Planetary CO₂ Impact: Methodology, Assumptions & Sources

1. Primary Data Sources

To mathematically project the systemic energy required by global LLM inference and the exact displacement yielded by the AEGIS cascade, the following third-party macroscopic sources were integrated:

• Energy per Query (Epoch AI): Baseline GPT-4o queries consume 0.3–0.43 Wh/query. Deep inference or long-context token windows escalate up to 40 Wh/query.
• Nuclear Baseline Benchmarking (US DOE): 1 GW reactor, 90% capacity factor = 7.9 TWh/year.
• Grid CO₂ Emissions (Ember 2024): Global electrical grids project a decline from 0.40 kg CO₂e/kWh down to ~0.15 kg CO₂e/kWh by 2050 via renewable transition.
• "Planet France" Equivalent benchmark: France consumed ~445 TWh in 2023 (RTE/Enerdata). AI sector approaches total national consumption parity.
• Growth Escalation: Goldman Sachs projects +160% data center power growth explicitly by 2030.

2. Structural Architecture & Key Baseline Assumptions

The derivation of the 21.71 Gt CO₂ gross savings relies on understanding the "Guardian LLM Inference Tax"—the hidden computational overhead of routing queries through tertiary safety models (like Llama Guard) prior to primary inference.

2.1 The Guardian LLM Inference Tax (+55%)

We derived a conservative midpoint of +55% compute overhead per safety-filtered query (compared to a +40–100% possible range). This represents the structural reality that Guardian LLMs (7-8B parameters) perform a full read/classify inference pass per IO transaction. For context:

• Llama Guard 3-1B benchmark (OWASP LLM 2025): 165ms / query threshold.
• Llama Guard 8B benchmark: ~750ms / query on A30 GPUs.

2.2 The NI-Stack Algorithmic Relief Metric (<1%)

Operating as a deterministic Edge/CPU-bound cascade, the NI-Stack consumes purely scalar algorithmic telemetry matching. The gross overhead resolves to <1% CPU taxation per prompt. Evaluated against GPU floating-point operations, the NI-Stack acts as an absolute energy sink, displacing NPU loads dynamically. (Secured via Patent USPTO #63/997,472).

3. Scaling Mechanics & Projection Factors

In projecting compound growth out through 2050, static extrapolation produces impossible energy demands. We applied rigid deceleration algorithms to normalize the simulation to reality:

• Safety Filter Application Volume: We model that ~65% of all global LLM queries must pass through safety and intent filters. This reflects the blend of highly guarded enterprise B2B queries and mass-market moderation demands.
• Hardware Efficiency Decay: Modeled at ~3x transistor/compute improvements per GPU generation (NVIDIA H100 → B200 → Rubin architectures). This is applied actively as a net efficiency divisor against token growth.
• Post-2035 Curve Deceleration: Token demand growth is modeled to strictly decelerate out of exponential climb—trailing from +65% CAGR down to a sustaining +15% by 2040, and further chilling to +8% linear growth by 2050 (representing market saturation parity).
• Economic Normalization: Extrapolated upon a hyperscale blended average electricity cost of $0.08/kWh, compounded with 2% persistent inflation, against a constant Datacenter target PUE of 1.3.

4. Conclusion on Planetary Displacement

By mapping the +55% Guardian GPU Tax onto the 77 Quadrillion projected token volume logic, versus replacing that safety layer with the <1% NI-Stack CPU Cascade, the compound energy delta translates directly to avoided MW generation. Accounting for the Ember grid-decay curve (0.40 -> 0.15 kg CO₂e), the integrated area under the curve establishes the 21.71 Gt CO₂e gross reduction.

The elimination of contradiction between computational safety and ecological solvency.