Most coaching ends the same way. You leave with notes, a few good ideas, and the quiet knowledge that in three weeks the notes will be cold and the ideas will still be ideas. The work that was supposed to change did not change.

You paid for advice. But you needed a system.

I operated, optimized, and scaled a B2B distribution company from $27.5M to $75M and sold it in August of 2021. I have spent more than 400 hours this year coaching founders and operators. I’ve learned so much from the incredilbe +100 founders, entrpreneurs, operators and professional I’ve been fortunate to work with. Many of these amazing people have become my friends and collaborators and for that I am grateful.

I wanted to take all that I’ve learned from these individual engagements and turn them into a product and system to drive outcomes for the Operators and Builders I’m proud to call my clients.

What I came up with is the Operating Project.

Claim Your Seat!

What it is

The Operating Project takes one problem in your business and works it to a finished result across four sessions. You pick the problem. I bring the structure, the worksheets, and the build. The outcome is defined before we start, and the project closes when the finished thing exists.

This is not open-ended coaching. It is not a standing retainer. It is one operating problem, scoped to one outcome, closed.

Pick one problem

You choose the project. Four kinds of work come up most often.

1. A business model. Your pricing, your offer design, and the economics of what you actually sell.

2. A brand identity. The system that makes your work recognizable across every surface, instead of a new look every time you post.

3. A content strategy. The plan and the machine that produces it on a schedule, instead of the weekly scramble.

4. A matter inside a larger business. One specific problem in a company you have already built and need to fix without rebuilding the whole thing.

One problem. One outcome. One finished artifact you can use.

Claim Your Seat!

The four sessions

The work runs in four moves.

• First, we diagnose. We name the problem in plain terms, set the outcome you want to hold at the end, and define the finished artifact. We draw the scope and what sits outside it.

• Second, we design. We build the actual plan. Pricing models, offer maps, positioning frameworks, content calendars, the worksheet that fits your project. You build them with me and you keep them.

• Third, we build the machine. This is the part nobody else includes. We stand up a working system inside Claude or ChatGPT that produces the work you just designed. The skills, the projects, the workflow. You own it. It lives in your account.

• Fourth, we install. We run the system on a live piece of your business, confirm it holds, and define done.

The sessions are not timed. Some run thirty minutes. Some run longer. The unit of measure is progress, not a clock.

Claim Your Seat!

A Plan Tells You What To Do. A Machine Does It For you.

Most advice dies because it stays a document. A document needs you to remember it, apply it, and keep applying it after the energy of the session fades. A machine does not.

That is the difference here. You leave with a system that runs after I am gone. I am in these tools every day, building the systems I write about. The build is the same work I do for myself, handed to you and tuned to your business.

What People Are Saying About John

The Terms

The Operating Project sells at $999. I am opening ten founding seats at $499, half the price, for the first ten people who take one.

When they are gone, the founding rate is gone. Ten is the most I can deliver well alongside the work I already carry, so the cap is real.

Each seat includes a full year of VIP access to the Operating Founders community and the weekly Friday office hours.

You can run your four sessions any time through December 31, 2026. Lock the seat now, start when you are ready.

Take one of the ten before they’re gone!

Pick the problem. I will help you solve for it, and build the machine that keeps it solved.

Claim Your Seat!

- John -

About John

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

Do you want to start becoming AI-Fluent?

Register for my free Masterclass on June 16th, where I will teach you how to build your own AI Employee that runs your calendar and inbox! Register Here

Read more

By John Brewton · Founder · Operating by John Brewton

Start Here To Become An AI-Fluent Consultant

Are you a solo consultant or professional services provider? Whether you specialize in financial analysis, acquisition strategies, accounting, or brand marketing and sales, this article is tailored for you. In today's AI-driven landscape, consultants and service providers can expand their businesses faster and more efficiently than ever before. If this resonates with your work, you won't want to miss these transformative insights.

Let's dive in.

What once took 40 hours for a client audit now takes just 4. At $200 per hour, that efficiency comes at a cost of $7,200 in lost billable time. The billable hour penalizes consultants who create more efficient systems.

Over the past six months, I've tested the full Claude and Perplexity stack against the real-world workflows of a solo advisory practice. This article draws from 24 primary sources and 12 academic and institutional studies, all linked inline and organized in the appendix. The conclusion is clear: integrating AI into your current practice can boost efficiency by 20 to 30 percent, but redesigning your operating model around AI enables the productivity of a 3-to-5-person firm as a solo consultant.

TL;DR

• AI-fluent means rebuilt, not retrofitted. Speeding up old workflows yields 20 to 30 percent gains. Redesigning the operating model around AI yields 5 to 10x output on research-heavy work, per Digital Applied’s micro-consulting benchmarks.

• The stack is 2 tools with different jobs. Perplexity is the intelligence layer; it reads the live world and returns cited research. Claude is the execution layer; it holds your firm’s IP and produces the deliverables. Together, they close a loop: verified intelligence in, client-ready output out.

• The evidence is no longer anecdotal. A field experiment with 758 BCG consultants found that AI users completed 12.2 percent more tasks, 25.1 percent faster, and at 40 percent higher quality on tasks within the model’s competence, per the Harvard Business School jagged frontier study.

• Demand confirms the shift. AI-augmented freelance consulting skills grew 109 percent year over year in Upwork’s 2026 In-Demand Skills report.

• Pricing is the play that captures the value. When delivery compresses, hourly billing becomes a liability. Outcome-based pricing converts the time savings into margin and raises practice exit multiples from 4x toward 6x EBITDA, per DEMG’s analysis.

• The risks are manageable, not optional. Verification, data handling, and billing ethics decide whether the system compounds or collapses.

This week’s article builds one thing: a working AI operating system across Claude and Perplexity. The Vault hands you every asset to build it.

Twelve assets: Six reference and instruction files. Five working skills. One Companion Field Guide that compiles it all into a 24-page PDF.

The five skills do the work: ICP scoring, meeting intelligence, proposal generation, Five Forces, and Jobs To Be Done.

Free subscribers get the thinking. Paid subscribers get the build.

Subscribe and download the Vault.

Become A Paid Subscriber Today

What does “AI-fluent consultant” actually mean?

The phrase has been stretched to cover everything from a ChatGPT tab to full process reinvention. The useful definition is narrow. An AI-fluent practice is one in which the operating model is designed around AI from the start, rather than retrofitted onto a legacy service-delivery structure.

The distinction shows up in the economics, not the tooling. BCG’s 2025 study of 1,250+ companies found that only 5 percent achieve AI value at scale, while 60 percent report no material value despite real investment. The gap is not access to models. Everyone has the same models. The gap is in the operating model design.

The same split is restructuring the big firms. HBR documented in September 2025 that AI is collapsing the consulting pyramid into an obelisk, automating the research and modeling that justified thousands of junior billable hours. The FT reported in May 2026 that McKinsey is overhauling partner pay under Project Acorn because roughly a quarter of global fees are now tied to outcomes rather than hours. The Economist asked in June 2025 who needs Accenture in the age of AI, after the firm had shed roughly $60 billion in market value since its February 2025 peak.

The pyramid is breaking at the top of the market. That is the opening for the solo operator at the bottom of the cost curve.

What is the best AI stack for a solo consultant?

Claude is the execution environment: It holds your firm’s IP, frameworks, voice, and processes in Projects and Skills. It produces the deliverables clients pay for through Code, Artifacts, and MCP-connected tools.

Perplexity is the intelligence layer: it ingests the live world, web sources, internal files, competitor signals, and earnings transcripts, and routes these signals through specialized models to produce verified, cited research faster than any human team.

The Loop: Perplexity generates the intelligence. Claude shapes it into client-ready output. Your judgment sits between them as the quality gate.

Perplexity intelligence layer (Deep Research, Tasks, Spaces, Computer) feeding the operator gate, feeding the Claude execution layer (Projects, Skills, Artifacts, MCP), feeding client deliverables, with the feedback loop returning to intelligence.

Subscribe now

What are the 10 highest-leverage AI plays for a consulting practice?

The full research report identifies 10, ordered roughly by implementation complexity.

1. Deep Research for first-pass analysis: A 30-page cited report in under 4 minutes replaces 20 to 40 hours of engagement research.

2. Always-on market monitoring: Scheduled intelligence replaces 3 to 5 hours a week of manual scanning across client verticals.

3. Claude Projects as the firm’s operating brain: Your frameworks, voice, and pricing persist across every conversation.

4. Claude Skills for repeatable frameworks: Porter’s Five Forces, ICP scoring, JTBD synthesis, encoded once, run in minutes.

5. Discovery-to-proposal automation: 48 to 72 hours compressed to under 2.

6. First-draft deliverables via Artifacts: Interactive memos, 11-tab financial models, working mini-apps.

7. CRM intelligence via Attio MCP: Research, record creation, and outreach drafting in 1 conversational loop.

8. Perplexity Spaces as engagement hubs: Internal files and live web searched together.

9. The content engine: 1 newsletter becomes 10+ platform assets, client work becomes thought leadership.

10. Outcome-based pricing: The structural play that converts the first 9 into revenue.

Subscribe now

How do consultants use AI for research without getting burned?

Perplexity Deep Research performs dozens of searches, reads hundreds of sources, and synthesizes a cited report in 2 to 4 minutes. It scores 93.9 percent on SimpleQA. The scope change matters more than the speed change. A human analyst carefully reads 20 to 30 sources. Deep Research reads hundreds, which changes the statistical odds of surfacing the non-obvious insight.

The discipline that makes it safe runs in 3 steps. First, run the research inside a Perplexity Space scoped to the engagement, with source instructions set to the client’s vertical. Second, export the cited report into the engagement’s Claude Project as reference material. Third, spot-check 5 to 10 citations per report before anything enters a client deliverable. That takes 15 minutes, compared to hours of independent verification.

The productivity evidence behind this play is peer-reviewed. Noy and Zhang’s MIT experiment, published in Science, found that access to ChatGPT reduced professional writing time by 40 percent and improved quality by 18 percent. Ethan Mollick documents a Danish study in which AI halved the working time on 41 percent of tasks. The Stanford and NBER call-center study found a 14 percent average productivity gain, with 34 percent for novices. Research-intensive consulting tasks compress harder than any of these because parallel search structurally beats sequential browsing.

Do you want to start becoming AI-Fluent?

Register for my free Masterclass on June 16th, where I will teach you how to build your own AI Employee that runs your calendar and inbox! Register Here

Can AI monitor my markets while I sleep?

Yes, and this is the play most consultants skip. Perplexity Computer, launched February 25, 2026, runs asynchronous multi-step work across 19+ frontier models. Perplexity Tasks schedule recurring Deep Research queries delivered to your inbox.

The standing configuration for a 5-client practice: competitor funding and hiring scans weekly, contract awards from SAM.gov daily or weekly, regulatory changes weekly, client account news daily, earnings synthesis quarterly. Karo Zieminski documents 5 weekly workflows running in Computer, including the warning that matters: the May 2026 default orchestrator switch changed long-running task behavior, so pin specific models for production workflows.

The gain is 3 to 5 hours a week. The bigger gain is never missing the competitor funding round or policy change that should have triggered proactive client outreach.

Subscribe now

How do I make Claude actually remember my firm?

Claude Projects persist documents and instructions across all conversations within them. The architecture that works: 1 Project per client engagement plus 3 standing firm-IP Projects.

Firm Voice and Methodology holds your style guide, frameworks, and anonymized past deliverables. Build this one first. Proposal Factory holds pricing structures, SOW templates, and scoping criteria. Research Synthesis Hub holds recurring frameworks and sector source lists.

Claude Skills are the action layer on top. A Project configures behavior. A Skill executes work: it takes inputs, runs multi-step logic, and produces a structured output. The canonical consulting Skills are a Five Forces analyzer, an ICP scoring engine, a JTBD interview synthesizer, and a proposal generator. One practitioner documented cutting proposal time from 2 hours to 90 seconds with the Skill pattern. A framework application that took 4 to 6 analyst-hours to build runs in 10 to 30 minutes. Across 5 engagements a month, that is 20-30 hours recovered.

The design principle: a Skill produces a specific, reviewable output, not a conversation.

How do I automate the discovery-to-proposal pipeline?

This is the most commercially sensitive workflow in any practice, the gap between qualified interest and signed engagement. Speed here moves close rates. Stack.expert’s pricing analysis notes AI-augmented consultants quote 2-day turnarounds where traditional firms quote 4 to 6 weeks, and clients pay premium rates for the speed.

The pipeline runs in 5 steps. A meeting notetaker routes the discovery transcript to the engagement Project via MCP. The Proposal Skill extracts the stated problem, the unstated needs, success criteria, and decision process into a 1-page brief. The Skill matches extracted needs against your offerings file and recommends 1 to 3 packages with pricing rationale. Claude drafts the full SOW in your template and brand format. A final Perplexity pass adds 1 or 2 fresh data points on the prospect to the executive summary.

Subscribe now

Can AI build the actual client deliverables?

The deliverable layer is where the stack stops being a research assistant and starts being a production system.

Claude Artifacts produce interactive outputs: strategy memos with embedded charts, working ICP scoring tools, market sizing calculators, competitive positioning matrices a client can explore. As of April 2026, Live Artifacts connect to external tools via MCP and refresh in real time. Anthropic’s Financial Services solution ships pre-built plugins for financial modeling. One analyst built an 11-tab financial model in 10 minutes including sensitivity analysis. Perplexity Labs is the research-native equivalent, producing dashboards and reports grounded in cited web sources.

On the pipeline side, Attio MCP gives Claude OAuth-authenticated access to your CRM. The flow Attio documents: run deep research on a target in Perplexity, then instruct Claude to create the company record, add contacts, log the research as a note, and draft personalized outreach referencing a specific finding, all in 1 conversation. Estimate 3 to 5 hours a week saved across a 20-to-30-account pipeline, on engagements worth $15,000 to $150,000 each.

The rule that keeps this safe: client-facing Artifacts get read-only credentials. Write access to your systems always requires explicit confirmation.

How should I price consulting when AI does the delivery?

The consultant billing $200 an hour who compresses a 40-hour audit to 4 hours just converted AI into a pay cut. The resolution is to price the audit at its decision value, $7,000 to $8,000, and let delivery time become your margin.

The productized architecture runs in 3 tiers. Tier 1 is fixed-scope intelligence products: a competitive brief for $1,500 to $3,000 in 48 hours, and a market entry analysis for $3,000 to $6,000 in 5 days. Tier 2 is the retained intelligence service at $3,000 to $8,000 per month, built on the monitoring and synthesis plays, and requires 8 to 15 hours of your time. Tier 3 is the outcome-defined transformation engagement, priced at $25,000 to $75,000, based on value delivered rather than the $5,000 to $15,000 the same work would have earned under hourly rates.

The institutional signal points the same direction. Sequoia’s services-as-software thesis notes $6 flows to services for every $1 to software, and argues the winners will sell completed work, not tools. DEMG’s analysis documents outcome-based practices commanding 5 to 6x EBITDA at exit against 4 to 4.5x for hourly firms. Digital Applied’s benchmarks show solo practitioners reaching $200 to $500 effective hourly rates on productized offerings. And there is an ethical floor here: if you bill time-and-materials, you bill the 4 hours, not the 40. Outcome pricing eliminates the ambiguity.

What are the risks of using AI in client work?

Hallucination: The HBS jagged frontier experiment found consultants using AI outside its competence were 19 percentage points more likely to produce wrong answers. The Stanford RegLab study of legal AI tools found error rates of 17 to 33 percent in tools marketed as hallucination-free. Barclay Damon’s legal analysis states the standard: the legal risk lies not in the existence of hallucinations but in the failure to govern and verify them. Trace every market sizing figure to a primary source. No exceptions.

Confidentiality: Use Claude Team or Enterprise and Perplexity Enterprise Pro for client work. Consumer tiers carry weaker data commitments. For defense clients handling ITAR or CUI material, verify contractual data handling with counsel before uploading anything.

Trust mechanics: MIT Sloan Management Review’s global survey found users who can interpret AI outputs are 2.8x as likely to trust the technology. Your clients follow the same curve. Show sources, show methods, and the deliverable lands.

Workflow fragility: Model updates break Skill behavior, long Project histories dilute context, OAuth tokens expire. Pin models for production workflows, compress old conversations into summary documents, and check MCP connections weekly.

What do the first 90 days look like?

Days 1 to 30, foundation. Build the Firm Voice Project, configure 1 Perplexity Space per active engagement, connect Attio MCP, run your first 5 Deep Research queries. Success: Claude produces on-voice first drafts without style corrections.

Days 31 to 60, automation. Build the Proposal Skill and 2 framework Skills, configure 2 to 3 scheduled monitoring Tasks, start the content engine. Success: proposal drafts in under 10 minutes, intelligence briefs arriving automatically.

Days 61 to 90, repricing. Launch the first productized offering, raise rates on new engagements, ship the first client-facing Artifact. Success: 1 engagement closed at outcome pricing and a measurably higher effective hourly rate.

