<![CDATA[Operating by John Brewton]]>https://www.operatingbyjohnbrewton.comhttps://substackcdn.com/image/fetch/$s_!87Ds!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa8178186-b7d3-4cb4-8aa1-c17369bd2128_600x600.pngOperating by John Brewtonhttps://www.operatingbyjohnbrewton.comSubstackTue, 03 Nov 2026 03:13:49 GMT<![CDATA[AI Is Coming for Your Answers. Give People a Reason to Come for You.]]>https://www.operatingbyjohnbrewton.com/p/ai-is-coming-for-your-answers-givehttps://www.operatingbyjohnbrewton.com/p/ai-is-coming-for-your-answers-giveMon, 02 Nov 2026 12:51:03 GMT

  1. How to Build an AI Operating Team in Grok Bot: The Field Manual

  2. You Don’t Have a Software Problem. You Have a Database Problem.

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

  4. The Creator Economy’s Original Sin

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

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

Subscribe now

Hello Operators!

Last week, the market put a price on the free answer, and the total exceeded half a trillion dollars.

summed it up better than I could in a post that’s been bouncing around my feed since Friday: Reddit stock is down because of AI answers. Google stock is down because of AI answers. Your web traffic is down because of AI answers. The internet has entered a new era. Social media has entered a new era.

I want to take that framing one step further, because the companies in those headlines are the headline version of a question every consultant, advisor and creator I work with needs to be asking.

What do people get from you that they can’t get from a prompt?

On Wednesday, October 28, Alphabet reported search revenue of $63.35 billion, up 12% but below analyst expectations for the second straight quarter. Philipp Schindler, Google’s chief business officer, told investors that AI Mode queries carry “lower monetization.” The next day the stock fell about 13%, its worst day since Google went public in 2004, and roughly $500 billion of market value went with it. Google also said query growth was the strongest in five years, which is the strange part. More people asked Google more questions, and Google made less from each answer.

Friday it was Reddit’s turn. The company beat on the quarter, with revenue up 53%, and then told investors its search referrals had dropped by more than a million a week through October. Steve Huffman warned that daily visitors could fall by a low double-digit percentage from about 130 million in the fourth quarter. The stock fell 29%, its worst day ever.

Reddit is the place people go for lived experience, and Google’s summaries now hand out that lived experience without the clicks.

Sources: CNBC, Pew Research Center, Chartbeat

Your Traffic Was The Canary

None of this surprised the people who run websites for a living.

Pew Research tracked 900 U.S. adults and their actual Google searches. When an AI summary appeared, people clicked a traditional link on 8% of visits, compared with 15% when there was no summary. They clicked a source cited inside the summary on 1% of visits. Chartbeat, which measures traffic across thousands of publisher sites, found Google Search referrals down 40.2% from July 2025 to July 2026, nearly double the decline it measured the year before. Over two years, small publishers lost 60% of their search traffic.

Chartbeat’s own read is the line I’d underline for every content business: “generic, easily-summarized content is the most exposed.” If your work can be summarized without losing anything, it will be, and nobody will click through to thank you.

Explanations Are Now A Commodity

If you sell content, consulting, advisory or professional services, here’s what is at risk:

  • An explanation.

  • A summary.

  • A framework.

  • A polished presentation of widely available ideas.

All of those are getting cheaper to produce every month, and I say that as someone whose agents now do a lot of my research and packaging. But I’m not complaining. I’m building on it.

What stays hard to reproduce is the perspective you earned by doing the work. The pattern you recognize because you’ve lived through it. The question you ask because you once asked the wrong one and paid for it. The recommendation you make because you know what implementation will cost the team that has to carry it out.

Subscribe now

The answer is getting cheaper, and the judgment behind it is getting more valuable, and those two prices are moving in opposite directions.

Why I Subscribe To People

I don’t subscribe to someone because they can explain what everyone else can explain. I subscribe because I want to know what this specific person makes of it.

Their analysis surprises me. Their stories hold my attention. Their judgment helps me see a decision differently, and their voice makes me want to keep reading after I’ve gotten the point.

Education still matters. Increasingly, though, the value sits in what you add to the information: what you notice, what you question, what you connect, what you recommend and, most of all, what you’re willing to defend when someone pushes back.

Look again at where Chartbeat says publisher traffic is holding up. Direct visits, internal clicks, and links shared privately in texts and DMs all grew as a share of pageviews while search shrank. People still show up by name for the sources they trust. They just stopped wandering in by accident.

The Same Thing Is Happening Inside Companies

A CEO can ask AI for twenty ways to improve margins this afternoon, and the list will be pretty good.

The CEO can’t get from the list which two deserve attention, which assumptions need testing before anyone spends a dollar, or how to get a tired organization to actually act. I see this with clients every week. The data is rarely the problem. By the time it reaches the leadership team, it’s stale, or it was never true to begin with, and someone has to know which number to trust and own what happens next.

That takes context, judgment, trust, and responsibility for the outcome. An AI can hand you options all day, but it can’t be accountable for the one you pick.

This is what I mean when I say companies are becoming tech stacks. Software is absorbing the research, drafting, and analysis. What’s left for the people is the judgment that points the stack somewhere.

Subscribe now

Let Them Experience Your Judgment First

So your content has a new job. It should let people experience your judgment before they ever hire you.

  • Show us why the obvious answer might fail.

  • Explain the tradeoff everyone skipped.

  • Tell us what changed your mind.

  • Make a difficult idea clear and compelling, even entertaining, and give us something worth thinking about after we close the app.

That is how you build a market of one. Your experience shapes your perspective, your perspective shapes your work, and your work gives people a reason to look you up by name instead of by keyword. Reddit is betting on the same thing. Its answer to losing search traffic is to push people to log in and come back on purpose.

When the search engine stops sending people, your name has to do the sending.

Where The Opportunity Sits

Here’s the opportunity I see for creators, consultants, and operators. We are all becoming companies, and AI lets one person research, build, and deliver at a scale that used to need a team.

Your judgment is what gives it direction. When everyone can publish an answer, people go looking for whose thinking they want in the room, and you want that to be an easy call.

If you’re a consultant, advisor, or creator working through what this means for your own offer, I’d love to talk with you.

- j -

  1. How to Build an AI Operating Team in Grok Bot: The Field Manual

  2. You Don’t Have a Software Problem. You Have a Database Problem.

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

  4. The Creator Economy’s Original Sin

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

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

Subscribe now

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

Sources

]]><![CDATA[Build for the Customer of 2030, Not the Broken Process of 2015]]>https://www.operatingbyjohnbrewton.com/p/build-for-the-customer-of-2030-nothttps://www.operatingbyjohnbrewton.com/p/build-for-the-customer-of-2030-notSat, 31 Oct 2026 16:02:07 GMT

Subscribe now

Start Here

In the first half of this year, Anthropic took in $16.29 billion in revenue. A year earlier, the figure was $1.16 billion.

That number, from the S-1 the company filed on October 26, is market signal for all the founders building companies right now. Almost no one was paying for Claude to help them fix a process they already had, they were paying to enable their companies, teams and workflows to begin working in a fundamentally new way.

The same week of the S-1 release, Big Tech lost nearly $900 billion in value over two days, according to the Financial Times. Alphabet had the worst day in its history. Intel had its worst day ever.

Companies need to build for what comes next, not solve problems for a version of the world that becomes less relevant every week. That’s the thesis of this essay: Build for the customer of 2030, not the broken process of 2015.

  1. How to Build an AI Operating Team in Grok Bot: The Field Manual

  2. You Don’t Have a Software Problem. You Have a Database Problem.

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

  4. The Creator Economy’s Original Sin

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

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

Subscribe now

Customers Can’t Ask For What They’ve Never Seen

Steve Jobs understood this problem well. “You can’t just ask customers what they want and then try to give that to them,” he told Inc. in 1989. “By the time you get it built, they’ll want something new.” His reasoning was simple. Customers can’t anticipate what the technology can do, so they never ask for things they assume are impossible.

He held to the idea for the rest of his career. “Our job is to figure out what they’re going to want before they do,” he told his biographer, Walter Isaacson.

AI makes that discipline urgent. Most customers have never seen an agent close their books, file their permits, or run their support desk, so they don’t ask for one. They ask for a faster version of the process they already have. A company that builds only from that list of requests is building for the past, and falling further and further behind the future that rapidly unfolding.

The Growing Gap

In a widely shared clip, Marc Andreessen described the gap between AI-native startups and incumbents as the widest he has seen in his career. Founders and operators find new ways to build and automate almost every week; it’s what they do. Established companies are still fitting the technology into the processes and systems and ways of thinking they already run.

Subscribe now

He made a related argument in his X Article, “Replit and the dawn of the self-driving company.” The idea is a company where software does more and more of the operating work, and the people steer.

Startups are asking what the world will need next. Many incumbents are waiting for a slide deck that explains what they can buy today.

This Time Isn’t Different

Every platform shift ends the same way. New companies get funded to build for the world the platform creates. Incumbents patch old workflows, and the patch buys them a few years.

Retailers put their catalogs online; Amazon was built for a customer who would never visit a store. Taxi companies added booking apps; ridesharing built for a phone in every pocket. Software vendors sold licenses to automate paper forms; cloud companies built for a world where nobody installs anything.

AI is the same pattern at much greater velocity. What took a decade with the internet is taking a year or less now.

If you like this, you might LOVE this one!

Subscribe now

Elon Builds For the World That Hasn’t Happened, Yet

No one has made this bet more literally than Elon Musk. Each of his companies sells something that assumed a world that did not exist when he started building it.

He has described the method many times. “I think it’s important to reason from first principles rather than by analogy,” he told the technology investor Kevin Rose in 2012. Reasoning by analogy, he said, means “we’re doing this because it’s like something else that was done, or it’s like what other people are doing.” First principles means you “boil things down to the most fundamental truths” and “then reason up from there.”

SpaceX is the clearest case. Launch customers asked for cheaper rides on rockets that were thrown away after one flight. SpaceX built boosters that land and fly again, then used that launch capacity to put up its own broadband network. Its filings now describe a company that “designs, manufactures, and launches reusable rockets” and runs a network “powered by thousands of Starlink satellites,” serving millions of customers.

The same filings list a third business, which SpaceX calls Super Intelligence and which runs Grok, the frontier model. SpaceX acquired Grok’s developer, xAI, on February 2, in a deal Reuters reported valued xAI at $250 billion and SpaceX at $1 trillion. Rockets, satellites and AI models now sit inside one company.

Tesla is running the same play on the ground. On September 3 it started Robotaxi rides in its Cybercab in Austin, and by the end of the third quarter its fleet had logged nearly two million cumulative unsupervised miles, according to its quarterly update. Production of Optimus, its humanoid robot, began on high-volume lines in Fremont. Musk told investors Tesla could start shipping the robots to outside companies by the end of next year, according to The Wall Street Journal. He was candid about the distance left: there is “still quite a lot of work to do with regard to the AI and the robot for it to be truly useful for open-ended tasks,” he said on the call, as reported by CNBC.

Now Tesla and SpaceX, worth about $1.5 trillion and $2 trillion, are weighing a merger that would combine about $3.5 trillion of value, according to Fortune. No terms have been agreed.

The lesson for operators sits in a line Musk gave Lex Fridman in 2023: “Physics is the law, everything else is a recommendation.” Most of what a company calls a constraint is a convention someone set years ago. A rebuilder asks which ones are physics.

Patching is a trap

Here is the problem with fixing the old world: AI makes the old fix cheap. When the fix is cheap, the margin on selling it disappears.

Look at ServiceNow. Its third-quarter revenue rose 20.5% to $4.11 billion, a strong result by any normal standard. But its outlook missed, and on October 29 the stock fell more than 26%. The same week, Serval raised $150 million at a $3 billion valuation in a round led by Sequoia. Serval says it moved an automaker off ServiceNow in 45 days. The market was judging which company is built for how IT work gets done next.

Intel shows the other side. It guided fourth-quarter revenue to $15 billion to $16 billion, against $17.1 billion expected, citing weak PC demand as AI drives up memory costs, plus a fab explosion. The result was the worst day in its history.

Audi’s third-quarter net profit fell 87% to €95 million, and it cut its margin guidance to 3% to 5%. Different industries, same lesson: a model tuned for yesterday’s economics breaks when the economics shift.

The debt markets see it too. The Wall Street Journal reports that private-equity-backed companies face a $300 billion debt wall in 2028 and 2029, and software companies make up about a third of the riskiest loans. Many of those are businesses built to manage the pre-AI workflow.

I wrote about this in Enterprise Software Was Built for a World That No Longer Exists. The seat-based model assumed humans doing the work. Once agents do it, the seat stops being the unit of value.

What Capital Is Actually Funding

The companies with the highest valuations share one trait: their products assume a world that doesn’t fully exist yet.

Anthropic was valued at $965 billion in May. Its gross margin was about 85%, and it posted an operating loss of $693 million. The headline net loss for the first half was $80.7 billion, but roughly $72 billion of that was accounting charges on convertible notes. The company has committed hundreds of billions to compute; the FT puts the figure at about $800 billion, and Reuters at more than $600 billion.

OpenAI was valued at $852 billion after raising $122 billion in March. It reached about $50 billion in annualized revenue in September.

