Comprehensive analysis of the 2025 energy crisis and presentation of the Power-Survival Stack architecture.
Publication Status:
Preprint. Work is under peer review.
400×
Efficiency Gain
LSR
Latent Reasoning
Dec 2025
Preprint Date
Total Efficiency
400×
EU Grid Deficit
-920 TWh (2025)
Compression
10× (Bytecode)
Architecture
MoE + Latent
2025 grid audit reveals zero surplus capacity for AI expansion in major markets
Mixture-of-Experts with FP8 delivers 9× efficiency as the new standard
LSR enables "quiet contemplation"—thinking without decoding tokens
Dense AI-native representation with 10× compression over Python
This paper addresses a critical bottleneck in modern AI scaling: the "Token Tax". Modern large language models (LLMs) waste vast amounts of energy decoding intermediate "chain-of-thought" tokens that serve merely as a draft for thinking.
Key Innovation: We introduce Latent-Space Reasoning (LSR), a paradigm that decouples reasoning from token generation. By performing multi-hop logical inferences within a high-dimensional hidden state vector, we eliminate the memory bandwidth costs of autoregressive decoding.
Results: Combined with the "Rational Scaling" base (MoE + FP8) and Neural Bytecode for output compression, the Power-Survival Stack achieves a 400-fold reduction in total system energy costs. This enables the deployment of reasoning-capable agents even amidst the energy crisis of 2025.
400×
ENERGY EFFICIENCY
Figure 1: Comparative efficiency of Legacy CoT vs. Power-Survival Stack