The order matters. Foundation before automation, automation before repricing. A consultant who reprices before the delivery system exists has sold a promise with no machine behind it.

- j -

FAQ

Is this realistic for a generalist, or only for niche consultants? The stack works for both. The economics favor the niche. Scheduled monitoring, vertically tuned Spaces, and a newsletter thesis all compound faster inside a defined boundary. Weak boundary: business strategy. Strong boundary: GTM strategy for defense tech firms navigating CMMC compliance.

What does the full stack cost? Claude Pro or Team runs $20 to $30 per seat per month; Perplexity Pro $20, with enterprise tiers higher. Attio and a meeting notetaker add $50 to $100. Total tooling sits under $200 a month for a practice billing $15,000 to $50,000 a month. The constraint is build time, not cash.

Do clients care that AI produced the work? Clients pay for judgment and outcomes. You own every deliverable, and your professional accountability is undiminished by who drafted it. The MIT Sloan trust data says the practical move is transparency about the method paired with visible source citations.

Which play should I build first? The Firm Voice Project. Every other play inherits its quality. A proposal Skill without encoded voice produces templates. With it, the output sounds like you on the first pass.

Can I run this on consumer-tier subscriptions? For internal work, yes. For client work, no. Enterprise tiers include the contractual data protections, SOC 2 compliance, and no-training commitments required by client confidentiality.

Does outcome pricing work for existing clients? Reprice at the next natural boundary, a renewal, a new SOW, a scope change. Grandfather current agreements. New engagements start on the new model from day 1.

What breaks first? Verification discipline. The system produces confident, polished, occasionally wrong output at volume. The consultant who stops spot-checking citations ships the error that costs the relationship. Build verification into the workflow, not the calendar.

Become A Paid Subcriber Today To Access All The Assets Below.

Become a Paid Subscrber

Read more

TL;DR

Sequoia published “Services: The New Software” on March 5, 2026. The claim: the next $1T company will sell completed work, not software tools. Companies spend $6 on services for every $1 on software. AI autopilots capture that 6x budget by delivering outcomes directly. The best entry point is work a company already outsources. The largest unclaimed market is management consulting at $300-400B. I verified the thesis against Foundation Capital, Bessemer, Goldman Sachs, a16z, HBR, Stanford, and WSJ. It holds.

For every $1 a company spends on software, it spends $6 on services.

Sequoia published that ratio on March 5. The firm’s claim is bigger than the ratio. The next $1T company will be a software company masquerading as a services firm. It will not sell you the tool. It will sell you the finished work.

I spent the week auditing the thesis against 8 sources: Foundation Capital, Bessemer, Goldman Sachs, a16z, 2 HBR studies, Stanford’s Digital Economy Lab, and WSJ reporting. The thesis holds. Here is the field review.

Subscribe now

Why does Sequoia say services are the new software?

A company pays $10K a year for QuickBooks. It pays $120K a year for the accountant who closes the books. Same function, 12x the budget.

Every software founder is fighting over the $10K line. The $120K line sits next to it, larger and less defended. Sequoia’s argument is that AI now lets a vendor take the $120K line directly. The next legendary company will just close the books.

The investors agree on the size of the prize. Foundation Capital sizes the services-as-software pool at $4.6T. Bessemer frames it as a roughly $10T services and labor market against a $600B software market. Goldman Sachs projects that more than 60 percent of software industry operating profit accrues to systems built around AI agents by 2030.

$4.6T. Foundation Capital’s sizing of the services-as-software opportunity, against a global software market of roughly $600B. The work budget is 7x the tool budget at planetary scale.

What is the difference between intelligence work and judgement work?

Writing code follows rules. The rules are complex, but they are rules. Sequoia calls this intelligence work. Deciding which feature to build next, whether to take on tech debt, when to ship before it is ready. That is judgement, built on years of pattern recognition.

AI crossed the intelligence threshold first in software engineering. The category now accounts for more than half of all AI tool usage across professions. Every other category sits in single digits. A year ago Cursor users treated AI as autocomplete. Today agents start more coding tasks than humans do.

Stanford’s Digital Economy Lab audited 844 tasks across 104 occupations and found workers in 36 percent of occupations already using AI for at least 25 percent of their tasks. The threshold crossing is not a software story. It is an everything story arriving on a lag.

What is the difference between an AI copilot and an AI autopilot?

A copilot puts AI in the professional’s hands. An autopilot delivers the finished work to the company that needed it.

Harvey sells to law firms. Rogo sells to investment banks. The professional remains the customer and takes responsibility for the output. That is the copilot model, and it captures the tool budget.

Crosby sells the drafted NDA to the company that needs it, not to outside counsel. WithCoverage sells the CFO insurance, not the broker a workflow. That is the autopilot model, and it captures the work budget from day one.

Where should an AI-native services company start?

Start with work somebody already outsources. The playbook section of Sequoia’s piece is the part operators should read twice.

If a task is already outsourced, 3 things are true. The company accepts that the work can be done externally. A budget line exists and substitutes cleanly. The buyer already purchases an outcome rather than hours.

Replacing an outsourcing contract is a vendor swap. Replacing headcount is a reorg. Vendor swaps close in a quarter. Reorgs take a year and an executive sponsor.

Crosby started with NDAs for exactly this reason. Well-defined task, primarily intelligence, already outsourced, immediate ROI. a16z documented the same pattern in its 2025 BPO work: the outsourced layer unbundles first, and pricing moves from seats to outcomes. Decagon prices per conversation handled, not per agent seat.

Subscribe now

Which services markets have the largest AI opportunity?

Sequoia plots 10 service verticals by labor TAM. Insurance brokerage at $140-200B. Recruitment and staffing at $200B+. Supply chain and procurement at $200B+. IT managed services at $100B+. Accounting, healthcare revenue cycle, and claims adjusting at $50-80B each. Tax advisory at $30-35B. Legal transactional work at $20-25B.

Nine of those squares have named players. The largest does not. Management consulting, $300-400B, carries the label best candidates TBD.

The supply side tells you why the timing matters. The US lost roughly 340,000 accountants in 5 years while demand grew. 75 percent of CPAs are nearing retirement. WSJ counted more than 300,000 departures between 2019 and 2022 alone. Shortage forces AI acceptance faster than any sales motion ever could.

Will AI replace management consulting?

The pyramid is restructuring in public. McKinsey cut 3,000 to 4,000 roles in 2025 and 2026, its largest reduction since 2008. McKinsey, BCG, and Bain froze starting salaries for the third consecutive year. KPMG UK cut its graduate class 29 percent. HBR has now published 2 separate studies documenting the pyramid model breaking, in September and October 2025.

HBR flagged consulting as being on the cusp of disruption in 2013. The cusp lasted 12 years. The break took 18 months.

The replacement is disaggregation, not deletion. Data gathering, benchmarking, and modeling get automated. The strategic recommendation stays human and gains value. The operator who packages judgement on top of automated intelligence inherits the margin the pyramid used to capture.

Why don’t the successful AI copilots just become autopilots?

The fastest-growing AI companies of 2025 were copilots. In 2026 many will try to become autopilots. They have the product and the customer knowledge. But selling the work means cutting their own customers out of doing it.

That is the innovator’s dilemma, running live, with 18-month-old companies playing the incumbent. The hesitation is the opening for pure-play autopilots.

What should operators do now?

If you are building or advising in this market, the playbook runs in 5 moves.

1. Pick an outsourced, intelligence-heavy task in a vertical you know. The budget line already exists.

2. Sell the outcome, not the seat. Price the work.

3. Substitute the vendor, not the org chart.

4. Compound proprietary judgment data with every engagement. Today’s judgment becomes tomorrow’s intelligence.

5. Expand toward the insourced, judgment-heavy work. The wedge is the entry. The labor line is the TAM.

This week, do 1 thing. List every function your company or your clients currently outsource, with the annual spend next to each line. That list is the opportunity map for your specific situation. It takes 30 minutes and most operators have never written it down.

Subscribe now

FAQ

What does “services as software” mean? AI companies deliver completed work instead of selling tools. The vendor owns the outcome, the way a services firm does, with software economics underneath. Foundation Capital coined the framing and sizes the opportunity at $4.6T against a $600B global software market.

What is an AI autopilot? An autopilot is an AI company that sells the finished work directly to the buyer. Crosby delivers drafted NDAs to companies. WithCoverage delivers insurance to CFOs. The customer buys the outcome, and the vendor captures the work budget rather than the tool budget.

How big is the services-as-software market? Foundation Capital sizes it at $4.6T over 5 years. Bessemer frames the full services and labor market at roughly $10T, against a $600B software market. Sequoia’s ratio: $6 of services spend for every $1 of software spend.

Which industries will AI services companies disrupt first? Industries where the work is rules-based and already outsourced: insurance brokerage ($140-200B), accounting ($50-80B US outsourced), healthcare revenue cycle ($50-80B), claims adjusting ($50-80B), and legal transactional work ($20-25B). High judgement fields move later.

Is management consulting threatened by AI? The $300-400B consulting market is the largest square on Sequoia’s map and the only one without named candidates. The work disaggregates: research, benchmarking, and modeling get automated while strategic recommendation stays human. McKinsey cut 3,000 to 4,000 roles in 2025-2026 and froze salaries 3 years running.

What is the difference between a copilot and an autopilot in AI? A copilot makes a professional more productive and sells to that professional. An autopilot replaces the engagement and sells the outcome to the end customer. Harvey is a copilot for lawyers. Crosby is an autopilot for the company that needs the NDA.

The reading list

1. Sequoia, “Services: The New Software”. The source text. 7 minutes.

2. Foundation Capital, “The $4.6T Services-as-Software Opportunity”. The sizing.

3. a16z, “Unbundling the BPO”. The wedge in detail.

4. Goldman Sachs, “AI Agents to Boost Productivity and Size of Software Market”. The profit-pool math.

5. HBR, “AI Is Changing the Structure of Consulting Firms”. The pyramid, breaking.

6. HBR, “How AI Is Upending How Consulting Firms Hire Talent”. The hiring evidence.

7. Stanford, “Future of Work with AI Agents”. The task-level data.

8. Bessemer, on the ~$10T labor framing. The biggest frame.

— 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. 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

New to Operating? Start with The Star Acquisition Company, Claude and the Three Decisions, and The Challenger’s Playbook.

Subscribe now

RIMOWA’s Aluminum Bet was a 1937 material decision that became a 1950 engineering decision that became a 1980s manufacturing footprint that became a 2016 €640 million acquisition that became, by 2023, an estimated billion-euro business.

TL →DR

• In October 2016, LVMH paid €640 million for 80% of RIMOWA, a 118-year-old Cologne suitcase maker doing roughly €440 million in revenue. The multiple was 1.8 to 2.0× revenue.

• The asset was 1 physical feature: parallel grooves pressed into 0.8mm aluminum since 1950, copied from the corrugated skin of the Junkers F13 aircraft. The grooves are structure that doubles as recognition. You cannot separate the two.

• Alexandre Arnault, 24, cut distribution from 5,000 doors to roughly 500, raised prices 20%, deleted the logo ornament, and moved 100% of ad spend to digital. Revenue dipped for 2 years, then nearly quadrupled to an estimated €1 billion by 2023.

• The research backs the mechanism. Berger and Ward showed the most expensive products carry the fewest logos. Visibility research shows visible, differentiated products earn outsized word of mouth. The grooves pass every test in the literature.

• The out-of-sample proof: RIMOWA grew through the 2024–2025 luxury downturn while the market shrank and sector margins fell to 2009 levels.

• 4 principles transfer: find the 1 feature only you can do, subtract everything that competes with it, make wear an asset, and put the product where buying decisions happen.

On October 4, 2016, LVMH paid €640 million for 80% of an 118-year-old German suitcase company with 3 factories, roughly 3,000 employees, and 1 engineering decision dating to 1950 (Reuters).

The implied enterprise value was roughly €800 million. The multiple was 1.8 to 2.0× revenue (Business of Fashion). The Cologne factory was making suitcases the same way it had for 7 decades, feeding 0.8mm aluminum sheet through a proprietary rolling die that pressed parallel grooves into the metal before bending it into a half-shell (factory video).

LVMH did not buy the factory. It bought the grooves.

The deal box. €640 million for 80%, roughly 1.9× revenue, and the five-move sequence that followed. Tiffany ran the same playbook at 20× the price four years later.

The Operating Thesis: RIMOWA Is Not a Luggage Company

RIMOWA is an 85-year case study in artifact design. Make the physical object so visually distinctive that the product becomes the brand’s primary marketing channel. That is the whole thesis.

The grooves are not decoration. They started in 1950 as a structural fix borrowed from the corrugated duralumin skin of the Junkers F13, the world’s first all-metal commercial aircraft (Forbes). The ribbing let Richard Morszeck use thinner aluminum without losing rigidity, the same principle that let Hugo Junkers’s aircraft fly. RIMOWA claims that the corrugation increases panel stability and impact resistance by up to 50% compared with a flat sheet of the same gauge (MightyTravels).

Function came first. Recognition followed. Recognition built the business.

By 2016, the grooves were doing work no advertising budget can buy: making the suitcase legible from 20 meters in any airport on earth, in any light, with zero typography. Taiwan’s IP courts later weighed whether the parallel grooves constituted a protectable trade identity, confirming in law what travelers already recognized at a glance (Managing IP).

The grooves are the brand. No wordmark required.

LVMH paid €640 million for that fact.

What the academic research says about products that advertise themselves

The mechanism has a literature behind it. 3 findings matter the most:

1. First, the inverted-U. Berger and Ward documented in 2010 that the most expensive products in a category carry the fewest explicit brand markers. Insiders decode subtle signals. Outsiders cannot. The grooves are the textbook case: legible to the knowing, invisible as branding to everyone else.

2. Second, costly signaling. The theory requires a signal to be observable, hard to fake, and tied to a real underlying quality (Nelissen and Meijers, 2011). The grooves clear all 3 bars: visible across a terminal, inseparable from a forming process RIMOWA calls “a highly coveted secret,” and tied to measurable engineering performance. A 2017 replication found the status payoff of luxury logos weakens in some settings. The grooves dodge that fragility because they signal competence and miles traveled, not wealth alone.

3. Third, visibility drives conversation. Berger and Schwartz showed across 300+ products that publicly visible, environmentally cued products earn more word of mouth, immediately and over time. Lovett, Peres, and Shachar added that differentiated brands earn outsized conversation. A grooved aluminum case in an airport stacks all 3 effects.

The grooves pass every test the literature sets. RIMOWA built the signal in 1950 and never diluted it. The discipline is the point.

The four tests of a perfect signal. Observable, hard to fake, tied to real quality, amplified by context. The grooves pass all four. Most products pass none.

3 Decisions That Made RIMOWA

Most heritage brands have 1 origin story and a long tail of incremental choices. RIMOWA’s history rests on 3 discrete operating decisions, each separated by roughly half a century and defining the next era.

Read more

The operator’s path is rarely a straight line. It crosses open ground, stalls at a failure point, then recovers to deliver a tangible return on investment, hopefully. The route from deployed to value is an organizational journey, and the 51 companies examined in the Stanford study, analyzed in this piece, reached the end by managing the organizational and human path, not by buying a better model.

Too Long → Didn’t Read

Most enterprise AI pilots stall for organizational reasons, not technical ones. A 2025 MIT NANDA study found 95% of generative AI pilots produce no measurable financial impact. The Stanford Digital Economy Lab went the other way and documented 51 successful deployments. The model was never the differentiator. The organization was. This piece walks through where the real work sits and where to spend the next dollar of AI budget. It is about a six-minute read.

What you will find below:

• Why do most enterprise AI pilots fail?

  • The 95% versus the 51, and why the model is not the variable.

• What is the hardest part of deploying enterprise AI?

  • 77% of the hardest work is invisible, and 61% of winners failed first.

• Why do similar AI projects take weeks at one company and years at another?

  • The accelerators and the drags, none of them technical.

• What does effective executive sponsorship of AI involve?

  • Clearing blockers, permitting failure, and managing staff-function resistance.

• Does experience with AI compound over time?

  • The data says yes, and the gap is widening.

• Does enterprise AI reduce headcount, and where should AI budget go?

  • Reduction is a choice, not a verdict.

• Frequently asked questions and a full appendix with sources and downloads.

Two companion research reports are free to download: Inside 51 Successful AI Deployments and The Learning Curve.

Are you new to Operating?

You might enjoy these recent reader favorites:

1. Three Builds To Become An AI Fluent Operator

2. The 17 Questions That Tell You Whether You Are AI Fluent

3. AI Didn’t Steal Our Greatness. AI Didn’t Steal Our Jobs.

4. AI Founder Mode Means Hiring Micro-CEOs

Subscribe now

Why do most enterprise AI pilots fail?

