The technologies still finding their category - the frontier beyond the frontier.
Uppsala researchers propose streaming LLM weights through spooled fiber instead of HBM. The design is a sketch; the DRAM market that provoked it is very real.
NVIDIA specs every MGX rack (GB200, GB300, Vera Rubin) for 45°C inlet water. Warm-water cooling is a decade old; the default on 200 kW-class racks is what's new.
Since Starcloud flew the first H100 last November, SpaceX, Blue Origin, Starcloud, and a five-month-old startup have filed with the FCC for constellations totaling more than a million satellites. The physics hasn't moved as fast as the paperwork.
HBM4 doubles bandwidth per package and still can't share capacity across a rack. The two architectural answers, pooled DRAM over CXL and compute pushed next to memory, are at very different stages of readiness.
By April 2026, Amazon's random-graph fabric had become the default for most new AWS datacenters. The efficiency claims behind it are still Amazon's own, with no independent benchmark yet.
At Computex 2026, Wiwynn and eight ecosystem partners showed a full optical scale-up rack built around compute-side optics. It is a useful moment to separate two technologies the supercomputing industry keeps filing under one acronym.
Normal Computing's CN101 is in characterization. Extropic has a prototype platform, an MIT-co-authored arXiv preprint, and an ETH Zurich hackathon in June. After two years as a manifesto, thermodynamic computing is producing the kind of artifacts readers can evaluate.
Hardware has reached orbit and SpaceX has filed for a million-satellite constellation. Thermal physics, launch cadence, and bandwidth still push gigawatt orbital AI to post-2030, at best.
The GCSA produced principles, not production targets. The Advanced Materials Act decides whether European fabs get domestic substrates.
IBM and Dallara published GIST, an AI surrogate for motorsport CFD. Neural Concept, Ansys SimAI, and NVIDIA reached production deployment first.
Anthropic’s 2.9× to 51.9× training-optimization curve signals that AI training infrastructure is becoming machine-optimizable, raising rebound demand and control-plane risks for HPC operators.
