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Start Here

The 7-S model is the longest-surviving organizational diagnostic in management. Peters and Waterman built it at McKinsey in 1980 and shipped it to the world in In Search of Excellence in 1982. Three million copies sold in four years. Every consulting firm picked up the model. For forty-three years, the seven-element diagnostic was the standard frame for thinking about organizational performance: Strategy, Structure, Systems, Staff, Skills, Style, Shared Values. Three hard. Three soft. One core, at the center, that the other six rotate around.

For forty-three years it held. Then the Stack arrived.

The Assumption

Peters and Waterman built the model on a single assumption that was unimpeachable in 1980. Tools were external. The IT department picked them. Strategy did not depend on which database the firm bought. Marketing did not depend on which CRM the firm rented. Performance came from how seven human and procedural elements aligned around Shared Values.

That assumption broke in 2023. Frontier models compressed the cost of capability. Tools stopped being external. Two firms with identical Strategy, Structure, Systems, Staff, Skills, Style, and Shared Values now produce different results because their Stacks differ. The seven-element model under-specifies the firm.

The 8th S

Stack is the model and tool layer the firm now runs on. It has four sub-layers.

• L1 Data: Owned, observable, indexed.

• L2 Models: Frontier and specialist.

• L3 Prompts: Codified team patterns.

• L4 Agents: Autonomous workflows.

Stack sits next to Structure as a determinant of performance. It is a hard S.

Codifiable. Assignable. Changeable in twelve months.

The 1980 diagnostic placed seven elements in the model because these were what consultants could move. The 2026 diagnostic adds the eighth for the same reason.

Strategy depends on which models the firm runs. Structure depends on which agents sit in which roles. Skills depend on which prompts the team has codified. Performance is now an eight-element function.

Three operator translations

The 7+1-S diagnostic translates differently for each operating tier.

1. Solo operator - Run weekly: Maintain one document that names all eight Ss for your practice. Update the Stack row weekly. Update the other seven quarterly. Read the document before any new tool decision.

2. Startup founder - Run on a 90-day build: Hire for stack literacy first. Then for the other seven. Builders who know their tools. Operators who codify their prompts. The Stack belongs in the onboarding pack, not the IT request queue.

3. SMB strategist - Run quarterly: Walk every existing 7-S diagnostic the firm uses. Strategy decks. Org charts. Operating reviews. For each row, write the year it was last refreshed and the assumption it was built on. If the row pre-dates 2023, add a Stack column and refresh the row alongside.

The full pack

The prompt pack: Five prompts that turn the 7-S into an 8-S diagnostic any team can run quarterly. DIAGNOSE. MAP STACK. REALIGN. DECIDE. LOOP. Built for the copy-paste-into-Claude workflow. [Download here →]

The worksheet: Ten-page fillable diagnostic. The Stack row is pre-printed as the eighth. Walk every row through ALIGNED, DRIFTED, or REFRESH. Pick the move. Schedule the loop. Built for the quiet ninety minutes. [Download here →]

The principle

Shared Values still sits at the center. Stack joins as a hard S. The new diagnostic has eight rows, where the old one had seven. Performance is now an eight-element function.

Every other framework in the McKinsey library is getting the same treatment. Stack column added. Assumption checked against 2023. Most rows predate the Stack. Most rows need to be refreshed.

Operating publishes one of these every week. The next framework, the next pack, the field notes from operators running the stack live. Subscribe to keep getting them.

- j -

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About the author

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

Philip Kotler taught a generation of marketers to think in three sequential acts. Segment the market by shared need. Target the segment where the offer wins. Position the offer in the buyer’s mind against the alternatives. The three-step discipline became the spine of marketing planning at every business school and consultancy that mattered for the next fifty years.

Personalization at scale was impossible. The cost of treating each customer as a unique market made the segment a necessary abstraction. The marketing organization could research, target, and position to a segment. It could not do that work for every individual buyer. The segment was the smallest economic unit of attention an organization could afford to treat as unique.

Bye Bye Bye Workaround

AI is collapsing the cost of personalization to near zero. Per-customer position generation now runs on the same operating budget as a marketing email. Behavioral signals can be observed continuously and predicted by a model. The smallest economic unit of attention is now the individual, not the segment.

Three of Kotler’s four segmentation bases collapse under that economics. Geographic, demographic, and psychographic segmentation were always proxies. They grouped buyers into bands that you could afford to treat differently because you could not afford to treat each buyer differently. With the cost gone, the proxies dissolve. Behavioral segmentation survives because behavior is the underlying signal the proxies were trying to estimate. Observed in real time and predicted by the model, it is the only base that earns its keep.

Segments do not disappear entirely. They survive where the channel forces them to aggregate. They survive where regulation forces aggregation. They survive in healthcare verticals, in legal tier marketing, in any context where the law or the surface itself demands a category. Outside those exceptions, most segments in your CRM are personalization-cost workarounds left over from human-era marketing economics.

Three Tiers

The solo operator runs a positioning loop on every reply. One document per active customer. AI updates after every interaction. The document contains the customer’s current job, alternative, objection, and position. Read it before every reply. Volume is what makes this competitive.

The startup founder skips the persona deck. The persona deck was the human-era artifact. Replace it in week one with a personalization stack. Capture every event. Maintain a live profile per buyer. Let AI generate a position per person. Deliver programmatically. The founder reads the deltas, not the personas.

The small business strategist audits the segment vocabulary. Walk through every segment label in the CRM, in email, in advertising, in product. Write the year it was created and the assumption it was built on. If the assumption was personalization cost, the segment is obsolete. Replace it with a model.

Operating Assets

Prompt Pack

The prompt pack ships five prompts that turn STP into a weekly positioning practice. Diagnose. Stress-test. Translate. Decide. Loop. Run them in sequence against any capable model. The first prompt scores your existing segments by predictive value. The second re-runs the analysis under the AI-era inversion and marks each segment KEEP, REPLACE, or SIGNAL. The third translates the output to your operating tier. The fourth forces a single move for the quarter. The fifth schedules the practice as recurring intelligence.

Worksheet

The worksheet runs the same logic on paper. Ninety minutes. Seven steps. One inventory of the segments you actually use. One audit of the year and assumption behind each. One scoring of the four bases. One mark of OBSOLETE, STANDS, or REVIEW per segment. One sketch of the personalization stack you would build today. One move for the quarter. One cadence for the loop.

Hope this helps. Tune in tomorrow for another framework, prompt pack and worksheet to help you become a better Operator.

- j -

About the author

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

All visuals for this article were produced in my Claude studio. If you’d like to be able to do the same, make sure you register for the Compound Content Studio, launching this Friday.

The Compound Content Studio™ is a live, 2‑week AI immersion where we build your Claude-powered content system that multiplies your ideas into an endless stream of on-brand content.

We kick off May 1st, and there are only 50 spots (25 of which have already been sold). If you want your content to compound rather than reset to zero every day, this is where you build the system that finally makes it happen.

This is the system I built to create all of my content across social platforms. Jumpstart and scale your brand and content this May!

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48 Hours

On February 13, 2026, Anthropic publicly launched Cowork, a Claude-powered collaborative workspace that can read, write, and operate across a user’s applications simultaneously. Within 24 hours, the combined market capitalization of the ten largest US enterprise software companies fell by $285 billion. Adobe, Salesforce, Intuit, Microsoft. They all moved the same direction.

That $285 billion is not the most interesting number. The most interesting number came eight weeks later. On April 16, 2026, Anthropic released Claude Opus 4.7. On April 17, it released Claude Design, an Anthropic Labs product that lets a founder describe a pitch deck in plain English and produce a working prototype. Figma’s stock dropped 7.28% on the day. Three days before the launch, Anthropic’s Chief Product Officer, Mike Krieger, had quietly resigned from Figma’s board. The market missed the signal. The operating lesson is that Krieger did not resign because he was getting uncomfortable. He resigned because he already knew what was about to ship.

Anthropic now generates $30 billion in annualized revenue. It carries a $380 billion valuation and has received unsolicited offers at $800 billion. It has raised $65 billion across eleven funding rounds in five years. Eight of the ten Fortune 10 companies are customers. More than 500 businesses spend over $1 million per year on Claude. Claude Code went from zero to $2.5 billion in annualized billings in nine months.

For an operator, the right question about Anthropic is not how they did this. The right question is what they chose correctly that their competitors chose incorrectly. The answer is three things. A method decision. A constraint decision. A vertical integration decision. Every other operating outcome flows from those three.

Operating Thesis

First, Anthropic treated safety as the product, not a constraint on the product. Constitutional AI, the Responsible Scaling Policy, and the Long-Term Benefit Trust were not brand elements. They were the technical and governance foundation on which enterprise trust compounded. Every pharmaceutical company, bank, and public-sector buyer that runs Claude in a regulated workflow runs it because Anthropic’s safety disclosures made procurement possible.

Second, Anthropic built from the model up, not from the chatbot down. Every product release, in sequence, is a move up the stack from the model to the workspace to the outcome. Claude 1 sold capability. Claude 3.5 sold a workspace. Claude Code sold a developer. Cowork sold a department. Claude Design sold a designer. Each move captured more of the value that used to flow to the application layer.

Third, Anthropic treated each capability release as a governance decision first and a revenue decision second. When a model was too dangerous to ship broadly, it did not ship broadly. Claude Mythos, announced on April 7, 2026, was restricted to forty cybersecurity partners because its capabilities threatened operating systems globally. A normal company would have sold it. Anthropic withheld it and gained trust instead.

Three decisions. Five years. $380 billion. The pattern maps to three historical operators who made the same three decisions in their own eras.

Velocity

The revenue arc is what it is. $100 million annualized at the end of 2022. $1 billion by December 2024. $5 billion by August 2025. $7 billion by October 2025. $30 billion by April 2026. Three consecutive years of roughly 10x annual revenue growth at this scale is unverified in the enterprise technology record. Salesforce took eleven years to hit $5 billion in revenue. Amazon Web Services took about seven years to hit $10 billion. Anthropic hit $10 billion in roughly 24 months after crossing $1 billion.

The driver is enterprise concentration, not consumer subscriptions. Accounts generating more than $100,000 in annual recurring revenue grew 7x in 2025 over 2024. The total enterprise customer base grew from fewer than 1,000 in late 2023 to more than 300,000 in September 2025. That is a 300x expansion in under two years.

Claude Code is the inside story. It launched as a research preview in February 2025 and hit general availability in May 2025. It went from zero to over $2.5 billion in annualized billings in roughly nine months. Business subscriptions on Claude Code quadrupled in the six weeks following January 1, 2026. That is the fastest revenue ramp of any product in enterprise software history.

Here is the paradox. Anthropic will spend more than $50 billion on compute infrastructure in 2026 alone against $30 billion in annualized revenue. Dario Amodei publicly said in February 2026 that a 12-month delay in AI progress would make him personally bankrupt. The company is simultaneously one of the fastest-growing technology companies in history and one of the largest consumers of capital per dollar of revenue ever. Both things are true. The flywheel requires it.

New to Operating?

Check out these reader favorites:

• Operating Economics: The Optimization Company (Amazon)

• Operating by Warren & Charlie

• Winning the Loser’s Game: The Creator Economy and the Companies We All Need to Build

• 0 to 55,000 - The First 90 Days Playbook - What I’d Do Differently Knowing What I Know Now

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Three Product Inflection Points

The full product history has fifteen releases. Three of them mattered most.

Constitutional AI, December 2022. Before Anthropic shipped a product, it published a paper. “Constitutional AI: Harmlessness from AI Feedback” described a method for training large language models to critique and revise their own outputs against a written set of principles, replacing most human annotation with a principled self-improvement loop. The technique cut human labeling costs by 10x to 100x while producing models that were simultaneously more helpful and less harmful than prior approaches. Every Claude model since has been trained on this foundation. The strategic implication was procurement, not research. Constitutional AI gave Anthropic’s enterprise sales team verifiable, documented claims about model behavior that pharmaceutical, financial, legal, and public-sector buyers required before deploying AI in consequential workflows. It turned safety into a product feature.

Model Context Protocol, November 2024. Anthropic released MCP as an open standard for connecting AI systems with external data and tools. MCP replaced fragmented, bespoke API integrations with a universal protocol. Any AI agent built on MCP could connect to any MCP-compatible data source without custom connector code. In December 2025, Anthropic donated MCP to the Agentic AI Foundation, a directed fund within the Linux Foundation. This was the platform move. By establishing MCP as the standard, Anthropic positioned itself as the architect of the infrastructure layer through which AI agents operate. The economics are analogous to controlling HTTP or the dominant JDBC driver.

Cowork and Claude Design, February and April 2026. Cowork is a collaborative workspace where Claude reads, writes, and acts across applications simultaneously. A Claude-powered agent inside Cowork can open a user’s email, pull data from a spreadsheet, run analysis, draft a response, and file a follow-up task, all without handoffs. Claude Design lets a founder or product manager describe a design in plain English and produce a functional interactive prototype. Inputs include text prompts, uploaded images, DOCX, PPTX, XLSX files, and a web capture tool for grabbing live UI elements from existing websites. Claude Design is included at no extra charge for Claude Pro, Max, Team, and Enterprise subscribers. Cowork is the vertical move into the business user’s deliverable. Claude Design is the vertical move into the designer’s deliverable. Both are Rockefeller moves.

Three Parallels

Every high-velocity operating company has antecedents. Anthropic’s three most useful antecedents are the Traitorous Eight who founded Fairchild Semiconductor in 1957, Andy Grove’s memory-to-microprocessor pivot at Intel in 1985, and John D. Rockefeller’s climb up the Standard Oil stack from 1870 to 1890. Each one made a method decision that looked wrong in the short run and built an industry in the long run. Each one matches a specific Anthropic move, year for year.

The Traitorous Eight: method over hierarchy

In September 1957, eight scientists walked out on William Shockley. In late 2020 and early 2021, eight researchers walked out on OpenAI. The parallel is not the number. It is the substance. Shockley believed authority and hierarchy produced scientific output. Robert Noyce and the other seven believed peer review and diffusion of authority produced it. The eight who left OpenAI, Dario Amodei, Daniela Amodei, Jared Kaplan, Tom Brown, Sam McCandlish, Jack Clark, Benjamin Mann, and Christopher Olah, believed scaling compute alone was insufficient without a parallel rigorous focus on alignment. Both walkouts were method disagreements. Both founder cohorts built the company that was right about the method.

Fairchild’s alumni went on to found more than 65 companies including Intel, AMD, and Kleiner Perkins. Anthropic’s diffusion effect is already visible in year five. Model Context Protocol, donated to the Linux Foundation in December 2025, is the integrated circuit of the agent era. The Long-Term Benefit Trust is the governance equivalent of Fairchild’s stock option plan.

