Systems Architect · AI Safety Researcher · Founder, Localis AI · Newcastle, UK
I design artificial intelligence systems that hold by structural constraint, not by instruction.
Fifteen years of urban masterplanning and spatial systems design across Europe, the Middle East, Africa, and the Americas taught me that complex systems cannot be governed by behavioral training alone. I develop Constitutional Physics and Aitiopoietic Cognition—a general-purpose, neurosymbolic methodology that binds generative language models to deterministic mathematical rules, physical engines, and verified legal frameworks. Across eight distinct domains, this architecture makes misalignment, hallucination, and structural failure thermodynamically expensive or code-level impossible.
Standard reinforcement learning (RLHF) produces allopoietic systems that learn what aligned output looks like without possessing an existential stake in structural validity. The Aitiopoietic Methodology re-introduces thermodynamic coupling, turning structural coherence into an operational condition of existence.
The methodology's generality rests on empirical validation across eight unrelated, high-stakes application domains—each constructed from first principles to demonstrate substrate-independent governance.
CausalChain graph (Metabolic Closure / VDK Phase 1) with an 8-pressure selection genotype.
Grounds claims against Monarch KG, ClinGen validity tiers, gnomAD (BA1 population frequency hard veto), HPO semantic
similarity, and BioGRID physical interactome ($S_{ab}$ network separation).
The transition from spatial design to computational systems design represents a continuous inquiry into structural integrity, systemic resilience, and non-arbitrary boundary conditions.