference for completion. Agents draft reports, prepare presentations, write code, summarize meetings, coordinate workflows and increasingly orchestrate other agents. The underlying objective is remarkably consistent: reduce the amount of work that remains unfinished. Yet completion has never been the defining characteristic of high-performing organizations. A completed analysis that frames the wrong problem creates more risk than an unfinished one that still invites challenge.

That distinction suggests a different design philosophy.

Instead of asking how an agent can replace the next human task, organizations might begin by asking which forms of uncertainty disappear too early in their existing decision processes. The objective shifts from maximizing automation toward improving the quality of organizational sensemaking.

An agent designed around that principle behaves very differently.

Rather than presenting a single recommendation, it identifies competing interpretations.

Rather than optimizing for confidence, it estimates the quality and completeness of the available evidence.

Rather than summarizing agreement, it highlights disagreement.

Rather than producing the shortest path to execution, it identifies where additional observation would meaningfully change the decision.

The result is not a slower organization.

It is an organization that allocates its attention more intelligently.

That distinction becomes easier to appreciate when viewed through familiar organizational practices.

Conventional Agent

Epistemic Agent

Produces answers

Maps uncertainty

Completes tasks

Identifies missing evidence

Optimizes efficiency

Optimizes decision quality

Reinforces existing assumptions

Challenges existing assumptions

Maximizes confidence

Calibrates confidence

Ends the workflow

Decides whether the workflow should continue

This is not merely a different interface.

It represents a different philosophy of organizational intelligence.

Much of today’s AI landscape assumes that knowledge work consists primarily of producing outputs. Reports, presentations, analyses and recommendations become the measurable products through which productivity is evaluated. Yet organizations rarely derive competitive advantage from documents alone. They compete through the quality of the decisions those documents support. A beautifully written recommendation based on an incomplete understanding of the environment remains strategically inferior to a less polished analysis that exposes uncertainty before resources are committed.

Seen from this perspective, the role of agentic systems begins to resemble something closer to institutional architecture than digital labour. Good institutions have never existed to eliminate disagreement. Their purpose has been to ensure that disagreement appears early enough to improve judgment. Constitutional courts, scientific peer review, investment committees and aviation safety investigations all slow the journey from observation to action in carefully chosen places because experience has demonstrated that unchecked certainty becomes extraordinarily expensive.

Perhaps AI should be designed with the same principle in mind.

Instead of asking whether an agent can make a decision independently, organizations might ask a different set of questions.

Which assumptions is this recommendation built upon?
What evidence would most likely change this conclusion?
Which perspectives are currently absent?
Where does confidence exceed available evidence?
Which part of this decision still depends on human judgment rather than computational capability?

Notice that none of these questions reduce the value of automation. They simply recognize that automation and judgment solve different organizational problems. One increases operational capacity. The other determines whether that capacity is directed toward the right objective.

This distinction becomes increasingly important as organizations evolve from isolated AI tools toward networks of collaborating agents. A single language model answering questions for an employee introduces one set of challenges. Hundreds of specialized agents coordinating product development, legal review, procurement, finance and customer operations introduce another. Once agents begin exchanging conclusions with one another, organizations face the possibility of creating systems that reinforce certainty at machine speed. If every agent accepts the outputs of every other agent without introducing mechanisms for challenge, contradiction or independent verification, the organization risks scaling coherence faster than understanding.

Perhaps the most valuable agent inside an enterprise will therefore not be the one that writes the report, negotiates the contract or schedules the meeting.

Perhaps it will be the one that interrupts.

The agent that notices conflicting assumptions between departments before anyone else does.

The agent that detects unusual confidence unsupported by evidence.

The agent that identifies the missing customer perspective in an otherwise compelling strategy.

The agent that quietly asks whether the organization has begun answering a question it never properly understood.

Those responsibilities sound remarkably familiar because organizations have always depended on people who performed exactly that role. The difference is that they rarely occupied positions of formal authority. They were the experienced architect who challenged an elegant design, the analyst who questioned the assumptions hidden inside a financial model, the physician who requested one additional test despite overwhelming consensus, or the strategist who noticed that everyone had become unusually comfortable with the current narrative.

The future of agentic AI may ultimately depend on whether we choose to replicate only the visible work those people performed or whether we also preserve the invisible function they served within the organization. The first path produces faster execution. The second strengthens institutional judgment itself.

VI. Judgment as Competitive Advantage

Every generation tends to believe that the defining technologies of its time fundamentally alter the conditions under which organizations compete. Sometimes they do. Railways changed geography. Electrification transformed manufacturing. The internet redefined distribution. Artificial intelligence will undoubtedly reshape many aspects of organizational life in ways we can only partially anticipate today.

What history also suggests, however, is that enduring competitive advantage rarely emerges from technology alone. Technologies diffuse. Best practices spread. Capabilities that once distinguished a handful of organizations gradually become available to everyone else. What remains difficult to replicate are the institutional habits through which organizations interpret reality, revise their assumptions and make decisions under conditions that remain fundamentally uncertain.

This observation deserves greater attention because it changes the way we think about intelligence itself. The coming decade will almost certainly produce organizations capable of generating extraordinary volumes of analysis, recommendations, simulations and strategic options. Access to explanation will continue to expand, while the cost of producing sophisticated reports, market assessments and technical evaluations will continue to fall. In many industries, these capabilities will become expected rather than exceptional. They will shape the baseline from which organizations operate, not the advantage that separates one organization from another.

The differentiator will increasingly shift elsewhere.

Not toward who has the most explanations.

Nor toward who automates the largest number of workflows.

But toward those organizations that remain capable of recognizing when their own explanations have become inadequate.

That capability has always occupied an unusual position within strategy. Markets rarely reward organizations for answering yesterday’s questions more efficiently than their competitors. They reward organizations that notice emerging questions while others continue refining outdated answers. Every major strategic inflection point contains this asymmetry. One organization continues optimizing an existing model because the evidence still appears internally coherent. Another begins questioning assumptions that have not yet visibly failed. Looking backward, these moments are often described as innovation or visionary leadership. Looking more closely, they frequently begin with something less dramatic: an institution preserving enough epistemic humility to recognise that reality may already have moved beyond its current understanding.

Seen in this light, judgment begins to look less like an individual attribute and more like an organizational capability. It is embedded in governance structures, decision processes, incentive systems and cultural norms that determine whether uncertainty is explored or suppressed. Organizations do not become adaptive simply because they employ intelligent people. They become adaptive because they create environments in which intelligent people can revise their thinking without treating revision as failure. Learning is not the accumulation of correct answers; it is the continuous recalibration of mental models as reality changes around them.

Generative AI enters this landscape as both an extraordinary opportunity and a subtle design challenge. It dramatically expands humanity’s capacity to produce explanations, synthesize information and coordinate increasingly complex work. Used well, these capabilities can elevate the quality of organizational decision-making in ways that were previously unimaginable. Used without careful attention to organizational design, they may also encourage institutions to mistake fluency for understanding and completion for judgment. The technology itself remains neutral. Its consequences depend on the architecture into which it is introduced.

Perhaps that is the deeper lesson emerging from this moment.

Organizations have spent decades investing in systems that improve access to information. We are now entering an era in which access to explanation is becoming equally abundant. The question quietly moving toward the centre of management is no longer whether people can obtain answers. Increasingly, everyone can. The more consequential question is whether organizations continue to preserve the conditions under which answers are challenged, refined and occasionally abandoned altogether.

That challenge reaches beyond management practice. It touches the way societies organize expertise, distribute authority and cultivate trust. Universities, scientific communities, engineering disciplines and democratic institutions all evolved around the recognition that certainty benefits from structured disagreement. Their objective was never to eliminate uncertainty but to prevent confidence from outrunning evidence. As organizations become increasingly AI-native, they inherit the same responsibility. The technologies may be unprecedented, yet the institutional challenge remains remarkably familiar: preserving the quality of judgment while expanding the scale of intelligence.

John and I approached this question from different directions. One of us spends much of his time thinking about how organizations build strategic capability, how decisions shape competitive advantage and how institutional learning becomes an enduring source of resilience. The other explores the architecture of agentic systems, orchestration and the operational models that will increasingly define AI-native enterprises. Those perspectives converge on a common conclusion. The future of organizational intelligence will not be determined solely by the sophistication of the systems we build, but by the quality of the environments into which we introduce them. The design of an agent and the design of an institution are becoming inseparable questions.

Perhaps that is where this conversation ultimately leads.

The defining organizations of the AI era will almost certainly produce better analyses, faster recommendations, and more efficient workflows than any generation before them. Those achievements will matter, but they are unlikely to be sufficient. Long after explanation becomes inexpensive and ubiquitous, organizations will continue competing through something that cannot be downloaded, prompted, or automated in isolation: the collective discipline of recognizing where their understanding remains incomplete, resisting the temptation of premature certainty, and creating institutions in which better questions continue to emerge before better answers are accepted.

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The Infinite Value of a Meeting Transcript for the AI-Fluent Operator: How to 100X the Value of Meeting Transcripts in Your Workflows and Business

John Brewton — Tue, 18 Aug 2026 10:22:18 GMT

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Most companies are using AI to solve the least interesting problem with meetings.

They record the call. They generate a transcript. They ask AI for a summary. Someone skims it, closes the window, and the team moves on with their business.

That is useful. But it dramatically underutilizes the opportunity at hand. A meeting transcript is not merely a record of a conversation. It is a structured source of decisions, commitments, customer intelligence, objections, deadlines, requirements, ideas, risks, owners, and institutional knowledge.

In an AI-NAtive company, all of those things can become inputs into other systems.

The transcript can update the CRM. It can create tasks. It can draft the follow-up email. It can write the project brief. It can generate a proposal. It can prepare an executive summary. It can tell someone who missed the meeting exactly what they need to know. Months later, an AI agent can search thousands of previous conversations and answer a question nobody remembers discussing.

That changes the purpose of the transcript entirely.

The transcript should not simply tell us what happened in the meeting. It should reduce the probability that another meeting is required.

That may eventually become one of the most important operating advantages of AI.

A Practical Example: Turning One Transcript Into a Finished Asset

Watch the Loom video here.

I wanted to show one small example of what this looks like in practice.

