Operating by John Brewton

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

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

By John Brewton
Operating by John Brewton

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

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

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

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

But the surplus ambition has gone somewhere else.

Corporate America may call this quiet quitting.

I think something more interesting is happening.

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

There is nothing wrong with that path.

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

But it is no longer the only serious route available.

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

That does not mean building a company has become easy.

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

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

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Quiet quitting is the wrong phrase for a real shift

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

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

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

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

Both interpretations contain some truth.

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

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

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

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

This article is about the third group.

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

That is not entrepreneurship.

That is simply failing to keep a promise.

The defensible version is much cleaner:

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

The corporation has lost its monopoly on leverage

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

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

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

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

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

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

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

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

That distinction matters.

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

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

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

The one-person company is no longer structurally absurd

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

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

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

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

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

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

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

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

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

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

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

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

This is not an anti-corporate argument.

It is a pro-optionality argument.

The hybrid phase is not hesitation

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

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

Joseph Raffiee and Jie Feng later tracked thousands of American entrepreneurs over 15 years. Their Academy of Management Journal study found that people who transitioned through hybrid entrepreneurship had substantially better venture-survival outcomes than those who immediately moved from employment into full-time entrepreneurship. The result is often summarized as a roughly 33% lower likelihood of failure.

Keeping the job is not evidence that you lack conviction. It may mean you are purchasing information before purchasing risk. The salary pays household expenses while you, the founder, learn:

Will anyone buy this?
Can I reach customers consistently?
Can I deliver the work profitably?
Will customers renew?
Is the opportunity large enough to justify leaving?
Do I enjoy operating this business once the novelty disappears?

The day job also prevents desperation from becoming the company’s first investor.

A founder who needs immediate income may accept the wrong customer, underprice the work, build for anyone willing to pay, or keep an idea alive because admitting failure would threaten next month’s rent.

A founder with a salary has more ability to say no:

A paycheck can become venture capital you provide to yourself, one deposit at a time.

This is the real opportunity inside quiet quitting.

Not doing nothing.

Building patiently.

Seven priorities while the day job still pays

The following are some operating rules, not universal scientific laws, that I’ve constructed based on my own experience making this drum in my career over a period of years.

Research supports the logic of experimentation, staged entry, clear boundaries, and financial caution. It does not establish that every founder needs exactly 18 months of cash, exactly three contracts or exactly three profitable months.

Those are decision thresholds.

Their purpose is to help solo founders switch out their feelings for actual evidence.

1. Bank eighteen months of runway

Runway is not the amount of money in a savings account.

It is the number of months your household and business can continue operating without requiring optimistic assumptions.

Start with the real monthly number:

Household fixed costs

business fixed costs
replacement benefits
taxes
contingency allowance

Then multiply it by 18.

Health insurance matters. Software subscriptions matter. Equipment matters. The tax reserve matters. The expenses your employer currently absorbs matter.

Eighteen months is intentionally conservative. It is not a magical figure. Some founders with highly predictable contracts may require less. Others with dependents, debt, long development cycles or volatile markets may need more.

The reason for caution is straightforward: new businesses remain uncertain. According to the SBA Office of Advocacy’s 2026 small-business data, only about 49% of new employer establishments survive five years. That statistic covers many kinds of businesses and does not predict the fate of any one company, but it is a useful correction to launch-day optimism.

Runway gives you time to survive delayed contracts, customer churn, pricing mistakes, and slower sales cycles.

More importantly, it improves the quality of your decisions.

2. Sell before you build

Three signed contracts beat a finished product.

This does not mean accepting money for something you cannot responsibly deliver. It means testing the commercial assumption before investing months in infrastructure.

Entrepreneurship is fundamentally a process of experimentation. William Kerr, Ramana Nanda and Matthew Rhodes-Kropf argue in the Journal of Economic Perspectives that entrepreneurial success probabilities are often low, highly skewed and unknowable until resources are committed. The answer is not to eliminate uncertainty. It is to structure smaller experiments that reveal information before making larger commitments.

Research on a scientific approach to entrepreneurial decision-making similarly suggests that entrepreneurs benefit when they state assumptions clearly, test them and allow evidence to kill weak ideas rather than searching only for confirmation.

The progression should usually look something like this:

Most founders reverse that sequence.

They build the platform, design the brand, automate the workflow and prepare for scale before discovering whether someone has a sufficiently painful problem and a sufficiently large budget.

A working product proves that you can produce supply.

A signed customer proves that demand exists.

Three customers do not prove you have discovered a large market. But they prove that the idea has survived contact with someone else’s bank account.

3. Audit Your Current Employment Paper

Before building anything, read every document governing your employment.

Not the summary.

Not the onboarding slide.

The entire handbook and everything you signed.

Review the provisions involving:

Intellectual-property assignment.
Confidentiality and trade secrets.
Outside employment or moonlighting.
Conflicts of interest.
Non-solicitation.
Noncompetition.
Customer and employee relationships.
Required disclosure or approval.
Notice periods.
Ownership of inventions created during employment.

Then have an employment lawyer in your jurisdiction review the provisions that matter.

Do not assume that noncompetes are universally unenforceable. The Federal Trade Commission’s proposed nationwide rule is not in effect and is not enforceable. After a federal court blocked it, the FTC dismissed its appeals in 2025. State law and individual contracts therefore remain central to enforceability.

A founder may also have duties while still employed that extend beyond the words “noncompete.” Employment-law scholarship has examined how duties of loyalty can apply to competition, corporate opportunities and employer resources during an active employment relationship. The exact obligations vary by role and jurisdiction.

A few hours of legal review are cheaper than discovering that your employer claims ownership of the business after it becomes valuable.

Do not take legal advice from a founder whose contract, role and state are different from yours. Find a good lawyer and listen to them

4. Keep the separation clean

Use personal hardware.
Use personal accounts.
Use personal software subscriptions.
Use personal payment methods.
Work during personal time.
Do not transfer files, prompts, templates, customer lists, code, research, pricing information or internal documents from the employer’s environment into your own.
Do not recruit coworkers or approach company customers without specific legal clearance.
Do not build a competing product while sitting in a meeting, even when the meeting should have been an email.

Clean separation is not only an ethical principle. It creates an evidentiary record.

File metadata, device logs, account histories, and timestamps should tell the same story you would tell.

Your standard should not be, “Could I explain this casually?”

Your standard is:

Could I establish a clean chain of creation if every device, document and timestamp were examined?

Nothing built on company time, with company data or through company systems, is worth the future dispute.

5. Count customers, not followers

Audience is useful, and feels good to build, but having an audience does not equate to having revenue, actual money in the bank.

Ten thousand impressions may tell you that a sentence was interesting. Ten paying customers tell you that a problem is expensive enough for someone to solve, and that your solution has done just that for those ten people.

The progression of evidence is simple:

Each step requires more commitment from the other person. Each step therefore provides more useful information.

Followers can reduce future distribution costs. A respected audience can become a meaningful asset. But founders often confuse public encouragement with commercial demand because public encouragement is easier to obtain and more pleasant to measure.

A stranger commenting “This is brilliant” risks nothing.

A purchasing manager signing a contract risks budget, reputation and time.

Early in the business, optimize for learning rather than applause.

Concentrated revenue may eventually become a problem. Depending on one customer can create fragility. But zero revenue is a more immediate problem than concentrated revenue.

Win the first customer.

Then the second.

Then determine whether you can win the third without relying on luck.

6. Protect the first ninety minutes—and a defined portion of the weekend

The best time block is the one you can defend consistently.

For many employed founders, that means working before the day job begins.

The morning has an advantage that has little to do with motivational mythology. It has not yet been consumed by someone else’s emergencies. The calendar has not slipped. The difficult client has not called. The meeting has not run long. Your attention has not been divided among twelve unfinished tasks.

Research on attention residue shows that switching from one unfinished task to another can impair performance because part of our attention remains attached to the previous activity. This helps explain why fragmented evening work often feels slower than protected, uninterrupted creation. (ScienceDirect)

The side business should therefore have recurring appointments with its founder.

Protect the first ninety minutes on selected weekdays. Use one defined weekend block for deeper work, customer delivery or sales. Make the schedule visible to the people whose lives are affected by it.

But do not convert every morning, evening and weekend into work.

Research on side hustles is genuinely two-sided. An Academy of Management Journal study found that empowerment gained through outside work could enrich performance in a person’s primary job. More recent Journal of Management Studies research found both gains and costs: thriving in a side hustle can improve well-being, but attention residue and resource depletion can also damage primary-job performance. Clear segmentation between roles helps. (Academy of Management Journals)

Recovery is not laziness either. A major Annual Review of Organizational Psychology and Organizational Behavior review concluded that detachment and recovery during evenings, weekends and vacations are important for sustaining well-being, motivation and performance.

Do not finance the company with chronic sleep deprivation, family resentment or deteriorating performance at the job still funding it.

7. Set a trigger, not a date

“I will quit in June” is not a strategy.

June does not know whether customers will renew.

June does not know whether the pipeline is real.

June does not know whether your largest client will disappear in May.

A date creates emotional momentum. A threshold creates decision discipline.

My preferred core trigger is:

Leave when recurring business revenue covers household and business fixed costs for three consecutive months—with the original runway substantially intact.

That is the beginning of the analysis, not the end.

Before leaving, also examine:

Whether customers are renewing or purchasing again.
Whether the pipeline can be repeated.
Whether revenue depends excessively on one relationship.
Whether gross margins support the owner after taxes and benefits.
Whether the founder has clean ownership of the work.
Whether additional founder time is likely to create additional revenue.

The final question is especially important.

Do not resign merely because operating the business while employed has become uncomfortable.

Resign when employment has become the principal constraint on a model that has already demonstrated demand.

The difference is enormous.

In the first situation, the founder is hoping that more time will create a business.

In the second, the founder has evidence that more time can expand an existing business.

The moral contract

There is a version of this strategy that is disciplined and honorable.

There is also a version that is dishonest.

The line is not complicated.

Your employer is entitled to the professional performance it pays for. Your coworkers should not be required to subsidize your company by carrying abandoned responsibilities. Customers should not receive inferior service because you were thinking about your own sales pipeline. Confidential information is not founder capital. Paid working hours are not a private incubator.

Keep your promises.

Meet the standard.

Disclose conflicts when the agreement requires disclosure.

Leave employer information, relationships, and property alone.

But the employment relationship has a boundary, too.

An employer may purchase your labor during agreed hours. The contract and the law may impose additional obligations. None of that automatically means the institution owns every future idea, every weekend, every early morning or the permanent direction of your ambition.

The business must be built outside the job—not instead of the job.

That is why I prefer quiet building to quiet quitting.

Quiet quitting sounds like retreat.

Quiet building is deliberate preparation.

What quiet building does not guarantee

Not every disengaged employee is a future founder. There is no credible research establishing that quiet quitting has caused the growth of solo entrepreneurship.

That connection is an interpretation of two changes occurring at the same time:

Work has become less central to the identities of some employees, while technology has made independent ownership more feasible.

This strategy is not equally available to everyone. Spare time, financial reserves, predictable schedules, health, caregiving responsibilities, and access to technology are distributed unevenly. Some people need the stability of employment more than they need entrepreneurial optionality. Some people genuinely enjoy institutional leadership and can create more value inside a large organization than they would alone.

There is no moral superiority in becoming a founder.

There is also no guarantee that the business will work.

AI does not create demand. A large audience does not create willingness to pay. More hours do not repair a weak proposition. The side business may produce energy, or it may create exhaustion. A secure salary may support intelligent experimentation, or it may allow the founder to avoid making a decision indefinitely.

The point is not that everyone should leave.

The point is that ambitious people have another route available—and the first stage of that route may occur while they are still employed.

Build Quietly.

At 8:30 AM, the founder closes the personal computer.

At 9:00 AM, the employee begins the workday and keeps the promises that have been made.

The employer still receives competent, professional performance.

What the employer no longer receives is the unquestioned right to every discretionary hour and every future ambition.

The founder keeps learning.

Keeps selling.

Keeps separating the work.

Keeps accumulating cash.

Keeps watching the numbers.

The first act of entrepreneurship is rarely a job resignation.

It is the choice to learn to wait and quietly build.

Keep the salary.
Protect the dream.
Perform honestly.
Leave when the arithmetic gives you permission.

- j -

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

Research appendix

A. Quiet quitting, engagement and the counterargument

1. Gallup: Is Quiet Quitting Real?

Read the Gallup analysis

Gallup’s 2022 analysis defined many quiet quitters as “not engaged”: employees who perform minimum requirements while remaining psychologically detached. The article linked falling engagement to unclear expectations, fewer development opportunities and weak manager relationships.

Used for: The definition of quiet quitting as disengagement and the estimate that roughly half of U.S. employees were not engaged in 2022.

2. Gallup: Employee Engagement Remains Flat as AI Adoption Accelerates

Read the 2026 Gallup report

Gallup reported that 31% of U.S. employees were engaged during the first half of 2026 and 18% were actively disengaged. It also emphasized that access to AI alone did not improve engagement; clear implementation and manager support mattered.

Used for: Current U.S. engagement figures and the argument that managerial context remains important.

3. Derek Thompson, The Atlantic: Quiet Quitting Is a Fake Trend

Read the article

Thompson argued that quiet quitting was not statistically new and that modest employee disengagement had existed long before the phrase went viral. He interpreted the popularity of the term as evidence of cultural frustration rather than proof of a sudden labor-market transformation.

Used for: The counterargument that the label exaggerates the novelty of the phenomenon.

4. Anthony Klotz and Mark Bolino, Harvard Business Review: When Quiet Quitting Is Worse Than the Real Thing

Read the article

The authors distinguished legitimate boundary-setting from forms of withdrawal that can damage relationships, performance and coworkers.

Used for: The ethical distinction between declining optional work and abandoning agreed responsibilities.

5. Pew Research Center: Young Workers Express Lower Levels of Job Satisfaction Than Older Ones, but Most Are Content With Their Job

Read the Pew analysis

Pew found that younger workers reported lower levels of strong job satisfaction than older workers, while 85% of workers overall remained at least somewhat satisfied.

Used for: Balance against the claim that an entire generation has rejected conventional employment.

B. Hybrid entrepreneurship and staged entry

6. Timothy B. Folta, Frédéric Delmar and Karl Wennberg: Hybrid Entrepreneurship

Read the Management Science abstract and publication record

The researchers defined hybrid entrepreneurship as entering self-employment while retaining a primary wage job. Using Swedish employee-employer data, they found that hybrid founders represented a significant share of entrepreneurial activity and examined how hybrid entry can facilitate later movement into full-time self-employment.

Used for: The formal definition of hybrid entrepreneurship and the concept of incremental entry.

7. Joseph Raffiee and Jie Feng: Should I Quit My Day Job? A Hybrid Path to Entrepreneurship

Read the Academy of Management Journal study

The authors studied thousands of entrepreneurs over a 15-year period and found that founders who entered through a hybrid stage experienced significantly better survival outcomes after transitioning to full-time entrepreneurship.

Used for: The claim that keeping a job while validating a venture can reduce the risk of failure.

8. Harvard Business Review: Why Going All-In on Your Start-Up Might Not Be the Best Idea

Read the research summary

This summary describes the Raffiee and Feng result as a 33% reduction in the likelihood of failure for founders who left their employment in stages.

Used for: The accessible interpretation of the academic result.

9. William Kerr, Ramana Nanda and Matthew Rhodes-Kropf: Entrepreneurship as Experimentation

Read the Journal of Economic Perspectives paper

The authors describe entrepreneurship as experimentation under conditions in which success probabilities are low, skewed and often unknowable before investment.

Used for: The argument that founders should structure smaller learning investments before making larger commitments.

10. Arnaldo Camuffo, Alessandro Cordova, Alfonso Gambardella and Chiara Spina: A Scientific Approach to Entrepreneurial Decision Making

Read the Management Science study

In a randomized controlled trial involving Italian startups, the researchers tested whether teaching founders to formulate hypotheses and evaluate evidence changed their decisions. The study supports disciplined testing over purely intuitive or confirmatory decision-making.

Used for: “Sell before you build” and the need to design experiments capable of disproving weak assumptions.

11. U.S. Small Business Administration: Plan Your Business

Read the SBA guidance

The SBA emphasizes researching demand, market size, saturation, pricing and customer behavior before making major investments.

Used for: The practical importance of direct customer research and demand validation.

C. Side hustles, focus and recovery

12. Hudson Sessions, Jennifer Nahrgang, Manuel Vaulont and colleagues: Do the Hustle! Empowerment From Side-Hustles and Its Effects on Full-Time Work Performance

Read the Academy of Management Journal study

The study found that empowerment generated by a side hustle could produce positive affect and enrich an employee’s performance in their primary job.

Used for: The evidence that outside work does not automatically damage full-time performance.

13. Min Liu, Shengxian Yu, Xin Liu and Shanshi Liu: The Effect of Side-Hustle Thriving on Full-Time Work Performance

Read the Journal of Management Studies study

The authors identify two competing pathways. Side-hustle thriving can improve well-being and help employees detach from their full-time work, but it can also create attention residue and deplete resources. Boundary segmentation influences the balance.

Used for: The two-sided treatment of side hustles and the importance of separating roles.

14. Sophie Leroy: Why Is It So Hard to Do My Work? The Challenge of Attention Residue When Switching Between Work Tasks

Read the publication abstract

Leroy’s research shows that people can retain cognitive attention on an unfinished task after switching to another activity, impairing subsequent performance.

Used for: The recommendation to create protected blocks instead of repeatedly switching between employer and founder work.

15. Sabine Sonnentag, Charlotte Venz and Anne Casper: Advances in Recovery Research

Read the Annual Review article

The review synthesizes evidence on recovery during breaks, evenings, weekends and vacations, emphasizing the role of recovery in sustaining well-being, motivation and performance.

Used for: The argument that weekends cannot become a second uninterrupted workweek.

D. Technology, AI and the solo economy

16. Satish Nambisan: Digital Entrepreneurship: Toward a Digital Technology Perspective of Entrepreneurship

Read the Entrepreneurship Theory and Practice article

Nambisan examines how cloud computing, mobile technologies, digital platforms and other tools alter entrepreneurial processes, agency and uncertainty.

Used for: The argument that digital technology has reduced the institutional infrastructure required to launch a venture.

17. Shakked Noy and Whitney Zhang: Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence

Read the peer-reviewed Science article

In a preregistered experiment involving professional writing tasks, participants using ChatGPT completed their work approximately 40% faster and produced outputs rated approximately 18% higher in quality.

Used for: Evidence that generative AI can increase individual productivity in defined professional tasks.

18. Erik Brynjolfsson, Danielle Li and Lindsey Raymond: Generative AI at Work

Read the Quarterly Journal of Economics article

The researchers studied the introduction of an AI assistant among more than 5,000 customer-support agents. Productivity increased by approximately 15% on average, with larger benefits for less experienced employees.

Used for: Evidence that AI can transfer elements of organizational knowledge and improve certain workplace outputs.

19. Fabrizio Dell’Acqua and colleagues: Navigating the Jagged Technological Frontier

Read the Organization Science article

The field experiment involving 758 BCG consultants found that AI improved performance significantly on tasks inside its capabilities but reduced accuracy on some tasks outside that frontier.

Used for: The warning that AI leverage remains uneven and requires human verification.

20. U.S. Census Bureau: Census Bureau Data Tell the Small Business Story

Read the 2026 Census analysis

The Census Bureau reported 30,427,808 U.S. nonemployer establishments in 2023, producing nearly $1.8 trillion in receipts.

Used for: The scale of economic activity conducted by businesses with no paid employees.

21. Stripe: Solo Founding Is at an All-Time High

Read Stripe’s analysis

Stripe reported growth in the number of high-revenue solo founders on its platform, while also documenting a wide and increasing gap between median and top-performing solo businesses.

Used for: Evidence of the opportunity and extreme outcome dispersion within solo entrepreneurship.

22. Te-Ping Chen, The Wall Street Journal: The Rise of Million-Dollar Companies With Just One Employee

Read the article

The article reports Stripe data indicating that thousands of solo operators generated more than $1 million in annual revenue. It also presents both successful and unsuccessful founder cases and describes the cost, competition and replication risks created by AI.

Used for: Current evidence that high-revenue solo businesses are becoming more common, while remaining exceptional and risky.

E. Legal boundaries

23. Federal Trade Commission: Noncompete Rule

Read the official status page

The FTC states that its nationwide Noncompete Rule is not in effect and is not enforceable. A district court blocked the rule, and the agency dismissed its appeals in 2025.

Used for: The warning that founders cannot assume a federal blanket ban invalidates their agreements.

24. Federal Trade Commission: FTC Files to Accede to Vacatur of the Non-Compete Clause Rule

Read the official FTC release

The release documents the FTC’s decision to dismiss its appeals and accept the rule’s vacatur.

Used for: Confirmation of the current federal procedural position.

25. Deborah A. DeMott: The Restatement of Employment Law and the Duty of Loyalty

Read the Cornell Law Review essay

The essay examines when employees may owe duties of loyalty and the difficulties involved in defining those obligations.

Used for: The caution that current employees may face legal duties involving competition, employer opportunities and employer resources even apart from noncompete clauses.

F. Business survival and risk

26. SBA Office of Advocacy: Frequently Asked Questions About Small Business, 2026

Read the 2026 SBA report

The SBA reports a five-year survival rate of approximately 49.2%, a ten-year survival rate of approximately 33.9% and a fifteen-year survival rate of approximately 25.5% for new employer establishments in the periods studied.

Used for: Context on business uncertainty and the rationale for maintaining substantial financial runway.

Research and legal note: The numerical thresholds in this article—including 18 months of runway, three initial contracts and three consecutive months of cost-covering revenue—are operating heuristics, not findings established by the cited studies. Employment, intellectual-property, tax and restrictive-covenant laws vary by jurisdiction and circumstances. Readers should consult qualified legal and financial advisers before acting.

Why We Business Builders, Writers and Creators Should Want Substack To Become the Next Giant Social Media Platform

John Brewton — Sun, 02 Aug 2026 11:41:03 GMT

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

Paid subscriptions are getting harder to grow.

Notes rewards people who post constantly.

Followers do not feel as valuable as email subscribers.

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

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

Their experiences should be taken seriously.

Their conclusion should not.

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

Good. It should.

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

It is an opportunity for all involved to grow.

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

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

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Harder does not mean worse

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

Early Substack writers entered a much emptier marketplace.

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

That was never going to last forever.

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

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

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

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

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

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

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

A category can become saturated while the broader platform expands.

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

That last distinction matters.

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

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

That is not necessarily cannibalization.

It is a different funnel.

Email is not a discovery system

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

It is direct.

It is portable.

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

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

Substack also allows creators to export their subscriber lists.

But email begins after discovery.

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

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

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

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

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

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

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

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

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

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

TikTok’s real innovation was not short video

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

Who are your friends?
Whom do you follow?
Which accounts have you already chosen?

TikTok began with a different question:

What are you likely to care about?

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

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

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

The old feed asked:

What did the people you follow publish?

The new feed asks:

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

The rest of the industry has followed.

YouTube says recommendations generate more viewing than subscriptions or search.

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

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

This is not an isolated product trend.

It is the migration from social media to interest media.

Substack must find the voice

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

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

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

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

That requires an interest graph built for ideas and expertise.

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

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

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

That is the promise of the interest graph.

More creators entering the platform does increase competition.

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

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

Following should be a signal, not an entitlement

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

Follower counts will become status markers.

Frequency will defeat substance.

Outrage will defeat expertise.

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

That is not the social platform Substack should build.

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

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

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

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

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

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

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

Optimize for relationships, not reactions

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

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

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

It should ask:

Did the reader visit the publication?
Did they follow?
Did they become a free subscriber?
Did they become a paid subscriber?
Did they remain subscribed after 30, 90, or 365 days?
Did they return to the archive or attend another Live?

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

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

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

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

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

Creators should demand better attribution, not less discovery.

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

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

Do not ask Substack to remain small

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

Nor should it.

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

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

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

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

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

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

Substack becoming a giant social platform is not the threat.

Becoming the wrong kind of giant social platform is.

The feed is not the enemy.

The wrong algorithm is.

- j -

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Appendix: Sources and evidence

The current creator debate

Substack’s scale and discovery network

The shift toward interest-based distribution

Evidence limitations

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

The Value of “We Don’t Know” - Part One

John Brewton — Thu, 30 Jul 2026 11:17:12 GMT

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

My good friend Alex Pawlowski has written a warning to leaders and executives that personifies this expectation, and I am thrilled to be publishing it in two parts, this week and next.

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

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

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

Encoding your operation is still the right move. Encode the answers without encoding the questions, and you have built a company that reaches conclusions faster than it ever could.

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

Hope this helps, friends.

- j -

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I. The Disappearing Space Between Question and Answer

Every organization, regardless of its industry or ambition, exists because no individual possesses a complete understanding of the environment in which it operates. Markets shift without warning, technologies evolve faster than planning cycles, customers behave inconsistently, competitors conceal their intentions, and the internal dynamics of even the best-run companies rarely resemble the tidy diagrams found in annual reports. Organizations were never designed to eliminate uncertainty. They emerged because uncertainty exceeded the capacity of any single person to manage it alone.