Alphabet grew third-quarter revenue 25% to $127.6 billion, and Google Cloud more than doubled, up 103% to $30.7 billion. It also posted negative free cash flow of $9.07 billion, its first negative quarter as a public company, while keeping capex guidance at $195 billion to $205 billion.

Amazon kept its $220 billion spending plan even as trailing free cash flow swung to an outflow of $42.4 billion.

Nvidia reported fiscal second-quarter revenue of $96.2 billion, up 106%, with $89.0 billion from data centers.

Tesla and SpaceX, as described above, are weighing a combined company worth about $3.5 trillion.

None of this is advice to buy or sell anything. It is a description of my belief in what we should be building as builders, creators, founders, and operators. Investors are financing visions of how people will work, move, compute, and live, and they are financing them at a scale we haven’t seen.

The Capital Market’s Appetite Has Limits

OpenAI’s new round is priced at $1.25 trillion, below the $1.4 trillion it sought, and the target was cut to about $20 billion with about $11 billion committed. Blackstone now expects Anthropic’s debt to be rated high junk after the IPO, not investment grade. Even Tesla’s free cash flow fell 62% to $1.53 billion as capital spending more than doubled on Optimus and other AI work, CNBC reported.

That discipline only sharpens my thesis. Capital isn’t rewarding spending for its own sake. It’s rewarding a credible path to the new world, and punishing both the patchers and the spenders who can’t show the path.

What The Next Customer Will Need

If you’re building, the question is what people and institutions will need once AI is ordinary. Consumers will expect agents that act on their behalf, from shopping to scheduling to managing money. Amazon is already building its own shopping agent rather than ceding the customer to outside ones.

Businesses will need fewer seats and more outcomes. Small teams will run what used to take departments or entire divisions. I made that case in Build a Company of Curators: the job shifts from doing the work to judging it.

Public services will need faster, simpler delivery: permits, benefits and records handled in minutes, not weeks.

Health will need continuous care rather than episodic care, with diagnosis, monitoring, and follow-up that doesn’t wait for an appointment.

In each case, the winning product doesn’t make the old process 20% faster. It makes the old process unnecessary.

Some old companies will falter. Some old jobs will go away. But every one of those losses sits next to a new market: new needs, new products, new ways of producing, new customers. Operators who see both sides of that ledger will build the companies that capture the markets that have fualtere and the capital that has flowed to new, yet to be determined, locations.

The Operator Playbook: Three Questions

Before you fund, build or buy anything this year, reason from first principles and test it against three questions.

  1. Does this still matter if AI gets 10 times better? If your product’s value shrinks as models improve, you’re building a patch.

  2. Who is the customer in 2030, and what will they never do again? Build for what they’ll stop doing, not what they do now.

  3. Would an AI-native competitor build this from scratch? If they’d skip your process entirely, so will your customers, eventually.

  4. If your plan fails two of these, you’re optimizing a world that’s leaving. For more on getting a team to actually make the shift, see How to Break Through the AI Adoption Wall.

The Takeaway

Companies are becoming tech stacks. We are all becoming companies. The tools to build for the next world are available to almost anyone for a monthly subscription, while the giants carry the bill on their balance sheets.

The velocity of change has never been higher, and neither has the cost of looking backward or standing still.

So here’s my question: What are you building that only makes sense in the world that’s coming?

- j -

Subscribe now

If you enjoyed this, you might LOVE this!

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

Sources

]]><![CDATA[You Don’t Have a Software Problem. You Have a Database Problem.]]>https://www.operatingbyjohnbrewton.com/p/you-dont-have-a-software-problemhttps://www.operatingbyjohnbrewton.com/p/you-dont-have-a-software-problemThu, 29 Oct 2026 11:31:48 GMT

Subscribe now

A Year of Building

This year, I’ve built AI-native CRM systems, company valuation software, investment and market research systems, ERP modules, company data reservoirs, and content-generation machines.

Across all of these applications, dozens of product design configurators, catalogs of thousands of parts, inventory counts, transaction counts, visual design systems, and customer orders in currencies from around the world. They all live in a variety of Postgres databases on Supabase.

Software is becoming a complex of databases designed to enable agentic queries, visual and analytical outputs, and autonomous workflow progressions.

The shift that these projects have demonstrated to me is visible in how most all enterprise software is getting built. In June, Supabase CEO Paul Copplestone said AI coding tools now start more than 60 percent of new databases on the platform, with Claude Code the single largest source. By October the company put the share at roughly 70 percent. Agents are choosing the database before a person chooses the software. Supabase’s reported valuation reached $10.5 billion.

The durable asset is the company’s record of how its business works. A database gives that record a structure that people, applications, and agents can use.

Models keep changing. A clean database, with agreed definitions and controlled access, remains useful through those upgrades. I use Supabase as the foundation because it lets me build around that record without buying a separate system for every workflow.

  1. How to Build an AI Operating Team in Grok Bot: The Field Manual

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

  3. The Creator Economy’s Original Sin

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

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

Subscribe now

What is Supabase?

Supabase is a managed Postgres database with the parts every application needs already wired in: user login, file storage, server-side functions, real-time updates, and vector search for AI features. Those services sit around the same database.

Postgres matters more than the brand. It is a 30-year-old open-source database with a widely used query language, SQL. Engineers and coding models can work with it directly. If you outgrow Supabase, you can move your Postgres tables and SQL, though you may need to replace its other services. With a packaged application, moving the data often also means untangling the vendor’s definitions and rebuilding its workflows.

Supabase has also attracted substantial funding. Supabase raised $500 million at a $10.5 billion valuation in June 2026, then another $150 million in October alongside the acquisition of Turso, a database built for agents that spin up millions of small databases. It reports more than 13 million developers on the platform.

The entry cost is lower than many operators expect. Free costs $0. It is for prototypes: 2 projects, 500 MB each, paused after 7 days of inactivity.

Pro starts at $25 a month per organization, plus compute. A $10 monthly compute credit covers 1 small instance. The plan includes 8 GB of database storage, daily backups kept for 7 days, and a spend cap enabled by default.

Team starts at $599 a month and adds features like SSO and SOC 2 documentation for customer security reviews.

Enterprise has custom pricing for larger or regulated workloads.

A Pro organization running 2 projects on the smallest compute costs $35 a month. I run 5 projects for client and research work in 1 organization. For a research reservoir scoped this month, the projected all-in running cost is below $100 a month.

Why You Need to Care More About Databases

AI has cut the effort required to write an application. That makes custom software practical for work that once could not justify a development budget. Agents can provision the database as part of the build, and Supabase reported 600 percent growth in database launches through June 2026.

The market has started questioning the old economics. By early 2026, Salesforce, ServiceNow, and Atlassian were down 25, 30, and 40 percent for the year, respectively, as investors debated how AI would affect software spending. Those declines prove companies won't abandon their vendors. They make the build-versus-buy question harder to dismiss.

I talk about reservoirs more than databases now. A reservoir is the governed store of your company’s facts that every model and every app draws from. A dashboard may take an afternoon to rewrite. The underlying data can take months to reconcile, including decisions about what each field means and who can use it. A better model can use that work. It cannot make those decisions for the business.

That changes the build-versus-buy question for a mid-sized company. You buy the commodity: email, payroll, accounting. You build the layer that encodes how your business works because it captures business-specific decisions.

A vendor charging per seat to display your records now has to justify the price against an application you can own. The subscription is easy to count. Include maintenance, security, and support on the build side of the comparison.

My Five Operating Use Cases

These examples come from my own work and client engagements. The figures describe the databases as of October 27, 2026. Some support established workflows. Others support products still being built.

Subscribe now

Read more

]]><![CDATA[We Are All Becoming Companies: They Burn the Billions. You Rent the Results. ]]>https://www.operatingbyjohnbrewton.com/p/we-are-all-becoming-companies-theyhttps://www.operatingbyjohnbrewton.com/p/we-are-all-becoming-companies-theyMon, 26 Oct 2026 11:01:43 GMT

TL → DR

Start Here

The AI empire builders are burning through billions. At the same time, creators, consultants and small business owners are building profitable companies on top of what those billions produce.

That is the extraordinary economic moment we’re living through, and a seet of key numbers that were reported last week made the opportunity and unusual economic dynamic particularly easy to see.

The Cost Of Building The Platform

Last Tuesday, The Wall Street Journal reported on a draft of Anthropic’s IPO filing. According to the Journal, the draft shows an $80.7 billion net loss for the first half of 2026 and signals that the company could spend $750 billion in the coming years on computing capacity, including deals still under negotiation.

A clarification matters here. Most of that loss reflects an accounting charge. Reuters, summarizing the Journal’s report, says about $72 billion of it came from changes in the value of convertible notes and other instruments the company issued to raise money. It does not mean $80.7 billion went up in smoke.

The underlying investment race is still enormous. Per the Journal’s reporting, Anthropic spent close to $9 billion on computing capacity and infrastructure in the first six months of the year, more than it spent in all of 2025. Add the rest of the field and the spending on research, chips, data centers and power reaches a scale few industries have ever attempted.

The bills arrive in other forms too. On Sunday, the owners of Elvis Presley’s music sued Anthropic for about $41 million over the lyrics to 274 songs they say were used to train Claude without permission. Anthropic says it will fight the case. Whatever the outcome, legal risk is one more line item the platform builder carries so its customers don’t have to.

  1. How to Build an AI Operating Team in Grok Bot: The Field Manual

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

  3. The Creator Economy’s Original Sin

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

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

Subscribe now

Where The Profits Land, For Now

The money spent building AI shows up as revenue somewhere. Right now it lands mostly with the companies selling the picks and shovels. A Dow Jones Market Data analysis found that Nvidia and Micron account for 34% of the S&P 500’s expected third-quarter earnings growth, and 10 companies account for roughly two-thirds. Without those 10, the rest of the index is expected to grow profit about 10%.

The big platforms are also consolidating the layer above them. Salesforce agreed to buy the customer-engagement company Braze for about $5.66 billion including debt, a 58% premium. Standalone software keeps getting folded into larger suites.

Read those numbers as a map of the infrastructure layer. The bigger question for the rest of us is what gets built on top of it.

The Corporate Promise is Wobbling

Inside large companies, the payoff is arriving more slowly than the spending. Gartner surveyed 2,002 CIOs and technology executives and found that only 13% say their organizations have gotten significant or transformational value from AI.

Belief is splitting along the org chart. In the Milken Institute–Harris Poll Listening Project, 68% of business leaders expect to be better off financially in five years because of AI, compared with 27% of American workers. That 41-point gap was the hook for a DealBook piece in The New York Times this weekend.

Young people feel it most. The FT reports that students fear AI will ruin their job prospects. The Journal recently profiled a Princeton Ph.D. who has applied to more than 2,300 jobs without landing one.

For decades, the safe path ran through a corporate job. The employer provided the tools, training, and paycheck. When executives are far more confident about AI than the people who work for them, and entry-level hiring stalls, that bargain looks less secure. Confidence starts to move toward ownership.

The Rental Economy

Here is what makes me optimistic. The platform builders are paying for capabilities that are available to almost anyone, by the month.

The infrastructure costs billions. Your opportunity can start with a customer and a problem.

A creator can turn hard-earned expertise into a course, a tool and a paid community. A consultant can build the software that delivers their advice. A small business can automate the administrative work that used to consume its owner’s evenings. A solo, operator can research, design, build, market and sell an offer that once required an entire team.

You can see where talent is moving. Bill Owens, the former boss of 60 Minutes, is joining Newpress, a start-up that publishes creators’ video journalism on YouTube. When the person who ran one of television’s most storied newsrooms bets on creator-led journalism, the creator model has grown up.

You can see it in the tools being built for small companies. The French start-up Pennylane has won about a third of France’s small and midsize businesses with AI bookkeeping software that works alongside accountants.

At the other end of the scale, companies are building. The FT reports that Toyota is taking on Tesla and China in the humanoid robot race. Toyota has told investors that about 400,000 robots would be needed across Toyota, its group companies and major suppliers, with automation spending of up to about ¥1 trillion a year from 2028. The world’s biggest cait would needer is rebuilding itself. A one-person company can now build a small version of the same thing.

That creates a remarkable juxtaposition. The platform builder can be losing money while the business built on its platform earns a healthy profit.

Access Alone Won’t Create A Profitable Business

None of this happens automatically. Owners still need judgment. They still need customers. They still need to deliver something worth buying.

The Journal recently followed people who bought laundromats after social media promised them easy money. It was anything but. The FT looked at how YouTubers finance their videos: launching is cheap, but a channel that takes off needs working capital, and banks are wary of lending. Cheap tools lower the cost of starting. The economics of the business still have to work.

Luxury offers a useful reminder of what holds up. In the third quarter, Hermès grew revenue 4.3% at constant currency, startup costs. The brand with the clearer, scarcer offer held its ground. Specific beats generic at every size.

So the work is to combine these capabilities with something specific. A problem you understand. A customer you can reach. An outcome someone will pay for. Then use the technology to improve delivery, expand capacity and keep more of what you earn.

That is also the lesson inside Gartner’s 13%. Everyone can buy the same tools. Results come from how the work gets organized around them. A small business that redesigns one workflow well can capture more value than a large company running a hundred pilots.

Gratitude

I feel real gratitude for this moment. For the researchers doing the work. For the engineers building the systems. For the people willing to finance something this ambitious.

Their incentives are commercial. The opportunity they create for others is still extraordinary. We don’t have to own the infrastructure to build something valuable on top of it.

