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Editorial narrative buckets — the way we talk about pieces of the AI supercycle. Themes are curated groupings used for screens; they exist alongside bubbles, which are validated empirically by 252-day capital-flow co-movement. When a theme's avg correlation is high, the editorial story is also tradeable as a bloc.
Launch, satellite comms, earth observation.
As AI clusters scale, copper hits physical limits and the next bottleneck becomes optical infrastructure. Lasers, transceivers, optical engines, specialty glass, and the test/burn-in capacity behind it.
UAVs and counter-UAS — defense and commercial.
Reactors, SMRs, uranium miners and enrichment.
Server CPUs, packaging and ARM-based silicon — the non-GPU compute layer.
AI-native cloud / GPU-as-a-service operators.
Power generation, fuel cells, distribution for AI datacenters.
Foundries, memory, lithography — the AI silicon supply chain.
Enterprise software and platforms productizing AI for end-users.
Custom silicon and connectivity for inference workloads.
Edge / serverless / dev infra enabling autonomous AI agents.
Robotics, autonomy, manipulators — AI in the physical world.
GOOGL admitted Google Cloud is leaving revenue on the table because it cannot build capacity fast enough. The bottleneck has shifted from chips to deployment — value accrues to operators with the power, real estate, and operational scale to actually deliver AI compute. These names anchor multi-billion-dollar contracted backlogs from hyperscalers (MSFT, META, GOOGL, AWS) and frontier labs (OpenAI, Anthropic), turning grid-connected MW and GPU clusters into long-dated revenue.
Software platforms productizing AI for defense, intelligence, and battlefield decisions.
Primes building the next-gen US missile defense and Golden Dome architecture.
Naval platforms — surface combatants, submarines, and shipyards.
Rare-earth and strategic materials supply chain — the substrate of defense and AI hardware.
Power conversion and management silicon — the unloved bottleneck of the AI buildout.
The software layer powering next-gen robots in 2026. Generalist vision-language-action (VLA) models that train across embodiments — humanoid, quadruped, manipulation arms — are the bottleneck shifting value from hardware to the "robot brain". The investable public surface is dominated by NVDA (Isaac sim + Cosmos + GR00T) and the mega-cap diversifieds (GOOGL Gemini Robotics, MSFT/OpenAI–Figure axis, AMZN industrial fund, TSLA Optimus vertical, BIDU Apollo). The actual technological leaders — Skild AI (omni-embodied brain) and Physical Intelligence (π0/π0.5 manipulation models) — are still private, alongside the humanoid platforms (Figure, 1X, Apptronik, Agility) consuming those models. Tracked as an editorial theme rather than a bubble because the public basket would beta-drift into AI Compute / Hyperscalers.