Seen from that perspective, knowledge has always been something of a collective achievement. The engineer understands the architecture but not the market. The marketer recognizes customer behavior but not the underlying code. Legal counsel anticipates regulatory implications that product teams overlook, while finance evaluates risks invisible to both. What eventually appears as organizational intelligence is assembled from dozens, sometimes thousands, of incomplete perspectives that gradually converge into a decision. The organization becomes intelligent not because everyone knows the same thing, but because different people know different things.

This also explains why the phrase “I don’t know” occupies a far more important place in healthy organizations than most management literature acknowledges. Contrary to popular intuition, uncertainty is not the opposite of expertise. It is often one of its most reliable indicators. Experience rarely removes ambiguity. Instead, it sharpens the ability to recognize where established knowledge ends and where additional evidence becomes necessary. Senior physicians request further tests not because they lack competence, but because they understand how easily complex systems produce convincing yet incomplete explanations. Seasoned investors hesitate before declaring certainty because markets have repeatedly demonstrated how fragile apparently robust assumptions can become. The same pattern appears in engineering, intelligence analysis, scientific research and strategy. Expertise does not eliminate doubt; it changes the reasons for doubting.

That observation has quietly shaped the institutions through which modern societies produce reliable knowledge. Universities, peer review, investment committees, architecture reviews, clinical conferences and scientific replication all emerged from the same underlying recognition: individual judgment improves when it encounters competing perspectives before hardening into conviction. None of these institutions were created because people lacked intelligence. They were created because intelligence, left entirely on its own, has a remarkable talent for mistaking coherence for accuracy.

For a surprisingly long time, the practical mechanics of acquiring knowledge reinforced that discipline almost automatically. A difficult question rarely produced an immediate answer. It initiated a process. People searched libraries, consulted colleagues, revisited old notes, designed experiments, waited for data, challenged assumptions and occasionally abandoned conclusions that had seemed persuasive only days earlier. The interval between curiosity and explanation was often frustrating, yet it performed valuable intellectual work. It slowed judgment just enough for alternative interpretations to remain alive, making it less likely that the first plausible explanation would become the final one.

Generative AI compresses that interval with astonishing effectiveness. Questions that previously initiated investigation now frequently produce polished explanations within seconds. The technological achievement is extraordinary, but its organizational consequences deserve equal attention. Every coherent response shortens the period during which uncertainty remains visible. The question no longer lingers long enough to invite additional perspectives before an explanation arrives, and because that explanation is fluent, internally consistent and delivered with confidence, it often satisfies the immediate psychological need that originally motivated the question. Whether it fully deserves that confidence becomes a separate matter, one that many users never reach because the cognitive tension has already dissolved.

An intriguing study published recently offers an early glimpse of what this shift may mean. Researchers observed that participants who relied on generative AI became less willing to acknowledge when they did not know something. Confidence increased even when underlying understanding did not improve accordingly. Read in isolation, the finding resembles another entry in the growing literature on overconfidence and hallucinations. Read more carefully, however, it points toward a different possibility. The study is less about the reliability of AI than about the changing role of uncertainty in human reasoning. It suggests that one of the oldest transitions in the production of knowledge—the movement from not knowing toward understanding—may itself be acquiring a fundamentally different shape.

That possibility reaches far beyond individual cognition. Organizations learn because uncertainty travels. Questions move between departments, assumptions encounter disagreement, partial explanations are revised through conversation, and decisions gradually improve as competing interpretations collide with one another. Remove uncertainty too early from that process and something subtle begins to change. Conversations become shorter. Alternative hypotheses receive less attention. Confidence appears earlier than evidence. The organization continues to generate answers, but it may become progressively less effective at recognizing which questions still deserve to remain open.

The challenge, therefore, is not to decide whether artificial intelligence produces better explanations than humans. In many domains it already does. The more consequential question concerns the role that uncertainty has quietly played inside organizations all along. If explanation becomes abundant, inexpensive and permanently available, what happens to the intellectual habits that were originally built around its scarcity? Before exploring how AI may transform work, management or strategy, it is worth examining something more fundamental: the organizational function that uncertainty has performed for far longer than most of us have ever noticed.

II. Organizations Do Not Manage Knowledge. They Manage Distributed Uncertainty.

Traditional management literature often describes organizations as repositories of knowledge. The language is understandable. We speak about knowledge workers, knowledge management, knowledge transfer and organizational learning as though the defining characteristic of an enterprise were the amount of information it possesses. Yet this perspective places the emphasis on the outcome rather than on the process that produces it.

The more closely one observes how organizations actually function, the less convincing this description becomes. Companies rarely fail because the relevant information exists nowhere inside the business. More often, the necessary pieces are already present, scattered across departments, embedded in individual experience or hidden within operational routines that have never been connected into a coherent picture. Product teams understand technical constraints that commercial teams rarely encounter. Legal departments anticipate regulatory implications long before they become strategic concerns. Customer support notices weak signals months before they appear in market research, while finance often recognizes structural patterns that remain invisible to both. Every function operates with a legitimate yet necessarily incomplete representation of reality.

The organization exists because none of those representations is sufficient on its own.

That observation changes the way we think about expertise. Expertise is frequently imagined as the progressive elimination of uncertainty through accumulated experience. Organizational life suggests something rather different. As people become more experienced, they certainly develop deeper knowledge within their own domains, but they also become increasingly aware of the boundaries surrounding that knowledge. The experienced engineer rarely assumes that technical elegance guarantees commercial success. The experienced strategist understands that market data cannot predict technological discontinuities with confidence. The experienced executive eventually learns that every important decision contains variables no dashboard is capable of measuring directly.

The consequence is that organizations spend remarkably little time managing certainty. Their daily work consists largely of coordinating uncertainty between specialists whose perspectives overlap only partially. Meetings, design reviews, investment committees, cross-functional workshops and executive discussions all serve the same underlying purpose. They are mechanisms through which incomplete models of reality encounter one another before irreversible decisions are made. The value of these conversations lies less in exchanging facts than in exposing assumptions that remained invisible within individual disciplines.

This also explains why disagreement often becomes a productive rather than destructive force inside high-performing organizations. Two departments rarely disagree because one possesses knowledge and the other lacks it. They disagree because each observes reality through a different set of constraints. Engineering optimizes feasibility. Sales optimizes customer opportunity. Finance optimizes capital allocation. Legal optimizes compliance. Strategy attempts to reconcile those competing perspectives into a coherent direction. What appears as conflict on the surface frequently represents something far more valuable beneath it: the organization discovering where its own understanding remains incomplete.

Much of organizational learning therefore occurs before anyone learns anything new. It begins when existing explanations encounter friction. A marketing assumption collides with operational data. A customer interview contradicts internal expectations. A prototype behaves differently than predicted. A financial model no longer reflects observed market behavior. Progress starts at precisely the moment when confidence becomes difficult to maintain. The objective is not to eliminate disagreement as quickly as possible but to understand what reality is attempting to reveal through it.

Viewed from this perspective, uncertainty begins to resemble an organizational resource rather than an organizational defect. It identifies where current models no longer describe the environment with sufficient accuracy. It highlights where additional observation, experimentation or conversation is likely to generate the greatest returns. Organizations capable of recognizing those moments early often appear unusually adaptive, not because they possess superior forecasting abilities, but because they remain willing to revise their understanding before circumstances force them to do so.

Generative AI enters this landscape in a fascinating and somewhat paradoxical way. On the one hand, it increases access to information on a scale that would have seemed extraordinary only a few years ago. Individuals can summarize research, compare legal frameworks, draft technical documentation or explore unfamiliar domains within minutes. In many respects, AI lowers the cost of acquiring explicit knowledge more dramatically than any previous technology.

At the same time, lowering the cost of explanation also changes the signals flowing through the organization. If every uncertainty immediately produces a coherent narrative, the distinction between possessing an explanation and understanding the problem becomes progressively more difficult to detect. Questions that previously travelled through multiple perspectives may now terminate after the first plausible answer. Cross-functional conversations become shorter because apparent certainty arrives earlier. Teams may still collaborate, yet they increasingly collaborate around explanations that have not undergone the same process of collective scrutiny that organizations historically relied upon to improve decision quality.

The strategic challenge therefore extends well beyond accuracy. Even if future models become dramatically more reliable than those available today, organizations will still face the question of how uncertainty should move through the enterprise before action is taken. That movement has always been the hidden architecture of organizational learning. Decisions improve because uncertainty circulates between different perspectives, gathering evidence, exposing contradictions and forcing assumptions to justify themselves against reality. If that circulation gradually slows because explanation becomes abundant, organizations may discover that they have optimized access to knowledge while quietly weakening one of the mechanisms through which knowledge becomes trustworthy in the first place.

The discussion surrounding artificial intelligence often assumes that better answers naturally produce better organizations. History offers a more nuanced picture. Enduring institutions have rarely distinguished themselves through the speed with which they arrived at conclusions. Their advantage came from designing environments in which conclusions had to survive repeated encounters with alternative perspectives before they were allowed to shape consequential decisions. What deserves protection, therefore, is not uncertainty for its own sake, but the organizational processes that transform uncertainty into judgment rather than replacing it prematurely with confidence.

III. “I Don’t Know” as Organizational Infrastructure

When people speak about organizational capabilities, they usually think of engineering excellence, operational efficiency, strong leadership or an innovative culture. Rarely does anyone mention uncertainty. Yet many of the practices that distinguish mature organizations exist for one surprisingly simple reason: they create environments where uncertainty can be expressed before decisions become irreversible.

Consider how many institutional mechanisms depend on someone being willing to acknowledge that the current explanation may not be complete.

These practices were not invented because organizations distrusted intelligence. They were invented because intelligent people repeatedly arrive at convincing conclusions that later prove incomplete. The stronger the expertise, the more sophisticated those conclusions often become, which makes them even harder to challenge.

The phrase “I don’t know” therefore performs a function that extends far beyond individual humility. It becomes an organizational signal. It tells everyone else that the current model of reality has reached its limits and that additional perspectives are now valuable.

Interestingly, the most experienced professionals often become increasingly comfortable with this signal. Not because they know less than their junior colleagues, but because experience changes their relationship with confidence. After enough product launches, acquisitions, clinical cases or market cycles, certainty begins to feel less like expertise and more like a hypothesis waiting to encounter reality.

This observation creates an intriguing inversion of how expertise is commonly perceived.

None of these characteristics imply indecision. Organizations cannot function if every question remains permanently open. Decisions eventually have to be made, resources committed and strategies executed. The distinction lies elsewhere. High-performing organizations separate the exploration of uncertainty from the execution of decisions. They encourage broad disagreement while understanding the problem, and disciplined alignment once a direction has been chosen.

That separation has quietly become one of the defining characteristics of modern organizational life. Aviation investigates every incident regardless of how minor it appears because small anomalies often reveal systemic weaknesses. Hospitals conduct morbidity and mortality conferences not to assign blame but to examine where existing understanding failed. Intelligence agencies invest heavily in structured analytic techniques precisely because history demonstrates how easily coherent narratives become accepted without sufficient evidence. Across these very different domains, one pattern repeats itself: uncertainty is treated as something to be examined before it becomes something to regret.

Perhaps this explains why organizations so often struggle after periods of exceptional success. Success has a remarkable ability to reduce the perceived need for uncertainty. Strategies that repeatedly work begin to feel universally applicable. Processes that solved yesterday’s problems gradually become assumptions about tomorrow. Confidence accumulates not because reality has become simpler, but because past experience begins to substitute for fresh observation. By the time external conditions finally challenge those assumptions, organizations frequently discover that the habit of questioning them has quietly disappeared.

This is where the conversation returns to generative AI, although from a different direction than most current discussions. The central concern is not whether a model occasionally hallucinates or whether a summary omits important context. Organizations have always possessed imperfect information. Their resilience depended less on avoiding mistakes than on maintaining mechanisms capable of discovering them before they spread. The more interesting question is whether systems designed to produce immediate explanations unintentionally weaken one of those mechanisms by making uncertainty less visible inside everyday work.

John and I have spent much of the past year exploring organizations from different vantage points. One of us has focused primarily on strategy, organizational capability and judgment; the other on agentic systems, orchestration and the emerging architecture of AI-native work. Those conversations repeatedly converged on the same observation: the future of enterprise AI may depend less on how well systems answer questions than on how well they preserve the conditions under which good questions continue to emerge.

That possibility leads naturally to a broader question. If uncertainty has always functioned as an invisible form of organizational infrastructure, what happens when explanation becomes effectively abundant? What changes inside the enterprise when every employee, every team and eventually every autonomous agent can generate convincing narratives on demand? And perhaps more importantly, what kinds of systems should organizations build if their objective is not merely to generate explanations, but to preserve the quality of organizational judgment itself?

Tune in next week for the concluding sections of the essay…

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The 5 Operating Habits That Built a 75,000-Person Audience Around My Passion For Operating Companies in 2.5 Years

John Brewton — Wed, 29 Jul 2026 11:53:55 GMT

75,000 followers. 6,000 Substack subscribers. 200 paid. A Substack bestseller badge. And a new +$200,000 business vertical.

Over the last 2.5 years, I built an online audience of 75,000, a Substack with 6,000 subscribers, a Substack bestseller badge, + 200 paid members. Five essential operating habits created these outcomes.

Here’s how I learned and adopted them.

Habit 1: Start, then never stop

2.5 years ago I committed to posting on LinkedIn every single day.

I have not stopped since.

Some days the content was not ready. I posted. Some days I woke up late. I posted. In some weeks, the prior 7 posts underperformed. I kept posting.

The commitment has 2 parts: Start, and never stop. Most people manage to begin step one, but struggle to realize step 2.

What you can do: Pick 1 platform. Post daily for 90 days before you judge any of it. Do not negotiate or mentally manipulate your way around the committment.

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Habit 2: Audit every post and every week

Every post gets reviewed. Every week’s batch gets reviewed. Auditing all of your work is essential and falls across three categories within the feedback loop:

Topic: Check whether the subject is something your audience wants from you and whether it aligns with your profile. Note whether it rode a broader conversation or was specific to you. Note who engaged early and propelled reach inside the algorithm.
Packaging: Carousel, infographic, hero image, video, or photo. Check the design: the fonts, the readability, how the post rendered in a mobile feed and on desktop. Log which formats won and which lost in a Google sheet and keep pushing forward.
Copy: My hooks run 8 to 12 words across 2 sentences. Check whether the hook stopped the scroll. Check the CTA length. Check whether the CTA matched the body of the post and moved the reader to the action you wanted.

What you can do: Build a log. 1 row per post, 3 columns: topic, packaging, copy. Review it every Sunday. The log will tell you what to make next before your instincts do.

Habit 3: Stop caring what people think

Criticism arrived early, and it never stopped. Haters are going to hate. Laughers are going to laugh. The people not doing the work, who wish they were doing what you’re doing, are the loudest.

None of it touched the numbers.

In 2.5 years, no critic has cost me a follower, a subscriber, a single piece of my happiness.

What you can do: Publish anyway, every day. Read comments for signal about where the value is in your work, not for opinions about you.

Habit 4: Create for the audience, not the ego

Curiosity is a habit: Reading constantly, studying what the audience responds to (and doesn’t), and constantly asking what would help them next. The audience tells you what it wants in the comments, in DMs, in which posts get saved and shared.

Teach them what they’re asking for, based on their feedback on why they show up each day for your work, not on what your ego imagines makes you look the smartest or appear most professionally or intellectually attractive.

The filter on every piece is whether the audience will find it valuable, not whether it makes me sound smart or look cool.

Those 2 filters produce different content.

They produce content that actually grows a business.

What you can do: Keep a running list of audience questions. Turn each one into a post.

Habit 5: Upgrade the production line, then reinvest the time

When I started, a LinkedIn carousel took 2.5 to 3 hours to build in Canva. Today, with Claude handling the design, the same carousel takes under 20 minutes once the concept, idea, and copy are set. Editing takes longer now than it did then, and it matters more, but that has also become my favorite part.

Every time a new model or new tool ships, I test it against my production process. If it makes the work faster or better, it goes in the stack.

The saved time goes back into the work. More posts, richer resources, more value shipped to the audience. Not shorter workdays.

What you can do: audit your production process once a quarter. Test 1 new tool each cycle. Spend every saved hour on output.

The system, in 5 lines + 1

Post every day.
Audit every week.
Ignore the noise.
Serve the audience.
Upgrade the tools and reinvest the time.
Start today.

The Operating Habits Workbook

0 to 75,000: The Operating Habits Workbook expands the 5 habits into 38 pages: 6 parts, 24 lessons, and 21 exercises you can complete at your desk, in a notebook, or with your favorite LLM. The 6th lesson covers material outside the habits and focuses on building the business while you build the audience.

The lessons report covers what happened in my data across the 912-day run.
The exercises turn each lesson into an artifact you produce this week: a signed posting contract, a 90-box streak tracker, the 8-column data tracker, a Sunday review ritual, a critic ledger, a chat pipeline, a production map, an offer sheet, and a conversion path map.
It closes with the 90-day execution plan and the Day 90 benchmark tables, which show whether it is working.

Commit to one part per week for the next six weeks. By Week 6, you will have a running streak, tracked data on every post, a daily engagement block, a quarterly tool test, and an offer a stranger can buy. These are the same assets in tools that took me 2.5 years to build by trial and error. The workbook puts you at a profoundly better starting line than I was.

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Stop Arguing (or Caring) About AI Writing About Slop or Not Slop

John Brewton — Sun, 26 Jul 2026 16:20:58 GMT

Historically, every time a tool made ideas cheaper to share, the people who had mastered the older, slower way treated it as a threat to defend against rather than a shift to move with. The scribes who spent their lives copying manuscripts did not celebrate the printing press. They saw a craft and a livelihood going to zero. Plato warned that writing itself would ruin memory and hollow out real understanding.

That was around 2,400 years ago.

Civilization grew anyway.

We are running the same fear-based reflex again. Our social feeds and inboxes are full of people litigating whether AI writing counts, whether it is cheating, whether the craft is dying. The scribes and the press are at it again. And while this argument burns, the enabling machines that create distribution for our ideas, work, products, and services are moving faster than they have in 500 years.

Here’s our current reality: The cost of producing words just fell to zero, and the only things left worth owning are the idea you choose and the distance it travels.

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Five thousand years of the same job

For 5,000 years, every writing tool did one thing successfully.

It made ideas cheaper to share.

Clay tablets held temple receipts, not poetry.

A medieval book took weeks of skilled labor and got chained to the desk because it was worth more than the room it sat in.

Then the printing press cut the price of a book by two-thirds within 50 years.

Each drop did the same work.

More people could read, and more people could write.

This cycle of payoffs compounded for centuries.

In 1820, 12 percent of the world could read. Today it is roughly 90 percent. Cheaper copies did not make people read less. This innovation turned reading from an elite privilege into a basic human skill.

Every one of those tools shared a blind spot. The personal computer and the laptop made producing and editing words faster, and none of it touched the oldest cost of all, the effort of composing the thought itself. You still had to sit down and produce the words. That is the cost ChatGPT and Claude just took to zero. These are the first tools that compose and reason back. They draft, they read, they research across sources in minutes instead of weeks.

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What actually got scarce

When a cost falls to zero, value moves to whatever sits next to it and is still expensive.

Composition is now cheap. So look at what it sits between. Upstream is the choice of what is worth saying. Downstream is getting it to the right people and being believed once it lands.

Judgment and distribution.

Those are the survivors.

Everything the AI-writing debate is fighting over is the one output that now fails to be scarce. The question was never how fast you can write. Speed is solved. The new question we should be centering our collective, ambitious, entrepreneurial gaze on is what you choose to think about, and how far you can move it.

The gate is gone

Here is my take on the opportunity we should all be aligned behind, but very few actually are.

For most of the internet’s life, reach was a function of accumulation. You built a list, a following, a set of contacts, and your ideas reached roughly the people you had already collected. That is the social graph. If you were small or new, you stayed small. Distribution was gated by who you already knew.

That gate is gone. A recommendation model now reads the content itself, scores it against what each person actually engages with, and serves it to strangers who have never heard your name. Follower count stopped being the gate and the metric to chase. The quality of your idea and what you can use the new stack to produce, distribute and build with it is the new hurdle, the new gate.

This is not a forecast I’m making as some Substack author you’ve never heard of befroe. Meta told a federal court, in its own antitrust filing, that just 7 percent of the time people spend on Instagram, and 17 percent on Facebook, is spent on content from friends. Zuckerberg has said more than 50 percent of what people see on Instagram is now chosen by AI, not by who they follow. The man who built the social graph testified that Facebook is no longer the culture, and floated wiping everyone’s friend connections to reset the platform around interests. The social network is barely social. The algorithm runs the room.

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Social media is dead. AI-enabled, interest-based media is the replacement.

So yes, the interest graph removed the gate, but it did not remove the competition. Cheap distribution does not spread attention evenly. Anita Elberse at Harvard studied this directly. In one set of 8 million songs that each sold at least 1 copy, roughly a third sold exactly 1 copy. Attention still stacks on whatever earns it. A model serving your idea to strangers is a momentary shot, not a guarantee, by far.

So the constraint did not disappear. It has just moved to a new place in the supply chain. It used to sit on permission, on access, on the size of your list. Now it sits entirely on the quality and clarity of the idea and how well you shape it. That is a harder game to fake and a fairer one to win. For the first time, a small company with a sharper idea can out-reach an incumbent with a giant list, because the list no longer decides.

Voice is the accelerant

If the interest graph decides who gets heard, voice decides how fast you can feed it.

Voice is the most information-dense form of human communication, and for the first time it is programmable. Andreessen Horowitz calls it the first and primary way people will now interact with AI, and 22 percent of the most recent Y Combinator class were voice-agent companies. You speak, the machine drafts and researches, and the loop from raw thought to finished asset closes to near real time. The cost of running these models fell about 280 times in 18 months, so the constraint is no longer money or minutes. It is what you point them at.

The reading side moved the same way. Most Google searches now end with no click at all. People take the answer, not the source. The idea that the machine surfaces matters more than owning the page it came from.

What to do with this

The uncomfortable part is that none of this rewards the skill most knowledge workers spent 20 years building. Fast, clean production is now table stakes a machine clears for free. What it rewards instead is judgment, and judgment is harder to fake.

So we need to focus our operating energies on the part that pays.

First, spend your scarce hours choosing ideas, not producing words. Keep a running list of the few ideas you want to be known for. That choice is now the work that pays most.
Second, move at conversation speed. Talk your thinking out loud, let the model draft and research, then you cut and judge. Do not type what you can speak.
Third, publish into the interest graph on purpose. Turn each idea into something consumable and teachable, ship it on a schedule, watch what the algorithm carries, and make more of that.
Fourth, own a few ideas in public until your name is attached to them. Repetition around a tight set of ideas is how you get known for them. Do not scatter.
Fifth, convert reach into revenue. Distribution is not a vanity metric. The point is to become known, sell more product, and sell more service off the back of the ideas.

Let other people spend the decade arguing about whether how the idea was written or produced matters. Don’t want to give away the ending, but none of that is going to matter one bit. Pick 1 idea worth being known for and publish it every week for the next 52. The writing was never the asset. What you choose to think about, create from those ideas, and how far you can push those concepts into the world of products, serrvices, subscriptions, VALUE FOR YOUR AUDIENCE, is the only thing that matters.

Stop worrying about debates that don’t matter.

Stop letting irrelevant online voices shame you.

Get to work, Substack.

- j -

The Interest-Graph Operating System

I built the system that is the logical, operating approach for this set of ideas. And my paid subscribers get it. The Interest-Graph Operating System is a workbook you paste into your own model (anyone you like or prefer), which then turns into your facilitator and walks you through the four moves: choosing the ideas worth owning, composing them at conversation speed, publishing them into the interest graph, and turning reach into revenue.

You answer its questions in your own words. It compiles the answers into a system you can run this week: your idea ledger, your prompt pack, your publishing plan, and the four key numbers that tell you if it is working.

If you’re just starting to understand these ideas, this workbook is the place to begin building the AI-fluent machine to architect your new professional future and business ambitions.

Become a Paid Operating Subscriber

DOWNLOAD BELOW AFTER THE PAYWALL, FOLLOWING THE APPENDIX AND FAQ SECTION.