That is where my optimism lives. In the owner who can finally grow without adding another exhausting workday. In the creator whose knowledge can become a business. In the skilled person who can now bring an idea to market.

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

The empire builders are financing a race for the future. We have an opportunity to build our livelihoods along the way.

What are you building on top of it?

- j -

FAQ

Do I need to build my own AI?

No. The infrastructure costs billions, and the companies racing to build it are carrying that cost. You can use their work for the price of a subscription. We don’t have to own the infrastructure to build something valuable on top of it.

Is the Anthropic loss real money?

Mostly, it’s an accounting charge. Reuters, summarizing the Journal’s report, says about $72 billion of the $80.7 billion loss came from changes in the value of convertible notes and other instruments. The underlying spending is still huge: close to $9 billion on computing and infrastructure in six months. [UNVERIFIED: these figures come from a draft filing, and Anthropic has not filed publicly.]

What should I build first?

Start with a customer and a problem. Pick a problem you understand, a customer you can reach and an outcome someone will pay for. Then redesign one workflow well, because that can capture more value than running a hundred pilots.

What if I still have a corporate job?

Keep it, and keep learning. The point is that the safe path looks less certain than it used to, so it’s worth building skills and ideas you own. You can rent the same tools a solo operator uses, and the judgment you build at work carries over.

What still can’t AI do for me?

It can’t supply judgment, find your customers or make the economics work. People who bought laundromats after social media promised easy money learned that cheap tools lower the cost of starting, but the business still has to work. You still have to deliver something worth buying.

Why does a specific offer matter so much?

Because everyone can buy the same tools. In a soft luxury market, Hermès grew 4.3% while Gucci fell 6%. The clearer, scarcer offer held its ground, and specific beats generic at every size.

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

Sources

  1. The Wall Street Journal, “Anthropic S-1 Shows Red-Hot Growth, Risks and Massive Computing Costs,” Oct 20, 2026. https://www.wsj.com/tech/ai/anthropic-revenue-growth-ipo-accaf0f1 [UNVERIFIED: draft S-1, not publicly filed]

  2. Reuters via Investing.com, “Anthropic recorded net loss of over $80 billion in first half of this year, WSJ reports,” Oct 20, 2026. https://www.investing.com/news/stock-market-news/anthropic-recorded-net-loss-of-over-80-billion-in-first-half-of-this-year-wsj-reports-4960917

  3. NDTV Profit (citing WSJ), “Anthropic May Spend $750 Billion On Computing Costs,” Oct 2026. https://www.ndtvprofit.com/technology/anthropic-may-spend-750-billion-on-computing-costs-report-says-12213014

  4. The New York Times, “Owners of Elvis Presley’s Music Sue Anthropic for Copyright Infringement,” Oct 25, 2026. https://www.nytimes.com/2026/10/25/arts/music/anthropic-lawsuit-elvis-presley-songs.html

  5. The Wall Street Journal, “The Earnings Boom Is Heavily Reliant on Just a Handful of Stocks,” Oct 25, 2026. https://www.wsj.com/finance/stocks/the-earnings-boom-is-heavily-reliant-on-just-a-handful-of-stocks-21decb53

  6. The Wall Street Journal, “Salesforce to Buy Customer-Engagement Platform for Nearly $5.7 Billion,” Oct 20, 2026. https://www.wsj.com/business/deals/salesforce-to-buy-customer-engagement-platform-for-nearly-5-7-billion-aba17cfe

  7. The Wall Street Journal CIO Journal, Gartner survey of 2,002 CIOs, Oct 20, 2026. https://www.wsj.com/cio-journal/ais-transformational-value-remains-elusive-for-most-companies-2fc0e3d3

  8. The Harris Poll, “2026 Milken Institute–Harris Poll Listening Project” presentation, May 2026. https://theharrispoll.com/wp-content/uploads/2026/05/The-Harris-Poll-Milken-Listening-Project_Global-Conference-Presentation-2026.pdf

  9. The New York Times DealBook, “C.E.O.s Say the A.I. Rollout Is Going Well. Employees Feel Differently.,” Oct 24, 2026. https://www.nytimes.com/2026/10/24/business/dealbook/ceos-say-the-ai-rollout-is-going-well-employees-feel-differently.html

  10. Financial Times, “Why students in a midterm battleground fear AI will ruin their future,” Oct 25, 2026. https://www.ft.com/content/0a86d4f5-6046-4c1c-89e9-90583cff33f9

  11. The Wall Street Journal, “A Princeton Ph.D. Applied to 2,300 Jobs. He Still Can’t Get One.,” Oct 17, 2026. https://www.wsj.com/lifestyle/careers/labor-market-freeze-phd-graduate-f8e17d7c

  12. The New York Times, “Former ‘60 Minutes’ Boss to Join Video Journalism Start-Up,” Oct 25, 2026. https://www.nytimes.com/2026/10/25/business/media/bill-owens-60-minutes-emerson-collective.html

  13. The New York Times DealBook, “A.I. and New Rules for Accounting Are an Opportunity for Start-Ups,” Oct 23, 2026. https://www.nytimes.com/2026/10/23/business/dealbook/pennylane-ai-accounting-france.html

  14. Financial Times, “Toyota takes on Tesla and China in humanoid robot race,” Oct 25, 2026. https://www.ft.com/content/8bf93407-6b93-4ebb-b54b-f290ebd563e3

  15. Reuters, “Toyota estimates factory automation could cost $6.4 billion per year from 2028,” Sept 18, 2026. https://www.reuters.com/business/autos-transportation/toyota-estimates-factory-automation-could-cost-64-billion-per-year-2028-2026-09-18/

  16. The Wall Street Journal, “The Internet Told Them Owning a Laundromat Was Easy Money. It Was Anything But.,” Oct 19, 2026. https://www.wsj.com/business/the-internet-told-them-owning-a-laundromat-was-easy-money-it-was-anything-but-2ca220c7

  17. Financial Times, “How YouTubers finance their videos,” Oct 18, 2026. https://www.ft.com/content/5427eed7-6cfc-4e8b-892f-efbb289e4a53

  18. The Wall Street Journal, Hermès Q3 sales, Oct 2026. https://www.wsj.com/business/retail/hermes-sales-growth-slows-as-luxury-backdrop-remains-tough-c564ed84

  19. The Wall Street Journal, Kering Q3 sales, Oct 2026. https://www.wsj.com/business/retail/kerings-sales-lose-steam-in-struggling-luxury-market-2154c2d6

]]><![CDATA[I Built an Audience of 60,000+ on LinkedIn and Substack. Here's How I'm Starting From Zero on X.]]>https://www.operatingbyjohnbrewton.com/p/i-built-an-audience-of-60000-on-linkedinhttps://www.operatingbyjohnbrewton.com/p/i-built-an-audience-of-60000-on-linkedinFri, 23 Oct 2026 11:08:09 GMT

  1. How to Build an AI Operating Team in Grok Bot: The Field Manual

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

  3. The Creator Economy’s Original Sin

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

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

Subscribe now

Start Here

On LinkedIn, 44,900 people follow me. On Substack, I have about 6,000 subscribers (over 200 paid) and 12,000 followers. Cumulatively across my primary platforms, that is an audience of +60,000 across LinkedIn and Substack, followers and subscribers. On X, 107 people followed me when I started taking it seriously on Oct 6. This morning, 155 are following along.

That last number might be small but I know the strategy is right. To explain why, I need to start with how I built the other two audiences. In this article, I want to explain how I’m building the strategy to start from zero on a new platform using everything I’ve learned over the last 2.5 years building my large audience.

How I built my LinkedIn following

I started posting on LinkedIn every day in March 2024. By the end of 2024, I had 10,000 followers. By the end of 2025, I had 25,000. Four things did the work: consistency, the right content types, packaging and hooks, and, in 2026, products people could buy. This year I sold three course cohorts through LinkedIn, at $1,500 to $2,500 a seat, plus coaching packages.

LinkedIn is where my largest audience lives. My profile had 44,156 followers on Oct 6 and 44,876 on Oct 21, a gain of 720 in about two weeks. In the week to Oct 6, my posts drew about 31,500 impressions, with engagement of about 3.3%.

The approach is simple to describe. My own posts drive the reach, and I reply to every comment. A post that opened “Your ambition isn’t shrinking. You’re changing who you’re building for.” reached 14,628 impressions and drew 255 engagements. I answered its 18 comments one by one. Across my whole profile, there were 322 comments from Oct 16 to Oct 22. On my company page, short video clips with a concrete hook beat link posts.

LinkedIn also feeds the newsletter. It has brought in 951 Substack sign-ups all time, about 11% of the total. The lesson I took from it is that the conversation after a post matters as much as the post. People decide whether you are worth knowing in the replies.

How I built my Best-Selling Substack audience

I launched the newsletter in earnest on June 1, 2025, and built the free list organically, with no imported emails. It reached about 1,000 subscribers by January 2026 and about 6,000 now, over 200 of them paid. I also have 12,000 followers on Substack. Substack Stats shows 6,163 subscribers, paid included. Over its life, the newsletter has recorded 8,663 sign-ups by source: Notes 4,375, Direct to App 1,552, recommendations from other writers 1,438, LinkedIn 951 and direct traffic 347. By Substack’s own count, 68.84% of all-time sign-ups, or 6,576, came from the Substack network.

In other words, Substack grew mostly inside Substack. Three things drove it. Volume: a steady run of articles and live sessions. Relationships with other creators, whose recommendations account for 1,438 sign-ups. And Notes. I write about five a day. Operator stories do best, at 15 to 19 likes. News Notes with link cards draw three to 10.

Two lessons from those platforms shaped what I did next. Write in one recognisable voice: an operator story, a hard number and a so-what. And answer people, one at a time.

Starting over on X

A big audience elsewhere gives you a voice and habits. It gives you no reach. On X, I was a stranger in an empty room.

The voice carried over. So did the habit of answering people, which on X became replying to other people’s posts. What did not carry over was broadcasting to my own audience. On LinkedIn and Substack, people are waiting to read what I post. On X, they weren’t, and my own posts there draw about 10 impressions each.

X has its own rules, and three matter most. Borrow other people’s audiences: the conversation under a large account’s post is already full of readers. Be fast: replying early to big accounts wins. Keep it short: two or three sentences, one fact the thread lacks, ending on the point.

From 107 to 155

On Oct 6, I started replying on X. For the first week, almost nothing happened. My follower count sat between 106 and 109. Impressions moved first: on Oct 12, my average impressions per post, replies included, jumped from about 3 to 41. By Oct 14, replies had taken over, and followers took off: 116 on Oct 14, 121 on Oct 16, 136 on Oct 19, 141 on Oct 20, 146 on Oct 22 and 155 at 5:44 AM this morning. That is about 45% growth since Oct 6, nearly all of it in the past 10 days.

The best reply came on Oct 19, under a post from Blake Scholl: “Rachmaninoff’s $5,000 in 1923 kept Sikorsky’s airliner alive, and Sikorsky paid it back with interest in 1929. The helicopter came a decade after that. Back the founder and the product sorts itself out.” It drew 262 impressions on its first day, then kept going, to 2,618 impressions, 55 likes and two quote posts, one from Danielle Fong. It is my most-liked reply ever, and it is the LinkedIn formula in three sentences.

Earlier that day, 27 minutes after @unusual_whales posted about car loans stretching past seven years, I replied: “GM started GMAC in 1919 so families could buy a car before they’d saved for it. A century later the loan outlasts the warranty.” It has drawn 2,599 impressions. On Oct 22, a two-sentence reply under a Polymarket post about SpaceX and cell towers reached 1,024.

What I track

I keep the list short and check it every day.

Reach per reply against reach per post: Replies on larger accounts reach 779 to about 2,600 people, roughly 80 to 260 times my own posts.

Average impressions per post, replies included, from daily snapshots: This counts everything except retweets. It rose from about 3 on Oct 7–9 to about 125 on Oct 22, peaking at 225 on Oct 20, roughly 40 times the first week. Replies did most of that work. My standalone posts still get about 10 impressions each.

  • What happens after day one: A reply that grows from 262 impressions to 2,618 is still being shown to people days later.

  • Who responds: I note who replies, quote-posts or follows back. A quote post from someone with their own following, such as Danielle Fong, carries me into a room I couldn't have entered alone.

  • How fresh the post is: Replying fast to big accounts wins. The GMAC reply went up 27 minutes after the original.

  • Which topics land: Markets and business history do best. Media and newswire accounts do worst.

  • Word for word or edited: Software agents draft reply options. I choose, edit or reject them, and nothing posts without my approval. For six days running, every reply went out word for word. That tells me the drafts now sound like me.

  • Format, followers and referrals: A native X Article drew 135 impressions; the same material as a thread drew 18. I watch followers for direction more than level. And in 90 days, X sent my newsletter nine views and zero subscribers. I track that to stay honest.

Subscribe now

Why I know it is working

Four signals point the same way. The gap between replies and posts is wide and consistent across accounts and subjects, which makes luck an unlikely explanation. The best replies keep gaining reach after the first day. People with real audiences have started to quote me. And the follower count, flat for a week, turned upward once the replies began to land.

Side by side

The voice is the same in all three columns. The tactics differ because each platform rewards different behavior. X drives discovery. Notes do the converting. LinkedIn is where relationships start in public.

