Artificial General Intelligence (AGI), and particularly large language models (LLMs), have achieved remarkable progress in language tasks. Yet their stochastic outputs, opaque reasoning, and reliance on unverifiable data make them unsuitable for critical infrastructures such as energy, where determinism, traceability, and regulatory compliance are indispensable. We propose an alternative path: a deterministic, domain-specific AGI for energy systems. The framework is built on three patent-defined components: a Global Energy World Model (GEWM) encoding physical laws and regulatory rules; Tokenized Energy Profiles (TEP) as minimal, verifiable units of lawful data; and a Deterministic Semantic Engine (DSE) that executes auditable, rule-based operations. In this design, LLMs serve only as orchestration and interface layers, while the deterministic core ensures reproducibility, compliance, and resilience. This architecture reframes intelligence itself: not as plausible text generation, but as the capacity to govern a domain under explicit rules and verifiable data. By embedding sovereignty, auditability, and compliance at the substrate of computation, vertical AGI emerges as a credible and ethically robust path toward trustworthy AI in energy and beyond.