CIO AI
2026-06-29 11:00 UTC
Score 45.0
USR-0125-20260629-global-ai-ne-d15412c9
Full article
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. The model is becoming a commodity Let us consider economics. Gartner projects that by 2030, performing inference on a trillion-parameter model will cost providers more than 90% less than it did in 2025, with models becoming up to 100 times more cost-efficient than the earliest versions of comparable size. When the cost of the underlying capability collapses by that magnitude, it stops being a differentiator. Anything that gets that cheap, that fast, is not where compet…