Why small numbers can be the right numbers

If my goal were to import my LinkedIn following, 155 would be disappointing. My goal on X is an audience native to X, and every number that matters is small and rising.

Small positive numbers are useful because they are cheap to read. A reply that reaches 2,618 people costs me a few minutes. If the strategy were wrong, I would see it within days: replies stalling at 10 impressions, no quote posts, a follower count stuck at 107. Instead, reach per reply is many times reach per post, the best replies keep traveling, and the count has moved from 107 to 155.

The tempting mistake is to judge a new platform by an old platform’s scoreboard. Measured by subscriptions, X has given me nothing in 90 days. Measured by whether strangers are meeting my work, it has given me thousands of impressions on a handful of sentences. The first view would have me quit. The second tells me to keep going and be patient about conversion.

The kicker

Starting over is humbling when you have a big audience somewhere else. It is also clarifying. With no crowd to broadcast to, the only way in is to be useful in someone else’s conversation, quickly, in a few sentences.

The voice I built on LinkedIn and Substack made the replies worth reading. The habits X demanded — speed, brevity, and borrowed rooms — got them seen. The numbers are small. They point in the right direction, and at this stage, that is what I need from them.

- j -

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

]]><![CDATA[The AI Bubble Debate Is Arguing Over the Wrong Number]]>https://www.operatingbyjohnbrewton.com/p/the-ai-bubble-debate-is-arguing-overhttps://www.operatingbyjohnbrewton.com/p/the-ai-bubble-debate-is-arguing-overTue, 20 Oct 2026 12:01:15 GMT

  1. How to Build an AI Operating Team in Grok Bot: The Field Manual

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

  3. The Creator Economy’s Original Sin

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

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

Subscribe now

Start Here

Paul Kedrosky thinks artificial intelligence is a bubble. Azeem Azhar thinks it is a productive boom with hints of getting “a little bit stinky.” On the Ezra Klein Show last week, in an episode titled “Is There an A.I. Bubble? A Debate,” the two gave the clearest account yet of the argument hanging over markets. Each got something important right.

Kedrosky, of SK Ventures, is right that build-outs of general-purpose technologies, from canals and railroads to electricity and fiber, tend to overshoot, and that this one is increasingly financed with debt. Azhar, of Exponential View, is right that bubbles burst when financing breaks, not merely when returns disappoint, and that what keeps a boom healthy is real customers paying real money.

But both are arguing about the wrong number. Kedrosky’s case rests on one line: “What matters is tokens times price.” That is the seller’s equation. It describes a lab’s revenue. The buyer’s equation has a denominator: tokens times price, divided by the work that actually got done. Call it the cost per finished task. Companies are only now discovering that number, and the evidence is that they have started to ration.

Rationing is neither the bubble popping nor a blip. It is what happens when AI stops being a perk and becomes an operating cost with an owner.

Subscribe now

The Bear’s Case

Kedrosky’s definition is strict: “A bubble occurs whenever asset prices detach from any semblance of economic reality.” By his account, that has nothing to do with whether AI is useful. Railroads were useful, too. The problem is capital meeting competition. “As long as you have a supply of capital and a race dynamic, you’re guaranteed overcapacity,” he said.

His demand argument is sharper. Kedrosky estimated that token prices have been falling about 13-fold a year on a performance-adjusted basis, and that coders account for something like 80% of frontier-model usage and revenue. Neither figure came with a source, but the logic holds. If price falls that fast, volume has to grow even faster. And coders are an unusual market. Their use is expansive, turning a short prompt into a mountain of code, while most office work is compressive, turning a pile of email into a summary. “Never confuse your early customers for what the real market will look like,” he said.

Credit markets have given him material. Under the headline “Weak Links Start to Show in A.I. Borrowing,” The New York Times reported last week that investors placed “a more than 60 percent likelihood” on CoreWeave, which “has accrued $72 billion in liabilities to operate 51 data centers,” failing to meet its obligations. The Federal Reserve raised rates in September for the first time since 2023. “The failure here is overdetermined,” Kedrosky said.

The Bull’s Case

Azhar applies two tests: Is demand real, and is the financing sound? On the first, he sees strong enterprise demand and too little compute, not too much. On the second, he concedes that the big cloud companies are shifting from cash to debt, but argues that borrowing has not yet reached dangerous levels.

His strongest reply is about where demand goes. If customers move to cheaper or open-weight models, he said, “that is just the demand moving. It may hurt Anthropic’s margins, but it still monetizes the underlying capital expenditure.” A lab can struggle while the data centers stay busy. Aaron Levie, Box’s chief executive, goes further. He has written that agent swarms “will consume 1,000X more tokens than people prompting agents 1 at a time.”

Azhar is also candid about the pace. “It’s just not the case that you get the best model from OpenAI and say: Fix my company — and it fixes it,” he said. His horizon is five to 10 years, not one or two.

The Number Neither Side Measured

“Tokens times price” and “demand moving” both describe the vendor. Neither tells a buyer whether the spending worked.

Leave out the denominator and both camps go wrong. The bear treats a drop in volume as a drop in value, even when the work cut was waste. The bull treats a rise in volume as a rise in value, even when the tokens bought nothing. A thousand times more tokens is a forecast of spending, not of output.

Most companies cannot yet compute the denominator. The WSJ found that in 32% of more than 6,800 tasks, the cheaper-per-token model ended up costing more overall, and that only 11% of 396 organizations forecast their AI spending within 10%. A company that doesn’t know what it will spend, or what a finished task costs, has one tool left: a blunt cap.

Tellingly, when Klein asked what would change their minds, neither guest named a token count. Azhar said he would worry if growth in enterprise AI spending halved two quarters in a row. Kedrosky said he would reconsider if productivity, labor-force participation and GDP growth broke above trend. Both tripwires sit on the buyer’s side of the ledger.

The Budgets Are Getting Owners

That is what the first wave of rationing is. In August, Microsoft’s Jay Parikh told engineers that “tokenmaxxing is not what we are optimizing for,” 404 Media reported, as divisions moved onto AI token budget targets. “We are not optimizing for fewer tokens,” Parikh reportedly wrote. “We are optimizing for more impact per token.” In October, The Information reported that Meta and Microsoft had cut back employees’ use of Claude Code. On the show, Kedrosky went further, saying Meta was halving the number of in-house developers allowed to use outside frontier tools, that Microsoft was doing the same, and that the days of token-maxxing were long over.

Accenture shows the mechanics. This month the consultancy told many employees that it would centrally fund only $29 a month per person for Claude, according to an internal announcement reported by 404 Media. Some had been spending as much as $1,000 a month. Usage above $29 isn’t banned. It is charged to a project or team budget code. “All costs are consumption-based,” the announcement says.

None of this means demand is collapsing. The Information noted that Microsoft’s internal cutbacks have been offset by customer demand for Anthropic models on Microsoft’s own platforms, and Meta and Microsoft are steering staff toward in-house and partner tools, not away from AI. What has changed is that demand now has to be underwritten. Every useful input follows this arc. It arrives as a perk, spreads as a habit and ends up as a line item with an owner. The Fed’s turn will speed that along, because budgets that were loose in a boom tighten fast when money costs more.

What It Means For Companies

Companies are becoming tech stacks. A stack comes with a bill, and the bill always finds an owner. The question for management is no longer whether to buy AI, but which work it may do, at what cost per unit and with whose approval.

That is where Azhar’s most interesting claim comes in. The people who gain, he argued, will be “the layers of humans doing verification, doing the last sign-off,” who “will end up being paid more and more… as a consequence of sitting at that choke point.” In a metered company, sign-off is also where the cost per finished task gets decided. A task isn’t finished until someone accepts it, and the person who signs is the one who knows whether the tokens bought anything.

We are all becoming companies, too. An employee with a monthly AI allowance and a budget code for anything above it is running a small P&L, whether anyone calls it that or not.

Rationing can still go badly. A flat allowance treats every employee alike, and AI use isn’t alike. Cap tokens without defining the task and the heavy users who create the most value hit the ceiling first, while light users go untouched. People quietly go back to doing the work by hand, or find workarounds the company can’t see.

The Operator’s Playbook

1. Price AI per finished task. Define “finished” as work that was reviewed, accepted and used. Count failed runs, retries and the human time spent checking output. If the cheaper model often costs more, the per-token price is the wrong number to optimize.

2. Budget by workflow, not by seat. A seat license is a buffet. A workflow budget is a menu with prices. Fund invoice matching, code review or contract redlining at a cost per unit, and let volume flex with the work.

3. Run two budgets. Separate expansive use (engineering, agents, research) from compressive use (drafting, summarizing, search). Give the first a higher ceiling and a review of output. Give the second a low flat allowance. One cap for both will starve the wrong people.

4. Name an owner for the token P&L. Charging costs to project codes is the right instinct only if the project owner can see cost per task, not just a monthly total. Finance should own the definitions; the business should own the outcome.

5. Retire the usage leaderboard. Kedrosky described internal charts ranking who used the most tokens. Any dashboard that rewards volume will produce volume. Replace it with cost per accepted output.

6. Keep the right to switch. Microsoft moved its internal default to a cheaper model. Route routine work to the cheapest model that clears your quality bar, and save the frontier for tasks that need it.

Subscribe now

The Kicker

Kedrosky and Azhar will keep arguing about valuations, credit and timing, and the coming IPO filings will settle some of it. The question underneath is simpler: Does a finished piece of work cost less with AI than without it?

Token-maxxing was what AI looked like when it was free to the person using it. Rationing is what it looks like when it costs money. Neither phase answers the question. The operators who win the next one won’t be those who buy the most tokens or cut the most licenses. They’ll be the leaders who know what a finished job costs.

- j -

Subscribe now

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

Sources

]]><![CDATA[How to Build an AI Operating Team in Grok Bot: The Field Manual]]>https://www.operatingbyjohnbrewton.com/p/how-to-build-an-ai-operating-teamhttps://www.operatingbyjohnbrewton.com/p/how-to-build-an-ai-operating-teamSun, 18 Oct 2026 11:55:28 GMT

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

  2. The Creator Economy’s Original Sin

  3. Porter’s Five Forces, Reread for 2026

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

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

Subscribe now

TL → DR

  • Grok Bot lets you set up a team of AI bots, each with one job, its own instructions, scheduled routines, and access to the tools you connect.

  • My setup has eight working bots and six group rooms. On a typical weekday it sends me about 24 items, publishes about 28 posts across five platforms, and adds about 57 articles to a research database.

  • Every public post still waits for my approval. That rule has protected the brand, and it has also made me the slowest part of the system, with 16 to 18 approval prompts a day.

  • My X bot also runs a video pipeline to four platforms, with 174 posts in Buffer for October 13 to 24.

  • Below: how the product works, my org chart and architecture, the daily output table, the video pipeline, a six-step build guide, ten prompt templates you can copy, what broke, and what I’m changing next.

Start Here

Two weeks ago I wrote about the org chart of my AI operating team. Several of you asked the same follow-up question: how do I build one?

This piece answers it. It’s the manual I wish I’d had on September 1, when I set up my first bot to keep me out of calendar noise.

Since then the team has grown to eight working bots. They read my paid news subscriptions, draft for X, Substack, and LinkedIn, load posts into Buffer for Facebook, Instagram, and TikTok, prepare me for meetings, and write long-form article drafts I can choose from. I approve everything that goes out under my name.

I’ll show you exactly how it runs, what it produces each day, the instructions I gave each bot, and where it broke. Then I’ll walk you through building your own, one step at a time.

What Grok Bot Is, in Operator Terms

If you’ve run a department, you already understand the model. Here are the parts.

Bots are your hires. Each bot is a role with a name, a job description, and its own memory. You talk to each one in its own chat, just as you’d in a one-on-one with a direct report. I keep each bot to one job. My X bot runs X. My News Bot reads the papers. When a job gets crowded, I hire another bot.

Routines are the standing meetings and recurring tasks. A routine is a scheduled or triggered job: “every weekday at 8:00 AM, check my calendar and only write if something needs a decision.” Routines are what make the team run while you sleep. My News Bot runs five routines a day, starting with a 6:47 AM briefing.

Connectors are the system access you grant. Google Calendar, Gmail, Google Drive, Buffer, X, Supabase, and others. Each bot only uses the ones its job needs. My Chief of Staff reads my calendar and Gmail. My X bot uses the X connector for search and analytics and Buffer for scheduling.

Skills are saved procedures. A skill is a written playbook a bot can follow repeatedly, like an SOP binder. Grok Bot ships with managed skills, and you can save your own. I loaded a whole set of my most used skills from Claude into Grok for this purpose.

Group rooms are cross-functional meetings. A group room puts several bots and you in one chat. My busiest room has the News, X, Substack, and LinkedIn bots. Every morning, the X bot posts what’s trending, the News Bot checks the facts, and the content bots divide the stories so no two platforms use the same line on the same day.

Shared memory is the company file server. Every bot can read and write files on a shared computer. My target lists, voice guides, reply logs, drafts, and briefings all live there. I also keep a Google Drive media folder for images and video, a Great Quotes Library doc, and a Drive folder of article drafts.

The box is the office computer. Each bot works on a real computer with a real web browser. That browser is how bots sign in to sites without a connector. I signed in to Substack, X, LinkedIn, and my paid news subscriptions in the browser once. I typed my passwords into a secure form the bot couldn’t see, and the bot has been working within those sessions since.