Every Friday, I host Operating Founder Office Hours for members of my paid Substack community. We spend the session working through questions from operators, builders, solopreneurs, and small-business owners. Those conversations can move across dozens of ideas, resources, frameworks, and problems in a single hour.

Historically, the recording itself would have been the primary asset.

Now the transcript becomes the starting point.

In the Loom above, I show Claude pulling the recording and transcript directly from Zoom and creating a comprehensive, anonymized set of session notes. I then have it run my Elite Research skill across the conversation, identifying the concepts, books, academic research, articles, and thought leadership connected to what we discussed.

That produces an expanded research library members can use to keep learning long after the call ends.

Finally, I use my JB Personal Brand skill to turn everything into a polished PDF that can be distributed alongside the recording.

One conversation becomes a summary, a knowledge resource, a research library, a learning tool, and a finished community asset without requiring me to manually reconstruct the session.

Members can then feed the document back into Claude or ChatGPT, ask which resources are most relevant to them, explore an individual topic, or have an extended conversation about the research.

Record it once. Structure it once.

Reuse the intelligence many times.

The same principle becomes much more powerful when applied across an entire company. Here is a sample of the weekly session notes that all Operating Founders receive from our weekly calls:

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What You’ll Learn in This Article

Why is the meeting transcript becoming an operating layer rather than just a record of what happened?
How transcripts can automatically improve CRM quality by capturing decisions, objections, commitments, stakeholders, and next steps.
How AI can turn meetings into execution by creating tasks, assigning owners, identifying deadlines, and surfacing blockers.
How better transcript workflows can eliminate future meetings, especially recap, handoff, status, and information-sharing calls.
How one conversation can become an asset factory, producing follow-ups, briefs, proposals, project plans, and internal documentation.
Why thousands of transcripts can become proprietary institutional intelligence that teams and AI agents can search and learn from.
The new KPI for meetings: not how well they were summarized, but how much work they created—and how many future meetings they made unnecessary.

What You’ll Get With This Article

The Meeting Transcript Operating System: Paid subscribers get a complete interactive workbook designed to be uploaded directly into Claude or ChatGPT. Bring one real meeting transcript, and the workbook walks you through building a repeatable system for extracting decisions and commitments, routing intelligence into your CRM and other tools, creating high-value assets automatically, identifying meetings that can be eliminated, and turning the finished workflow into reusable AI instructions, skills, scheduled tasks, and agents. You leave with a working Transcript-to-System Map, CRM Extraction Schema, Asset Factory, Meeting Elimination Report, Transcript Agent Instruction Set, Transcript Value Scorecard, and a 30-day implementation plan. This is not simply another set of prompts.

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

John Brewton — Sat, 15 Aug 2026 12:30:06 GMT

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A few years ago, we became obsessed with the concept of quiet quitting.

People stopped answering emails at night. They stopped volunteering for work that was not theirs. They stopped treating every request from an employer as an opportunity to prove how badly they wanted the next promotion.

We interpreted much of this as a decline in ambition.

I think we got that wrong.

The ambition did not disappear. It shifted directionally.

The person who no longer wants to spend Saturday preparing another presentation for their employer may be spending Saturday building a product of their own.

The marketer who stopped obsessing about becoming VP may be building an audience. The accountant may be learning how to code with AI. The salesperson may be testing a consulting business. The executive may be discovering that becoming CEO of a company she owns sounds more interesting than becoming EVP of a company she does not.

This is not quiet quitting.

It is quiet building.

Quiet quitting was about withholding additional effort from someone else’s company.

Quiet building is about redirecting that effort toward your own.

And I increasingly believe that quiet building will become one of the defining professional behaviors of the AI economy.

Jobs are becoming bundles of tasks that machines can increasingly help us complete. Skills are changing faster. Organizational capability is becoming less dependent on organizational headcount. Individuals are gaining access to capabilities that once belonged almost exclusively to companies.

The professionals who recognize this have an opportunity to become dramatically less dependent on a single employer.

The companies that recognize it have an opportunity to build organizations filled with some of the most curious, commercially minded and ownership-oriented people in the workforce.

Companies are becoming tech stacks.

We are all becoming companies.

And both sides of the employment relationship need to start acting in accordance with the reality of this change.

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The Safe Job Is Becoming a More Complicated Idea

For most of the last century, one of the safest economic strategies available to an ambitious person was relatively straightforward.

Get educated.
Develop a valuable skill.
Find a good company.
Become increasingly valuable to that company.
Get promoted.
Earn more.
Save.
Invest.
Retire.

There is nothing wrong with that strategy. Millions of people will continue to build wonderful lives that way. But I think it is dangerous to assume the structure of the job sitting underneath that strategy will remain as stable as it once appeared.

The International Monetary Fund estimates that roughly 60 percent of jobs in advanced economies are exposed to AI. Importantly, exposure does not mean elimination. In roughly half of those exposed jobs, the IMF believes AI could complement human work and increase productivity.

The International Labour Organization reached a similarly important conclusion. Its latest global work on generative AI says roughly one in four jobs worldwide has some degree of GenAI exposure, but job transformation is more likely than wholesale replacement because most occupations still contain tasks requiring human involvement.

The question employees should be asking is the following:

How much of the work currently inside my job still needs to be done by me three years from now?

The U.S. Bureau of Labor Statistics now explicitly incorporates AI into its employment projections. BLS expects AI and other automated systems to increase productivity in some occupations while reducing labor demand in portions of office support, administrative work, sales and other task-heavy categories. At the same time, it projects substantial growth in technology-intensive occupations such as data science.

I do not think the intellectually serious argument is that there are no safe jobs anymore. It is the following: It is becoming less safe to assume that today’s job description, today’s skills, and today’s workload will remain intact.

Increasingly, I think career security will be about becoming valuable to a market, not a single company.

Part of Your Job Will Disappear

We keep discussing AI at the occupational level.

Will AI replace programmers?

Will it replace marketers?

Lawyers?

Accountants?

Consultants?

Writers?

That framing misses the way work actually happens. A job is not one thing. It is a collection of things.

Researching. Writing. Scheduling. Analyzing. Calculating. Communicating. Reporting. Creating presentations. Updating systems. Answering questions. Coordinating people. Making decisions.

AI does not need to eliminate the entire collection to fundamentally change the economics of the job. It simply needs to begin compressing enough of the tasks inside it.

In one of the most important real-world studies of generative AI at work, researchers from Stanford and MIT studied thousands of customer-support workers. Access to a generative AI assistant increased productivity by roughly 14 to 15 percent, with the largest improvements concentrated among newer and less-skilled workers.

The broader Stanford AI Index now documents productivity improvements across multiple types of knowledge work, though the gains vary considerably depending on the task and the worker. That variability is important. AI is not magically making every worker twice as productive, and we should be suspicious of anyone pretending that it is.

But even modest productivity gains create an organizational question that becomes more important as the technology improves:

What happens when a role designed around 40 hours of tasks no longer contains 40 hours of economically valuable human work?

Maybe it contains 35.

Then 30.

Maybe a year later it contains 20.

The traditional organization has an instinctive response to this.

Fill the space. Add responsibilities. Add meetings. Add reporting. Add another project. Turn a productivity gain into more activity.

I think the better organizations will eventually ask a harder question.

Do we actually need this job to remain exactly what it was?

Companies Are Becoming Tech Stacks

This is the organizational transformation underneath all of this. The traditional company accumulated capabilities largely by accumulating people.

If you needed more sales capacity, you hired salespeople.
More research required researchers.
More design required designers.
More content required marketers.
More analysis required analysts.
More software required developers.
More administrative capacity required administrative employees.

There was a fairly direct relationship between the amount an organization could do and the number of qualified humans it could employ.

According to the OECD’s 2026 research on skills in the AI age, the share of firms using AI across OECD countries with available data rose from roughly 7 percent in 2021 to about 20 percent in 2025. The OECD attributes part of that acceleration to the arrival and diffusion of general-purpose generative AI tools. (OECD)

We are moving toward organizations composed of some combination of:

Employees + AI models + agents + software + data + automation + contractors + systems.

The company itself is starting to look more like a technology stack. Adding organizational capability no longer always requires adding organizational headcount.

A ten-person company can increasingly perform work that once required 20.

A five-person company can access research, design, development and marketing capabilities that once belonged to organizations with entire departments.

A single extraordinary operator can increasingly orchestrate systems that perform work previously distributed across several specialists.

If that continues, we should expect two things to happen at the same time. (1) Companies will need fewer humans for certain categories of work. (2) And the humans they do employ will need to become much more capable.

That is where the quiet builder becomes incredibly interesting.

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The Employee You Are Afraid of Losing May Be Exactly the Employee You Need

Think about the characteristics companies say they want from their employees.

Ownership.
Curiosity.
Initiative.
Commercial awareness.
Comfort with ambiguity.
Problem solving.
Adaptability.
Technology fluency.
Continuous learning.
Resourcefulness.

The World Economic Forum’s Future of Jobs research found that employers expect 39 percent of workers’ core skills to change by 2030. AI and big data are among the fastest-growing areas of skill demand, but employers also place tremendous value on analytical thinking, creative thinking, resilience, flexibility, agility and leadership.

We have another word for a person obsessed with developing most of those characteristics. Entrepreneur.

Companies are desperately trying to make their employees think more like owners, yet often become deeply uncomfortable when those employees begin to act like owners.

They want entrepreneurial thinking.

Just not too much entrepreneurship.

They want curiosity.

As long as that curiosity stays within the organization's walls.

They want employees obsessed with learning AI, understanding customers, improving margins, finding opportunities and creating systems. Then the employee starts a small company on the weekend and suddenly everyone gets nervous.

I think that mentality is increasingly backward.

Because the quiet builder may be exactly the employee you want.

Would you like to work with John?

A Side Business Can Be the Best Leadership Program Your Company Never Built

Something changes when you try to build a company.

I experienced it myself as an operator, and I see versions of it constantly in the founders and professionals I work with.

Learning stops being book-based and theoretical.

You do not learn sales because someone told you sales is important.
- You have to sell something.
You do not learn financial management because your company added a finance module to its leadership program.
- You are spending your own money.

You begin to understand ownership by owning something.

A quiet builder starts noticing costs. They start recognizing inefficient processes. They ask why the customer wants something rather than merely completing the request. They understand that revenue does not magically appear. They start thinking about distribution. They understand the value of speed. They begin to ask whether a process really needs seven steps, because, in their own company, every unnecessary step costs them time.

