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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.

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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.

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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.

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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

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

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Most operators I talk to have a growth problem.

Not the kind they think they have.

They don’t lack ambition. They don’t lack ideas.

They lack the right scoreboard.

They’re tracking what was easy to measure one year ago and wondering why the number goes up but nothing moves.

That’s not a strategy. That’s a habit dressed up as one.

This week: one habit, one …

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The 15-Minute KPI Audit

A one-sheeter for operators who are done measuring the wrong things.

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One lens, a few sharp moves, and a short list of inputs to help you operate better in an AI-shaped economy this week. If you only have 10 minutes: read Pine on customer aspirations, skim the Palantir agent thread, and try the tiny experiment at the end.

And most importantly, have an amazing, productive, killer week!

- john -

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Become An Operating Founder

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In 2026, I’m working directly with 100 creators building real businesses.

I want to bring operating strategy, competitive positioning, and financial planning to a community that’s fundamentally different from my typical industrial and technology clients.

For the first 100 creator founders: Four 60-minute 1:1 advisory sessions for $95.

Context: My standard engagement starts at $10K/month. This isn’t that. This is me learning from you while helping you build something sustainable.

Limited to 100 spots. Message me with questions or to claim yours.

Become An Operating Founder

The week in Davos clarified the operating baseline for the next few years: modest but resilient growth, structurally fragmented trade, AI treated as infrastructure rather than experiment, and a labour market being rewritten in real time. The spectacle around Trump, Greenland and NATO made headlines; the more important signals were in the side rooms where CFOs, CHROs and operators compared notes on how to grow into this new regimented era.

This piece extracts those signals and turns them into an operating agenda for the weeks ahead.

- john -

Full appendix with links to all cited research and publications can be found at the end of the article.

The macro message from Davos was not exuberance but durability. The IMF raised its 2026 global growth forecast to around 3.3%, up 0.2 percentage points from October, citing stronger investment in AI and digital infrastructure as a key offset to trade and policy headwinds. The United States was revised up to 2.4% growth, China to 4.5%, with the global figure anchored in the low‑3s

IMF 2026 Growth Outlook: Global, US and China real GDP forecasts underpin Davos’s ‘resilient but noisy’ narrative.

Chief economists convened by the WEF described this as “reassuring resilience”: lower than pre‑GFC norms but stronger than the gloomy consensus entering 2025. The composition of growth matters more than the headline number. A significant share is now coming from capex into AI, cloud and data centres, which supports equipment, construction and energy demand, and from fiscal and industrial policy, particularly around green technologies and semiconductors.

Bloomberg and FT coverage underscored the tension: equity markets have already capitalised years of expected AI productivity gains, while interest rates remain structurally higher due to debt overhangs, re‑shoring and defence spending. Central banks and investors now treat AI investment as both growth engine and potential source of financial instability if returns disappoint.

For operators, the planning baseline is straightforward: assume low‑3s global growth with high regional dispersion, and treat AI capex and energy prices as first‑order macro variables, not background noise.

If the macro picture is resilient, the institutional picture is not. The WEF’s Global Risks Report 2026 ranks geoeconomic confrontation as the top global risk over the next two years, ahead of state-based conflict, extreme weather and misinformation. A large majority of surveyed leaders expect a more fragmented or multipolar global order within a decade.

The Greenland episode provided a vivid illustration. In the run‑up to Davos, the Trump administration threatened EU tariffs amid a dispute over Greenland, briefly reviving fears of a transatlantic trade war. At Davos, Trump announced a “framework” deal with NATO allies on Greenland and dropped the immediate tariff threat, triggering a relief rally in markets such as India’s equity indices. The sequence showed how quickly trade conditions can be weaponised and then reversed.​

At the same time, the EU and India used Davos to flag that their long‑negotiated free trade agreement is close to conclusion, with Brussels branding it “the mother of all deals.” The agreement promises lower tariffs and improved access across autos, alcohol, pharma and textiles, while giving both parties a diversification hedge against US–China volatility.

The underlying pattern is a shift from hyper‑globalisation to risk‑routed globalisation:

• Trade, investment and supply chains are being rewired around geopolitical fault lines rather than cost alone.

• New corridors (EU–India, Gulf–India, intra‑Asian and South–South links) are gaining strategic weight relative to legacy US‑centric flows.

