Cohere has shared eight observations on how companies build lasting competitive advantage, or moats, around generative AI products, drawing on recent commentary from the company's blog and accompanying video content.
Why Moats Are Harder to Define in AI
The generative AI market has been marked by rapid model commoditization: capabilities that were differentiating a year ago are now available through multiple providers at falling prices. That dynamic has pushed the debate about defensibility away from raw model access and toward other levers, including proprietary data, workflow integration depth, distribution advantages, and switching costs built into how a product is used day to day.
What This Means for Builders
For teams building on top of foundation models rather than training their own, the practical question is less about which model is used and more about whether the surrounding product creates value that persists even as the underlying model layer keeps shifting. Cohere's framing suggests that companies betting purely on model quality as their differentiator are building on the least stable part of the stack.