Original Sin: Hardware Sovereignty and the Physical Limits of Artificial Intelligence

Technical White Paper: QN-SIN-2026-V2.0QuantNature Global | September 7, 2026 Modern deep learning operates under a flawed assumption: that hardware is an isolated, passive substrate. This white paper uncovers AI’s physical "Dual Original Sin" across silicon and cyber-physical domains, introducing empirical proofs and architectural solutions: The First Original Sin (Inside Silicon): Autoregressive transformers leak a deterministic microarchitectural signature—the Neural Pulse (2.99 Hz on RTX 3080). Utilizing unprivileged Active Micro-Contention Spectroscopy, we present Ghost Coding (G-CODE), a software-invisible execution control framework that induces a 600 ms memory latency wall to physically freeze active LLMs (Llama-3) mid-sentence without OS/TDR exceptions. Furthermore, we prove Cross-Domain Power Substrate Interference, where GPU current transients (ΔI/Δt) destabilize the host CPU’s hardware TRNG (RDRND: 0xffffffff), breaking the physical root-of-trust. The Second Original Sin (Outside Silicon): Bounded by a six-decade temporal chasm (~ms vs. ≤ 25 ns), statistical neural networks are physically disqualified from high-frequency sub-cycle power governance and mission-critical safety compliance (ISO 26262 ASIL-D). This leaves gigawatt AI infrastructure hostage to legacy 1990s 32-bit microcontrollers, wasting 4.2 MW of continuous thermal power per 100 MW facility ($5.5M/yr). Sub-Cycle Silicon Governance: To resolve this duality, we formalize a deterministic 25 ns FPGA architecture (AMD/Xilinx Zynq-7000 SoC) that asserts sub-cycle Zero-Voltage Switching (ZVS) locking, eliminates datacenter cooling waste, doubles hydrogen fuel cell stack durability (+115%), and secures a 120-minute pre-warning for battery thermal runaway. [안내] 본 레코드 파일 목록에는 영문 원본 백서(Original_Sin_V2.0(ENG).pdf)와 한국인 연구자 및 엔지니어를 위한 한국어 완역본 백서(Original_Sin_V2.0(KOR).pdf)가 함께 포함되어 있습니다.