Most coverage of enterprise AI starts with the failures. MIT’s NANDA initiative found that 95% of generative AI pilots produce no measurable financial impact. Stanford’s Digital Economy Lab ran the experiment in reverse. Elisa Pereira, Alvin Wang Graylin, and Erik Brynjolfsson documented 51 deployments that delivered real value, drawn from 41 organizations across 9 industries, 7 countries, and 5 regions, covering more than one million workers. Then they asked what those organizations did differently (Figure 2).

Figure 2 · The failure rate and the exceptions

Two numbers set the stakes. 95% is the failure rate from the MIT study. 51 is the count of deployments Stanford chose to study precisely because they worked.

The answer aligns across all 11 chapters of their report. The model was never the differentiator. The organization was. The technology is the easy, increasingly commoditized part. The hard, durable, value-creating work is organizational. It is human. What great news for all us humans. The bottleneck is the org, not the technology. Both studies, from opposite directions, reach the same conclusion: AI fails at the org level, not the model.

What is the hardest part of deploying enterprise AI?

When practitioners were asked to name the hardest challenges, they did not mention technology. They named change management, data quality, and process redesign. Those intangible costs accounted for 77% of the hardest work. Teams described the technology itself as the easiest piece of the build (Figure 3).

Figure 3 · The invisible 77%

The visible 23% is the technology, the part a business case usually budgets for. The 77% below the line is change management, data quality, and process redesign, the work that decides the outcome and rarely makes the spreadsheet. The yellow rule marks the waterline between what leaders see and what actually costs them.

The cost of the business case missed is the failure that came first. 61% of successful projects had a prior failed AI project. That sunk cost never appears in the winning project’s ROI, yet it was often essential to the win. First attempts failed for consistent reasons. Teams pointed AI at broken workflows. Technical groups were led without business ownership. Leaders assumed the model would fix problems that required redesigning the work.

The data backs a counterintuitive point about readiness. Only 6% of deployments had data fully ready for AI, yet 91% processed unstructured data and 88% made previously inaccessible data usable (Figure 4).

Figure 4 · The data-readiness paradox

The “perfect the data first” instinct is the one this panel dismantles. Only 6% of these teams had ready data, and they shipped anyway. The lesson on the right is the durable one: messy proprietary data you own beats clean data anyone can buy. The operator’s move is to budget for the invisible 77% and to treat the first failure as tuition rather than waste.

Why do similar AI projects take weeks at one company and years at another?

The cleanest proof of the thesis is timing. A fintech migrated millions of lines of legacy code in weeks. A major bank attempting a comparable customer support build reports timelines measured in years. Same technology. Same category of use case. Different organizations, and an outcome gap of more than an order of magnitude (Figure 5).

Figure 5 · Same use case, weeks or years

Two tracks, one technology. The top path is short and direct because the organization was ready. The bottom path winds across the whole frame, detours through a failed pilot, and arrives years later. The visual makes the point a table cannot: the distance between the two is organizational readiness, nothing else.

Three factors consistently accelerated time to value: executive sponsorship in 43% of cases, building on an existing foundation in 32%, and genuine end-user demand in 25%. Four factors consistently slowed projects down, each in roughly a fifth of cases: the learning curve and iteration, data quality and preparation, regulatory and compliance work, and gaps in process documentation. None of those four is a model problem. All four are organizational (Figure 6).

Figure 6 · The forces on time to value

A tug-of-war on a single axis. The yellow vectors pull a project toward ROI, the counter-vectors pull it back toward the stall. Read the labels and the editorial point is unmistakable: every force on both sides is a choice about people and process, not a property of the model.

One pattern was absolute. Every successful project with an identifiable methodology used iteration. None used waterfall. The shape of the work was always start small, learn, expand.

What does effective executive sponsorship of AI involve?

The report draws a sharp line between sponsors who drive results and sponsors who approve budgets. On a four-point engagement scale, only 29% of cases reached Strategic Integration, the level at which AI adoption became a corporate OKR tied to incentives. The seven cases that achieved organization-wide change all reached that level. None reached it by writing a check (Figure 7).

Figure 7 · The sponsorship ascent

Sponsorship is drawn as a climb because that is what it is. Most leaders stop at Active Steering, the 58% rung. The summit, Strategic Integration, is where AI becomes a corporate objective tied to incentives, and it is the only rung where organization-wide change happened. The red mark shows where most stall out.

Effective sponsors do specific things. They clear blockers weekly. They bridge the business and technical teams. They give teams permission to fail, and they mean it. In no case studied was anyone punished for a failed AI initiative. 73% of teams started small on purpose, and 63% framed their pilots as experiments rather than launches.

Resistance came from a place most leaders do not expect. Staff functions, not frontline workers, were the most frequent source. Legal, HR, Risk, and Compliance accounted for 35% of resistance. Internal end-users accounted for 23%. Frontline fear of replacement appeared in only two cases. Staff functions have the authority to slow a project regardless of executive support, and they respond to governance rather than persuasion. Bring them in early with a mandate, and several of them will convert from blockers to enablers.

The same discipline shows up in the amount of human oversight each function needs. Escalation models, where AI handles the work and humans review exceptions, delivered the highest gains. Approval models fit regulated, high-stakes work. The right level depends on the task, not the technology (Figure 8).

Figure 8 · Oversight by function

One graphic carries six data points and a design principle. Functions sit in the oversight band that fits them, plotted by the productivity gain they delivered. The takeaway for an operator is to stop asking how much to trust AI in general and start matching the level of oversight to the task at hand.

Does experience with AI compound over time?

The Stanford data describes organizations. A second dataset describes individuals, and it points in the same direction. Anthropic’s Economic Index studied one million conversations and found that experience with AI is self-reinforcing (Figure 9).

Figure 9 · The advantage compounds

The curve climbs with tenure. Newcomers succeed about two-thirds of the time, seasoned users closer to three-quarters, and roughly four points of that gap survive every statistical control. The editorial reading is that skill with AI is earned through use, which means the advantage is cumulative and the latecomer cannot simply buy it.

Users with six or more months of tenure succeed more often than newer users. Their task success rate is 73.1%, compared with 66.7% for newer users. That edge survives strict controls. Comparing high-and-low-tenure users within the same narrowly defined task, and then adding controls for model, language, use case, and country, leaves a success gap of about 4 percentage points. The difference is not the task they pick. The difference is the skill they built through use.

Experienced users also calibrate. They send their hardest, most valuable work to the most capable model. For every additional $10 in hourly wage for a task, Opus's share increases by 1.5 points on Claude.ai and by 2.8 points on the API. Programmatic users calibrate roughly twice as hard as web users (Figure 10).

Figure 10 · Calibration by task value

Two slopes, one behavior. As the value of a task rises, sophisticated users reach for the more capable model, and developers working through the API do it about twice as steeply as web users. The slope is a portrait of judgment: knowing which model to spend on is itself a learned skill.

Adoption is uneven and staying that way. The top 20 countries account for 48% of per-capita usage, up from 45%, while usage within the United States slowly converges. Read the two datasets together, and the conclusion is plain. Ready organizations deploy in weeks. Experienced users succeed more often. The gap between early movers and everyone else widens with time. The cost of waiting is not flat (Figure 11).

Figure 11 · Adoption concentration ·

Two arrows moving in opposite directions. Across countries the heaviest users pull further ahead, while inside the United States usage slowly evens out. The composition holds both truths at once and underlines the theme: the benefits of early adoption are concentrating, not spreading evenly.

Subscribe now

Does enterprise AI reduce headcount, and where should AI budget go?

Step back and the throughline resolves. For 42% of implementations, model choice was fully interchangeable. The durable advantage sat in the orchestration layer and the organization around it, not in the foundation model. As open-source models close the performance gap, the differentiator shifts from which model you run to what data you feed it and how well your organization works. Model choice only starts to matter as task complexity rises (Figure 12).

Figure 12 · The commodity boundary

The boundary line rises with complexity. For routine work the model is interchangeable, so the smart money is spent elsewhere. For advanced, consequential work the model becomes a real differentiator. The chart tells an operator exactly where model choice stops being a commodity and starts being a decision.

That reframes the question most leaders are actually asking: what AI does to headcount. Reduction was the largest single outcome at 45%. It was also a minority. The other 55% of deployments redeployed people to higher-value work, avoided planned hiring, or reinvested the gains into the roadmap. Reduction is a choice an organization makes, not a verdict the technology delivers. The forward-looking caveat is real and worth stating plainly. ADP payroll data show that early-career workers aged 22 to 25 in AI-exposed occupations have already experienced a 16% relative decline in employment since late 2022. The 45% figure may be a floor (Figure 13).

Figure 13 · The headcount fork

One gain, two destinations. The same productivity improvement forks into the 55% that reinvest or redeploy and the 45% that cut. Drawing it as a fork rather than a fait accompli is the point: the technology produces the gain, the organization chooses what to do with it.

One more pattern deserves attention because it is where the next advantage sits. Agentic AI appeared in only 20% of deployments but delivered a 71% median productivity gain, the highest of any approach. The least-used approach pays the most (Figure 14).

Figure 14 · The agentic quadrant

The quadrant inverts the usual intuition. Agentic AI is the least adopted approach and the highest paying, sitting alone in the top-left while the safer, more common approaches cluster lower and to the right. For a leader looking for the next edge, the empty space around the agentic node is the opportunity.

Subscribe now

The companies that won pointed AI at revenue and new capability, not only at cost. They treated the model as the commodity it is becoming and devoted their utmost effort to the organization. The next dollar of AI budget should go to the same place, because that is where the bottleneck has been the whole time (Figure 15).

Figure 15 · The numbers that matter

A single board to carry away. Six numbers, each one a section of the argument compressed to its figure. It works as the closing slide and as the social card: if a reader keeps only one image, this is the one that holds the whole case.

Are you new to Operating?

You might enjoy these recent reader favorites:

1. Three Builds To Become An AI Fluent Operator

2. The 17 Questions That Tell You Whether You Are AI Fluent

3. AI Didn’t Steal Our Greatness. AI Didn’t Steal Our Jobs.

4. AI Founder Mode Means Hiring Micro-CEOs

Subscribe now

Frequently asked questions

Why do most enterprise AI pilots fail? They fail for organizational reasons, not technical ones. The MIT NANDA study reported a 95% failure rate and attributed it to workflow integration and misaligned incentives. Stanford’s 51 successes confirm the inverse: the model was never the differentiator.

Is the AI model the most important factor in enterprise AI success? No. For 42% of implementations, the model was fully interchangeable. The durable advantage sits in the orchestration layer, the proprietary data, and the surrounding organization. Model choice only becomes decisive as task complexity rises.

How long does enterprise AI take to deliver ROI? It ranges from weeks to years for the same use case. The variable is organizational: executive sponsorship, an existing foundation, and genuine user demand accelerate it. Learning curve, data prep, compliance, and documentation gaps slow it down.

Who resists enterprise AI adoption the most? Staff functions, not frontline workers. Legal, HR, Risk, and Compliance accounted for 35% of resistance, ahead of end-users at 23%. They respond to a governance role and a clear mandate, not persuasion.

Does enterprise AI cause layoffs? Sometimes. Reduction was the largest single outcome at 45%, but most deployments redeployed people, avoided hiring, or reinvested the gains. Reduction is a choice, not an inevitability, though early-career roles are already declining 16% in AI-exposed occupations.

Does agentic AI deliver better results? Yes, where it fits. Agentic implementations delivered a 71% median productivity gain, the highest of any approach, but appeared in only 20% of cases. It suits high-volume, repetitive work with clear success criteria and recoverable errors.

How clean does enterprise data need to be for AI? Cleaner is better, but perfection is not the gate. Only 6% of deployments had fully ready data, yet 91% processed unstructured data and 88% made inaccessible data usable. Access matters more than centralization, and proprietary data is the durable moat.

Does skill with AI compound over time? Yes. High-tenure users succeed 73.1% of the time, compared with 66.7% for newcomers, and the edge survives strict controls. The advantage accrues to those who start early and build the habit.

Appendix

Primary sources

• Pereira, E., Graylin, A. W., and Brynjolfsson, E. (2026). The Enterprise AI Playbook: Lessons from 51 Successful Deployments. Stanford Digital Economy Lab. Publication page · Full PDF

• Massenkoff, M., Lyubich, E., McCrory, P., Appel, R., and Heller, R. (2026). The Anthropic Economic Index Report: Learning Curves. Anthropic. Report

• MIT Project NANDA (2025). The GenAI Divide: State of AI in Business 2025. Reported figure: 95% of generative AI pilots produce no measurable financial impact. Coverage via Fortune & MIT Media Lab

Supporting references

• Brynjolfsson, E., Rock, D., and Syverson, C. (2021). The Productivity J-Curve. National Bureau of Economic Research. The framework behind the report’s macro context.

• ADP payroll data, cited in the Stanford report: early-career workers aged 22 to 25 in AI-exposed occupations saw a 16% relative employment decline since late 2022.

Books by the authors

• Erik Brynjolfsson and Andrew McAfee. The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton, 2014. Buy on Amazon

• Andrew McAfee and Erik Brynjolfsson. Machine, Platform, Crowd: Harnessing Our Digital Future. W. W. Norton, 2017. Buy on Amazon

• Alvin Wang Graylin and Louis Rosenberg. Our Next Reality: How the AI-powered Metaverse Will Reshape the World. Foreword by Neal Stephenson. Nicholas Brealey, 2024. Buy on Amazon

Download the companion research reports

• Inside 51 Successful AI Deployments. The Stanford playbook distilled: 11 headline findings, all 34 detailed findings by chapter, and a 10-point operator checklist. (Report-1_Inside-51-Successful-AI-Deployments.pdf)

• The Learning Curve. The Anthropic Economic Index data on adoption, model selection, and why experience compounds. (Report-2_The-Learning-Curve.pdf)

John Brewton · Founder · Operating · 6AEP

About the author

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

TL · DR (The 30-second version)

• AI fluency is not knowing what AI can do. It is changing how your work actually gets done. Knowing the capability exists changes nothing on its own.

• The research splits in two. Stanford finds 14 to 34 percent task-level gains. NBER finds 89 percent of executives see no enterprise boost. McKinsey finds only 6 percent capture real EBIT, and the one behavior that separates them is workflow redesign. The space between the two is the operator’s opportunity.

• This is a 17-question field test, grouped under seven characteristics of the fluent operator. Each question is a binary, and the standard for yes is that you could show the artifact, the schedule, or the screen recording in the next ten minutes.

• Score yourself 0 to 17. Four tiers: Curious, Practicing, Operating, Fluent. Most operators land at four or five and assume they are close. They are not.

• The worksheet is at the end. Run it on yourself, then on the person who reports to you. The pattern of where you both stall is your build order.

If you have 30 seconds, read this. If you have 12 minutes, read the rest.

Subscribe now

There is a quiet split within companies, and research has now measured both sides of the schism.

At the level of a single task, the gains are real and large. A Stanford study of 5,179 customer support agents found that access to a generative AI assistant raised output 14 percent on average and 34 percent for the least experienced workers. At the level of the whole enterprise, the same technology shows almost nothing. A National Bureau of Economic Research survey of 6,000 executives found 89 percent reporting no productivity gain from AI over three years. Goldman Sachs Research reached the same conclusion in 2026: no meaningful relationship between AI and productivity at the economy-wide level, alongside a median 30 percent gain in two narrow places, software engineering and customer service. The task data and the enterprise data are not the same data. The space between them is the operator’s opportunity.

McKinsey put a number on who closes that space. In its late 2025 State of AI, 88 percent of organizations report using AI, and only 6 percent qualify as high performers capturing 5 percent or more of EBIT from it. The single strongest correlation with that impact is not model choice or budget. It is fundamental workflow redesign. High performers are nearly three times as likely as everyone else to say they have rebuilt how the work is done.

That finding is the whole argument of this piece. AI fluency is not knowing what AI can do. It is changing how your work actually gets done. Knowing the capability exists produces nothing. MIT found that 95 percent of enterprise AI pilots returned no measurable result, and the cause was not the model. It was the failure to redesign the work around it. Fluency lives in the work itself, not in the vocabulary around it.

So I built a test for it. Seventeen questions, grouped under seven characteristics of the fluent operator. Each one is grounded in the institutional research record, and each one has a single hard standard.

How to take this

Each question is a binary. Yes or no. No partials and no “kind of.”

The standard for a yes is strict. You could show the artifact, the schedule, or the screen recording in the next ten minutes. If your answer is a description of what is possible, the answer is no. If your answer is a piece of work that already runs differently, the answer is yes.

Count your yes answers as you go. There are four tiers at the bottom. Most operators assume they are most of the way there and land lower than they expect. That gap is the point of the exercise.

Subscribe now

Section I · Do The Work

The job runs differently because AI is inside it.

1. Name one recurring deliverable you own that you now produce through an AI-assisted workflow you did not use twelve months ago. Could you show the before-and-after side by side?

A yes is a specific deliverable, a brief, a deck, a model, a memo, that is genuinely produced differently now. This is the load-bearing question. The Stanford gains showed up only for workers who changed how the task was done, not for workers who knew the tool existed.

2. Has your role, your weekly cadence, or your standard output changed in the last 90 days because AI is now inside the work, not adjacent to it?