For corporate strategy, this argues for portfolios and operating models optimised for option value rather than pure efficiency. The critical questions for leadership teams this week are concrete: where is revenue or supply chain exposure overly concentrated in a single political node, and which emerging corridors are under‑represented in the three‑to-five‑year plan relative to likely policy support and demand growth?

Resilience in this setting is a precondition for growth.

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In 2026, I’m working directly with 100 creators building real businesses.

I want to bring operating strategy, competitive positioning, and financial planning to a community that’s fundamentally different from my typical industrial and technology clients.

For the first 100 creator founders: Four 60-minute 1:1 advisory sessions for $95.

Context: My standard engagement starts at $10K/month. This isn’t that. This is me learning from you while helping you build something sustainable.

Limited to 100 spots. Message me with questions or to claim yours.

Become An Operating Founder

January 19, 2026 | by John Brewton

On January 11, 2026, Fortune reported something extraordinary: U.S. worker productivity surged 4.9% while hiring remained frozen. Companies are growing—revenues climbing, profits expanding, but the headcount that once powered that growth has flatlined. Wells Fargo shed 65,000 employees since 2019 while maintaining operations. Shopify told managers: no new hires unless you can prove AI can’t do the job. IBM’s voluntary attrition hit a 30-year low because workers know the exit doors have narrowed.​

This is the defining economic story of 2026, and it has a second act that most people are missing: When corporate growth decouples from headcount growth, the creator economy doesn’t just grow, it becomes structurally necessary.

Fewer entry-level roles mean more people must build their own income engines. Flatter organizations mean companies increasingly “rent” distribution, trust, and expertise from the outside, hiring creators and contractors, not employees. The same AI tools letting Shopify operate with frozen headcount are letting solopreneurs run $500K businesses from their laptops with <$500/month in tool costs.​

Two operating systems are emerging:

1. The corporate OS: productivity leverage without hiring.

2. The creator OS: owned audiences, productized expertise, micro-teams of AI agents.

And they’re not separate, they’re feeding each other.

Starting this week, Operating Week Ahead will do something different.

• Part one will track the big stories—Fortune 500 strategy, macro trends, geopolitics, the stuff that excites entrepreneurs and shapes how we all think about business.

• Part two will translate those forces into actionable intelligence for the one-person builder—the newsletter writer, the solopreneur, the operator figuring out how to build durable income in a world where corporate ladders are narrowing.

The big world sets the context. The small builder captures the value.

Let’s begin.

I. The Decoupling Is Real—and Accelerating

The macro confirmation came last week. Fortune reported that U.S. labor productivity jumped 4.9% in Q4 2025, while hours worked rose just 0.5%. Translation: companies extracted dramatically more output from existing workers, not by adding people. This follows years of AI investment, process automation, and organizational redesign. Economists frame this as “good news”—growth without wage pressure or inflation risk.​

“Wells Fargo’s headcount has fallen roughly 24% since 2019 even as U.S. labor productivity accelerates to 4.9% growth—illustrating how corporate growth is increasingly driven by technology and process leverage rather than hiring.”

But zoom into the Fortune 500, and the human story becomes stark. A Yale School of Management CEO survey found that 66% of CEOs plan to cut staff or maintain current team sizes in 2026. Only 34% plan to expand headcount. Wells Fargo has gone from 275,000 employees in 2019 to 210,000 today—a 24% reduction—while maintaining operations. IBM’s voluntary attrition fell below 2% in the U.S., the lowest rate in 30 years, because people aren’t leaving—they know jobs are scarce.​

Shopify’s CFO put it bluntly: “I don’t see us needing to increase headcount in any way. We’ve been at this headcount for over two years.” And in April 2025, Shopify CEO Tobi Lütke sent a memo that’s become the unofficial manifesto of this new era: no new hires unless managers can prove that AI cannot perform the required tasks.​

Operating Insight: The corporate ladder didn’t just get harder to climb—it got narrower. Entry-level and mid-tier roles are compressing as AI and process redesign absorb what juniors used to do. If you’re early in your career or considering a pivot, plan for independence. The path up is uncertain; the path out—building your own thing—is increasingly viable.