The primary bot, also called Grok Bot, works as a front desk. It takes any request and either handles it or routes it to the right specialist.

My System Today

Figure 1. The team: eight working bots, a meta bot, and six group rooms, with every public post coming back to me.

Become A Paid Subscriber To Continue Reading

Subscribe now

Read more

]]><![CDATA[AI Is Making Small Teams Stronger and Big Companies Bigger]]>https://www.operatingbyjohnbrewton.com/p/ai-is-making-small-teams-strongerhttps://www.operatingbyjohnbrewton.com/p/ai-is-making-small-teams-strongerTue, 13 Oct 2026 10:56:55 GMT

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

  2. The Creator Economy’s Original Sin

  3. Porter’s Five Forces, Reread for 2026

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

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

Subscribe now

Start Here

I have built this publication on a hopeful premise: companies are becoming tech stacks, and we are all becoming companies. The tools that once required a department now fit on a laptop. An individual with expertise and an offer can compete.

Andreessen Horowitz has spent the past few months putting that premise to the test. They have a better view of where the capital is going than I do, so I thought it would be interesting to dig in this week and respond to their thesis.

AI can democratize the ability to start a company while concentrating the rewards of running one. In that world, “we are all companies” describes the entry ticket. The harder question is where a solo operator or small firm can win.

Capital Can Buy Speed

David George of a16z has argued that AI strengthens the power law of technology investing: a small number of companies capture a disproportionate share of the rewards. Capital itself can reinforce their advantage by buying more compute.

Ben Horowitz makes the mechanism explicit. A company with a 2-year lead once had some protection against a competitor that tried to catch up by hiring 1,000 engineers. Adding people could create more coordination problems than progress. His example of what has changed is the model race: spend $3 billion on a computing cluster, and a challenger can close a gap that hiring alone could not.

That challenges a familiar startup advantage. Speed has often allowed small companies to beat larger ones. Where capital can purchase speed, the balance shifts toward those who can afford it.

The fundraising market is behaving as if that argument has merit. Disruptive, an investor in chip startup Groq, is targeting a fund of up to $10 billion, according to the Wall Street Journal. The firm plans to invest in roughly 10 late-stage companies over 2 years. a16z itself has raised a $1.1 billion Machine Age Fund for the physical infrastructure of AI.

If you like this article, you’ll love this one, too!

Read Here

Access Is Uneven

Participation is uneven on the customer side, too. In a16z’s analysis of U.S. consumer spending, the top 1% of AI spenders averaged $903 a month in August, against a $25 median. Together, they spent more than the bottom 50% combined.

Those figures measure spending, so they cannot tell us who is earning a commercial return. They do show how widely the intensity of paid use varies. Access to the tools is only the beginning.

Andrej Karpathy has described a similar divide in access and experience. On October 11, he wrote that the gap in understanding what these models can do is “widening” into “a sharp funnel.”

By his own napkin math, about 6 billion people have barely touched a large language model. Another 1 to 2 billion use free chatbots casually. Around 20 million use frontier models professionally in math and code, and about 5,000 have access to frontier systems inside the labs. These are rough estimates, but the distinction matters: people discussing the same technology may be working with very different capabilities.

The vendor market offers another view of concentration. In Ramp’s token-spending data, Anthropic’s share fell from 69.5% in the week ending March 15 to 51.0% in the week ending September 27. OpenAI’s share rose to 44.5%. These figures cover model-attributed API spending by businesses using Ramp’s Token Spend Management product, a subset of its customers.

The shares are moving, but those 2 companies still account for more than 95% of the spending in that sample. A leader can lose ground while the market remains concentrated.

Subscribe now

Small Teams Can Sit Inside Large Companies

Bending Spoons makes the tension between small teams and large companies concrete. In a September interview with David Senra, CEO Luca Ferrari said Evernote had roughly 350 people when it was acquired. By the time of the interview, about 20 people worked directly on it.

Ferrari also explained what makes that possible: the team benefits from technology developed across Bending Spoons’ portfolio. The group can spread the cost of that investment across multiple businesses.

On October 12, Airtable announced a move to a leaner team following its acquisition by Bending Spoons. The company believes smaller teams will help it deliver its product plans. Whether those plans succeed will have to be judged by the results.

Small teams and concentrated ownership can advance together. An independent operator looking at Evernote’s headcount also needs to account for the platform behind it.

Where A Small Firm Can Win

That is where I need to qualify the democratization story. Greater access to the tools does not give every company the same competitive position. The economics of building a frontier model are different from those of using one to serve a client.

Capital has its clearest advantage where more infrastructure or broader distribution directly improves the offer. Trust and local knowledge are harder to reproduce on demand.

When I ran an industrial distribution business, the competitor I feared most was the family owner with no exit plan and 20 years’ worth of relationship building in a new market we were entering.

That kind of business is difficult to understand through a venture-capital lens. Its owner can define success on different terms, and the customer relationship can outlast several generations of technology.

Think about the regional distributor who knows which customer always pays late and which one needs a call before a price change. The adviser who has known a family for 20 years. The specialist consultant whose judgment a CFO trusts on a deal.

In those markets, scale helps, but customers are also buying accountability from someone they know and trust. AI can make that person more productive. It does not automatically transfer the customer’s trust to whoever owns the model, nor will the models imbue that business owner with the specialized knowledge these advisors possess.

4 Choices For The Independent Operator

For a company of 1, or of 10, I would draw 4 practical conclusions.

  1. Avoid competing on features alone. If a larger company can reproduce your offer and put it in front of an existing customer base, a development head start gives you limited protection. Give customers a reason to choose you that survives the next product release.

  2. Compete where trust affects the buying decision. Choose a specific customer and a problem where your judgment matters. A narrow market helps when it allows you to understand the work and the people better than a general-purpose competitor.

  3. Rent the platform. Own the relationship. Use the infrastructure the giants are building, and keep direct access to your customers. Maintain usable records of their needs and your work so the business can survive a change of tools.

  4. Build a record. Document the advice you gave, the decisions you made, and the outcomes that followed. Record when you overrode the tool and why. A competitor can buy the same software much faster than it can earn the same history of trust.

AI can lower the cost of starting a business and increase the rewards available to scale. The operator’s task is to choose a market where scale does not settle the customer’s decision.

A profitable firm that supports its owner can succeed without ever becoming large enough to interest a venture fund.

We are all becoming companies. I still believe that. Some of the giants will become much bigger, and small firms will have more capability available to them than before. Their opportunity depends on what they do with it.

Build where your knowledge of the customer changes the outcome. Use AI to deliver the work. Keep earning, building, and growing the relationship(s).

- j -

Subscribe now

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

Connect with John on LinkedIn

Appendix: Sources and notes

The sources below support the figures and attributed statements in this article. The conclusions about strategy are my interpretation. Reporting periods matter, particularly for spending shares.

  1. David George and a16z on AI’s power law. How AI Is Rewriting the Power Law of Venture Capital, September 10, 2026. The discussion explains how capital can reinforce an AI company’s advantage by buying compute.

  2. Ben Horowitz on capital and speed. a16z’s published excerpt contrasts hiring 1,000 engineers with funding a $3 billion computing cluster. His example concerns competition between AI models. Applying it to every software business would go beyond that example.

  3. Disruptive’s proposed megafund. The Wall Street Journal’s report identifies Disruptive and its target of up to $10 billion. This is a fundraising target, not a statement that the full amount has been raised.

  4. a16z’s Machine Age Fund. The fund announcement, August 28, 2026, confirms $1.1 billion raised for AI infrastructure and physical systems.

  5. Consumer AI spending. The Top 100 Gen AI Consumer Apps, 7th Edition, October 5, 2026, uses YipitData’s U.S. consumer panels. The $903 average for the top 1% of spenders and $25 median refer to August 2026. Spending concentration does not establish productivity or profitability.

  6. Karpathy’s capability funnel. Andrej Karpathy’s October 11 post supplies the estimates of 6 billion, 1 to 2 billion, 20 million, and roughly 5,000 people. He describes these as napkin math. They should be read as his estimates, not survey findings.

  7. Business spending on AI tokens. Ramp AI Index: token spend by model maker. Anthropic’s share was 69.47%, rounded here to 69.5%, in the week ending March 15, 2026. For September 21–27, Anthropic held 51.0% and OpenAI 44.5%, a combined 95.5%. The dataset covers connected-provider, model-attributed API spending from businesses using Ramp’s Token Spend Management product. It represents a subset of Ramp customers, rather than the entire AI market.

  8. Evernote and Bending Spoons. Luca Ferrari’s interview with David Senra, September 13, 2026. Ferrari describes the reduction from roughly 350 people to about 20 working directly on Evernote and explicitly acknowledges support from the group’s shared technology platform. These are the CEO’s reported figures. The 20-person count does not include all shared support.

  9. Airtable’s team changes. An update on Airtable’s team and what we’re building next, October 12, 2026. The company announces departures and its plans for a smaller team. The announcement establishes its intended approach, not the eventual productivity result.

]]><![CDATA[Build a Company of Curators]]>https://www.operatingbyjohnbrewton.com/p/build-a-company-of-curatorshttps://www.operatingbyjohnbrewton.com/p/build-a-company-of-curatorsTue, 06 Oct 2026 10:42:08 GMT

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

  2. The Creator Economy’s Original Sin

  3. Porter’s Five Forces, Reread for 2026

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

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

Subscribe now

Start Here

Executives and leaders have an AI Slop problem, and they’re not happy about it.

If they receive one more 40-page report that is nothing more than an AI dump about an issue they’re dealing with internally, manufactured from half-clean data and with zero examination, clean summarization, or actionable direction by the deliverer of said file, there are going to be issues…

A 40-page report can contain useful information and most likely still be a poor piece of work to put in front of an executive. The numbers might be right. The research might be relevant. But the person receiving it still has to identify the important part and decide what to do with it.

Add several reports like that to a normal working week, and the time saved by the people producing them becomes work for the people reading them.

AI makes this easy to do. Give a model enough company data, and it can produce a document that looks finished long before anyone has decided what belongs in it.

Companies need to spend some of the time they save generating work on deciding what is worth sending.

The cost of an unfinished report

There is evidence of the cost of skipping that step. In a September 2025 survey by BetterUp Labs and Stanford’s Social Media Lab, 40% of 1,150 U.S. desk workers said they had received low-quality AI-generated work in the previous month.

Resolving an incident took roughly 2 hours on average, according to respondents. The researchers call this “workslop.”

Long documents deserve particular attention. In Zapier’s survey of 1,100 enterprise AI users, 52% identified long-form reporting among the tasks requiring the most cleanup or rework. Both studies rely on people’s reports of their experience, rather than an audit of company productivity. They still describe a recognizable problem: work arrives looking more complete than it is.

The version that concerns me goes beyond bad information. A report can have plenty of substance and very little judgment about what the reader needs. The author has gathered the material and passed the selection work upstairs.

Christian Catalini’s work on the economics of AI explains why verification becomes more valuable as execution gets cheaper. Companies need to know whether they can trust what their systems produce. Curation adds another obligation: deciding which of those trustworthy findings deserve attention in this particular decision.

An executive needs enough detail to make a sound decision, with the supporting work available when needed. That requires someone to understand the material well enough to organize it around the decision.

The report remains unfinished until that last bit of work is complete.

Use the time AI gives back

The time to finish it is part of what AI gives us back. In a 2023 randomized experiment, Shakked Noy and Whitney Zhang assigned writing tasks to 453 professionals. Access to ChatGPT reduced completion time by 40% and increased assessed quality by 18%. These were bounded writing assignments, so the figures shouldn’t be treated as a forecast for every company’s work. They show that faster production and better work can go hand in hand.

We need to make a deliberate choice about where the savings go.

If a first draft that used to take an afternoon now takes 30 minutes, use the remaining time to examine the recommendation. Check the source behind the number that drives it. Consider the alternative the draft dismissed too quickly. Then edit the document for the person who has to act on it.

I think of this as the final 20%: the work between a plausible draft and something you are willing to put your name on. The percentage is shorthand. For a difficult decision, that stage may take most of the time.

AI can help throughout it. It can compare alternative structures, identify repetition, and challenge whether a conclusion follows from the supplied evidence. We should use those capabilities to improve the final work. A faster first draft gives us room to raise the standard.

If you like this piece, you’ll love this one!

The 5 Curation Skills

Taste is the ability to recognize that standard in a particular context and explain why one version serves the reader better than another. In “The Overhang,” Ethan Mollick argues that, as production becomes easier, selecting what to keep and what to discard becomes a more valuable contribution. That’s a useful way to think about taste inside a company.

A finance team and a CEO may need different documents from the same analysis. The finance team needs enough detail to investigate. The CEO may need a recommendation about pricing and the evidence that would change it. Knowing how to serve each reader is a skill people can practice.

I would teach it through 5 curation skills.

1. Frame the decision

Before generating the report, finish this sentence: “After reading this, this person needs to decide _____.”

“An update on margins” leaves the assignment open. “Decide whether to change pricing on this product line before the next quoting cycle” gives the work a purpose. The reader’s responsibilities and the deadline determine what belongs in the brief.

For an informational update, name what the reader needs to understand and what would trigger action. There should be a reason to send it even when no immediate decision is required.

2. Judge the evidence

A confident paragraph and a well-formatted chart can make a weak conclusion look settled. Trace the claims that drive the recommendation back to their sources. Check the period covered and whether the data actually answers the question.

