Over the past two years, working inside the enterprise AI infrastructure world, tracking where the industry is heading, I have noticed the same question surface repeatedly: should we build our own large language model? I understand the instinct. The model feels like the thing, the engine, the brain, the asset worth owning. But after significant years as a product manager in the AI world in both customer experience and grounding infrastructure I concluded that it tends to unsettle the room: the model is the least durable part of your AI strategy. I say this not to be provocative, but because over the last few years we have seen organizations pour their scarcest resources, executive attention, engineering talent, capital, into the one layer of the stack that is commoditizing fastest. Meanwhile, the layer that determines whether their AI is trustworthy, accurate and defensible gets treated as plumbing. That inversion is, in my experience, the single most expensive mistake enterprises are making with AI right now.

Full opinion : Models are just rented, fast-depreciating engines that everyone has access to. Your actual AI moat is the custom data grounding pipeline you build around them.