Andy Grove at Intel: trust as the moat

By 1985, Intel faced commodity pressure from Japanese DRAM manufacturers. Memory margins collapsed to 1%. Grove and Gordon Moore had the conversation. If the board replaced them, a new CEO would exit memory. So they walked out the door, came back in, and did it themselves. Revenue fell from $1.63 billion in 1984 to $1.27 billion in 1986 before recovering to $3.13 billion in 1989. By 1992, Intel was the largest semiconductor company in the world. Grove documented the framework in Only the Paranoid Survive.

Anthropic ran the Grove move twice. First in mid-2022 when it withheld Claude as OpenAI was about to release ChatGPT. Anthropic walked away from the quarter’s revenue because it had correctly identified the constraint was trust, not speed. Second in April 2026 when it restricted Claude Mythos to forty cybersecurity partners through Project Glasswing. Anthropic had a model capable of hundreds of millions in annual revenue if released broadly. It released it narrowly. Both decisions made trust the product. The enterprise perception that Claude is the only frontier model you can safely deploy in regulated contexts is the moat. That moat is worth more than Mythos revenue would have been.

Rockefeller and Standard Oil: the stack climb

Rockefeller did not build Standard Oil by refining oil better than his competitors. He built it by controlling the stack above and below refining. By 1890, Standard Oil controlled 88% of refined oil flows in the United States, most of the pipelines, a material share of rail bulk freight, and the retail distribution network that ended at the customer’s kerosene lamp.

Anthropic’s playbook is a compressed Standard Oil. The model is the refinery. Claude.ai is the retail distribution. Memory and Projects are the storage tanks. Skills are the refined products. MCP is the pipeline network. Claude Code is the vertical move into the developer’s output. Cowork is the vertical move into the business user’s output. Claude Design is the vertical move into the designer’s output.

The Standard Oil pattern is why the Cowork release wiped $285 billion in market cap in 24 hours. The market understood, correctly, that Anthropic had just completed the Rockefeller move on the SaaS layer. Every enterprise software category charging per-seat for a specific UI on top of specific data had just been informed that the UI was optional and the data was accessible through a new control layer.

For the full business history underneath these three parallels, including the Cleveland Massacre’s six-week timeline, Grove’s six-force framework for strategic inflection points, and the structural reasons the Fairchild diaspora produced 65+ companies, see the Historical Parallels Compendium in the asset hub below.

Four operating principles

Answer the correct question completely, not the approximate question fast. The operating discipline is to identify the correct question before anyone else does. For Anthropic, the correct question was not how to build the best chatbot. The correct question was how to build AI that enterprises can actually deploy in regulated workflows. Constitutional AI, the Responsible Scaling Policy, the Public Benefit Corporation structure, the Long-Term Benefit Trust, and the MCP protocol are all different angles on the same correct answer. Each took longer than the equivalent ChatGPT feature would have taken. Each produces more durable value.

Make the constraint the product. In every business there is a constraint competitors treat as a cost that you can treat as the product. For Anthropic, safety was that constraint. The received wisdom in AI in 2021 was that safety was an overhead, a drag on capability that you had to accept because regulators would eventually demand it. Anthropic inverted the assumption. Safety became the thing enterprises were buying. Constitutional AI was a feature, not a tax. The Responsible Scaling Policy was a selling point, not an apology. Find the overhead line item in your own business that your competitors all treat the same way. That line item is probably your product.

Climb the stack toward the deliverable. Every business that sells a tool gets commoditized. Every business that sells an outcome gets to charge for the value of the outcome. You start by selling the tool. You add a workspace that captures the user’s state. You add integrations that make the workspace the default. You climb down the stack to the deliverable the user was going to produce with your tool anyway. Each step up the stack increases switching costs. Each step down the stack captures value that was flowing to adjacent vendors. For Anthropic, the climb from Claude 1 (tool) to Cowork (outcome) is the most compressed version of that pattern on record.

Let governance precede revenue, every time. Every time Anthropic has gained a new capability, it has made a governance decision before making a revenue decision. Claude 2022 was held back. Mythos 2026 was restricted to forty partners. Opus 4.7 shipped with automatic detection and blocking of high-risk cybersecurity requests. Each decision was made against a public, specific, and auditable written standard. Each decision forewent short-term revenue to reinforce a long-term trust position. Your strongest long-term asset is the thing your competitors are willing to compromise that you are not.

The Operating Stories analysis continues below for paid subscribers, including the broader implications for professional services, the operating lesson for founders, and the full asset hub: three research compendiums (Product and Capability, Financial and Strategic, and the Historical Parallels business history compendium covering the Fairchild founding, Grove’s DRAM exit, and Rockefeller’s stack climb in full), the five single-page operating guides for Claude Projects, Skills, Cowork, Claude Code, and Claude Design, and the complete primary sources appendix.

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All visuals for this article were produced in my Claude studio. If you’d like to be able to do the same, make sure you register for the Compound Content Studio, launching this Friday.

(If you’re an Operating Founder member, message me directly for details)

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Read more

Stop grinding out posts one by one.

The Compound Content Studio™ is a live, 2‑week AI immersion where we build your Claude-powered content system that multiplies your ideas into an endless stream of on-brand content.

Instead of another course, this is a build-along: Operating by John Brewton and Unfazed Founder™ walk you step-by-step as you design, train, and launch a content engine that runs on autopilot while you run the business.

We kick off May 1st, and there are only 50 spots (25 of which have already been sold). If you want your content to compound rather than reset to zero every day, this is where you build the system that finally makes it happen.

This is the system John has built to create all of his content across social platforms. Jumpstart and scale your brand and content this May!

Grab Your Seat In the Studio Today

Start Here

Clayton Christensen published The Innovator’s Dilemma in 1997. He studied four industries:

1. Disk drives

2. Hydraulic excavators

3. Mini-mills

4. Discount retail

He found one pattern across all four.

Well-managed companies failed for rational reasons. Their best customers wanted sustaining improvements. Resources flowed to those customers. The bottom of the market and the non-consumers stayed open. Disruptors entered there. Then climbed.

The arc took fifteen years in disk drives. Twenty in mini-mills. The framework assumed the incumbent had time to see the disruptor coming.

That assumption no longer holds.

The arc compressed.

AI-mediated production collapses the entrant’s cost curve to weeks. A junior analyst with copilots ships in two hours what a partner shipped in two days. Non-consumers reach the entrant through autonomous discovery, not through gatekeeper introductions. The decade Christensen documented is the year you are living through.

The framework still holds. The clock changed.

The framework now applies to careers.

If your role exists because clients valued the capability that an entrant can deliver at one-tenth the cost, you are the incumbent. Your sustaining improvements are your annual reviews. Your best customers are the bosses and partners who reward the work that no longer earns its premium.

Doing more of what is rewarded is exactly what causes the position to be lost.

The dilemma is identical at the personal scale.

The Worksheet

The worksheet is a single document with six steps.

Run it twice. Once for the company. Once for the career.

The six steps surface the dilemma in the order that matters. Name the customer. Audit the sustaining improvements. Identify the bottom of your market. Run the substitute scan.

Score the separation. Pick one move for the next ninety days.

Each step takes about ten minutes. Both passes take an hour. The worksheet has fillable answer blocks because the value is in writing the answers down. The discipline is the deliverable.

The career pass is where the framework earns its keep. Most operators have run a Christensen analysis on a company. Almost none have run one on themselves. The output is uncomfortable on purpose. The customers paying you for capability they no longer use are the ones you need to identify before they identify you.

The Prompt Pack

The prompt pack is five prompts that turn the worksheet into a recurring practice with a capable model.

• Prompt 1: Diagnose your situation against the framework.

• Prompt 2: Stress-test the diagnosis under the AI-era inversion, where the arc collapses from a decade to twelve months.

• Prompt 3: Translate the analysis to your operating tier. Solo operator, startup founder, or small business strategist.

• Prompt 4: Forces a decision and a ninety-day commitment.

• Prompt 5: Schedules the practice with named trigger conditions for early re-runs.

Run them in sequence. Each prompt builds on the previous one. The fifth prompt makes this a quarterly review, not a one-time exercise. That is the only version of the practice that survives contact with a busy quarter.

The pack works against any capable model. Curly-brace placeholders mark the fields you fill in. The output is structured. The whole sequence runs in about forty minutes once you have the inputs.

Why both?

The worksheet is for the operator who wants to think the framework through with a pen. The prompt pack is for the operator who wants the model to do the structural work and reserve their attention for the decisions that matter.

Most subscribers will use both. The worksheet anchors the first pass and produces a baseline you can hand to a colleague or a coach. The prompt pack runs every ninety days after that and compares the new analysis to the baseline so you can see what moved.

The combination converts an idea you already know into a practice you actually run and build from. Christensen wrote the book twenty-nine years ago. The reason the framework still gets cited is that the underlying logic is correct. The reason most readers do not act on it is that the framework was written as an analytical instrument, not as an operating methodology. These two assets are my attempt to help you close that gap.

Failure can be the result of rational decisions made well. The implication is that doing more of what is already rewarded is not the answer. Doing different work is.

The disruption is not coming. It arrived while you’ve been waiting to do this work.

- j -

[Download PDF’s of the Prompt Pack and Worksheet below →]

New to Operating?

Check out these reader favorites:

• Operating Economics: The Optimization Company (Amazon)

• Operating by Warren & Charlie

• Winning the Loser’s Game: The Creator Economy and the Companies We All Need to Build

• 0 to 55,000 - The First 90 Days Playbook - What I’d Do Differently Knowing What I Know Now

Become a paid subscriber to join the Operating Skool community and attend our weekly live Office Hour Q&A’s w/ John.

Subscribe now

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? He still cringes at his early LinkedIn posts and loves making content each and everyday, despite the protestations of his beloved wife, Fabiola, at times.

Stop grinding out posts one by one.

The Compound Content Studio™ is a live, 2‑week AI immersion where we build your Claude-powered content system that multiplies your ideas into an endless stream of on-brand content.

Instead of another course, this is a build-along: Operating by John Brewton and Unfazed Founder™ walk you step-by-step as you design, train, and launch a content engine that runs on autopilot while you run the business.

We kick off May 1st, and there are only 50 spots (25 of which have already been sold). If you want your content to compound rather than reset to zero every day, this is where you build the system that finally makes it happen.

This is the system John has built to create all of his content across social platforms. Jumpstart and scale your brand and content this May!

Grab Your Seat In the Studio Today

Start here.

In 1957 Walt Disney built three rooms in his Burbank studio.

One for dreaming.

One for planning.

One for criticism.

Walt moved his concepts from room to room. The walls provided the discipline that meetings rarely do.

Robert Dilts named the practice the Disney Method in 1994. It became standard reading for design firms, advertising agencies, and product teams. Three modes, one sequence, never simultaneous.

The reason it worked is the reason it was needed. Most ideation fails the same way. Three voices speak at once. The dreamer suggests an idea. The critic dismisses it. The realist never gets a chance to test it. The room produces half-formed work, defended or killed before it matures. Mixing the modes is what kills the output.

Walt separated the rooms because a human can only hold one mode at a time. The architecture was disciplined and forced a mindset switch.

What has changed in the Age of Agents?

That constraint disappeared.

An agent can dream, plan, and critique inside a single prompt. So that is what most operators ask it to do. They open one chat window, paste a problem, and ask for a solution. The agent obliges. It returns a half-formed plan, defended and killed in the same paragraph. Creativity collapses inside the prompt designed to produce it.

The Disney Method matters more in the agent era, not less. The discipline that used to require three rooms now requires three agents and an orchestrator who refuses to let them overlap.

Three agents. One sequence.

The Dreamer agent generates fifteen candidate solutions with no constraints, no budgets, and no prior failures. The Realist agent takes the dream literally and designs a six-step build sequence with cost, owner, and a thirty-day milestone. The Critic agent pressure-tests the plan, names three failure modes, and demands a go or no-go decision. You hold the orchestrator’s seat to enforce the wall between rooms.

The tier translation is straightforward. Solo operators run three threads and never open two at once. The friction of opening a new chat is the wall. Startup founders assign each mode to a teammate and their agent stack, and rotate roles quarterly to break up expertise capture. SMB strategists build three named agents with locked system prompts and convene the team in the right room for the right mode.

Run the sequence once on a real problem. Then put it on the calendar. Friday Dreamer session. Monday Realist review. Quarterly Critic round.

Hope this helps.

- j -

Download the workbook

I built a ten-page workbook that walks the full method on one specific problem you are trying to solve. It includes the framework, the AI-era inversion, four worksheet exercises, and five copy-paste prompts that turn each room into a specialized agent.

Download it for free here.

New to Operating?

Check out these reader favorites:

• Operating Economics: The Optimization Company (Amazon)

• Operating by Warren & Charlie

• Winning the Loser’s Game: The Creator Economy and the Companies We All Need to Build

• 0 to 55,000 - The First 90 Days Playbook - What I’d Do Differently Knowing What I Know Now

Become a paid subscriber to join the Operating Skool community and attend our weekly live Office Hour Q&A’s w/ John.

Subscribe now

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? He still cringes at his early LinkedIn posts and loves making content each and everyday, despite the protestations of his beloved wife, Fabiola, at times.

New to Operating?

Check out these reader favorites:

• Operating Economics: The Optimization Company (Amazon)

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In 1979, Michael Porter published “How Competitive Forces Shape Strategy” in Harvard Business Review. The article reframed strategy. Industry profitability is determined by five structural forces: rivalry among existing competitors, threat of new entrants, threat of substitutes, bargaining power of buyers, and bargaining power of suppliers. Forty-six years on, the framework still anchors nearly every MBA strategy syllabus.

It still holds. The operating context is inverted underneath it.

Porter’s framework rests on a single assumption that was unimpeachable in 1979 and questionable in 2026. Industry boundaries were stable enough to draw a line around. Actors inside that line were knowable, countable, and slow-moving. Information was asymmetric, expensive to gather, and bound by human cycle time. The framework was built on that floor.

That floor moved.

AI-mediated discovery breaks the asymmetries Porter took as foundational. Buyers compare hundreds of options in seconds through autonomous agents. New entrants face near-zero capability barriers in software-mediated work. Substitutes appear from adjacent capability stacks, not from within the named industry. The five forces still apply. Their relative weights inverted.

Most strategic plans still front-load rivalry analysis. The two forces that matter most in 2026 sit at the bottom of most leaders’ attention. Buyer power mediated by AI agents. Substitution from adjacent capability stacks. The framework holds. The reading order is inverted.

The solo operator stops positioning against named competitors. They map their business as a node in a capability stack and ask which adjacent capabilities to absorb and which to hand to an agent. The stack moves faster than the org chart.

The startup founder runs Five Forces weekly, not annually. The threat of new entrants is measured in months. Build the analysis into the Friday review. The first force to spike tells you where the next quarter’s capital goes.

The small-business strategist treats the buyer power inversion as the urgent threat. Customers are choosing through AI-mediated discovery. The buyer relationship built over decades is now intermediated by an agent that did not exist 18 months ago. The other four forces matter. This one moves first.