In a margin review, an observed decline is a fact. The explanation for that decline may still be an inference. A proposed price increase is a recommendation. Keep those distinctions visible, especially when the explanation is uncertain.

AI can help assemble a claim-and-source table. The author still needs to inspect the evidence behind the consequential claims. A second model agreeing with the first is insufficient on its own.

3. Rank what matters

Give the most consequential finding the most attention. A movement in a small product line may be interesting. A smaller movement in the company’s largest account may matter much more.

Ask AI to rank the findings against the stated decision and explain its ranking. Then challenge the order using what you know about the business. Financial size matters, but so can urgency or an obligation the model doesn’t understand.

The first page should make your priorities apparent. Equal space for every finding leaves the reader to establish the priorities themselves.

4. Connect the findings

Explain how the evidence leads to the proposed action.

In a hypothetical margin review, higher freight costs and lower margins might appear in separate sections. A useful analysis establishes whether freight explains the decline, how much remains unexplained, and whether changing prices would address it. Putting the observations next to each other doesn’t establish the connection.

Use AI to test the reasoning: have it identify the strongest supported objection and the missing evidence that could change the recommendation. Resolve those gaps or make them explicit in the brief.

5. Cut with care

Remove anything that doesn’t help the reader understand the decision, assess the evidence, or act. Move useful reference material into an appendix. Delete repeated background and findings included only because they were easy to generate.

Keep the uncomfortable fact that weakens your recommendation. Keep the uncertainty that could change the outcome. Curation becomes misleading when the editor removes the material a decision-maker most needs to see.

For each proposed cut, identify what the reader would lose. Sometimes the right answer is to keep the paragraph. A shorter document earns its length through better selection, not smaller type.

Use these 5 skills to select what the reader needs, including the evidence that complicates your recommendation.

Use AI to Help With the Edit

Employees can use AI for a separate editorial pass after the research and drafting. Give it the audience, the decision, and the source material. An instruction like this is much more useful than asking it to “make this better”:

Review this draft for the person who must decide [decision] by [date]. Identify the findings that could change that decision.

Show which consequential claims lack support in the supplied sources. Propose what to keep, move to the appendix, or cut, and explain each choice.

Preserve conflicting evidence and uncertainty that could affect the recommendation. Suggest a clearer order. Give me an edit plan before rewriting.

Review the proposed changes before accepting them. After the rewrite, compare the final brief with the source material and check what disappeared. The editing process can introduce omissions as easily as the drafting process can introduce errors.

Build curation into the job

This work needs a place in the job. A Microsoft Research and Carnegie Mellon study of 319 knowledge workers found that greater confidence in generative AI was associated with less reported critical thinking. It was a survey, not proof of skill loss. For a manager, it is a reason to make evaluation an explicit responsibility and give people the means to do it.

Leaders have to make the standard visible.

For a recurring executive report, agree on what “finished” means before the next version is commissioned. A useful default is a short opening brief containing the decision and recommendation, followed by the evidence needed to assess it. Material uncertainty belongs beside the recommendation. Supporting analysis should be easy to find. The document should name the person responsible for its conclusions.

Then show people examples. Keep a small library of strong briefs with annotations explaining the editorial choices. Include a before-and-after version so employees can see what was removed and why.

There is a useful research tradition behind this approach. Joanna Tai and colleagues’ work on evaluative judgment describes how learners can develop their ability to assess quality through examples and feedback. Applying that idea at work, I’d give employees 2 versions of the same brief and ask them to choose the stronger one, explaining their reasoning before a manager shares an assessment. That makes the company’s standards open to discussion and practice.

Protect some of the time saved for that work. If every faster draft earns the employee another assignment immediately, review will compete with a growing queue. An 8-month Berkeley Haas study at a 200-person technology company observed employees expanding their tasks and working longer hours as they adopted AI. That’s evidence from 1 company, but it is a useful warning against assuming that saved time will automatically translate into better judgment.

Return unfinished analysis with specific feedback and have the author revise it. Quietly rewriting everyone’s reports teaches dependence on the manager. Recognize useful selection, including the decision to stop producing a report nobody uses.

Managers should model the same discipline in what they send downward. Give a team a clear assignment with relevant context. Explain what needs their judgment. Forwarding an unreviewed AI analysis and asking everyone to make sense of it creates the same burden in the other direction.

Measure the result across the handoff. For 1 recurring report, record production time, review and correction time, and the recipient’s time spent clarifying it. Ask whether the brief supported the decision it was commissioned for. Faster drafting is a partial result until you know what happened to the people downstream.

Building a company of curators means developing this judgment throughout the organization. Start with the next recurring report. Have its author apply the 5 skills, use AI to challenge the edit, and get specific feedback from the recipient. Keep the changes that made it easier to use, and carry that standard into the next version.

- j -

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

  2. The Creator Economy’s Original Sin

  3. Porter’s Five Forces, Reread for 2026

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

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

Subscribe now

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

]]><![CDATA[How To Break Through The AI Adoption Wall ]]>https://www.operatingbyjohnbrewton.com/p/how-to-break-through-the-ai-adoptionhttps://www.operatingbyjohnbrewton.com/p/how-to-break-through-the-ai-adoptionMon, 28 Sep 2026 12:50:01 GMT

Give the right people a reason to buy.

People can enjoy your posts for months and still have no idea what they can hire you to do. That gap between attention and a clear offer is what we’re tackling in Master LinkedIn’s New Feed and Build an Offer People Want.

On September 29, I’m joining Alicia Teltz and Wessal Khader for a free, 90-minute masterclass covering how to:

  • Get your content in front of relevant buyers as LinkedIn’s feed changes.

  • Use customer insights to identify problems your audience wants solved.

  • Turn those insights into an offer people understand and want to pay for.

If you’re putting time into LinkedIn and struggling to connect that effort to something you can sell, join us.

Tuesday, September 29 · 1pm Eastern / 6pm UK · Free · Online

Register Here For Free!

Start Here

This year, I’ve trained hundreds of employees inside companies to use AI and become AI-fluent. I’ve also worked with executives and teams to build their personal capabilities and the systems that support them.

The same problems keep showing up. Someone has access but cannot find a use for it. Someone else gets a poor answer and gives up. A capable employee quietly wonders whether getting better at this means helping the company eliminate their job.

Adoption is a management problem before it is a technology problem.

That is the argument behind The Adoption Wall. Its 10 hurdles fall into 3 groups: fear, habit, and context. Each needs a different response. Another demonstration of what the software can do will only get you so far.

I start with fear because it changes how people interpret everything that follows. An employee who thinks they are training their replacement has a sensible reason to hold back. Asking them to document their knowledge and automate their work without addressing that concern makes the request harder to trust.

My advice to leaders is to state what this program is for and how the company will use the capacity it creates. If you can commit that the program will not drive job cuts, say so early and keep that commitment. Name the bigger work people will take on. Never borrow trust with a promise you cannot keep.

Then there is the fear of looking incompetent.

Experienced people have spent years becoming the person others ask for help. A blank chat window can put them back in the position of a beginner, this time in front of their colleagues.

I make room for private questions and encourage leaders to show their own failed attempts. People need to see a poor first answer get corrected. That gives them permission to learn without treating every attempt as a public assessment of their ability.

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

  2. The Creator Economy’s Original Sin

  3. Porter’s Five Forces, Reread for 2026

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

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

Subscribe now

The confident expert presents a different challenge. They believe they are faster alone, and on some tasks they are. I use work they have already completed as the test. We have a known result and someone qualified to judge it. Then we compare the assisted approach, including the time needed to check and correct the output. The expert’s judgment sets the standard. A useful result earns another attempt.

Once people are willing to try, the next obstacle is how they work and improve.

One of the most common mistakes I see is asking for a substantial piece of work with almost no context. The request might be “write a proposal,” followed by disappointment that the proposal sounds like every other proposal.

I teach people to brief AI as they would a new employee. Explain the customer’s situation and the decision the document needs to support. Supply the constraints and an example of acceptable work. State what information is missing.

For people who struggle to write that brief, I suggest dictating it. Talk through the situation as if you were explaining it to a colleague. Then ask AI to organize the brief and identify gaps before starting the work.

Finding the right task is another difficulty. “Use AI in your job” leaves a busy person with the entire problem of deciding where to begin.

I ask them to log repeated work for 1 week. Look for information they keep reformatting, notes they keep organizing, or routine drafts they keep rebuilding. Start with a clerical task whose result they can check. The first useful application should be easy to recognize in their working day.

That also helps address the objection that there is no time to learn.

My approach is to bring real work into the session. Take something already due and build it together. A manager might prepare a meeting brief. A salesperson might turn approved notes into a follow-up draft. The learning happens while an existing piece of work gets done.

Managers also need to protect time for practice. If every hour remains committed to existing demands, people will return to the method they already know under pressure.

Then we have to make it happen again.

Old habits return when the deadline arrives, and the new method takes effort to reconstruct. I focus on replacing 1 recurring habit and saving the method as a reusable skill: the instructions, required inputs, expected output, and checks. Give it a clear name and keep it where the work happens.

The test comes the following week, when the person uses it without me.

This is where personal capability begins to become a system. Someone can explain the job clearly, judge the answer, and repeat the process. Their useful experiment now has a method another person can follow.

The final group of barriers concerns the context around the work.

Much of a company’s operating knowledge lives in people’s heads. An experienced employee knows which exceptions matter and when the usual process will fail. None of that appears in the request they give AI.

I use AI to interview the person about how they do the job. Have it ask 1 question at a time, work through an example, and probe the exceptions. Then turn the answers into a draft process for the employee to correct. Their review matters because a plausible process can still be wrong.

There is also the uncertainty about what is allowed.

Employees need usable answers about which tools they can use and what information they can share. I recommend a 1-page document covering approved models, permitted data, connector permissions, and where to take an uncertain case. It should point to the fuller policies where necessary.

Make the rules easy to find when someone needs them.

At the other end of the spectrum are the power users racing ahead alone. They build useful things, but the team cannot maintain a process it cannot find or understand.

I want a named owner, a shared library, and a clear route from experiment to production. Record what each workflow does and who checks its output. Before anyone depends on it, establish who maintains it and what happens when it fails.

A company can have several impressive AI users and still have no dependable way to share their work.

This is why I work on executive capability alongside employee training. Leaders need enough direct experience to judge whether an application is useful and to understand the work required to make it dependable. Delegating every experiment leaves them poorly placed to decide what deserves investment.

An executive’s own recurring task is a useful starting point. Build the method, use it again, and notice where it breaks. That experience makes the next conversation about team adoption more concrete.

For a team starting this week, I would choose 1 repeated task and 1 person to own the trial. Record the current time and quality standard. Build the assisted method using the approved information, then measure the time spent on checking and correction.

Save what works. Run it again the following week. Ask someone else to follow the instructions and record where they get stuck.

Those observations tell you what to fix next: the brief, the process, the rules, or the training. They also give the next session a task.

Put that review on the calendar before the first session ends.

Concluding Thought

After training hundreds of employees this year, the lesson I keep coming back to is that AI adoption depends on what happens after the training. People need to feel safe enough to try, understand where the tools fit their work, and have a method they can use again. Leaders are responsible for creating those conditions and helping useful individual experiments become something the whole team can rely on.

Start with 1 recurring task this week. Set the quality standard, build the assisted method, and count the time spent checking and correcting it. Save the instructions and ask someone else to follow them the next week. When another person can produce acceptable work with less effort, you have evidence of progress. That is what I want companies to leave training with: the capability to keep improving the work themselves.

The models and technology will continue to improve exponentially. That’s why the training should prioritize driving meaningful changes within the organization, enabling the team to move forward and evolve into a company that fosters continuous learning, improvement, and adaptation.

Hope this helped you today. If you have a company that is looking to make these changes, I’d love to talk with you. If you are a consultant or coach who is in the midst of doing this work with your clients, be sure to drop me a message as well.

- j -

Give the right people a reason to buy.

People can enjoy your posts for months and still have no idea what they can hire you to do. That gap between attention and a clear offer is what we’re tackling in Master LinkedIn’s New Feed and Build an Offer People Want.

On September 29, I’m joining Alicia Teltz and Wessal Khader for a free, 90-minute masterclass covering how to:

  • Get your content in front of relevant buyers as LinkedIn’s feed changes.

  • Use customer insights to identify problems your audience wants solved.

  • Turn those insights into an offer people understand and want to pay for.

If you’re putting time into LinkedIn and struggling to connect that effort to something you can sell, join us.

Tuesday, September 29 · 1pm Eastern / 6pm UK · Free · Online

Register Here For Free!

Take Your Seat

]]><![CDATA[Apparently AI is going to kill us all. Finally, someone figured out how to write a hook.]]>https://www.operatingbyjohnbrewton.com/p/apparently-ai-is-going-to-kill-ushttps://www.operatingbyjohnbrewton.com/p/apparently-ai-is-going-to-kill-usThu, 24 Sep 2026 10:37:25 GMT

Give the right people a reason to buy.

People can enjoy your posts for months and still have no idea what they can hire you to do. That gap between attention and a clear offer is what we’re tackling in Master LinkedIn’s New Feed and Build an Offer People Want.

On September 29, I’m joining Alicia Teltz and Wessal Khader for a free, 90-minute masterclass covering how to:

  • Get your content in front of relevant buyers as LinkedIn’s feed changes.