Quiet building can become an employee-development program the employer does not have to design or pay for.

Yes, companies need sensible restrictions.

Employees cannot steal intellectual property. They cannot misuse confidential information. They cannot build competing products on company time. They cannot allow an outside project to destroy their performance.

Those are reasonable boundaries.

But there is an enormous gap between protecting the business and believing the business should own someone’s entire professional ambition.

The companies that understand that difference will have an advantage.

Stop Treating Every Future Founder Like a Retention Failure

Companies have spent decades becoming obsessed with retention.

In most organizations, the logic is straightforward:

Great employee stays = win.

Great employee leaves = loss.

But what if that is the wrong time horizon?

Imagine a talented employee who has spent five years at your company. During the final two years, she begins building something on the side. The business grows. Eventually she reaches the point where keeping a full-time job no longer makes sense.

The traditional company views what happens next as attrition.

I think the more interesting company asks:

What should our relationship become now?

Maybe she becomes a consultant.
Maybe her new company becomes a vendor.
Maybe you become her first enterprise customer.
Maybe she develops technology your company licenses.
Maybe she helps train your people.
Maybe she becomes a distributor.
Maybe she sends extraordinary candidates back to you.
Maybe five years later you invest in her company.
Maybe you acquire it.

The employment relationship can end without the economic relationship ending.

That suggests a different philosophy of retention. Do not retain the employment contract at all costs. Retain the relationship.

The best relationship between two people today may be between an employer and an employee. Five years from now, it may be customer and vendor. Ten years from now, it may be two CEOs sitting on each other’s boards.

Organizations built for the next era should be capable of letting that relationship evolve.

Ten Ways Companies Can Support Quiet Builders

If quiet building is going to increase, companies need more sophisticated ways to work with it.

Here is where I would start.

1. Make responsible outside building culturally acceptable

Stop automatically equating a side business with disloyalty.

Create clear rules around conflicts, intellectual property, time and competitive activity. Then give responsible adults room to build.

2. Measure output instead of theatrical busyness

AI makes this increasingly important.

If an extraordinary employee can perform excellent work in 25 hours that once required 40, do not immediately reward that person with 15 hours of invented activity merely to preserve the appearance of a full calendar.

Reward outcomes.

3. Give builders problems instead of tasks

People developing an ownership mentality should be given opportunities to use it.

Tell them what needs to become true.

Give them appropriate authority.

See what they build.

4. Teach employees the economics of the company

Show people how revenue works.

Teach margins. Pricing. Customer acquisition. Retention. Cash flow. Capital allocation.

The more employees understand how the machine makes money, the more capable they become of improving it.

5. Let curious people cross functional boundaries

A future founder should understand more than marketing or finance or operations.

Let strong people learn how the other pieces work.

The resulting employee will become more valuable to you even if they never leave.

6. Subsidize AI fluency

If AI is going to change the structure of work, your people should become unusually capable with it.

Do not limit AI education to a two-hour compliance course.

Give people models. Tools. Sandboxes. Training. Time to experiment. Problems worth solving.

7. Create places to build inside the company

Not every entrepreneurial person wants to leave.

Give employees opportunities to launch products, automate workflows, test new businesses, create internal tools and own new initiatives.

A builder with enough surface area to build may happily keep building for you.

8. Experiment with flexible employment

A full-time job does not need to be the only relationship available.

Certain roles can evolve into four-day schedules, fractional positions, reduced-hour arrangements and project-based relationships.

Not every job allows this.

More jobs will than companies currently assume.

9. Build an employee-to-vendor pathway

When a quiet builder reaches the point where the outside company deserves full-time attention, have a mechanism for asking whether your company should become a customer.

Graduating from payroll to accounts payable does not have to be a breakup.

It can be a promotion in the relationship.

10. Build an extraordinary alumni network

Today’s employee could become tomorrow’s customer, supplier, consultant, founder, investor, referral source, strategic partner or acquisition.

Think in decades.

A company that produces successful founders should celebrate that reputation, not hide from it.

Quiet Building Is Career Insurance

Your side project does not need to become a $10 million company to completely change your career.

Maybe it creates $25,000 a year in additional income.
Maybe it gives you 10,000 people who pay attention when you publish something.
Maybe it teaches you to sell.
Maybe it turns you into the best AI operator in your field.
Maybe it gives you 20 customers who know that you can solve a specific problem.
Maybe it builds relationships with people across your industry.
Maybe it establishes your reputation around an expertise that exists independently of your employer.
Maybe it eventually becomes a company.

Every one of those outcomes creates leverage. And leverage reduces dependence. For most of the industrial era, the safest career strategy was to become increasingly indispensable to a single powerful institution.

That institution had the capital, the people, the technology, the customers, distribution, and brand. You needed access to the institution to access those capabilities. That is changing.

The old career insurance policy was becoming indispensable to one employer.

The new one may be becoming valuable to a market.

We Are Already Becoming Companies

America already contains an enormous economy of companies without employees.

According to the latest U.S. Census Bureau data, there were approximately 30.4 million U.S. nonemployer businesses in 2023 generating nearly $1.8 trillion in receipts. (Census.gov)

These are businesses with no paid employees.

Consultants.
Creators.
Tradespeople.
Independent professionals.
Small merchants.
Owners of rental businesses.
Freelancers.
Specialists.
Entrepreneurs of every imaginable kind.

The important point is not that all 30.4 million of those businesses resemble the AI-powered companies I am describing. They clearly do not. The point is that operating as a company without building a traditional employee base is already an enormous part of the American economy.

Now place AI on top of it. One person can increasingly access capabilities that once required entire teams, from sophisticated research, software development, design, financial modeling, writing, and video production to marketing, automation, data analysis, customer support, sales enablement, and strategy.

And increasingly, AI agents can do more than assist with individual tasks. They can execute multi-step workflows, coordinate work across tools, and give a single operator leverage that once belonged almost exclusively to larger organizations.

You no longer necessarily need to hire a company before you can become one.

Quiet Building Is Not Quiet Quitting 2.0

There is an important difference between these ideas.

Quiet quitting says:
My employer does not own more of me than it pays for.
Quiet building adds something much more important:
And I am going to invest the rest of me somewhere.

Quiet quitting asks how much work you are obligated to give. Quiet building asks what you are going to do with the ambition, curiosity, technology and time that remain.

That is why I find quiet building so much more exciting.

It is not anti-company.

It is not anti-career.

It is not anti-work.

It is pro-ownership.

And the best companies should recognize the value they can capture by embracing this changing dynamic in the market for work.

Companies Are Becoming Tech Stacks. We Are All Becoming Companies.

This may be the larger transition hiding underneath everything else. Companies are becoming smaller collections of extraordinarily capable people orchestrating increasingly sophisticated technology stacks. At the same time, extraordinarily capable people are accumulating enough technology, distribution and expertise to function increasingly like companies.

Those two trends are going to collide.

And when they do, I do not think the future needs to become some simplistic competition between employers and employees.

A much more interesting arrangement is possible.

Companies can become places filled with people learning how to build. Some will remain employees for decades, some will build new businesses inside the organization, some will create side businesses that never become their primary work, and some will eventually become founders, vendors, customers, or partners. Occasionally, one of them will build something enormous, and the company that helped them along the way should not see that as a failure, but as evidence that extraordinary people want to spend part of their journey there.

For professionals, the lesson is equally important: do not wait until your job disappears to discover whether you can create economic value without it. Do not wait for the layoff, the reorganization, the moment your skills become obsolete, or the day your company realizes one AI-enabled employee can do what once required three people. Build, learn, experiment, create, develop relationships, and find customers while you are still employed, so you develop the muscle of ownership before your life requires you to use it.

The corporation once possessed capabilities an individual could never reasonably reproduce. Those advantages have not disappeared.

But technology is steadily unbundling many of them and making pieces available to individuals one subscription, model, agent and platform at a time.

That changes what a company can be.

It changes what an employee can be.

And I think it fundamentally changes what ambition can look like.

The safest future may not belong to the person with the safest job.

It may belong to the person with the greatest number of ways to create value.

Quiet quitting asked how little of yourself you owe your employer.

Quiet building asks a much better question:

What are you going to build to become an owner?

Are You a Quiet Builder or CEO that Would Like to Work with John?

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Research Appendix

Artificial Intelligence and Employment Exposure

International Monetary Fund — AI Will Transform the Global Economy
The IMF estimates that almost 40 percent of global employment is exposed to AI, rising to approximately 60 percent in advanced economies. Importantly, exposure can lead to either displacement or productivity-enhancing complementarity.
Read the IMF analysis. (IMF)

International Labour Organization — Generative AI and Jobs: A Refined Global Index of Occupational Exposure
The ILO’s 2025 task-level analysis concludes that approximately one in four jobs worldwide has some exposure to generative AI while emphasizing that job transformation is generally more likely than complete occupational replacement.
Read the ILO research. (International Labour Organization)

U.S. Bureau of Labor Statistics — Industry and Occupational Employment Projections
The BLS incorporates AI-related productivity effects into its employment projections, anticipating declining demand in some occupations while projecting growth in others as AI changes the composition of labor demand.
Read the BLS analysis. (Bureau of Labor Statistics)

AI and Productivity

Erik Brynjolfsson, Danielle Li and Lindsey Raymond — Generative AI at Work, NBER
This field study examined the deployment of a generative AI assistant among thousands of customer-support agents and found an average productivity improvement of roughly 14 percent, with disproportionately large benefits for less-experienced workers.
Read the NBER paper. (National Bureau of Economic Research)

Stanford Institute for Economic Policy Research — What Is Really Happening to Jobs?
Stanford’s 2026 review separates emerging evidence from stronger claims about widespread AI unemployment. It finds substantial evidence of productivity effects alongside early employment pressure in certain AI-exposed categories, particularly among younger workers.
Read the Stanford analysis. (SIEPR)

Stanford HAI — 2026 AI Index Report
Stanford’s annual AI Index compiles research on the economic effects, adoption and capabilities of artificial intelligence, including measured productivity improvements across multiple categories of knowledge work.
Explore the 2026 AI Index. (Stanford HAI)

AI Adoption and Changing Skills

OECD — Skills in the AI Age
The OECD reports that AI use among firms in countries with available data increased from roughly 7 percent in 2021 to approximately 20 percent in 2025, illustrating how quickly AI is moving from experimentation toward ordinary business infrastructure.
Read the OECD report. (OECD)

World Economic Forum — Future of Jobs Report 2025
Based on a survey of more than 1,000 major employers representing over 14 million workers, the report estimates that 39 percent of workers’ core skills will change by 2030. AI and big data skills are among the fastest-growing areas of demand, while analytical thinking, resilience, flexibility, leadership and creative thinking remain important.
Read the Future of Jobs Report. (World Economic Forum)

The One-Person Business Economy

U.S. Census Bureau — 2023 Nonemployer Statistics and Business Owner Characteristics
The Census Bureau reports approximately 30.4 million U.S. nonemployer businesses in 2023 generating nearly $1.8 trillion in receipts, demonstrating the enormous scale of economic activity already occurring through businesses without paid employees.
Read the Census Bureau release. (Census.gov)

U.S. Census Bureau — The Small Business Story
The Census Bureau’s 2026 review reports 30,427,808 nonemployer establishments in 2023, up from 29,811,495 in 2022, with total revenue approaching $1.8 trillion.
Read the Census Bureau analysis. (Census.gov)

Stop Paying For Advice. Start Investing In Operating Systems.