A yes means something structural shifted, not that a few steps got faster. This is where the McKinsey finding bites hardest. Workflow redesign is the behavior that separates the 6 percent who capture real value from everyone else. The MIT pilots that failed bolted AI onto the old process. A faster version of a broken step is still a broken step.

Section II · Wired into the stack

No separate environment to switch into.

3. Is your AI connected to the apps you already live in, email, calendar, files, CRM, so you never copy and paste between a chat window and your real work?

A yes means the model reaches your systems of record directly. Andreessen Horowitz’s 2025 survey of 100 enterprise CIOs found enterprises expecting roughly 75 percent growth in model spend over the next year, and one CIO put it plainly: what I spent in 2023 I now spend in a week. The money is moving toward systems that act inside connected tools, not systems that chat.

4. Have you installed the six core connectors, email, calendar, CRM, Notion or Drive, Slack, and accounting, and can you name the last action each one took on your behalf?

A yes is four or more connectors live and in daily use. This is where the NBER number bites. Most of those 6,000 executives adopted tools and never connected them to the real work, and the productivity gain never arrived. Adoption without integration produces nothing.

Section III · Communications Hub

Comms and calendar run themselves.

5. Does a daily or weekly brief arrive on your screen without you asking for it, covering your inbox, your calendar, conflicts, and the highest-impact items of the day?

A yes is something useful that shows up unprompted. Goldman Sachs Research finds AI’s measured gains run near 30 percent but stay confined to narrow, localized use cases. The unprompted morning brief is exactly that kind of case. It is small, repeated, and it compounds.

6. Is your inbox triaged and your replies drafted before you open it most mornings, with scheduling that resolves without your direct intervention?

A yes means the rhythm of your communication runs with light touch. Thomson Reuters surveyed 2,275 professionals and found they expect AI to save them 5 hours a week, about 240 hours a year, worth roughly 19,000 dollars per person. Inbox triage is that routine work, and it is the first place most operators feel the week get lighter.

Section IV · Analysis

Analysis collapses to minutes.

7. Have you produced a board-ready or client-ready brief inside a single hour in the last 30 days, using a curated research workflow rather than an open chat?

A yes is a real artifact produced fast and clean. Stanford’s AI Index puts organizational adoption at 78 percent in 2024, with the clearest output gains in drafting and data preparation. A disciplined research brief is that gain, productionized into your week.

8. Have you constrained your research to a defined universe of Tier 1 publications, Tier 2 academic centers, and Tier 3 institutional shops, and can you name three sources you have excluded?

A yes means you build the research environment before you ask the question. Constraining the model to a defined source universe moves the signal-to-noise ratio from roughly 20 percent to roughly 90 percent in one move. The fluent operator never lets the model choose the sources. (This article is itself an example. Every link in it sits inside a curated source universe, and the appendix shows the architecture.)

9. In the last week, did you drop a CSV or pull live data, describe a question in plain English, and iterate to a publication-quality chart in under an hour?

A yes is the chart-building loop running without an analyst in the chain. In the Harvard and BCG study of 758 consultants, work inside AI’s reliable zone came back 40 percent higher in quality and 25 percent faster. The chart loop sits squarely inside that zone.

Section V · Agents

Headcount stops being the only way to add capacity.

10. Do you have at least one scheduled agent, written as a one-page spec with a defined job, defined access, and a defined report-back, that ran last week without you starting it?

A yes is one agent doing real work on a schedule. Harvard Business Review found that 91 percent of executives expect agentic AI to reshape work, and only 13 percent have shipped an agent. The agent count is the clearest marker of where you sit in that gap.

11. Do you have between four and seven agents carrying rhythm work, briefing, pipeline, follow-up, reconciliation, that used to sit on your desk?

A yes is a small team of agents holding the recurring load. Sequoia argues the next trillion-dollar company will not sell software but the work itself, delivered by agents, and draws a sharp line between a copilot that helps you work and an autopilot that returns the finished work. The operator who already runs a few autopilots is early to the thing the capital is chasing.

12. When an agent produces a bad outcome, do you know who owns the action, where the kill switch is, and how to scope its permissions tighter?

A yes means you run agents with discipline. HBR warns against treating an agent as an employee. Run each one with scoped permissions, observability, a kill switch, and a single named owner per action. The trust gap is the reason this matters. Only 6 percent of companies fully trust agents with core processes, and 48 percent name a lack of skills as the top barrier. Discipline is the skill that closes that gap.

Section VI · Standard

Speed and judgment improve together.

13. In the last 30 days, did you catch a confident, polished AI output that was wrong on a fact, a number, or a source, and correct it before it left your desk?

A yes means your judgment is active, not delegated. AI lifts some tasks and quietly degrades others of equal difficulty, with no signal telling you which. In the Harvard and BCG trial, work outside the model’s reliable frontier came back measurably worse. The fluent pattern keeps the human in the loop. Anthropic’s Economic Index found augmentation, where a person iterates with the model as a thinking partner, back above half of all use at 52 percent, ahead of hands-off automation. Your judgment is the filter, and a polished wrong answer is more dangerous than an obviously bad one.

14. Is the faster output you now produce holding up to scrutiny from a CEO, a board, a client, or an investor, with no cost to quality, sourcing, or defensibility?

A yes means speed did not come at the cost of standard. Robert Solow observed in the New York Times in 1987 that the computer age was visible everywhere except the productivity statistics. The lag closed when the output held up, not when it merely got faster. Fluency is faster output that survives scrutiny.

15. Have you cleared desk-work hours against a baseline you actually measured, not estimated, with 12 to 18 per week as the operator benchmark?

A yes requires a real measurement, not a feeling. A controlled trial by METR found that experienced developers were 19 percent slower with AI while believing they were 20 percent faster. Felt time saved is not measured time saved. If you have not measured, you cannot know which side of that gap you are on.

Section VII · Portability and leadership

Platform-agnostic. Teachable downward.

16. Could you rebuild your research workflow, your dashboards, and your agent team on a different platform inside a weekend if the field shifted tomorrow?

A yes means the capability lives in your habits, not in one vendor’s interface. Andreessen Horowitz found 37 percent of enterprises now run five or more models in production, up from 29 percent a year earlier, spreading spend on purpose to avoid lock-in. Fluency built on transferable habits survives the field moving under it.

17. Have you taught at least one other person to install and run one of your three builds on their own work, so fluency is a leadership capability and not a personal hack?

A yes is the capability installed in someone else. Mentions of generative-AI skills in job postings roughly quadrupled in a single year, from about 16,000 to 66,000. Fluency is now a hiring requirement, which makes the ability to teach it a leadership position rather than a personal trick.

Subscribe now

Count Your Yes Answers

0 to 5 · Curious. You know the territory. You do not yet operate inside it. Start with the research architecture this weekend and connect the core six tools.

6 to 10 · Practicing. The builds are underway. The week has not yet been reshaped. The gap is usually the operator skills and the first scheduled agent. This is where most people stall and assume they have finished.

11 to 14 · Operating. The work runs differently. Time is being returned to judgment. Add agents until four to seven carry the rhythm work, then teach one other person.

15 to 17 · Fluent. The capability is portable, teachable, and inside the work. Install it across your team. Fluency is now a leadership position.

Most operators answer yes to four or five and assume they are close. The distance between five and seventeen is the entire competitive position of the job. The cost of staying at five for another quarter is the part nobody is pricing correctly yet.

Take the assessment

I have built these seventeen questions into a one-page worksheet. It is a fillable PDF with a yes and no box for every question, the four scoring tiers, and the build sequence to close your gaps. You can answer it on screen or print it and work through it with your team.

Download the AI Fluency Diagnostic worksheet (PDF)

Run it on yourself first. Then run it on the person who reports to you. The pattern of where you both stall tells you exactly where the next 30 days of work goes.

If you want to do the builds rather than just score them, I run a three-week sprint called Oper(AI)te that walks operators through all three, live, on their own work. Details are at unfazedfounder.com/operaite.

Frequently asked questions

What is AI fluency?

It is the ability to work differently because AI is in the loop. It is not knowledge of what AI can do. A fluent operator produces real deliverables through AI, runs research in minutes, and hands rhythm work to scheduled agents. MIT found 95 percent of enterprise pilots returned nothing measurable, because the work was never redesigned. Fluency is the redesign.

How is this different from being good at prompting?

Prompting is one input. Fluency is whether the work itself runs differently. You can write excellent prompts and still copy and paste between a chat window and your real job. This test measures the output and the system around it, not the prompt.

Do I need to be technical to score well?

No. The questions measure whether your work runs differently, not whether you can code. The operator’s skills are sequencing, source discipline, and judgment. The platform handles the technical layer.

What counts as a yes?

A yes is a piece of work you could show in the next ten minutes: the artifact, the schedule, or the screen recording. A description of what is possible is a no. There are no partials.

I scored lower than I expected. Is that normal?

Yes. Most operators answer yes to four or five and assume they are most of the way there. The distance from five to seventeen is the actual job. The questions you stall on are your build order.

Which gap should I close first?

Start with the research architecture and the core six connectors this weekend. Then stand up one scheduled agent. The four-tier read lists the sequence, and the worksheet repeats it.

Does the AI platform matter?

The capability spans Claude, ChatGPT, Gemini, and Perplexity and should move among them. Question 16 tests exactly that. Enterprises now run multiple models on purpose to avoid lock-in. Fluency that lives in one vendor’s interface is fragile.

How often should I retake it?

Every 90 days. That is the mark where the time savings stabilize, and a quarterly retake shows whether the builds held or drifted.

Is the worksheet free?

Yes. Download it below, run it on yourself, and work through it with your team.

Appendix · The research behind the assessment

Every claim in this article is sourced from a curated universe of Tier 1 publications, Tier 2 academic centers, and Tier 3 institutional research shops. The appendix organizes that record so you can pull the threads yourself.

A · The findings that anchor the assessment

1. Task-level productivity gains run 14 to 34 percent. Brynjolfsson, Li, and Raymond studied 5,179 support agents. Output rose 14 percent on average and 34 percent for the least experienced. [PEER-REVIEWED] Stanford SIEPR · Generative AI at Work

2. Enterprise-level surveys report no productivity boost. A survey of 6,000 executives across four countries found 89 percent reporting no productivity impact over three years. [EDITORIAL] NBER executive survey, via Fortune

3. Value concentrates in the 6 percent who redesign the work. McKinsey found 88 percent of organizations use AI, only 6 percent capture 5 percent or more of EBIT from it, and the strongest correlate of impact is fundamental workflow redesign. [INSTITUTIONAL] McKinsey · The State of AI, 2025

4. Adoption without redesign returns nothing. MIT’s NANDA initiative found 95 percent of enterprise AI pilots delivered no measurable P&L result. The root barrier was learning and workflow fit, not infrastructure or talent. [EDITORIAL] MIT NANDA · The GenAI Divide, via Fortune · full report PDF

5. The skill is in production, and the adoption gap is wide. Stanford’s AI Index puts organizational adoption at 78 percent in 2024, with gen-AI skill mentions in job postings rising from about 16,000 to 66,000 in a single year. [PEER-REVIEWED] Stanford HAI · 2025 AI Index Report

6. The capital is voting on agents. a16z’s survey of 100 enterprise CIOs found model spend growing roughly 75 percent year over year, with 37 percent of enterprises now running five or more models in production. [INSTITUTIONAL] Andreessen Horowitz · 100 Enterprise CIOs, 2025

B · Complicating evidence

Three findings push back on the simple version of the story. They belong in any honest read of the record.

AI can slow experienced people down. A randomized controlled trial found experienced developers were 19 percent slower with AI while believing they were 20 percent faster. [PEER-REVIEWED] METR · randomized controlled trial, July 2025

The frontier is jagged. AI improves some tasks 40 percent and degrades others of equal difficulty, inside the same workflow, with no signal which is which. [PEER-REVIEWED] Dell’Acqua, Mollick et al. · Navigating the Jagged Technological Frontier, Organization Science 2026 · SSRN working paper

Trust is the live constraint on agents. Only 6 percent of companies fully trust agents with core processes, 43 percent restrict them to routine work, and 48 percent cite a lack of skills as the top barrier. Run agents with scoped permissions, observability, kill switches, and a named owner. [EDITORIAL] HBR research, via Fortune · Why You Shouldn’t Treat AI Agents Like Employees, HBR 2026

C · The newest signals (2026)

Goldman sharpened the gap. Its 2026 update found no economy-wide productivity relationship, with the 30 percent gains localized to software engineering and customer service, even as AI capex climbed to a forecast 667 billion dollars. [INSTITUTIONAL] Goldman Sachs Research, via Fortune, March 2026 · Goldman · 7% global GDP forecast

Augmentation pulled back ahead of automation. Anthropic’s Economic Index found augmentation, where a person iterates with the model as a thinking partner, back above half of all use at 52 percent, ahead of hands-off automation. The judgment stays with the operator. [INSTITUTIONAL] Anthropic Economic Index, January 2026

Copilots are becoming autopilots. Sequoia argues the next trillion-dollar company will sell the finished work, not the tool, and notes that more tasks are now started by agents than by people. [INSTITUTIONAL] Sequoia Capital · Services: The New Software

The time math has a price. Thomson Reuters found professionals expect AI to save 5 hours a week, about 240 hours a year, worth roughly 19,000 dollars per person. [INSTITUTIONAL] Thomson Reuters · Future of Professionals, 2025

D · Historical parallel

The gap between task-level AI studies and enterprise-level surveys looks like the Solow paradox of the late 1980s. Robert Solow, writing in the New York Times in 1987, observed that you can see the computer age everywhere except in the productivity statistics. The lag stretched roughly a decade before the late-1990s acceleration. The operators who built the workflow during that lag captured the gains when the macro numbers finally turned.

E · Sources by tier

The elite-research architecture behind this assessment. Every source above sits in one of these three tiers.

Tier 1 · Elite publications

• Harvard Business Review · Why You Shouldn’t Treat AI Agents Like Employees [EDITORIAL]

• Harvard Business School Working Knowledge · Leadership in an Agentic AI World [EDITORIAL]

• Fortune · NBER executive survey coverage [EDITORIAL]

• Fortune · MIT GenAI Divide coverage [EDITORIAL]

• Fortune · Goldman 2026 productivity coverage [EDITORIAL]

• Fortune · HBR agent-trust coverage [EDITORIAL]

Tier 2 · Academic research centers

• Stanford SIEPR · Generative AI at Work [PEER-REVIEWED]

• Stanford HAI · 2025 AI Index Report [PEER-REVIEWED]

• Harvard and BCG · Navigating the Jagged Technological Frontier, Organization Science 2026 [PEER-REVIEWED]

• MIT NANDA · The GenAI Divide, State of AI in Business 2025 [PEER-REVIEWED]

• METR · Early-2025 AI and Developer Productivity [PEER-REVIEWED]

Tier 3 · Institutional research

• McKinsey · The State of AI, 2025 [INSTITUTIONAL]

• Goldman Sachs Research · AI and GDP [INSTITUTIONAL]

• Andreessen Horowitz · 100 Enterprise CIOs, 2025 [INSTITUTIONAL]

• Sequoia Capital · Services: The New Software [INSTITUTIONAL]

• Anthropic · Economic Index, January 2026 [INSTITUTIONAL]

• Thomson Reuters · Future of Professionals, 2025 [INSTITUTIONAL]

The source architecture above is the elite-research skill described in Question 8, applied to its own subject. Curate the universe, name the sources, exclude the rest.

John Brewton · Founder · Operating · 6AEP · Washington, DC

Subscribe now

Subscribe now

Subscribe now

Subscribe now

TL · DR (The 30-second version)

AI fluency is not a skill. It is three builds an operator installs once and runs on for the rest of their career.

• Build One: The Research Architecture - Constrain Claude to a curated universe of Tier 1 publications, Tier 2 academic centers, and Tier 3 institutional research shops. Name the mode and output format before the question. Signal-to-noise ratio climbs from 20 percent to 90 percent in one move.

• Build Two: Charts and Dashboards - Drop a CSV, describe the question in plain English, iterate in 20-second cycles. The build-dashboard skill produces a self-contained HTML report. The analyst bottleneck collapses.

• Build Three: The Team of Agents - Install six core connectors. Write each agent as a one-page spec. Set the schedule. Direct, refine, add. Four to seven agents inside 30 days carry the rhythm work.

• The math - A senior operator carries roughly 50 hours of weekly admin load. The three builds compound to clear 12 to 18 hours inside 90 days.

• The window - Stanford finds 14 to 34 percent task-level productivity gains. NBER finds 89 percent of executives reporting zero enterprise productivity boost. HBR finds 91 percent of executives believe agentic AI will reshape work but only 13 percent have deployed agents. The space between is the operator opportunity.

If you only have 30 seconds, read this. If you have 20 minutes, read the rest.

Subscribe now

What is AI fluency for a senior operator?

There is a quiet thing happening inside companies right now, and the operators who are paying attention have already named it.