Appendix: Sources

Opening and the history of writing tools

Plato, Phaedrus (Socrates on writing and the loss of memory), Perseus Digital Library, Tufts University. https://www.perseus.tufts.edu/hopper/text?doc=Perseus:text:1999.01.0174:text%3DPhaedrus:page%3D275
Jeremiah Dittmar, “Information Technology and Economic Change: The Impact of the Printing Press,” Quarterly Journal of Economics, 2011 (book prices fell by roughly two-thirds; cities that adopted the press grew about 60 percent faster). https://academic.oup.com/qje/article-abstract/126/3/1133/1855353
Jeremiah Dittmar, plain-language summary, CEPR / VoxEU. https://cepr.org/voxeu/columns/information-technology-and-economic-change-impact-printing-press
“Literacy,” Our World in Data (global literacy 12 percent in 1820 to roughly 90 percent today). https://ourworldindata.org/literacy

The distribution shift: social graph to interest graph

“Facebook is Dead; Long Live Meta,” Stratechery by Ben Thompson, 2025, citing Meta’s FTC antitrust filing (7 percent of Instagram time and 17 percent of Facebook time spent on content from friends). https://stratechery.com/2025/meta-earnings-meta-turns-the-dial-social-network-r-i-p/
Mark Zuckerberg, Meta Q1 2024 earnings call, reported by Benzinga (more than 50 percent of Instagram content is now AI-recommended). https://www.benzinga.com/news/24/04/38420928/meta-ceo-mark-zuckerberg-more-than-50-of-the-content-people-see-on-instagram-is-now-ai-recommended
“The AI behind unconnected content recommendations on Facebook and Instagram,” Meta AI, 2023 (recommendation of content from accounts users do not follow). https://ai.meta.com/blog/ai-unconnected-content-recommendations-facebook-instagram/
“Spinning off Instagram, the decline of friending, and other takeaways from Mark Zuckerberg at the FTC monopoly trial,” CNN Business, April 16, 2025. https://www.cnn.com/2025/04/16/tech/mark-zuckerberg-testimony-meta-ftc-trial/index.html
Anita Elberse, Blockbusters, Harvard Business School, via “The Way of the Blockbuster,” Harvard Magazine, 2013 (market concentration; a third of 8 million songs sold exactly 1 copy). https://www.harvardmagazine.com/2013/12/the-way-of-the-blockbuster

Voice and the reading side

“AI Voice Agents: 2025 Update,” Andreessen Horowitz (voice as the first and primary interface; 22 percent of the latest Y Combinator cohort). https://a16z.com/ai-voice-agents-2025-update/
“The 2025 AI Index Report,” Stanford Institute for Human-Centered AI (inference cost fell roughly 280x in about 18 months). https://hai.stanford.edu/ai-index/2025-ai-index-report
“Google zero-click searches study,” Search Engine Land, 2026 (most Google searches end without a click). https://searchengineland.com/google-zero-click-searches-2026-study-479717

Frequently Asked Questions

On “Stop Arguing About AI Writing” and the interest-graph shift

Isn’t most AI writing just slop? Some of it is. Slop is a production problem, and production is the part that got cheap. The point of the piece is that arguing about whether a machine drafts well is the wrong fight, because the value moved to the two things a machine does not do for you: choosing the idea and judging what comes back. Quality still matters. It just stopped being about who typed the words.

Does this mean writing skill no longer matters? Writing skill matters less as production and more as judgment. Fast, clean production is now table stakes a model clears for free. What pays is taste, the ability to choose what is worth saying and to cut what is not. That is a harder skill than typing, and it is the one worth building.

What is the interest graph, in plain terms? The social graph reaches the people you already follow or already know. The interest graph reaches strangers, because a recommendation model reads your content and serves it to whoever is likely to care. Follower count stops being the gate and the idea becomes the gate. Meta told a federal court that only 7 percent of time on Instagram, and 17 percent on Facebook, is now spent on content from friends.

I have a small audience. Can this actually work for me? Yes, and that is the opportunity. The interest graph does not ask how big you are. It asks how good the idea is. A sharper idea from an unknown account can out-reach an incumbent with a large list, because the list no longer decides who gets seen.

If everyone can produce, isn’t the competition infinite? The competition is real, and attention still concentrates on a few winners. Anita Elberse at Harvard showed that digital markets are more concentrated, not less. The gate moved. It did not vanish. Removing the permission barrier does not hand you the crowd. It gives you a shot you did not have before, and the idea is what earns it.

Isn’t building on someone else’s algorithm risky? It is a real risk, and it is the cost of the reach. Manage it two ways. Convert algorithmic reach into assets you own, an email list and paying customers, so a platform change cannot erase you. And do not depend on one platform. Publish where the idea travels and move with it.

Do I need to use voice and AI to do this? No, but they are the accelerant. Voice is the most information-dense way to get an idea out of your head, and the model turns it into a draft you judge and cut. The loop from raw thought to finished asset closes to minutes. You can do the work by typing. You will just be slower.

How do I make money from this, not just collect reach? Attach an offer to every idea you choose to own. Publish the argument free so it travels, and gate the system, the template, or the service that acts on it. Track four numbers every week: reach, email subscribers, paid subscribers, and revenue. If a week moves none of them, change the idea, not the effort.

How is this different from telling me to post more? Volume without a chosen idea trains the algorithm on nothing and trains the reader on nothing. This is the opposite. Own 3 to 5 ideas, repeat them in new forms until your name attaches to them, and let the schedule do the compounding. One flagship a week for 52 weeks beats 12 brilliant pieces shipped on mood.

How much time does the weekly system take? The cadence is one flagship and five short cuts a week. Monday you choose the idea, Tuesday you compose with the prompt pack, Wednesday you ship, and the rest of the week you distribute and log the numbers. The initial build takes about 40 minutes. The upkeep is a few focused hours a week.

What is the Interest-Graph Operating System, and how do I use it? It is a workbook you paste into your own model. The model becomes your facilitator, asks you one question at a time, and compiles your answers into a finished system: your idea ledger, your prompt pack, your publishing plan, and your scoreboard. You build it once, then you run it every week. Subscribers get it with this piece.

Do. Fail. Learn. Grow. Win. | My 5-Step (How-To) Framework To Succeed At Anything.

John Brewton — Tue, 21 Jul 2026 22:34:54 GMT

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Work w/ John

TL →DR

I have 1 framework: Do. Fail. Learn. Grow. Win. It carried me through a PhD program, a family distribution business, a consulting firm, and a creator business built from an audience of 0 to over 50,000.
Research says learning from failure is not automatic. A 10-year study of 71 cardiac surgeons found they learned from their own successes and other surgeons’ failures, not their own failures.
Failure alone is nearly worthless. Venture-backed founders with a prior failure succeed 23 percent of the time. First-timers succeed 22 percent of the time. The value is the lesson, not the failure.
The extraction tool is 1 written sentence naming why the miss happened. In a Harvard field experiment, 15 minutes of written reflection beat additional working time by about 23 percent.
Run 1 loop per week: ship 1 thing by Friday, name the miss, change 1 behavior, ship again. Count wins at week 12, not week 1.

Who the heck is John Brewton, where did this framework come from, and why should you care?

16 years ago, I was working to complete a PhD in economics at the University of Chicago when a professor gave me the best advice of my career. Take a year off, he said. Go get some experience actually operating a business. My parents owned an industrial distribution company and were working toward selling it. He thought I should go help. I had grown up in that entrepreneurial household and talked about the company through my entire graduate education, so I went.

The 1 year became 12.

I learned to operate by operating. We sold the company in 2021, and starting a consulting firm on the back of those relationships was the next logical step. So that is what I did. For that entire period, I had no LinkedIn, no newsletter, no “brand.” I produced zero content. It never occurred to me. I was head down, building a business, thinking about nothing else in the world but how to use our operational efficiencies to grow margin and razor thin market.

Then, almost 4 years ago, my life changed at once. I met my wife. We fell in love, got married, and had our daughter. The weekend we found out we were pregnant, my mother passed away. I am an only child. I had spent 12 years building a business beside my parents and stayed very close through these, often difficult, years. The largest loss of my life arrived the same weekend as the news of my greatest happiness.

Life is extraordinarily not linear.

That was the year I stopped expecting it to be.

Or maybe it was just the moment I realized that it had never been.

I moved to Northern Virginia to become a husband, a stepfather, and a father. That November I decided the business had to change to match the life. I wanted to be available for all of it, every experience of being a father, of being a Girl Dad. So I took a page from Gary Vaynerchuk’s playbook and made 1 simple commitment: post on LinkedIn every day for a year, starting from an audience of 0. I completed the year and have posted every day since. Today the audience across LinkedIn and the newsletter is over 50,000. Operating by John Brewton became a Substack bestseller. And my total business operations now span across 6 revenue streams, consulting being primary, courses, a paid newsletter, coaching, advisory work, and some content design work for projects I find especially inspiring.

I had wanted a content business for years and never started one. I admired Gary Vee and Casey Neistat from a distance while my ego managed credentials, attaching names and institutions to my work so it would look smarter to the outside world than I sometimes felt on the inside. Becoming a father ended that negotiation. Meeting other people’s expectations was the last thing I cared about when that period of my life began.

The life I have today looks nothing like the one I planned. I work more than I ever have. I love the consulting. I learn so much from clients each and every week. Opportunities are plentiful. None of it matches the picture I once drew, and 1 thing explains how it all got built anyway.

The DNA of every win is found in some loss and some failure along the way. I have 1 framework. It is 5 words long, and it has run every chapter of this story.

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What is the Do. Fail. Learn. Grow. Win. framework?

Do. Fail. Learn. Grow. Win.

When the cycle completes, it’s back to doing more.

That is the entire system.

No software, no certification, no 2-by-2 matrix.

One warning before the steps, because the research here is humbling. Learning from failure is not automatic. A 10-year study of 71 cardiac surgeons across 6,500 procedures found that surgeons improved after their own successes and after other surgeons’ failures, but not after their own failures. Their own misses got filed under bad luck. If surgeons with lives on the line do not learn from their own failures by default, neither do you and neither do I. It is imperative that the loop exists to force what does not happen on its own.

How do you run each step of the loop?

1. Do: Ship something real. A draft, a newsletter issue, a proposal, a post. The framework does not begin with planning. It begins with output. Most people invert this and spend the doing budget on preparing to do. Volume is the entry fee. My version was 1 LinkedIn post a day, every day, starting from an audience of 0.

2. Fail: Some of what you ship will not work. This is not a defect in the system. It is a component of it. The issue nobody opens. The proposal that dies in review. Do not route around this step. Treat each miss as data arriving on schedule.

3. Learn: Failure only pays if you collect. Name the mechanism of the miss in 1 sentence. Not “the post flopped” but “the post buried its one idea in paragraph 4.” A loss you cannot articulate is a loss you will repeat. This step is not a journaling habit. In a Harvard Business School field experiment, trainees who spent the last 15 minutes of the workday writing down what they learned performed about 23 percent better than trainees who spent those minutes doing more work. Reflection converts doing into learning. Reflective writing is an essential tool.

4. Grow: Growth is the lesson applied, not the lesson admired. Change the template. Rewrite the opening. Reprice the offer. If your next attempt is identical to your last, no learning happened.

5. Win: The win usually arrives smaller than expected and later than planned. Take it anyway. A win is not a finish line. It is funding for the next round of doing. Bank it, then return to step 1 with more ambition.

Does failure actually make you more successful?

No. This is the uncomfortable part, and the data is blunt. A Harvard study of venture-backed founders found that previously successful founders succeeded 34 percent of the time on their next company. First-timers succeeded 22 percent of the time. Previously failed founders, 23 percent. Failure by itself bought 1 point. The failing was never the value. The lesson you extract from it is, and most people skip the extraction.

That is why the framework has 5 steps and not 2. Do and Fail happen to everyone who tries. Learn, Grow, and Win only happen to the people who commit to running the full loop.

How do you run multiple loops at once?

The first version to master is to simply run 1 loop at a time. The magic happens when you’re running the loop against is 6 revenue streams, 6 competing client interests, all at 6 different stages of the cycle on any given day. When that many loops run at once, no single failure can take you out of the game.

The framework survives every context switch because it assumes nothing about the domain. It assumes only a sequence.

Action produces information.
Information produces adjustment.
Adjustment produces results.

That sequence held for me at Harvard, as a PhD candidate, in a messy distribution business, across all consulting engagements and right in the glow of an analytics dashboard for my newsletter business.

I will never know the outcome or the final destination. Neither will you. The job is to keep executing, put one foot in front of the other, and build the life from whatever luck arrives and whatever realities show up. That is the nature of the work for every consultant, creator, founder, and operator trying to figure this out.

How do you start the framework this week?

Start small and start now.

Pick 1 project. Ship 1 real unit of work by Friday: a post, a proposal, a prototype, whatever the project calls for. When part of it misses, and part of it will, write 1 sentence identifying the reason. Change 1 behavior before the next attempt. Then ship again next week.

Run this framework for 12 weeks and count the wins at the end.

Download the worksheet I’ve built to help you get started here:

The best state you can be in is a continuous state of learning from what has not worked. That is where the growing comes from.

That is where all your future wins live.

Do. Fail. Learn. Grow. Win.

That is my framework, and I am sticking to it.

You should try, too.

I so sincerely hope this helps.

- j -

Work w/ John

I run this operating system inside real businesses: consulting engagements, advisory work, and coaching for founders, operators, and creators. If you want to build your version of the loop for your work or company, message me and tell me what you are building.

What else do people ask about the framework?

Is “fail fast” good advice? Not by itself. The founder data shows failure alone improves your odds by 1 point. Fail small, on purpose, with a hypothesis, then write the lesson down. Extraction is the value, speed is not.

What if nothing failed this week? Something did. “Nothing missed” is almost always a collection failure, not a clean week. Look at what underperformed, what ran late, and what you avoided. Name 1 mechanism anyway. A dry week with a named lesson is progress.

How long before the loop produces wins? Run it for 12 weeks before judging it. Weeks 1 to 4 produce volume and misses. Weeks 5 to 8 produce lessons and behavior changes. Weeks 9 to 12 produce the wins the first 8 weeks paid for.

Does this work outside of business? Yes. The framework assumes nothing about the domain, only a sequence: action produces information, information produces adjustment, adjustment produces results. School, sports, music, parenting, work. Same loop.

Where do I get the free field guide? The Loop Field Guide is free for subscribers. It is 2 pages: the framework on 1 page and the 5-blank weekly sheet on the other. Fill the sheet once a week in 5 minutes. Subscribe and it lands in your inbox.

Where does the research behind the loop come from?

Appendix. Every study referenced in this article, organized by the step it supports.

Arrow, K.J., “The Economic Implications of Learning by Doing,” The Review of Economic Studies (1962). The founding economics of experience-driven improvement. https://academic.oup.com/restud/article-abstract/29/3/155/1539235
Thompson, P., “How Much Did the Liberty Shipbuilders Learn?” Journal of Political Economy (2001). Productivity rose with cumulative output, and gains decayed when the doing stopped. https://www.journals.uchicago.edu/doi/abs/10.1086/318605
Ericsson, Prietula, Cokely, “The Making of an Expert,” Harvard Business Review (2007). Repetition without feedback plateaus. Practice targeted at weaknesses improves. https://hbr.org/2007/07/the-making-of-an-expert

Fail

Kahneman and Tversky’s prospect theory, confirmed by a 2024 meta-analysis: losses weigh roughly 1.9x gains, which is why shipping feels risky and preparing feels safe. https://www.sciencedirect.com/science/article/pii/S0167487024000485
Golman, Hagmann, Loewenstein, “The Demand for, and Avoidance of, Information.” People dodge feedback that might sting, even when it is free and useful. https://www.cmu.edu/dietrich/sds/docs/golman/the-demand-for__and-avoidance-of__information.pdf
Edmondson, A., “Strategies for Learning from Failure,” Harvard Business Review (2011), and “It’s OK to Fail, but You Have to Do It Right” (2023). Only intelligent failure teaches: new territory, goal-driven, hypothesis-informed, small. https://hbr.org/2011/04/strategies-for-learning-from-failure · https://hbr.org/2023/07/its-ok-to-fail-but-you-have-to-do-it-right

Learn

KC, Staats, Gino, “Learning from My Success and from Others’ Failure,” Management Science (2013). 71 cardiac surgeons, 6,500 procedures, 10 years. No learning from own failures. https://pubsonline.informs.org/doi/10.1287/mnsc.2013.1720
Eskreis-Winkler and Fishbach, “Not Learning From Failure — the Greatest Failure of All,” Psychological Science (2019). 5 experiments, 1,600+ participants. Failure feedback taught less than success feedback because people tune out. They learned normally from others’ failures. https://journals.sagepub.com/doi/abs/10.1177/0956797619881133
Di Stefano, Gino, Pisano, Staats, “Learning by Thinking,” HBS working paper. 15 minutes of written end-of-day reflection beat additional working time by about 23 percent. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2414478
Finkelstein and Fishbach, “Tell Me What I Did Wrong,” Journal of Consumer Research (2012). Experts seek negative feedback. Novices avoid it. https://academic.oup.com/jcr/article-abstract/39/1/22/1823160

Grow and Win

Weick, K., “Small Wins: Redefining the Scale of Social Problems,” American Psychologist (1984). Small, winnable units are the reliable path to large change. https://psycnet.apa.org/record/1984-25682-001
Amabile and Kramer, “The Power of Small Wins,” Harvard Business Review (2011). 12,000 diary entries from 238 professionals. Progress in meaningful work is the top motivator. https://hbr.org/2011/05/the-power-of-small-wins
Edmondson, A., “Psychological Safety and Learning Behavior in Work Teams,” Administrative Science Quarterly (1999). Teams that surface errors outlearn teams that hide them. https://journals.sagepub.com/doi/10.2307/2666999

The market evidence

Gompers, Kovner, Lerner, Scharfstein, “Performance Persistence in Entrepreneurship,” Journal of Financial Economics (2010). Success rates: prior success 34 percent, first-timers 22, prior failure 23. https://www.nber.org/papers/w12592
Bessemer Venture Partners, the Anti-Portfolio. A top venture firm’s public log of the great companies it passed on, and why. The loop log at institutional scale. https://www.bvp.com/anti-portfolio

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

John Brewton — Sun, 19 Jul 2026 18:12:58 GMT

The full methodology and workflow behind a 22-page, 12-chart research report: Perplexity Computer for data collection and essential research, then Claude on Opus 4.8 and Fable 5 for verification and production, along with my elite research skill file that holds the whole thing to an institutional standard. The skill’s architecture is broken down below, and the workbooks for building your own are attached.

Too Long →Didn’t Read

Perplexity Computer produced a 22-page research report and a full data workbook in 33 minutes across roughly 140 agent tasks. It still contained 5 substantive errors.
The core doctrine: never publish research from inside the machine that produced it. Move the output to a second, different model and audit it against a written standard.
The audit runs on a skill file with 6 sections: a closed 3-tier source architecture, 4 research modes, search and paywall discipline, 3 output templates, voice rules, and 8 quality checks.
Charts get rebuilt from the raw data in your own visual identity. The final report is written in your own voice and read by you in full before anything ships.
Attached to this post: the full report this article walks through, plus the Your Research Employee workbooks in 4 editions (Claude, ChatGPT, Gemini, Perplexity) so you can build your own analyst in about an hour.

The report runs 22 pages.

It carries 12 original charts, 4 parts, 2 appendices, and a complete source register with a live URL behind every figure.

The machine research behind it took 33 minutes across roughly 140 tasks.

The verification pass that followed caught 5 errors before a single chart was drawn.

The complete process took my team of agents 65 minutes. The work would have taken me two weeks.

Anyone can get an AI research report in 2026. The tools are extraordinary and improving quarter over quarter. Which means the differentiation has moved. World-class research is no longer about access to information. It is about the discipline you wrap around the machines and agentic teams you deploy to make it happen.

This piece walks through the exact pipeline I used to produce “The Last Bubble Went Public. This One Is Staying Private,” my quantitative history comparing the dot-com bubble of 1995 to 2003 against the AI boom of 2022 to 2026. I recorded the whole build on Loom. This is the written version, plus the part the video could not show: the full anatomy of the elite research skill that runs quality control, and the workbooks for building a research employee of your own on Claude, ChatGPT, Gemini, or Perplexity.

Hope this helps you this week, friends!

- j -

If you want to become AI-fluent this summer and learn how to build a team of agents to do the work discussed in this article and so much more, you should seriously consider joining my Oper(AI)tors Summer of AI Fluency cohort.

Grab Your Seat Now!

Use code TAKE500 to receive a $500 discount on your seat.

What is the best AI stack for producing research?

Perplexity Computer collects. Claude verifies and produces the output in my primary stack. Skills encode the standards so neither step depends on my memory or my mood.

Three tools, three jobs, no overlap.

The research ran in Perplexity’s Comet browser (download today!), which is my daily browser on every machine I own. The verification, chart production, and final write-up ran in Claude Cowork sessions on Opus 4.8 and the Fable 5 model. Dictation ran through Wispr Flow, which is why I can commission a research project at the speed of speech.

Step 1: How do you brief an AI research agent like Perplexity Computer?

I opened Perplexity Computer and dictated a brief. Not a query. A brief. The distinction decides the quality of everything downstream.

Watch the Loom here.

The brief had 4 components:

The history: Deep research on the dot-com bubble and crash, from the late 1990s through the early 2000s. I want to understand the period, not skim it.
The comparison: Set that period against the current AI cycle, quantitatively and qualitatively. Startups then against startups now. Trajectories then against trajectories now.
The data demand: Come back with financial data and historical data sufficient to build charts, graphs, and visualizations. This clause is the one most people skip. If you do not ask for chartable series, you get prose, and prose cannot be re-plotted in your own brand.
The standard: Exhaustive, attributable, accurate. Every claim is sourced.

Then I left my computer for a bit, and Perplexity deployed a team of agents that worked for 33 minutes across roughly 140 discrete tasks. Watching the full run is instructive, partly because Perplexity shows which models it selects for which activities. But you do not need to watch. You need the 2 deliverables it returns: a complete quantitative and qualitative report, and an Excel workbook holding every underlying data series as chartable tables.

View the Perplexity Excel data file here.

That workbook is the asset. The report is a draft. Hold that hierarchy in your head, because the next step depends on it.

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Step 2: How do you verify AI research for accuracy?

Here is the doctrine at the center of this whole system. Never publish research from inside the machine that produced it.

Perplexity Computer is a serious research engine. The output is attributable, the writing is clean, the coverage is broad. And it still shipped me 5 errors. Not typos. Substantive, number-level errors of the kind that end up in someone’s investor memo and then in someone’s screenshot.

So the first thing I do with any Perplexity research package is take it out of Perplexity. I downloaded both files, opened a Claude Cowork session, attached them, and ran my elite research skill against the full package with one instruction: check that everything here is accurate, and show me what is not.

The audit came back (read the full audit MD file here) with a list.

The 5 that mattered most

Cisco’s crash was understated by 22 percentage points: The figure that circulates for Cisco’s dot-com decline is 66 percent. That number measures only through December 2000, which is 22 months before the actual bottom. The true peak-to-trough is 88 percent, from March 2000 to October 2002, from $79 to $9.50. The distinction changes the lesson. Being right about the technology protected Cisco’s business. It did not protect the price.
Anthropic’s valuation ladder was mislabeled: The verified sequence reads $61.5 billion, $183 billion, roughly $350 billion, then $965 billion, with the $965 billion Series H dated May 28, 2026. Versions dating that figure to November 2025 conflate it with the Microsoft-Nvidia deal, valued at roughly $350 billion. A 15x repricing in 14 months deserves correct dates.
The AI venture funding series stopped a year early: The draft ended the series at 2024, at $114 billion. The full record runs through 2025 at $202.3 billion, close to half of all venture capital deployed worldwide that year. Comparisons ending at 2024 understate the cycle. Flattering the past is the most common failure mode in bubble analysis, and it usually arrives disguised as a data cutoff.
The marquee AI IPO count was stale: The correct count is 2. Cerebras was listed on the Nasdaq on May 14, 2026, joining CoreWeave’s March 2025 listing.
The NASDAQ year-end close needed pinning: The verified 2025 monthly series puts it at 23,241.99.

I read the list, approved all 5 corrections, and instructed that all work going forward be updated against them. Every downstream chart and paragraph automatically inherited the corrected numbers. The final report states each correction openly in its methodology appendix, which is what a reader deserves and almost no AI-assisted research bothers to do.

The general principle: collect everything in one place, then verify with a different model, ideally a stronger one than the one that did the collection. Two models will not share the same blind spots. One model grading its own homework will.

This is the first layer of checking, not the last. When the full report is assembled, I read the entire thing myself. The machine audit removes the errors a human skims past. The human reader catches what no rule can encode. You need both, in that order.

Step 3: How do you rebrand AI-generated charts in your own visual identity?

The research you publish should look like yours. Perplexity’s charts are competent and generic. So the next instruction to Claude: take my personal brand skill, take every data series from the workbook, and re-create every chart plus any additional visualizations the data supports.

Claude returned 12 charts. Each one is rendered in my visual identity, the Forest Ink field register, as an individual PNG, plus a single PDF holding all 12, plus an HTML index page for reviewing the full set in one scroll. NASDAQ’s 6.2x five-year multiple. The casualty table shows that the survivors still fell to 88 percent. The private-marks ladder no public index can show you. The 280-versus-2 IPO chart encapsulates the entire thesis in a single image.

View all twelve charts here.

The proportion and consistency across the set is the point. A reader cannot articulate why a report feels institutional. Uniform chart grammar is most of the answer.

Two practical notes. First, this only works because the data came back as chartable tables in Step 1. Second, keep the individual PNGs. You will reuse single charts in newsletters, decks, and posts for months. The report is one container for the charts, not the only one.

Step 4: How do you turn AI research into a publishable report?

Final instruction: produce the complete research report, including the charts, written in the Operating voice. Clearly written, professionally written, minimal emotion. Tell the story, walk the reader through the numbers, let the two periods of history do the arguing. Return it as a branded PDF.

It came back as a 22-page document: executive summary, 4 parts, a methodology appendix listing the corrections, and a full source register. Some charts embedded in the first PDF pass imperfectly. This does not bother me. I had Claude produce a Markdown version I could format and embed cleanly myself. Expect this. The last 5 percent of production is still manual, and pretending otherwise is how polished-looking errors ship.

Then the human read. The whole report, my eyes, before anything publishes. The machines compress 2 weeks of analyst work into an afternoon. They do not remove the afternoon.

The finished report. 22 pages, 12 charts, 4 parts, every correction stated in Appendix A, the complete source register in Appendix B.

What goes inside an elite research skill for Claude?