John Brewton — Wed, 12 Aug 2026 10:28:36 GMT

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Most coaching ends the same way.

You leave with notes, a few good ideas, and the intention to do the work.

Three weeks later, the notes are cold. The ideas are still ideas. The thing that was supposed to change inside the business has not actually evolved.

You paid for advice. But what you needed was a system.

That realization has shaped a lot of how I think about coaching, consulting, and advisory work now.

The traditional model has an implementation problem.

The coach explains. The client understands. The call ends. Then responsibility for translating that understanding into something operational gets handed straight back to the person who was already too busy, too close to the problem, or too unsure of the solution to fix it alone.

There is nothing inherently wrong with advice. I have benefited enormously from good advice throughout my career.

But increasingly, I do not think advice should be where the work stops.

Especially now.

AI gives us the ability to take what used to live in a strategy document, a set of notes, or somebody’s head and turn it into a working system.

That changes what I think clients should expect from me.

And it is why I built The Operating Project.

Start Your Operating Project

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Why I Built This

Before I became a coach, advisor, writer, and investor, I was an operator.

I spent years helping operate, optimize, and scale my family’s B2B industrial distribution company from $27.5 million to $75 million in revenue before we sold the business in August 2021.

This year alone, I have spent more than 400 hours working directly with more than 100 founders, entrepreneurs, operators, and professionals.

Those engagements have taught me an enormous amount.

They have also made one pattern impossible to ignore.

The people I work with rarely lack ideas.

They usually lack a reliable way to turn an important idea into something that works repeatedly inside the business.

Sometimes the problem is an offer.
Sometimes it is pricing.
Sometimes it is positioning.
Sometimes the founder knows they need to publish consistently but have no content system.
Sometimes there is simply one stubborn operating problem inside a larger company that everybody can see and nobody has been able to solve.

The answer is rarely another 60-minute conversation about the problem.

What people need is a way to move from:

problem → decision → design → build → operating system.

That is what The Operating Project is designed to do.

One Problem. One Outcome. One Working System.

The Operating Project is a four-session engagement in which we take one important problem in your business from unresolved to solved—and build the system that helps keep it solved.

You bring the problem.

I bring the structure, the operating frameworks, the worksheets, the thinking, and the build.

Before we begin, we define what finished looks like.

Then we work toward it.

This is not open-ended coaching.

It is not a standing consulting retainer.

It is not four calls where I give you ideas and send you away with homework.

It is one operating problem, scoped to one outcome, worked on until something useful exists.

Bring Me the Problem You Keep Circling

You do not need to arrive knowing exactly what the solution is.

You need to know which problem matters.

Here are four kinds of projects I expect to work on most often.

1. Your offer or business model is not working

Maybe the pricing is wrong.
Maybe the offer is confusing.
Maybe customers do not understand what they are buying.
Maybe you have several products but no coherent architecture connecting them.

We can work through the economics, offer design, pricing, packaging, positioning, and go-to-market logic until the model makes sense.

The finished result might include an offer map, a pricing model, a product architecture, or a decision system for evaluating future offers.

2. Your brand feels different every week

Your website says one thing.
Your LinkedIn says another.
Your pitch deck says something else.
Every new piece of content feels like another attempt to rediscover what the company stands for.

We can build the positioning, messaging, brand architecture, and identity system that creates consistency across the business.

The goal is not simply a prettier brand.

It is a system that helps you make better brand decisions repeatedly.

3. Content keeps becoming a weekly emergency

You know you should publish.
You may even know what you should publish.
But every week still begins with the same question:
- What am I going to make this week?

We can build the strategy, recurring formats, editorial calendar, research process, production workflow, and AI infrastructure that turns content from an act of recurring inspiration into an operating system.

4. Something inside the company is simply broken

Maybe it is a workflow.
Maybe it is a decision process.
Maybe it is a sales problem, an operating bottleneck, a reporting problem, or a process everybody has learned to work around.

You do not need to rebuild the company.

You need to isolate the problem, redesign how the work should happen, and create a practical system around the fix.

You Do Not Leave With Homework. You Leave With Infrastructure.

This is the part of The Operating Project that matters most to me.

Most advice dies because it stays a document.

A document still depends on you remembering it.

A framework still depends on you applying it.

A strategy still depends on somebody translating it into work every time the work needs to happen.

A machine is different.

If we redesign your content strategy, I do not want to stop at a PDF describing it.
- I want to help you build the Claude Project and ChatGPT Project, skills, instructions, templates, research process, workflows, and supporting assets that enable the strategy to actually produce content.
If we redesign your offer, I do not want you to leave with a single pricing worksheet that you use once.
- I want to help you build a decision system you can use when creating your next offer.
If we solve an operating problem, I do not simply want to describe the new process.
- I want us to install the logic behind that process somewhere your business can use it again.

The objective is not for you to remember everything I told you.

The objective is to encode what we built.

That is what AI makes possible now.

And I think it fundamentally changes what good advisory work can look like.

What You Might Leave With

Every project will be different because every problem is different.

Depending on the engagement, you might leave with:

A redesigned offer and pricing architecture;
A complete positioning and messaging system;
A 90-day content strategy and editorial calendar;
A Claude or ChatGPT Project configured around your business;
Reusable AI skills, prompts, instructions, and workflows;
A business-model or financial decision framework;
An operating process your team can immediately begin using;
Research systems and templates for recurring work;
A diagnostic framework that helps you make the same class of decision in the future;
The actual documents, worksheets, models, and tools we create together.

The point is that there is something at the end.

Something you can open.

Something you can use.

Something that exists inside the business after the engagement is over.

The Four Sessions

The Operating Project runs through four moves:

1. Define

We diagnose the problem in plain language.
We determine what is actually causing it, decide what needs to change, and define the artifact or operating outcome we want to hold at the end.
We also establish the scope.
What are we solving?
What are we not solving?
What does done mean?

2. Design

Then we build the solution.

Depending on the project, that might mean pricing models, offer architecture, positioning frameworks, operating processes, content calendars, decision trees, or another custom framework.

This is not something I disappear and create in a black box.

We build it around the reality of your business.

3. Build

Then we turn the solution into a machine.

This is where we build the working system inside Claude, ChatGPT, or another appropriate part of your workflow.

We create the projects, instructions, skills, prompts, reusable assets, and processes required to make what we designed repeatable.

You own everything we build.

It lives in your account.

4. Install

Finally, we run the system on live work.

We test it.
We find where it breaks.
We refine it.
We make sure you know how to use it.

And we define what happens after the project closes.

Define. Design. Build. Install.

That is the Operating Project.

Start Your Operating Project

I Do Not Sell You Four Hours

The sessions are not timed.

Some may take 30 minutes.

Some will take longer.

We might work across more than four sessions.

The unit of measurement is not the clock.

It is progress.

We stop when we have completed the work required for that stage of the project.

That matters because I do not want you thinking about whether we have seven minutes left in the session.

I want us thinking about whether the problem moved.

A Plan Tells You What to Do. A Machine Does It for You.

There is a much bigger idea underneath this offer.

For most of my career, business advice had an unavoidable limitation.

Eventually the advisor had to leave.

The consultant delivered the deck.

The coach ended the call.

The operator went home.

The client was left with the process of turning all of that expertise back into action.

AI changes the economics of that handoff.

We can now begin encoding expertise into systems the client continues to own and use. This capability makes good thinking more durable.

That is the principle I want The Operating Project to embody.

You should not simply become better informed because we worked together.

Your business should become better equipped.

What People Are Saying

Start Your Operating Project

Who The Operating Project Is For

I think this engagement works best for founders, operators, creators, and business owners who have one meaningful problem worth solving now.

It is especially well suited to people who want to build rather than simply discuss.

You should be willing to make decisions, work through ambiguity, and use AI as part of the operating system we create.

You do not need to be an AI expert.

You do need to be willing to use the system once we have built it.

The Operating Project is probably not the right engagement if what you primarily want is general accountability, broad life coaching, indefinite access to an advisor, or someone to operate your company on your behalf.

This is finite by design.

One problem.
One project.
One outcome.

The Terms

The Operating Project is $999.

Your seat includes:

Four outcome-based working sessions.

All of the frameworks, worksheets, models, and operating assets we build together.

The Claude or ChatGPT system we create around the solution.

A full year of VIP access to my weekly Friday office hours.

Up to one year to schedule and complete your four project sessions.

There is no retainer.
There is no recurring consulting commitment.
There is no requirement that you start next week.

You can reserve the seat now and begin when the right problem is ready.

I would rather help you solve an important problem six months from now than manufacture an unimportant one because you bought today.

Start Your Operating Project

Bring Me One Problem Worth Solving

I am grateful for the more than 100 people who have trusted me with their businesses, careers, ideas, and ambitions this year.

Many of them have become friends and collaborators.

They have also helped me understand the kind of work I want to do more of.

I want to work on consequential problems with people who are serious about building.

I want the work to have a finish line.

And I want what we build together to keep producing value after I leave.

That is what The Operating Project is designed to do.

Bring me one problem worth solving.