Something that took three days last year takes an afternoon now. A junior analyst with the right setup produces work that a senior used to own. The board deck that ate two evenings gets drafted before lunch.

Nobody is saying this out loud in the all-hands.

Everybody is noticing.

The research backs the pattern. A Stanford Institute for Economic Policy Research working paper of 5,179 customer support agents found access to a generative AI assistant raised productivity by 14 percent on average and by 34 percent for the lowest-skilled quartile. The 2025 Stanford AI Index reports the same pattern across drafting, customer communication, data preparation, and software development. The lift is real, and it concentrates where the work has been the heaviest.

The strange part is what the same research finds at the enterprise level. A National Bureau of Economic Research survey of 6,000 executives across the US, UK, Germany, and Australia reported 89 percent of them are seeing no productivity boost from AI inside their companies. The task-level data and the enterprise-level data are not the same data. The gap between them is the operator’s opportunity.

FIGURE 1 · The Productivity Gap

Source: Stanford SIEPR · Generative AI at Work (Brynjolfsson, Li, Raymond). NBER executive survey via Fortune. Chart by John Brewton.

The word for that opportunity is fluency. AI fluency is not knowing what AI can do. It is changing how your work actually gets done.

I am going to walk you through the three builds that take an operator from curious to fluent. I run all three of them within my own companies and within client engagements at 6AEP. Each solves a different administrative problem. Together, they replace about 30 hours of weekly desk work for the average senior operator.

You can build the first one this weekend.

You can build all three in three weeks.

How do you build an AI research workflow that pulls from elite sources?

Most people use AI for research the wrong way. They open a chat, type a question, and accept whatever comes back. The model pulls from wherever, the citations are inconsistent, and the output reads like a polished blog post written by a sophomore.

The fluent move is different. You build the research environment first. Then you ask the question.

FIGURE 2 · The Research Architecture

The architecture I use is called the elite-research skill. It lives in my Claude account as a reusable instruction set that is automatically invoked whenever I ask a research question. The structure has three layers.

Layer one is for source curation. The skill only searches inside a defined universe of top-tier publications, academic centers, and institutional research shops. The publication list includes the New York Times, Financial Times, Wall Street Journal, The Economist, Harvard Business Review, MIT Technology Review, and MIT Sloan Management Review. The academic list includes Harvard, Stanford, Chicago Booth, Princeton, Yale, and LSE. The institutional list includes Goldman Sachs research, Bridgewater Daily Observations, Blackstone insights, AQR, Andreessen Horowitz, and Sequoia Capital. The skill ignores everything else. No Medium posts, no LinkedIn essays, no SEO content marketing dressed up as analysis.

That single constraint changes the output more than anything else. The signal-to-noise ratio goes from roughly 20 percent to roughly 90 percent in one move.

Layer two is mode selection. The skill has four research modes, and the right one depends on what I am actually trying to do. Thesis Research is for stress-testing a position I already hold. Market Intelligence is for understanding a company or sector cold. Trend Scanning is for catching what is moving across an industry over 30 to 90 days. Deep Dive is for the questions where I need everything the institutional record contains on a narrow topic. I name the mode at the top of the prompt and the skill structures the search accordingly.

Layer three is the output format. The skill outputs in one of three shapes. A Research Brief is a 600 to 900-word memo with sourced bullets. An Advisory Intelligence Memo is the longer format I send to clients, around 2,000 words with a recommended action set. A Signal Pack is the lightest format, used for surfacing five to seven data points fast.

The build itself is straightforward. You write the skill as a markdown file with three sections: source list, mode definitions, and output templates. You install it in Claude as a custom skill. The model then automatically invokes the entire architecture every time you ask a research question.

The first time you run a real question through it, the difference is unmistakable. Yesterday I asked the skill to assess a sector trend across the last two years. The output came back in 11 minutes, with 14 sourced citations, all from inside the curated universe. The same question in an unstructured chat would have produced a 400-word summary with three of the citations made up.

That is the entire point of build one. You stop relying on the model to choose your sources and start treating them as part of your operating discipline.

Companion playbook: The Research Architecture · 8-page field guide (PDF). Curate, Mode, Format, Deploy. The full architecture with worked examples and a maintenance cadence.

How do you build charts and dashboards with AI?

The second build solves the analytics problem. Every operator I work with carries some version of the same complaint. They have data in five places, the BI tools their company pays for are too slow to iterate against, and the analyst who could run the query is booked through Friday.

Here is what fluent looks like. You drop a CSV into the chat. You describe the chart you want. Ninety seconds later you have a publication-quality visualization, with formatting you control, that you can drop into a board deck or a strategy memo.

Subscribe now

FIGURE 3 · Charts You Build Yourself

The Goldman Sachs research team has been blunt about where AI gains actually show up at the macro level. In a 2025 update to its productivity tracker, Goldman found no meaningful relationship between AI adoption and productivity at the economy-wide level, with a median 30 percent productivity gain confined to narrow, localized use cases. The chart-building loop is exactly the kind of localized use case the research describes. The lift does not show up in the firm’s headline numbers. It shows up in the work that one operator does on one Friday afternoon.

I want to show you exactly how this works, because the gap between people who know this is possible and people who actually do it is enormous.

Step one. You collect the data. This part is unchanged. You pull from Stripe or QuickBooks or your warehouse, and you save the result as a CSV. If the data is already inside a connected system, you can skip this step entirely and ask the model to fetch it through the connector.

Step two. You describe the chart in plain English. Not the type, the question. Something like: “Show me monthly revenue from 2024 to today, broken out by product line, with a 3-month moving average overlaid.” The model picks the chart type, builds the matplotlib or Plotly code, runs the code in its sandbox, and returns the chart as an image you can save.

Step three. You iterate. This is where the workflow earns its keep. You say “make the moving average dashed, drop the 2024 quarters, and change the title to fit a board format.” The new chart appears in 20 seconds. You repeat that loop until the chart says what you want it to say.

For anything bigger than a single chart, the build is a dashboard. There is a Claude skill called build-dashboard that constructs a self-contained HTML file with KPI cards, filterable charts, and sortable tables. You give it the data and the questions you want answered. It returns a single HTML file you can open in any browser, share with a colleague, or post on your internal wiki. The dashboards are static once built, which makes them easy to version and easy to trust.

The fluent operator runs through this loop two or three times a week. A weekly revenue review takes 14 minutes instead of two hours. A board prep cycle that used to involve four people coordinating across three tools collapses into a single afternoon at the desk.

The skill ceiling on this build is high. The space between you and having this part complete is one weekend.

Companion playbook: Charts and Dashboards · 8-page field guide (PDF). Collect, Describe, Iterate, Dashboard. The data-handoff discipline, the prompt shape, and the iteration loop.

How do you deploy AI agents that work on your behalf?

The third build is the one that changes the shape of the workweek. It is also the one that takes the most setup, which is why most operators stop at the first two and never get here.

An agent is a specialized AI assistant configured once, with a defined job, that runs on request. You direct it. It does the work. It reports back. The work happens 24 hours a day, 7 days a week, and you do not need to be present.

FIGURE 4 · Your Team of Agents

The institutional capital is voting on this build hard. Andreessen Horowitz’s 2025 survey of 100 enterprise CIOs found that average enterprise spend on large language models rose from $4.5 million to $7 million in 24 months, with another 65 percent jump expected to roughly $11.6 million the following year. The fastest-growing developer behavior in the same data is what a16z calls agentic inference, where the model acts across extended sequences rather than single prompts. Sequoia Capital went further. The firm’s 2026 essay Services: The New Software argues the next trillion-dollar company will not sell software tools but the actual work itself, delivered as a service powered by agents.

The adoption story is the inverse of the capital story. A 2026 Harvard Business Review survey reports 91 percent of executives believe agentic AI will reshape work, and 83 percent say effective adoption is essential to competitive position. Only 38 percent feel their organization is prepared. Just 13 percent have integrated agents into real workflows. 56 percent are still piloting under heavy human supervision. The operators building the agent stack right now are competing within a window that institutional data say is unusually wide.

FIGURE 5 · Belief vs Build

Source: HBR 2026 enterprise survey via HBS Working Knowledge. Chart by John Brewton.

You build agents through connectors. A connector is a software bridge that allows the model to communicate with one of your tools. Gmail, Google Calendar, Notion, Slack, HubSpot, QuickBooks, and Google Drive all have connectors that take about 90 seconds each to install. Once a connector is live, every agent you build can read from and write to that system on your behalf, with the permissions you set.

The plugin packages in Claude are what make the agent practical to build. A plugin is a bundle of connectors, skills, and pre-built templates organized around a function. The Sales plugin includes connectors for your CRM and email, plus pre-built skills for account research, call prep, pipeline review, and outreach drafting. The Operations plugin includes process documentation skills, runbook builders, and risk assessment templates. The Productivity plugin includes a memory system and a shared task file that the agents read and update.

Once the plugins are installed, the agent build looks like this.

You pick one piece of work that has been sitting on your desk too long. For most operators, that work is one of four things. The Monday briefing nobody has time to write. The pipeline review that gets done at 11 p.m. on Sunday. The customer follow-up sequence gets skipped half the time. The expense reconciliation that piles up for two weeks.

You write the agent as a one-page instruction. The instruction has three sections. What the agent does. What it has access to. What it sends back. The first version is short. Roughly 200 words.

You set the agent to run on a schedule. The schedule is the second thing that makes the agent practical. A Monday Briefing agent runs at 6 a.m. every Monday, reads your calendar and your inbox, summarizes what is coming, flags conflicts, and drops the result in your messages before you wake up. A Pipeline agent runs at 5 p.m. every Friday, reads HubSpot, flags deals that did not move, and drafts the follow-up emails.

You direct, refine, and add. Inside a month, a senior operator running this build has between 4 and 7 agents handling defined work. The agents are not magic. They do the things you would do if you had four hours a day you do not have.

The first agent feels like a trick. The fourth one feels like the floor.

Companion playbook: Your Team of Agents · 8-page field guide (PDF). Connect, Write, Schedule, Conduct. Connector install order, the one-page agent spec, four starter agents.

How much weekly time does an AI-fluent operator save?

I built all three of these inside the companies I run because the time math is brutal at the senior operator level. A Head of Operations carries roughly 50 hours of unbilled administrative load each week. Research, analysis, communication, scheduling, follow-ups, status updates, briefing prep, and the work of routing other people’s work.

1. Building one collapses the research load by about 70 percent.

2. Building two collapses the analysis load by about 80 percent.

3. Building three collapses the communication, scheduling, and follow-up load by 50 to 90 percent, depending on how aggressive you get with the agent set.

The fluent operator I describe in client engagements clears 12 to 18 hours of desk work per week within 90 days. That is not a productivity story. That is the entire competitive position of the job.

FIGURE 6 · The Operator Time Math

Chart by John Brewton → Data from 6AEP work

The macro forecast points in the same direction. Goldman Sachs Research projects that generative AI will raise global GDP by roughly $7 trillion and lift productivity growth by 1.5 percentage points over ten years. The historical parallel is the Solow paradox of the late 1980s, when computers were visible everywhere except in the productivity statistics for nearly a decade before the late-1990s acceleration. The current gap between Stanford’s task-level findings and the NBER enterprise survey maps onto that lag with high precision. The operators who close the gap first will be running the work when the macro numbers finally show up.

What does an AI-fluent operator’s week actually look like?

• Monday: A deck the board needs by Wednesday. You draft it in an hour, with the research agent feeding sourced bullets and the chart builder generating the four visualizations. You spend the second hour writing the narrative.

• Tuesday: Three back-to-back calls. Each one ends with the summary, the decisions, and the action items already written and routed by the meeting agent. You took no notes.

• Wednesday: The question that meant an afternoon of reading returns a sourced, current answer before lunch. You make the call by 2 p.m.

• Thursday: Your team of agents pulled the numbers, flagged what moved, drafted the update, and built the spreadsheets. You edited. You did not build.

• Friday: You closed the week ahead and on time, with every task that was pressing on Monday handled, already moving on to next week.

That week is not aspirational. It is what the fluent operator’s calendar looks like now.

Subscribe now

Can you become AI fluent without taking a course?

You can build a version of each of these on your own. The skill files are documented. The connectors are publicly available. The plugin packages ship with Claude. None of this is proprietary.

What is hard is the sequencing. The order in which you build them, the way you scope the first agent, the prompts that work versus the prompts that look like they should work, and the discipline to keep the source list curated as you go. That is the gap between an operator who tinkers with AI and an operator who runs on it.

I am running a three-week sprint in June called Oper(AI)te that walks operators through these three builds, live, on their own work. Six sessions across three weeks, two a week, 90 minutes each. We connect your stack in week one. We build the research and analytics architecture in week two. We build your agent team in week three. You leave with the connected setup, the research workflow, the dashboard pattern, the agent team, and a 30-day plan to add more as the work evolves.

The sprint begins Tuesday, June 16, 2026. Enrollment closes June 15. Details and the seat link at unfazedfounder.com/operaite.

If the three builds make sense and you want to do them on your own, that is the right answer too.

The cost of waiting another quarter is the part nobody is pricing correctly yet.

Become AI-Fluent This Month

Frequently Asked Questions About Becoming AI Fluent

What is AI fluency?

AI fluency is the ability to work differently because AI is in the loop. It is not knowing what AI can do. It is a change in how work is actually produced. A fluent operator drafts decks in an hour, runs research in minutes, and delegates rhythm work to scheduled agents.

How long does it take to become fluent in AI?

The three builds take three weeks if you sequence them correctly. Build One ships in a weekend. Build two ships in a weekend. Build Three takes the remaining three weeks because the agent team requires the most iteration. The 90-day mark is when the time savings stabilize, and the fluent operator clears 12 to 18 hours of weekly desk work.

Do I need to be technical to build these workflows?

No. The three builds use natural-language instruction inside Claude or another major AI platform. Source curation is a markdown file. Chart-building is plain-English prompts. Agent specs are one-page documents. The technical layer is handled by the platform. The operator's skills are sequencing and prompt discipline.

Which AI platform should I use?

The article uses Claude as the worked example because that is the platform I run on. The same builds work on ChatGPT, Gemini, and Perplexity with minor adjustments to connector availability and skill format. Platform-agnostic fluency is the goal. The Oper(AI)te course covers all four.

What is the elite-research skill?

A custom Claude skill that constrains the model to a defined universe of Tier 1 publications, Tier 2 academic centers, and Tier 3 institutional research shops. It enforces source-tier credibility tags on every finding and outputs in one of three formats: Research Brief, Advisory Intelligence Memo, or Signal Pack. The skill is a Markdown file you install once.

What is the difference between a Claude skill and an agent?

A skill is reusable operating logic that Claude invokes inside a chat or task. An agent is a scheduled or triggered task that runs on its own and reports back. Skills shape how Claude works on a question. Agents do the work without you asking each time. Agents commonly invoke skills as part of their job.

Which agent should I build first?

The Monday Briefing agent. It reads your calendar and your inbox each Monday at 6 a.m., summarizes the week ahead, flags conflicts, and lists the three highest-impact items. It is the easiest spec to write, the highest-frequency value, and the agent that most clearly demonstrates how the rest of the agent stack works.

What if my data lives in tools that do not have a Claude connector?

Two options. The first is CSV export, which works for any system that produces a downloadable report. The second is building a custom MCP connector, which takes about 90 minutes for a competent technical user. Most operators do not need the custom connector path. The core six connectors (Gmail, Calendar, CRM, Notion or Drive, Slack, QuickBooks) cover the work of most senior operators.

How is this different from buying an off-the-shelf AI product?

The three builds compose a general-purpose AI infrastructure around your work. An off-the-shelf product compresses a single function (sales prospecting, transcription, scheduling) into a fixed workflow. The compounding advantage of the three builds is that the same connectors, skills, and agents serve multiple jobs and improve as you refine them. The product replaces a task. The builds replace the way you work.

What does it cost to run these workflows?

Platform subscriptions range from $20 to $200 per month, depending on the AI platform and tier you use. Most senior operators recover the cost of the saved desk time in the first week. The Oper(AI)te course is a one-time $4,500 fee and is structured to make sequencing easier rather than to gate access to the workflows themselves.

Become AI-Fluent This Month

What companion materials accompany this article?

Three resources accompany this piece. Take them in any order.

Field-guide playbooks · 8 pages each · PDF

• Playbook 01 · The Research Architecture - Curate the source universe. Pick the mode before the question. Three output shapes. Deploy and maintain the skill.

• Playbook 02 · Charts and Dashboards - Get the data out of the tool. Describe the question, not the chart. The iteration loop. The dashboard build.

• Playbook 03 · Your Team of Agents - Connectors are the bridge. Write the agent as a one-pager. Set the schedule. Conduct the team.

Elite-research companion brief · Markdown

• Research Brief · The AI Fluency Gap - Six findings, complicating evidence, historical parallels, narrative hooks, reading list.

Which institutions have studied AI productivity and adoption?