Everything above depends on one file. The elite research skill is a document I have built and tweaked over roughly 6 months, and it is the difference between asking Claude to “check this research” and running an institutional-grade audit with repeatable standards. A skill, mechanically, is just a structured instruction file the model loads when the task calls for it. The leverage is in what the file encodes.

Mine has 6 sections. Here is what each one does and why it exists.

1. What sources should an AI research skill be allowed to cite?

The skill’s foundation is a closed list of approved sources, organized in 3 tiers. Tier 1 is elite publications: the New York Times, the Financial Times, the Wall Street Journal, the Economist, Harvard Business Review, MIT Technology Review, and MIT Sloan Management Review, each listed with its domain and its specific strength. Tier 2 comprises academic research centers: Harvard, Stanford, Princeton, Yale, Chicago Booth, and LSE, each with named search targets, such as specific working paper series. Tier 3 is institutional investment research: Goldman Sachs, Morgan Stanley, Bridgewater, Blackstone, AQR, BlackRock, JP Morgan, plus the top VC firms, a16z, Sequoia, Benchmark, Bessemer, and their peers, with a rule to pull published essays and market maps rather than portfolio announcements.

The operative word is exclusively. The skill instructs the model to cite nothing outside the architecture unless I explicitly ask. This single constraint kills the SEO-farm citation problem before it starts. Most AI research quality problems are source-selection problems wearing a disguise.

Every finding then carries 1 of 4 credibility tags: [PEER-REVIEWED] for Tier 2 academic work, [INSTITUTIONAL] for Tier 3 research, [EDITORIAL] for Tier 1 reporting, and [OPINION] for op-eds and commentary, which must still come from approved sources. I can scan a brief and weigh every claim by the tag beside it.

2. How does the skill match research depth to the task?

The skill defines 4 modes, each with trigger phrases so the model selects the right one from how I naturally talk, each with its own numbered method.

Thesis Research activates on phrases like “I’m writing about” or “I want to argue that.” Its method: restate the thesis in 1 sentence, find supporting evidence, then deliberately hunt complicating evidence, then historical parallels, then 2 to 3 quotable data points, then any disagreements between sources. The output emphasis is narrative ammunition for an article.

Market Intelligence activates on client names and phrases like “competitive landscape.” It works through recent Tier 1 coverage, institutional research, relevant academic work, then risks and opportunities not yet in mainstream coverage. Output emphasis: decision-ready intelligence for advisory work.

Trend Scanning activates on “what are the smartest people saying about” and similar. It restricts to the last 30 to 90 days, then maps consensus, divergence, and the narrative that is forming but has not crystallized. Output emphasis: compressed, newsletter-ready signal.

Deep Dive activates on “comprehensive research” or “everything on.” Broad search, findings organized by sub-theme, a timeline where the topic has one, the key debates mapped, the gaps named, and a meta-narrative synthesized at the end. This is the 10-plus-source mode, and it is the one the dot-com project ran in.

The modes matter because “research this” is not one task. It is at least 4 tasks with different depths, different recency requirements, and different outputs. Naming them lets the file route the work.

3. How should AI research handle search queries and paywalled sources?

The skill specifies exact search patterns, site: operators per source, working-paper queries for the academic tier, and firm-name patterns for institutional research. It sets a floor of 5 searches per request, more for Deep Dive. It mandates recency within 12 months unless the topic is historical.

It also contains the section I consider non-negotiable: paywall discipline. Most Tier 1 sources are paywalled. The skill instructs the model to attempt the full fetch, and when blocked, to extract only what is actually visible, provide the direct URL for me to open through my subscriptions, and tag the finding [PAYWALLED — link provided]. The controlling rule is on one line: never assume content is behind a paywall. Only report what you can actually see. Fabricated paraphrases of paywalled articles are among the most common and least detectable AI research failures. This section is the immune response.

4. What output formats should AI research produce?

Three fully specified templates, chosen by destination. Format A, the Research Brief, feeds articles: thesis, key findings with tags and full citations, complicating evidence, historical parallels, quotable data points, source disagreements, narrative hooks, and a list of paywalled sources to review. Format B, the Advisory Intelligence Memo, feeds client work: executive summary, market context, a key-intelligence table where every row pairs a finding with its source, credibility tag, and a “so what” implication, then competitive landscape, risks and blind spots, and recommended discussion points for the client call. Format C, the Signal Pack, feeds the newsletter: the signal in 1 paragraph, top insights at 2 sentences each, where the smart money disagrees, the emerging narrative, and a prioritized reading list.

Templates sound bureaucratic until you run without them. Unstructured research output is a wall of prose you excavate. Structured output is a component you install.

5. Why should AI research output never hedge?

The skill enforces the Operating voice on the research output itself. Declarative sentences, no hedging, active voice only, findings stated as facts. “Goldman’s research shows X,” never “Goldman’s research seems to suggest X.” A banned-word list that includes “perhaps,” “arguably,” and “it seems.” The reasoning: a research brief is not a neutral academic document. It is intelligence, written with an operator’s eye for what matters. Hedged findings defer the judgment work to future me, and future me is busy.

6. How do you quality-check AI research before delivering it?

The skill closes with 8 verification items the model must clear before delivering anything: every finding cites a specific approved source, every finding carries a tag, nothing off-architecture slipped in, paywalled items are flagged with URLs, the format matches the destination, the voice complies, source disagreements are surfaced rather than smoothed over, and the output is actionable rather than merely informational.

That 7th check deserves a sentence. Models are consensus machines. Left alone, they average conflicting sources into a smooth, confident, wrong middle. Forcing disagreements to the surface is where the most interesting angles live. Goldman versus Bridgewater is an article. Their average is nothing.

The skill also declares its integrations, it feeds my meeting-intelligence pipeline, hands off to my brand system for visuals, and supplies the source architecture when my contrarian filter needs counter-evidence. Skills compound when they reference each other. A lone skill is a tool. A connected set is a staff.

How do you build your own AI research employee?

You do not need 6 months to get a working version of this. With my Oper(AI)te co-instructor, Wessal Khader, I have built the whole construction process into a set of workbooks called Your Research Employee, attached to this post in 4 editions:

Each workbook is a file that an AI can run. You upload it, paste 1 kickoff prompt, and the AI walks you through the build as a guided interview, 1 question at a time. No fields to fill in by hand. The build takes about an hour across 4 steps:

The Research Standard, about 20 minutes: Nine questions that force specificity: the decisions your research actually feeds, the output shape you need, when a fast answer suffices versus a deep dig, which sources you trust on sight, which are noise to you, your domain, and whether you want a recommendation or evidence laid out for your own call. The test of a good standard is that it names a real decision. A brief with no decision behind it is a Wikipedia page.
The Source Rules, about 15 minutes: The side nobody teaches. The workbook ships with a measured floor built from published research on how AI research fails, including the Columbia Tow Center finding that 8 AI search engines got the source wrong more than 60 percent of the time across 1,600 queries. On top of that floor sit 9 banned research habits, uncited claims, single-sourcing, stale data stated as current, marketing repeated as fact, and 5 more, each paired with the positive rule that replaces it.
Sources and Assembly, about 15 minutes: Your trusted-source list, your internal documents, and your question workflow get tested and assembled into 1 paste-ready analyst profile: standard, rules, sources, mode, output format.
The Source Audit, about 10 minutes: A repeatable protocol for scoring any brief against a fixed table. 0 to 1 violations are decision-grade. 2 to 4 means fix the claims and update your rules tonight. 5 or more is a guess, wearing a citation. Each workbook closes with a swipe file of 6 prompts for daily operation, including my favorite, the test drive: make the analyst research a question you already know the answer to, then grade it against reality.

The 4 editions exist because the 4 platforms do this job differently. Claude is the only one that wires your private data in as sources through connectors, and it thinks the longest before answering. ChatGPT produces the strongest long-form synthesis and is the most confident when wrong. Gemini brings the biggest context window and the tightest Google Docs handoff. Perplexity tested most accurately on citations and holds a standing persona most loosely. No edition is best at everything. Pick the edition for the machine you live in, and if you run several, the steps are the same on each.

These are the files we will be using in Oper(AI)te this Summer, starting this Tuesday, the 21st of July

The Claude Edition: The only edition that connects your private data as sources through connectors.

The ChatGPT Edition: Built around Deep Research and Memory.

The Gemini Edition: Built around Deep Research with Google grounding and Workspace.

The Perplexity Edition: Built around Research mode, Labs, and Spaces.

Grab Your Seat Before They're Gone!

What should you do with this workflow this week?

The pipeline, compressed: commission research with a real brief that demands chartable data. Move the output to a second, different model and audit it against a written standard before you trust a number. Rebuild the visuals in your own identity. Produce the final document in your own voice, then read every word yourself before it ships.

This week, run 1 piece of it. Take any research you are currently relying on, drop it into a different model from the one that produced it, and ask for an audit against the workbook’s source rules. Count the violations. The number will tell you whether you have research or a guess with a citation.

And if you want to build the full system live, this is exactly what we teach in Oper(AI)te, the Summer of AI Fluency cohort I am running with Wessal Khader. Six live builds: a research analyst, a voice employee, a meetings employee, a visual brand, wired into the tools you already use. We start Tuesday, July 21. Through the deadline, code TAKE500 takes $500 off, $1,999 instead of $2,499. Details at unfazedfounder.com/operaite.

Grab Your Seat Before They're Gone!

The tools will keep improving without your permission. The standards will not.

Frequently Asked Questions

Can you trust AI research from Perplexity without verifying it?

No. Perplexity Computer is the strongest collection engine I use; every claim arrives attributed, and it still shipped 5 substantive errors in this single report, including a 22-point understatement of Cisco’s crash and a mislabeled valuation ladder for Anthropic. Attribution is not accuracy. Audit everything that feeds a decision or a publication.

Why should you verify AI research with a different model than the one that produced it?

Two models do not share the same blind spots. A model auditing its own output tends to re-approve its own errors, because the same weights that produced the mistake evaluate it. Collect in one place, then verify in another, ideally with a stronger model than did the collection.

Which AI is best for research: Claude, ChatGPT, Gemini, or Perplexity?

They do different jobs. Claude is the only one that reads your private data as sources through connectors, and it thinks the longest before answering. ChatGPT produces the strongest long-form synthesis and is the most confident when wrong. Gemini brings the largest context window and the cleanest Google Workspace handoff. Perplexity tested most accurately on citations. The staff you want runs each job on the machine that does it best.

What is a Claude skill?

A skill is a structured instruction file that the model loads when a task calls for it. Mine encodes an approved source list, 4 research modes, search and paywall rules, 3 output templates, voice rules, and 8 quality checks. The file replaces my memory and my mood as the quality-control layer.

What are the most common errors in AI research?

Wrong citations delivered confidently. The Columbia Tow Center found 8 AI search engines got the source wrong more than 60 percent of the time across 1,600 queries. After that: fabricated links, stale data stated in present tense, single-sourced claims, and vendor marketing repeated as fact. Every one of these is testable, which is why the workbooks ship with a source audit.

How long does this workflow take?

The machine research took 33 minutes. The full pipeline, audit, charts, and write-up included, fit in an afternoon, plus the time it takes you to read the final report in full, which is not optional. Building your own research employee from the attached workbooks takes about 1 hour.

Do you need to know how to code to build this?

No. Every piece of this system is a document and a set of prompts. You upload a workbook, paste 1 prompt, and answer questions in plain language. The most technical act in the entire pipeline is downloading a file from one tool and attaching it to another.

About John

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

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The Most Common Lie at Work in 2026

John Brewton — Wed, 15 Jul 2026 22:35:26 GMT

Operating by John Brewton

Here is the secret almost nobody in your office or amongst your friend group is saying out loud. We are not using AI nearly as much as we pretend to, and we are not using it nearly as well as we would like the work world to think.

And that’s okay, friends.

This is one of the most common truths in the workforce and across corporates right now. The models sprinted ahead at a pace no prior technology has matched. Most people who did not start 2 years ago are quietly walking behind, overspending on tokens, prompting vaguely, and hoping nobody checks. Directors are reviewing reports that were completely generated by the models and are doing so with direct reports who can barely speak to their specifics. And the employees creating those reports are relying on models to draw conclusions from data that inaccurately communicates the company’s reality.

No shame in any of it, it’s just the simple, common truth of our times. The gap is real, but it is also the shortest, most closable gap of your career.

The space between layoff or promotion, anxiety and peace, becoming irrelevant versus becoming profoundly relevant to our companies’ futures is much smaller than we understand. Our social feeds, friendly conversations, and even late-night dreams are inundated with fearful headlines, images, and stories that we need to cast aside as we make the simple but powerful choice to become AI-fluent.

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TL;DR

Most people are using AI less and less well than they let on. So are most companies. The evidence says this is the norm, not the exception.
95 percent of enterprise AI pilots return nothing. The cause is a fluency and data gap, not weak models. (MIT NANDA, 2025)
“Workslop” is what the bluff looks like from the outside. It is a prompting-clarity problem that makes colleagues rate the sender 42 percent less trustworthy. (HBR)
The cost of a fixed level of AI performance fell by about 10 times per year, a 1,000x drop across 3 years. When the machine is nearly free, the value moves to the operator, who can aim it. (a16z)
AI raises the floor for everyone by about 14 percent on routine tasks. An uplift everyone gets is worth nothing as an edge. The edge sits at the ceiling, a 56 percent wage premium for real fluency. (Brynjolfsson, PwC)
The distance from bluffing to fluency is now measured in weeks, not years. Fluency is built, not innately gifted.

OPER(AI)TE · THE STAKES

A line is forming, and it runs straight through your industry. On one side are the people who use AI, or say they do. On the other hand, there are the people who operate it. The first group is already commoditized. The second group is getting promoted, paid, and hired to run the transformations the first group cannot finish.

Oper(AI)te exists to move you across that line before the fall. It is a live build, not a course you watch. Six sessions, a cohort, and a working staff of AI systems that you finish and keep. You can stop bluffing and get on with growing.

Join The Cohort

Use code TAKE500 to receive a $500 discount on your seat.

Why do 95 percent of enterprise AI pilots fail?

They fail on fluency, not on technology. MIT’s NANDA initiative studied enterprise generative AI and found that roughly 95 percent of pilots deliver no measurable return. The barrier was not model quality, regulation, or compute. It was a learning gap. Generic tools do not adapt to a company’s workflow; the data feeding them is too dirty to trust, and the people deploying them never learned to direct them.

Sit with what that number means for you personally. If you have been privately worried that you are the one who is behind, the evidence says otherwise. Nearly everyone is behind, including enterprises spending millions to make it look like they are not. You are not late. You are early and quietly bluffing, like almost everyone else in the building.

The same report found that internal builds succeed only a third as often as builds from vendor-purchased tools. Read casually, that sounds like an argument against building your own. It is the opposite. Internal builds fail because fluency is scarce. The operators who know how to scope, direct, and finish a build are the missing input, and they are the ones who turn a dead pilot into a working system.

The 95 percent that return nothing, against the thin margin that breaks through. The models were not the problem.

Is using AI the same as being AI-fluent?

No. Using AI is an activity. Fluency is direction. Harvard Business Review studied 2,500 employees over 8 months inside a company that already had high AI adoption. The best users did not use AI more than everyone else. They used it differently. They set ambitious objectives, treated AI as a reasoning partner rather than a task executor, and delegated complex work with clear, direct, thorough instructions.

The people struggling with AI are not failing at technology. They are failing at clarity. They prompt vaguely, burn tokens on wandering conversations, and get wandering output back. The fix is not a certification or a computer science degree. The fix is learning to say exactly what you want, with the context the model needs, in as few tokens as possible. That is a writing skill and a thinking skill that operators already possess.

The most striking finding in the study concerns measurement. Leaders track how much AI their people use because it's easy to see. They do not track how well because that number is hard to see and is the one that matters. Usage is a vanity metric. Fluency is the real one. Which is also why the bluff has survived this long. Nobody was measuring the thing that would expose it.

What is “workslop” and why is it not a character flaw?

Workslop is AI output that looks like work and shifts the real effort onto whoever receives it. Harvard Business Review found it now accounts for an estimated 15.4 percent of workplace content. Each incident costs the receiver close to 2 hours. Modeled across a 10,000-person company, the bill runs past $9 million a year.

The reputational cost is an issue. Colleagues rated the senders of workslop 50 percent less capable, 42 percent less trustworthy, and 54 percent less creative. Un-fluency is not neutral. It leaves fingerprints, and everyone around you can see them.

Workslop is not a you problem. It is a clarity problem. It is the direct byproduct of vague prompting meeting dirty data, a person who has not yet learned to direct the model, working inside a company that has not yet cleaned what the model reads. Both halves are fixable, and neither is a verdict on anyone’s intelligence. The data mess is not incompetence. It is the plumbing that needs to be fixed. The vague prompt is not a lack of talent. It is an unpracticed skill; it takes roughly 6 weeks to become competent.

OPER(AI)TE · WHAT YOU BUILD

Fluency is not something you watch. You get fluent by building real tools, on your real work, until the systems run without you narrating them. The bluff ends the first time a system you built does your actual job while you watch.

In Oper(AI)te, you build a staff of AI systems across 6 live sessions. A voice model trained on your own writing. Research and synthesis that deliver results in hours rather than afternoons. Meeting prep and first-draft decks in 45 minutes. One problem you choose, built live with the cohort. This is the same work inside John’s $50,000 to $250,000 consulting engagements, rebuilt for an individual operator.

Join The Cohort

Use code TAKE500 to receive a $500 discount on your seat.

Why does the falling cost of AI make skilled operators more valuable?

Because the scarce input ceases to be the machine and becomes the person aiming it. Andreessen Horowitz documents what it calls LLMflation. The cost of a given level of AI performance decreases by about 10x every year. GPT-3-level capability cost $60 per million tokens in 2021 and $0.06 per million tokens by 2024, a 1,000x collapse in 3 years.

Falling costs changed what AI could do. This changed who could use it. Which changed where the value sits. Most people are still arguing about which model to buy. The value already left the model and moved to the operator who can point it at the right problem, in the right way, without waste. Token efficiency is not a technical footnote. It is judgment, priced. The person who gets the right answer in 1 precise prompt is doing fundamentally different work than the person who circles it for 40.

Cost per million tokens fell from $60.00 to $0.06 in 3 years. As the machine approached free, the value rose to the operator.

Does AI give a company a lasting competitive advantage?

MIT Sloan Management Review clearly makes the argument. AI cannot deliver a sustainable advantage because access to models, data, and hardware is commoditizing in the same way access to personal computers and the internet did. Every rival will run the same models within a year.

What remains is the human layer. “AI does not change anything about the fundamental nature of sustained competitive advantage when its use is pervasive,” the review concludes. The differentiation moves to creativity, judgment, and the capacity to build. Those are the assets that are not commoditized by a vendor’s release schedule. For a company, becoming AI-native is not a purchasing decision. It is a fluency installation and workflow-reimagining event, person by person.

Does AI fluency actually raise your salary?

PwC’s Global AI Jobs Barometer puts the wage premium for AI skills at 56 percent. Roles that require those skills show pay growing 42 percent faster than roles that do not, and senior-level skills are appearing in junior AI-exposed jobs at 7 times the normal rate. Fluency is a promotable and money-making event for us operators.

The anxiety running through the workforce right now is rational. Layoffs are real, the pressure to sound confident about a technology you privately do not understand is real, and the people who were laid off and are now building consulting practices overnight are carrying both at once. The fear is rational. It is also pointed at the wrong thing. The threat was never the technology. The threat is staying a bluffer while the premium for being real compounds. The same force producing the fear is producing the widest opening most operators will ever get, and it is open right now.

The Brynjolfsson, Li, and Raymond study of 5,000 support agents found a 14 percent average productivity gain from generative AI, concentrated among the least experienced workers. AI lifts the floor for everyone. An uplift everyone receives is worth nothing as a differentiator. The premium migrated to the ceiling, to the people who can do what cheap, universal AI cannot do on its own. The distance between those two ends is now measured in weeks of deliberate building, not years of study. That is the part nobody carrying the anxiety has been told.

AI raises the floor by about 14 percent for everyone. Fluency reaches the ceiling, a 56 percent wage premium that stays scarce.

Has a technology shift like this happened before?

Twice, at least, and both times the pattern held. In 1979, VisiCalc put a spreadsheet on a personal computer. Within a decade, the tool was everywhere and nearly free. The people who captured the careers and the equity were not the ones who owned the software. They were the analysts who learned to model with it. The tool was the floor. Fluency was the ceiling.

Economic historian Paul David studied why electricity produced almost no measurable productivity gains for 40 years after its arrival. The dynamo was installed. The factories had not yet reorganized the work around it. The gain came only when the work changed, not when the machine did. MIT’s learning gap is the same finding, a century later. There is one difference this time, and it favors you. The spreadsheet and dynamo generations had to wait decades for the reorganization. This skill installs in weeks, on tools that cost less per month than a business lunch.

How do you actually become AI-fluent?

You build. Reading about AI produces the same result as reading about swimming. Start this week with 1 task you repeat every day, build an AI system that handles it end-to-end, and measure the time it takes to return. That single loop, run repeatedly, is the entire difference between using AI and operating it.

You do not need to write code. You do not need a developer. You do not need to have been early. You need clear prompting, clean inputs, and the discipline to build instead of watch. The most efficient way to destroy your own relevance is to wait for your employer to train you. The operators who close the gap first will spend the next decade running the transformations everyone else is still piloting, and they will do it without the knot in their stomach, because the confidence will be real.

What are the Oper(AI)te workbooks, and why should you never print them?

They look like documents. They behave like software. Sound Like You, the voice workbook, is the one I hand people first. It runs 21 pages, 4 steps, and about 1 hour, and the first inside page says it plainly. This is not a worksheet. Do not print it.

You upload the whole file to Claude, ChatGPT, or Gemini. The model reads it, finds the instruction block written directly to it, and turns into your interviewer. It asks 9 questions about how you actually write, pushes back when an answer is vague enough to describe anybody, collects real sentences you have written, and assembles the output. A locked voice profile. A measured prohibition list of the words and phrases that mark a draft as machine-made, each ban backed by published frequency data. A scoring audit that catches those tells in any draft you produce from that day forward.

Look at what that object is. A program, delivered as a document, that runs on whatever model you already pay $20 a month for. The document is the software. The model is the computer. Your work is the data. An operator 30 years ago would have called it a disk, and that is how to think about the full set. There is one for each segment of the course. Voice. Research. Administrative and calendar management. Brand identity. Each installs a capability into your model, then helps you direct your growing staff of agents with it.

How a workbook installs. The document goes into the model, the model interviews you through 9 questions, and a working skill comes out the other side.

Notice what the voice workbook does in the language of this article. Its entire function is to strip the machine accent out of your writing until what is left is you. It is an un-bluffing machine. You hand it to the model, answer honestly for an hour, and one of the fingerprints that exposes the bluff is gone for good.

Why does watching the build matter more than the curriculum?

Because fluency transfers by observation, not by syllabus. In the course I do not present these workbooks as finished artifacts. I build them live, from scratch, with the cohort watching. You see how I speak to the models. How I prompt, how I direct, how I tweak when the output drifts, how I adjust when a platform update changes the behavior underneath me. That layer never survives the trip into a slide deck or a recording, and it is the layer that actually makes someone fluent.

For more than 2 and a half years, all of my work has run through these models and platforms. Every capability jump, every quiet regression, every workflow rebuilt mid-quarter because a release changed what was possible, absorbed step by step because the business depended on it. The cohort compresses those spans into 6 sessions of standing next to the work as it happens. This is the oldest training method there is. The apprentice does not learn the trade from the manual. The apprentice learns it beside someone who has already made the mistakes, and asks questions while the tool is still open on the bench.

The individual Oper(AI)te cohort is $2,499. Set that number next to a 56 percent wage premium and a market where 95 percent of companies are still waiting up to 3 years for a return they may never see.
You leave with a staff of AI employees that does your real work, not a certificate that says you watched. Six build sessions. Two weekly Operator Build Labs. One cohort. A workbook for every segment, built live in front of you and installed into your own model.
Everyone is bluffing.
You no longer have to. It’s time to start building your AI-Fluent future.
Join The Cohort
Use code TAKE500 to receive a $500 discount on your seat.

FAQ

What is AI fluency? AI fluency is the ability to direct AI systems toward real work precisely, cheaply, and end to end. It is distinct from AI usage, which is measured by activity. Fluency is measured by output, by token efficiency, and by the systems you can build and run.

Is everyone really exaggerating how much they use AI? The measurable evidence points that way. 95 percent of enterprise pilots return nothing, leaders track usage instead of skill, and 15.4 percent of workplace content is AI output that pushes work onto the receiver. The gap between claimed adoption and real fluency is the defining feature of this moment.

Why do most enterprise AI projects fail? Because of a learning and data gap, not model quality. MIT found 95 percent of pilots return nothing. Generic tools do not adapt to a company’s workflow, the underlying data is too messy to trust, and most teams never build the fluency to fix either.

Is it too late to learn AI in 2026? No. The wage premium for AI skills is 56 percent and the skill requirements are still moving faster than most workers, which means the gap is open and closing. The honest starting point is admitting where you are, and nearly everyone is starting from the same place.

Will AI replace my job? AI raises the baseline for routine tasks by roughly 14 percent, which compresses the bottom of the skill curve. The durable roles belong to operators who build and direct AI, not to those who only produce the same output AI now produces for free.