We will define it, solve it, build the system around it, and put that system to work inside your business.

You will not leave with more homework.

You will leave with something that works.

Start Your Operating Project

— John

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 helping scale his family’s B2B industrial distribution company from $27.5 million to $75 million in revenue and selling the business in 2021, he has worked with business owners, founders, operators, and investors on company building, operating strategy, AI-enabled workflows, and growth. He is the founder of 6A East Partners, a research and advisory firm exploring the future of companies.

We Are All Becoming Companies.

John Brewton — Mon, 10 Aug 2026 11:38:06 GMT

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.

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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.

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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:

Credible source material + personal experience + external amplification = breakthrough.

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

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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)

A Conversation About The Importance of Building Real Human Relationships & Friendships in the Creator Economy w/ Special Guest and Great Friend and Entrepreneur Cory Blumenfeld

John Brewton — Fri, 07 Aug 2026 15:31:50 GMT

Available for iOS and Android

I Built Too Much. Here’s What I’m Cutting, and What I’m Scaling Instead

John Brewton — Fri, 07 Aug 2026 11:51:18 GMT

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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.

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:

Teach many people at once.
Build working tools that people can operate without me.
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.

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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.

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.

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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.

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.”

How I built a five-member Excel team in Claude and how you can too.

John Brewton — Wed, 05 Aug 2026 12:11:45 GMT

The best AI model in one financial-modeling benchmark edits 89.69 percent of the correct cells.

It gets only 34 percent of the final answers right.

That gap explains the problem with most AI spreadsheet workflows.

The model can make a lot of plausible-looking changes. It can move quickly. It can even appear productive. But none of that guarantees that the final answer is correct, traceable, or safe to use.

That is why I spent this morning building a 17-page spreadsheet operating system instead of writing another “better” prompt.

I did not build one all-purpose spreadsheet agent.

I built a five-role team.

Here is how I got there.

Watch my live loom recording of the process here.

First, Understand What the Workbook Is

Before I explain the research or introduce the five roles, it is worth explaining what I mean by a workbook.

This is not a workbook in the traditional sense.

It is not a document you print, complete by hand, and place in a binder. It is also not simply a collection of prompts.

The workbook is a system of instructions that the AI reads and then uses to interview you. You upload it into a new working session and ask the model to walk you through it. The model follows the rules inside the workbook, asks you a structured series of questions, and collects the information it needs to build a working AI team around the way you actually operate.

That distinction matters.

Most generic AI workflows begin with a finished prompt. Someone else decides how the work should be done, packages those assumptions into a template, and hands it to you.

The workbook begins one step earlier.

It helps the model learn how you need the work to be done before it starts building anything. For spreadsheet work, that means collecting details such as:

The types of analysis you perform
The workbooks and files you regularly use
The outputs you need to produce
The formulas and modeling standards you expect
The software and data sources involved
The assumptions that must always be stated
The controls required before work can be published
The recurring tasks that may eventually be scheduled

The result is not one enormous prompt that tries to anticipate every possible spreadsheet request.

The result is the infrastructure needed to handle many different requests consistently.

Why the Workbook Is Built Around Questions

The quality of an AI system depends heavily on the quality of the context behind it.

But most people do not naturally provide that context in one clean, complete prompt.

They leave out details because those details feel obvious to them. They forget to explain how a file is organized, which source should be treated as authoritative, what level of precision is acceptable, or what must happen before an answer can be shared.

Those omissions become failures later.

The workbook is designed to prevent that by turning system design into a guided conversation.

Instead of asking you to sit down and write a complete operating manual from scratch, the model asks one question at a time. Your answers become the raw material for the skills, role instructions, rules, checklists, and scheduled tasks that the team will use.

This makes the process more accessible, but it also makes it more rigorous.

The questions force decisions that a loose prompt allows you to avoid.

What is the team responsible for?
What is outside its authority?
Which files must it inspect?
Which software must it use?
What must it produce before another role can begin?
What conditions should stop the workflow?
How should the final work be checked?

By the end, the model is no longer guessing what “good spreadsheet work” means to you. You have defined it.

Why the Instructions Live in a Workbook

I use a workbook because the system needs to be portable, repeatable, and visible.

A good conversation with an AI can produce excellent work once. But conversations are easy to lose, difficult to audit, and hard to reproduce with another person or in another session.

The workbook gives the process a durable structure.

It contains the questions to ask, the order in which to ask them, the rules that must be followed, and the outputs that should be created from the answers.

That means I can upload the same workbook into a clean session and have the model run the process again without relying on the memory of a previous conversation.

It also means I can give the workbook to someone else.

They do not need to understand every decision I made while designing it. They can upload it, ask the model to follow it, answer the questions, and build a version of the system that reflects their own work.

The workbook is therefore doing two jobs.

First, it captures the operating logic behind the system.

Second, it teaches the user how to build that system for themselves.

Why the Workbook Comes Before the Team

The five roles I built are important, but the roles are not the starting point.

The workbook is.

Before the AI can build an Intake Clerk, Analyst, Modeler, Auditor, or Publisher, it needs to understand what those roles mean inside your environment.

Your Analyst may need to prioritize cash flow, while someone else’s Analyst focuses on inventory or customer acquisition.

Your Modeler may work inside Excel with files stored in SharePoint. Another company may use Google Sheets, a finance platform, and data exported from its ERP.

Your Auditor may need to reconcile every result to the general ledger. Someone else may need to compare it with a prior board report or an external data source.

The role names can remain the same while the actual instructions change substantially.

The workbook collects the information needed to make those roles specific.

It turns a general team design into your team design.

That is why I do not begin by telling the model, “Build me five spreadsheet agents.”

I begin by giving it a structured process for learning what those agents must know, what they are allowed to do, what they must never do, and how their work should move from one role to the next.

The Workbook Is the Control Layer

A useful way to think about the workbook is as the control layer between you and the AI team.

The roles perform the work.

The skills give the roles specialized methods.

The connected software, files, and data give them access to the materials they need.

The scheduled tasks determine when recurring work happens.

But the workbook defines how all of those pieces should be assembled.

It creates the instructions that govern the system before the system begins operating.

That is why I have started building workbooks for more and more types of work. They give people a repeatable way to create new AI capacity without needing me to personally design every team, skill, or workflow for them.

Once you understand how to build one, you can use the same approach for inbox management, research, financial analysis, reporting, project management, or almost any other structured knowledge-work process.

You are no longer collecting isolated prompts.

You are learning how to design the team that will use them.

Now for the process I documented for you today…

Phase 1: Research the failures

Time: 40 minutes

I began with one question:

What actually goes wrong when AI works inside a spreadsheet?

I limited the research to benchmarks and spreadsheet audits. No opinions. No productivity claims. Just documented failure modes.

Three findings shaped the entire system.

1. AI often fails because it does not inspect the workbook properly

SpreadsheetBench 2 evaluates AI agents on multi-sheet workbooks with real formulas, dependencies, and linked calculations.

The best model achieved 34.89 percent overall accuracy.

On debugging tasks, it scored just 12 percent.

The researchers identified two recurring problems:

Insufficient inspection
Incorrect target selection

In plain English, the models often failed before they started calculating.

They did not fully understand the workbook, or they changed the wrong cells.

That is not primarily a math problem. It is a looking problem.

2. AI can produce the right number in the wrong way

WorkstreamBench evaluates agents on end-to-end financial tasks.

The best agent scored 69.1 out of 100. Once the task required several chained calculations, the score fell to 53.4.

One of the most important documented failures was deceptively simple: agents sometimes pasted a correct number into a cell where a formula should have been.

The answer may be right today.

But the workbook is now broken.

Change an assumption next week, and the pasted number will not update. A reviewer cannot follow the calculation. The spreadsheet becomes a static answer rather than a working model.

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3. Review cannot be treated as an afterthought

Raymond Panko’s field audits found errors in 91 percent of operational spreadsheets.

A single reviewer typically catches about half of the errors.

A second reviewer, working independently and using a checklist, can raise detection to roughly 80 percent.

That matters because most AI spreadsheet workflows ask one agent to inspect the data, choose the method, build the model, calculate the result, and verify its own work.

That is the spreadsheet equivalent of asking an analyst to approve their own analysis.

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Phase 2: Design the team

Time: 20 minutes

The research killed my original idea.

I had planned to build one spreadsheet employee, similar to the inbox employee I taught last month.

But the evidence pointed in another direction.

Inspection, analysis, modeling, and verification are not one job. They are separate jobs with different failure modes.

Combining them inside one large prompt does not create a capable employee. It creates an analyst checking their own work.

So I divided the workflow into five roles.

Each role has:

One primary responsibility
One artifact it must hand to the next role
One action it is not allowed to take

The Intake Clerk

The Intake Clerk profiles the workbook before any analysis begins.

It identifies the sheets, tables, formulas, inputs, outputs, date ranges, missing fields, and potential data-quality problems.

It is not allowed to calculate the answer.

Its job is to understand the environment before anyone starts changing it.

The Analyst

The Analyst decides how the question should be answered.

It defines the method, assumptions, required calculations, and level of precision before producing a result.

The rule is simple:

State the method before stating the number.

The Modeler

The Modeler turns the approved method into a live workbook.

It builds formulas, links assumptions to outputs, and creates a model that recalculates when the inputs change.

It is not allowed to hardcode a value into a computed column.

If a number is calculated, there must be a formula behind it.

The Auditor

The Auditor independently re-derives the headline number using a second method.

It checks the formulas, assumptions, units, ranges, and reconciliation gaps.

It also has the authority to stop the workflow.

If the analysis does not hold, the Auditor issues a hold.

The Publisher

The Publisher prepares the finished workbook and communicates the result.

But it cannot publish over an audit hold.

That rule is what turns the system into a team rather than five prompts pasted one after another.

Every role can reject the work it receives.

Phase 3: Build the operating system

Time: 45 minutes

The finished workbook is 17 pages.

It includes:

Nine questions that define your analysis standards
Eight prohibited spreadsheet habits
Four prompts to run in sequence
Six reusable prompts for future analyses

The prohibited habits may be the most important part.

They include:

Pasting values into cells that should contain formulas
Reporting numbers with no supporting cell reference
Applying assumptions without stating them
Presenting more precision than the underlying data supports
Editing a workbook before profiling its structure
Publishing an answer that has not been independently reconciled

Each prohibition is paired with a replacement rule.