The companion prompt pack turns these ideas into a practice.

Five prompts: Diagnose, Stress-test, Translate, Decide, Loop. The first runs Five Forces on your company. The second re-runs it under AI-era conditions. The third translates to your operator tier. The fourth forces a decision and names the trade-off. The fifth schedules the practice as a weekly cadence with a 90-day review.

Porter was correct for 1979. The translation is our work in 2026.

Companies are becoming tech stacks.

We are all becoming companies.

- j -

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Download a pdf of the prompt pack here.

John Brewton documents the history and future of operating companies at Operating by John Brewton. He is a graduate of Harvard University and began his career as a Phd. student in economics at the University of Chicago. 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? He still cringes at his early LinkedIn posts and loves making content each and everyday, despite the protestations of his beloved wife, Fabiola, at times.

New to Operating?

Check out these reader favorites:

• Operating Economics: The Optimization Company (Amazon)

• Operating by Warren & Charlie

• Winning the Loser’s Game: The Creator Economy and the Companies We All Need to Build

• 0 to 55,000 - The First 90 Days Playbook - What I’d Do Differently Knowing What I Know Now

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Stop picturing a chat window. Picture a company.

A small, quiet, ferocious company with four divisions, all reporting to one person. That person is you. You are not an employee, a prompt engineer, a team member, a golden cog in a machine.

You are the CEO of a team that produces more finished work in ninety minutes than a traditional team of a dozen people produces in a week.

This is the shift most Operators have missed. Stop thinking of AI as a tool. Start thinking of it as a team.

Then, manage, run, direct, and performance evaluate the team.

Running the team has two modes…

Orchestrating and Operating

Orchestration is the meta-work. You write the playbooks. You stand up the divisions. You teach each department what “good” looks like. You wire up the systems, the APIs, the connections to the software you already live in. Orchestration is everything you do before any task arrives. It is how you raise the ceiling on what your organization can produce.

Operating is the daily work. You look at what landed on your desk. You decide which division handles it. You write the brief. You send it. You review what comes back within minutes. You edit, synthesize, and fire off the next request. Operating is the production line. It runs on rhythm, on volume, on your ability to keep it full.

Orchestration builds the production line. Operating runs it.

Most people try to do everything as an operating function. They open a fresh chat window. They dump context. They wait. They copy. They paste. They curse.

Then they do it again tomorrow from scratch.

That is not operating.

That is typing.

Real operating happens after you have orchestrated. After the divisions are standing. After the playbooks are written. After the tools are connected. Then a morning of operating looks like handing out four briefs and reading four finished outputs before lunch.

Here are the four divisions.

Projects are your Divisions

A Project is a division of your company. Each division has its own mandate, its own files, its own institutional memory, and its own standing orders.

I have a Project called Operating Stories. That division conducts my weekly research for my Operating Stories, drafts the article for me to edit and generates all of the associated digital assets and visuals. It knows my voice rules, my banned word list, my engagement data, and has access to every article in the series that I’ve published, as well as performance data.

A second division is 6A East Partners, my advisory firm. It knows the clients, meeting notes, and frameworks I implement with my corporate clients.

A third manages a client's corporate brand. And a fourth manages the assets and output for a client, for whom I advise relative to their operational strategy. A fifth handles everything relative to my personal brand.

Each division deliberately knows nothing about what the others are doing. Division-level separation is how a holding company keeps every unit sharp.

The orchestrator’s move: If you are re-explaining the same context every morning, stand up a new division.

Skills are your Playbooks

A Skill is the standard operating procedure that travels with the division that needs it. Skills are folders of instructions. They teach each division how to do a specific job the way you want it done.

I have a Skill called elite-research. When any division needs a research pass, that playbook fires. It tells Claude to only pull from the Financial Times, HBR, Stanford, Goldman, WSJ, The Economist, and the rest of my approved source list. The quality of my research is no longer a function of the hours I have available.

Meeting-intelligence turns Notion transcripts into branded client briefs. Contrarian-filter tears every draft apart before publication. An accountant friend built a Skill that closes her books. One button. Claude runs journal entries, builds the depreciation schedule, flags anomalies, and saves the workpapers. A day of work has been reduced to one hour of her time.

The orchestrator’s move: Any job you do more than three times is a Skill waiting to be written.

Cowork is your Operations Floor

Cowork is where the company actually produces the output. This is the shop floor. This is the production line.

Claude sits at your desk. Claude reads your files. Claude writes new ones. Claude opens a sandboxed Linux shell and crunches spreadsheets. Claude drafts the Word doc, formats it, saves it to the folder you picked, and hands you the link. Before it ships any work, it shows you the plan. You approve or redirect. You are the manager. Claude is the associate who has the rest of the day to finish.

Last month I pointed Cowork at six months of Substack engagement data and asked for a pattern report. Claude pulled the data, ran the analysis, drew the charts, wrote the commentary, and dropped a Word document on my desktop.

In 11 minutes.

The operator’s move: Stop copying and pasting files into chat windows. Send the folder to the floor. Let the line run.

Code is your Engineering Team

Claude Code is the engineering division. Same Claude, with full reach into the file system and a longer attention span. It writes code. It reads code. It refactors code. It also, and this is what most people miss, runs the biggest versions of the jobs the other divisions handle.

You do not have to program to staff this division. A product manager at Lenny’s Newsletter uses Code to organize his desktop, build expense reports, and sort six years of tax documents into the right folders. A writer I know uses it to maintain her book manuscript.

If Cowork is the shop floor, Code is the crew that runs the heavy machinery. Big refactors. Multi-file builds. Overnight batch jobs. You leave the work. You come back in the morning. The job is done.

I’ve personally used it to build production-ready applications for research universities and hospital environments, as well as complex client diagnostics.

The operator’s move: the moment a job touches more than five files, dispatch it to Engineering.

The systems underneath

Orchestration is not only about the four divisions. It is about the wires that connect them to the software you already use. Notion. Gmail. Google Drive. Substack. Your CRM. Claude plugs into all of it through APIs and MCP servers. Every connection you add raises the ceiling on what the company can take on.

You are not building a chatbot. You are building an operating system. Every Project, every Skill, every API, every MCP server is one more piece of production capacity. What you can produce is no longer capped by your typing speed.

It is capped by how much operational plumbing you have successfully built.

Start with one division.

Write one playbook.

Send one folder to the floor.

The rest will build itself.

We are all becoming companies.

- john -

Build it with me

If this all sounds great and you want help actually standing it up, I teach the whole system in the Unfazed Founder Sandbox.

It is a hands-on course for operators. We stand up your divisions live. We write your first Skills together. We wire Cowork and Code into the tools you already use. You leave with a real production line running.

Register for our Free Webinar, happening tomorrow (April 21st @ 1:00 PM EST) to take an intial deep dive, walk away with incredible learnings and decide if the full course offering is for you. And if you can’t attend live, just be sure to register and a reording of the event will be sent to you.

Register for the Sandbox →

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? He still cringes at his early LinkedIn posts and loves making content each and everyday, despite the protestations of his beloved wife, Fabiola, at times.

A personal note before the trilogy

This is the first article in a three-part Operating Stories arc on Uber. Article two looks at Uber as a multi-product logistics and commerce operator. Article three looks at the autonomy strategy as a textbook application of aggregation theory. I am publishing them as a deliberate arc (an Operating Stories tryptych) because the story of Uber under Dara Khosrowshahi is the most under-read operating turnaround of the last decade, and it rewards reading in order.

If you want the fast version of the whole trilogy in one paragraph, here it is.

Between 2017 and 2026, a single CEO and a disciplined team executed three turnarounds in series inside one company. A cost turnaround. A platform turnaround. A capital turnaround. Every one of them is stealable by an operator running a ten-person company or a ten-thousand-person company. This first piece is about the first one.

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A $20 billion swing that nobody credits to operating discipline

In 2019, Uber reported a $5.2 billion GAAP net loss on $14.1 billion in revenue.

In 2025, Uber reported $10.0 billion in free cash flow on $52.0 billion in revenue. Adjusted EBITDA reached $8.7 billion. Adjusted EBITDA margin on gross bookings reached 4.5 percent, up from 4.1 percent the prior year. Gross bookings for the full year crossed $193 billion. The company was in the S&P 500, which it had joined in December 2023, and returned roughly half of its free cash flow to shareholders through buybacks during that two-year window.

The swing from a $5.2 billion loss to $10 billion in cash generation over six calendar years represents roughly $15 billion in annual P&L improvement, within a company that was already producing more revenue than the 85th-largest country by GDP. Against 2017, when Dara Khosrowshahi took over an Uber bleeding $4.5 billion in operating losses, the cumulative swing between the 2017-2019 loss window and the 2023-2025 profit window is closer to $21 billion.

Twenty-one billion dollars is roughly the combined market capitalization of Hertz, Etsy, and Yelp. It is three times the price Verizon paid for AOL, about twice what Microsoft paid for LinkedIn, and more than the entire market value of the United States Postal Service’s implicit government guarantee. It is not a rounding error. It is the largest nominal P&L swing ever achieved by a consumer technology company within a single CEO's tenure.

The stock market’s response over the same window is the second surprise. The IPO priced in May 2019 at $45 per share and closed its first trading day at $41.90, down seven percent. By March 2020, in the depths of COVID, the stock had fallen to $13.71. The 2026 trading range sits above $67. The gain from IPO is around 50 percent. The gain from the COVID low is over 400%. Over the same window, the Nasdaq Composite roughly doubled. Uber, the company Wall Street spent most of 2019 and 2020 describing as structurally unprofitable, compounded at a pace that the word structurally was never designed to accommodate.

That is the opening.

The tension the article will work with is as follows: The conventional explanation for the turnaround is that Uber grew its way into profit. The financial record shows that the turnaround happened before the growth reacceleration, not because of it, and that growth was the consequence of the turnaround, not the cause.

The thesis

The Khosrowshahi-era Uber turnaround was not a growth story that finally achieved scale. It was a capital-allocation turnaround executed under the cover of a growth narrative. Uber did not grow its way to profit. It divested, deconsolidated, refused, and re-priced its way to profit. Then it grew.

The financial spine

I want to walk through the financial evidence carefully because the headlines have buried what actually happened.

Start with the pre-Khosrowshahi baseline. In 2017 Uber reported a $4.5 billion GAAP operating loss and a roughly $4.5 billion net loss on approximately $7.0 billion in revenue.

The company was carrying eight figures of monthly burn, a pending Waymo lawsuit, an internal cultural crisis, and a board fight that ended with the departure of the founding CEO. Dara Khosrowshahi took the job in August 2017.

His first full year in the seat, 2018, produced $11.3 billion of revenue and a $1.8 billion GAAP net loss. Operating loss was still above $3 billion.

The headline most often written about 2018 was that Uber’s losses were narrowing. That was true, and it was also beside the point.

The turnaround is visible in one image. Losses narrow from 2018 through 2022. GAAP operating profit arrives in 2023. Free cash flow separates from net income sharply in 2025.

The IPO arrived in May 2019. The filing priced at $45 per share, valuing the company at approximately $82 billion on a fully diluted basis. Day one closed at $41.90. The stock spent most of its first twelve months as a public company below $35. Inside the 2019 filings, Uber took a $5.2 billion GAAP net loss, of which a significant portion was stock-based compensation that Khosrowshahi described on the earnings call as a once-in-a-lifetime charge. The description was financially accurate. It was also a marker. The company had chosen to front-load the most painful accounting into the earliest post-IPO quarter. A management team planning to compound over a decade does not spread the pain. They take it.

Between 2020 and 2022 the company pushed through two things in parallel. The first was a set of divestitures that removed unprofitable or non-core businesses from the balance sheet. The second was a cost-structure overhaul that cut headcount by roughly 6,700 people in 2020 alone, trimmed variable driver incentives back toward sustainable unit economics, and flattened organizational hierarchy inside the core business. By the end of 2022, gross bookings had more than doubled from the 2020 trough, operating losses were inside one billion dollars, and adjusted EBITDA had crossed zero.

2023 is the inflection. First full-year GAAP operating profit: $1.8 billion. First full-year GAAP net income: $1.8 billion. First inclusion in the S&P 500, announced in December. Free cash flow of $1.1 billion. Revenue of $31.9 billion, up 17 percent year over year.

2024 consolidated the win. Revenue of $44.0 billion. Adjusted EBITDA of $1.8 billion at the annual level, expanding to a $2.5 billion quarterly run rate by Q4. GAAP net income of $4.4 billion, boosted in part by a deferred tax asset release but durable on an underlying basis. Free cash flow of $1.7 billion. Monthly active platform consumers crossed 170 million. Trips per monthly active user reached an all-time high in Q2 2024 and kept climbing.

2025 compounded it. Revenue of $52.02 billion, up 21 percent year over year. Adjusted EBITDA of $8.7 billion, up 35 percent year over year. Adjusted EBITDA margin on gross bookings of 4.5 percent. Gross bookings of $193 billion, up 20 percent. Free cash flow of $10.0 billion, up 42 percent year over year, and crossing 90 percent of adjusted EBITDA conversion. Mobility take rate held in the 27 to 30 percent range across quarters. Advertising crossed a $1.5 billion run rate at 60 percent year-over-year growth. Uber One membership passed 30 million subscribers.

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Cash conversion is the real story. Free cash flow ran ahead of GAAP net income in 2025, signaling that the business is now structurally cash-generative rather than accounting-optimized.

Guidance for Q1 2026, filed alongside the Q4 2025 release in early February 2026, called for $52.0 to $53.5 billion in gross bookings, up 17 to 21 percent in constant currency, and adjusted EBITDA of $2.37 to $2.47 billion. Non-GAAP earnings per share at the midpoint implied 37 percent year-over-year growth. The Q1 2026 earnings call is scheduled for May 6, 2026. I will update the series with live Q1 numbers when they print.

Now I want to stop and pull one specific number out of the spine. Compare 2019 to 2024. Revenue grew from $14.1 billion to $44.0 billion, a factor of roughly 3.1. Headcount grew from approximately 22,000 to approximately 30,800, a factor of roughly 1.4. Revenue per employee grew from roughly $640,000 to roughly $1.43 million, a factor of 2.2. The 80 percent growth in gross bookings against a 10 percent growth in employees that Khosrowshahi cited on the 2024 investor day was not a favorable ratio. It was a structural re-engineering. The company built a cost curve that separates from its revenue curve, and operating leverage now drops through the whole P&L.

Revenue per employee more than doubled under the turnaround. Operating leverage is a hiring-plan concept before it is a financial one.

That is the spine. The losses narrowed, then they stopped, then they reversed, and now the cash conversion is widening faster than revenue is growing. The interesting question is how.

The mechanism: a capital-allocation turnaround hiding inside a growth story

The conventional explanation for Uber’s profitability is that the business finally scaled, so unit economics finally turned positive. That explanation is not wrong. It is incomplete. It misses the three things that actually produced the turnaround.