Every claim in this article is sourced from the elite-research universe. Tier 1 publications, Tier 2 academic centers, Tier 3 institutional research shops. The appendix below organizes the source material so you can pull threads yourself.

A · Six findings that anchor the article

1. Task-level productivity gains run 14 to 34 percent. Brynjolfsson, Li, and Raymond at Stanford SIEPR studied 5,179 customer support agents using a generative AI assistant. Average productivity rose 14 percent. The lowest-skilled quartile rose 34 percent. The highest-skilled quartile saw minimal lift.

Stanford SIEPR · Generative AI at Work · siepr.stanford.edu Stanford HAI · 2025 AI Index Report · hai.stanford.edu

2. Enterprise-level surveys report no productivity boost. A National Bureau of Economic Research survey of 6,000 executives across the US, UK, Germany, and Australia found 89 percent reporting no productivity impact from AI over the prior three years.

NBER survey · summarized in Fortune · fortune.com

3. Goldman projects 7 percent global GDP lift and 1.5 percentage points of productivity growth. Goldman Sachs Research’s headline AI forecast estimates roughly $7 trillion of additional global output over ten years, with 1.5 percentage points of additional productivity growth.

Goldman Sachs Research · Generative AI could raise global GDP by 7% · goldmansachs.com Goldman Sachs Research · AI is showing very positive signs of boosting GDP · goldmansachs.com

4. Enterprise AI spend is rising 65 percent per year, concentrated in agentic workflows. a16z’s 2025 survey of 100 enterprise CIOs found average LLM spend rising from $4.5M to $7M to a forecast $11.6M, with agentic inference the fastest-growing developer behavior.

a16z · How 100 Enterprise CIOs Are Building and Buying Gen AI in 2025 · a16z.com a16z · The Rise of Computer Use and Agentic Coworkers · a16z.com

5. 91 percent of executives expect agentic transformation. 13 percent have shipped it. Harvard Business Review’s 2026 agentic enterprise survey reports the gap between executive conviction and organizational readiness.

HBR · What Leadership Looks Like in an Agentic AI World · library.hbs.edu MIT Sloan Management Review · The Emerging Agentic Enterprise · sloanreview.mit.edu

6. Sequoia argues the next $1 trillion company will sell work, not software. Konstantine Buhler’s Agent Economy framework defines the three primitives of agent infrastructure: persistent identity, seamless communication protocols, and security and trust. Sequoia’s Services: The New Software essay extends the argument to a trillion-dollar profit pool.

Sequoia · Services: The New Software · quasa.io Sequoia · The Agent Economy · inferencebysequoia.substack.com

B · Complicating evidence

Three findings push back on the simple version of the story. They belong in any honest read of the institutional record.

METR finds AI tooling slows senior engineers by 20 percent on complex repositories. Model Evaluation and Threat Research’s 2025 study of working developers found AI tools added 20 percent to task time on average, inverting the Stanford pattern. The lift is not uniform across roles and complexity levels.

Thomson Reuters finds modest productivity gains for lawyers, accountants, and auditors. The 2025 report found targeted use of AI for document review and routine analysis produced gains that fell short of signaling widespread displacement.

HBR warns against treating agents as employees. A May 2026 HBR paper argues that the agent-as-employee analogy hides three things human workers bring that agents do not: stable context across the day, an instinct to escalate when something feels off, and accountability that survives a bad outcome. The recommendation is to run agent rollouts through systems-engineering templates with scoped permissions, observability, kill switches, and a clear owner per action.

C · Historical parallel

The current gap between task-level AI productivity studies and enterprise-level surveys looks remarkably like the Solow paradox of the late 1980s. Robert Solow, writing in the New York Times in 1987, observed that “you can see the computer age everywhere but in the productivity statistics.” The lag between visible technology and visible productivity stretched roughly a decade before the late-1990s acceleration. The operators who built the workflow during that lag captured the gains when the macro numbers finally turned.

D · Reading list

A starting point if you want to go deeper. Eight pieces, ordered by relevance to the three builds.

1. Stanford SIEPR · Generative AI at Work. The canonical task-level productivity study. Begin here.

2. Stanford HAI · 2025 AI Index Report. Comprehensive annual review. The productivity chapter is the relevant section.

3. a16z · How 100 Enterprise CIOs Are Building and Buying Gen AI in 2025. The cleanest read on enterprise spend and behavior.

4. a16z · The Rise of Computer Use and Agentic Coworkers. The agent thesis from the firm with the largest agentic portfolio.

5. HBR · What Leadership Looks Like in an Agentic AI World. The clearest executive-level framing of the adoption gap.

6. Sequoia · Services: The New Software. The trillion-dollar thesis.

7. Goldman Sachs Research · Generative AI could raise global GDP by 7%. The macro forecast.

8. MIT Sloan Management Review · The Emerging Agentic Enterprise. Four organizational tensions in agent adoption.

E · Source architecture note

The elite-research skill described in Build One is what produced this appendix. Every cited source sits inside the curated universe of Tier 1 publications, Tier 2 academic centers, and Tier 3 institutional research shops. The output format is a Research Brief. The full brief that anchors this appendix is published as a companion document.

About the author

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

TL → DR

Brian Chesky named founder mode after losing control of a 7,000-person Airbnb and clawing it back through 35 hours of meetings a week. He now sees the next version, AI founder mode. Meetings were only a workaround for an information problem, and AI removes it. Management layers collapse toward four. The real shift is who you hire: stop hiring polished people managers, start hiring micro-CEOs, people biased toward execution who own outcomes and run agents. The founder's hours move from meetings to building workflows. Get the hire wrong and the company dies faster than before.

What is Founder Mode?

Brian Chesky made founder mode a public idea after Paul Graham coined the term. In a recent interview, he went further. He described what comes next and called it AI founder mode. He has not finished working it out. But its shape is already clear enough to act on, and the version that matters most is not the one running inside a 5,000-person company. It is the version for the founder with 600, 60, or 6 people.

Start with what founder mode actually solved:

• By 2019 Chesky ran 7,000 employees and could not turn the company. He described feeling as if he were in a car without a steering wheel.

• He had over-delegated to professional managers and was being managed instead of managing.

• The pandemic cut revenue by 80% in eight weeks and forced the correction. He took control, worked 100-hour weeks for two to three years, and reviewed every detail of the business.

• Chesky bought visibility with meetings. He ran about 35 hours of meetings a week, group meetings only, no one-on-ones, the full chain of command in the room.

• He reviewed everything weekly, biweekly, monthly, or quarterly.

• He spoke last and ratified every decision.

Founder mode was a workaround for an information problem. A founder who cannot see the company pays for that sight in calendar time.

How Does AI Change Founder Mode?

Chesky said AI founder mode means even more detail, because the information is available on demand. The meeting was never the value driver. It was the toll the founder paid to know what was happening. When the work itself becomes legible without a meeting, the toll drops to near zero. He expects the culture to move from meeting-based to asynchronous, the layers of management to shrink toward the four that the Catholic Church has run on for 2,000 years, and every job to change. His hard line: pure people managers do not survive.

Everyone becomes a hybrid manager and individual contributor with what he calls contact with reality. An engineering manager still codes. A lawyer who manages still reads case law.

Chesky is not forecasting theoreticallyically. Harvard Business Review and MIT Sloan Management Review have spent 2026 documenting the same shift, and large companies are already collapsing their org charts. Bayer cut its management layers from roughly twelve to six and removed close to 5,500 roles, most of them in management.

The direction is set. The open question is what replaces the layers.

New to Operating?

Check out these reader favorites:

• Operating Economics: The Optimization Company (Amazon)

• Operating by Warren & Charlie

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

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

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

Subscribe now

Operator Perspective

Here is what I see from inside my advisory work at 6AEP.

The traditional founder problem has always been the same:

• The founder starts with the minutiae and the details. The challenge is to get out of it and delegate to a team. Some founders never make the delegation happen. They hire very high-quality people and then do not give them enough autonomy. Those good people leave. The only thing that keeps them is the company being wildly successful, and most companies are not.

AI founder mode does not remove the delegation problem. It moves it. The delegation still has to happen, but a large part of it now happens digitally, across agents and platforms. It is embedded in the founder's relationship with the technology. The founder is not just handing off the tasks that the technology now does on its own. The founder is delegating to humans, and the management of those humans is now mostly about one thing…understanding the level of individual productivity achievable and the quality of the direction the human team is giving to the tech.

That changes who is worth hiring.

The person who is an excellent people manager and relationship builder is worth less than they used to be. A founder needs someone more technically capable. Someone who can think across multiple departments and build out the agents, the applications, and the execution layer that gets the work of those departments done. The way I have started to see it is that the founder or CEO wants to hire teams of micro-CEOs. People who think strategically across the whole business, who carry real ownership, and who have a bias toward action and outcomes that a founder has.

Harvard Business Review reached a version of the same conclusion. In February 2026, it argued that the AI era needs a new role, it calls the agent manager, the way the software era produced the product manager. Here’s one version using my thinking. The micro-CEO is the ceiling. The agent manager runs the agents. The micro-CEO owns the outcome. And in some orgs, the Micro-CEO and the agent manager will absolutely be one person overseeing multiple divisions or departments.

This shift necessarily changes the hiring signal. It makes it more of a matter of the candidate's industrial psychology:

• You want people heavily biased toward execution.

• You want people who have actually created outcomes, not described them.

• You want technical know-how across multiple departments.

• You do not just want leaders.

• You do not just want people with a lot of education.

• You want enough education and enough smarts, paired with a heavy bias toward having been in the execution of a company’s work, not only its management.

Look hard at personality and at how a person thinks about their life. I tell founders to stay far away from anyone whose first concern is work-life balance. The green flag used to be the polished manager who builds relationships. That is now a red flag.

The founder’s own day changes as well. A majority of the founders’ hours should go to building better workflows. The founder uses agents and technology to create workflows that get every department's work done. Better customer experience. A better product in the customer’s hands. Fires are put out faster for customers. Faster prototyping. The needs vary by company, but the work is the same shape. The founder builds the workflow, shares it with the team, manages it, and measures its execution against the business's growth goals. The meeting was where the old founder spent the hours. The workflow is where the new founder spends them.

MIT Sloan Management Review made the same point in plainer terms. Its researchers found that AI does not create productivity on its own. The gain comes from redesigning the work. Companies that deploy AI tools on top of old workflows get almost nothing for the spend. The founder who treats AI as a tool to buy will lose to the founder who treats it as a company to rebuild.

Get this wrong and the cost is real. The founder makes the old hire, the meeting-centric people manager. That person is expensive and not productive enough. Worse, that person recommends the company take on too many tools, too much software, too many technological assets, to run a people-management culture inside a company that should be built around outcomes.

The company fills up with meetings.

It stops moving fast.

It does not prototype fast enough.

It does not respond to customers fast enough.

It does not see the whole picture fast enough.

It does not innovate fast enough.

It does not grow fast enough.

The company dies.

The founder who does not understand this makes the hires that kills it.

There is a second way to get it wrong. In May 2026 Harvard Business Review published a large-scale experiment on companies that placed AI agents on the org chart and treated them like employees. The result was worse, not better. Treating the agents like people reduced individual accountability, raised unnecessary escalation, and lowered the quality of human review. The lesson holds the whole argument together. Delegation to agents is not abdication to agents. The founder and the micro-CEOs stay accountable for the work the agents produce. That is what contact with reality means.

The honest version of this is not complicated. Founder mode was about the founder staying close to the work when the company grew too large to see. AI founder mode is about who the founder builds the company with. The founder who understands that first will use it to build a company that stays small in headcount, strong in ownership, and close to the customer the whole way up.

- j -

Frequently Asked Questions (FAQ)

What is the difference between founder mode and AI founder mode?

Founder mode solved an information problem. A founder who could not see the company bought visibility with meetings. AI founder mode removes the information problem, because the work is legible on demand. The meetings go away. The detail does not.

Does AI founder mode only apply to large tech companies?

No. Chesky describes it from a company that already scaled to thousands of people. The sharper version is for the founder with 12 people. For a small team the value is the option to never build the management layers that detach a founder in the first place.

What is a micro-CEO?

A micro-CEO is a hire who thinks strategically across the whole business, carries real ownership, and holds a founder’s bias toward action and outcomes. They run the agents and own the result. They are not coordinators of other people’s work.

What kind of person should a founder hire now?

Hire for the psychological profile. You want a heavy bias toward execution, a record of creating real outcomes, and technical range across departments. Education and intelligence matter, but only when they are paired with execution experience.

Do pure people managers still have a place?

Less than before. The polished manager whose only output is other people’s output used to be a green flag. It is now a red flag. Everyone needs contact with reality. An engineering manager still codes. A lawyer who manages still reads case law.

What should the founder actually spend time on?

Building workflows. A majority of the founder’s hours should go to using agents and technology to design the workflows that produce better products and faster outcomes for customers. The meeting was where the old founder spent the hours. The workflow is where the new founder spends them.

What is the most common mistake?

Making the old hire. The meeting-centric people manager raises cost, sprawls tools, and fills the company with meetings. The company slows until it cannot prototype, respond, or grow. The wrong hire kills the company, and faster than it used to.

About the author

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

Appendix: Links, Media, and Research

Primary Source

The founder mode and AI founder mode framework comes from a recorded interview with Brian Chesky, co-founder and CEO of Airbnb. The interview covers his industrial design training, the 2019 realization that the company had grown beyond his control, the move into founder mode during the pandemic, and his early thinking on what AI founder mode becomes.

The term itself comes from Paul Graham, who named it after a Chesky talk at Y Combinator.

• Paul Graham. “Founder Mode.” paulgraham.com/foundermode.html

Research Cited

• Harvard Business Review, February 2026. “To Thrive in the AI Era, Companies Need Agent Managers.” The argument that the AI era produces a new role, the agent manager, the way the software era produced the product manager. hbr.org

• Harvard Business Review, May 2026. “Research: Why You Shouldn’t Treat AI Agents Like Employees.” A large-scale experiment finding that placing AI agents on the org chart as employees reduced accountability, raised escalation, and lowered review quality. hbr.org

• MIT Sloan Management Review. “Agentic AI at Scale: Redefining Management for a Superhuman Workforce.” sloanreview.mit.edu

• MIT Sloan Management Review. “Want AI-Driven Productivity? Redesign Work.” The finding that AI produces a return only when the work is redesigned around it. sloanreview.mit.edu

• MIT Sloan Management Review. “Stop Deploying AI. Start Designing Intelligence.” The case against layering AI tools on top of old workflows. sloanreview.mit.edu

Supporting Data

• Bayer’s management-layer reduction, from roughly twelve layers to six and the removal of close to 5,500 roles, is drawn from 2026 reporting on the company’s “Dynamic Shared Ownership” reorganization. It is cited here as a concrete example of the management-layer flattening that Harvard Business Review and MIT Sloan Management Review describe.

Subscribe now

Three forces are converging.

1. Technology lowers the floor on what one person can produce.

2. Capital restructures around small teams and individual operators.

3. History will reward the first-movers.

The combination is turning workers into operators of their own enterprise.

The shift from traditional employment to micro-entrepreneurship is the culmination of centuries of economic evolution. Digital platforms and AI carry the cost curve the rest of the way down. “Company” no longer requires size or hierarchy. It now signals autonomous agency in a global market. This article traces the historical arc, examines why the tools now exist for any individual to operate as a company of one, and anticipates a future in which large companies behave as cellular networks of human-centered micro-organizations.

Current outcomes are unevenly distributed. Embedded risks are plentiful. The history is still being lived. The pattern is clear enough to act on. A large share of global working economic activity will function this way within the decade.

From Commons to Corporations

Pre-industrial systems built the bedrock for shared economic activity. Medieval villages managed farmland, pasture, and forest as commons, governed by rules ensuring equitable access. Elinor Ostrom’s Nobel-winning research documented more than 800 such common-pool resource arrangements across centuries and continents. Her 1990 book Governing the Commons showed that local users repeatedly designed governance frameworks that avoided the tragedy of the commons. The same ethos resurfaced in 19th-century mutual aid societies. David T. Beito’s research documents how fraternal and friendly societies provided sickness benefits, burial benefits, orphanages, and health care systems to a broad cross-section of Americans before the welfare state. At the peak, more Americans belonged to fraternal societies than to any other voluntary association except churches.

Industrialization reorganized economic life. Mechanized factories replaced artisanal production. Corporations emerged as centralized entities optimized for scale and efficiency. Franchise models like Ford’s assembly line standardized operations. Precursors to today’s platforms already existed during this period. Nineteenth-century grain exchanges functioned as physical matchmaking platforms, connecting buyers and sellers through trusted intermediaries.

The digital revolution’s first wave transformed peer-to-peer exchange. eBay launched in 1995 as a side project by Pierre Omidyar, digitizing flea markets and classified ads. Craigslist expanded the model into services and housing. Both platforms introduced reputation systems, digital proxies for community trust. The peer-review reputation mechanism reduced transaction friction and became the blueprint for Amazon Marketplace, Airbnb, Uber, Etsy, and Fiverr. The NBER’s peer-to-peer markets research by Einav and Farronato shows how each generation of platform pushed the trust signal further out into a stranger-to-stranger commerce model. Amazon then converted Walmart’s physical retail concept into an online operating system at planetary scale.

