Simon Leyland

Sequoia's thesis that the next $1T company will sell work, not software, is the most important reframe in AI right now. The argument: if you sell a copilot, you're competing with every new model release. But if you sell the outcome — books closed, contracts reviewed, claims handled — every AI improvement makes your margins better, not your product obsolete. The key insight most people miss: for every $1 spent on software, ~$6 is spent on services. The entire SaaS playbook was about capturing the software dollar. The AI playbook is about capturing the services dollar — at software margins. Not "AI for accountants." The AI accounting firm. Not "AI for lawyers." The AI law firm. The companies that figure this out won't look like SaaS companies. They'll look like services firms rebuilt on software infrastructure. That's a fundamentally different company to build, fund, and scale. And most founders are still building copilots.

Amazon's internal metrics said customers waited under 60 seconds for customer service. Jeff Bezos picked up the phone in a meeting and waited more than 10 minutes. The head of customer service had been defending the number. Bezos said "Ok, let's call." He dialed Amazon's 1-800 line on speaker. The room sat there for over ten minutes before a rep answered. The metric didn't survive the meeting. Bezos has a saying: when the data and the anecdotes disagree, the anecdotes are usually right. Metrics don't measure reality. They measure what you designed them to measure. Customer service dashboards commonly filter out abandoned calls, cap hold time at the IVR timeout, and start the clock after the menu tree completes. Every one of those choices pushes the average down. The customers hanging up at minute 9 are not in the denominator. The 60-second number was technically accurate and practically wrong. That call broke through a defended metric in a way no spreadsheet could have. The head of CS had dashboards and a team whose job was to report that number going down quarter over quarter. Bezos had 10 minutes of hold music and a room full of people watching. This is the executive test almost nobody runs. Call your own 1-800 line. Try to buy your own product in incognito. Every senior leader can do it in under 15 minutes. Almost none do, because the dashboards feel like the truth and the dashboards say things are fine. The measurement got redesigned. Wait times actually fell. When your data says you're winning and your customers say you're losing, the customers are right. The data was built by people whose job depends on it going down.

Agents are going to use software 100X more than people will in the future. As a result, enterprise platforms will become headless and be able to work with any agent on or off platform. If you don’t do that you’re DOA. What some have missed is that this creates vastly more use-cases for these platforms than even existed pre-AI. This isn’t zero sum. Software value props have traditionally been capped at the number of users you have in a company. Agents have no upper limit. We’re going to run agents to process data at a scale humans never could, they’re going to be running 24/7 in parallel doing work for us, and they can integrate workflows across systems to generate all new value propositions. Once you embrace this approach, it becomes obvious how much more upside there is.

The hyperscalers have already outspent the most famous US megaprojects

Sequoia's thesis that the next $1T company will sell work, not software, is the most important reframe in AI right now. The argument: if you sell a copilot, you're competing with every new model release. But if you sell the outcome — books closed, contracts reviewed, claims handled — every AI improvement makes your margins better, not your product obsolete. The key insight most people miss: for every $1 spent on software, ~$6 is spent on services. The entire SaaS playbook was about capturing the software dollar. The AI playbook is about capturing the services dollar — at software margins. Not "AI for accountants." The AI accounting firm. Not "AI for lawyers." The AI law firm. The companies that figure this out won't look like SaaS companies. They'll look like services firms rebuilt on software infrastructure. That's a fundamentally different company to build, fund, and scale. And most founders are still building copilots.

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