AI Companies Have a Distribution Problem Disguised as a Model Problem.

Every AI company's pitch deck has a slide about the model. Few have a slide about how customers will find out the model exists.

That imbalance made sense for a few years. Capability was scarce, demonstrations were astonishing, and a product that worked could find its first thousand users through a launch post and a waitlist. Those years are over. Capable models are available to everyone, including the company's competitors and, increasingly, its customers. The scarce input has moved.

What is scarce now is distribution: a repeatable way to reach a specific buyer, earn enough trust to be tried, and become part of a workflow before the next model release makes the current one look ordinary.

The three questions that replace "how good is the model"

Whose workflow do we own? An AI product that assists a task is a feature waiting to be absorbed by whichever platform already owns the task. An AI product that becomes the place a task is done, with the data, the history, and the habits that accumulate there, is a business. Distribution should be aimed at the buyer whose workflow the product can plausibly own, not at everyone who might find it interesting.

What has to be true for a buyer to trust it? AI products ask buyers to accept outputs they cannot fully check. In regulated or high-stakes categories, that trust is the actual product, and it is earned through references, proof of process, and visible restraint about claims, not through demonstrations. This is closer to fintech GTM than to consumer software, and the channels that carry it are the same: partners, practitioners, and people the buyer already believes.

Which channels can carry the claim? AI advertising is crowded, expensive, and increasingly policed for unverifiable claims. A product whose central promise is "better outputs" is competing in the most crowded auction in software. Meanwhile, the channels that explain rather than assert, creators who show workflows, partners who integrate, communities of practitioners who compare tools in public, are thinly bid, because they take work.

The wedge, again

The same logic that governs any GTM applies with more force here. A narrow buyer with an expensive version of the problem, an urgent reason to act, and a workflow the product can own, beats a broad audience who finds the demo impressive. The narrower the wedge, the cheaper the attention, because fewer competitors are bidding for that specific buyer, and the more defensible the position, because the product accumulates something the next model cannot replicate: context.

Building distribution that survives the next release

A distribution system for an AI company should assume that the model advantage is temporary and build for what persists.

Practitioner communities and creators, because they explain, compare, and recommend in ways a platform ad cannot. Partner and integration programs, because a product embedded in a customer's existing tools is harder to replace than one visited in a browser. Owned content and email, because they compound and cannot be revoked. Performance terms wherever an outcome can be observed, because a product that converts should pay for outcomes and a product that does not should learn that quickly.

And measurement the company controls, because the platforms' own reporting will describe the product's advertising, not its adoption.

The uncomfortable version

Many AI companies do not have a model problem or even a product problem. They have a customer they cannot name precisely, a claim the crowded channels cannot carry, and no plan for the channels that could. The model will keep improving. That will not fix it.

lowob takeaway: When capability is available to everyone, distribution is the advantage. Choose a workflow to own, earn trust the way regulated products do, and build the channels that explain rather than assert.