One. The divestiture program.

Between 2016 and 2022, Uber systematically removed businesses from its balance sheet that either could not be won or could not be made profitable under its cost of capital. Russia went to Yandex. Southeast Asia went to Grab. China went to Didi. Uber Eats India went to Zomato. Uber Advanced Technologies Group, the in-house autonomous vehicle effort, went to Aurora in December 2020 for equity and a board seat. Drizly, acquired in 2021 for $1.1 billion, was shut down in 2024 after the integration failed to deliver. Cornershop’s minority partners were consolidated and absorbed into the Uber Eats grocery surface rather than run as a separate brand.

The gross cash generated from those divestitures through Q1 2026 is estimated between $8 billion and $12 billion. Net of the losses taken on publicly traded stakes (Grab, Zomato, Aurora), the realized balance sheet gain was smaller, on the order of $2 billion to $3 billion. The cash mattered less than the removal. Every divested business had been consuming management attention, regulatory bandwidth, and capital that was now available for redeployment into mobility, delivery, advertising, and membership.

Most public-company CEOs do not sell. Selling is unglamorous. It triggers impairment charges. It is read by the press as retreat. The Khosrowshahi-era Uber sold aggressively from a position of weakness between 2018 and 2021, and then it kept selling from a position of strength between 2022 and 2024. Treat divestiture as a product. Operators who want to emulate the turnaround should start here.

Two. The unit-economic rebuild.

Uber rebuilt driver economics before it touched consumer pricing. This is backwards from most consumer companies. Most consumer companies cut product quality or raise prices on consumers first. Uber did the harder thing, which was to reconstruct the match between rider, driver, and platform incentive, and then push pricing through once the match was structurally sound.

The work happened inside the 2019 to 2022 window. Driver incentive variable spend was rationalized away from the aggressive match rates that had been used in 2016 and 2017 to build supply during the land-grab years. Surge pricing was re-tuned to reduce variance on the driver side while preserving the spike-day revenue capture on the consumer side. Mobility take rate moved from a mid-20s range compressed by incentive spend to a stable 27 to 29 percent range. In Q4 2025 mobility take rate reached 29.9 percent.

On the delivery side the path was different. Delivery take rate declined from roughly 20 percent in 2022 to an estimated 18 to 19 percent through 2025, as DoorDash competitive intensity forced operators to keep restaurant and merchant economics attractive. Uber did not fight that compression. It stacked a higher-margin revenue layer (advertising) and a higher-attach revenue layer (membership) on top, so that delivery could run at slightly thinner transaction margins while delivering rising profit per active user.

The two segments diverge on take rate and converge on contribution. Mobility compresses supply cost to protect rate. Delivery accepts rate compression and stacks margin on top through advertising and membership.

Three. The capital-return commitment.

At the February 2024 investor day, the company committed to returning roughly 50 percent of free cash flow to shareholders through buybacks and, in time, dividends. That commitment was made while free cash flow was still $1.7 billion, which made the commitment non-trivial. The 2025 number of $10 billion in free cash flow vindicated the commitment. The first year of buybacks took place under that policy and retired meaningful share count. Net share count now trends down over time rather than up.

The discipline matters more than the absolute number. Returning capital forces a company to treat free cash flow as a resource that has a cost. It breaks the internal habit of treating cash as a rainy-day buffer that management can spend on acquisitions of declining strategic relevance. Uber committed to the discipline before the result. That ordering is what separates an operating company from a treasury.

Historical Operating Parallels

The mechanics above map cleanly onto three of the most studied corporate turnarounds of the last thirty years. I want to walk through each one because the mapping is where the playbook becomes transferable.

Parallel one: Lou Gerstner at IBM, 1993-2002.

In April 1993 Lou Gerstner took over an IBM that had lost $8.1 billion the prior year on $64.5 billion of revenue. The analyst consensus, and it was a loud consensus, was that IBM had to be broken up into independent business units, each of which would have a chance to compete in its own market. Gerstner declined. He believed that IBM’s durable advantage was the ability to deliver integrated solutions to enterprise customers, and that breaking up the company would forfeit exactly the thing that made the whole worth more than the sum of its parts.

Gerstner’s operating playbook across the next nine years included three moves that are directly relevant to Uber. The first was decisive cost action. IBM reduced headcount by roughly 100,000 people between 1993 and 1994. The second was divestiture of non-core businesses. IBM sold the Federal Systems Company to Loral in 1994 for approximately $1.5 billion and walked away from a semiconductor strategy that was consuming capital without producing strategic return. The third, and the most important, was the construction of a higher-margin business (IBM Global Services) on top of the lower-margin legacy business (hardware).

The financial result. IBM’s net income moved from a $8.1 billion loss in 1992 to a $4.2 billion profit by 1994, a swing of $12.3 billion inside two years. Services became the majority revenue contributor by the end of the decade. The stock compounded at approximately 14 percent annually from Gerstner’s arrival through his departure in 2002, against an S&P 500 that returned approximately 11 percent over the same window.

The mapping to Uber is precise. Khosrowshahi, like Gerstner, declined the breakup narrative. Activists and analysts during 2019 and 2020 argued that Uber should be split into mobility and delivery, with mobility returned to shareholders as a cash cow and delivery sold or spun off. Khosrowshahi refused. He argued, correctly, that the two businesses shared a customer acquisition funnel and that over one third of Uber Eats first-time users arrived through the mobility app. The cost action mirrors Gerstner’s. The 6,700 layoffs in 2020 were the Uber equivalent of Gerstner’s 100,000 cuts. The divestiture program (Russia, Southeast Asia, China, ATG, Eats India) is directly analogous to Gerstner’s non-core asset sales. And the construction of the advertising and membership businesses on top of mobility and delivery is the direct structural analog of Global Services on top of IBM hardware.

Operators should take from the Gerstner comparison one specific principle. A legitimate operating turnaround does not require breaking up the company. It often requires refusing to. The integrated platform is frequently the source of the turnaround, not the obstacle to it.

The full Operating Stories deep dive continues below.

Paid subscribers get the remaining two historical parallels (Satya Nadella at Microsoft, Howard Schultz at Starbucks), the four operating principles extracted as a directive playbook, the broader implications section, the conclusion, and

The AI Prompt Pack with sixteen prompts that walk you through applying the playbook to your own business inside a working session with Claude, ChatGPT, Gemini or Perplexity,

The Operating Playbook Worksheet

The 22-page Deep Dive Financial and Historical Report.

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What is Operating Stories?

This series is built differently. It comes in two speeds.

The primary article, above the fold, gives you the essentials of the story being told, the charts, and the four operating principles in under five minutes.

But the article is just the center of a larger system designed to meet you wherever you are in your operating journey. If you run a company doing $50 million, the Operating Audit will score your business against the same playbook that built Nike, On Running, and New Balance, and the Prompt Pack will turn your favorite AI tool into a working session partner that produces a 90-day action plan specific to your business.

If you are a solopreneur selling your first product out of a metaphorical car trunk, the same frameworks scale down to your level because the principles are the same ones Phil Knight used when his total capital was $500 and his distribution strategy was a Plymouth Valiant.

And if you are the kind of operator who wants to see the raw research, the five Research Briefs give you every number, every source, and every data table so you can build your own analysis on top of mine, or just gain a better understanding of a fascinating piece of business history.

The goal is not to hand you a newsletter you read once. It is to hand you a system you use until the principles become embedded in your thinking and operating strategy.

For the first week’s release of this new design, I’m making the Operating Audit and Prompt Pack available for free, here and here.

- j -

Start Here

In 1964, Phil Knight sold 1,300 pairs of Japanese running shoes out of a Plymouth Valiant and grossed $8,000. His total startup capital was $500. He kept his day job for five years. By 1980, Nike commanded 50% of the US athletic shoe market with $269 million in revenue. Sixteen years to acquire and 50% of the market. That’s the first operating lesson.

The playbook was three moves.

1. Product obsession: Bill Bowerman cut shoes apart with a band saw and poured rubber into a waffle iron.

2. Grassroots distribution: Knight sold at track meets because that is where runners were.

3. Capital discipline: 3% of revenue on marketing in 1977. Every dollar earned its place.

Sixty years later, two companies are running that same playbook. Against Nike.

On Running started in 2010 when a retired Swiss triathlete glued garden hose segments to shoe outsoles. He pitched the idea to Nike first. Nike said no. On hit $3 billion in revenue in 2025 with a 62.8% gross margin. Roger Federer invested $54 million for 3% equity in 2019. That stake is now worth $400-500 million.

New Balance has been privately owned since Jim Davis bought it for $100,000 in 1972. He turned down a blank-check acquisition offer from Warren Buffett. The company reached $9.2 billion in revenue in 2025.

Its fifth consecutive year of double-digit growth.

Both challengers grew because Nike opened the door. Between 2017 and 2025, Nike made three strategic mistakes that map precisely to the three moves of its own founding playbook.

1. It stopped obsessing over product. Nike replaced sport-category teams with demographic silos. The innovation pipeline thinned. Nike disappeared from the top five in US performance running specialty.

2. It abandoned grassroots distribution. Nike cut its wholesale partners from 30,000 to 40. It dropped DSW, Macy’s, Urban Outfitters, Zappos, and dozens more. Challengers walked into the vacated shelf space.

3. It traded capital discipline for franchise extraction. Nike flooded the market with Air Force 1, Jordan 1, and Dunk colorways until the cultural equity collapsed.

Revenue fell from $51.3 billion to a $46 billion run-rate. The stock dropped 56% over five years. More than $200 billion in market capitalization erased.

The company that invented the Challenger playbook became the incumbent that the playbook was designed to beat.

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The Challenger Playbook 4 Operating Principles

1. Sell to the community that confers credibility before you sell to anyone else. Knight sold at track meets. On launched through REI exclusives and run clubs. New Balance partnered with Teddy Santis and Aimé Leon Dore. Find the smallest, most influential community that cares about what you are building. Serve them first.

2. Product obsession is a founder’s job, not a department’s job. Bowerman’s waffle iron. Bernhard’s garden hose. Davis’s domestic factory investment. The product advantage was created by a founder with domain expertise making decisions that a professional management team would not make.

3. Capital constraints are a competitive weapon, not a disadvantage. Knight spent 3%. On gave Federer equity instead of a $300 million endorsement deal. New Balance stayed private for 52 years. Every constraint forced a better decision.

4. Distribution is brand strategy expressed operationally. Every distribution decision is a brand decision. Choose channels that tell the story you want told. Track meets told the story that Nike was for serious runners. DSW never told a story anyone remembers.

Score your business against the Challenger Playbook. Take these three questions to ChatGPT, Claude, or Gemini with your own business context:

1. Name the last three product changes you made based on direct feedback from your most demanding customers. If you cannot, your Product Obsession score is low.

2. Rank your distribution channels by credibility with your core customer. If your highest-volume channel is your lowest-credibility channel, your Distribution Credibility score needs work.

3. What percentage of revenue do you spend on paid customer acquisition? Knight spent 3%. If yours is above 15%, your Capital Discipline is renting attention instead of building equity.

The full Operating Stories Deep Dive continues below for paid subscribers, including the complete Nike origin story, the On Running and New Balance operating profiles, running boom market analysis, the Challenger’s Operating Audit worksheet, a 16-prompt AI working session pack, and three branded research briefs you can use for your own strategic planning.

- john -

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I used to write 3,000-word essays. Then I looked at the data. The average newsletter reader gives you 51 seconds. Even on Substack, most people scroll to about 65% depth on a long piece. So I’ve created Starting Points.

Each edition gives you a sharp thesis in a few sentences, two to three AI prompts built to interrogate that thesis, and a curated pack of the best articles I found on the topic.

Read it in 90 seconds. Take it to Claude, ChatGPT, Gemini, NotebookLM…

The creator market does not need more content explaining what AI can do. It needs clearer thinking about what AI changes. This edition is about the operating model most businesses are still running — and why the window to rebuild it from scratch has never been wider for smaller firms.

I write starting points. You build the rest.

— j —

Last week I watched an ERP software demo.

At step nine, the presenter said: “And then your ops lead simply reviews the exception report and makes the appropriate adjustment.”

Simply?

I’ve been in rooms like this for fifteen years. The word simply has never once described what actually happened.

The software we built businesses on for the last thirty years was never designed for the people running it. ERP, CRM, Excel, BI dashboards, approval workflows — these were elaborate intermediary systems built for computers that couldn’t yet do the work themselves. They structured the world so humans could perform tasks that machines lacked the intelligence to handle. Parsing context. Routing exceptions. Reconciling mismatched data across departments. Making judgment calls buried inside process flows.

The systems didn’t do the work.

They organized the workspace so humans could.

The AI era is dissolving that arrangement by removing the human from the loop where the human never belonged: procedural execution, data capture, pattern recognition at scale, and cross-system orchestration. The interface is no longer a set of screens that a human clicks through. The interface is intended.

My contrarian bet: The firms that win the next decade won’t be those with the most sophisticated tech stack. They’ll be the companies who rethink their operating architecture from first principles, before they bolt more AI features onto the same broken assumption.

The smallest firms can redesign fastest. That is not a consolation prize. In an era where structural advantage shifts from scale to speed of architectural adaptation, it is the opening.

Work with me directly.

You’ve read the framework. Most people stop there.

If you want to implement it — map your operating architecture, build your content system, design the AI-first workflows your business actually needs — this is how we do it together.

Three 1:1 strategic operating sessions with me.

Full access to the Operating Founders Skool community and all three course curriculam (43 DIY assets across 45 lesson plans)

• The Operating Foundation,

• The Brand Operating System,

• The Content Engine.

Weekly live Zoom calls to keep you moving between sessions.

$99

This offer is for founders who are ready to stop reading about operating differently and start doing it.

Become an Operating Founder

Start Here

For decades, small operators looked to larger companies for operational guidance. There was logic in that. But the transfer was always imperfect. AI-first operations invert the dynamic entirely.

The very things that made large firms strong — layers of coordination, massive headcounts, deep process libraries — are now impediments. A $5 billion company cannot rearchitect its ERP overnight. A $15 million company can build a custom data model, deploy agents against its core workflows, and iterate in weeks. The World Economic Forum profiled AI-first startups in early 2026 running lean teams of fewer than ten people across hundreds of agents spanning full operational functions. The structural advantage is speed, proximity to real problems, and the absence of legacy process debt.

For decades, small operators looked to larger companies for operational guidance. There was logic in that. But the transfer was always imperfect. AI-first operations invert the dynamic entirely.

The very things that made large firms strong — layers of coordination, massive headcounts, deep process libraries — are now impediments. A $5 billion company cannot rearchitect its ERP overnight. A $15 million company can build a custom data model, deploy agents against its core workflows, and iterate in weeks. The World Economic Forum profiled AI-first startups in early 2026 running lean teams of fewer than ten people across hundreds of agents spanning full operational functions. The structural advantage is speed, proximity to real problems, and the absence of legacy process debt.

