Research, Development, and Holdings
Projected critical threshold: 2030
This is a hypothetical, model-inferred Life Cycle Analysis dashboard projecting global AI compute, water, carbon, and training-data trajectories from 2005 through 2045. It is built on a small set of public 2024–2025 anchors (IEA, EIA, LBNL, UC Riverside, Epoch AI, Villalobos et al.) and a transparent set of growth, efficiency, and adoption assumptions — every one of which is exposed as a knob. Numbers reshape live; nothing is hard-coded.
Section I models MythOS recycling as a global compute-reduction multiplier (market saturation × per-instance recycling efficiency) applied to AI energy, water, and grid load. Section II models GamePump as a continuous training-data ingestion layer that replaces catastrophic full retrains with incremental updates — reducing both lifecycle training compute and model knowledge staleness. The two pillars compose: a fully-deployed MythOS+GamePump stack reduces AI's environmental footprint at the inference edge AND extends model relevance without retraining the world from scratch.
The baseline values below are the empirical anchors and conversion factors the model rests on. Sliders move; baselines do not.
| Parameter | Baseline |
|---|---|
| Section I · Resource anchors (2025) | |
| AI energy consumption | 625 TWh / yr |
| Total compute energy | 2,500 TWh / yr |
| AI water footprint | 295 B gal / yr |
| Total compute water | 950 B gal / yr |
| Global grid capacity | 25,000 TWh / yr |
| Global grid usage | 22,000 TWh / yr |
| AI share of compute | 25% |
| AI share of grid | 2.5% |
| Section II · Training-data anchors | |
| Public high-quality text exhaustion | ~2027 |
| Avg knowledge-cutoff staleness today | ~14 mo |
| Frontier retrain cycle | 12–24 mo |
| Per-retrain compute (frontier model) | ~100 GWh |
| Hallucination baseline (fresh model) | 5% |
| Time-sensitive accuracy (fresh model) | 95% |
| Conversion factors | |
| Electricity cost | $0.20 / kWh |
| Grid carbon intensity | 0.38 kg CO₂ / kWh |
| US homes powered per TWh | ~90,000 |
| Coal plant output | 5 TWh / yr |
| Olympic pool | 660,000 gal |
| Model defaults · knob start positions | |
| HW efficiency gain | +18% / yr |
| AI adoption rate | 100% (baseline) |
| Forward AI energy growth (pre-efficiency) | 32% YoY |
| MythOS market saturation | 25% |
| MythOS per-instance recycling | 75% |
| GamePump adoption | 0% |
| GamePump continuous-update efficiency | 85% |
Reasoning capital is the OPEX→CAPEX line — it scales with inference volume, not just energy. ROI compounds because every reuse is realized at zero marginal compute. The chart's reasoning-capital share is the addressable market the rest of the field hasn't priced yet.
The energy + water + carbon lines map directly to your P&L. Set saturation in Section I to your deployment plan, recycling to your governance maturity, and the annual-savings number is your CFO's slide.
The TWh saved upstream are demand that never reaches your interconnect. Avoided buildout — substations, transformers, transmission — is deferred CapEx not shown here. Treat the energy line as the conservative floor.