When you read that headline, it probably sounds like former Intel CTO and CEO Gelsinger’s got a case of sour grapes. Of course, the man’s far more mature and experienced than that. His comments came while speaking to the Acquired podcast as an invited guest at NVIDIA’s GTC 2025 conference, where the GPU vendor unveiled Blackwell Ultra GB300. Gelsinger, speaking in the pre-show podcast, said this to the hosts:
Jensen and I had numerous conversations about ‘throughput computing’—today we refer to it as ‘accelerated computing’—versus scalar computing. You know: branch prediction, and short latency pipelines versus, “hey, who cares how long the pipeline is? just maximize throughput and create the programmability.” And obviously at the time, the CPU was the king of the hill, and I applaud Jensen for his tenacity in just saying, “No, I am not trying to build one of those; I am trying to deliver against the workload starting in graphics” and, then, it became this broader view, and then he got lucky, right? with AI.
In the interview, Gelsinger goes on to say this after another question from the hosts:
Today, if we think about, for instance, the training workload, okay—but that’s got to to give way to something much more optimized for inferencing. You know, a GPU is way too expensive; I argue it is 10,000x too expensive to fully realize what we want to do with the deployment of inferencing for AI, and then, of course, what’s beyond that?
In other words, if AI is going to proliferate the way the tech giants want it to, there’s going to have to be devices that are capable of rapid AI inference at low power, and crucially, low cost. Gelsinger stops short of saying what type of processors those might be, but he could be talking about “NPUs”—slimmed-down ASICs dedicated to the task of performing AI inference.
The whole interview is pretty interesting and worth watching. There are many other guests from across the industry who have relevant insights on the future of technology and AI.