The question is what you build instead. Here are the ten principles.

1. Value streams, not departments: Design operations around end-to-end flows — lead to cash, procure to pay, ticket to resolution — not around the functional silos that make org charts tidy and decision-making slow.

2. Tasks, not software categories: Before selecting any tool, decompose your operations into discrete tasks: what is actually being done, what data does it require, what decision does it produce. Buy for the task. Not the category.

3. One canonical data model: A single governed source of truth for customers, transactions, and operations. Everything reads from it and writes to it. Twelve SaaS tools and thirty spreadsheets is not a data strategy. It is a liability.

4. Capture data at the source: If a conversation generates information, the system captures it in real time — not after a human logs it later. Post-meeting data entry is not a workflow. It is a gap waiting to fail.

5. Agents execute, humans judge: Assign procedural, repetitive, and pattern-recognition tasks to agents. Reserve human attention for exceptions, ambiguity, and relationship judgment. The human who reviews every step is a bottleneck. The human who judges what matters is an asset.

6. Operate on events, not reports: An agent that alerts you when pipeline velocity drops 15 percent on Tuesday is more useful than a dashboard you check on Friday. Shift from periodic review cycles to continuous, event-driven signals.

7. Encode your decision logic: Your policies, thresholds, escalation rules, and next-best-actions should live as explicit, executable logic — not as tribal knowledge in someone’s head or inconsistent calls across a team.

8. Buy commodity, build differentiation: Use off-the-shelf infrastructure for identity, payments, and communications. Build custom systems only around the workflows and data models that constitute your actual competitive advantage.

9. Minimize sprawl, maximize interoperability: Every tool in your stack should expose clean APIs. If a system cannot be queried or written to programmatically, it is a dead end. Eight tools duct-taped with manual exports is not a stack. It is a maintenance burden.

10. Govern through exceptions: Governance in an AI-first model is not approval chains. It is exception handling: defining what the system escalates to a human, maintaining a clear audit trail of agent actions, and building feedback loops so the system improves over time.

The question is what you build instead.

These are not software recommendations. They are design decisions. Before selecting any tool, decompose your operations into discrete tasks:

• What is actually being done?

• What data does it require?

• What decision does it produce?

Then build around the answer, not around a software category.

Stop thinking in CRM. Stop thinking in ERP.

Start thinking in tasks, data, decisions, and loops.

Build from there.

Prompts to explore:

“Map your own business against the ten principles. For each one, rate your current state: 0 (fully legacy), 1 (in transition), or 2 (already AI-first). Where do you score lowest? What would it cost — in time, money, and process disruption — to move that specific principle from 0 to 2 in the next 90 days? Be specific about what changes and what you stop doing.”

→ Before you go further — take the free operating model evaluation:

The Operating Model Audit Quiz

Score your organization against the ten principles. Come back with your three lowest scores. The prompts below are built for that.

Paid subscribers get the full prompt pack for this edition — the workflow redesign prompt, the evaluation-linked diagnostic, and the complete reading pack with NotebookLM instructions.

Here’s the prompt one preview:

“Using principles 1, 2, and 5 — value streams, task decomposition, and agents-execute-humans-judge — redesign one end-to-end workflow in your business from scratch. Describe the workflow as it exists today, then rebuild it from first principles. Be specific about what an agent handles, what a human judges, and how you would know when something goes wrong.”

“Before running this prompt, take the evaluation below. Come back with your three lowest-scoring principles. Then ask: what is the single workflow in my business where improving that score would have the most immediate impact on revenue or margin? Now design the intervention, not the tool, the architecture.”

Most operators will read a framework like this and change nothing.

The full prompt pack is for those who want to do the work.

Two more prompts behind the wall: a workflow redesign built on principles 1, 2, and 5, and an evaluation-linked diagnostic that converts your lowest scores into a specific architecture decision. The complete reading pack — six sourced articles with NotebookLM instructions — included.

The work is the point. The prompt pack is how you do it.

Paid subscribers get the full prompt pack for this edition.

“Get the full prompt pack below →

Subscribe now

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I studied the economics of companies at Harvard and the University of Chicago for ten years. Then I operated, optimized, sold and advised companies for 20 more.

I spent the last two turning everything I learned into a system while building my personal brand online (Zero to 70,000+ followers. 6,000+ Substack subscribers. Bestseller. 100-person cohort sold out in 12 days. No paid ads. No launch strategist. One system.)

This week, I’m opening the doors to my first course and digital product.

The Operating Founders Curriculum

This is NOT a standard newsletter upsell or lead magnet.

This is NOT a PDF bundle with a Calendly link.

I am going to walk you through every piece.

If you’ve been following along, you already know the frameworks.

You have watched the Operating Habits install one system per week with a downloadable asset behind each one.

You have learned how some of the most iconic firms in business history have optimized their operations via our Operating Stories pieces.

You have learned how to Scale Down the Big Ideas.

The articles are the thinking.

What I built is the doing.

The Operating Founders Curriculum gets you into action.

Become An Operating Founder

What Is Inside

Three structured courses, 100+ operators, and a live lesson walk through and Q&Q with me every Friday for 52 weeks.

Course 1: The Operating Foundation

Five modules. Fifteen lessons. The operating system I used to scale a family-owned B2B distribution company, repackaged for one-person and small-team businesses. (The principles did not get smaller when the company did.)

• You build a business model canvas.

• Calculate your effective hourly rate by activity.

• Find the Denominator Problem buried in your calendar.

• Define your moat.

• Install the Friday Forensic, the Decision Journal, and a time-blocking system.

• Build an Operating Dashboard tracking seven numbers.

• Design a 90-day Priority Map reducing 47 competing priorities to three.

• Run a Quarterly Business Review using the same structure I use with 6A East advisory clients paying five figures a month.

Nine Excel trackers. Six Word templates. Actionable steps and business deifning infrastructure as oppossed to surface level theory.

Course 2: The Brand Operating System

The course between operating and content. Most founders skip to posting before defining what their brand is. The output is competent and forgettable. Like a well-organized filing cabinet with nothing interesting inside.

• You build a Brand Association Map (what you want associated with you, and the equally important list of what you refuse).

• Map your competitive field and find the positioning gap nobody occupies.

• Construct your three-part brand story.

• Design the 80/20 content mix (80% authority, 20% the stuff making you human and not a content-producing automaton).

• Document your brand voice and test it against real audience perception.

Fourteen assets including a Brand Voice Reference Document (the documentation your AI systems will use to write content sounding like you instead of sounding like ChatGPT wearing your name).

Course 3: The Content Engine

The system behind Operating by John Brewton. Zero to 70,000+ followers. 6,000+ Substack subscribers. Bestseller. 100-person cohort sold out in 12 days. No paid ads. No launch strategist. One system.

• You define your editorial thesis and content pillars.

• Build the Engine-and-Exhaust model turning one flagship piece into eight to ten derivatives.

• Learn the Counterintuitive Reframe.

• Master the paywall architecture.

• Install a Content Dashboard.

• Run a Content Audit at four weeks.

• Design a 90-Day Content Operating Plan.

Fifteen tools, including a Repetition Rate Tracker (the target is 60% reinforcement, and I have never met a founder hitting it without measuring).

45 lessons. 43 assets.

And this is just the start…

Become An Operating Founder

The Courses Change. Your Access Does Not.

Most online courses are time capsules.

Someone records the videos, uploads the PDFs, moves on.

A year later, the whole thing feels like business advice from a previous administration.

AI is rewriting how work gets done at a pace I have not seen over the span of my career.. The tools I teach today will be different in six months.

A static course in 2026 is a decorative item.

New lessons get added. Stale lessons get removed. Assets get rebuilt when the tools underneath them change. The course six months from now will be better than the course today.

Your access is permanent. New lessons land in your Classroom.

Updated assets replace old ones.

You do not pay again. You do not re-enroll.

I am inside this community building every week because it is the foundation of my business, not a side project I stopped thinking about on day 31.

Become An Operating Founder

The Community

The courses are the curriculum.

The community delivers the game-changing, compounding value.

Seven feeds:

1. Wins and Milestones (with numbers attached),

2. Ask the Room (advice from operators with the scar tissue to back it up),

3. Collab Corner (cross-promotion, newsletter swaps, referrals),

4. From the Desk (my updates and asset drops),

5. Resource Vault (every tool, organized),

6. AI Operators (workflows, prompts, tech stacks)

7. Introductions

200+ operators have already been given access. Consultants, newsletter operators, coaches, founders, investors, academics, creators, artists. A room full of people doing the same thing produces groupthink. A room full of people applying the same principles to different businesses produces signal above the noice and learning community that will remain evergreen.

When someone hits 100 subscribers or publishes a post with 3X their normal views, the room is their to celebrate with them. When someone lands a client from the quality of the content they’ve been producing, the room studies the mechanics. When someone asks a question, the answers come from an experienced, global community of friends and colleagues.

Members recommend each other’s Substacks. Swap newsletter features. Co-host Lives. The reciprocal engagement alone is worth $99. Everything else is gravy baby.

Become An Operating Founder

The Friday Sessions

Every Friday. One hour lesson walk through and Q&Q (for all 52 weeks of the year)

My real data. Not curated screenshots from a good week. Open Q&A where nothing is off-limits. If you want to know my revenue, my conversion rate, my worst-performing content, ask.

There is a second layer most people do not notice until they are inside. You are watching an operator build a product. Every decision, every course I add, every framework I test and discard, every mistake (and they are instructive), shared openly. The team operations. The tech stack. The launch sequence. What worked. What I will never do again.

If you plan to build your own course or community, this is the 52-week build log. The live version, including the weeks where the data said something I did not want to hear. Nobody was offering this when I was building Operating from scratch or when I started my personal brand building journey two years ago. I wish someone had. I would have paid well above $99 for it.

Become An Operating Founder

The 1:1 Sessions

Three sessions. Sixty minutes each. Your pace.

Not coaching calls. Coaching calls are someone asking how you feel about your progress. These are working sessions.

We apply the course materials to your business.

• Your canvas.

• Your positioning.

• Your content strategy.

• Your dashboard.

• Your priorities.

You are getting three hours for $99. The cohort gives me a front-row seat to 100+ businesses, and the relationships inside this room will produce engagements and collaborations worth far more than the per-seat revenue on the table.

Become An Operating Founder

$99

• Permanent access to all three courses.

• 45 lessons, 43 assets, and every future update. You pay once.

• 52 weekly live sessions. Real data, real Q&A. Plus the full build log so you have the perspective to build your own.

• Three 1:1 sessions applied to your business. Scheduled when you are ready.

Less than $2 per week.

Scalability is overrated. Helping people is the business model.

[BECOME AN OPERATING FOUNDER]

Become An Operating Founder

Who This Is For

You are building a business, a newsletter, a coaching practice, a consulting firm, or a creator company, and you want a system underneath the work. Not more content. The foundations on which you will build a profitable business.

You have been reading this newsletter and thinking “I agree with all of this and I have done none of it.” This program was built for that sentence.

You want a room to really refine your offers and moat.

You want to build your own productized offering and want to watch one get built live to learn best practices over 52 weeks.

Do not join for passive consumption. Every lesson produces a deliverable. Every lesson can be conusmed in under 10 minutes and completed in under an hour. If you are collecting courses the way some people collect books they display but never open, this is the course that was built for you to be able to proudly finish.

Enrollment Is Open. It Will Not Stay Open.

Limited window.

The offer remains for only as long as my calendar can manage the 1:1 sessions.

Quality matters more than size. When enrollment closes, the Operating Founder tier with the 1:1 sessions goes away.

Free subscribers: becoming an Operating Founder includes full paid access. Every article, every asset, every tool.

Paid subscribers: upgrading adds the Skool community, the courses, the sessions, and the 1:1 time.

$99. One click.

Let’s go build your business, friends!

[BECOME AN OPERATING FOUNDER]

Subscribe now

- john -

Companies are becoming tech stacks.

We are all becoming companies.

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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 6A East Partners, a research and advisory firm asking the question: What is the future of companies? He still cringes at his early LinkedIn posts and loves making content each and everyday, despite the protestations of his beloved wife, Fabiola, at times.

I Write Starting Points. You Build The Rest.

I used to write 3,000-word essays. Then I looked at the data. The average newsletter reader gives you 51 seconds. Even on Substack, most people scroll to about 65% depth on a long piece. So I’ve created Starting Points.

Each edition gives you a sharp thesis in a few sentences, two to three AI prompts built to interrogate that thesis, and a curated pack of the best articles I found on the topic.

Read it in 90 seconds. Take it to Claude, ChatGPT, Gemini, NotebookLM…

The premise is simple. The creator market does not need more content teaching people how to use AI. What it needs is a point of view, synthesis and curation of which ideas matter, sharp framing, and the tools to explore them yourself.

You don’t need more things to read, you need more starting points to inspire your curiosity and creativity.

I write starting points. You build the rest.

- j -

Subscribe now

Start Here

Consumer enthusiasm for AI-generated content dropped from 60% to 26% in two years. More than 20% of videos shown to new YouTube users are now classified as AI slop. Content volume is at an all-time high and content trust is at a structural low.

My contrarian self is not betting that AI content fails, it is betting that AI content succeeds so completely at flooding every channel that the only remaining scarce resource is editorial judgment.

Taste, in the economic sense, is becoming a capital asset.

The people who can decide what matters, what to ignore, and what to remix will build the next class of media companies.

Prompts to explore:

“Make the economic case that editorial judgment and curation are becoming scarce resources in an AI-saturated content environment. Use the economics of scarcity, signaling theory, and network effects to explain why human taste could become a defensible competitive advantage. Include at least three historical parallels where information abundance created new scarcity-based businesses.”

“Analyze the business model of three curator-driven media companies or newsletters (for example: Dense Discovery, The Browser, Stratechery). What do they have in common structurally? What is the ‘moat’ of a curation business when AI can aggregate information at near-zero cost? Where does the moat break down?”

“I am building a personal brand around [topic]. If the contrarian bet is correct that taste and editorial judgment become the scarce asset in media, how should I restructure my content strategy to optimize for curation authority rather than content volume? Be specific about format, frequency, and positioning choices.”

Subscribe now

Tool: NotebookLM (notebooklm.google.com). Run the prompts against the set of articles below. NotebookLM will query across all sources simultaneously, find contradictions between them, and generate synthesis that no single article provides.

The Reading Pack

Six articles. Upload all of them. They are the source material the prompts were built to interrogate.

Sloponomics: Who Wins and Loses in the AI-Content Flood — Tom Wainwright, The Economist (Oct 2025). The most rigorous economic analysis of the AI content glut. Documents the barbell effect: hobbyists and superstars thrive, the professional middle gets squeezed.