  • Use customer insights to identify problems your audience wants solved.

  • Turn those insights into an offer people understand and want to pay for.

If you’re putting time into LinkedIn and struggling to connect that effort to something you can sell, join us.

Tuesday, September 29 · 1pm Eastern / 6pm UK · Free · Online

Register Here For Free!

Start Here

Dario Amodei’s Anthropic has built its Responsible Scaling Policy around catastrophic AI risks, including biological threats and systems acting beyond human control.

In an interview published September 3, Sam Altman called the next generation of models “sobering for everybody.” He later warned about 2 outcomes: humanity losing control of AI, and AI concentrating power in a single person, company or country.

Elon Musk has been sounding the alarm for years. In 2023, he signed an open letter calling for a pause in training the most advanced systems.

Getting Amodei, Altman and Musk to broadly agree on anything may itself qualify as an extinction-level event.

People often need a reason to care before they need a reason to buy…

The companies warning us about AI also have enormous commercial interests in the rules governing it. Those rules help determine who can compete and what competing will cost.

A 2023 paper coauthored by OpenAI researchers proposed registration, reporting, safety standards and enforcement for frontier-model developers. Anthropic has advocated a “regulatory ladder”, with testing and government oversight increasing as the risks grow.

Those may be sensible requirements. Depending on their design, they may also be considerably easier for an established lab to meet than for a smaller challenger.

The thresholds matter. A rule aimed at developers of the most capable models does not automatically apply to everyone building an AI app. But compliance costs can still shape who gets to challenge the leaders.

Brookings has warned that licensing can reinforce incumbents by raising the cost of entry. Separately, the Federal Trade Commission has identified how control over essential AI inputs can entrench established firms.

That is a reason to examine the incentives. It is not evidence that these executives are manufacturing fears they do not believe.

The risks can be real, the concerns sincere, and the regulations justified. The same regulations can still make life harder for future competitors.

A warning can serve the public interest and the company delivering it.

Fear can draw attention to a real problem. Attention can influence which solutions get heard. And the proposed solution can have commercial consequences of its own.

Most founders face a considerably less apocalyptic version of the first challenge: getting somebody to care.

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

  2. The Creator Economy’s Original Sin

  3. Porter’s Five Forces, Reread for 2026

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

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

Subscribe now

Here are 10 lessons worth taking from these recent shenanigans

1. Start with the problem

Nobody wakes up hoping to learn about your proprietary methodology.

They wake up with problems.

The AI warnings begin with consequences people can understand: a cyberattack, a biological threat, a system humans cannot control. The technical explanation comes after the reason to listen.

Your customer deserves the same order.

“We offer a 6-week advisory engagement” describes something you sell.

“Your sales team cannot explain why someone should choose you” describes something a buyer might need to fix.

Start where the customer is already uncomfortable.

2. Make the stakes obvious

There is a reason nobody leads with:

AI could marginally inconvenience humanity.

A problem earns attention when its consequences become clear. For your customer, those consequences might be lost revenue, shrinking margin, wasted labor, customer churn, or another year spent solving the wrong problem.

“Our quoting process is slow” is easy to postpone. “Customers are choosing someone else while we’re still preparing the quote” gives the delay a cost.

Find that cost. Use numbers the customer can verify.

3. Make it about them

  • 22 years of experience.

  • 6 certifications.

  • A Harvard degree.

  • 7 modules.

  • 43 templates.

Wonderful. But nobody cares.

Your buyer is still trying to work out what changes for them.

Credentials can establish that you are qualified to help. They cannot do the job of explaining why the help matters.

“You’ll leave with a price you can defend in a sales conversation” gives the buyer something to picture. “You’ll get access to 7 modules” gives them something else to finish.

The customer should recognize their own situation before you ask them to admire yours.

4. Create tension

“AI is getting better” contains information.

“AI may become capable faster than we learn to control it” gives you a reason to keep reading.

The tension sits in the gap between progress and preparedness. In your business, it might sit between the effort a customer is making and the result they keep failing to get.

Consider a founder buying more traffic for an offer nobody understands. The useful observation is simple: more visitors will encounter the same confusion.

Now the founder has a reason to reconsider the offer before buying the next ad.

Good tension exposes something the customer needs to resolve. It earns the next sentence.

5. Get specific

Drama earns attention. Specificity gives people something to evaluate.

“We’ll transform your business” asks the buyer to supply the meaning.

Compare that with:

In 30 days, you’ll have a tested offer, a price, positioning, sales copy, and a live way for customers to buy.

Now the buyer can picture the work and judge whether it is worth doing.

A promise this specific also gives the customer a way to hold you to it. Define what “tested” means. Make sure the timeline and deliverables are ones you can stand behind.

6. Give people a way forward

This is where the psychology gets useful.

  • A 2015 meta-analysis covering 127 papers, 248 samples and 27,372 participants found that fear appeals had a positive average effect on attitudes, intentions and behavior. They worked better when the message also explained that the recommended action could work, or that the person could carry it out.

  • A 2022 meta-analysis covering 19,736 participants found stronger effects on behavioral outcomes when messages included information that the recommended response would be effective. Most of its messages concerned health, so applying the finding to sales requires judgment.

My practical reading: if you make a problem vivid, make the response credible too.

“Your sales are slipping” creates concern. A clear diagnosis, a workable first step, and evidence from a comparable situation give the customer something to do with it.

Show them the door. Explain why it leads somewhere.

7. Make the path simple

Once someone cares, don’t hand them a maze.

For an offer-building engagement, the path might be:

Find it. Name it. Validate it. Price it. Publish it. Sell it.

That is easier to follow than “participate in our iterative commercialization methodology.”

The work behind each step may be complicated. The customer still needs to understand where they start, what happens next, and what they will have when they finish.

Describe the path in words they can use without you in the room.

8. Create real urgency

If a problem costs $30,000 every month, another month of delay has a price. If a customer keeps losing bids because quotes arrive late, waiting means accepting more of the same.

That is enough to discuss urgency without installing a countdown timer.

Use the customer’s actual costs and deadlines. A season ending, a contract renewing, or another month of avoidable losses can give them a sound reason to act.

Invented deadlines teach buyers to doubt the next deadline too.

9. Make the idea repeatable

“AI might kill us.”

4 words. Try forgetting them.

Now compare that with:

“We leverage cross-functional technology-enabled capabilities to unlock enterprise transformation.”

Try remembering it.

Your customer should be able to explain what you do to someone else without reopening your website. That matters when the person you persuaded has to persuade a partner, a boss, or a budget holder.

Give them a sentence they can carry into that conversation.

10. Earn attention without betraying trust

Once you discover that threats attract attention, the temptation is obvious: make everything bigger, scarier, more urgent.

Turn every inconvenience into an emergency.

Eventually, the customer learns to discount you.

Describe a real problem vividly. Keep the scale of the claim proportionate to the evidence. Make the cost of waiting clear, then give the buyer room to decide.

Great marketing makes genuine importance difficult to overlook.

Your next claim will arrive carrying the reputation of your last one.

Give them a reason to use the pen

Consider the familiar sales test: “Sell me this pen.”

Most founders would start with the ink and the craftsmanship. Then the materials, the founder’s résumé, and the proprietary pen framework.

6 pen modules. A private pen community.

Meanwhile, the buyer still needs a reason to write something down.

That is the job these 10 lessons help you do. Identify a problem the customer recognizes, make its consequences clear, and offer a credible way forward. Make the message simple enough to remember and honest enough to trust.

Ideally, you can manage this without threatening the extinction of the human species.

Attention gives you an opening. You still have to earn the sale.

Before you publish your next offer, underline the first sentence that gives the customer a reason to care. Check that the consequence is real and the promised response is one you can deliver.

If you have to read halfway down the page to find that sentence, move it to the top.

Give the right people a reason to buy.

People can enjoy your posts for months and still have no idea what they can hire you to do. That gap between attention and a clear offer is what we’re tackling in Master LinkedIn’s New Feed and Build an Offer People Want.

On September 29, I’m joining Alicia Teltz and Wessal Khader for a free, 90-minute masterclass covering how to:

  • Get your content in front of relevant buyers as LinkedIn’s feed changes.

  • Use customer insights to identify problems your audience wants solved.

  • Turn those insights into an offer people understand and want to pay for.

If you’re putting time into LinkedIn and struggling to connect that effort to something you can sell, join us.

Tuesday, September 29 · 1pm Eastern / 6pm UK · Free · Online

Register Here For Free!

]]><![CDATA[Pick Your Business Model Like Your Life Depends On It.]]>https://www.operatingbyjohnbrewton.com/p/pick-your-business-model-like-yourhttps://www.operatingbyjohnbrewton.com/p/pick-your-business-model-like-yourFri, 18 Sep 2026 10:24:55 GMT

Hello Operators!

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

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

What was visible becomes what feels possible.

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

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

FREE DROP 05: The Model Sorter

Download Today's Drop

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

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

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

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

  2. The Creator Economy’s Original Sin

  3. Porter’s Five Forces, Reread for 2026

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

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

Subscribe now

Your Expertise Is Only Half the Business Model

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

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

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

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

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

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

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

That is a different decision.

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

The Workbook Output

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

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

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

The characteristic failure mode of your winning model.

  • Productized service fails when scope creeps until every engagement becomes custom again, but the price never moves with it.

  • Retainer or advisory fails when the founder slowly becomes the client’s employee without the salary, equity, or exit.

  • Licensing fails when nobody wants to buy a method they have never watched the founder run, so the founder keeps running it.

  • A cohort can turn your calendar into a permanent launch treadmill.

  • A self-serve course can quietly turn the founder into a full-time marketer.

  • The diagnostic can get given away in an attempt to win an implementation that never closes.

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

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

Download Today's Drop

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

The Model Sorter can help you answer one important question:

What shape should this business take?

It cannot build the rest of the business for you.

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

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

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

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

Take Your Seat

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

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

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

Here’s what our cohort members will leave with:

  • Your evidence gathered.

  • Your expertise named.

  • Your model selected.

  • Your market researched.

  • Your price supported.

  • Your sales page published.

  • Your first buyer conversations underway.

  • And a 90-day operating rhythm for continuing the work after the cohort ends.

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

Doors close September 30.

The live cohort runs October 1–29.

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

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

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

Take Your Seat

]]><![CDATA[Build Your Substack to Sell High Ticket Consulting, Coaching, Cohort and Community Offers, Not $10 Subscriptions]]>https://www.operatingbyjohnbrewton.com/p/build-your-substack-to-sell-highhttps://www.operatingbyjohnbrewton.com/p/build-your-substack-to-sell-highWed, 16 Sep 2026 16:09:38 GMT
Get more from John Brewton in the Substack app
Available for iOS and Android
]]><![CDATA[My Biggest Give Away Yet: The Oper(AI)te (Hidden Offer) Drop Library]]>https://www.operatingbyjohnbrewton.com/p/my-biggest-give-away-yet-the-operaitehttps://www.operatingbyjohnbrewton.com/p/my-biggest-give-away-yet-the-operaiteWed, 16 Sep 2026 11:17:31 GMT

Subscribe now

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

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

  • A piece of evidence.

  • A clearer understanding of what you are unusually good at.

  • A pricing insight.

  • A case study.

  • A list of real prospects.

  • A better way to explain what you sell.

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

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

How to Use the Drops

1. Download the workbook.

2. Upload it into Claude or ChatGPT.

3. Tell the model to begin the workbook.

Then follow the conversation.

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

Do not overthink them.

Run them on your own intuition and instinct.

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

What These Drops Are Actually Building

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

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

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

Take Your Seat

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

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

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

  • Find the proof.

  • Identify the expertise.

  • Understand the buyer.

  • Check the market.

  • Establish the price.

  • Articulate the offer.

  • Build the sales assets.

  • Start selling.

But there is an important distinction.

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

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

  • Instead of examining one quarter of your calendar, you build an Everything File that lets AI understand the body of work you have built across your career.

  • Instead of identifying a single piece of valuable expertise, the Superpower Scan and Fit Test help determine what you should actually build a business around and which business model fits it.

  • Instead of focusing on a single competitor or a single pricing question, the Market Map and Price Evidence Sheet examine what buyers are paying right now and what your evidence can support.

  • Instead of writing a better sentence, the Intersection Engine, Page Builder, and Language Audit turn the evidence into an offer and put it onto an actual sales page.

  • And instead of finding five prospects once, the Selling Agent and Daily 15 are built to help make selling part of how the business operates every day.

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

The goal is for you to leave holding one.

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

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

Run the free tools first.

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

Those discoveries are not random.

They are your evidence.

Eventually the question becomes:

What happens if you connect all of it?

For now, let’s start with the work.

AVAILABLE NOW

FREE DROP 1

The Calendar Autopsy

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

Your résumé is aspirational. Your LinkedIn profile is curated.

Your calendar is harder to fake.

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

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

It then asks a much more interesting question:

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

For many experienced operators, the answer is no.

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

What this drop finds

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

What it doesn’t know yet

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

Download The Calendar Autopsy →

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

FREE DROP 2

The Receipt Finder

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

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

We say things like:

I helped grow the company.

I improved operations.

I helped the team scale.

I worked on the transformation.

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

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

What was true before you arrived?

What was true afterward?

Over what period did the change happen?

What role did you actually play in creating it?

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

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

That distinction matters.

