AI changed what a product is

AI agents, MCP, and open protocols broke the assumption that products have edges. The entire go-to-market stack—positioning, competitive analysis, pricing, sales enablement—assumes a bounded thing. Something you can draw a box around, position, price, sell.

An agent discovers your API and wires it into workflows you didn’t design for. Your product becomes one node in a chain that didn’t exist yesterday. Surface area is emergent, not shipped. The agent defines it at runtime.

You can’t position a moving target. The competitor isn’t just the category anymore. It’s anything in the agent’s toolkit that approximates the same function.

Subscription and per-seat pricing assume human purchasing decisions, but agent-mediated usage is bursty and autonomous. Your sales motion now has two buyers: the human with budget and the developer or agent choosing tools. And your analytics show what the agent does, not what the human values. Product-market fit gets harder to read.

Old moats erode fast when agents swap tools per-call with no loyalty. Features, brand, switching costs. None of them hold. Data quality, reliability, and composability depth do. Trust does too—but when the buyer is an LLM, who evaluates trust?

The API surface is the product now. Features matter less than reliability when the buyer never sees a UI. And the real leverage is the curation layer—tool registries, agent defaults, discovery protocols. Whoever writes those defaults is doing distribution whether they know it or not.