The State of the Culture, 2024 — Ted Gioia, The Honest Broker (Feb 2024). Gioia’s most viral essay. Maps the cultural food chain from art to entertainment to distraction to addiction. 25 million views. The framework that explains why the slop problem is structural, not temporary.

A.I. Is Homogenizing Our Thoughts — Kyle Chayka, The New Yorker (Jun 2025). MIT Media Lab research showing ChatGPT users produce more homogenized writing and demonstrate less brain activity. The empirical case that AI does not just flood the market. It flattens the thinking.

Drowning in Slop — Max Read, New York Magazine (Sep 2024). The investigative deep dive into the AI content farm economy. How abandoned domains get stuffed with machine-generated articles. The supply side of the crisis, reported from the inside.

This Is Just the Internet Now — Charlie Warzel, The Atlantic (Oct 2025). The definitive 2025 assessment that AI slop is no longer a trend piece. It is the default state of online content. The piece that stops pretending the problem is fixable at the platform level.

AI and the Human Condition — Ben Thompson, Stratechery (Jan 2026). Thompson’s argument that human-created content builds community in ways AI structurally cannot. The strategic case for why taste is not just a cultural preference but a business moat.

Ran the prompts?

What did you discover?

What are you going to build next?

Share in the comments.

Hope this helped.

I write starting points. You build the rest.

- j -

Companies are becoming tech stacks.

We are all becoming companies.

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Become a paid subscriber to access all the content ($7.99 / month)

Subscribe now

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? He still cringes at his early LinkedIn posts and loves making content each and everyday, despite the protestations of his beloved wife, Fabiola, at times.

I’ve spent 100 hours coaching Substack founders so far this year.

It’s actually been 105 so far, with hundreds more hours booked for the months ahead. In 2026, scalability is overrated. Helping people is the only thing that matters.

I’ve spent 100 hours listening and helping people like you who are trying to build a business of one..

• Consultants productizing services.

• Newsletter operators building paid subscription engines.

• Coaches designing scalable practices.

• Corporate refugees are trying to figure out what their 20 years of experience are actually worth on the open market.

I’ve kept notes, and the patterns are far too clear to ignore.

By session 20, the same struggles were showing up across completely different businesses. By session 50, I could predict the order. By session 100, I had a field guide I did not expect to write.

These are not lessons I invented. They are lessons I learned from people through their lived experience, contrasted with the strategic direction and lens of my 20 years of operating experience.

The struggles are universal.

The breakthroughs are specific and life changing.

The people who generated these lessons deserve more credit than I do for writing them down.

I organized them into 10 categories.

The first 10 are free.

The remaining 90 are behind the paywall because they represent the depth of what this cohort built together over just the first two months of our work together.

If you see yourself in these lessons, good. I hope this helps.

If you want to learn these lessons and more, join our community.

- j -

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Become a paid subscriber to access all the content ($7.99 / month)

Subscribe now

The First 10

1. We are not losing because we lack talent. We are mid-transition.

Measure against where you were 90 days ago. Not against someone who started five years before you.

2. The first $1,000/month is almost never an audience problem. I watched founders with 200 followers close $3K deals. I watched founders with 10,000 followers struggle to clear $500. The difference was clarity, not reach.

Rewrite your offer in one sentence. Send it to 10 people you know. The bottleneck is not distribution.

3. Nearly every founder from corporate underpriced by 40-60% in year one. Corporate taught them that someone else decides what they are worth. That conditioning does not vanish on day one of self-employment.

Calculate your delivery cost. Add 50%. That is your floor.

4. The fastest builders were not the best content creators. They were the best follow-uppers. One warm follow-up per day. That is it. The correlation between this habit and revenue was the strongest signal in the entire cohort.

Follow up with one warm contact today. Before you create any content.

5. Most people did not set out to build a personal brand. They set out to build a business. The brand is the distribution channel. Not the destination. That reframe changed how a lot of people spent their mornings.

Write about your client’s problem. Not about you.

6. The identity shift from employee to founder takes 6-12 months. Month 3 or 4 is the hardest. The excitement fades, results have not arrived, and your old colleagues just posted about a team offsite. Nearly everyone hit this wall.

Do not make permanent decisions based on temporary doubt. 12-month runway before you evaluate.

7. Almost everyone already had their first 50 clients in their existing network. They just had not looked at those relationships through a founder lens yet.

List 20 people who trust your judgment. That is not your network. That is your pipeline. Reach out to three this week.

8. Nobody needs another course. They need someone to look at their numbers and tell them the truth with kindness. That is what the sessions became.

Find one person who will look at your business with honest eyes this week.

9. One-sentence businesses outperformed multi-tier product suites every time. “I do this one thing for this one type of person and I can start Monday” beat the 30-slide deck at a rate that stopped surprising me around session 40.

If you cannot explain your offer in one sentence, it is not clear enough to sell.

10. Perfectionism showed up in almost every session. Not as laziness. As fear. The people who shipped at 80% learned faster, earned faster, and built more. Every time. Across 100 sessions. No exceptions.

Ship the thing you have been polishing. 48 hours. Whatever state it is in.

These are the first 10. There are 90 more below, organized into the 10 categories I wish someone had handed me 20 years ago. I wrote them for the people in this cohort. I hope they will be useful to you, too.

Before you hit the paywall, I want to give you something.

I built a free interactive scorecard based on these 100 lessons.

You rate yourself 1-5 on each one. It calculates your scores across all 10 categories and shows you exactly where you are strong and where you are leaking. It takes about 15 minutes. The output is a personalized operating diagnostic that would cost serious dollars if a consultant built it for you.

It is free. No email gate. No signup. Just take it.

[Take the Operating Founder Scorecard]

Paid subscribers also receive this as a Google sheet with a graphical dashboard and quarterly tracker. It’s pretty awesome.

I built it using Perplexity Computer. Which, if you think about it, is a pretty good proof of concept for Category 8 of this playbook.

Now consider this. That scorecard is what I give away for free.

The 90 lessons behind the paywall, organized into 10 operating categories with specific action steps for each one, are what I give to paid subscribers. Along with every framework, template, and tool I build across Operating every week.

Become a paid subscriber for $7.99 per month.

Become a founder so we can being working together 1:1.

Subscribe now

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Start Here

On December 31, 1600, Queen Elizabeth I signed a charter that created the East India Company. It required a royal decree, an act of Parliament, and months of political maneuvering. The company would eventually command half the world’s trade, employ tens of thousands, and operate its own military.

The word “corporation” comes from the Latin corpus. A body.

Many people acting as one.

On a Tuesday in 2026, a 34-year-old in Austin can establish their corporation of one in 11 minutes on Stripe Atlas.

No charter.

No act of Parliament.

A laptop and a credit card…

Are you new to Operating?

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Become a paid subscriber to access all the content ($7.99 / month)

Subscribe now

The corporation was never about people.

It was a coordination technology. A legal structure that let groups of humans pool capital, share risk, and act as a single entity. For four hundred years, if you wanted access to the capabilities a corporation provided, you had to join one or build one.

Finance, distribution, legal protection, marketing, operations.

These were the departments you had to staff.

That is what makes the last twenty years of corporate culture discourse so funny, in retrospect. All those demands for work-life balance, for better treatment, for “cultures that value the whole person.” Those were not signs of a healthy employee-employer relationship under stress. They were early symptoms of a workforce that had already mentally quit the premise of employment. Every person who asked their company to treat them like an individual was already thinking like a free agent. They just had not filed the LLC yet.

Now millions of them have.

29.8 million nonemployer businesses exist in the United States.

That is 81.9% of all small businesses in the country.

The SBA recorded 440,000 new business applications per month during the Biden administration, a rate 90% above pre-pandemic averages. In 2025, 5.67 million new business applications were filed. An all-time record.

They collectively generate $1.7 trillion in annual revenue. That is the GDP of Australia. It is 6.8% of total U.S. GDP, produced by businesses with zero employees.

The demographic profile does not match the cliche either:

• Women make up 54.4% of solopreneurs. 64% are over 45, led by Gen X and Boomers.

• The median solopreneur is not a 26-year-old with a ring light. She is a mid-career professional who looked at the corporate wellness program, the mandatory fun Slack channel, and the seventh reorg in three years, and decided to stop pretending.

• 20% earn between $100K and $300K annually.

• 77% are profitable in year one.

• The U.S. spent $47 billion on employee wellness programs in 2024. Most employees said it did not help with burnout.

Turns out the cure for corporate misery was not a meditation app. It was an LLC.

The enabling mechanism is what I call the Corporate Stack. It is the business equivalent of a tech stack. Every person building a solo operation is assembling one, whether they use the term or not.

Payments (Stripe). Distribution (Substack, Beehiiv). Automation (Zapier, Make). AI reasoning (Claude, ChatGPT). Design (Canva). Accounting (Wave, QuickBooks). Scheduling (Calendly).

The average company runs 106 SaaS applications.

A solopreneur needs 8 to 12.

Monthly cost: $200 to $600.

In 2005, starting a product business meant hiring an accountant, a designer, a web developer, a marketing team, and a lawyer. In 2015, you could outsource most of those roles but still managed the relationships. In 2026, the functions themselves are software. You do not hire a marketing department.

You configure a distribution stack.

You do not hire a CFO.

You connect Stripe to Wave and read the dashboard.

The Corp Stack replaced the org chart.

The API integration market, the connective tissue between these tools, is projected to grow from $15.6 billion to $78.3 billion by 2032. That is 25.9% compound annual growth. The infrastructure for solo operations is not a niche. It is one of the fastest-growing segments of the technology economy.

There is a precise historical parallel. In 1844, the UK passed the Joint Stock Companies Act, which let ordinary people incorporate through simple registration for the first time. Before that, you needed a Royal Charter or an Act of Parliament. The 1844 Act democratized the corporate form. It took decades to reshape the economy.

We are at that inflection point again, except decades are compressing to years.

Stripe Atlas formations were up 41% in 2025.

Stripe now incorporates one in five new Delaware corporations. 20% of Atlas startups charged their first customer within 30 days of incorporation, up from 8% in 2020. The 2025 cohort is growing 50% faster than the 2024 cohort.

Anthropic CEO Dario Amodei predicted in 2025 that AI would enable the first billion-dollar, one-person company by 2026. The timeline may slip. The direction will not.

Microsoft’s 2025 Work Trend Index introduced the “Frontier Firm”, an organization built around on-demand AI intelligence with human-agent teams. 71% of Frontier Firm leaders said their company was thriving versus 39% globally. 55% of their workers said they could take on more work versus 25% globally.

That is not marginal. That is a different operating model.

Paul Jarvis wrote Company of One in 2019. Back then, staying small was a philosophical choice. A lifestyle decision. In 2026, it is increasingly the default. AI lets one person do what a team of twenty did in 2015. The philosophy caught up with the infrastructure. Or the infrastructure caught up with what people actually wanted all along.

Your Corp Stack is your operating system. The decisions you make about which tools, which platforms, which revenue models, which distribution channels are not administrative details. They are strategic decisions. They are the modern equivalent of corporate strategy, made by individuals, every day, most of whom do not realize that is what they are doing.

The company did not disappear. It miniaturized. The people who see this clearly will build what comes next.

Companies are becoming tech stacks. We are all becoming companies.

Millions of Americans have incorporated. Millions more operate as unregistered businesses. The question is not whether you are a company. The question is whether you are a well-run one.

Share this essay with someone building something today.

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The Productivity Inversion: AI-first startups now generate $1.5 million to $4 million in annual recurring revenue per employee. The previous SaaS generation averaged $175,000. A solo founder with an AI-augmented stack in 2025 generates more revenue per person than the average 2010s venture-backed startup’s entire team. More people no longer means more output.

Why it matters: The labor theory of company-building is dying. The winners will be the leanest, not the largest.

The Unilever Signal: Unilever CEO Fernando Fernandez announced a shift of 50% of the company’s total media budget to social channels and a 20x increase in creator partnerships. He called his vision “desire at scale.” The largest consumer goods company in the world is disaggregating its marketing function and outsourcing it to individuals who built audiences.

Why it matters: When Unilever restructures around solo creators, this is not a trend. It is a supply chain redesign.

The Creator Economy’s Actual Distribution: The creator economy is reported as a $250 billion industry. Less reported: nearly half of creators earned under $500 in 2025. The top 4% capture the vast majority of revenue. The distribution mirrors software economics. Near-zero marginal cost, winner-take-most outcomes. Creators who diversified across 3+ revenue streams earned $75,000 more on average than those with one.

Why it matters: Creators who think like businesses outperform creators who think like creators. The Corp Stack applies here too.

81.9%

The share of all U.S. small businesses that have zero employees. The “small business” that politicians invoke on campaign trails, the corner store, the neighborhood restaurant, the local contractor with a crew, is the minority of what the data actually calls a small business.

The dominant form of American enterprise is a one-person LLC.

Most people have no idea.

Most policy is written as if this is not true.

The dominant narrative around solopreneurs and creators is liberation.

Escape the corporation.

Become your own boss.

Build on your terms.

The platforms love this story. It is half-true.

Every solopreneur who builds on YouTube works for YouTube’s algorithm.

They just do not get health insurance.

Every creator on Substack pays a 10% revenue haircut plus Stripe processing fees and accepts the risk that the platform changes its terms at any time.

Platform algorithm changes are the number-one barrier to business growth for creators worldwide, ranked above burnout and inconsistent brand deals. When TikTok’s U.S. status was uncertain, sponsored post volume dropped 4.6% in two weeks, then surged 16.8% once the deal closed. Millions of solo businesses held their breath over a geopolitical dispute they had no seat at.

The old boss was a corporation.

The new boss is a platform. The corporation was legally obligated to pay you and offer protections. The platform owes you nothing, and its Terms of Service say so explicitly. 48% of solopreneurs have gone at least a month without income.

Yale Law Journal research from 2025 argues the independence narrative of the gig economy is corporate PR designed to justify contractor classification that moves wealth from workers to platforms.

This newsletter’s thesis is not just that every person is becoming a company. The deeper point is this: Every person is becoming a company operating inside a larger company’s ecosystem.

The winners know it. They treat platforms like rented retail space, useful but not trustworthy, and build accordingly. Diversified revenue streams. Owned audiences. Portable IP. Multi-platform distribution.

The losers treat the platform like a partner. The winners treat it like a landlord.

Build your strategy wisely, friends.

Hope this helped. See you next week!

