GPU

Plain English. A GPU (graphics processing unit) is the general-purpose workhorse of AI. Built originally to render graphics, its knack for doing thousands of calculations in parallel turned out to be exactly what training and running neural networks need. Nvidia's GPUs are the default hardware for nearly all frontier AI.

Why it moves money. The GPU is the picks-and-shovels trade of the AI boom, and Nvidia's dominance rests less on the silicon than on CUDA — the software layer developers have spent fifteen years building on. That lock-in is the moat: it is why buyers tolerate the pricing and why every rival must match not just the chip but the ecosystem. The flip side is concentration risk — a huge share of AI capex flows to one vendor. The alternatives are Google's in-house TPU and fixed-function ASICs; labs weighing them is the story of custom silicon.

What to watch. Any real erosion of the CUDA moat — credible portability layers, or large buyers shifting workloads to rival hardware — and whether demand stays supply-constrained. A thinning moat would matter more than any single competing chip.

From the signals. Mark's call: CUDA's moat could thin faster than the chip cycle. AMD argues it is agents, not silicon, that will close the CUDA gap. Nvidia guided to 70% revenue growth and said supply, not demand, is the cap.