Sequoia Capital: The next trillion-dollar company won't sell software, but will directly sell outcomes

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

Author: Julien Bek

Translation: Deep潮 TechFlow

Deep潮 Guide: Sequoia Capital partner Julien Bek wrote a clear framework article with the core argument: the next trillion-dollar company won’t sell software tools but will directly sell work outcomes. For every $1 spent on software, companies spend $6 on services. When AI makes “doing work” nearly free, the real opportunity isn’t in Copilot (assistive tools) but in Autopilot (automatically completing tasks).

He dissects automation opportunities across insurance, accounting, healthcare, legal, IT, procurement, recruiting, consulting, and more, accompanied by an opportunity matrix chart plotted along “Intelligence vs. Judgment” and “Outsourcing vs. In-house” dimensions. This provides valuable insights for AI entrepreneurs and investors alike.

Full Text:

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

Every founder building AI tools is asking the same question: what if the next version of Claude turns my product into a feature? This concern is valid. If you’re selling tools, you’re racing the model. But if you’re selling the work itself, each AI advancement makes your service faster, cheaper, and harder for competitors to replicate. A company might spend $10,000 annually on QuickBooks and $120,000 on accountants to close books. The next legendary company will directly handle your accounting.

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 ultimately rule-based. Judgment is different. It requires experience and taste, intuition built over years of practice. Deciding what features to develop next, whether to incur technical debt, or when to release before being fully ready.

A year ago, most Cursor users used AI for autocomplete. Today, more tasks are initiated by agents than humans. Software engineering accounts for over half of AI tool usage across professions, with other categories still in single digits. The reason is that software engineering is mainly intellectual work. AI has crossed that line—it’s capable of autonomously completing most intellectual tasks, leaving judgment to humans. Software engineering was the first to reach this point, but it will spread to every profession.

Caption: AI tool usage across professions, with software engineering far surpassing others.

Copilot and Autopilot

Copilot sells tools. Autopilot sells work.

Until recently, AI models were still developing in both intelligence and judgment. The correct approach was to start with Copilot: put AI into the hands of professionals, letting them decide how to use it. Harvey sells to law firms, Rogo to investment banks. Professionals are the customers; 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 directly to Autopilot. Crosby sells to companies needing NDA drafting, rather than external legal advisors. WithCoverage sells to CFOs needing insurance, not insurance brokers. Customers are buying results directly. In any profession, work budgets far exceed tool budgets, and Autopilot can capture the work budget from day one.

The higher the proportion of intelligence in a domain, the faster Autopilot will win.

Integration

Today’s judgment will become tomorrow’s intelligence. As AI systems accumulate proprietary data on what good judgment looks like in their fields, the frontier shifts. Copilot and Autopilot will converge. The transition from Copilot to Autopilot has already begun in several categories. But the starting point matters because it determines where Autopilot can now win customers and begin accumulating the data that will eventually enable it to handle judgment tasks.

Autopilot Entry: Outsourcing as a Gateway

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

The TAM for Autopilot is the total labor expenditure in a category, combining internal and outsourced work. But the best starting point is where outsourcing already exists.

If a task is already outsourced, it tells you three things: First, the company accepts that this work can be done externally. Second, there’s a ready budget line that can be cleanly replaced. Third, the buyer is purchasing results. Replacing an outsourcing contract with an AI-native service provider is a vendor switch; replacing internal staff is organizational restructuring.

The strategy is to start with outsourced, knowledge-intensive tasks. Manage the distribution. As AI accumulates data, expand into internal, judgment-intensive work. Outsourced tasks serve as wedges; internal work represents the long-term TAM.

Crosby begins with NDA drafting: a well-defined, primarily intellectual task that most companies already outsource to external lawyers. Budget is clear, scope is defined, ROI is immediate, and replacement is frictionless.

Opportunity Map

Plot each service vertical along the “Intelligence to Judgment” spectrum and “Outsourcing to In-house” ratio to create a priority map. The following is not exhaustive.

