The Question Most Companies Get Backwards When Evaluating an AI Project
Comparing AI vendors by which model they use is a common starting point, but it answers a smaller part of the question than most buyers assume. Here's what to evaluate instead. Nvidia published a result recently that's worth pausing on: the same underlying model produced wildly different outcomes on a difficult reasoning benchmar k, once with a purpose-built layer of engineering wrapped around it and once without, and the gap between those two outcomes was larger than the gap between most competing models on the market today. Researchers described that surrounding layer, the tools, memory handling, and coordination logic that turn a raw model into something that can act reliably over a longer task, as the real driver of performance, more so than the model doing the reasoning underneath it. That single result is a useful jumping-off point, but the more important conversation isn't really about that benchmark, or even about Nvidia's research specifically. It...
