[
 {
  "id": "models",
  "name": "Models and thresholds",
  "thesis": "Capability levels the rest of the map is measured against: reasoning, perception, reliability, and the AGI and superintelligence thresholds.",
  "tier": "foundation"
 },
 {
  "id": "algo",
  "name": "Algorithms and learning",
  "thesis": "How models are trained and improved: pretraining, reinforcement learning, test-time compute, efficiency gains and continual learning.",
  "tier": "foundation"
 },
 {
  "id": "data",
  "name": "Data",
  "thesis": "What models learn from: human writing, synthetic data and traces of experience, and the rights to use them.",
  "tier": "foundation"
 },
 {
  "id": "measure",
  "name": "Measurement and understanding",
  "thesis": "Verifiers, evals and interpretability: knowing whether a model is right, and why. Verifier’s law says this sets the pace of everything else.",
  "tier": "foundation"
 },
 {
  "id": "hw",
  "name": "Compute hardware",
  "thesis": "Chips, memory and the supply of compute, and how cheap a unit of capability becomes.",
  "tier": "foundation"
 },
 {
  "id": "energy",
  "name": "Energy",
  "thesis": "Electricity for datacentres: generation, grid connections and firm power.",
  "tier": "foundation"
 },
 {
  "id": "capital",
  "name": "Capital and talent",
  "thesis": "The money and the people that pay for and push the frontier.",
  "tier": "foundation"
 },
 {
  "id": "finance",
  "name": "Finance and capital allocation",
  "thesis": "AI in investing, trading and capital markets, and the flood of capital into AI itself. This is where the VC question of who captures the returns gets answered.",
  "tier": "domain"
 },
 {
  "id": "software",
  "name": "Software",
  "thesis": "The domain where generative AI is most mature, because code can be checked automatically. It runs ahead of every other lane and previews what they may face next.",
  "tier": "domain"
 },
 {
  "id": "agents",
  "name": "Agents and organisations",
  "thesis": "Models that act, remember and coordinate inside companies and markets. This was the whole graph until v4.",
  "tier": "domain"
 },
 {
  "id": "science",
  "name": "Science and health",
  "thesis": "Models as instruments for discovery and care. The bottleneck is wet-lab data and clinical time, not ideas.",
  "tier": "domain"
 },
 {
  "id": "physical",
  "name": "Physical world",
  "thesis": "Perception and world models meet robots and vehicles. Gated by physical data and safety cases.",
  "tier": "domain"
 },
 {
  "id": "media",
  "name": "Media and creative",
  "thesis": "Images, video, voice and music made by models. The capability arrived fast; rights, authenticity and audience trust now set the pace.",
  "tier": "domain"
 },
 {
  "id": "society",
  "name": "Interfaces and society",
  "thesis": "How people meet generative models day to day, and where second-order effects land: information, work, learning and wellbeing.",
  "tier": "domain"
 },
 {
  "id": "geo",
  "name": "Security and geopolitics",
  "thesis": "AI as national power: export controls, sovereign compute, cyber operations and military use. Already in production, with the least public scrutiny of any lane.",
  "tier": "cross-cutting"
 },
 {
  "id": "govern",
  "name": "Trust and governance",
  "thesis": "Security, rights, liability and oversight that every branch needs. It cuts across the others.",
  "tier": "cross-cutting"
 }
]