Caption: Opportunity matrix for Autopilot across service verticals (based on intelligence/judgment ratio and outsourcing/in-house ratio).

Insurance Brokerage ($140-200 billion).

The largest market on this list. Standard commercial insurance is highly standardized: brokers’ value is essentially in comparing quotes and filling forms—purely intellectual work. Distribution is highly fragmented, with thousands of small brokers running similar processes, none controlling the customer relationship. WithCoverage and Harper are interesting new entrants.

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

Over the past five years, the US lost about 340,000 accountants, even as demand grew. 75% of CPAs are nearing retirement; licensing paths are long, and starting salaries lag behind tech and finance. This structural shortage is pushing accounting firms to adopt AI faster than almost any other profession. Rillet is building AI-native ERP for direct bookkeeping. Basis starts with AI-assisted accounting copilots.

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

People think of healthcare as judgment-intensive, but billing is almost purely intellectual. Medical coding involves translating clinical notes into about 70,000 standardized ICD-10 codes. Rules are complex but ultimately rule-based. Outsourcing is mature and billed by results. Autopilot only needs to do the same work at lower cost. Anterior has gone the furthest.

Claims Adjustment (including TPA, $50-80 billion).

On the insurance policy side, claims adjustment is another autonomous Autopilot scenario. Standard claims are adjudicated based on policy language and damage lists, with reserves set using actuarial tables. The claims adjuster workforce is aging, with no new recruits. The market heavily outsources to independent adjusters and TPAs like Crawford and Sedgwick. One industry, at least two Autopilot opportunities: Pace is building claims processing Autopilot; Strala is developing AI-native TPA.

Tax Advisory ($30-35 billion).

CPA licensing creates a regulatory moat, but 80-90% of underlying work is intellectual. Tax Autopilot becomes more valuable as it covers more jurisdictions, deepening the data moat. Multi-jurisdiction complexity is precisely why SMEs outsource—no internal accountant can cover everything. TaxGPT is an early entrant; Europe has Skalar and Ravical.

Legal Work ($20-25 billion).

Contract drafting, NDAs, regulatory filings: high in intellectual content, often outsourced routinely. Work output is standardized enough for quality to be verifiable, allowing buyers to trust AI-generated results without deep legal expertise. Harvey is emerging as a leader, rapidly shifting toward Autopilot; Crosby and Lawhive are new entrants built as Autopilot-native.

IT Managed Services ($100+ billion).

Every SME outsources IT. Patching, monitoring, user configuration, alert routing: repetitive intellectual work across identical environments. Existing software layers (ConnectWise, Datto) sell tools to MSPs. No one yet sells “your IT is up and running” as a result. Edra automates IT workflows; Serval automates IT support.

Supply Chain and Procurement ($200+ billion).

Most companies only negotiate seriously with their top 20% suppliers. Long-tail suppliers are unmanaged because it’s not cost-effective. Contract leaks account for 2-5% of procurement spend. Entry points are the neglected tasks: no budget lines to argue over, no incumbents to replace—just easy money. Magentic develops direct procurement AI; AskLio handles indirect procurement. Tacto builds record systems and Copilot for mid-market simultaneously.

Recruiting and Staffing ($200+ billion).

The largest service market on this list. The top of the recruiting funnel (screening, matching, outreach) is purely intellectual, but closing and cultural fit assessments rely on years of pattern recognition—judgment. Autopilot’s entry point is high-volume, low-judgment roles where matching is standardized. Juicebox, Mercor, Jack & Jill are emerging leaders across the spectrum.

Management Consulting ($300-400 billion).

A huge market, but work is mainly judgment-based. An interesting question is whether AI can decompose consulting into intellectual components (data collection, benchmarking) and judgment components (strategy advice), automating the former while leaving the latter to humans. The best candidates are yet to be determined.

By 2025, the fastest-growing AI companies will be Copilot. By 2026, many will attempt to evolve into Autopilot. They have product and customer awareness. But they also face an innovator’s dilemma: selling work means pushing clients out of their traditional workflows. This is the window of opportunity for pure Autopilot companies.