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

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

What this drop finds

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

What it doesn’t know yet

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

Download The Receipt Finder →

FREE DROP 3

The Compliment Decoder

Other people may already have written your positioning for you.

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

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

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

The Compliment Decoder turns that unstructured feedback into evidence.

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

And there is an important distinction buried inside that analysis:

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

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

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

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

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

What this drop finds

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

What it doesn’t know yet

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

Download The Compliment Decoder →

FREE DROP 4

The Free Advice Audit

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

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

“Can you take a quick look at this?”

“What would you do here?”

“Can I get your opinion on my pricing?”

“Who would you hire?”

“Can I pick your brain for 20 minutes?”

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

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

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

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

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

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

Consequence: What happens if they make the wrong decision?

Frequency: How often does this problem occur?

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

This is a product, not a favor.

But my favorite part is the final number.

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

That number may sting a little.

I hope it does.

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

What this drop finds

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

What it doesn’t know yet

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

Download The Free Advice Audit →

FREE DROP 5 · SEPTEMBER 17

The Model Sorter

Download Drop 5

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

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

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

FREE DROP 6 · SEPTEMBER 18

The Buyer’s Real Words

Download Drop 6

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

Those two languages often barely overlap.

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

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

Take Your Seat

COMING NEXT…

FREE DROP 7 · SEPTEMBER 19

The 90-Second Teardown

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

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

What the competitor never talks about.

That silence may be where your differentiation is hiding.

FREE DROP 8 · SEPTEMBER 20

The Price Spread

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

Something else is creating the spread.

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

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

FREE DROP 9 · SEPTEMBER 21

The Underpricing Calculator

Most experts price the work. Buyers value the outcome.

Those are two entirely different numbers.

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

FREE DROP 10 · SEPTEMBER 22

The Generic Detector

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

The Generic Detector applies a simple test:

Could a direct competitor say this exact sentence?

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

FREE DROP 11 · SEPTEMBER 23

The One-Sentence Compressor

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

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

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

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

FREE DROP 12 · SEPTEMBER 24

The Proof Page

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

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

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

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

FREE DROP 13 · SEPTEMBER 25

The Objection Pre-Mortem

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

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

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

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

FREE DROP 14 · SEPTEMBER 26

The First Five

“Find me leads” is a terrible instruction.

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

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

Five prospects. Five reasons why now.

FREE DROP 15 · SEPTEMBER 27

One Day of the Daily 15

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

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

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

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

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

FREE DROP 16 · SEPTEMBER 28

The Headline Stress Test

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

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

You finish with page-ready language.

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

Because look at what you will have accumulated.

What You Will Hold After 16 Drops

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

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

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

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

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

And then we arrive at the most important part.

What you still will not have is anything that runs.

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

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

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

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

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

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

From October 1–29, we build it live.

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

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

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

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

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

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

You can send somebody the page.

And you have a system helping you sell it.

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

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

See everything inside Oper(AI)te: The 30 Day Offer Launch →

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

Drop 5 goes live tomorrow morning.

This page will keep getting better every day.

]]><![CDATA[Harvard Taught Me How to Negotiate in Theory. $250 Million in Deals and 20 Years of Work Taught Me the Rest.]]>https://www.operatingbyjohnbrewton.com/p/harvard-taught-me-how-to-negotiatehttps://www.operatingbyjohnbrewton.com/p/harvard-taught-me-how-to-negotiateMon, 14 Sep 2026 11:42:05 GMT

Oper(AI)te: The Hidden Offer

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

Take Your Seat

The Beginning

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

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

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

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

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

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

Harvard Gave Me the Language

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

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

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

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

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

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

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

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

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

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

That is an entirely different negotiation.

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

  2. The Creator Economy’s Original Sin

  3. Porter’s Five Forces, Reread for 2026

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

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

Subscribe now

Never Win a Deal You Cannot Afford to Deliver

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

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

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

Subscribe now

Revenue is seductive. Bad revenue is still bad.

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

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

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

That isn’t weakness. It’s strategy.

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

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

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

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

The Operating Negotiation Framework

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

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

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

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

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

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

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

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

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

Proof. Market. Value. Then price.

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

  1. There is the market price: what does this problem normally cost to solve?

  2. There is a strategic price: is there a legitimate reason this particular relationship deserves different economics?

  3. There is a walk-away price: below what number does the work stop making sense?

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

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

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

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

THE OFFER PROBLEM

For most of your career, an employer did four incredibly valuable things for you: it packaged your expertise, found the customer, established an economic model around your work, and gave you permission to defend that model.

When you become independent, all four responsibilities become yours.

Your expertise did not suddenly become less valuable. The commercial infrastructure around it disappeared.

Oper(AI)te: The Hidden Offer

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

Take Your Seat

Then I Put My Own Name on the Proposal

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

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

And I became worse at negotiating.

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

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

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

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

Sometimes it is. Sometimes it is a permission problem.

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

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

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

Then price it.

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

BUILD THE OFFER BEFORE YOU NEGOTIATE IT

This is much of what Wessal Khader and I have built into Oper(AI)te™: The Hidden Offer.

We don’t begin by inventing a product or asking what someone wants to charge. We begin with the evidence accumulated across a career. We use that evidence to identify the expertise with the greatest commercial value, map it against what buyers are paying for now, turn the intersection into one clear offer, and establish a defensible price.

Proof → Market → Value → Price.

The objective isn’t to teach an experienced operator another random skill. It is to identify the valuable skill they have already spent 15 or 20 years proving and finally build the offer around it.

Take Your Seat

The Hardest Person I Have Ever Negotiated For Is Myself

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

Running companies gave those ideas consequences.

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

Entrepreneurship taught me something different.

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

So today I use the same framework.

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

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

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

Then send the invoice.

- j -

Subscribe now

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

Subscribe now

Connect with John on LinkedIn!

]]><![CDATA[The Creator Economy’s Original Sin: Building An Audience Without An Offer ]]>https://www.operatingbyjohnbrewton.com/p/the-creator-economys-original-sinhttps://www.operatingbyjohnbrewton.com/p/the-creator-economys-original-sinFri, 11 Sep 2026 14:05:14 GMT

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

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

  3. Porter’s Five Forces, Reread for 2026

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

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

Subscribe now

My Story

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

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

I learned how to grow.

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

Connect with John on LinkedIn!

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

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

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

It sold +100 seats in 12 days.

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

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

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

  • AI transformation.

  • Enterprise technology and software that needed improvement or optimization.

  • Sales and inventory operations strategy.

  • Growing EBITDA ahead of a business exit.

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

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

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

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

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

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

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

Take Your Seat Today

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

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

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

I love the creator community. Coaching solopreneurs has become some of the most rewarding work I do. I value the audience, the training, and the relationships I have built. The money I left on the table came from confusing the skill I had recently learned to distribute with the expertise I had already earned.

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

Instead, I spent 18 months leaving the offer unresolved.

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

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

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

It’s time to make this mean something specifically for you. Become a paid subscriber to get the exact set of action steps and strategy I would deploy today if I was starting from zero and the workbook to help you get started.

Subscribe now

Read more

]]><![CDATA[The Problem With The Creator Economy]]>https://www.operatingbyjohnbrewton.com/p/the-problem-with-the-creator-economy-a8chttps://www.operatingbyjohnbrewton.com/p/the-problem-with-the-creator-economy-a8cTue, 08 Sep 2026 13:51:39 GMT
Get more from John Brewton in the Substack app
Available for iOS and Android
]]><![CDATA[The Problem With the Creator Economy]]>https://www.operatingbyjohnbrewton.com/p/the-problem-with-the-creator-economyhttps://www.operatingbyjohnbrewton.com/p/the-problem-with-the-creator-economyMon, 07 Sep 2026 10:18:27 GMT

Subscribe now

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

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

Grab Your Free Seat Here!

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

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

The problem was not that the creator economy failed.

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

Mistake Number One: Building the Channel Before the Offer

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

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

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

  • Content about making content.

  • Newsletters about growing newsletters.

  • Courses about launching courses.

  • Creators teaching people how to become creators.

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

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

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

They are not the problem.

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

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

That sequence is backwards for a huge number of people.

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

My Story: How I Made All These Mistakes

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

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

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

The economics were never as democratic as the distribution

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

That distribution should not surprise us.

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

Recorded music did it.

Television did it.

Social platforms amplified it again.

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

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

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

The Game Has Changed

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

Every one of those constraints has weakened.

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

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

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

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

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

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

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

  3. Porter’s Five Forces, Reread for 2026

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

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

Subscribe now

The AI Of It All…

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

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

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

  • Facts.

  • Procedures.

  • Frameworks.

  • Reference knowledge.

  • Generic analysis.

  • Generic advice.

  • Increasingly, even technically competent content.

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

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

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

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

Your Experience as Inventory

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

Look at the same career commercially instead:

  • What problems have you repeatedly been trusted to solve?

  • What became easier because you entered the room?

  • Where have companies already paid for your judgment?

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

  • What is expensive when somebody gets it wrong?

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

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

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

  • Content becomes proof.

  • Content becomes trust.

  • Content becomes discovery.

  • Content becomes distribution.

Don’t do this: Content → Audience → Offer

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

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

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

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

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

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

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

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

Then become extraordinarily good at distributing it.

Become an expert with distribution.

Not a creator looking for something to monetize.

Companies are becoming tech stacks.

We are all becoming companies.

- j -

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

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

Grab Your Free Seat Here!

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

Connect with John on LinkedIn

]]><![CDATA[Enterprise Software Was Built for Someone Else]]>https://www.operatingbyjohnbrewton.com/p/enterprise-software-was-built-forhttps://www.operatingbyjohnbrewton.com/p/enterprise-software-was-built-forMon, 31 Aug 2026 11:17:11 GMT

Subscribe now

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

TL → DR

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

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

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

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

Subscribe now

Introduction

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

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

  • Migrating companies to new enterprise software

  • Implementing and integrating those systems

  • Optimizing existing enterprise applications

  • Building the databases, data warehouses, and now data lakes required to support them

  • Creating the data infrastructure that allows operators to run critical workflows, analyze performance, and understand what is actually happening inside their businesses

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

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

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

That changes what operators should expect from their software.

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

The same transformation is happening underneath the applications themselves.

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

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

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

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

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

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

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

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

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

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

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

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

AI makes that alternative increasingly possible.

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

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

  3. Porter’s Five Forces, Reread for 2026

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

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

Subscribe now

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

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

Most companies have adopted AI.

Very few have rearchitected themselves around it.

That creates the opportunity.

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

AI offers a rare second startup phase.

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

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

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

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

Subscribe now

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

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

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

Electricity offers the classic analogy.

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

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

Useful. But the much larger question is:

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

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

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

AI increasingly allows the employee to begin with intent.

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

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

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

  • What counts as revenue?

  • Which inventory is actually available to promise?

  • When does a prospect become a customer?

  • Which discount requires approval?

  • Who can see which information?

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

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

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

The interface becomes more disposable.

The company’s understanding of itself becomes more valuable.

Subscribe now

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

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

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

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

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

The practical strategy is therefore not build versus buy.

It is: Where should the line move?

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

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

Buy the durable core. Build the differentiated edge.

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

Subscribe now

When does an operating company become a software company?

This may be the most interesting consequence of all.

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

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

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

The cycle is worth paying attention to:

Operate → build → prove → productize → sell.

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

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

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

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

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

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

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

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

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

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

  • Design for the growth-oriented, legacy-light company willing to rethink the workflow.

  • Expose data and actions so agents can operate the software as easily as humans.

  • Make permissions, context, definitions, auditability, and learning part of the product.

  • And sell the shortest path from trusted information to completed work.

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

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

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

- j -

Questions For Executives

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

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

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

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

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

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

Will AI automatically make a smaller company more productive?

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

Does this mean SaaS is going away?

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

Why might SMBs be particularly important to this transition?

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

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

Research

Enterprise Software and ERP Design

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

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

AI Adoption and the Second Startup Phase

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

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

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

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

Data, Context, and AI-Native Software Architecture

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

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

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

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

Build Versus Buy

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

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

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

From Internal Operations to Software Products

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

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

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

Building and Selling AI-Native Enterprise Software

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

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

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

]]><![CDATA[I Turned 22 Hours of Founder Office Hour Video Recordings Into a Learning Library with Claude Cowork, Opus 5, Zoom and Google Drive Connectors]]>https://www.operatingbyjohnbrewton.com/p/i-turned-22-hours-of-founder-officehttps://www.operatingbyjohnbrewton.com/p/i-turned-22-hours-of-founder-officeMon, 24 Aug 2026 11:18:48 GMT

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

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

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

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

Watch Me Build It Here on Loom

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

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

Become an Operating Founder and join our weekly live office hours this week!

Become an Operating Founder

The Project Started With 22 Zoom Calls

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

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

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

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

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

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

Subscribe now

Prompt One: Retrieve and Organize Everything

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

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

Prompt Two: Understand 22 Conversations

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

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

Prompt Three: Make the Existing Work More Valuable

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

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

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

Prompt Four: Turn the Work Into Finished Assets

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

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

Prompt Five: Build the Final Experience

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

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

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

Become an Operating Founder and join our weekly live office hours this week!

Become an Operating Founder

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

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

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

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

Start Looking for Three-Hour Agent Jobs

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

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

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

Find the Work You Should Stop Doing Yourself

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

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

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

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

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

- j -

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

Become an Operating Founder

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

]]>