The Boring Businesses That Will Dominate the AI Era

Tina He is back at Every. The writer and investor—now at Pace Capital—explored what happens when AI agents become your primary users last spring. Now she’s going deeper: In a world where AI picks vendors based on ruthless logic, she identifies five kinds of businesses that become irreplaceable. These aren’t the companies with the best models or the slickest interfaces—they’re the ones that own the boring yet essential infrastructure AI must flow through but cannot replace. If you’re building for a future where your customer is an algorithm, this is your map.—Kate Lee

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At 2:17 a.m., your customer fires you and your customer relationship management software. The reason? Their sales rep calculated that your CRM’s $30,000 annual contract only delivers $12,000 in value. There was no meeting and no negotiation—because the sales rep was an AI agent.

Agents don’t need training like human users of software, and they don’t have loyalty. They evaluate economics in milliseconds and switch the moment it makes business sense, even in the middle of the night. It’s pure ruthlessness.

When AI models commoditize—when the latest GPT, Claude, and Gemini models are all making similar decisions based on similar capabilities—the competitive edge for companies shifts from having the best model to having the infrastructure between algorithmic decisions and real-world consequences. This is the infrastructure that AI can’t route around: the boring, essential systems that control access to data agents need, workflows they must execute, and regulations they can’t bypass. And the next few years will determine who owns this layer while everyone else is still building better AI agents.

This infrastructure breaks down into five archetypes. I’ll show you how to recognize which one fits your business, where there are opportunities to build outsize businesses, and how to build a competitive advantage before this window closes. If your edge is a model or a coat of user interface like an app or chatbot, it isn’t a moat. What endures is infrastructure AI must flow through, but cannot replace.

The new infrastructure for software

Before we get into the first archetype, there’s one shift to explain. Think of it like the early internet: Before companies could build Google or Amazon, we needed HTTP and TCP/IP—the invisible communications rules and protocols that let computers talk to each other. We’re in that moment again, but for AI agents. New protocols—AG-UI (agent-user interaction) for agents talking to users, Google’s A2A (agent-to-agent) for agents talking to each other, MCP (model context protocol) for agents using tools—have become the standard pipes and wires. The archetypes I’ll walk through next are maps to where businesses are built as this infrastructure solidifies.

When the AI itself becomes cheap and interchangeable, the edge becomes what you feed it and what you can do with its outputs—data it can’t find elsewhere, and actions it can’t take alone.

Knowledge compounders: Data that learns while you sleep

The first winners are what I call knowledge compounders. These companies control organized data that agents need and that improves continuously through real-world usage, and requires years of operations to replicate. This data isn’t widely available, so most AI agents wouldn’t have it (and it hasn’t been used to train the major models).

Every time a player makes a strategic move in a video game or a doctor confirms that a diagnosis made by AI is indeed correct, the dataset grows stronger. That learning stacks up in ways that competitors can’t shortcut through capital alone (similar to the concept of compound engineering).

The best companies in this archetype create environments where customers unknowingly generate training data through usage. Medal is a platform where gamers can record and share clips of gameplay. Medal’s community creates over 2 million gaming clips per day—more than 700 million clips per year—each rich with signals about player behavior, reaction timing, and strategic decision-making. At typical data-collection and labeling costs, building an equivalent dataset from scratch would require hundreds of millions of dollars, yet Medal’s users generate it organically in their quest for recognition.

Another example of a company building an edge in data is Mercor, which provides feedback from experts such as lawyers and doctors to AI labs to help improve their models. Even if each generation of frontier models is getting better, in domains such as medicine, law, or finance, the cost of errors is high enough that “good enough” AI isn’t acceptable, and ongoing human judgment is essential. Mercor is betting that demand for human-in-the-loop verification persists even as raw capabilities improve.

Knowledge compounders aggregate human judgment at scale, like gamers expressing preferences or doctors confirming diagnoses. When AI agents need to check if their outputs match reality, not just sound plausible, they have to consult these datasets. The alternative is hallucination.

Examples: Explorium (B2B data infrastructure), Mercor (human-in-the-loop verification), Medal.gg (user-generated training data)

Become a paid subscriber to Every to unlock this piece and learn about:

The four other archetypes that win when AI is your customer
Why one workflow platform’s valuation jumped from $300 million to $2.5 billion while everyone else chased better models
What a fintech collapse reveals about the only real moat in the agent economy