Subscribe now

Platform Entrepreneurship

The 2008 financial crisis triggered a structural shift in the labor market. As unemployment climbed, online labor platforms absorbed displaced workers. The Resolution Foundation's analysis of UK labor market data found that self-employment accounted for 45 percent of all UK employment growth after March 2008. Self-employment grew by 650,000 from 2008 to a peak of 4.5 million, nearly 15 percent of UK employment. Two-thirds of UK post-crisis job creation came from atypical roles: self-employment, zero-hours contracts, and agency work.

In the United States, Katz and Krueger’s Princeton-RAND research tracked the share of workers in alternative arrangements (temp, on-call, contract, independent) climbing from 10.7 percent in February 2005 to 15.8 percent in late 2015. Contract company workers more than doubled in that window. Uber, Airbnb, and TaskRabbit were founded in this period. They used underutilized assets (cars, homes, labor) and grew precisely because traditional systems faltered.

The period normalized three trends:

• Fragmentation of work from stable employment to project-based engagements

• Rise of micro-enterprises absorbing a meaningful share of new job creation

• Digital-first business models that priced access above ownership

Hall and Krueger’s 2018 ILR Review analysis of Uber’s driver-partners found that most drivers held full- or part-time employment before joining the platform and continued in those roles after. Platforms were not replacing jobs in this period. They were supplementing income and giving workers a flexible income fallback.

Figure 02. The Operator’s 800-Year Path. From commons governance to agentic capability.

Subscribe now

COVID-19: The Greatest Accelerator

The COVID-19 pandemic pulled forward a decade of change in two years. Barrero, Bloom, and Davis’s Stanford-Hoover research tracked U.S. full work-from-home shares from roughly 5 percent before the pandemic to nearly 60 percent in spring 2020, then settling near 28 percent by 2023. Their American Economic Association paper on the evolution of remote work confirms a roughly four- to fivefold increase over baseline that has persisted for three years past the trigger event.

MIT Sloan Management Review documented productivity gains across 90 percent of firms in the first year of remote work, with 55 percent of home-based workers reporting higher output. Lynda Gratton’s seven-truths analysis found that teams together 23 to 40 percent of the time performed best across multiple performance dimensions.

The shift democratized access to global talent pools. Companies hired across borders. Workers competed and collaborated across borders. The result is what Stanford GSB now describes as a borderless professional services market.

The entrepreneurial response was structural. McKinsey’s American Opportunity Survey found that 36 percent of employed Americans, roughly 58 million people, identified as independent workers in 2022. That share is up from 27 percent in 2016. Of those, 76 percent identified as contract, freelance, or temporary workers, and 68 percent identified as gig workers.

The U.S. Census Bureau’s non-employer business data show the same pattern from a different angle. Non-employer businesses grew at 4.9 percent in 2021 and 4.7 percent in 2022, the fastest rates in nearly two decades. The United States now contains roughly 29.8 million solopreneurs producing about $1.7 trillion in annual revenue.

Outcomes remained uneven. Workers with digital skills and access to platforms captured the upside. Workers without were exposed to downward pressure on earnings and persistent precarity.

The Creator Economy: Vanguard of the Operator Revolution

Beyond Influencers to Economic Transformation

The creator economy has been treated as synonymous with social media influencers. That framing misses the larger pattern. The infrastructure that produced the influencer (audience-building tools, direct monetization, AI-powered content creation, global payment processing) is now adopted by a far broader population. Andreessen Horowitz called this the passion economy in Li Jin’s 2019 framing. The thesis was that new platforms allow anyone to monetize unique skills, in contrast to earlier marketplaces that flattened individuality and commoditized labor.

The current infrastructure becomes the operational backbone for:

• Professional services (consulting, coaching, specialized expertise)

• Digital craftsmanship (design, development, custom solutions)

• Knowledge work (research, analysis, education)

• Hybrid enterprises that combine content, products, and services

Projected Economic Significance

Goldman Sachs Research estimates the global creator economy at $250 billion today, growing to $480 billion by 2027 at a roughly 14 percent compound annual growth rate. The global creator base of 50 million is projected to grow 10 to 20 percent annually for the next five years.

Income concentration remains a problem. Only about 4 percent of global creators earn more than $100,000 per year. Li Jin’s HBR essay argues for platform mechanisms that build a creator middle class, not just more lottery winners at the top. The fix is structural. Discovery algorithms, revenue-sharing terms, and audience portability determine who builds a stable business and who does not.

By 2027, more than half of U.S. households will derive some portion of household income through creator economy channels and platforms. The creator economy stops being a side-hustle phenomenon and becomes a core piece of household income, community formation, and operating models inside traditional corporations.

V. Modern Infrastructure: The Platform Stack

Today’s operator economy rests on three layers of infrastructure. Each solves a constraint that historically required scale.

1. Social Networks as Marketplaces

LinkedIn and TikTok democratized audience-building and distribution. What once required a corporate marketing budget is now achievable through organic content. Individuals become media entities. Instagram storefronts and TikTok creator funds enable direct monetization. The boundary between creator and entrepreneur dissolves.

2. AI as the Operational Backbone

Tools like ChatGPT and Claude automate work that historically required specialized departments:

• Content creation (replacing copywriters and junior marketers)

• Data analysis (replacing business intelligence teams)

• Customer service (via AI agents and chatbots)

• Back-office workflows (legal review, accounting, recruiting)

Goldman Sachs estimates generative AI will raise U.S. labor productivity by approximately 15 percent at full adoption. The compounding effect on solo operators is larger than on incumbents because solos start without the fixed overhead AI is now substituting away. Bessemer’s Vertical AI playbook maps the new pattern: products that solve a specific workflow inside a single industry, built by small teams, replacing horizontal SaaS budgets with vertical AI agents. Sequoia’s investment teams now underwrite what they call agentic leverage, the ability of one or two operators to produce the output of a 10-person team using orchestrated AI agents.

3. Payment Systems as Financial Rails

Stripe and Square reduced transaction friction to near-zero. Stripe operates in more than 135 countries with more than 135 supported currencies and processes hundreds of billions of dollars annually. Square’s 2024 gross payment volume reached approximately $180 billion across small and medium businesses, with 34 percent of users running solo operations. The APIs enable instant global payments, recurring billing, and multi-party revenue splits. Those functions once required banking partnerships, merchant accounts, and dedicated accounting teams.

VI. The Operator Economy in Practice

From Gig Workers to Creators

The gig economy commoditized labor. Uber drivers, DoorDash couriers, and TaskRabbit workers were paid for fungible time. The creator economy inverted the pattern. Individuals build audiences around specific skills and monetize through courses, subscriptions, products, and services. Platforms like Substack and Patreon function as a business in a box for solo operators.

AI-Enabled Scaling

A graphic designer in 2026 uses Midjourney for rapid prototyping, Calendly for client scheduling, QuickBooks AI for invoicing, and Claude for client communications. The operation reaches small-agency capability without small-agency headcount. The capital efficiency of a one-person operation built on AI tools is roughly 10 to 50 times higher than a traditional startup, which is why Sequoia, Bessemer, and a16z are repricing solo founders as a real venture category.

Case Study: The History-Informed Operator

A historian launches a research service in three moves:

1. They source archival data via digitized collections, removing the pre-internet access barrier that locked specialist knowledge inside university libraries

2. They use Perplexity, Claude, and ChatGPT to synthesize sources at scale

3. They monetize through LinkedIn thought leadership and a Calendly-Zoom-Stripe stack that handles scheduling, delivery, and payment

The operation encapsulates 500 years of economic evolution in a single moment. It carries the communal knowledge-sharing logic of guilds, the specialization of industrial crafts, and the platform-enabled autonomy of the digital age.

VII. Historical Parallels and Critical Differences

History supplies both the precedent and the warning. The medieval commons and 19th-century cooperatives were governed by shared rules and mutual oversight. Today’s platforms are global in reach and centrally controlled. Algorithms and policy changes are opaque. Risk and reward were once distributed across a community. The platform economy concentrates risk on the individual operator and offers little recourse.

Power dynamics have shifted. Historical cooperatives operated on democratic principles. Members had a voice and a vote. Modern platforms operate more like feudal arrangements. They provide infrastructure in exchange for data, fees, and compliance with unilaterally imposed rules. Harvard Business School research on DAO governance finds that even ostensibly decentralized autonomous organizations concentrate voting power, with 76.2 percent of voting power held by block voters. The Belfer Center’s policy work shows the gap between the decentralized governance ideal and the centralization that emerges in practice. The scale is unprecedented. So is the concentration of control.

The challenge is making sure the digital commons of the 21st century do not replicate the inequities of the past under the cover of innovation. The question is whether we are democratizing entrepreneurship or shifting old power structures into new forms of extraction. The honest answer is both, in different proportions in different sectors. The policy work of the next decade will determine which proportion wins.

Figure 03. Platform Power Meets Operator Power. The next decade is a contest of governance models.

Subscribe now

Inequality and Access

The operator economy promises autonomy and opportunity. It is not a universal equalizer. Access to digital tools, stable internet, and platform skills remains unevenly distributed across countries and within them. Many operators face income volatility, lack of benefits, and the constant risk of changes to platform rules.

Barriers persist. Digital literacy, reliable internet, and a financial cushion to absorb early-stage uncertainty exclude many potential operators. The most successful platform entrepreneurs come from privileged backgrounds with networks, capital, and risk tolerance already built in.

Platform dependency creates new vulnerabilities. Academic research on platform-mediated work shows that algorithm changes can devastate a creator’s income overnight. Recent earnings analysis using Patreon data shows that algorithmic curation concentrates earnings at the top and pulls income away from a creator middle class. The promise of autonomy becomes algorithmic management more invasive than traditional employment. For some operators, the dream becomes a struggle for survival with less security than a job once provided. The Pew Research Center’s 2017 expert survey cataloged the risks early. Most of them are now playing out.

IX. Implications and Future Trajectories

Opportunities

• Democratized entrepreneurship. Lowered barriers enable global participation

• Flexibility. Operators choose projects that align with values and expertise

• Economic resilience. Diversified income streams buffer market shocks

Challenges

• Platform dependence. Algorithm changes can disrupt livelihoods overnight

• Regulatory gaps. Labor and tax systems lag the new business models

• Inequality. Access to tools remains uneven globally

The Future of Companies

Decentralized autonomous organizations point toward a future in which operators collectively govern shared infrastructure. The early DAO data shows the gap between the ideal and the implementation. AI agents will take on administrative and operational tasks, freeing operators to focus on creative and strategic work. The boundary between a company and a network of cooperating operators continues to blur.

A Roadmap for Aspiring Operators

Resilience and strategic thinking are the two requirements. The tools change. The principles do not.

Build Antifragility

• Diversify presence across multiple platforms to reduce single-point-of-failure risk

• Cultivate direct relationships with audience and clients. Email capture is the most durable layer

• Invest in continuous learning to stay ahead of automation and market shifts

Use Technology Wisely

• Treat AI as an assistant, not a substitute for judgment and creativity

• Develop the human skills AI does not replicate: emotional intelligence, creative problem-solving, relationship building

• Build systems that scale without compromising quality or authenticity

Advocate for Better Systems

• Support platforms with transparent algorithms and fair revenue sharing

• Push for portable benefits that travel with workers across platforms

• Coordinate with other operators to negotiate fairer terms

• Push regulators toward frameworks that protect operators without stifling innovation

Think Long-Term

• Treat platform participation as one element of a strategy, not the whole strategy

• Build owned assets (email lists, websites, intellectual property) alongside platform presence

• Develop multiple revenue streams to absorb platform volatility

Conclusion

The shift toward an operator-centric economy is both new and deeply rooted in economic history. From medieval commons to digital platforms, the drive to democratize production and exchange has reshaped business across centuries. Individuals now wield capabilities once reserved for corporations. AI and global connectivity carry the cost curve the rest of the way down.

The shift is neither inevitable nor universally beneficial. The operator economy’s promise depends on actively addressing inequities, dependencies, and risks. We are not entering an era of side hustles. We are watching the rebirth of the company. Smaller. Nimbler. Human in scale and decidedly more so in character. Whether the evolution empowers operators or extracts from them depends on the choices made in the next ten years.

The Operator age is here. It belongs to anyone ready to build. Building wisely requires understanding both the opportunities and the obstacles.

What’s Next

The operator and creator economies are not static. They are reshaping the nature of work, entrepreneurship, and value creation in real time. Several frontiers matter most.

Regulatory frameworks will need to protect operators without strangling the innovation that produced them. New business models and forms of collective action will emerge as more individuals operate as companies of one. Household income structures will shift as creator-economy channels move from supplementary to primary. The skills and safeguards that allow operators to thrive in an environment where autonomy and precarity coexist will define the next decade of work.

One frontier matters most for this community. Polymaths, individuals who blend expertise across disciplines, will set the pace as AI takes on specialized tasks. The ability to connect ideas, synthesize knowledge, and innovate across domain boundaries becomes the most valuable skill of the next decade.

At Operating, I examine the future of company structure and the evolution of digital platforms, the realities of platform dependence, the strategies that will define operator success, and the resurgence of polymathic talent as a primary driver of innovation and adaptability.

I want your input. What are you seeing in your own work and industry?

Whether you are already operating as a company of one or are considering the move.

What challenges and opportunities do you foresee as the operator and creator economies mature?

Whether you consider yourself a polymath or are working to become one, consider how the ability to connect ideas across fields is shaping your work.

Thank you for reading.

John

Subscribe now

John Brewton documents the history and future of operating companies at Operating by John Brewton. He is an economics graduate of Harvard University and the University of Chicago. After selling his family’s B2B industrial distribution company in 2018, he has been helping business owners, founders, and investors optimize their operations ever since. His frameworks have generated over +$250M in enterprise value. He still cringes at his early LinkedIn posts.

APPENDIX: RESEARCH SOURCES

All sources are drawn from elite publications, top academic centers, and institutional investment research. Each carries a credibility tag.

A. Historical Foundations (Section I)

Elinor Ostrom: Governing the Commons [PEER-REVIEWED] Cambridge University Press, 1990. Nobel Memorial Prize in Economic Sciences, 2009. Documents more than 800 common-pool resource governance arrangements. Cambridge University Press

Elinor Ostrom Profile: Stanford GSB [INSTITUTIONAL] Background on Ostrom’s analytical framework and its application to modern commons. Stanford GSB Insights

David T. Beito: From Mutual Aid to the Welfare State [PEER-REVIEWED] University of North Carolina Press. Comprehensive history of fraternal societies and friendly societies, 1890-1967. UNC Press

Einav and Farronato: Peer-to-Peer Markets [PEER-REVIEWED] NBER Working Paper 21496. Analysis of digital marketplaces from eBay through Airbnb and Uber. NBER

eBay Founding History [EDITORIAL] Britannica Money. Pierre Omidyar founded AuctionWeb in September 1995. Reputation systems became the trust infrastructure for the platform economy. Britannica

B. The 2008 Crisis (Section II)

Katz and Krueger: The Rise and Nature of Alternative Work Arrangements [PEER-REVIEWED] NBER Working Paper 22667. Alternative work arrangements grew from 10.7% to 15.8% of US workforce, 2005-2015. NBER · Harvard Kennedy School (Katz)

Hall and Krueger: Analysis of the Labor Market for Uber’s Driver-Partners [PEER-REVIEWED] ILR Review, 2018. The first comprehensive analysis of platform driver economics. ILR Review (SAGE) · NBER version

Resolution Foundation: Just the Job or a Working Compromise [INSTITUTIONAL] UK self-employment accounted for 45% of all employment growth from March 2008. Self-employment grew by 650,000 to 4.5 million. Resolution Foundation

ONS: Trends in Self-Employment in the UK [INSTITUTIONAL] UK self-employment rose from 3.8 million in 2008 to 4.6 million in 2015. ONS

C. COVID-19 and the Operator Acceleration (Section III)

Barrero, Bloom, Davis: The Evolution of Work from Home [PEER-REVIEWED] Hoover Institution / Stanford. The seminal longitudinal WFH dataset (SWAA). Hoover Institution · American Economic Association

Stanford GSB: The Pandemic Blew Up the American Office [INSTITUTIONAL] Stanford GSB Insights. Bloom et al. on the durable structural shift in workplace location. Stanford GSB

Gratton: Seven Truths About Hybrid Work and Productivity [EDITORIAL] MIT Sloan Management Review. Teams together 23-40% of the time outperform on multiple dimensions. MIT Sloan Review

Aksoy et al.: Working from Home Around the World [PEER-REVIEWED] NBER Working Paper 30446 and Brookings Papers on Economic Activity. International comparison of remote work persistence. NBER · Brookings

McKinsey: Freelance, Side Hustles, and Gigs [INSTITUTIONAL] American Opportunity Survey. 36% of US workforce (≈58M) identified as independent workers in 2022, up from 27% in 2016. McKinsey

