Spend an afternoon looking at new AI products and a pattern gets hard to unsee. A huge share of them are a thin layer over the same handful of frontier models, plus a prompt, a UI, and a niche. The clearest example is the ai writing tool category, where a dozen products are functionally the same model with different onboarding, but it’s true across chatbots, deck makers, and “agents” too. The context that makes this interesting: if the intelligence itself is a commodity that anyone can rent through an API, then the model is no longer the moat. It’s an input everyone has equal access to. Which means the actual competition moved to the parts nobody likes to talk about, distribution, workflow lock-in, proprietary data, and how little the output looks like everyone else’s. There’s a real strategic question under this. In most software eras the technical core was the defensible thing. Here the technical core is the one part you don’t own and can’t differentiate on, because your competitor is calling the same endpoint. So the value has to live somewhere else or it doesn’t exist, and a lot of these companies are one price change from their supplier away from having no business. My take is that “wraps a model” stopped being an insult and became the actual shape of the industry, and the winners will be decided by boring things like retention and specific data, not by whose model is two points better on a benchmark. Where do you think the durable moats actually are once the model is a shared commodity? Is it data, distribution, workflow, or is the honest answer that most of this layer just gets absorbed by the labs themselves? submitted by /u/Civilmats_992
Originally posted by u/Civilmats_992 on r/ArtificialInteligence