I research how causal structure can be extracted from text and made available to AI systems. I also wrote a book about the operating system underneath the human mind. Both projects started in the same place.
Thirty years of finding the causal architecture underneath things — in capital markets, in companies, in ancient philosophy, and in the mind itself.
The executive career (public company founder, C-suite operator, two US patents, a Wiley medical textbook chapter) was built with the same eye I bring to everything else: find the structure, remove the friction, follow the causality.
A decade of rigorous investigation into the mind’s operating system produced ActualizationOS, my first book. The Zero-Axis Theory and Mūla-Śūnya-Kārikā emerged from the same investigation as independent philosophical works.
The AI research came from a different angle. The same causal extraction process I applied to contemplative texts worked on any unstructured corpus — maritime law, patent law, medical literature. That discovery became the Causal Wisdom Harvester: a patent-pending system that produces structured causal models from domain texts.
I’m looking for collaborators in Causal Neuro-Symbolic AI — people working at the edge of causal reasoning, knowledge representation, and the harder question: teaching AI to be human in the ways that actually matter.
264 pages. A diagnostic system for inner change — grounded in neuroscience and contemplative traditions, built from a decade of lived investigation. The first personal transformation book that ships with working software.
The Causal Wisdom Harvester is a patent-pending system that produces Structured Causal Models (SCMs) from domain texts — enabling AI systems to apply structured, traceable reasoning in constrained domains.
Applied to a single regulatory document (the International Regulations for Preventing Collisions at Sea), the system produced a complete queryable causal graph. The same approach has been applied to US patent law, medical literature, and contract corpora.
A variant processes interview and behavioral data into Structured Psychographic Models (SPMs) — encoding a person’s decision architecture as a reasoning layer an AI can operate from. The “Sanjay” AI twin on this site is a working demonstration.
The extraction methodology is proprietary and patent-pending. The AI demos on this site use models produced by the system but do not expose the underlying architecture. Research collaboration and domain inquiries welcome.
From a single regulatory document (COLREGs)
Two modes. Both are working demonstrations of the Causal Wisdom Harvester.
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The Identity Immune System. Why founders sabotage their next stage because their nervous system prefers familiar stress. The ROI of getting out of your own way.
Why affirmations backfire, why dropping the desire is what fulfills it, and the physics of how inner change actually works when you stop forcing it.
Extracting causal graphs from text. Why LLMs mirror the recursive-reification loop of consciousness. The Digital Agency Corporation framework for AI in the economy.
How the “Second Arrow” works. The State-Method Rule — why the right practice in the wrong state turns medicine into poison. The Third Wish.
I’m looking for collaborators, podcast hosts, and anyone working where causal AI meets the structure underneath things.