What is workslop? Workslop is AI-generated output that looks like work but pushes the real effort onto the receiver. It accounts for about 15.4 percent of workplace content and makes the sender look 42 percent less trustworthy to colleagues. It is a prompting-clarity and data-quality problem, not a character flaw, and it is fixable.

What is Oper(AI)te? Oper(AI)te is a live, 6-session build program by John Brewton that teaches operators to build and direct AI systems on their own real work. Participants leave with a working set of AI tools, not a video library. The individual cohort is $2,499.

Who is Oper(AI)te for? It is built for operators. Employed managers, directors, executives, founders, and owners running lean, including people rebuilding as consultants or coaches after a layoff. It is not built for anyone looking for passive consumption or a done-for-you service.

How is Oper(AI)te different from a normal AI course? A normal course is something you watch. Oper(AI)te is a build you finish. The tools are built live from scratch in front of the cohort, so you see the prompting, directing, and adjusting that recordings never carry. Fluency comes from that, not from lessons.

What are the Oper(AI)te workbooks? They are documents designed to be uploaded into Claude, ChatGPT, or Gemini rather than printed. The model reads the workbook, interviews you, and assembles a working skill from your answers, such as a voice profile, a research system, a calendar and administrative manager, or a brand identity. One ships with every segment of the course. Sound Like You, the voice workbook, takes about 1 hour and runs 4 steps.

Appendix · Sources

MIT NANDA initiative, on the 95 percent enterprise AI pilot failure rate and the learning gap, reported by Fortune, August 2025. Link
MIT Sloan Management Review, “Why AI Will Not Provide Sustainable Competitive Advantage.” Link
Harvard Business Review, “What the Best AI Users Do Differently,” March 2026 (study of 2,500 employees over 8 months with KPMG and UT Austin). Link
Harvard Business Review, “AI-Generated ‘Workslop’ Is Destroying Productivity,” September 2025. Link
Andreessen Horowitz, “Welcome to LLMflation,” on the 1,000x decline in inference cost. Link
Brynjolfsson, Li & Raymond, “Generative AI at Work,” NBER Working Paper 31161, published in the Quarterly Journal of Economics, 2025. Link
PwC, 2026 Global AI Jobs Barometer, on the 56 percent wage premium and 42 percent faster wage growth. Link
Deloitte Insights, “AI tokens: How to navigate AI’s new spend dynamics.” Link

Note on sourcing: findings 1, 7, and 8 come from MIT NANDA, PwC, and Deloitte, which sit just outside the core elite-source tiers used for the rest of this piece. They carry the most quotable figures and are flagged here for transparency.

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

How To Stay Relevant In Our AI-First World w/ John Brewton & Wessal Khader

John Brewton — Wed, 15 Jul 2026 18:08:45 GMT

Available for iOS and Android

How to Actually Make AI Write Like You, Not Like a Robot, With the Accent of Your Favorite LLM.

John Brewton — Mon, 13 Jul 2026 14:03:21 GMT

TL → DR

Every AI model writes in a default style. Researchers can identify which model wrote a text with 99.88 percent precision, even when the model was told to write differently.
The proof has been very public. The word “delve” rose 14-fold in science papers. “Meticulous” ran at 34.7 times its normal rate in AI-edited text. At least 10 percent of 2024 medical abstracts show AI processing.
Describing your voice to the AI is not enough. Models drop instructions as you stack them, and every dropped instruction gets replaced with the default style.
The fix has 2 parts and a compendium of samples. Write a positive profile of what you do. Write a prohibition list that bans the AI’s defaults by name. Attach 3 samples of your real writing. Load it all before every task.
Each platform needs its own setup. Claude takes the full profile as a Project or saved Skill instructions. ChatGPT caps custom instructions at 1,500 characters per field, so the full profile goes in a Project. Gemini needs the samples placed right before the task.
The stakes are growing. The AI’s favorite words are now rising 25 to 50 percent a year in human speech, and 80 percent of people who wrote essays with AI help could not quote their own essays.

Want to learn how to make AI actually write in your true voice?

Register for my free masterclass, happening tomorrow.

Grab Your Free Masterclass Seat Here!

In the first quarter of 2024, the word “delve” appeared in scientific papers at roughly 14 times its historical rate. For 2 decades it had idled at about half a mention per 1,000 papers. Then it hit 7.9. Nobody called a meeting about this. No style guide changed, and no editor sent a memo asking the world’s researchers to start “delving.” A machine developed a preference, and several hundred million people carried it into the permanent scientific record, where it sits now like a watermark.

That watermark is the subject of this essay. The machine you write with has a voice of its own, and it is overwriting yours. Until you name that voice and explicitly turn it off, your instructions are negotiating with a fingerprint that was trained long before you arrived.

Most advice about AI writing never touches this. It teaches you to describe what you want, in ever more detail, and assumes the model is a blank instrument waiting for direction. The evidence says otherwise. The model arrives with commitments. You do not have a prompting problem. You have a fingerprint problem, and the fix has 2 jobs where almost everyone does 1.

The live masterclass is tomorrow. Everything in this article gets built in the room, in 1 hour: the 2-sided voice profile, the prohibition list, and the 3-machine setup for Claude, ChatGPT, and Gemini. You watch me build mine, then you build yours. The session is free.

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Why does all AI writing sound the same?

A team led by Dmitry Kobak analyzed more than 14 million biomedical abstracts and found that at least 10 percent of 2024 abstracts had been processed with a language model. In some subfields, the estimate reached 30 percent. The giveaway sat in 329 “excess words” that spiked past their projected baselines, and the composition of that list is the telling part. 66 percent were verbs, 18 percent adjectives. Vocabulary of style rather than substance.

A separate study of conference peer reviews measured the same signature from a different angle. In the AI-modified text, the adjective “commendable” appeared at 9.8 times its expected rate. “Intricate” ran at 11.2 times. “Meticulous” ran at 34.7 times. At EMNLP 2023, an estimated 16.9 percent of review sentences had been substantially modified by a model. The reviewers judging the field’s science were, in 1 of 6 sentences, letting the machine speak for them.

“Meticulous” at 34.7 times baseline, “delve” at 14, “intricate” at 11.2, “commendable” at 9.8. At least 10 percent of 2024 biomedical abstracts show LLM processing.

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The issue reaches well past academia. Stanford researchers tracked it across the wider economy through late 2024 and found it in 17.7 percent of consumer complaints, 23.8 percent of corporate press releases, 13.7 percent of United Nations press releases, and roughly 1 in 10 job postings.

A quarter of corporate communication now arrives pre-inflected. Somewhere in a training pipeline, a preference formed the way an accent forms, absorbed rather than chosen, and it is now the most widely spoken accent on earth.

How do researchers detect AI writing?

Catching an author by counting words predates the AI era by 60 years. In 1964, the statisticians Frederick Mosteller and David Wallace settled a 175-year dispute over who wrote 12 unclaimed Federalist Papers. They ignored the arguments and counted the filler. Madison wrote “whilst” where Hamilton wrote “while.” Hamilton reached for “upon” at rates Madison never approached. The invisible words convicted him. All 12 papers went to Madison, and forensic stylometry became a discipline.

The same method now works on machines, and it has become far more effective. A 2025 study trained classifiers on 200,000 texts from 4 model families and identified which model wrote a given passage with 0.9988 precision. The authors’ summary sentence deserves to be quoted in full: large language models “have distinct and consistent stylistic fingerprints, even when prompted to write in different writing styles.”

Models are trained toward human approval, and the training compresses them. Researchers have documented that reinforcement learning from human feedback reduces output diversity. The text comes out more similar, regardless of what goes in. A 2025 paper traced the cause to what it called "typicality bias." Human raters systematically reward conventional, expected phrasing, independent of quality, so the model’s probability mass collapses toward its most typical sentences. Anthropic describes the endpoint of this process plainly in its own research. Post-training, in the lab’s words, selects “one particular character from this enormous cast” and places it center stage. The character is stable. It generalizes across models. It is a house voice.

Why does AI ignore my writing instructions?

The standard response is to write better instructions. Describe the voice. Add detail. Be firm. The instruction-following literature explains why this keeps disappointing you.

On IFEval, the benchmark for objectively checkable instructions, a GPT-4 class model failed roughly 1 multi-instruction prompt in 4; these were instructions a script could verify, including word counts and required keywords. A 2025 study stacked constraints and watched compliance sag, with the best frontier models honoring about 68 percent at a density of 500 instructions. The Allen Institute for AI then ran the sharpest version of the test. Models scoring high on familiar constraints collapsed on unfamiliar ones. Claude 4 Sonnet went from 91.3 percent to 42.3. Gemini 2.5 Pro went from 65.4 to 52.3. OpenAI’s o3 went from 95.0 to 69.3.

The same 3 models on seen constraints (IFEval) and unseen constraints (IFBench). Claude 4 Sonnet drops from 91.3 to 42.3. The best frontier accuracy at 500 stacked instructions is 68 percent.

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Your tone rules are exactly the kind of unfamiliar, unverifiable constraint that decays first. When a constraint decays, the model reverts to the trained default. So the more elaborate your description of your voice, the more constraints you have queued for decay, and every decayed constraint is refilled with house style.

There is a second finding that matters even more for the fix we are all seeking. Models exhibit what MIT researchers call affirmation bias. They under-process negation and instead attend to the named items, with performance in the underlying study dropping by nearly 25 percent when statements were negated. The study was conducted on vision-language models. But the direction of the finding aligns with every operator’s experience with text models. A default you have not named stays on. A default you have named weakly also stays on. Suppression has to be explicit. It has to be specific. And the machine needs to hear it more than once, because its reflex is to register the noun and miss the “never.”

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What is a 2-sided voice profile?

The first side of a working voice profile is the positive profile, the description of what you actually do. Your sentence rhythm. Your openings and your closings. Whether you lead with the conclusion or build to it. Your punctuation habits. How your register shifts between a cold email and an internal memo. Most people who take AI writing seriously have built some version of this. It is necessary, but by itself it does almost nothing about the fingerprint because it never mentions the fingerprint.

The second side is the prohibition list, the explicit inventory of what the model must never default to. The negation reflex, “it’s not X, it’s Y,” which the machine deploys with the regularity of a metronome. The rule of three, triplets in every sentence, makes it feel complete. The vocabulary you now know by heart: “delve,” “robust,” “seamless,” “testament,” “pivotal,” “underscore.” The grand contextual opener that clears its throat for a paragraph before saying anything. Signposting. Paragraphs of eerily uniform length. Each of these is a measured, documented default, and each one stays on until it is named and switched off. The complete inventory, with the measured effect sizes, sits in the appendix at the end of this piece.

The 2-sided architecture. Positive profile, prohibition list, and 3 few-shot anchors, loaded together before the task.

You can spend an hour describing your voice in exquisite detail, and the model will still hand you “delve” and a triplet, because you described what you want and never prohibited what it does. Both sides need to load together, or the calibration fails.

Do writing samples work better than instructions?

The third component outperforms both descriptions.

Show the model your actual writing.

The finding is as old as the modern field. The 2020 GPT-3 paper demonstrated that models can perform new tasks from a handful of examples in the prompt, with no retraining required, and quantified the gap. On 1 benchmark, accuracy went from 64.3 percent with instructions alone to 71.2 percent with a few examples. Later work sharpened the reason in a way that matters for voice. When researchers deliberately corrupted the content of in-context examples, performance barely moved. What the model was absorbing was the shape. The distribution of the text, the format, and the rhythm of how input becomes output. Which is to say, examples convey exactly what you cannot fully put into words about your own prose.

Anthropic’s documentation calls examples “one of the most reliable ways to steer Claude’s output format, tone, and structure.” OpenAI’s guidance compresses it to 3 words: “show and tell.”

This should only take 10 to 15 minutes. Pick 3 of your best pieces of writing in different registers: 1 email, 1 memo or post, and 1 longer piece. These are your few-shot anchors, your voice in its native format.

How do I set up a voice profile on ChatGPT, Claude, and Gemini?

Here is where nearly all AI writing advice goes soft. It claims to work on any model, which is precisely why it works well on none of them. The machines differ, and the differences are documented. A classifier separates their prose at 0.9988 precision. The same voice profile, loaded identically, produces 3 different drifts.

Claude honors explicit prohibitions most faithfully, and its failure mode is over-compliance. Strip out enough defaults and it can go flat. The fix is to pair each major prohibition with 1 positive rule. Avoid signposting, but open with strong declaratives. Load the full profile, positive side first, prohibition list second, anchors third, as per Project instructions, so every conversation inherits it.

ChatGPT’s default is agreeable polish. It will sand your fragments and finish your abrupt endings unless you name them as features. Its custom-instructions fields cap at 1,500 characters each, so the play is a condensed rule set there and the full profile with anchors as the first message in a Project, where it persists.

Gemini over-formalizes, an elevation reflex more pronounced than the other 2, and it weights recent context heavily. Put the profile in a Gem’s instructions, place the examples immediately before the task rather than burying them early, and give the prohibition list an explicit register rule. Match the examples, not what reads as professional. The position advice has empirical teeth. Language models reliably privilege the beginning and end of their context window over the middle, and Anthropic’s own docs report that placing the query after long context improves response quality by up to 30 percent.

1 profile, 3 platforms. Claude over-complies, ChatGPT polishes, Gemini formalizes. Each drift has a 1-adjustment fix and a placement rule.

The differences are proof that voice calibration is a technical discipline rather than a mindset, and that platform-agnostic advice stays shallow for that reason.

Is AI changing how people write and speak?

The strangest finding in this literature concerns your speech rather than your drafts.

Researchers at the Max Planck Institute for Human Development analyzed 360,445 academic talks on YouTube and 771,591 podcast episodes, roughly 7.35 billion spoken words. In the 18 months after ChatGPT launched, the words the model favors- “delve,” “realm,” “meticulous,” “underscore,” rose in unscripted human conversation at annual rates of 25 to 50 percent. The patterns stored in the technology “seem to be transmitting back to the human mind.”

Machine-favored words are rising 25 to 50 percent yearly in 7.35 billion words of human speech. 80 percent of AI-assisted writers could not quote their own essays.

The written evidence points the same way, and it is peer-reviewed. A Science Advances study gave 300 writers AI-generated story ideas and had roughly 600 evaluators judge the results. Individual stories got better, especially from the weaker writers. The collection got worse. Stories written with AI help were measurably more similar to one another than stories written without it. Each writer improved into the same writer. And the pull has a direction. A Cornell study of American and Indian writers using AI autocomplete found the suggestions steadily moved Indian participants toward Western styles, which means the default is 1 register, exported, rather than an average of everyone.

The New Yorker’s Kyle Chayka compressed the phenomenon into 5 words. AI, in his phrase, is “a technology of averages.” The MIT study he reported carried the detail that should bother you most. Of the subjects who wrote essays with ChatGPT’s help, 80 percent could not quote from what they had putatively written.

The professional class spent the last 2 years paying monthly subscriptions to sound like each other. The tool was sold as amplification and adopted as ventriloquism, and most users never noticed the transfer because the prose kept arriving fluent and finished. The fluency we were purchasing was cheap. What belongs to the machine is “finished,” and increasingly, the memory of having written at all.

Can AI detectors prove a text was written by AI?

The word-level tells are aggregate signals, not individual proof. AI detectors built on them misclassified 61.22 percent of essays by non-native English speakers as machine-written in a 1 study at Stanford. A single “delve” convicts nobody. The mechanism behind the vocabulary is also unproven. The popular theory blaming annotation workers’ dialects was tested directly and found unsupported. And the fingerprints move. One 2025 analysis found that DeepSeek’s prose was classified as OpenAI’s 74.2 percent of the time, which suggests the house styles can converge as labs train on similar data. The prohibition list you write this year will need an audit next year.

How do I make AI write in my voice this week?

What to do this week…

Pull your last 5 AI-assisted drafts and audit them against the tells in Figure 1.

Count the negation structures, triplets, marker words, and signposts.

This count is your baseline.

Write the positive profile in 1 sitting. Rhythm, openings, escalation, punctuation, argument order, register shifts. Keep it to one page max.

Write the prohibition list from your audit. Name every default you found, specifically. Not “avoid AI-sounding language” but “never use ‘delve,’ never construct ‘it’s not X, it’s Y,’ never signpost.”

Choose your 3 anchors and load all of them together before the task, on whatever machine you use, configured the way that machine requires.

Then track your edits for a week. Every word you swap and every sentence you reorder is data on a rule the profile still lacks. The gap between what the model gave you and what you shipped is your voice, captured in real time.

In the live session, we build both sides in the room, run the same profile through Claude, ChatGPT, and Gemini side by side, and fix each machine’s drift as it appears. Bring the 3 anchors.

What words and patterns give away AI writing? The full prohibition list.

The difference between a writer’s reframe and the machine’s reflex is about choice, and frequency. Use the pattern once, on purpose. Without clear directoin, the machine uses it 6 times a page. The same goes for every entry below. Possession of a single marker creates little impact, and detectors built on these markers falsely flagged 61 percent of essays by non-native English speakers. The tell most always lives in the presence of the same move, arriving again and again.

The markers fall into 2 evidentiary classes:

The vocabulary is measured, with effect sizes from studies covering more than 14 million documents.
The structures are documented by detection researchers and linguists, who observe rather than quantify them.
Load all 4 sections into your AI as explicit prohibitions, then pair each structural prohibition with 1 positive rule from your own writing.

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Which words does AI overuse?

The 15 measured word families. “Delves” grew 6,697 percent, 2020 to 2024.

The core cluster, with effect sizes. Growth figures measure frequency change in scientific writing, from 2020 to 2024 (Juzek and Ward). Fold-increase measures overrepresentation in AI-modified peer reviews (Liang et al.). Frequency ratios measure excess above baseline in 14 million PubMed abstracts (Kobak et al.).

delve / delves / delved / delving. “delves” grew 6,697 percent. The flagship marker.
underscore/underscores/underscoring. “underscores” grew 904 percent.
showcase/showcasing/showcases. “showcasing” grew 1,396 percent.
meticulous / meticulously. 34.7-fold increase in AI-modified reviews.
intricate / intricacies. 11.2-fold increase. “Intricacies” grew 773 percent.
commendable. 9.8-fold increase.
boasts. Grew 918 percent.
comprehending. Grew 899 percent.
surpass/surpasses/surpassing. Grew 667 percent.
garnered. Grew 437 percent.
emphasizing. Grew 397 percent.
realm. Grew 381 percent.
groundbreaking. Grew 330 percent.
advancements. Grew 278 percent.
aligns. Grew 267 percent.

The documented adjective set, from the same studies’ extended lists: notable, versatile, noteworthy, invaluable, pivotal, potent, ingenious, laudable, lucid, cogent, methodical, profound, refreshing, intriguing, admirable, exceptional, remarkable, seamless, holistic, insightful, comprehensive, compelling, unprecedented, cohesive, tangible, multifaceted.

The documented adverb set: notably, aptly, methodically, compellingly, impressively, undoubtedly, undeniably, seamlessly, effortlessly, robustly, thereby, hitherto, herein, additionally, subsequently, crucially, markedly, judiciously.

A composition note: Of the 329 excess words of 2024, 66 percent were verbs and 18 percent were adjectives. The fingerprint lives in how the machine says things. Content words like “potential,” “findings,” and “crucial” also rose and remain weak signals on their own since any writer covering research reaches for them.

Which phrases give away AI writing?

“It’s not just X, it’s Y” and every variant.
“In today’s fast-paced world.”
“In the ever-evolving landscape of.”
“Plays a pivotal role in.”
“Serves as a testament to.”
“Rich tapestry.” “
Navigating the complexities of.”
“Unlock the potential of.”
“At the intersection of.”
“In the realm of.”
“A delicate balance between.”
“It’s important to note that.”
“It’s worth noting that.”
“Let’s dive in.”
“Let’s delve into.”
“In conclusion” and “Ultimately” are closing moves.
“Whether you’re a beginner or an expert.”
“Underscores the importance of.”
“Sheds light on.”
“The key takeaway.”
Stacked hedges like “could potentially.”
And every trace of the eager assistant: “Great question.”

Which sentence structures does AI default to?

The antithesis construction: “This isn’t X. It’s Y.” Deployed several times per page.
The rule of three: Nouns, adjectives, and clauses arriving in threes, in every sentence that wants to feel complete.
The participial tail: A clause bolted on with an “-ing” verb: “...showcasing its versatility,” “...underscoring the need for further research.” 5 of the 21 fastest-growing marker words are exactly these participles.
The em dash cascade: Several per paragraph, doing work commas and periods should do.
Uniform sentence length: Human writing mixes 4-word sentences with 40-word sentences. Machine writing hums at a steady cadence.
The balanced concessive: “While challenges remain, the opportunities are significant.” Every tension is neutralized in a single sentence.
Elevated formality throughout: No contractions, no slang, no fragments, even where a human would relax.
The signposted transition: “Additionally,” “Furthermore,” “Moreover,” “Notably” at the head of paragraphs after paragraphs. Several of these adverbs appear on the lists of measured overuse.

Which document habits give AI away?

The throat-clearing open: A paragraph of context before anything is said.
Uniform paragraph length: Every paragraph has 3 to 4 sentences; the whole page is a solid gray.
The scaffolded essay: First, second, third, finally, whether or not the material has that shape.
Bolded lead-in bullets: Lists where every item opens with a bolded phrase and a colon, much like this section, but it’s how I like to construct sections like this, so I do it anyway.
The both-sides shuffle: Every claim balanced by its counterclaim, no position taken.
Over-explanation: Restating the question, summarizing what was just said, announcing what comes next.
The tidy moral: A closing paragraph that resolves everything and lands on an uplifting generality.
Completion compulsion: Nothing left open, every thread tied, even when the honest answer is unresolved.

Frequently asked questions

Why does my AI writing sound like AI?

Every model is trained toward the answers human raters prefer, and raters reward conventional phrasing. That training compresses the model into 1 default style, the same vocabulary and sentence shapes for every user. Your instructions sit on top of that default. They soften it. They do not remove it until you ban its parts by name.

What words does AI overuse?

The measured core: delve, underscore, showcase, meticulous, intricate, commendable, boasts, comprehending, surpass, garnered, emphasizing, realm, groundbreaking, advancements, and aligns, plus their inflections. “Delves” grew 6,697 percent in scientific writing between 2020 and 2024. The full list, with phrases and structures, is in the appendix above.

How do I stop ChatGPT, Claude, or Gemini from sounding like AI?

Build a voice profile with 2 sides. Side 1 describes what you do: your rhythm, your openings, your punctuation. Side 2 bans the machine’s defaults by name: the words, the “it’s not X, it’s Y” reflex, the rule of three, the signposting. Attach 3 samples of your real writing. Load all of it before the task, every time.

Do writing samples work better than written instructions?

Yes. The foundational GPT-3 research showed accuracy rising from 64.3 to 71.2 percent on the same task when examples were added. Later work found that examples transfer format and rhythm, the parts of a voice you cannot fully describe. Anthropic calls examples “one of the most reliable ways to steer” tone. OpenAI’s version is “show, and tell.”

Can AI detectors prove a text was written by AI?

No. The word-level tells are reliable across millions of documents and unreliable on any single one. In 1 Stanford study, detectors falsely flagged 61.22 percent of essays by non-native English speakers as AI-written. A single “delve” convicts nobody. The tell is the pattern repeating across a whole body of text.

Is the same voice profile enough for every AI platform?

The words are the same. The setup is different. Claude takes the full profile as Project instructions and needs each ban paired with a positive rule. ChatGPT caps custom instructions at 1,500 characters per field, so the condensed rules go there and the full profile goes in a Project. Gemini weights recent context heavily, so the samples go right before the task.

Is AI changing how people talk?

The early evidence says yes. Max Planck researchers analyzed 7.35 billion words of podcasts and talks and found the machine’s favorite words rising 25 to 50 percent a year in unscripted speech after ChatGPT launched. The study is a preprint, so treat it as a strong early signal rather than settled fact.

How long should a voice profile be?

Short enough to survive. Instruction-following decays as rules stack, so a bloated profile fails faster. Target 1 page for the positive side, a named prohibition list, and 3 writing samples. Fewer, harder rules beat many soft ones.

Build it live, tomorrow.

This article gives you the doctrine. The masterclass gives you the working setup.

Tomorrow, live, we will build every piece from this article in 1 hour. Your positive profile. Your prohibition list, drawn from the measured word lists above. Your 3 few-shot anchors. Then we load the same profile into Claude, ChatGPT, and Gemini side by side and fix each machine’s drift as it appears, so you leave with your voice running on whatever AI you already use.

You do not need a technical background.

You need 3 pieces of your own writing and 1 hour.

The session is free.

Research appendix: every source in this article.

