Sequoia Is Wrong: Services Aren’t the New Software

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Sequoia Is Wrong: Services Aren’t the New Software<br>The Physical World Is

Guillermo Flor<br>Jul 30, 2026

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Sequoia recently told the market that the next trillion-dollar AI companies will sell work, not software. Instead of charging for software seats, they will complete the work itself and capture a share of the much larger global labor budget.<br>A few weeks ago, I broke down why that thesis is so compelling.

Guillermo Flor@guilleflorvs

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

5:54 PM · Apr 15, 2026 · 2.14M Views

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Then Andrew Côté published a rebuttal that exposes the uncomfortable weakness inside it:<br>“Using AI for a task trains AI to do that task, so every services niche eventually becomes a niche the model owns outright .”

That is not always technically literal. Enterprise providers may not train on customer data, companies may self-host models, and proprietary workflows do not automatically flow back to foundation-model labs.<br>But economically, the direction is the same: as foundation models improve, more of the value created by thin AI service layers becomes commoditized.<br>That breaks the simplest version of Sequoia’s thesis.<br>So where does the durable value go?<br>I pulled apart both theses to map where the money may actually go next, including physical markets where a single industrial process can represent hundreds of billions of dollars in annual activity.<br>In this issue you’ll find:<br>Why “sell work, not software” may train its own killer

Software investing is close to over

The five enormous atoms markets Côté is pointing toward

How AI and robotics could bring software-like leverage to physical industries

Who is already building the atoms version of this thesis

What founders and investors should start, back or partner with before this becomes consensus

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1. Why “sell work, not software” trains its own killer

The flaw in Sequoia’s “sell work, not software” thesis is captured in Côté’s line:<br>“Using AI for something trains it to do that task.”

Again, this should be understood as an economic principle, not always as a literal description of model training.<br>A customer interaction does not necessarily become a training example sent directly to OpenAI, Anthropic or Google. But every successful AI workflow reveals what can be automated, which tools are required, where humans intervene and what a correct output looks like.<br>That knowledge spreads.<br>Model providers improve their reasoning and tool use. Competitors replicate the workflow. Open-source models catch up. Customers learn to perform more of the process themselves. What was once a differentiated service gradually becomes a native model capability or a standard feature.<br>Côté’s conclusion is that “every possible niche where you can use AI to do something better eventually becomes a niche occupied by AI. ”<br>That does not mean every AI services company disappears. It means the service itself is not automatically the moat.<br>An AI services business that wraps a foundation model and sells its output may initially capture attractive margins. But its advantage often comes from a temporary gap between what the customer can do directly and what the company can orchestrate on the customer’s behalf.<br>That gap closes on the model’s schedule, not the startup’s.<br>Sequoia’s own feed illustrates the direction. Promoting Factory AI’s Matan, the firm wrote that almost every token today begins with a human asking a model to do something, but that within 12 to 24 months, 90% of tokens could be machine-initiated.

That human typing “hey, go...