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A Bifurcating Operating Model

The week’s coverage from the Wall Street Journal, Financial Times, The Economist, Harvard Business Review, MIT Technology Review, Wired, Fast Company, and creator-economy trend pieces reveals a single underlying pattern: the future of operating is splitting into two distinct architectures, each with radically different governance, cost structures, and competitive advantages.

Traditional firms are exhausted by perpetual transformation cycles. Creator-led micro-firms are just beginning to scale. Both are converging on the same understanding: adaptive capacity, embedded in operating systems and governance, is the only sustainable moat.

The Transformation Paradox

“Get Off the Transformation Treadmill” (Harvard Business Review, Jan–Feb 2026) landed this week with data that should terrify executives: 95% of large organizations have undergone 2+ major reinventions in the prior two years; 61% have hit four or more transformations.

Serial transformations breed employee burnout, erode investor confidence, and drain resources. McKinsey’s CEO Bob Sternfels, marking the firm’s 100-year milestone in an HBR interview, is shifting McKinsey from pure advisory to outcomes-based partnerships, a signal that consultants no longer believe in advice-giving alone; they now want to share risk with clients.

The implication is stark: perpetual transformation is failing. The escape route is not another big-bang reinvention. It is embedding adaptive capacity directly into the operating model (faster decision-making cycles, clearer governance, continuous sensing) so that organizations can adapt without periodic organizational trauma.

Board Literacy and AI-Agent Governance

While digital board literacy has improved dramatically (24% to 72%), only 26% of boards are equipped to govern next-generation AI systems—revealing a critical new gap

“AI-Savvy Boards Drive Superior Performance” (MIT Sloan Management Review, Winter 2026) reveals a striking finding: only 26% of large firms have boards equipped to understand and govern next-generation AI (AI agents, robotics, specialized tech like fintech and regulatory tech). This gap is a leading indicator of which firms will make smart AI investments and which will squander capital.

Why? Because boards govern the boundary between human and machine decision-making, the delegation of discretion to autonomous systems, and the governance of data-driven strategy. If boards don’t understand these questions, they cannot effectively oversee them.

The data point that matters: 70% of large enterprises now have Chief Data Officers in established, successful roles (up from 50% last year). Only 3% view the CDO role as a failure. This suggests that data and AI governance, properly embedded in the C-suite, is becoming a competitive differentiator.

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Markets reached a pivotal moment this week as confidence in AI-driven equities collided with real-world economic anxieties. Eli Lilly became the first pharma firm to reach $1 trillion valuation on surging demand for obesity drugs. Yet the American household experience is increasingly recession-like: consumer sentiment fell to its second lowest level in two decades, layoffs surged, and inflation’s aftershocks reshaped daily life.

Simultaneously, more than $380B in capital flowed into AI infrastructure, an investment cycle comparable to the railroads, highways, and electrification booms of American history. This outlier growth masks an otherwise bifurcated economy: technology, healthcare, and infrastructure are surging, while manufacturing, construction, retail, and middle-income spending are stagnant or shrinking.

Ahead: essential economic data (including Black Friday/Cyber Monday retail results, inflation readings, and confidence reports) will test whether 2025 ends with optimism or caution. For investors and operators, the divide between AI winners and traditional losers has never been starker.

A. The AI Correction and Market Volatility

The artificial intelligence “Super Seven” stocks finished the week deep in the red—Nvidia down 3.6%, Tesla off 6.6%, Oracle, Meta, and Alphabet all tumbling. The tech-led Nasdaq posted its steepest three-week slide since spring, fueling broader concerns about concentration risk.

Line chart: S&P 500 (-4%), Nasdaq (-6.5%) from Nov 1–22, 2025, highlighting the accelerating tech selloff.

Goldman Sachs’s CEO publicly warned of a likely 10–20% correction as even noted AI bull Michael Burry revealed heavy short positions against Nvidia and Palantir, signaling a shift in institutional sentiment. The “Magnificent Seven” now comprise an unprecedented 34% of S&P 500 market capitalization—a record share once matched only in the most euphoric moments of the dot-com bubble or “Nifty Fifty” era.

Pie chart: 34% of S&P 500 attributed to seven tech stocks—Apple, Microsoft, Alphabet, Amazon, Nvidia, Meta, and Tesla.

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