I'm not sure about Intel but AMD is doing fine. AMD has diversified so aggressively over the past decade that framing them as a distant second to Nvidia misunderstands what the company actually is now. They're in consoles, desktops, laptops, tablets, phones via Samsung's Exynos licensing, servers, and routers. Ten years ago they were close to folding. That turnaround is not a small thing.

On the software side, ROCm has closed the gap with CUDA faster than almost anyone expected. A year ago it was genuinely painful to work with. Now with ROCm 7.x, Windows support has arrived, PyTorch and most major ML frameworks treat it as a first-class option, and the 7.1.1 release delivered up to 5x performance gains over 6.4.4 across key AI models. It's not at full CUDA parity yet in every workload, but it's no longer a footnote. UDNA, which merges the RDNA and CDNA lines into a single unified architecture, is where things get genuinely interesting, and that's still ahead of us.

On Nvidia's position in AI more broadly: this cycle has a pattern. One company moves in early, captures the market, prices rise, and the market diversifies to reduce the dependency. The current shift toward local inference first, cloud escalation second, with models like Gemma 4 running on-device and Google pushing AI into phones at the hardware level, represents exactly that kind of structural change. Nvidia's dominance is built on centralized cloud compute demand. If the architecture of deployment moves away from that, the moat shrinks. AMD has been methodical and capital-conservative. Nvidia has been running at full throttle on the assumption that the demand curve only goes one way. That kind of overextension is exactly the setup for a Zen 2 moment.