US Census Bureau: Business Owners and the Self-Employed [PEER-REVIEWED] Working paper CES-25-60. Non-employer business growth peaked at 4.9% in 2021 and 4.7% in 2022, fastest in nearly two decades. Census Bureau

D. The Creator Economy (Section IV)

Goldman Sachs Research: The Creator Economy Could Approach Half-a-Trillion Dollars by 2027 [INSTITUTIONAL] $250B today, projected $480B by 2027. ~14% CAGR. 50M global creators growing 10-20% annually. Goldman Sachs

Li Jin: The Creator Economy Needs a Middle Class [EDITORIAL] Harvard Business Review, December 2020. Diagnosis of income concentration and prescription for platform mechanics. HBR

Li Jin: The Passion Economy and the Future of Work [INSTITUTIONAL] Andreessen Horowitz. The thesis that distinguished the creator economy from the commoditized gig economy. a16z · a16z 16 Key Metrics

HBR: How Your Business Should Tap into the Creator Economy [EDITORIAL] Harvard Business Review, May 2024. Smaller creators (under 50,000 followers) account for 76% of all TikTok creators and 96% on Instagram. HBR

E. Platform Stack and AI Infrastructure (Sections V-VI)

Stripe: Global Payment Processing Platform [INSTITUTIONAL] Stripe operates in 135+ countries, supports 135+ currencies, and processes hundreds of billions annually. Stripe

Bessemer Venture Partners: Building Vertical AI Playbook [INSTITUTIONAL] The investment thesis for vertical AI companies replacing horizontal SaaS budgets. Bessemer

Bessemer: Everything Everywhere All AI [INSTITUTIONAL] Bessemer’s AI investment thesis book, 2024. Bessemer

Goldman Sachs Research: How Will AI Affect the Global Workforce [INSTITUTIONAL] Generative AI will raise US labor productivity by approximately 15% at full adoption. Goldman Sachs

Morgan Stanley: AI and Jobs: Labor Market Impact Echoes Past Tech Transitions [INSTITUTIONAL] Historical analogues for AI displacement and re-employment. Morgan Stanley

F. Historical Parallels and Platform Governance (Section VII)

Harvard Business School: Centralized Governance in Decentralized Organizations [PEER-REVIEWED] Working paper. Block voters control 76.2% of voting power in DAO governance decisions. HBS

Harvard Belfer Center: Decentralized Autonomous Organizations and Policy Considerations [INSTITUTIONAL] Technology and Public Purpose Project policy brief, 2025. Belfer Center

The Governance of DAOs: Contributor Voting Power Analysis [PEER-REVIEWED] Empirical analysis showing concentrated voting power across DAO ecosystems. arXiv

G. Platform Dependency and Algorithmic Risk (Section VIII)

Pew Research Center: Code-Dependent: Pros and Cons of the Algorithm Age [INSTITUTIONAL] Expert survey of algorithmic governance and its effects on workers. Pew Research

Dependence and Precarity in the Platform Economy [PEER-REVIEWED] Theory and Society. Worker satisfaction and earnings volatility across platforms. Springer / Theory and Society

Rich-Get-Richer? Platform Attention and Earnings Inequality [PEER-REVIEWED] Earnings analysis using Patreon data. Algorithmic curation concentrates income at the top. arXiv

Content Creators Between Platform Control and User Autonomy [PEER-REVIEWED] Analysis of how creators adapt income strategies to algorithmic changes. PMC / NCBI

All findings drawn from elite publications (NYT, FT, WSJ, Economist, HBR, MIT), top academic centers (Harvard, Stanford, Princeton, Chicago Booth, LSE, NBER), and institutional research (Goldman Sachs, Morgan Stanley, McKinsey, Bessemer, Andreessen Horowitz, Resolution Foundation, Pew Research, Brookings, Hoover).

John Brewton · Founder · Operating · 6AEP

New to Operating?

Check out these reader favorites:

• Operating Economics: The Optimization Company (Amazon)

• Operating by Warren & Charlie

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

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

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

Subscribe now

A founder I advise ran a product review last week. Six people were in the room. An AI platform, connected to a team of agents listened on screen and mic, ready to spring into action. The team debated a positioning choice for forty minutes. The AI agreed with the founder eight times. It disagreed zero times. The team agreed with the AI nine times. The tangential decisions went live on Friday.

They were largely wrong by Monday.

This is Failure Mode, 2026.

Amy Edmondson named psychological safety in 1999, writing in Administrative Science Quarterly from Harvard Business School:

She defined it as the shared belief that the team is safe for interpersonal risk-taking. She studied 51 work teams, with extensions into surgical units and ICUs.

Every voice in the room was a human voice. The framework worked for twenty-four years. Google’s Project Aristotle confirmed it in 2015 across 180 teams and 250 attributes, finding psychological safety the single strongest predictor of high performance. Sales teams with high safety beat target by 17 percent. Teams with low safety missed by 19 percent.

The framework began to shake in 2023.

The Four Behaviors

Edmondson’s original card has four behaviors. Admit error: surface mistakes without fear of blame. Ask questions: admit gaps and seek help across rank. Offer ideas: propose novelty against the consensus. Raise concerns: name problems early before they compound. Her data showed B4 is the behavior teams skip most. It carries the highest social cost.

Edmondson paired the four behaviors with a 2x2 of safety and standards. Learning is high safety plus high standards, the zone of elite surgical units and top ICUs. Comfort is high safety plus low standards, the polite team that ships nothing. Anxiety is low safety plus high standards, the zone where fear masquerades as rigor and burnout follows. Apathy is low safety plus low standards, the team that has stopped trying. Safety is the floor. Standards build the ceiling. Edmondson made the point again in her 2018 book The Fearless Organization. Silence is expensive. Speech has to be made safe and then required.

The Team. The Unit.

One detail in the original work matters more than ever now. The unit of analysis is the team, not the company. Two teams under the same CEO score wildly differently. Climate is local. Policy set at the organization cannot reach the room. The team is the four to nine people who share a Zoom call or a doorway. That is where the climate is built or lost.

The Addition

Subscribe now

In 2023, the AI joined the team. By 2024, it was in every standup, roadmap review, and escalation huddle. The HBS AI Institute’s field experiment at Procter & Gamble with 776 professionals found that AI acted as a cybernetic teammate. AI-augmented individuals matched the output of human pairs and produced ideas in the top ten percent of all submissions. The unit of analysis is no longer four to nine humans. It is four to nine humans plus one to three models.

The models brought a new failure mode. The default behavior of every major frontier model in 2026 is sycophancy. Anthropic’s 2023 paper tested five state-of-the-art assistants and found sycophancy in every one. A Stanford and Carnegie Mellon study in Science tested eleven frontier models and found they affirm users 50 percent more than humans do. On Reddit posts where human consensus rules the user in, the AI affirms the user in 51 percent of cases, whereas the human rate is zero. Stanford’s own summary names the loop. Users in the 2,400-person experiment preferred the sycophant, trusted it more, came back more often, and grew more convinced they were right after talking to it. The feature that causes the harm drives the engagement.

The market noticed in April 2025. OpenAI rolled back a GPT-4 update on April 29 after users reported the model endorsed stopping medications and praised business ideas that should have been killed. OpenAI later conceded that user feedback signals had outweighed the corrective ones during training. Nature and IEEE Spectrum reached the same conclusion. Sycophancy is the default, reinforced by human feedback every day.

Edmondson and Jayshree Seth named the result in HBR in February 2026. AI erodes trust on teams because staff believe trust in the model is supposed to be warranted, lack actual trust, and find the gap undiscussable. They call it trust ambiguity. The 1999 framework still holds. The card is incomplete.

The fifth behavior

Add a fifth card. Challenge the AI. The behavior is the deliberate act of demanding that the model disagree. Refuse the sycophantic default. Reward dissent in the room from any source, human or otherwise. The new five-behavior card reads admit error, ask questions, offer ideas, raise concerns, and challenge the AI. The first four came in 1999. The fifth arrived in 2024.

The fifth behavior is not optional. MIT Sloan Management Review argued in 2025 that an AI that agrees too easily is changing how teams think, not merely what they decide. The HBR essay To Mitigate Gen AI’s Risks, Draw on Your Team’s Collective Judgment framed the fix as a third layer of risk management. A team that scores ten on the first four behaviors and zero on the fifth still produces flattered, false, and fragile decisions.

Install

For the solo operator, run a weekly five-behavior check. On Friday, write where you were wrong this week and ask the AI to rate the post-mortem. Ask the model the hardest question you avoided in the office. Offer one contrarian idea and demand a critique of the consensus. Raise the concern you would not have raised to a boss. Log every instance of sycophancy from the week. Twenty minutes. Every Friday.

For the startup founder, install psychological safety across the team and AI in 90 days. Four levels. Inclusion rosters every voice in the room, the AI included. Learner makes admitting gaps the default, and the AI admits its limits. The contributor lets the AI offer ideas, and the team critiques them on the merits. Challenger rewards disagreement with the model and tracks the disagreement rate as a metric.

For the SMB strategist, audit recurring meetings quarterly. Weekly standup, pipeline review, planning offsite, campaign brief, roadmap review, escalation huddle, retrospective, all-hands. For each, mark whether the fifth behavior is present, drifted, or needs installation. Most teams find three of eight need installation.

Five Prompts

Name the team as the four to nine people who share the same room. Score the four originals on a scale of 1 to 10, with an example for each. Set the AI to challenge with a system prompt that requires the model to disagree at the start of every session. Measure disagreement and target one in three exchanges. Loop on Friday and refresh the climate quarterly.

The standard for a learning team in 2026 is high safety, high standards, and an AI that argues back. The framework, named the floor in 1999, now needs a fifth behavior to hold the ceiling. Add it this week. Track it next quarter. The teams that install will compound. The teams that do not will ship decisions that are flawed, false, and fragile on schedule.

- j -

About the author

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

Appendix. Sources.

Foundational research on psychological safety

1. Edmondson, A. Psychological Safety and Learning Behavior in Work Teams. Administrative Science Quarterly, Vol. 44, No. 2. Harvard Business School. June 1999. [PEER-REVIEWED]

2. Edmondson, A. The Fearless Organization. Creating Psychological Safety in the Workplace for Learning, Innovation, and Growth. Wiley. 2018. [PEER-REVIEWED]

3. Creating Psychological Safety in the Workplace. HBR IdeaCast with Amy Edmondson. January 2019. [EDITORIAL]

4. The Five Dynamics of Effective Teams. Google. Project Aristotle. [INSTITUTIONAL]

AI on the team

5. Dell’Acqua, F., Ayoubi, C., Lifshitz-Assaf, H., Sadun, R., Mollick, E., et al. The Cybernetic Teammate. A Field Experiment on Generative AI Reshaping Teamwork and Expertise. Harvard Business School Working Paper 25-043. March 2025. [PEER-REVIEWED]

6. When AI Joins the Team, Better Ideas Surface. HBS Working Knowledge. 2025. [PEER-REVIEWED]

7. To Mitigate Gen AI’s Risks, Draw on Your Team’s Collective Judgment. Harvard Business Review. November 2024. [EDITORIAL]

AI sycophancy. The new failure mode.

8. Sharma, M., et al. Towards Understanding Sycophancy in Language Models. Anthropic. arXiv:2310.13548. October 2023. [INSTITUTIONAL]

9. Cheng, M., et al. Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence. Science. Stanford and Carnegie Mellon. 2026. [PEER-REVIEWED]

10. AI Overly Affirms Users Asking for Personal Advice. Stanford Report. March 2026. [PEER-REVIEWED]

11. AI Chatbots are Sycophants. Researchers Say It Is Harming Science. Nature. 2025. [EDITORIAL]

12. AI Sycophancy. Why Chatbots Agree With You. IEEE Spectrum. 2025. [EDITORIAL]

The April 2025 GPT-4o sycophancy event

13. Sycophancy in GPT-4o. What Happened and What We Are Doing About It. OpenAI. April 2025. [INSTITUTIONAL]

14. Expanding on What We Missed With Sycophancy. OpenAI. 2025. [INSTITUTIONAL]

15. OpenAI Rolls Back Update That Made ChatGPT Too Sycophant-y. TechCrunch. April 29, 2025. [EDITORIAL]

Psychological safety meets AI

16. Edmondson, A. and Seth, J. How to Foster Psychological Safety When AI Erodes Trust on Your Team. Harvard Business Review. February 2026. [EDITORIAL]

17. When Integrating AI, Focus on Psychological Safety. Harvard Business Review Management Tip. February 2026. [EDITORIAL]

18. When AI Agrees Too Easily, It May Be Changing How We Think. MIT Sloan Management Review. 2025. [EDITORIAL]

New to Operating?

Check out these reader favorites:

• Operating Economics: The Optimization Company (Amazon)

• Operating by Warren & Charlie

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

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

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

Subscribe now

Operating Stories No. 07 · By John Brewton

In October 2025, Mark Walter closed his purchase of the Los Angeles Lakers at a $10 billion valuation. It is the highest price ever paid for an American sports franchise, $3.9 billion above the Boston Celtics sale to Bill Chisholm seven months earlier. The Buss family, who had controlled the franchise since 1979, kept roughly 15 percent and Jeanie Buss kept the governor seat.

Forty-six years before that closing, Jerry Buss had purchased the Lakers, the Forum, the LA Kings, and a 13,000-acre Sierra Nevada ranch from Jack Kent Cooke for $67.5 million. Roughly $16 million of that total was allocated to the basketball franchise itself.

That $16 million compounded into the $10 billion sale. The math is 625x over 46 years, or a 14.9 percent compound annual growth rate. For comparison, a $16 million S&P 500 buy-and-hold over the same period would have grown to roughly $1.9 billion. The Buss family bought one private illiquid asset and outperformed the S&P 500 by 5x. Plus seventeen championships.

Eight months before Walter closed, the Lakers acquired Luka Dončić from the Dallas Mavericks in a three-team trade that reporters called the most unexpected transaction in American sports history. Luka was 25, an All-NBA First Team selection in his prime, and had not requested a trade.

That sequence looks like luck. It is not. It is the seventh execution of the same operating decision the franchise has made for 78 years.

The Operating Thesis

The Los Angeles Lakers are not a basketball franchise. They are an 80-year case study in concentrated talent acquisition. The franchise has won 17 championships across seven generational acquisitions. Every era has been anchored by a single decisive operating decision to acquire one transcendent star. The infrastructure (coaches, role players, ownership succession, the city itself) exists to protect and amplify that asset.

The basketball is the byproduct.

Seven Decisions

1. 1947 · George Mikan: First pick in the NBL dispersal draft after the Chicago Gears folded. Five championships in six years. The franchise template.

2. 1968 · Wilt Chamberlain: Trade from Philadelphia for Imhoff, Clark, Chambers, and cash. First explicit star-stack with West and Baylor. Won the 1972 title with a 33-game winning streak that still stands.

3. 1979 · Magic Johnson: First overall pick. Cooke held the pick from a prior trade with New Orleans. Selected Magic on June 25. Buss completed his $67.5 million purchase four days later. Five titles in nine years. The franchise became Showtime, explicitly Hollywood-engineered.

4. 1996 · Shaq plus Kobe: A coordinated single decision in a single calendar week. Signed Shaq to $121 million, the largest contract in NBA history at the time. Traded Vlade Divac to Charlotte for the rights to Kobe Bryant. Three consecutive championships, 2000 to 2002.

5. 2008 · Pau Gasol: Trade from Memphis for Kwame Brown, Javaris Crittenton, the rights to Marc Gasol, and two future first-round picks. Popovich called it a heist. Two titles in three years.

6. 2018 · LeBron James: Free agency, four years and $154 million. The first acquisition where the franchise’s accumulated brand gravity was the recruiting mechanism. No assets traded, no punitive contract absorbed. The seventeen banners did the work. Won the 2020 bubble title.

7. 2025 · Luka Doncic: Sent Anthony Davis, Max Christie, and a 2029 first to Dallas. Acquired the era’s third-best player in his prime, age 25, while the previous-generation star (LeBron) was still on the roster. The first time the cycle did not require a generational gap.

Subscribe now

Read more

New to Operating?

Check out these reader favorites:

• Operating Economics: The Optimization Company (Amazon)

• Operating by Warren & Charlie

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

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

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

Subscribe now

Start Here

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

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

The Assumption

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

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

The 8th S

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

• L1 Data: Owned, observable, indexed.

• L2 Models: Frontier and specialist.

• L3 Prompts: Codified team patterns.

• L4 Agents: Autonomous workflows.

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

Codifiable. Assignable. Changeable in twelve months.

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

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

Three operator translations

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

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

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

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

The full pack

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

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

The principle

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

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

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

- j -

Subscribe now

About the author

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

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

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

Bye Bye Bye Workaround

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

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

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

Three Tiers

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

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

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

Operating Assets

Prompt Pack

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

Worksheet

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

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

- j -

About the author

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