(NVDA) NVIDIA's biggest news on their investor call? CPUs. No, not the Nano line. Think like Apple silicon. Faint analyst coverage. Yet this is bigger news than Chinese exports or even Vera Rubin for them. $200B-$1T+ TAM for their taking. Especially if they move to RISC-V in the future.
Musings
2026 MAY
HiPEAC has a call for posters; see you in London! https://www.hipeac.net/~jasoncbraatz/#/
A vision-language model fine-tuned on your CAD library and a year of factory-floor footage is a digital-twin starter kit on rails. It catches the gap between as-designed and as-built — the gap that eats manufacturing margin — without instrumenting a single new sensor.
Kolmogorov–Arnold networks run slower than MLPs at inference. In regulated industries — banking, pharma, defense — that trade is worth it: KAN edges are fixed, learnable functions you can read. MLP weights are an opaque chord. When auditors come knocking, an open book beats a black box.
A quantized 12B-parameter SLM with full PubMed RAG runs on a Jetson-class board. Doctors Without Borders, off-grid, 400:1 patient ratios, no internet. The frontier is not always where you think it is — sometimes it is exactly where the cloud cannot reach.
Most SMB AI projects start life as fine-tuning and end as RAG. Fine-tuning teaches the model to sound like you; RAG teaches it to know what you know. Almost everyone needs the second. They rarely need the first.