Direct energy forecasts on generation capacity, demand, infrastructure events, climate-fund allocation. The founding vertical of Phronesis. Customer class: capital allocators in energy infrastructure, utility and grid operators, energy-sector corporate strategy, climate-aligned funds.
Archetypes
- E1 Generation capacity additions by region and technology, multi-year horizon
- E2 Locational marginal price (LMP) trajectories under capacity scenarios
- E3 Interconnection queue throughput and project-completion probability
- E4 Critical-minerals supply chain availability for energy-storage build
- E5 FERC ruling and state PUC docket outcome probabilities
Co-primary vertical with Energy. Forecasts on AI compute power demand, data center electrification, GPU and HBM production, hyperscaler capex, training run trajectories. Cross-vertical Phronesis Grid-Compute Forecast is the flagship intersection product. Customer class: data center operators, hyperscaler infrastructure, utility forecasting desks needing compute-side demand, semiconductor analysts, AI-infrastructure-focused capital allocators.
Archetypes
- I1 Phronesis Grid-Compute Forecast — cross-vertical AI-compute power demand × grid-capacity intersection (FLAGSHIP)
- I2 GPU + HBM production capacity vs hyperscaler capex demand
- I3 Inference cost trajectory by model class and provider
- I4 Data center build cadence by region and power-availability profile
- C1 AI training run trajectory (cluster size, model scale, time-to-first-token)
Disease incidence, population-health, public-health system forecasts. Operates exclusively under HIPAA Safe Harbor enforcement at the code layer via the Themis-Healthcare substrate; PHI is architecturally excluded from the inference path. Customer class: payers and providers, public-health agencies, pharmaceutical demand forecasting, clinical-trial site planning.
Archetypes
- H1 US disease incidence per 100k by demographic and region (e.g., Type 2 diabetes; first H-archetype minted)
- H2 Population-health metric trajectories (life expectancy, age-adjusted mortality)
- H3 Health system capacity by service line and region
- H4 Public-health intervention effect estimates
- H5–H8 Additional H-archetypes in pre-launch substrate
Physical-risk forecasts: hurricane and wildfire frequency, sea-level-rise impact zones, extreme-weather probability, asset-level exposure modeling. Customer class: insurance and reinsurance underwriting, commercial real estate diligence, climate-disclosure funds, infrastructure resilience planning.
Substrate prepared via V#4 Scope Addendum v1.1; data-plane research dossier ready; archetypes K1-K6 defined in scope addendum.
Regulatory environment forecasts: legislation passage probability, agency rulemaking timing, jurisdictional outcome variability. Cross-cutting Themis-Regulatory substrate layer at architecture level. Customer class: lobbyists and trade groups, corporate government-affairs, regulators themselves, compliance-aware capital allocators.
Supply-chain resilience forecasts: critical-minerals availability, logistics throughput, manufacturing-supply-chain disruption probability, commodity flow scenarios. Customer class: logistics operators, commodity traders, manufacturing supply-chain teams, critical-minerals investment funds.
Space-economy forecasts: launch cadence, IPO probability, orbital-debris density, mission-budget trajectories, commercial-space-station deployment, lunar infrastructure timing, space-based solar power feasibility. Themis-Space cluster enforces ITAR/EAR + national-security-payload + crewed-mission + insurance-grade actuarial hard lines. Customer class: space-economy capital allocators, commercial-space companies, defense-aerospace investors, regulatory observers.
Archetypes
- C1 SpaceX IPO probability by Q4 2027 (52% canonical p50; first C-archetype minted)
- C2 Commercial launch cadence by provider and orbit class
- C3 Orbital infrastructure deployment cadence
- S1–S6 Additional S-archetypes in pre-launch (orbital debris density, STM, mission-budget, etc.)
Humanoid and autonomous-system deployment cadence forecasts. Universal cross-pollinator vertical: intersects with V#1 (energy infrastructure robotics), V#2 (compute substrate for robotics), V#7 (space robotics). Per Karpathy diligence Q7: archetype weighting favors digital-interface forecasts (sensors, actuators, software stack maturation) over full-stack autonomy timing. Customer class: industrial robotics buyers, humanoid-platform investors, automation supply-chain analysts, defense-robotics observers.
Archetypes
- R1 Humanoid robot industry deployment cadence by region and customer-segment (first R-archetype minted)
- R2 Industrial robotics market sizing by sector
- R3 Autonomous vehicle deployment timing by jurisdiction
- R4–R6 Cross-vertical I_V2×V8 + I_V7×V8 archetypes in pre-launch