- j -

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Sources and Research Appendix

Solopreneurship and Business Formation Data

U.S. Census Bureau, Nonemployer Business Growth (2022 data, released July 2025). 29.8 million nonemployer businesses in the U.S., representing 81.9% of all small businesses. $1.7 trillion in combined annual revenue (6.8% of GDP). Growth rate of 2.7% annually from 2012 to 2023, with post-pandemic spikes of 4.9% in 2021 and 4.7% in 2022.https://www.census.gov/library/stories/2025/07/nonemployer-business-growth.html

U.S. Small Business Administration, January 2025. 21 million new business applications over the Biden administration period. 440,000 applications per month, 90% above pre-pandemic averages. https://www.sba.gov/article/2025/01/17/us-hits-record-21-million-new-business-applications-sba-publishes-report-outlining-how-biden-harris

Founder Reports, March 2026. 5,671,836 total business applications filed in 2025. All-time record, surpassing the previous high set in 2023. https://founderreports.com/how-many-businesses-are-started-each-year/

Inc. Magazine / LinkedIn. Solopreneurs represent 81.9% of all U.S. small businesses. Over 41 million solopreneurs including freelancers and contractors. MBO Partners counts 72.9 million total “independents” in the U.S. workforce.https://www.linkedin.com/pulse/solopreneurs-booming-making-6-figures-heres-why-incmagazine-zvo5e

Solopreneur Demographics and Income

Ken Yarmosh / Leapmesh / World Economic Forum. Women make up 54.4% of solopreneurs. 20% earn between $100K and $300K annually. 3.6% earn over $1 million. Average solopreneur income: $39,273.https://kenyarmosh.com/blog/solopreneur-statistics/

Branch x Mastercard Solopreneur Report, January 2026. 64% of solopreneurs are over 45 years old. Gen X (30%) and Baby Boomers (31%) lead the category. https://branchapp.com/blog/the-rise-of-the-solopreneur

Simply Business, 2025 Solopreneur Report. 77% of solopreneurs are profitable in their first year. Nearly half started with less than $5,000 in capital. 48% have gone at least one month without income.https://www.simplybusiness.com/resource/2025-solopreneur-report/

Stripe Atlas and Startup Formation

SaaStr / Stripe Data, February 2026. Stripe Atlas formations up 41% in 2025. Stripe incorporates one in five new Delaware corporations (over 23,000 in 2025). 20% of Atlas startups charged their first customer within 30 days of incorporation, up from 8% in 2020. 2025 startup cohort growing 50% faster than 2024 cohort. Number of companies reaching $10M ARR within 3 months of launch doubled year-over-year. https://www.saastr.com/stripes-latest-data-startups-are-growing-50-faster-computer-demand-drove-50-of-gdp-growth-60-more-apps-yoy-and-more/

Technology Infrastructure and the Corp Stack

BetterCloud, 147 SaaS Statistics for 2026. Average company runs 106 SaaS applications, down from a peak of 112 in 2023. Companies with 1,500-4,999 employees average 342 SaaS apps. 75% of company software is now SaaS.https://www.bettercloud.com/monitor/saas-statistics/

Landbase, Fastest-Growing API Integration Platforms. API integration platform market projected to grow from $15.63 billion (2025) to $78.28 billion (2032) at 25.9% CAGR. Postman user base has grown from 25 million to 35+ million. https://www.landbase.com/blog/fastest-growing-api-integration-platforms

Backlinko, Substack User and Revenue Statistics (2026). Substack has 5 million paid subscriptions. Top 10 authors earn $40 million per year. https://backlinko.com/substack-users

AI, the Frontier Firm, and the Billion-Dollar Solopreneur

Inc. Magazine, May 2025. Anthropic CEO Dario Amodei predicts the first billion-dollar, one-person company by 2026.https://www.inc.com/ben-sherry/anthropic-ceo-dario-amodei-predicts-the-first-billion-dollar-solopreneur-by-2026/91193609

Microsoft Work Trend Index 2025: The Frontier Firm. Survey of 31,000 knowledge workers. 82% of leaders say 2025 is a pivotal year to rethink strategy. 71% of Frontier Firm leaders report thriving (vs. 39% globally). 55% of Frontier Firm workers say they can take on more work (vs. 25% globally). https://www.microsoft.com/en-us/worklab/work-trend-index/2025-the-year-the-frontier-firm-is-born

Tiny Empires / Substack. AI-first SaaS startups generating $1.5M to $4M ARR per employee, up to 10x higher than previous SaaS generation median of $175K per employee.

Creator Economy

IAB, 2025 Creator Economy Ad Spend Strategy Report. Creator economy valued at approximately $250 billion. Creator ad spend growing 4x faster than total media industry. https://www.iab.com/insights/2025-creator-economy-ad-spend-strategy-report/

2025 Creator Earnings Report (via LinkedIn). Median full-time creator income reached $133,000 in 2025, up 33% year-over-year. Creators with 3+ revenue streams earned $75,000 more on average than those with one.https://www.linkedin.com/pulse/2025-creator-earnings-report-what-1000-full-time-creators-cgdjc

The Motherhood, Influencer Marketing Resources, March 2025. Unilever CEO Fernando Fernandez announced shift of 50% of total media budget to social channels and 20x increase in creator partnerships.https://www.themotherhood.com/influencer-marketing-resources-march-2025/

Platform Dependency and the Contrarian Case

eMarketer / CreatorIQ, March 2026. Platform algorithm changes ranked as the number-one barrier to creator business growth (18% of respondents), ahead of inconsistent brand deals (17%). TikTok U.S. uncertainty caused a 4.6% drop in sponsored post volume over two weeks, followed by a 16.8% surge once the deal closed.https://www.emarketer.com/content/creators--biggest-threat-business-growth-isn-t-burnout

Yale Law Journal, “Gig Economy Myths and Missteps” (2025). Argues the independence narrative of the gig economy is corporate PR designed to justify contractor classification. At least half of gig platform work is done by full-time workers with regular routines, contradicting the “flexibility” framing. https://yalelawjournal.org/forum/gig-economy-myths-and-missteps

Historical Context

Wikipedia, Corporation. East India Company chartered December 31, 1600. Dutch East India Company (VOC) chartered 1602, introducing permanent capital lock-in and transferable shares. “Corporation” derives from Latin corpus(body). https://en.wikipedia.org/wiki/Corporation

Wikipedia, Corporate Personhood. UK Joint Stock Companies Act of 1844 allowed ordinary citizens to incorporate through simple registration for the first time, previously requiring a Royal Charter or Act of Parliament.https://en.wikipedia.org/wiki/Corporate_personhood

Additional Sources Consulted but Not Directly Cited

Forbes, January 2026. Carta Solo Founders Report data. Solo founders hiring first employee after median of 399 days (vs. 480 for multi-founder companies). https://www.forbes.com/sites/elainepofeldt/2026/01/28/as-more-founders-aim-to-build-billion-dollar-one-person-businesses-new-research-points-to-high-potential-niches/

Inc. Magazine, February 2026. Solopreneurs as 82% of small enterprises, freelance economy data.https://www.inc.com/brian-honigman/the-freelance-economy-is-booming-right-now/91302893

Chargeflow / Stripe Statistics. 43% of Stripe Atlas users are first-time founders. 25% credit Atlas as the direct catalyst for starting their business. https://www.chargeflow.io/blog/stripe-statistics

U.S. Census Bureau, Demographic Characteristics of Nonemployer Business Owners, May 2025. Gender and demographic breakdown of solo business ownership. https://www.census.gov/newsroom/press-releases/2025/nonemployer-business-characteristics.html

Fordham Journal of Corporate & Financial Law, 2018. History of the corporate form, the pre-1600 partnership problem, and why perpetual existence mattered. https://news.law.fordham.edu/jcfl/2018/11/18/a-brief-history-of-the-corporate-form-and-why-it-matters/

Yale Law School, “The Emergence of the Corporate Form” (VOC/EIC academic paper).https://law.yale.edu/sites/default/files/documents/pdf/cbl/VOC_050_GDM.pdf

Research compiled March 22, 2026. All statistics verified against primary sources. This appendix is provided for transparency and further reading, not included in the published Substack edition.

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? He still cringes at his early LinkedIn posts and loves making content each and everyday, despite the protestations of his beloved wife, Fabiola, at times.

Start Here

Every digital platform you use is a marketplace.

LinkedIn, YouTube, Substack, Amazon, Google, TikTok, eBay, Instagram, etc.

Each platform exists to solve a coordination problem.

Economists call these n-sided markets.

You probably call them your “content strategy.”

The moment you understand that on each of these platforms you are participating in a market governed by specific pricing mechanics, information asymmetries, and power structures, you stop subsidizing their platform and start negotiating your position inside an economic system.

That is a different game entirely.

Thrilled to have collaborated on this article with my esteemed friend and colleague, Jonas Braadbaart:

Jonas Helps business leaders become AI operators. He is an AI architect and AI agency owner with 10+ years of experience designing, building and delivering AI solutions. Currently, he is building an AI film making studio:

https://www.project-skia.com/

Here is a link to a prior Substack Lives we enjoyed doing together:

1. Creator Economics and the N-Sided Marketplace

2. What Works on Substack Will Get You Ignored on LinkedIn (And Vice Versa)

The Economics Governing Your Platform Earnings Potential

In 2003, Rochet and Tirole published the paper that formalized how platforms actually set prices. Their central insight was deceptively simple:

In a two-sided market, the structure of prices matters more than the total price level. A platform does not just charge what the market will bear.

It strategically subsidizes one side to attract the other:

• Substack charges readers nothing and takes 10% from writers.

• Amazon charges buyers nothing for Prime search results and takes 50% from sellers.

• Google charges searchers nothing and monetizes publishers’ traffic into ad revenue.

• Youtube Shorts earn next to nothing compared to long-form content published on the platform.

The pattern is identical every time.

One side is the product. The other side is the customer.

If you are not sure which one you are, check how you are paying.

Three years later, Mark Armstrong identified something darker.

He called it the “competitive bottleneck.”

When one side of a market “single-homes” (readers who use one app, buyers who search one store) and the other side multi-homes (creators who post everywhere, sellers who list everywhere), the platform develops a local monopoly over access to the single-homing side.

It can then price the multi-homing side as aggressively as it wants.

Armstrong’s Proposition 4 states, almost clinically, that platforms will systematically under-invest in the experience of the multi-homing side. They have nowhere else to go to reach that particular audience.

This is the market’s structural equilibrium.

As a creator, you are typically on the multi-homing side (notable exceptions include Joe Rogan’s USD 100 million Spotify deal, but those are very rare indeed).

Just as new investors should assume they are not Warren Buffett, it would be prudent to assume here that you are not Joe Rogan.

The Algorithm Is the Marketplace

Susan Athey’s work at Stanford reframes platforms not as transaction venues but as information engines.

The real service is not hosting your content. It is reducing the information asymmetry between you and the person who might want to read, watch, or buy from you.

Reviews, ratings, recommendation algorithms, verification badges…

These are the mechanisms preventing the market from collapsing into what Akerlof called the “lemons problem,” where low quality drives out high.

This is why algorithmic change feels existential, because it actually is for most creators. The algorithm is the marketplace’s allocation mechanism for attention. When LinkedIn replaced thousands of fragmented ranking models with a single 150-billion-parameter foundation model that reads your content, evaluates your profile, and matches you to audiences, it did not update a feature.

It redesigned the market’s central pricing mechanism.

The Result: LinkedIn company page reach dropped 60 to 66% between 2024 and early 2026. Personal profiles now receive roughly 561% more distribution. The platform decided that corporate broadcasting is less valuable than human expertise.

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The Four Seasons of Getting Squeezed

Every platform follows the same extraction lifecycle:

Phase One–Early Growth: The platform subsidizes both sides. PayPal paid users $10 to sign up. Substack recruited journalists with guaranteed advances. TikTok handed virality to unknowns like confetti.

Phase Two–Late Growth: Network effects compound. Users become dependent. Switching costs rise. Not because leaving is technically hard, but because your audience, your reviews, your history, and your data all live behind someone else’s login screen.

Phase Three–Early Maturity: Monetization tightens. Take rates climb. Organic reach quietly declines, rebranded as “algorithm improvement.” Paid promotion becomes the price of visibility you once earned for free.

Phase Four–Active Extraction. Amazon’s effective seller take rate has crossed 50%. Google’s AI Overviews have driven organic click-through rates down 61%, with 58.5% of U.S. searches now ending in zero clicks. The platform answers the question itself. The publisher who created the information maybe gets a citation, but is not guaranteed traffic.

In general, the best time for getting onto any platform is in phase one or phase two of their lifecycle. You’re not fighting the platform.

Competition is light.

Attention is heavily subsidized.

In phases three and four, achieving ROI (return on investment) on your position will get harder and harder. Platforms become pay-to-play.

Think about Amazon or Google as rent-extracting monopolies.

Three Examples

If all of this still sounds very abstract, don’t worry, we’ve got you covered.

Let’s look at three platforms in more detail:

LinkedIn–multi-sided professional platform (6+ sides):

LinkedIn is one of the strongest “information engines” out there in terms of real identity, verifiable employment history, and peer endorsements. Their 360Brew algorithm (rolled out gradually from March 2025) scans all text (headlines, about sections, captions, comments) for meaning. It prioritizes “authentic engagement” and industry-specific insights over generic viral posts.

This was the most significant algorithm change in LinkedIn’s history.

Phase: Early Maturity: The platform algorithms have started suppressing organic reach for companies → to force ad spend → and selectively reward top creators to maintain content quality → then gradually expand monetization to mid-tier creators.

These changes, coupled with a feed composition shift (11% ads, 31% Top Creator content) means the average user’s organic reach is structurally declining.

Get started today or lose out on the platform.

Substack–two-sided creator platform evolving toward multi-sided (3–4 sides):

Substack’s information engine has grown significantly these last two years.

Data from 2025 showed the recommendation network drove 40–50% of all new subscriptions and 25% of new paid subscriptions, and is still the platform’s most powerful discovery mechanism. 40% of all subscriptions now come from inside the Substack network (app + recommendations).

This means Substack is becoming a genuine discovery platform, not just an email delivery tool, which both strengthens its value to writers and deepens its competitive bottleneck.

Phase: Late Growth: the best creator economics in the market, but the first cracks are appearing

Amazon–N-sided commerce platform (6+ sides):

Amazon’s information engine is undergoing its biggest transformation since reviews were introduced.

The Rufus AI assistant and COSMO semantic knowledge graph mean sellers now optimize for three distinct systems simultaneously: the A9/A10 keyword algorithm, COSMO’s semantic understanding, and Rufus’s conversational layer.

This dramatically increases the opacity of the matching engine and the resulting information asymmetry between Amazon and its sellers–allowing Amazon to smartly position their own products and brands.

Phase: Active Extraction - One of the oldest platforms, and not entirely coincidentally, also one of the most advanced cases of rent-seeking in big tech.

download high res pdf here

The High Over, March 2026

Best positions (invest heavily)

1. Substack — Late Growth phase, lowest take rate (10%), subscriber list portable, discovery engine strengthening (40%+ of subs from in-app). The window is now before monetization pressure from the $1.1B valuation creates extraction incentives.

2. YouTube (long-form) — Strong bottleneck works FOR you if you’re established. Early Maturity but stable economics. 55% revenue share is the best rate in video at scale. The Shorts format shift is extractive, but long-form remains the gold standard for creator revenue.