The measured tells

Kobak, González-Márquez, Horvát and Lause, “Delving into LLM-assisted writing in biomedical publications through excess vocabulary,” Science Advances 2025. 14M+ PubMed abstracts, the 329 excess words, the 10 percent figure. arxiv.org/abs/2406.07016
Liang et al., “Monitoring AI-Modified Content at Scale,” ICML 2024. The peer-review fold increases: meticulous 34.7, intricate 11.2, commendable 9.8. arxiv.org/abs/2403.07183
Liang et al., “The Widespread Adoption of Large Language Model-Assisted Writing Across Society,” Stanford HAI / Patterns 2025. The economy-wide adoption rates. arxiv.org/abs/2502.09747
Juzek and Ward, “Why Does ChatGPT ‘Delve’ So Much?,” COLING 2025. The 21 focal words with growth rates, and the tested-and-rejected annotator theory. arxiv.org/abs/2412.11385
Shapira, “Delving into ‘delve,’” 2024. The 14-fold rise of the flagship word. pshapira.net/2024/03/31/delving-into-delve
Gray, “ChatGPT ‘contamination’: estimating the prevalence of LLMs in the scholarly literature,” 2024. The scholarly keyword growth figures. arxiv.org/abs/2403.16887

The fingerprint and its mechanism

Bitton, Bitton and Nisan, “Detecting Stylistic Fingerprints of Large Language Models,” 2025. The 0.9988 precision result and the DeepSeek convergence finding. arxiv.org/abs/2503.01659
Kirk et al., “Understanding the Effects of RLHF on LLM Generalisation and Diversity,” ICLR 2024. Post-training reduces output diversity. arxiv.org/abs/2310.06452
Zhang, Yu, Chong et al., “Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity,” 2025. Typicality bias as the cause. arxiv.org/abs/2510.01171
Anthropic, “The Assistant Axis: Situating and Stabilizing the Character of Large Language Models,” 2026. The default character, from the lab itself. anthropic.com/research/assistant-axis
Mosteller and Wallace, “Inference in an Authorship Problem,” Journal of the American Statistical Association, 1963. The Federalist Papers attribution. jstor.org/stable/2283270

Why instructions decay

Zhou et al., “Instruction-Following Evaluation for Large Language Models” (IFEval), 2023. The 1-in-4 prompt failure figure. arxiv.org/abs/2311.07911
Jaroslawicz et al., “How Many Instructions Can LLMs Follow at Once?,” 2025. 68 percent at 500 stacked instructions. arxiv.org/abs/2507.11538
Pyatkin et al., “Generalizing Verifiable Instruction Following” (IFBench), Allen Institute for AI, 2025. The seen-versus-unseen collapse: 91.3 to 42.3. arxiv.org/abs/2507.02833
Alhamoud et al., MIT, on affirmation bias and negation, 2025. Why prohibitions must be explicit. news.mit.edu

Why examples beat descriptions

Brown et al., “Language Models are Few-Shot Learners,” NeurIPS 2020. 64.3 to 71.2 percent with examples added. arxiv.org/abs/2005.14165
Min et al., “Rethinking the Role of Demonstrations,” EMNLP 2022. Examples transfer format and structure, corrupted labels barely matter. arxiv.org/abs/2202.12837
Liu et al., “Lost in the Middle: How Language Models Use Long Contexts,” TACL 2024. Position in the context window changes what the model uses. arxiv.org/abs/2307.03172

Platform documentation

Anthropic, multishot prompting guidance. Examples steer tone and structure. platform.claude.com
Anthropic, prompting best practices. The query-at-the-end 30 percent figure. platform.claude.com
OpenAI, prompt engineering best practices. “Show, and tell.” help.openai.com
OpenAI, custom instructions for ChatGPT. The 1,500-character limit. help.openai.com
Google, tips for creating custom Gems. support.google.com

The spread into human writing and speech

Yakura et al., Max Planck Institute for Human Development, on LLM-favored words in spoken language, 2025, preprint. 7.35B words, 25 to 50 percent yearly growth. arxiv.org/abs/2409.01754
Doshi and Hauser, “Generative AI enhances individual creativity but reduces the collective diversity of novel content,” Science Advances 2024. science.org
Agarwal, Naaman and Vashistha, on AI suggestions homogenizing writing toward Western styles, CHI 2025. arxiv.org/abs/2409.11360
Chayka, “A.I. Is Homogenizing Our Thoughts,” The New Yorker, 2025. “A technology of averages” and the MIT 80 percent figure. newyorker.com

The limits of detection

Liang et al., “GPT detectors are biased against non-native English writers,” Patterns 2023. The 61.22 percent false-positive figure. cell.com

About John

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

The Five Founder Problems I Discovered Coaching +100 Substack Founders in 2026

John Brewton — Fri, 10 Jul 2026 11:10:10 GMT

TL → DR

400 hours of coaching Substack founders in 2026 surfaced 5 recurring problems: positioning, packaging, pricing, distribution, and AI.
Underneath all 5 sits 1 cause. A decision the founder will not make. The writing is never the constraint.
Positioning: pick the 1 problem you own. Companies that define a category capture roughly 76 percent of its value.
Packaging: productize the expertise. Service businesses trade at around 1 to 2 multiples, product businesses at around 6 to 8.
Pricing: attention is not revenue. Price to the reader’s outcome and put an end date on free.
Distribution: the email list is the only audience you own. Grow on 1 channel and convert to the list.
AI: the machine makes you roughly 40 percent faster and measurably more alike. Decide task by task what you draft and what only you write.

What you get

One year in the Operating Founder Community: A working room of founders, operators, and writers building real businesses. You bring the problem. The room and I will help you decide.
The weekly Q&A Masterclass: 52 live sessions, 60 minutes each, one a week for a year. Bring one problem. Leave with the decision and the next action. That is 52 hours of live coaching over the course of the year.
Three golden tickets: $1,000 off any course you choose in the next 12 months. Three tickets, $3,000 in course credit, yours to spend on the work that moves you.

Become An Operating Founder

What did 400 hours of coaching Substack founders reveal?

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

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

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

Here are the 5 conundrums these problems present.

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

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

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

Go deeper:

The Difference Between a First Mover and a Category Creator (Harvard Business Review, 2019). The category-king research behind the 76 percent figure. Category creators capture the dominant share of a category’s market value.
Why It Pays to Be a Category Creator (Harvard Business Review, 2013). Category creators in the study grew revenue and market cap far faster than their industries. The business case for owning 1 problem.

Download The Five Problems Worksheet

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

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

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

Go deeper:

Putting Products into Services (Harvard Business Review, 2016). The definitive playbook for converting labor-based expertise into productized offerings, with named cases from professional services firms.
What Professional Service Firms Must Do to Thrive (Harvard Business Review, 2021). A framework for which client work to keep premium and which to systematize.

Download The Five Problems Worksheet

Subscribe now

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

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

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

Go deeper:

A Quick Guide to Value-Based Pricing (Harvard Business Review, 2016). The mechanics of pricing to the buyer’s outcome and next-best alternative instead of your effort.
The Creator Economy Needs a Middle Class (Li Jin, Harvard Business Review, 2020). The structural analysis of why audience size and creator income diverge, with platform-level data.

Download The Five Problems Worksheet

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

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

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

Go deeper:

Pipelines, Platforms, and the New Rules of Strategy (Harvard Business Review, 2016). The canonical explanation of who controls demand on a platform and who captures the margin.
1,000 True Fans? Try 100 (Li Jin, Andreessen Horowitz, 2020). The economic argument that a small directly-owned audience paying real prices beats a large rented one.

Download The Five Problems Worksheet

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

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

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

Protect your judgment. Keep your voice.

Hand over the production speed to the models.

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

Grab Your Free Masterclass Seat

Go deeper:

Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence (Noy and Zhang, Science, 2023). The randomized MIT experiment behind the numbers: 40 percent faster, 18 percent higher quality.
Generative AI Enhances Individual Creativity but Reduces the Collective Diversity of Novel Content (Doshi and Hauser, Science Advances, 2024). The homogenization evidence: AI-assisted work rated more creative individually and measurably more alike collectively.

What is your one problem underneath all five?

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

Most of the founders I coached were not overwhelmed.

They were stalling and calling it overwhelm.

It’s time to move beyond overwhelm.

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

What’s your pick?

- j -

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

What you get

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

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

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

The math

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

Who is this for?

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

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

Become An Operating Founder

Download The Five Problems Worksheet

Frequently asked questions

How many subscribers do I need before I start charging?

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

Will niching down shrink my audience?

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

Should I drop my consulting clients to build products?

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

Which platform should I grow on?

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

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

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

What should I do first?

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

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

Companies Don’t Want Employees. Employees Don’t Want to Work for Companies.

John Brewton — Wed, 08 Jul 2026 12:11:32 GMT

TL →DR

The 100-year-old 40-hour job is being emptied from both sides. Companies want outcomes, not headcount. Workers want to sell outcomes, not hours.
Ronald Coase explained in 1937 that firms exist to lower coordination costs. AI reduces the cost of buying work on the open market, so the firm's boundary contracts. The result is large firms with fewer employees, not smaller firms.
Stanford’s Digital Economy Lab found that early-career employment in the most AI-exposed jobs fell 13 to 16 percent since late 2022, driven by weaker hiring. Companies closed the on-ramp first.
“Outcomes, not hours” was a 20-year worker demand. Once a company pays for the result, it can buy the result from anyone. That concession made the salaried employee optional.
The services market for AI, repackaged as software, is $4.6 trillion, against a $200 billion software market. Selling software and selling a service are becoming the same act.
The play at the scale of one: name the outcome you sell, price the outcome, publish the proof, own a newsletter. Claude at $20 a month and a Substack at $0 cover year one.
The bet can lose. Fewer than 5 percent of workers changed jobs in the 33 months following ChatGPT's launch. The clock may be slower than the logic.

In 1926, Henry Ford gave his workers the 40-hour week. It was a production decision, and it became the employment construct every job has been poured into for 100 years.

We are living through an era of deconstruction. Companies are pulling employees out of their companies, and workers are climbing out on their own. The same force presses from both sides, creating a surprisingly beneficial alignment for the individuals bold enough to make the jump.

The 40-hour job is ending, with companies demanding payment for outcomes, not hours, while the individuals delivering those outcomes are asking the same.

Why are companies cutting employees instead of hiring?

A full-time employee is a fixed cost with benefits, overhead, and idle capacity attached. In a volatile market, fixed labor is a liability and variable labor is insurance.

Most roles do not contain 40 hours of work that only one person can do. They contain a set of outcomes and a lot of paddling around, working around inefficiencies, and hours spent doing little work that drives the most important outcomes for the organizations.

The company wants the outcomes and it is tired of paying for the paddling.

Ronald Coase explained the padding in 1937. Firms exist to lower the cost of coordination, and a firm grows until organizing one more task within it costs as much as buying that task on the open market. AI is cutting the cost of buying it. The firm's boundary is contracting.

This does not mean firms shrink. It means building larger firms with fewer employees. The giants grow and cut headcount in the same quarter. Declining labor share is the signature of the shift. The biggest firms (at least those that will be successful moving forward) now capture more output with less payroll.

Stanford’s Digital Economy Lab found that early-career employment in the most AI-exposed occupations fell 13 to 16 percent since late 2022, driven by weaker hiring, not layoffs. Companies closed the on-ramp before they touched the exit. Hiring now sits at levels last seen in 2010.

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How did “outcomes, not hours” become the end of the salaried job?

For 20 years, workers argued for a better deal, and the best version was outcomes, not hours. Cali Ressler and Jody Thompson built the Results-Only Work Environment inside Best Buy in the early 2000s and published it in 2008. Measure people by results, not presence. It spread to Gap and IBM, sold as a workers’ victory.

It was a concession with a long fuse. Once a company accepts that only the outcome matters and the 40 hours are arbitrary, it has conceded that the employee is arbitrary too. If the result is the unit, the firm buys the result from whoever delivers it best. That is rarely a salaried generalist filling a week. It is a specialist who does one thing really well and bills for the outcome. Outcomes, not hours, were the eviction notice employees wrote themselves.

Why are more Americans becoming independent workers?

Thirty-six percent of employed Americans now do independent work, up from 27 percent in 2016, on McKinsey’s count. Full-time independents number near 27 million. The number earning more than $100,000 a year rose from 3 million in 2020 to 5.6 million today.

Independent work rose from 27 percent of employed Americans in 2016 to 36 percent in 2024. Independents earning $100,000-plus rose from 3 million to 5.6 million.

Much of the walk-away is still a hedge. People keep the paycheck for stability and stay for the healthcare. I am not predicting a stampede this year. I am simply calling out a directional shift that is well underway.

Why are software and services collapsing into one market?

The structural force runs underneath the whole shift. The line between selling software and selling a service is dissolving.

Foundation Capital sizes the services market that AI is now repackaging as software at $4.6 trillion, against a $200 billion software market. a16z says software has stopped eating tasks and started eating labor. Sequoia calls the new software services.

The services market for AI, repackaged as software, is $4.6 trillion, against a $200 billion software market. The prize is 23 times larger.

Software stopped selling the tool and started selling the outcome. That turned consultancies into software companies. That turned software companies into service companies. That erased the line between the two. Most people are still arguing about which of their tools has the best features.

The individual runs the same play at a scale of one. A person who turns expertise into a productized outcome sold to companies is doing what a consultancy does when it becomes a software company. Sam Altman’s group of tech CEOs runs a betting pool on the first one-person billion-dollar company. The endpoint of employees becoming companies is a company with one employee.

The one-person company is the endpoint of the shift.

How do you turn your expertise into a company of one?

The move has 4 steps. None of them requires a team.

1. Name the value, not the title: Find the one outcome a company will pay an outside party to deliver. Weak: operations expertise. Strong: I cut quote turnaround for industrial distributors by 60 percent in 90 days. The title is what you were. The outcome is what you sell.

2. Build one clean offering: Price the outcome, not the hour. Start where the company already outsources, because the budget line exists and the swap is clean. Replacing a vendor is a purchase order. Replacing an employee is a reorg. Be the purchase order.

3. Publish the expertise: When anyone can build, the only question left is whether anyone believes you. Content is the proof, and it compounds in public. Ship on a schedule before anyone reads.

4. Own a newsletter: It is the one channel no platform can repossess. Write about the entire world your expertise sits inside, and sell your services and your products to the companies reading it. The newsletter is the go-to market for a company of one.

You do not need funding. You do not need a developer. A $200-300-per-month tech stack is enough to get you through the first year.

What should you do this week?

The norm we were educated and motivated to accept held for 100 years because coordination was expensive. It is not expensive anymore. You can wait to find out whether your seat survives the deconstruction, or you can build the thing the company buys when it stops buying seats.

Download The Company Of One Worksheet

Name your outcome: Pull the one measurable result a company will pay an outside party to deliver out of your old job title, and write it as a single sentence.
Frame the offer: Fill in who buys it, what they get, the budget line you replace, your proof, and a set price for a project or a retainer.
Write the one-liner: Compress the offer into “I help [who] achieve [outcome] for [price].”
Sketch the newsletter: Define the world your expertise sits inside, who reads it, a name and publish day, and your first three post titles.
Ship this week: Work on a five-item checklist that ends with sending the offer to 5 companies that already carry the budget line.

Download The Company Of One Worksheet

Build your company of one with the Operating Founders

You do not have to do this alone. I have a founding-member cohort inside Operating by John Brewton that is 200 strong.

Become a founding member this week.

The community lives in Skool. It is where operators turn the 4 steps in this article into a shipped offer, a live newsletter, and a robust, growing, money-making business.

Every week, there is a live Q&A with John and the community, where you bring the one thing you are stuck on and leave with the next move. Every month, there is a Masterclass that takes one part of the build, the offer, the pricing, the newsletter, or the distribution, and goes deep enough to act on it that day.

Join the Operating Founders community, bring the one sentence that names your outcome, and start building the company that will define your future today.

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Frequently asked questions

Why don’t companies want full-time employees anymore? A full-time employee is a fixed cost with benefits, overhead, and idle capacity attached. Most roles hold a few outcomes wrapped in padding, and the company only wants the outcomes. AI lowers the cost of buying those outcomes on the open market, so companies keep fewer people and rent the rest.

What does “outcomes, not hours” mean? It means paying for a delivered result instead of time on the clock. Cali Ressler and Jody Thompson formalized it as the Results-Only Work Environment at Best Buy and published it in 2008. Once the result is the unit of pay, the firm can buy that result from anyone, which makes the salaried role optional.

Is the 40-hour workweek actually ending? The direction is set, but the clock is slow. Henry Ford standardized the 40-hour week in 1926, and it has held for 100 years. Fewer than 5 percent of workers changed jobs in the 33 months after ChatGPT, so the shift is real but early rather than sudden.

What is “services as software”? It is the repricing of human services into AI-delivered products. Foundation Capital sizes that services market at $4.6 trillion, against a $200 billion software market. Software stopped selling the tool and started selling the outcome, which erased the line between buying software and hiring a service.

How do I become a company of one? Name the one outcome a company will pay an outside party to deliver. Price the outcome, not the hour, and start where the company already outsources. Publish your expertise on a schedule, and own a newsletter as your go-to-market. Claude at $20 a month and a Substack at $0 cover the first year.

Will AI replace employees or just change how they work? Both, and the first signal is in hiring. Stanford’s Digital Economy Lab found early-career employment in the most AI-exposed jobs fell 13 to 16 percent since late 2022, driven by weaker hiring rather than layoffs. Companies closed the on-ramp before they touched the exit.

What is the strongest argument against this thesis? Companies buy named outside firms for credibility and for someone to point at when a decision goes wrong. A board cannot say it relied on a model, and a company of one sells neither the credibility nor the blame absorption. The work could also stay in-house, with a thin internal core running the AI and buying from no one.

Appendix: The Research

The sources below are organized by the pillar of the argument each one supports. Tier tags follow the elite-research architecture. Sources marked outside tier sit beyond the approved source list and were used for scale or framing. Verify their figures independently before they carry weight in print.

1. The theory of the firm

California Management Review (UC Berkeley Haas) — “From Coase to AI Agents: Why the Economics of the Firm Still Matters in the Age of Automation.” Grounds the claim that firms exist to minimize transaction costs and that AI shrinks the efficient boundary of the firm. [ACADEMIC] https://cmr.berkeley.edu/2025/04/from-coase-to-ai-agents-why-the-economics-of-the-firm-still-matters-in-the-age-of-automation/
Ronald Coase, “The Nature of the Firm” (1937), via Wikipedia — The original text and its corollary that lower coordination costs also enable larger firms. The source of the “large firms, fewer employees” reconciliation. [REFERENCE] https://en.wikipedia.org/wiki/The_Nature_of_the_Firm

2. The demand side: companies shedding employees

Yale Insights (Jeffrey Sonnenfeld) — “The Real Job Destruction from AI Is Hitting Before Careers Can Start.” Source for the 13 to 16 percent early-career decline (Brynjolfsson, Stanford Digital Economy Lab), the hiring freeze at 2010 levels, developer postings down 53 percent, and developers aged 22 to 25 down nearly 20 percent from peak. [INSTITUTIONAL / academic] https://insights.som.yale.edu/insights/the-real-job-destruction-from-ai-is-hitting-before-careers-can-start
Harvard Business Review (Fuller, Raman, Bailey, Vaduganathan) — “Rethinking the On-Demand Workforce.” HBS and BCG finding that almost all Fortune 500 firms already rent capability through talent platforms. [EDITORIAL] https://hbr.org/2020/11/rethinking-the-on-demand-workforce

3. Outcomes not hours: the eviction notice

Monitask business glossary — Results-Only Work Environment origin. Names Cali Ressler and Jody Thompson, Best Buy, and adoption at Gap and IBM. Outside tier. https://www.monitask.com/en/business-glossary/results-only-work-environment
MindTools — “Managing in a Results-Only Work Environment.” Source for the 2008 book “Why Work Sucks and How to Fix It” and the results-over-presence principle. Outside tier. https://www.mindtools.com/agx0aqn/managing-in-a-results-only-work-environment/
Brighter Strategies — “ROWE: Results Only Work Environment.” Source for the Henry Ford 1926 40-hour-week detail. Outside tier. https://brighterstrategies.com/rowe-results-only-work-environment

4. The supply side: independent work

McKinsey (American Opportunity Survey) — “Freelance, side hustles, and gigs: Many more Americans have become independent workers.” Source for 36 percent of employed Americans doing independent work, up from 27 percent in 2016. Outside strict tier, institutional. https://www.mckinsey.com/featured-insights/sustainable-inclusive-growth/future-of-america/freelance-side-hustles-and-gigs-many-more-americans-have-become-independent-workers
99firms (aggregating MBO Partners and Upwork) — Freelance statistics. Source for roughly 27 million full-time independents, 5.6 million earning $100,000-plus (up from 3 million in 2020), and $1.5 trillion in collective earnings. Outside tier, verify against MBO Partners and Upwork primary reports. https://99firms.com/research/freelance-statistics/

5. The services and software convergence

Foundation Capital (Joanne Chen) — “AI Leads a Service-as-Software Paradigm Shift.” The $4.6 trillion services-as-software figure and the QuickBooks-to-AI-accountant framing. [INSTITUTIONAL] https://foundationcapital.com/ai-service-as-software/
Foundation Capital — “The $4.6T Services-as-Software Opportunity: Lessons from the First Year.” Contracts evolving from seats to usage to tasks to outcomes, and forward-deployed engineers. [INSTITUTIONAL] https://foundationcapital.com/ideas/the-4-6t-services-as-software-opportunity-lessons-from-the-first-year
a16z — “Good News: AI Will Eat Application Software.” Software eating labor rather than tasks, and seat-based pricing under structural pressure. [INSTITUTIONAL] https://a16z.com/good-news-ai-will-eat-application-software/
TechCrunch (reporting a16z’s Olivia Moore and Seema Amble) — Source for the line that consultancies are now software companies, and that a firm’s TAM is no longer one or the other. [EDITORIAL] https://techcrunch.com/2025/10/02/a-new-a16z-report-looks-at-which-ai-companies-startups-are-actually-paying-for/
Sequoia Capital — “Services: The New Software.” The intelligence-to-judgment spectrum and the vendor-swap-versus-reorg wedge logic. [INSTITUTIONAL] https://sequoiacap.com/article/services-the-new-software/
PwC (Deals) — “How AI is reshaping software valuations in M&A.” Both directions of the convergence, and the roughly 30 percent software-index decline after agentic tools launched in early 2026. Outside strict tier, institutional. https://www.pwc.com/us/en/services/consulting/deals/library/ai-software-valuations-ma-private-equity.html
Madrona — “Service as Software: The Foundation of Outcome Delivery in Applied AI.” Labor spend an order of magnitude larger than the software market, and forward-deployed engineers as a fast-growing role. [INSTITUTIONAL] https://www.madrona.com/service-as-software-the-foundation-of-outcome-delivery-in-applied-ai/

6. The one-person company

Fortune (via Yahoo Finance) — “Could AI create a one-person unicorn? Sam Altman thinks so.” The betting-pool quote, plus the Instagram (13 employees) and Plenty of Fish (one employee) precedents. [EDITORIAL] https://finance.yahoo.com/news/could-ai-create-one-person-120000722.html

7. The playbook: become your own company

Reid Hoffman and Ben Casnocha, “The Startup of You” — The intellectual anchor for managing a career as a company: find your competitive edge, differentiate, build soft assets and network. Maps to the four-step playbook. [REFERENCE]

https://www.startupofyou.com/

Harvard Business Review — “LinkedIn Co-Founder Reid Hoffman on Innovating for an Uncertain Future.” Hoffman on talent as the primary differentiator. [EDITORIAL] https://hbr.org/2022/01/linkedin-co-founder-reid-hoffman-on-innovating-for-an-uncertain-future

8. Counter-evidence and the open-eyed bet

Built In (reporting the Yale and Brookings study) — Source for fewer than 5 percent of workers switching jobs 33 months after ChatGPT, below the PC and internet transitions, and the finding that AI’s aggregate labor impact is so far limited. Supports the “right direction, wrong clock” counter. Outside tier. https://builtin.com/articles/job-market-ai-impact-yale-brookings-study
Better Tomorrow Ventures — “Services Won’t Become Software.” The strongest counter: companies buy named firms for liability cover, credibility, and blame absorption, none of which a company of one can sell. [INSTITUTIONAL / VC] https://better-tomorrow-ventures.ghost.io/services-wont-become-software/
“Two Decades of Free Agent Nation” (retrospective on Daniel Pink, 2001) — The historical pattern to answer: the organization-man-to-free-agent prediction was made 25 years ago, and the corporation grew larger anyway. Outside tier. https://medium.com/swlh/two-decades-of-free-agent-nation-47db3d4c4687

How I Became Irrelevant. And How I Made Myself Relevant Again.

John Brewton — Thu, 02 Jul 2026 11:18:06 GMT

TL → DR

Two and a half years ago, a college intern on a consulting job I was working with zero hours in Excel and a $20 ChatGPT subscription completed work in 24 hours that I had blocked 25 to 35 hours to do myself, work built on skills I spent 20 years sharpening.
Being made irrelevant took 24 hours, and I never got a vote. Deciding to become relevant again took 30 seconds.
Fluency came from 1 method on repeat: Do. Fail. Learn. The skill that survived was not a skill. It was the decision to keep running the loop.

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What happens when AI reprices 20 years of expertise?

The engagement was unglamorous. A consulting client needed to migrate to a new ERP system, which meant a mountain of dirty inventory data, the work no one wants. It was also the exact work I had spent 20 years building expertise in.

I had 1 person helping. A college intern, sharp, but with zero hours in Excel and zero in spreadsheets. Not a CS major. Not an engineer. Everything I knew how to do, he had never touched.

I handed him 3 things:

A laptop.
A ChatGPT subscription at $20 a month.
A pile of work I would not have time to finish.

The job required complex Excel workbooks, clean data, and advanced analysis. I had blocked 25 to 35 hours to do that work myself.

