Sequoia Capital: The Next Trillion-Dollar Company Sells Results, Not Software

اصل تالیف: ٹیک فلو

تعارف: Sequoia Capital partner Julien Bek has written a clear and structured article. The core thesis is: the next trillion-dollar company won’t sell software tools, but will sell work outcomes directly. For every $1 spent on software, businesses spend $6 on services. When AI drives the cost of “doing things” towards zero, the real opportunity lies not in Copilots (assistive tools), but in Autopilots (automated task completion).

He breaks down the automation opportunities in service industries like insurance, accounting, healthcare, law, IT, procurement, recruitment, and consulting one by one, and includes an opportunity matrix chart based on two dimensions: “Intelligence vs. Judgment” and “Outsourced vs. In-house.” This has reference value for both AI entrepreneurs and investors.

Full text as follows:

The next trillion-dollar company will be a software company disguised as a services company.

Every founder building AI tools is asking the same question: what if the next version of Claude turns my product into a feature? This fear is justified. If you sell tools, you’re racing against the models. But if you sell the work itself, every improvement in the model makes your service faster, cheaper, and harder to compete with. A company might spend $10,000 a year on QuickBooks and $120,000 on an accountant to close the books. The next legendary company will close your books for you directly.

Intelligence vs. Judgment

Writing code is primarily “intelligence.” Knowing what to do next is “judgment.”

Translating a requirements document into code, testing, debugging: the rules are complex, but they are still rules. Judgment is different. It requires experience and taste, an intuition built up over years of practice. Deciding what feature to build next, whether to incur technical debt, when to ship before something is fully ready.

A year ago, most Cursor users used AI for autocomplete. Today, more tasks are initiated by Agents than by humans. Software engineering accounts for over half of all AI tool usage across professions, while all other categories are still in single digits. The reason is that software engineering is largely intelligence work. AI has already crossed that line—it can autonomously handle most of the intelligence work, leaving judgment to humans. Software engineering got there first, but it will spread to every profession.

Caption: AI tool usage share by profession, software engineering far exceeds other categories

Copilot and Autopilot

Copilot sells tools. Autopilot sells work.

Until recently, AI models were still developing in both intelligence and judgment, so the right path was to start with a Copilot: put AI in the hands of professionals and let them decide how to use it. Harvey sells to law firms, Rogo sells to investment banks. The professionals are the customers; the tools make them more efficient, and they are responsible for the output.

Today, models are smart enough that in some categories the best starting point is to go straight to Autopilot. Crosby sells to companies that need NDAs drafted, not to external legal counsel. WithCoverage sells to CFOs who need insurance, not to insurance brokers. The customer buys the outcome directly. In any profession, the budget for work is far greater than the budget for tools, and Autopilot captures the work budget from day one.

The higher the proportion of intelligence in a field, the faster Autopilot wins.

کنورجنسی

Today’s judgment will become tomorrow’s intelligence. As AI systems accumulate proprietary data on “what good judgment looks like” in their respective domains, the frontier will shift. Copilot and Autopilot will converge. The transition from Copilot to Autopilot has already begun in several categories. But the starting position matters because it determines where Autopilot can win customers now and start accumulating the data that will eventually allow it to handle judgment-based tasks as well.

The Autopilot Play: Outsourcing is the Entry Point

For every $1 spent on software, $6 is spent on services.

The TAM for Autopilot is all labor expenditure in a category, both internal and outsourced. But the right starting point is where outsourcing already exists.

If a task is already outsourced, it tells you three things. First, the company has already accepted that this work can be done externally. Second, there is a ready-made budget line item that can be cleanly replaced. Third, the buyer is already purchasing an outcome. Replacing an outsourcing contract with an AI-native service provider is a vendor switch. Replacing an internal employee is an organizational restructuring.

The play is: start with outsourced, intelligence-intensive tasks. Nail distribution. As AI accumulates data, expand into internal, judgment-intensive work. Outsourced tasks are the wedge; internal work is the long-term TAM.

Crosby started with NDAs: a well-defined task, mostly intelligence work, already outsourced by most companies to external lawyers. Budget is ready, scope is clear, ROI is immediate, replacement is frictionless.

Opportunity Map

Plotting each service vertical on a spectrum from “Intelligence to Judgment” and by the ratio of “Outsourced to In-house” yields a prioritization map, with labor TAM in parentheses. The list below is not exhaustive.