3. LinkedIn — Early Maturity means the platform is still investing in creators via BrandLink. 360Brew algorithm rewards niche expertise. Its core professional audience is high-value. But the organic reach suppression for company pages signals the direction, personal brand investment is time-sensitive as the platform moves towards the active extraction stage.

Maintain presence (don’t over-invest)

4. TikTok — Regulatory risk resolved (Jan 2026 USDS deal). Great for reach/discovery with improved Creator Rewards ($0.40–$1.00/1K views, up 20x). Its new follower-first algorithm changes are ending the “anyone can go viral” era. Use as a top-of-funnel. TikTok Shop commerce is still a great opportunity in 2026, despite increasing take rates 4% -> 6% -> 8%.

5. YouTube Shorts — Necessary for discovery, but structurally extractive (55% platform take, 50–100x lower RPM than long-form). Feed long-form. Don’t use the channel as a standalone pillar in your content strategy–only for discovery.

6. Instagram — Mature and strategically confused but 3B MAU makes it impossible to ignore. Commerce retreat means it’s an advertising platform, not a marketplace. Use for brand building in visual niches.

7. eBay — If you sell collectibles, pre-owned, or parts, this is experiencing a genuine renaissance (focus categories +16% in Q4 2025). AI features are a real competitive advantage. Otherwise, Amazon dominates.

Proceed with caution

8. Amazon (as seller) — Active Extraction: 45–51% effective take rate and rising. Be there because you have to, but build direct channels aggressively. The FTC trial (October 2026) is the single most important event for marketplace sellers this decade — plan for both outcomes.

9. Google Search (as publisher) — Active Extraction via AI Overviews. Organic CTR drops 61% when AI Overviews appear. Plan for 40%+ traffic decline over 3 years. Diversify to email, social, and direct urgently.

Exit or minimize

10. X (Twitter) — Declining platform (Threads overtook X on mobile DAU, Jan 2026). User base shrinking 10–15% YoY. Ad revenue recovering from a deeply depressed base but CPMs still 70% below pre-Musk levels. Maintain a minimal presence; invest content-creation time on LinkedIn, Substack, YouTube, or Threads. Worth noting: Communities around technology (AI engineering), venture capital, crypto, and the investing community maintain a strong presence on the platform, making presence an imperative if you want to build awareness for your work in these important sectors.

The Power Law You Are Living Inside

A 2025 study analyzing 104,000+ Patreon creators found that earnings across platforms follow a power law distribution with an exponent around α = 2.0. The mechanism generating creator income online behaves more like capital accumulation than labor income.

Initial advantage compounds.

Algorithmic recommendation accelerates the compounding.

The finding that should concern you: Platforms with stronger recommendation engines exhibit more inequality and lower median earnings. Platforms with weaker algorithmic curation show higher median incomes. The algorithm does not just pick winners. It mathematically suppresses the middle class.

Recent data confirms the trend is accelerating.

The top 10% of creators earned 62% of total payments in 2025, up from 53% in 2023. Median creator earnings declined from $3,500 to $3,000. More creators. More money. Less of it reaching most of them.

Three Moves To Strengthen Your Business Today

Stop thinking like a creator. Start thinking like a strategist in markets with known structural properties.

Build portability. Your email list is the only asset the platform cannot reprice, throttle, or algorithmically suppress. Every interaction on a platform you do not own should have one objective: move the relationship to a channel you do own. Everything else is rented reach.

Layer revenue. If more than 30% of your income depends on any single platform’s algorithm or fee structure, you are not building a business. You are being allocated income by someone else’s information engine. That allocation can change on a Tuesday afternoon with no notice.

Read the lifecycle. When organic reach declines and paid promotion becomes necessary to maintain prior visibility, you are entering Phase Three. When the platform launches its own competing product or its AI begins answering questions with your content without sending you traffic, you are in Phase Four. These are not abstract signals. They are measurable. Monitor take rates, reach trends, and new fee introductions like the operating metrics they are.

The platforms will keep evolving. They need to.

And N-sided markets solve real coordination problems and create genuine economic value. For example, a 2019 study by Brynjolfsson et al found that the median US consumer would have been willing to pay up to $17,530 per year for access to search. Google’s US Search ad revenue that year was roughly $45–47 billion across 150 million users, or about $300 per user.

So in this estimate, in 2019, with Google Search in the late growth stage of its lifecycle, Google captured roughly 2% of the value created by its n-sided marketplace.

In 2026, with Google in active extraction mode, revenue per user has roughly doubled to ~$675 (still under 4%) but organic click-through rates have been halved by AI Overviews. Google isn’t taking a bigger slice of a shared pie, but it is absorbing more and more of the value that used to flow to content publishers, historically, one of the key participants in their marketplace.

Final Thought

The economics that make platforms powerful are the same economics that make participants vulnerable.

The difference between being extracted from and being strategically positioned is knowing which side of the equation you are standing on.

Every platform you build on today was designed to solve someone else's coordination problem. Not yours.

The economics are not hidden. They are published in quarterly earnings calls, buried in fee schedule updates, and visible in every percentage point of reach you lost last year. The only question is whether you read them like an economic analyst or experience them as the commodity you can easily become.

Companies are becoming tech stacks.

We are all becoming companies.

Act accordingly.

Hope this helped.

- John & Jonas -

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? He still cringes at his early LinkedIn posts and loves making content each and everyday, despite the protestations of his beloved wife, Fabiola, at times.

84% of people have never used AI. 0.3% pay for it.

The gap between what the technology can do and what people actually do with it is the biggest business opportunity of our professional lives.

That is what we work on inside Operating.

Paid subscribers ($7.99/month) get the full resource library, every framework, and the complete archive of essays, case studies, and toolkits.

Founders ($500/year) get everything above plus two 1:1 strategic sessions with me. Your business. Your growth problem. We work and solve together.

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The Fed meets this week. Rates stay at 3.5% to 3.75%. Inflation above target. Unemployment at 4.3%. Oil spiking. The honest summary for operators: plan for ambiguity, because that is the weather forecast not matter which way you look.

Cool. Here’s the useful part.

In a foggy economy, the operators who pull ahead are not those with better predictions, they’re the individuals who get more done per person, per hour, per dollar.

Fewer handoffs.

Fewer meetings where nothing happens.

Fewer people waiting on other people.

That is the leverage question.

It is the only strategic question that matters right now.

Not “should we use AI?”

How do we redesign our work so every hour produces 10X more than it did six months ago, and produce 100X more one year from now.

The Timing: 5% of enterprise apps had AI agents in 2025. Gartner expects 40% by year-end. That’s an eightfold jump in 12 months. The bottleneck is not technology. The bottleneck is human capacity for change. The bottleneck is our own competitiveness and design to win. It is workflow design. The “boring stuff” ia how you win in today’s markets. I’d argue that’s always been true, but now it looks a little sexier to say.

If you run a small team, you can rewrite how work gets done without a task force. Your lack of organizational hierarchy is your moat.

A distinction of this particular economic revolution is that the competitive opportunities a biasing to small orgs, not large. Big ships moves slowly.

Take your shot, Operators.

- j -

Note: Be sure to check out the research appendix at the end of the article for more depth and perspective.

One Article to Read

“OpenAI Launches Frontier, a Platform to Build and Manage AI Agents” — TechCrunch

[Read it here]

This readse like an IT memo.

It is not.

OpenAI built a platform to deploy and manage fleets of AI agents across business systems. They are calling them “AI coworkers.”

Onboarding. Permissions. Performance reviews.

Not a chatbot. An entire operating department (not just a layer)

Their CRO said it plainly: “What is missing is a way to deploy agents inside the business without rebuilding everything underneath. The technology works. The plumbing does not.”

For Operators: The moat is about process design. Frontier exists because enterprises cannot get agents past the pilot stage. The demo works. Then it breaks the moment it touches a real workflow because nobody redesigned the workflow. Model intelligence is not the constraint. Human, organizational intelligence the limiting variable. Uber and State Farm need six months and a compliance review to change anything. You need a laptop and forty-five minutes. Competitive advantage is going to accrue to smaller firms for the time being. You need to be ready to grab it.

Do this now. Pick one recurring workflow. Three columns: human judgment, repeatable manual steps, automatable steps. Hand one item from column three to an AI tool this week. The point is not to save twenty minutes. It is to prove the workflow can be rewritten.

One Book to Listen To

“High Output Management” by Andy Grove

Audible | Amazon

Thirty years old.

More useful than anything published this quarter about AI and productivity.

Grove ran Intel. Engineer who thought like an economist. One question drives the whole book: What is the output of a manager? Not effort. Not hours. Output. Then he redesigns meetings, decision rights, and information flow around that answer.

Software changes every eighteen months. How you design handoffs, allocate attention, and structure decisions so the right person makes them at the right time, will compound over decades, across infinite software updates.

Grove figured it out in the 1980s. The vocabulary changed somewhat.

The value only grew. You need to know this book backwards and forward.

(If you’re interested, here’s a full playbook I wrote months ago)

One Podcast to Watch

“Bill Gurley: 6 Out of 10 People Are Making This Mistake” —

My First Million, Ep. 803

Apple | Spotify

Sam Parr and Bill Gurley (Uber, Zillow, GrubHub). The whole conversation circles one idea: most people are working extremely hard on the wrong thing.

Catch the moment early. Six out of ten people dislike their work, and Gurley reframes it as an allocation issue, not a motivational conundrum. People optimize for intensity when they should optimize for fit. They confuse effort with leverage.

When was the last time you audited how your time, resources and activity were being allocated?

Not how fast you are moving, but whether the direction makes sense.

Try it today!

One New Person to Follow

Pieter Levels (@levelsio) — Follow on X

Runs Nomad List, Remote OK, and Photo AI. Revenue: $3M+/year. Employees: zero. Tech stack: PHP, jQuery, SQLite. Funding: zero. Infrastructure: under $200/month. That last number is probably less than your Slack bill.

He does not talk about leverage.

He just operates with it.

Ships fast, automates what works, kills what does not, posts his numbers publicly.

Follow the operators. Not the commentator.

The Through-Line

The article shows agent infrastructure arriving now, bottlenecked by human limits, not the technological (it also makes the clear supposition that you shouldn’t sleep on what OpenAI is continuing to produce, even as Claude outperforms for the moment).

The book gives you the framework for redesigning work around output.

The podcast asks whether you are optimizing effort when you should be optimizing your various resource allocations.

The person is doing all of it, solo, profitably, in public.

Leverage > Systems > Proof > Signal.

See you next Monday. Hope this helped.

— John

Forward this to one person building something on their own.

That is how Operating grows.

Most people are cosplaying with technology they have opened twice.

84% of humans have never used AI. 0.3% pay for it. Actual adoption is a fraction of what the tools can do. And yet the feed is full of “AI-native founders” who could not build an automated workflow if their revenue depended on it.

The gap between what the technology can do and what people actually do with it is the biggest business opportunity of the next decade.

That is exactly what we work on inside Operating.

Paid subscribers ($7.99/month) get the full resource library, the Operating Working Group, every framework I build, and the complete archive of essays, case studies, and operating toolkits. That is less than two dollars a week to have an economist who actually ran a company decoding the economics of what is happening and translating it into tools you can build with.

Founders ($500/year) get everything above plus two 1:1 (60-minute) strategic sessions with me. Your business model. Your positioning. Your growth problem. We work on it together. This is a second set of eyes from someone who has scaled a company, exited it, and now advises founders building in this economy every day.

At Operating, we’re not posting about AI. We’re building our futures with it. Join us!

Subscribe now

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? He still cringes at his early LinkedIn posts and loves making content each and everyday, despite the protestations of his beloved wife, Fabiola, at times.

Appendix: Research, Sources, and Links

Macro and Fed

• The Dual Mandate in Conflict: Balancing Current Tensions between Inflation and Employment — St. Louis Fed, March 2026. Unemployment at 4.3%, PCE inflation at 2.9% in 2025, Fed holding at 3.5%-3.75%.

• FOMC Statement, January 28, 2026 — Federal Reserve. 10-2 vote to hold rates.

• FOMC Minutes, January 27-28, 2026 — Federal Reserve. Market expectations of one to two cuts in 2026.

• Fed Leaves Rates Unchanged to Start 2026 — J.P. Morgan Wealth Management.

• Fed Rate Cut Bets Rise After Weak Jobs Data — Reuters, March 6, 2026.

• Markets’ Hopes for Fed Interest Rate Cuts Are Rapidly Fading Away — CNBC, March 12, 2026. Iran conflict, oil prices, and shifting expectations.

• Looming Fed Meeting Shifts Bets for 2026 Interest-Rate Cuts — TheStreet, March 15, 2026.

AI Agents and Enterprise Adoption

• OpenAI Launches a Way for Enterprises to Build and Manage AI Agents — TechCrunch, February 5, 2026. Primary article recommendation.

• Introducing OpenAI Frontier — OpenAI blog. Full product announcement.

• OpenAI Frontier: Enterprise Platform — OpenAI. Product page with technical details.

• OpenAI Launches Platform to Manage AI Agents — Axios, February 5, 2026. Includes early customer data (90% more time back for client-facing teams, 1,500 hours/month saved in product development).

• OpenAI Launches New Enterprise Platform — CNBC, February 5, 2026. Enterprise customers account for ~40% of OpenAI’s business.

• OpenAI Expands Enterprise Push With Frontier — Reworked, February 2026.

• OpenAI Launches Frontier: Platform to Build, Deploy, and Manage AI Agents — InfoQ, February 20, 2026.

• OpenAI Frontier: Close the Enterprise AI Opportunity Gap, or Widen It? — Futurum Research, February 9, 2026.

Gartner Forecasts

• Gartner Predicts 40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026 — Gartner, August 2025. 5% to 40% adoption, $450B+ in enterprise AI revenue by 2035.

• Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027 — Gartner, June 2025. Counterpoint: escalating costs and unclear ROI remain risks.

• 40% of Enterprise Apps Will Embed AI Agents by End of 2026 — Yahoo Finance / Motley Fool.

Featured Media

• My First Million, Episode 803: Bill Gurley — Apple Podcasts. Also on Spotify.

• High Output Management by Andy Grove — Audible. Also on Amazon.

• Pieter Levels (@levelsio) — X/Twitter. Products: Nomad List, Remote OK, Photo AI.

Solo Operator and Leverage Context

• The One-Person Unicorn: How Solo Founders Use AI to Build Billion-Dollar Companies in 2026 — NxCode, February 2026. Solo-founded startups now 36.3% of all new ventures.

• The Rise of the Solopreneur Tech Stack in 2026 — PrometAI, January 2026.

• The 30 Highest-Valued Solo Startups of 2026 — WeAreFounders, January 2026.

• How Pieter Levels Built a $3M/Year Business with Zero Employees — FastSaaS.