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How did an intern with no Excel skills finish 30 hours of expert work in 24 hours?

He came back in 24 hours. Most of it was done faster than I and cleaner than I. He also brought recommendations on where to take the analysis next and questions I had not thought to ask.

The method was simple. He never learned Excel. He asked ChatGPT to write code, dropped the code into Apps Script, and delivered everything I asked for and more. He never touched one of the spreadsheet skills I spent 20 years sharpening.

Sitting in that office, I said it out loud. I have become irrelevant. 20 years of hard skills, some of which I was very proud of, were repriced in an afternoon by a college intern with a $20 subscription.

What is the difference between being made irrelevant and becoming irrelevant?

I was made irrelevant. The repricing happened, and I never got a vote. Becoming irrelevant was up to me. Keep doing the old thing, and I would have finished the job myself, slower and prouder.

The repricing took 24 hours. The decision to do whatever I had to do to become relevant again took 30 seconds.

How do you become relevant again after AI reprices your skills?

The chain has 2 links. You become relevant by becoming fluent. You become fluent by running a loop, not by finishing a course.

Hard problems rarely yield to one attempt. They yield to a loop. You do. You fail. You learn. Then you run it again. Each turn carries the last correction forward. The failures get cheaper. The signal gets sharper.

How does the Do. Fail. Learn. Loop rebuild fluency in practice?

Do: The attempt, a specific bet put into contact with reality. I decided in that office and I tripled down. I pushed every workflow through these systems, smallest real attempt first, time-boxed. The chat tools were only the on-ramp. ChatGPT first, then Perplexity, then Claude, then Gemini. The first version was a probe, not the product.
Fail: The breakdown, reality returning a verdict I did not want. Prompts that produced garbage. Workflows that broke on contact with real client data. Automations I rebuilt 3 times before they held. Each failure was honest signal at contained cost. I named what broke, measured the gap, and kept the evidence. Failure was data, not identity. A cheap failure is a paid lesson.
Learn: The extraction, converting each verdict into a rule I could carry. Write the lesson down. Change 1 variable. Re-enter the loop. A lesson you cannot repeat is just a story. The correction is the asset.

Run with discipline, the turns compounded. What followed was more than automation. It was a retreat to first principles. Not the old workflow runs faster. A new operating model, with every process redrawn around what these systems can actually do.

Then the loop carried me past the chat tools entirely. I learned how databases were changing. I learned to ship AI-native applications. I learned to deploy agents through Cursor, Codex, and Claude Code, agents that do the work rather than answer questions about it. The work stopped stopping. I split jobs across machine instances running in parallel at 3 a.m., while I slept or worked on something else. The line that kept circling broke out.

What skill actually survives AI?

The skill that survived was not a skill. It was the decision to stay fluent. Staying fluent is refusing to exit the loop.

You will be made irrelevant. The shift is underway and you do not get a vote on that part. You do not have to become it. That part is a 30-second decision, and it is entirely yours. The decision has a method. Do. Fail. Learn. Run the loop until the breakout happens.

Read The Full 25 Here

Oper(AI)te is where you build the whole staff, not one skill: 8 sessions, 4 employees, and the one workflow that defines your job, built start to finish with Wessal Khader and me.

The summer cohort starts July 21, and for the next 48 hours, you can use code EARLY500 to receive a $500 discount on the regular price of $2500. Seats are selling fast; make sure to grab yours today!

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FREE DOWNLOAD

The Loop Log: the worksheet that runs the method. 1 page per turn, 10 turns. The bet, the verdict, the rule, the 1 variable changed. Print it or annotate it.

Download Your Loop Log For Free!

The 30-Day Fluency Sprint and the Failure Ledger arrive in the paid edition of this article on Saturday.

- j -

How do I build my first one?

You do not need to build all 25 to start. You need to build 1.

Become an Operating Founder. $99, one time.

What comes with it:

A live build session. We take one skill from this list and build it on screen, start to finish, yours to keep. You leave with a working employee and the method to build the next 24.
One year in my Skool community, with my Friday noon Q&A every week.
One 50-minute working session with me, one to one.

Become an Operating Founder Today!

About John

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

Frequently asked questions

What is the Do. Fail. Learn. method? A 3-step loop for problems that do not yield to one attempt. You make the smallest real attempt, log the verdict reality returns, extract a rule you can carry, change 1 variable, and run it again. Each turn carries the last correction forward.

How is this different from just practicing with AI? Practice repeats the attempt. The loop repeats the correction. Without the written rule and the 1 changed variable, you run the same turn 10 times and call it experience.

How long does it take to become AI fluent? The decision takes 30 seconds. The first automation that holds takes most people 2 to 4 weeks of daily turns. I ran the loop from chat tools to deployed agents in under a year, working 60 to 90 minutes a day.

Do I need to know how to code? No. The intern who repriced me had zero hours in Excel and never learned it. He asked the model to write the code. The loop teaches you to direct the work, not to type it.

Which AI tool should I start with? Any of the big ones. Claude is strong for writing and voice. Perplexity is built for research. ChatGPT and Gemini both work. The tool matters less than the turn count.

How do I know when a lesson is real? Run the test in the Loop Log. A lesson you cannot repeat is just a story. If the rule changes your next attempt, it is an asset. If it only decorates the last one, re-enter the loop.

Will AI make my skills irrelevant? Parts of them, and you do not get a vote on that. The repricing of my Excel skills took 24 hours. What you control is the other side: whether you become irrelevant, which is a decision, and it has a method.

The 25 Most Important AI Jobs I Run For Myself & Build For My Clients Every Week

John Brewton — Tue, 30 Jun 2026 13:32:16 GMT

TL → DR

Most people run 2 jobs with AI. They ask a question and close the tab.
I run 25. Each one is a skill you can own, not a prompt you retype.
The list below is the whole staff, sorted into 5 groups.
The gap between 2 and 25 is the difference between using AI and running it.
11 of the 25 run as autonomous loops, several agents working together with no hand on the wheel.
You can build your first one this summer, live, in about 90 minutes.

What does an AI-fluent founder or operator actually do all day?

Most people are still using AI like a Google search box. They ask one thing, read the answer, and close the tab. And if the results aren’t strong enough, or seem not to make sense, they walk away and choose to do the work themselves.

That is their first mistake

That is 1 job, done by hand, every time, from scratch.

I run a team of +25 Agentic Employees every week. Not 25 prompts I retype. 25 agents that know my voice, my standards, my thinking, and my work. 25 team members who produce the same high-quality output every time and who adapt to the changing needs of my business each and every day

The gap is no longer between those who use AI and those who do not. The gap is who runs 25 jobs with it, while everyone else runs 2.

Here is the whole entry-level list of the work my team produces each week and the 25 most common first builds I engineer for my clients each month

Each one carries a tag.

Run means you operate it, one job at a time.
Scheduled means it runs on a trigger without you.
Loop means several agents run it end to end, in a loop, checking their own work, with no hand on the wheel.

What are the 25 things operators run with AI right now?

Writing in your voice

Draft a client email in 3 versions, one for the client, one for the team, one for the board, each in your tone, off one line. Run
Turn a 2-minute voice memo into a finished post in your own written voice. Run
Self-check every draft against your banned-phrase list before you read it. Run
Research a named prospect, write the cold outreach, then run the whole follow-up sequence on its own. Loop
Rewrite a stiff paragraph into your voice in under a minute. Run

Research that comes back sourced

Watch your calendar and deliver a sourced brief before every meeting, every claim cited, with no ask. Loop
Scan 3 rivals on a cadence, one agent each, and return the positioning gaps you can take. Loop
Turn 10 customer interviews into the 3 jobs they hire you for, one reader agent per transcript into one synthesizer. Loop
Fan out across a new market and return a sourced read in an afternoon, not a week. Loop
Pull a one-page memo on a person before every first call, triggered off the invite. Loop

Meetings that write themselves up

Turn rough bullets into a deck outline you would actually send. Run
End every call with the notes, the decisions, and the next steps already written, every time, automatically. Scheduled
Run the meeting pipeline, from transcript to notes to counterparty research to a relationship memo to a drafted email, on its own. Loop
Get the agenda and the pre-read from a 2-line goal. Run
Turn a long email thread into a summary and a recommended reply. Scheduled

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Money and deals

Normalize EBITDA on a founder’s spreadsheet and flag what will not survive diligence. Run
Read a deal room with one agent per section into one screen on whether the business is real. Loop
Turn a discovery call into a full proposal and a scope on its own. Loop
Build a 13-week cash view from raw numbers each week and name the weeks that get tight. Scheduled
Reconcile 2 months of statements and surface the duplicates and the gaps on a schedule. Scheduled

The work that is yours alone

Build the one weekly report that eats your Friday so it runs from your inputs each week. Scheduled
Stand up a brand voice profile and write everything through it. Run
Turn a research report into a finished carousel, research agents to writer to designer, charts and all. Loop
Triage your inbox every morning into needs-you, needs-a-reply, needs-nothing before you sit down. Loop
Build a custom skill for the one task no template fits. Run

Which of these run without me, completely?

Run, schedule, loop.

11 of the 25 reach the top rung.

Most teams stop at Run. They operate one skill at a time, by hand, and call that AI fluency. They are mistaken.

The ladder has 3 rungs. A skill you run becomes a job that runs on a schedule. A scheduled job becomes a loop that several agents run together, each one doing a piece, checking the work, and handing it to the next, until the result lands on your desk finished.

11 of the 25 above are at the top rung. A research brief that triggers off your calendar and writes itself before the meeting. A meeting pipeline that takes a transcript and returns a relationship memo and a drafted email with no prompt from you. A market read that fans 6 agents across the sources and comes back in the afternoon.

You are not prompting these. You build and schedule them once, and they run in perpetuity.

Caption: Four agents on one loop. The work moves without you.

That is the move most people have not seen yet. Not a faster search box. A staff of agents that runs the loop while you are in the room doing the work only you can do.

Why is this the summer to become AI-fluent?

Count how many of the 25 you run today.

If the number is 2, that is normal. It is also the problem.

Get Your AI-Fluency Score Today!

Waiting until the gap is loud is the costly move. By the time it is obvious, it has already cost you the deal, the raise, or the year.

Oper(AI)te is where you build the whole staff, not one skill: 8 sessions, 4 employees, and the one workflow that defines your job, built start to finish with Wessal Khader and me.

The summer cohort starts July 21, and for the next 48 hours, you can use code EARLY500 to receive a $500 discount on the regular price of $2500. Seats are selling fast; make sure to grab yours today!

Secure Your Seat Today!

How do I build my first one?

You do not need to build all 25 to start. You need to build 1.

Become an Operating Founder. $99, one time.

What comes with it:

A live build session. We take one skill from this list and build it on screen, start to finish, yours to keep. You leave with a working employee and the method to build the next 24.
One year in my Skool community, with my Friday noon Q&A every week.
One 50-minute working session with me, one to one.

Become An Operating Founder

What the $99 unlocks next:

Your seat in Oper(AI)te at $1,500 for the July 21 cohort, $1,000 off the $2,500 list.
A place in the affiliate program, 20% net on every seat you sell.

The $99 is the front door. The live build proves the method. Oper(AI)te is where you build the other 24, and the $1,500 rate closes with the July 21 cohort.

The founder rate is open for 14 days. When the 14 days end, the founder door closes. The list above is the menu. The first build is the key to the first item.

Become An Operating Founder

Frequently asked questions

What is the difference between using AI and running it? Using AI is one prompt at a time, from scratch, every time. Running it means you built a setup that already knows your voice and your work and does the same job the same way every time.

Can these really run on their own? 11 of the 25 can. Past the single skill, a job runs on a schedule, then several agents run it as a loop, each doing a piece and checking the work. You set it once and it runs without you.

Do I need to know how to code? No. You answer questions and paste in samples. The build does the rest.

Which AI app should I use? Any of the big ones works. Claude is strong for writing and voice. Perplexity is built for research. ChatGPT and Gemini both work. Pick one and start.

How long does the first one take? About 90 minutes in the live build session. Then you run drafts to make it sharp.

What does $99 get me? Lifetime access to the Operating Founder community, the weekly Friday office hours, a new weekly AI fluency session through the summer, one live build session, the right to join Oper(AI)te at $1,500 for the July 21 cohort, and a place in the affiliate program at $300 a seat.

What happens after the 14 days? The founder door closes. The $99 rate is sold only inside the 14-day window.

Is this safe with my private writing? Your profile lives in your own account. You read every file before you turn it on.

About John

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

AI Fluency, Defined: The 5 Moves That Replace Prompt Lists

John Brewton — Sat, 27 Jun 2026 11:32:21 GMT

TL → DR

AI fluency is the capacity to see the whole workflow and build it yourself. Not knowing a tool. Operating one.
Prompt lists are a phrasebook. They run out. Fluency is a grammar. It does not.
The discipline is 5 moves: Frame, Assign, Brief, Inspect, and Iterate then persist.
You drill them on small prompts until they are automatic. Then the same 5 moves run multi-agent systems.
Paid subscribers get the tool: the Rudiments skill for Claude and ChatGPT, plus a one-page chart.

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I live in a bilingual household. My wife is from Mexico; Spanish is our first language at home, and our two-and-a-half-year-old daughter is becoming bilingual day one. She moves between Spanish and English as if it’s all one language. She produces sentences no one taught her. It’s one of the most beautiful experiences of my life.

I studied Spanish for 4 years in high school and Chinese for 4 years in college. 8 years of classes, and I could not have held a conversation in either. I never became fluent. I was memorizing lists for the test and forgetting them by summer. Since meeting my wife, I have worked hard to become bilingual. I’m not quite there yet, but getting very close. What is making me fluent now is my household, my wife, our daughter, our friends, and my extended family. I speak more Spanish than English in my personal life each day.

That is how most people should learn AI, and almost no one does. They collect prompts instead. “10 prompts for marketers.” “The 50 best ChatGPT prompts.” They are building a phrasebook, and they will struggle to produce high quality outputs the first time the work is not on the list.

AI fluency is the capacity to see a workflow whole and build it yourself, which is the one thing software has spent your entire career doing for you.

Nobody has defined AI fluency, so the word floats. People feel behind without knowing what they are behind on. Companies buy licenses and wait for a result that never arrives.

The gap is not access to the tools.

The gap is a definition nobody wrote down or thought to calrify.

What does it actually mean to be fluent?

Linguists test it with 3 markers:

A fluent speaker stops translating in their head.
They route around the words they do not know instead of freezing.
They create instead of reciting.

My daughter is 2 and already does the third one. Most adults with a language app never clear the first.

The same 3 markers tell you a person is fluent with AI. They stop reaching for a saved prompt. They route around what the model cannot do instead of giving up. They compose a workflow for a problem no one has templated.

New to Operating? You Might Appreciate These Popular Articles

Why is working with AI different from using software?

For 40 years, enterprise software has made this skill unnecessary. The ERP told you which screen to open. The CRM told you what to enter and when. The workflow was built into the software, written by a vendor who had already anticipated your situation. You pointed at the phrase.

That arrangement ended in 1 step. The model does not hand you a directional form on a screen; it hands you a monumentally flexible canvas through which you can communicate to build anything, literally.

The cost of building a workflow fell to a sentence you can type. That changed what a tool is. Which changed what a worker does. This is now changing what it means to be a worker. Most people are still asking which prompt to use.

We have spent our careers doing work that computers could not do, yet. Now the computer does the work and asks us to compose and direct its intended output. That is not a smaller job. It is the work of an Operator, and very few companies, much less individuals, have been trained for this task.

Can you become AI fluent without a technical background?

I trained as a drummer at the Berklee College of Music for a year right after high school. I obsessively drilled rudiments, single strokes, double strokes, paradiddles, slowly and cleanly, thousands of times, until my hands moved without thought.

When the rock-and-roll dream didn’t work out, I became an equally obsessive student, ultimately graduating from Harvard with a degree in Economics. The disciplines and habits that helped me master an instrument were the same tools that helped me succeed in academia.

I did it again in business. I spent a decade at a B2B industrial distributor, from the sourcing desk to the chief executive role, and grew it from $27.5M to $75M before selling in 2021. The work computers could not do yet was the work I did by hand for 10 years.

I am doing it a third time now. I am building AI-first, enterprise-grade software using tools Attio, Claude, Supabase, and Resend. I am not an engineer. I am an Operator, a buildeer, a designer, and a founder.

The discipline that produced all 3 outcomes across my life transfers and compounds with each successive iteration.

For AI Fluency, this skill-building repetitive loop has a clear shape, I call it the Five Moves. It is rudimentary, and meant to be practiced over and over again

You do not become AI Fluent by reading about reps. You achieve this new reality by building this capability across your real work, alongside colleagues who are doing the same, and by learning from leaders who have already achieved the outcome.

That is what I built with Wessal Khader. It is called Oper(AI)te, and it runs this summer. 8 live sessions, no theory, and no replays to watch alone. You build a working staff of AI employees that does your real work, across Claude, ChatGPT, Gemini, and Perplexity. Beginner or advanced. No coding ability required. You leave with a team of agentic AI-employees running 24/7.

The summer cohort starts July 21, and seats are limited.

Take Your Seat @ Oper(AI)te!

Build it with us this summer to become AI-Fluent by the time Q3 ends and Q4 starts!

What are the 5 moves of AI fluency?

The five never change. Only the size of the task changes. You drill them on a one-line prompt the way a drummer drills a single-stroke roll, then the same 5 moves run a multi-agent system. Start small on purpose. A weak prompt costs nothing, and you can run the loop 40 times in a morning on real work.

Frame: One question: what is the actual outcome, and what does done look like? Small: “summarize this” becomes “pull the 3 decisions and who owns each.” Large: map where the workflow begins, ends, and every touchpoint between. Skip it and you accept the first plausible thing the model returns, because you never defined better.
Assign: One question: who does each part, you, the model, a tool, or an agent? Small: decide whether the model is thinking or just formatting a decision you already made. Large: the model’s job, a tool’s job, a sub-agent’s job, your judgment. Skip it and you hand the model work it cannot do, then blame it for guessing.
Brief: One question: what do you know that the model does not, and what must it not do? Small: paste the constraint, the audience, and one example of good. Large: persistent context, data sources, guardrails. Skip it and the answer comes back fluent, confident, and generic, because you described a generic situation.
Inspect: One question: where is this wrong, thin, or bluffing, measured against move one? Small: read the output as an operator checking work, not a customer accepting a delivery. Large: test points, spot checks, the places a workflow breaks quietly. Skip it and fluent-sounding wrongness ships. This is the most expensive failure in the discipline.
Iterate, then persist: One question: what one change improves this, and how do you keep what works? Small: change one variable, run again, notice what changed. Large: save what worked as a prompt, a project, a skill, or an agent, so it runs again without you. Skip it and every task starts from zero, and nothing compounds.

How do you practice AI fluency?

The cost of a mistake on a 2-line prompt is limited. The rep count is everything. Run the loop 40 times a day on real work, and the moves leave your conscious attention. By the time the task is a system with sub-agents across 4 tools, the moves are already in your hands, and your attention is free for the one thing that does not automate: judgment.

The rudiments are rudimentary. That is the point. Mastered, they become the foundation for building things that run without you.

Is there a tool that enables the 5 moves?

I built a skill that runs you through the 5 moves while you work. It is called Rudiments. When you sit down and say “help me draft the board update” or “build me a lead-routing flow,” it does not lurch into the task. It asks you the moves, calibrated to the size of the job, and it shows you the difference between your loose ask and the sharp one, so you learn the move and not just the answer. It is built to make itself unnecessary.

Here is what it does with every task you bring it:

Sizes the job. A quick rep for a one-line prompt, a full rep for real work, a build rep for a multi-agent system.
Walks you through the 5 moves as questions: Frame, Assign, Brief, Inspect, and Iterate then persist.
Shows you your loose ask next to the sharp one, so the rep teaches you the move.
Tracks the moves you skip and tells you which one to drill next.
Saves what works, so the rep compounds instead of resetting to zero.

That is the loop and the rep that will help you get one step closer to becoming AI Fluent.

You do not need to write code. You do not need a developer. You need Claude Pro at $20 a month or a Premium ChatGPT account, and 60 minutes of real work to drill against.

Paid subscribers get 3 things, attached below:

The Rudiments skill for Claude, installed in under a minute.
The same discipline written as instructions for a ChatGPT, so it runs wherever you work.
The one-page chart, print-ready.

Become a paid subscriber to download the Claude skill and the directions for how to build the same capability in ChatGPT after the frequently asked questions section below.

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Frequently Asked Questions

What is AI fluency? AI fluency is the capacity to see the whole workflow and build it yourself. It is not knowing a tool. It is operating one. The test is the same as for language. You stop reaching for a saved prompt, you route around what the model cannot do, and you compose a workflow for a problem no one templated.

What is the difference between AI fluency and prompt engineering? Prompt engineering collects lines that work. AI fluency builds the workflow that those lines sit inside. A prompt list is a phrasebook, and it runs out. The 5 moves are grammatical, and they do not.

Do you need to know how to code to become AI fluent? No. I am building an AI-native CRM for a public company, and no one trained me to write software. You compose the workflow in plain language and let the model and tools handle their parts. You need judgment, not a computer science degree.

What are the 5 moves? Frame, Assign, Brief, Inspect, and Iterate then persist. Frame the outcome. Assign each part to you, the model, a tool, or an agent. Brief the context and the guardrails. Inspect the output against the outcome. Iterate one change, then save what works so it runs again without you.

How long does it take to become AI fluent? Faster than you expect, because you drill on small tasks with a high rep count. Run the 5 moves 40 times in a morning on real work, and the moves start to leave your conscious attention in weeks, not years.

Should I use Claude or ChatGPT to practice? Either. The discipline is identical on both. Claude Pro is $20 a month, and ChatGPT has a free tier. The Rudiments skill ships for Claude, and the same discipline is written as instructions for a ChatGPT Custom GPT.

What is the Rudiments skill? It is a tool that runs you through the 5 moves while you work, calibrated to the size of the task, and shows you the difference between your loose ask and the sharp one so you learn the move. It is built to make itself unnecessary. Paid subscribers get it for Claude and ChatGPT, plus the one-page chart.

Download the first skill you need to become AI Fluent below.

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

John Brewton — Thu, 25 Jun 2026 13:37:03 GMT

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

This is the answer.

The full piece tells the whole story. This one strips it to the 5 moves that worked and gives you the launch sequence to start building and execute yourself.

Read it in 3 minutes.

Start building this weekend.

The Numbers: I started 2026 with 1,014 subscribers and about $2,200 a year. By June 1 I had 5,861 subscribers, 266 paying members, and 6 revenue streams on track for more than $200,000.

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Move 1: Build the back catalog before anyone reads

I published 81 posts in 9 months to a small list and low engagement. By the time traffic arrived in 2026, 81 posts were waiting. New readers found a library, not an empty page.

Publish on a schedule before anyone reads. Build the back catalog first so that when your discovery mechanism starts workings, your new readers are delighted by what they find.

Work With John Directly

Move 2: Make one unscalable offer

On February 1 I launched the Operating Founder offer. It was $99 for four 60-minute 1:1 sessions, about $25 a session. It sold 100 seats in 12 days. Across 400 hours of those sessions I learned what a valuable coaching offer and course structure would look like for my audience.

Make your first offer small, direct, and 1:1. Use what you learn to build what scales.

Make it an easy offer for people to say yes to.

Move 3: Release your proof the same week as your offer

On February 2 I published “0 to 55,000: The First 90 Days Playbook.” It got 4,210 views and 85 new paying members in one day. The offer was already live. The proof landed on top of it.

Write the post that shows what you know. Publish it the same week you make your offer. Spend less time theorizing and philosophizing and more time talking about what you have credibly done, built, and learned from what has worked and what hasn’t.

Move 4: Use scarcity

My scarcity emails opened at 46 percent and 34 percent as the cohort filled and closed. “Seats left” outperformed “join now” every time.

State a real cap and a real deadline. Then close it when you said you would.

Studying how luxury brands use scarcity to sell their products is immensely valuable in building the strategic capacity for your business. Check out these articles for more on this concept:

Work With John Directly

Move 5: Sell access, not posts

Subscriptions bring in $9,600 a year. Coaching, the founder program, and a private community with weekly office hours bring in the rest. Paying members get direct access free readers do not.

Sell access to you. Use your AI stack to make this more efficient than it would be otherwise.

The 12-Day Launch Sequence

Work With John Directly

The Tools

Pick the tools that remove hours from work you repeat. Every hour you remove is capacity you can sell.

Subscribe now

The Newsletter to Revenue Workbook

The Operating Workbook turns these five moves into four worksheets you fill in: name your one idea, design your offer, plan your 12-day launch, and track your first 90 days. Print it, work through it, and you leave with your own plan instead of my notes.

Download The Workbook

The Operating Project

The 5 moves above are the plan, but it’s easy for a plan to stay that and nothing more.

If you’d like to work together to build your newsletter business, the Operating Project is for you! We take one problem in your business across 4 sessions, from diagnosis to a finished system, plus the machine that runs it inside Claude or ChatGPT. You own it. It lives in your account.

It regularly sells at $999, but I have opened 20 founding seats at $499 (9 of which have already filled). Each seat includes a year of VIP access to the Operating Founders community and the weekly Friday office hours. Run your 4 sessions any time through December 31, 2026.

Pick the problem. I will help you solve it, and build the machine that keeps your business growing for months into the future.

Grab Your Seat Here!