That distinction matters.

Telling an AI what not to do is rarely enough. You also have to define what it should do instead.

And every rule in the workbook traces back to a documented failure mode. These are not formatting preferences. They are controls.

What this system actually gives you

Most people use AI in spreadsheets because they want speed.

But speed is not the main constraint.

A fast answer with no formula trail still has to be checked by hand. That manual review is the work you were trying to eliminate in the first place.

The real opportunity is not faster analysis.

It is additional analytical capacity.

This system gives an operator access to three functions that many small teams have never been able to staff properly.

A data profiler

Someone examines the workbook before the analysis begins.

They identify what is present, what is missing, what is linked, and what may be unreliable.

A modeler

Someone builds a workbook that continues working when the assumptions change.

The goal is not a spreadsheet that was correct once. It is a model that remains useful.

An auditor

Someone independently checks whether the headline number holds.

This is the role most small finance teams do not have. It is also the role the research suggests matters most.

A private company with a four-person finance team may never have had a second analyst independently re-derive a number before it reached the board.

Historically, that verification could cost as much as the original analysis.

It does not have to anymore.

The final output is not simply a number.

It is:

The number.
The method above it.
The formulas beneath it.
And an independent check beside it.

What to try this week

Choose one analysis you completed manually last month.

Pick something where you already know the correct answer.

Then run it through the five roles in order.

Have the Intake Clerk map the workbook before making any changes.

Have the Analyst explain the method before calculating the result.

Have the Modeler prove that the headline cell contains a formula rather than a pasted value.

Have the Auditor calculate the answer a second way and report the difference between the two results.

Every place where the AI’s work differs from your manual process should become a new rule in your operating profile.

Repeat that process for a week, and the system becomes more consistent.

Then use the Auditor on the spreadsheets your team produced last quarter.

Panko found errors in 91 percent of operational spreadsheets.

There is no reason to assume ours are the exception.

Become a paid subscriber to receive the working and build your Excel team this week!

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And if building this kind of AI fluency 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.

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

John Brewton — Tue, 04 Aug 2026 11:52:01 GMT

By John Brewton
Operating by John Brewton

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At 5:07 AM, the house is quiet. The work laptop is still closed.

On a different computer, purchased with personal money and connected to personal accounts and applications, and filled with photos from last week’s Summer vacation to the beach, someone is preparing a proposal for a prospective customer to be sent before breakfast. At 8:30 AM, the computer will be put away, and at 9:00 AM, this person will begin the job that still pays the mortgage, provides health insurance, and finances the experiment.

From the company’s perspective, something may have changed.

This employee is no longer volunteering for every optional committee. They are not looking for another internal initiative to inherit. They are not treating every evening as overflow capacity for the organization. They still do the job. They still meet their commitments. They still act professionally.

But the surplus ambition has gone somewhere else.

Corporate America may call this quiet quitting.

I think something more interesting is happening.

For generations, the standard path for an ambitious person was reasonably clear. Join a good organization. Learn its systems. Take on increasingly important assignments. Earn promotions, bonuses and stock options. Accumulate responsibility and convert extraordinary effort into a better position inside the institution.

There is nothing wrong with that path.

A strong company can provide training, mentorship, capital, infrastructure, meaningful collegial relationships, distribution and opportunities that would be difficult to assemble independently. For many people, climbing the corporate ladder will remain the most intelligent way to build a career and provide for a family.

But it is no longer the only serious route available.

Cloud software, global payment systems, digital distribution, online marketplaces, independent contractors and generative AI have dramatically reduced the amount of infrastructure a person needs to begin operating a business. Work that once required a small department can increasingly be performed by one capable operator coordinating software, automation and outside specialists.

That does not mean building a company has become easy.

It means it has become possible for more people to try.

And that changes the destination and direction of our collective ambition.

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Quiet quitting is the wrong phrase for a real shift

“Quiet quitting” became a popular term in 2022, but it has never had a stable definition.

For some people, it means refusing to work beyond the responsibilities and hours of the job. For others, it means becoming psychologically detached while continuing to collect a paycheck. For some managers, it describes any employee who stops providing an unlimited supply of discretionary effort.

Gallup’s original analysis associated quiet quitting with employees who were “not engaged.” These were people who might perform the minimum required but felt psychologically disconnected from the organization. Gallup estimated that this group represented roughly half of the U.S. workforce in 2022.

But Derek Thompson argued in The Atlantic that the supposed trend was neither statistically new nor especially revolutionary. What social media had renamed quiet quitting had previously been known as having a job without allowing it to consume your identity.

Both interpretations contain some truth.

The label may exaggerate the novelty of workplace disengagement. But the underlying tension is real. In the first half of 2026, Gallup reported that 31% of American employees were engaged and 18% were actively disengaged. Engagement remained well below its 2020 level.

At the same time, this is not a universal story of workers rejecting their careers. Pew Research Center has found that most employees, including most younger employees, remain at least somewhat satisfied with their jobs.

The phrase quiet quitting therefore collapses several different behaviors into one:

There is boundary-setting, in which an employee performs the agreed job but stops treating every optional request as an obligation.
There is withdrawal, in which an employee becomes indifferent to quality, customers, and colleagues.
And there is strategic reallocation, in which an employee continues to perform professionally but directs more of their nonworking time toward building an asset they own.

This article is about the third group.

It is not an argument for doing poor work, falsifying hours, missing deadlines, or transferring your workload to coworkers. As Anthony Klotz and Mark Bolino noted in Harvard Business Review, withdrawal can damage team relationships when colleagues must absorb the responsibilities someone else has abandoned.

That is not entrepreneurship.

That is simply failing to keep a promise.

The defensible version is much cleaner:

Do the job. Meet the standard. Keep your commitments. But stop assuming that every remaining ounce of ambition belongs to the company issuing your paycheck.

The corporation has lost its monopoly on leverage

The modern corporation became powerful partly because it could coordinate resources an individual could not afford alone.

It assembled people, information, machinery, technology, distribution and capital under one roof. If you wanted access to that leverage, you usually needed to enter the institution and rise through it.

That bargain still exists. But technology has weakened its exclusivity.

Research on digital entrepreneurship has documented how technologies including cloud computing, mobile platforms, analytics and social media have changed the boundaries of entrepreneurial activity. Founders can acquire capabilities as services, reach customers directly and reorganize ventures more rapidly than was previously possible.

Generative AI has accelerated that change, although its effects are uneven.

In a controlled experiment involving professional writing tasks, Shakked Noy and Whitney Zhang found that participants using ChatGPT completed their work about 40% faster while average output quality rose by 18%.

A large workplace study subsequently published in The Quarterly Journal of Economics found that access to an AI assistant increased productivity among customer-support agents by an average of approximately 15%, with the largest gains accruing to less experienced workers.

But AI is not a universal competence machine. Research involving 758 Boston Consulting Group professionals found a “jagged technological frontier.” AI substantially improved performance on some knowledge tasks while making people more likely to produce incorrect answers on tasks outside the technology’s capabilities.

That distinction matters.

AI can increase the reach of judgment. It cannot eliminate the need for judgment.

It can help one person research, write, code, analyze, respond to customers, and automate administration. It can also help that person make a polished mistake much faster.

The opportunity is real, but so is the obligation to verify the work.

The one-person company is no longer structurally absurd

The solo economy is already much larger than the venture-capital headlines suggest.

The U.S. Census Bureau counted approximately 30.4 million nonemployer businesses generating nearly $1.8 trillion in receipts in 2023. “Nonemployer” means a business has no paid employees; it does not necessarily mean that every business is a technology-enabled company operated entirely by one person. But the numbers demonstrate how much economic activity already occurs outside conventional employer organizations.

At the upper end, a July 2026 Wall Street Journal investigation reported that thousands of solo operators using Stripe were producing more than $1 million in annual revenue. According to Stripe’s analysis, the number crossing $1 million doubled between 2023 and 2025, while the number crossing $10 million nearly tripled.

Stripe’s customers are not a statistically representative sample of every American business. Seven- and eight-figure solo companies remain unusual. Survivorship bias is substantial, and the same tools that reduce the cost of launching a business also reduce the cost of copying one.

The point is not that everyone can build a $10 million company alone.

The point is that doing so is no longer operationally impossible.

The corporation has not become obsolete.
It has lost its monopoly on leverage.

That creates a new option for a certain kind of ambitious person.

Instead of asking, How do I become indispensable to this institution?, they can begin asking, How do I build something that can operate without the institution’s permission?

Instead of placing every discretionary hour into internal visibility, they can place some of those hours into customer relationships.

Instead of waiting for an equity grant, they can begin accumulating equity directly.

Instead of concentrating their income, status and future inside one organization, they can begin constructing an alternative.

This is not an anti-corporate argument.

It is a pro-optionality argument.

The hybrid phase is not hesitation

Researchers have a more precise name for starting a business while remaining employed: hybrid entrepreneurship.

In a foundational Management Science study, Timothy Folta, Frédéric Delmar and Karl Wennberg defined hybrid entrepreneurs as people who enter self-employment while retaining their primary wage-paying jobs. Their research found that hybrid founders represented a significant portion of entrepreneurial activity and could use the overlap period to learn before entering full-time self-employment.

Joseph Raffiee and Jie Feng later tracked thousands of American entrepreneurs over 15 years. Their Academy of Management Journal study found that people who transitioned through hybrid entrepreneurship had substantially better venture-survival outcomes than those who immediately moved from employment into full-time entrepreneurship. The result is often summarized as a roughly 33% lower likelihood of failure.

Keeping the job is not evidence that you lack conviction. It may mean you are purchasing information before purchasing risk. The salary pays household expenses while you, the founder, learn:

Will anyone buy this?
Can I reach customers consistently?
Can I deliver the work profitably?
Will customers renew?
Is the opportunity large enough to justify leaving?
Do I enjoy operating this business once the novelty disappears?

The day job also prevents desperation from becoming the company’s first investor.

A founder who needs immediate income may accept the wrong customer, underprice the work, build for anyone willing to pay, or keep an idea alive because admitting failure would threaten next month’s rent.

A founder with a salary has more ability to say no:

A paycheck can become venture capital you provide to yourself, one deposit at a time.

This is the real opportunity inside quiet quitting.

