AI Notebook · Live systems map
The AI Systems Map
Every layer of the AI value chain as one dependency graph: from the frontier labs at the top down through cloud, chips, foundries, equipment, memory, materials and minerals, to the energy, data centers and capital the whole thing rests on. Trace what any company depends on, what feeds it, and where the chokepoints are.
Entities
455
Dependencies
1161
Chokepoints
121
Layers
7
Frontier labs & models
The demand layer: the labs racing to build frontier models, and the models themselves.
123 entities
Cloud & compute
Hyperscalers and neoclouds that rent the compute the labs train and serve on.
32 entities
Accelerators & chips
GPU, TPU and custom-silicon designers: the engines of AI compute.
54 entities
Foundries & packaging
The factories that physically manufacture and package the chips.
30 entities
Equipment, EDA & memory
The machines, design software and high-bandwidth memory the fabs and chips depend on.
45 entities
Materials & minerals
Wafers, photoresist, gases and the critical minerals mined at the bottom of the stack.
39 entities
Energy, infrastructure & capital
The power, data centers, networking, capital and policy that the whole stack rests on.
132 entities
Hover any entity to light up what it depends on and what it feeds. Tap to open its topic: story, 3D placement in the stack, and the news attached to it. ⬦ marks a supply-chain chokepoint.
The chokepoints
A handful of single points of dependency hold up the entire stack. Remove any one and the frontier stalls, which is exactly why they define the geopolitics of AI.
Training Data & Web Corpus
The raw material of models
Scale AI
Human data labeling, RLHF, and eval provider (Meta-invested)
Common Crawl
The open web corpus underlying most LLM pretraining
NYT v. OpenAI/Microsoft (copyright)
Landmark litigation defining training-data legality
AI Copyright Litigation (NYT et al.)
IP lawsuits reshaping training-data rights
Apple Intelligence / On-Device AI
Edge AI and the device-distribution chokepoint
ChatGPT (Consumer Distribution)
800M-user funnel, the AI consumer default
AI Talent Market / Researcher Comp
$100M+ packages as a scarce-input chokepoint
Content & IP Licensing Deals
Publisher/media data licensing for training & grounding
Content Licensing & Copyright Regime
Publisher licensing deals & EU/US copyright constraints on training
Inference Economics / Token Margins
The unit economics that decide who profits
Enterprise Data Cloud (Snowflake/Databricks)
Where regulated enterprise data & AI converge
Arm Holdings
CPU instruction-set and IP licensor underpinning most custom AI-server silicon
Nvidia
Dominant AI GPU designer and de facto industry chokepoint
CUDA / software moat
The lock-in layer of the accelerator stack
Nvidia Data-Center GPU (Blackwell/Rubin)
The GPU product line that is the default AI training and inference accelerator
NCCL (NVIDIA Collective Comms)
The collective-communications library gluing multi-GPU training
MLIR / OpenXLA / StableHLO
Compiler IR layer decoupling models from accelerators