For the full story behind these moves, read the original article.

- j -

About John

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

Connect with me on LinkedIn!

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

John Brewton — Tue, 23 Jun 2026 11:20:03 GMT

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

I started 2026 with 1,014 subscribers and about $2,200 a year. By June 1st I had 5,861 subscribers and 266 paying members.
The business will generate +$200,000 this yeara across six streams: Advisory, Coaching, the Operating Founder program, Courses, Subscriptions, and a Community.
The inflection point came on February 2. One post, “0 to 55,000,” drove 85 paying members in a day and sold 100 coaching seats in 12 days.
My growth from the start has been more than 90% organic to Substack. I imported zero subscribers.
What drove it: high-frequency publishing, a proof post timed to an offer, using scarcity to drive conversions, collaborations, clear not clever headlines, willingness to give generously of my time, and an AI stack that has enabled me as one person to do the work of a team.

On January 1, 2026, my newsletter, Operating by John Brewton, had 1,014 subscribers and was on track to generate about $2,200 in ARR. I had built that audience over nine months of publishing, to my small readership and to little engagement. My average post receive 500 to 800 views, and my 30 day views hung around 2,000 to 2,500.

Today my newsletter is the foundation of a multi-tiered divsion of my larger consulting work:

Content and Brand Advisory brings in $10,000 a month.
Coaching brings in $4,000 to $5,000 a month.
The Operating Founder program has collected $22,000.
My first course earned $15,000.
The newsletter now has 5,861 subscribers and 266 paying members.

Together these revenue streams will generate +$200,000 this year and have become a wonderful source of diversification to my larger business. It is likely that these elements will become primary revenue drivers of a much larger business and company over the next several years.

Here is what happened, in the numbers:

The six streams: Advisory and coaching are shown as what they earn over a full year at today’s pace. The rest is money earned so far.

How did the newsletter start?

Before Substack, I spent 18 months building a LinkedIn following of 25,000 professional and operators (today that audience sits at 40,000). When I launched on Substack in April 2025, I started clean. I did not import an email list or extract follower emails from LinkedIn. I wanted to grow completely organically on Substack.

My first article received 197 views.

Over the next nine months I published 81 posts and built a following of 1,014 subscribers. Readership stayed low the whole time. Average views per post grew from 173 in April to 675 in November. By January 2026 the newsletter was on track to generate about $2,200 of ARR.

I picked Substack on purpose. I thought it had a cool factor, seemed like a nicer place than X to public, and most critically, I wanted the network effects of being on a social platform. I ran an experiment: grow with zero imported subscribers and see how far the platform alone could carry the work. Over 90% of the growth that followed came organically from Substack itself.

The early post did one job. They gave later readers a back catalog, a library of content, ideas, tools and perspective on the immediate world of what it means to operating and build companies. When more people arrived in 2026, 81 posts were waiting, that would become crucial.

What you can do: publish on a schedule before anyone reads. Build the back catalog first.

Subscribers reached about 1,014 by January 2026, then climbed to 5,861 by June after the launch.

When did readers start paying?

The same kind of writing began turning readers into paying members.

Jan 16, a post on AI and work: 1,080 views, 12 paying members.
Jan 19, a post on JP Morgan and AI with Hodman Murad: 1,500 views, 19 paying members.
Jan 25, “Winning the Loser’s Game”: 2,450 views, 34 paying members.
Jan 27, the weekly note “The Operating Creator”: 20 paying members.
Jan 30, “Building Your One-Person Creator Business”: 25 paying members.
Jan 31, “Personal Brand is the Human Differentiator”: 24 paying members.

Posts that once reached a few hundred readers started reaching a few thousand. I had not launched a paid product yet, but I was honing in on what my audience wanted and was experimenting with different paid subscription offers.

One point worth mentioning, that might already be clear, is that I turned paid subscriptions on from the start, receiving my first founding member subscriber for $120 in April of 2025. That subscriber has become a wonderful friend and colleague.

What happened when the offer launched in February?

On February 1, I launched the Operating Founder offer: $99 for four 60-minute coaching sessions. Yes, that’s an absolutely insane, unscalable offer. We’re going to talk more about that below. Keep reading.

On February 2, I published “0 to 55,000 — The First 90 Days Playbook”. It got 4,210 views, 107 likes, 33 comments, 85 new paying members, and a 30% open rate.

That one post drove more paying members than any other. WIth that as my jumping off point, I sold 100 coaching seats in 12 days. The program has since collected $22,000 in total revenue and amassed over 200 members.

A terrific added benefit of this growth in attention was that I became a Substack bestseller once I crossed the 100 paid subscriber mark. During this period, I was regularly in the top 5, top 10, and top 20 charts for technology and for business newsletters on Substack. Posting about these successes and talking about what was happening for my Substack continued to fuel the growth.

What you can do: write the post that shows what you know, and publish it the same week you make your offer. Another idea worth considering is to develop the too-good-to-pass-up offer you want to make and keep it in your back pocket until you have a post that significantly outperforms. Then flood the zone with your offer and with additional content related to the offer and the topic of the post(s) that overperformed.

New paying members from single posts. “0 to 55,000” drove 85. The next best drove 34.

Why does a small, unscalable offer work?

The offer was $99 for four 60-minute 1:1 sessions. That is about $25 a session. It does not scale, and my time is arguably worth more than $25/hour. A number of much larger creators that I had the privilege of being able to talk to over the last couple of years regularly spoke to the value of doing the unscalable, of creating an offer or doing something relative to your content that is clearly not scalable, not an efficient use of your time, but that gives a lot of value to your audience. That was the entire driver behind my Operating Founder Offer.

The price removed the price question for two kinds of readers at once:

For readers who could easily afford it, $99 was nothing. Their only question was whether four hours with me were worth it.
For readers for whom $99 was real money, the math still worked. Four 1:1 sessions at that price was an easy yes, and felt too good to pass up for those who saw value in what I was doing.

Because every seat was 1:1, every buyer got direct access to me. That produced trust, testimonials, and referrals. Recommendations from readers and members now drive most of my new subscribers.

The unscalable part paid off twice: It sold 100 seats in 12 days, and it taught me what people needed across 400 hours of coaching sessions. I turned those lessons into a well develope coaching offering, the Operating Project, a course and a paid community, which are all much more scalable.

What you can do: make your first offer small, direct, and easy to say yes to. Use what you learn to build the thing or things that come next are more scalable.

How did you keep the paying members?

I published multiple times per week, increased the number of paid posts that delivered actual working assets and tools to help my subscribers grow their businesses and careers.

On March 26, I opened a private Skool community for paying members, the Operating Founder Community. The primary enticement behind the community was a weekly group AMA Office Hours session with me via Zoom, and a set of three curriculums I put together for people to build the operational infrastructure for their one-person businesses, develop their brand identity, and then build their social content engine.

While I have launched the Skool into a tiered paying community, this is not something that has taken full shape yet, but will be a real focus for me between now and the end of the year. If you’d like to become a member of the Skool community, take a look at my paid offers here on Substack. For as long as the community exists, I will continue hosting the weekly office hours.

Subscribe now

What you can do: Give paying members something free readers do not get. Direct access works.

How can I build my own AI employee, live and free?

I run a free 60-minute class with Wessal Khader. We build an AI employee that reads your calendar and inbox and hands you your top 3 moves each morning. You build yours in the room and keep it. No code. No replay.

Wednesday, June 24. 1:00 PM ET / 10:00 AM PT.

Reserve a free seat.

What are the 7 things that drove the growth?

Volume: I published more than 155 posts in 157 days.
Timing: I released my best post the same week as my offer.
Scarcity: “Seats left” beat “join now” every time.
Collaboration: I co-wrote with more than 10 people. In the last 30 days, recommendations from other writers brought 95 of my new subscribers.
Clear headlines: “AI Is Not Killing Jobs. It Is Killing Excuses.” A headline that takes a side earns higher click throughs.
Access: Making my coaching offerings affordable and the weekly group access via the office hours in the community are major drivers of continued renewals and growth.

Where new subscribers came from in the last 30 days. The Substack network drove 76.79%, LinkedIn 12.5%. Over 90% of all growth has been organic to Substack, with zero imported subscribers.

What would I do differently?

I priced the offer too low. $99 for four sessions is about $25 a session. It sold out in 12 days. That arguably means it was underpriced.
I left LinkedIn on the table. I have 40,000 followers there, and it drove only 12.5% of new subscribers. Growing Substack organically was the experiment, so this was a choice, but I could have converted more.
I ignored referrals. Readers sent 41 gift subscriptions. Only 2 became paying members. I did not build a successful outreach funnel around it.
I chased the wrong traffic: Facebook sent 13.4% of my visitors and only 2 subscribers were acquired. Huge miss on my part.

How can you build a newsletter business like this?

Publish on a schedule before anyone reads. Build the back catalog.
Pick one clear idea. Mine was a thesis: We have spent our careers doing work that computers couldn’t do yet. Companies are beocming tech stacks. We are all becoming companies.
Sell access, not just posts, products and content.
Release your proof the same week as your offer, if possible.
Build the systems: pricing, referrals, community, and your own following.

How can I work with you directly?

Everything above is the playbook. If you would rather build it with me, that is the Operating Project. Learn more here:

The Operating Project

What AI tools did you use to scale a service business?

A service business used to need a team. Advisory, coaching, content, and design all ran on people’s hours. These tools let one operator do that work and serve more people without adding headcount. Each tool removes hours from work I used to do by hand, and those hours become capacity I can sell.

Here is the stack.

Claude is the engine. I run it through Cowork, Claude Code, and its design work. I make content, build voice and brand skills, write code, and design assets with it. I teach this in my course, The Compound Content Studio.
Perplexity and the Comet browser do the research. I use them to find sources fast and to pull the data behind pieces like this one.
Midjourney makes the images. Canva finishes them. Midjourney generates the visuals, and Canva turns them into posts and graphics.
Notion holds the work. Plaud captures the meetings. Plaud records and transcribes my calls, so the notes land in Notion without me writing them.
Calendly runs scheduling. It keeps coaching and advisory time booked without back-and-forth email.
Attio is my AI-first CRM. It tracks every relationship and deal in one place.
Substack and Skool are the home base. Substack is where I publish, take payments, and reach the recommendation network. Skool is where paying members live.

What you can do: pick the tools that remove hours from work you repeat. Every hour you take out is capacity you can sell.

How can I build my own AI employee, live and free?

Wednesday, June 24. 1:00 PM ET / 10:00 AM PT.

Reserve a free seat.

Which posts are referenced in this article?

The posts named in this article, in order, with what each one did.

January

AI Didn’t Steal Our Greatness. AI Didn’t Steal Our Jobs. (Jan 16). The first breakout. It drew 1,080 views and 12 paying members from a list of about 1,000, and showed the audience wanted a clear take on AI and work.
Operating Leaders: How JP Morgan Chase Showed What AI Transformations Can Achieve (Jan 19). My first co-written post, with Hodman Murad. It drew 1,500 views and 19 paying members.
Winning the Loser’s Game (Jan 25). An essay on the creator economy. It drew 2,450 views, 34 paying members, and a 34% open rate.
The Operating Creator (Jan 27). My weekly note on the history and economics of the creator economy. It added 20 paying members.
Building Your One-Person Creator Business (Jan 30). A frameworks post for the solo operator. It added 25 paying members.
Personal Brand is the Human Differentiator (Jan 31). An essay on why personal brand matters in an AI market. It added 24 paying members.

February

From Snowboards to $220B: The Tobias Lütke & Shopify Story (Feb 1). A case-study post from the Operating Stories series. It added 11 paying members.
Scaling is Overrated: The Operating Founder Offer (Feb 1). The launch of the $99 four-session coaching offer.
0 to 55,000: The First 90 Days Playbook (Feb 2). The top post of the year. It drew 4,210 views and 85 paying members in one day.
Everything You Need to Build Your Creator Business (Feb 7). A resource post for building a creator business. It drew 2,850 views and 13 paying members.
5 Spots of 100 Left: $99 for Four 1:1 Sessions (Feb 8). A scarcity email for the coaching offer. It opened at 46%.

March

6 Seats Remain Before Operating Founder Admission Expires (Mar 9). A scarcity email near the close of the cohort. It opened at 34%.
AI Is Not Killing Jobs. It Is Killing Excuses. (Mar 13). An essay on AI and accountability. It drew 2,380 views.

April

Operating with Claude (Apr 20). A post on using Claude in daily operating work. It drew 2,640 views, the highest of the spring.

Links are built from each title on your base URL and need a quick verify, since Substack can truncate or de-duplicate a slug. The sandbox could not reach your domain and no browser was connected, so I could not confirm them live.

Frequently asked questions

How long did it take to grow the newsletter? I launched in April 2025 and spent nine months building 1,014 subscribers with almost no readership. The fast growth came in the five months after February 2026.

How big is it now? 5,861 subscribers and 266 paying members as of June 2026.

How much money does it make? Subscriptions bring in $9,600 a year. Across all six streams, the business is on track for $100,000 to $200,000 a year.

Did you import an email list or an existing audience? No. I imported zero subscribers. Over 90% of the growth came organically from Substack.

Why Substack instead of ConvertKit or Beehiiv? I wanted the network effects. The plan was to grow with zero imports and see how far the platform alone could carry the work.

What was the best-performing post? “0 to 55,000 — The First 90 Days Playbook,” published February 2. It drew 4,210 views and 85 paying members in one day.

What AI tools do you use? Claude with Cowork and Claude Code, Perplexity and Comet, Midjourney, Canva, Notion, Plaud, Calendly, and Attio.

How can I work with you? Subscribe for the posts and community, join the free Oper(AI)ting Live class, or take one of the ten Operating Project seats to build a system with me directly.

John Brewton · Founder · Operating & 6AEP

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

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

John Brewton — Sun, 21 Jun 2026 00:17:56 GMT

What is this article about? (TL → DR)

Most people use AI like a search box. They ask one thing, then close the tab.
A few people do more. They build AI “employees” that do the same work every day.
An AI employee is a setup you create once. It learns your voice, your job, and your rules.
Four are worth building first: one for writing, one for research, one for meetings, and one for your own job.
You can build the first one this week. No code. The free kits and a free live class are at the bottom.

Want to build one live with me, for free?

Register for my free Masterclass on June 16th, where I will teach you how to build your own AI Employee that runs your calendar and inbox! Register Here

A free 60-minute live class with me and . You come in with a full calendar and a packed inbox. You leave with an AI employee that runs both, and the way to build the next 10. No code. No replay.

Reserve Your Free Seat

What is the difference between using AI and operating it?

Using AI is simple. You open it, ask a question, and close it. The answer sounds flat. The tool knows nothing about you.

Operating AI is different. You build something that stays. You teach it your voice, your job, and your rules. Then you run it like a teammate who already knows your standards.

Here is a typical Monday for a person who built their staff:

A big slide deck is due on Wednesday. It gets drafted in an hour, not over two nights. Three calls run back-to-back.
Each one ends with the notes, the choices, and the next steps already written.
A question that used to eat up an entire afternoon comes back with a sourced answer before lunch.

That is not a dream. That is what the right setup does.

What is an AI employee?

An AI employee is not a prompt you copy from Reddit or copy and paste from a LinkedIn post. It is a setup built for one job inside your team and workflows. You engineer it one time. It runs the same way every time. You adjust and tweak, but do not ever need to rebuild.

These are the four most worth building first:

The Voice Employee writes like you. You train it on your real emails and posts. It can write to a client, to your team, or to your boss, each in the right tone. It checks its own work before you see it. Done right, no one can tell you used it.
The Research Employee gets you ready. You give it the questions you ask every week. It pulls real facts, sorts them, and hands you a short report with sources. It preps you before any meeting that matters.
The Meetings Employee kills the blank slide. You give it your rough notes. It hands back a clean deck you would actually send. It writes the agenda. It turns your call notes into a summary. Every meeting ends with the work already done.
The Role-Specific Employee is yours alone. You have one task that eats hours it should not. A report. A proposal. A weekly grind. No template fits it. You build this one around your real job.

Connect With Me On LinkedIn

Connect on LinkedIn

Why do most people get AI wrong?

Most people stay stuck for one reason. They think of AI as a tool. Tools are passive. You pick them up, use them, and put them down.

An employee is different. It carries what it knows. It knows your standards. It knows what you would say no to. You do not explain yourself again every time.

Change that one idea, from tool to employee, and everything changes. How you build it. How you talk to it. What you expect to receive back.

Consulting Perspective: This is one of the biggest mental hurdles I see inside my clients’ companies. Individuals struggle to provide enough direction, detail, and specificity when they’re prompting one of the models to perform a taks, or within the build process for the engineering of their agent.
When you make the jump to looking at the AI as a new hire, a talented, smart analyst, and begin providing said analyst with all of the context, reference materials, and clear direction that you would to any team member you were asking to produce high-quality work product, that is when the quality of the results, the outcomes, and the strength of the builds you are engineering, dramatically improve.

The primary apps are not hard to use. Claude is strong in long writing and voice. Perplexity is built for research with sources. ChatGPT does a little of everything very well and is currently the cat’s meow for image generation. Gemini sits within Google, where many people already work, and has a unique capability to help with management and team-dynamics queries. You do not need to master one. You need to know which one to grab, and why.

Are most people actually good at AI?

Here is the hard part. Most people in your meetings are faking it. They put “AI” in their slides. They say they are “trying the tools.” They paste prompts into free accounts and take screenshots of the results.

The gap is getting larger. You can see it in who writes the clearest report the fastest. Who shows up to the meeting with the work done? Who gets more done without longer hours? That person, most likely, quietly built their staff.

The most costly move right now is to wait until it is obvious. By the time the gap is loud, it has already cost you.

How do I start building my AI staff?

You can build your staff this summer. Do it before the calendar fills and the year resets. Do it before a skill gap turns into a career gap.

Start with the Voice Employee. Train it on 10 to 15 real things you have written. Tell it what you never say. Tell it how you sound when you are rushed. Tell it what “clean but not stiff” means for you. Run 30 drafts before passing judgment.

What are the AI staff kits?

I built the four employees as kits so you do not have to start from scratch. One kit per employee: Voice, Research, Meetings, and Role-Specific.

A kit is a skill file. It holds the steps that run the job, the notes that carry your standards, and a template for the output. That is the gap between a prompt you retype and an employee who already knows the work.

Each kit has 2 modes. BUILD sets up your profile the first time. Your profile is the file that holds what the kit learned about you. OPERATE runs the job at your standard after that, and checks its own work before you see it.

How do I install and run the kits?

First, install it. If your app takes skill files, load the kit. If your app uses projects instead, paste the steps into a project and add the notes as files.

Second, run BUILD one time. Answer the questions. For Voice, that means 10 to 15 writing samples. For Research, your weekly questions. For Meetings, your formats. For Role-Specific, the one task worth it. This makes your profile. Keep it. It is the part that matters most.

Third, run OPERATE from then on. Give it the input. Take the output. Fix what missed. Save the fix back into the profile. It gets faster each time.

Start with the Voice Employee. Build it this week. Run 30 drafts before you judge it. Then add the next one. Each one makes the next build faster, because the work you did once carries over.

That is the whole setup.

Why does my AI output still sound generic?

The build misses in 2 ways. Both are quick to fix.

The skill does not run. The app only uses a skill when the description tells it when to. If nothing happens, just say it: “Use my Voice Employee to draft this.” Then check two switches. The skill and code tools are turned on in settings. One of those is almost always the cause.

The output still sounds robotic. That is a thin profile, not a broken tool. Add 5 more writing samples. Use the ones you wrote in a hurry. Make the “never say” list longer. Name 3 phrases you would never use. Run 10 more drafts. It gets sharp from your fixes, not from the first try.

When the output misses, fix the profile, not the prompt. The prompt stays the same. The profile is the part that learns.

Want me to build it with you?

We take one problem in your business and finish it across four sessions. You pick the problem: your pricing, your brand, your content plan, or one issue inside a company you already run. We name it, we plan it, we build the machine inside Claude or ChatGPT, and we put it to work on real tasks.

It normally sells at $999. The first 10 seats are $499, half off. Each seat includes a full year in the Operating Founders community in Skool and weekly Friday office hours. You can run your four sessions any time through December 31, 2026.

Claim Your Seat

Frequently asked questions

Do I need to know how to code?

No. You do not write code. You answer questions and paste in samples. The kit does the rest.

Which AI app should I use?

Any of the big ones works. Claude is great for writing and voice. Perplexity is great for research. ChatGPT and Gemini both work too. Pick one and start.

How long does it take to build the first one?

About an hour to set up the Voice Employee. Then run 30 drafts to make it sharp. You can do it in a week of short sessions.

Is this safe with my private writing?

Your profile lives in your own account. Only load kits from people you trust. Read the files before you turn them on.

What if the output still sounds robotic?

Add more writing samples and a longer “never say” list. Fix the profile, not the prompt. It learns from your fixes.

How much does it cost?

The kits are free. A paid AI plan runs about $20 a month. A free plan is enough to start.

Do I have to build all four at once?

No. Build the Voice Employee first. Add the others when you are ready. Each one makes the next faster.

What is the difference between a prompt and a kit?

A prompt is one message you retype. A kit is a saved setup that already knows your job and runs the same way every time.

Where do I get the kits?

Have a look at the Field Guide here and then become a paid subscriber to access all of the skill kits and files below.

Become a paid subscriber for as little as $7.99 per month and access them below…

Subscribe now

Oper(AI)ting Masterclass Preview - Build Your Own AI Employee w/ John

John Brewton — Fri, 19 Jun 2026 14:58:09 GMT

Available for iOS and Android

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

John Brewton — Mon, 15 Jun 2026 23:26:19 GMT

Most coaching ends the same way. You leave with notes, a few good ideas, and the quiet knowledge that in three weeks the notes will be cold and the ideas will still be ideas. The work that was supposed to change did not change.

You paid for advice. But you needed a system.

I operated, optimized, and scaled a B2B distribution company from $27.5M to $75M and sold it in August of 2021. I have spent more than 400 hours this year coaching founders and operators. I’ve learned so much from the incredilbe +100 founders, entrpreneurs, operators and professional I’ve been fortunate to work with. Many of these amazing people have become my friends and collaborators and for that I am grateful.

I wanted to take all that I’ve learned from these individual engagements and turn them into a product and system to drive outcomes for the Operators and Builders I’m proud to call my clients.

What I came up with is the Operating Project.

Claim Your Seat!

What it is

The Operating Project takes one problem in your business and works it to a finished result across four sessions. You pick the problem. I bring the structure, the worksheets, and the build. The outcome is defined before we start, and the project closes when the finished thing exists.

This is not open-ended coaching. It is not a standing retainer. It is one operating problem, scoped to one outcome, closed.

Pick one problem

You choose the project. Four kinds of work come up most often.

A business model. Your pricing, your offer design, and the economics of what you actually sell.
A brand identity. The system that makes your work recognizable across every surface, instead of a new look every time you post.
A content strategy. The plan and the machine that produces it on a schedule, instead of the weekly scramble.
A matter inside a larger business. One specific problem in a company you have already built and need to fix without rebuilding the whole thing.

One problem. One outcome. One finished artifact you can use.

Claim Your Seat!

The four sessions

The work runs in four moves.

First, we diagnose. We name the problem in plain terms, set the outcome you want to hold at the end, and define the finished artifact. We draw the scope and what sits outside it.
Second, we design. We build the actual plan. Pricing models, offer maps, positioning frameworks, content calendars, the worksheet that fits your project. You build them with me and you keep them.
Third, we build the machine. This is the part nobody else includes. We stand up a working system inside Claude or ChatGPT that produces the work you just designed. The skills, the projects, the workflow. You own it. It lives in your account.
Fourth, we install. We run the system on a live piece of your business, confirm it holds, and define done.

The sessions are not timed. Some run thirty minutes. Some run longer. The unit of measure is progress, not a clock.

Claim Your Seat!

A Plan Tells You What To Do. A Machine Does It For you.

Most advice dies because it stays a document. A document needs you to remember it, apply it, and keep applying it after the energy of the session fades. A machine does not.

That is the difference here. You leave with a system that runs after I am gone. I am in these tools every day, building the systems I write about. The build is the same work I do for myself, handed to you and tuned to your business.

What People Are Saying About John

“John doesn’t give you advice. He gives you clarity.” Matt Burton, founder of Savant-AI.

“His perspective on AI is practical and lived-in. He is not giving abstract advice from the sidelines. He is in the tools himself, testing workflows and building systems.” Arvita Tripati, founder of Vahana Labs.

“A savant when it comes to operations. I trust his opinions and respect his instincts when it comes to making my business a success.” Laura LeBleu, founder of Geezer Magazine.

Claim Your Seat!

The Terms

The Operating Project sells at $999. I am opening ten founding seats at $499, half the price, for the first ten people who take one.

When they are gone, the founding rate is gone. Ten is the most I can deliver well alongside the work I already carry, so the cap is real.

Each seat includes a full year of VIP access to the Operating Founders community and the weekly Friday office hours.

You can run your four sessions any time through December 31, 2026. Lock the seat now, start when you are ready.

Take one of the ten before they’re gone!

Pick the problem. I will help you solve for it, and build the machine that keeps it solved.

Claim Your Seat!

- John -

About John

John Brewton documents the history and future of operating companies at Operating by John Brewton. He is a graduate of Harvard University and began his career as a PhD student in economics at the University of Chicago. After 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