Not doing nothing.

Building patiently.

Seven priorities while the day job still pays

The following are some operating rules, not universal scientific laws, that I’ve constructed based on my own experience making this drum in my career over a period of years.

Research supports the logic of experimentation, staged entry, clear boundaries, and financial caution. It does not establish that every founder needs exactly 18 months of cash, exactly three contracts or exactly three profitable months.

Those are decision thresholds.

Their purpose is to help solo founders switch out their feelings for actual evidence.

1. Bank eighteen months of runway

Runway is not the amount of money in a savings account.

It is the number of months your household and business can continue operating without requiring optimistic assumptions.

Start with the real monthly number:

Household fixed costs

business fixed costs
replacement benefits
taxes
contingency allowance

Then multiply it by 18.

Health insurance matters. Software subscriptions matter. Equipment matters. The tax reserve matters. The expenses your employer currently absorbs matter.

Eighteen months is intentionally conservative. It is not a magical figure. Some founders with highly predictable contracts may require less. Others with dependents, debt, long development cycles or volatile markets may need more.

The reason for caution is straightforward: new businesses remain uncertain. According to the SBA Office of Advocacy’s 2026 small-business data, only about 49% of new employer establishments survive five years. That statistic covers many kinds of businesses and does not predict the fate of any one company, but it is a useful correction to launch-day optimism.

Runway gives you time to survive delayed contracts, customer churn, pricing mistakes, and slower sales cycles.

More importantly, it improves the quality of your decisions.

2. Sell before you build

Three signed contracts beat a finished product.

This does not mean accepting money for something you cannot responsibly deliver. It means testing the commercial assumption before investing months in infrastructure.

Entrepreneurship is fundamentally a process of experimentation. William Kerr, Ramana Nanda and Matthew Rhodes-Kropf argue in the Journal of Economic Perspectives that entrepreneurial success probabilities are often low, highly skewed and unknowable until resources are committed. The answer is not to eliminate uncertainty. It is to structure smaller experiments that reveal information before making larger commitments.

Research on a scientific approach to entrepreneurial decision-making similarly suggests that entrepreneurs benefit when they state assumptions clearly, test them and allow evidence to kill weak ideas rather than searching only for confirmation.

The progression should usually look something like this:

Most founders reverse that sequence.

They build the platform, design the brand, automate the workflow and prepare for scale before discovering whether someone has a sufficiently painful problem and a sufficiently large budget.

A working product proves that you can produce supply.

A signed customer proves that demand exists.

Three customers do not prove you have discovered a large market. But they prove that the idea has survived contact with someone else’s bank account.

3. Audit Your Current Employment Paper

Before building anything, read every document governing your employment.

Not the summary.

Not the onboarding slide.

The entire handbook and everything you signed.

Review the provisions involving:

Intellectual-property assignment.
Confidentiality and trade secrets.
Outside employment or moonlighting.
Conflicts of interest.
Non-solicitation.
Noncompetition.
Customer and employee relationships.
Required disclosure or approval.
Notice periods.
Ownership of inventions created during employment.

Then have an employment lawyer in your jurisdiction review the provisions that matter.

Do not assume that noncompetes are universally unenforceable. The Federal Trade Commission’s proposed nationwide rule is not in effect and is not enforceable. After a federal court blocked it, the FTC dismissed its appeals in 2025. State law and individual contracts therefore remain central to enforceability.

A founder may also have duties while still employed that extend beyond the words “noncompete.” Employment-law scholarship has examined how duties of loyalty can apply to competition, corporate opportunities and employer resources during an active employment relationship. The exact obligations vary by role and jurisdiction.

A few hours of legal review are cheaper than discovering that your employer claims ownership of the business after it becomes valuable.

Do not take legal advice from a founder whose contract, role and state are different from yours. Find a good lawyer and listen to them

4. Keep the separation clean

Use personal hardware.
Use personal accounts.
Use personal software subscriptions.
Use personal payment methods.
Work during personal time.
Do not transfer files, prompts, templates, customer lists, code, research, pricing information or internal documents from the employer’s environment into your own.
Do not recruit coworkers or approach company customers without specific legal clearance.
Do not build a competing product while sitting in a meeting, even when the meeting should have been an email.

Clean separation is not only an ethical principle. It creates an evidentiary record.

File metadata, device logs, account histories, and timestamps should tell the same story you would tell.

Your standard should not be, “Could I explain this casually?”

Your standard is:

Could I establish a clean chain of creation if every device, document and timestamp were examined?

Nothing built on company time, with company data or through company systems, is worth the future dispute.

5. Count customers, not followers

Audience is useful, and feels good to build, but having an audience does not equate to having revenue, actual money in the bank.

Ten thousand impressions may tell you that a sentence was interesting. Ten paying customers tell you that a problem is expensive enough for someone to solve, and that your solution has done just that for those ten people.

The progression of evidence is simple:

Each step requires more commitment from the other person. Each step therefore provides more useful information.

Followers can reduce future distribution costs. A respected audience can become a meaningful asset. But founders often confuse public encouragement with commercial demand because public encouragement is easier to obtain and more pleasant to measure.

A stranger commenting “This is brilliant” risks nothing.

A purchasing manager signing a contract risks budget, reputation and time.

Early in the business, optimize for learning rather than applause.

Concentrated revenue may eventually become a problem. Depending on one customer can create fragility. But zero revenue is a more immediate problem than concentrated revenue.

Win the first customer.

Then the second.

Then determine whether you can win the third without relying on luck.

6. Protect the first ninety minutes—and a defined portion of the weekend

The best time block is the one you can defend consistently.

For many employed founders, that means working before the day job begins.

The morning has an advantage that has little to do with motivational mythology. It has not yet been consumed by someone else’s emergencies. The calendar has not slipped. The difficult client has not called. The meeting has not run long. Your attention has not been divided among twelve unfinished tasks.

Research on attention residue shows that switching from one unfinished task to another can impair performance because part of our attention remains attached to the previous activity. This helps explain why fragmented evening work often feels slower than protected, uninterrupted creation. (ScienceDirect)

The side business should therefore have recurring appointments with its founder.

Protect the first ninety minutes on selected weekdays. Use one defined weekend block for deeper work, customer delivery or sales. Make the schedule visible to the people whose lives are affected by it.

But do not convert every morning, evening and weekend into work.

Research on side hustles is genuinely two-sided. An Academy of Management Journal study found that empowerment gained through outside work could enrich performance in a person’s primary job. More recent Journal of Management Studies research found both gains and costs: thriving in a side hustle can improve well-being, but attention residue and resource depletion can also damage primary-job performance. Clear segmentation between roles helps. (Academy of Management Journals)

Recovery is not laziness either. A major Annual Review of Organizational Psychology and Organizational Behavior review concluded that detachment and recovery during evenings, weekends and vacations are important for sustaining well-being, motivation and performance.

Do not finance the company with chronic sleep deprivation, family resentment or deteriorating performance at the job still funding it.

7. Set a trigger, not a date

“I will quit in June” is not a strategy.

June does not know whether customers will renew.

June does not know whether the pipeline is real.

June does not know whether your largest client will disappear in May.

A date creates emotional momentum. A threshold creates decision discipline.

My preferred core trigger is:

Leave when recurring business revenue covers household and business fixed costs for three consecutive months—with the original runway substantially intact.

That is the beginning of the analysis, not the end.

Before leaving, also examine:

Whether customers are renewing or purchasing again.
Whether the pipeline can be repeated.
Whether revenue depends excessively on one relationship.
Whether gross margins support the owner after taxes and benefits.
Whether the founder has clean ownership of the work.
Whether additional founder time is likely to create additional revenue.

The final question is especially important.

Do not resign merely because operating the business while employed has become uncomfortable.

Resign when employment has become the principal constraint on a model that has already demonstrated demand.

The difference is enormous.

In the first situation, the founder is hoping that more time will create a business.

In the second, the founder has evidence that more time can expand an existing business.

The moral contract

There is a version of this strategy that is disciplined and honorable.

There is also a version that is dishonest.

The line is not complicated.

Your employer is entitled to the professional performance it pays for. Your coworkers should not be required to subsidize your company by carrying abandoned responsibilities. Customers should not receive inferior service because you were thinking about your own sales pipeline. Confidential information is not founder capital. Paid working hours are not a private incubator.

Keep your promises.

Meet the standard.

Disclose conflicts when the agreement requires disclosure.

Leave employer information, relationships, and property alone.

But the employment relationship has a boundary, too.

An employer may purchase your labor during agreed hours. The contract and the law may impose additional obligations. None of that automatically means the institution owns every future idea, every weekend, every early morning or the permanent direction of your ambition.

The business must be built outside the job—not instead of the job.

That is why I prefer quiet building to quiet quitting.

Quiet quitting sounds like retreat.

Quiet building is deliberate preparation.

What quiet building does not guarantee

Not every disengaged employee is a future founder. There is no credible research establishing that quiet quitting has caused the growth of solo entrepreneurship.

That connection is an interpretation of two changes occurring at the same time:

Work has become less central to the identities of some employees, while technology has made independent ownership more feasible.

This strategy is not equally available to everyone. Spare time, financial reserves, predictable schedules, health, caregiving responsibilities, and access to technology are distributed unevenly. Some people need the stability of employment more than they need entrepreneurial optionality. Some people genuinely enjoy institutional leadership and can create more value inside a large organization than they would alone.

There is no moral superiority in becoming a founder.

There is also no guarantee that the business will work.

AI does not create demand. A large audience does not create willingness to pay. More hours do not repair a weak proposition. The side business may produce energy, or it may create exhaustion. A secure salary may support intelligent experimentation, or it may allow the founder to avoid making a decision indefinitely.

The point is not that everyone should leave.

The point is that ambitious people have another route available—and the first stage of that route may occur while they are still employed.

Build Quietly.

At 8:30 AM, the founder closes the personal computer.

At 9:00 AM, the employee begins the workday and keeps the promises that have been made.

The employer still receives competent, professional performance.

What the employer no longer receives is the unquestioned right to every discretionary hour and every future ambition.

The founder keeps learning.

Keeps selling.

Keeps separating the work.

Keeps accumulating cash.

Keeps watching the numbers.

The first act of entrepreneurship is rarely a job resignation.

It is the choice to learn to wait and quietly build.