Caption: Autopilot opportunity matrix for various service verticals (distributed by intelligence/judgment ratio and outsourced/in-house ratio)

Insurance Brokerage ($140-200 billion).

The largest market on this list. Standard commercial insurance is highly standardized: the broker’s added value is essentially comparing prices and filling out forms across different underwriters—pure intelligence work. The distribution layer is extremely fragmented, with thousands of small brokers each running the same process, none controlling the client relationship. WithCoverage and Harper are interesting new entrants.

Accounting & Auditing ($50-80 billion outsourced in the US alone).

The US has lost about 340,000 accountants over the past five years, while demand has grown. 75% of CPAs are nearing retirement, the licensure path is long, and starting salaries lag behind tech and finance. This structural shortage is driving accounting firms to adopt AI faster than almost any other profession. Rillet is building an AI-native ERP to close books directly. Basis started as a Copilot for accountants.

Healthcare Revenue Cycle Management ($50-80 billion outsourced in the US).

Hearing “healthcare” makes people think judgment-intensive, but the billing layer is almost pure intelligence work. Medical coding is translating clinical notes into about 70,000 standardized ICD-10 codes. The rules are complex but still rules. Outsourcing is already mature and billed by outcome. Autopilot just needs to do the same thing at lower cost. Anterior is the furthest along.

Claims Adjusting ($50-80 billion including TPAs).

On the other side of the insurance policy, claims adjusting is another distinct Autopilot scenario. Adjusting claims for standard policies involves adjudicating against a list of damages based on policy language and setting reserves using actuarial tables. The adjuster workforce is aging with no one to replace them. The market is heavily outsourced to independent adjusters and TPAs like Crawford and Sedgwick. One industry, at least two different Autopilot opportunities. Pace is building an Autopilot for claims processing, Strala is building an AI-native TPA.

Tax Advisory ($30-35 billion).

The CPA licensure system creates a regulatory moat, but 80%-90% of the underlying work is intelligence. The tax Autopilot’s data moat deepens with each additional jurisdiction covered. The complexity of multi-jurisdiction work is precisely why SMEs outsource it, as no internal accountant can cover it all. TaxGPT is an early mover; Skalar and Ravical are in Europe.

Legal Transactional Work ($20-25 billion).

Contract drafting, NDAs, regulatory filings: high intelligence share, routinely outsourced. The work output is standardized enough and quality is verifiable, so buyers can trust AI output without deep legal expertise. Harvey is the rising leader, quickly moving towards Autopilot; Crosby and Lawhive are Autopilot-native new entrants.

IT Managed Services ($100+ billion).

Every SME outsources IT. Patching, monitoring, user provisioning, alert triage: intelligence work repeated across thousands of identical environments. The existing software layer (ConnectWise, Datto) sells tools to MSPs. No one yet sells “your IT is running” as an outcome to companies directly. Edra is automating IT processes, Serval is automating IT support.

Supply Chain & Procurement ($200+ billion).

Most companies only seriously negotiate with their top 20% of suppliers. The long tail is completely neglected because it’s not cost-effective to have people do it. Contract leakage accounts for 2%-5% of total procurement spend. The entry point is the abandoned work: no budget line to justify, no incumbent to displace, just money left on the table. Magentic is building AI for direct procurement, AskLio for indirect. Tacto is building both the system of record and a Copilot for the mid-market.

Recruitment & Staffing ($200+ billion).

The largest service market on this list. The top of the recruitment funnel (screening, matching, outreach) is pure intelligence work, but closing deals and assessing cultural fit is judgment built on years of pattern recognition. The Autopilot entry point is high-volume, low-judgment roles where matching is standardized. Juicebox, Mercor, Jack & Jill are emerging leaders building across the spectrum.

Management Consulting ($300-400 billion).

Huge market, but the work is mostly judgment. The interesting question is whether AI can decompose consulting into intelligence components (data gathering, benchmarking) and judgment components (strategic advice), automating the intelligence layer and leaving the judgment to humans. Best candidates TBD.

The fastest-growing AI companies in 2025 were Copilots. In 2026, many will try to become Autopilots. They have product and customer awareness. But they also face the innovator’s dilemma: selling work means kicking their own customers out of their jobs. This is the opportunity window for pure Autopilot companies.

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