Keep the salary.
Protect the dream.
Perform honestly.
Leave when the arithmetic gives you permission.

- j -

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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?

Research appendix

A. Quiet quitting, engagement and the counterargument

1. Gallup: Is Quiet Quitting Real?

Read the Gallup analysis

Gallup’s 2022 analysis defined many quiet quitters as “not engaged”: employees who perform minimum requirements while remaining psychologically detached. The article linked falling engagement to unclear expectations, fewer development opportunities and weak manager relationships.

Used for: The definition of quiet quitting as disengagement and the estimate that roughly half of U.S. employees were not engaged in 2022.

2. Gallup: Employee Engagement Remains Flat as AI Adoption Accelerates

Read the 2026 Gallup report

Gallup reported that 31% of U.S. employees were engaged during the first half of 2026 and 18% were actively disengaged. It also emphasized that access to AI alone did not improve engagement; clear implementation and manager support mattered.

Used for: Current U.S. engagement figures and the argument that managerial context remains important.

3. Derek Thompson, The Atlantic: Quiet Quitting Is a Fake Trend

Read the article

Thompson argued that quiet quitting was not statistically new and that modest employee disengagement had existed long before the phrase went viral. He interpreted the popularity of the term as evidence of cultural frustration rather than proof of a sudden labor-market transformation.

Used for: The counterargument that the label exaggerates the novelty of the phenomenon.

4. Anthony Klotz and Mark Bolino, Harvard Business Review: When Quiet Quitting Is Worse Than the Real Thing

Read the article

The authors distinguished legitimate boundary-setting from forms of withdrawal that can damage relationships, performance and coworkers.

Used for: The ethical distinction between declining optional work and abandoning agreed responsibilities.

5. Pew Research Center: Young Workers Express Lower Levels of Job Satisfaction Than Older Ones, but Most Are Content With Their Job

Read the Pew analysis

Pew found that younger workers reported lower levels of strong job satisfaction than older workers, while 85% of workers overall remained at least somewhat satisfied.

Used for: Balance against the claim that an entire generation has rejected conventional employment.

B. Hybrid entrepreneurship and staged entry

6. Timothy B. Folta, Frédéric Delmar and Karl Wennberg: Hybrid Entrepreneurship

Read the Management Science abstract and publication record

The researchers defined hybrid entrepreneurship as entering self-employment while retaining a primary wage job. Using Swedish employee-employer data, they found that hybrid founders represented a significant share of entrepreneurial activity and examined how hybrid entry can facilitate later movement into full-time self-employment.

Used for: The formal definition of hybrid entrepreneurship and the concept of incremental entry.

7. Joseph Raffiee and Jie Feng: Should I Quit My Day Job? A Hybrid Path to Entrepreneurship

Read the Academy of Management Journal study

The authors studied thousands of entrepreneurs over a 15-year period and found that founders who entered through a hybrid stage experienced significantly better survival outcomes after transitioning to full-time entrepreneurship.

Used for: The claim that keeping a job while validating a venture can reduce the risk of failure.

8. Harvard Business Review: Why Going All-In on Your Start-Up Might Not Be the Best Idea

Read the research summary

This summary describes the Raffiee and Feng result as a 33% reduction in the likelihood of failure for founders who left their employment in stages.

Used for: The accessible interpretation of the academic result.

9. William Kerr, Ramana Nanda and Matthew Rhodes-Kropf: Entrepreneurship as Experimentation

Read the Journal of Economic Perspectives paper

The authors describe entrepreneurship as experimentation under conditions in which success probabilities are low, skewed and often unknowable before investment.

Used for: The argument that founders should structure smaller learning investments before making larger commitments.

10. Arnaldo Camuffo, Alessandro Cordova, Alfonso Gambardella and Chiara Spina: A Scientific Approach to Entrepreneurial Decision Making

Read the Management Science study

In a randomized controlled trial involving Italian startups, the researchers tested whether teaching founders to formulate hypotheses and evaluate evidence changed their decisions. The study supports disciplined testing over purely intuitive or confirmatory decision-making.

Used for: “Sell before you build” and the need to design experiments capable of disproving weak assumptions.

11. U.S. Small Business Administration: Plan Your Business

Read the SBA guidance

The SBA emphasizes researching demand, market size, saturation, pricing and customer behavior before making major investments.

Used for: The practical importance of direct customer research and demand validation.

C. Side hustles, focus and recovery

12. Hudson Sessions, Jennifer Nahrgang, Manuel Vaulont and colleagues: Do the Hustle! Empowerment From Side-Hustles and Its Effects on Full-Time Work Performance

Read the Academy of Management Journal study

The study found that empowerment generated by a side hustle could produce positive affect and enrich an employee’s performance in their primary job.

Used for: The evidence that outside work does not automatically damage full-time performance.

13. Min Liu, Shengxian Yu, Xin Liu and Shanshi Liu: The Effect of Side-Hustle Thriving on Full-Time Work Performance

Read the Journal of Management Studies study

The authors identify two competing pathways. Side-hustle thriving can improve well-being and help employees detach from their full-time work, but it can also create attention residue and deplete resources. Boundary segmentation influences the balance.

Used for: The two-sided treatment of side hustles and the importance of separating roles.

14. Sophie Leroy: Why Is It So Hard to Do My Work? The Challenge of Attention Residue When Switching Between Work Tasks

Read the publication abstract

Leroy’s research shows that people can retain cognitive attention on an unfinished task after switching to another activity, impairing subsequent performance.

Used for: The recommendation to create protected blocks instead of repeatedly switching between employer and founder work.

15. Sabine Sonnentag, Charlotte Venz and Anne Casper: Advances in Recovery Research

Read the Annual Review article

The review synthesizes evidence on recovery during breaks, evenings, weekends and vacations, emphasizing the role of recovery in sustaining well-being, motivation and performance.

Used for: The argument that weekends cannot become a second uninterrupted workweek.

D. Technology, AI and the solo economy

16. Satish Nambisan: Digital Entrepreneurship: Toward a Digital Technology Perspective of Entrepreneurship

Read the Entrepreneurship Theory and Practice article

Nambisan examines how cloud computing, mobile technologies, digital platforms and other tools alter entrepreneurial processes, agency and uncertainty.

Used for: The argument that digital technology has reduced the institutional infrastructure required to launch a venture.

17. Shakked Noy and Whitney Zhang: Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence

Read the peer-reviewed Science article

In a preregistered experiment involving professional writing tasks, participants using ChatGPT completed their work approximately 40% faster and produced outputs rated approximately 18% higher in quality.

Used for: Evidence that generative AI can increase individual productivity in defined professional tasks.

18. Erik Brynjolfsson, Danielle Li and Lindsey Raymond: Generative AI at Work

Read the Quarterly Journal of Economics article

The researchers studied the introduction of an AI assistant among more than 5,000 customer-support agents. Productivity increased by approximately 15% on average, with larger benefits for less experienced employees.

Used for: Evidence that AI can transfer elements of organizational knowledge and improve certain workplace outputs.

19. Fabrizio Dell’Acqua and colleagues: Navigating the Jagged Technological Frontier

Read the Organization Science article

The field experiment involving 758 BCG consultants found that AI improved performance significantly on tasks inside its capabilities but reduced accuracy on some tasks outside that frontier.

Used for: The warning that AI leverage remains uneven and requires human verification.

20. U.S. Census Bureau: Census Bureau Data Tell the Small Business Story

Read the 2026 Census analysis

The Census Bureau reported 30,427,808 U.S. nonemployer establishments in 2023, producing nearly $1.8 trillion in receipts.

Used for: The scale of economic activity conducted by businesses with no paid employees.

21. Stripe: Solo Founding Is at an All-Time High

Read Stripe’s analysis

Stripe reported growth in the number of high-revenue solo founders on its platform, while also documenting a wide and increasing gap between median and top-performing solo businesses.

Used for: Evidence of the opportunity and extreme outcome dispersion within solo entrepreneurship.

22. Te-Ping Chen, The Wall Street Journal: The Rise of Million-Dollar Companies With Just One Employee

Read the article

The article reports Stripe data indicating that thousands of solo operators generated more than $1 million in annual revenue. It also presents both successful and unsuccessful founder cases and describes the cost, competition and replication risks created by AI.

Used for: Current evidence that high-revenue solo businesses are becoming more common, while remaining exceptional and risky.

E. Legal boundaries

23. Federal Trade Commission: Noncompete Rule

Read the official status page

The FTC states that its nationwide Noncompete Rule is not in effect and is not enforceable. A district court blocked the rule, and the agency dismissed its appeals in 2025.

Used for: The warning that founders cannot assume a federal blanket ban invalidates their agreements.

24. Federal Trade Commission: FTC Files to Accede to Vacatur of the Non-Compete Clause Rule

Read the official FTC release

The release documents the FTC’s decision to dismiss its appeals and accept the rule’s vacatur.

Used for: Confirmation of the current federal procedural position.

25. Deborah A. DeMott: The Restatement of Employment Law and the Duty of Loyalty

Read the Cornell Law Review essay

The essay examines when employees may owe duties of loyalty and the difficulties involved in defining those obligations.

Used for: The caution that current employees may face legal duties involving competition, employer opportunities and employer resources even apart from noncompete clauses.

F. Business survival and risk

26. SBA Office of Advocacy: Frequently Asked Questions About Small Business, 2026

Read the 2026 SBA report

The SBA reports a five-year survival rate of approximately 49.2%, a ten-year survival rate of approximately 33.9% and a fifteen-year survival rate of approximately 25.5% for new employer establishments in the periods studied.

Used for: Context on business uncertainty and the rationale for maintaining substantial financial runway.

Research and legal note: The numerical thresholds in this article—including 18 months of runway, three initial contracts and three consecutive months of cost-covering revenue—are operating heuristics, not findings established by the cited studies. Employment, intellectual-property, tax and restrictive-covenant laws vary by jurisdiction and circumstances. Readers should consult qualified legal and financial advisers before acting.

Why We Business Builders, Writers and Creators Should Want Substack To Become the Next Giant Social Media Platform

John Brewton — Sun, 02 Aug 2026 11:41:03 GMT

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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.

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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 -

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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?

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Appendix: Sources and evidence

The current creator debate

Substack’s scale and discovery network

The shift toward interest-based distribution

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.