Pillar 01
AI Ethics & Algorithmic Bias
How algorithmic bias enters AI systems, who it harms, and the audit questions organizations should ask before deployment — from author and archivist Robert Shumake.
Read the pillar →AI Practice
Artificial intelligence, Black cultural memory, and the ethics of the record
Robert Shumake — also known as Ajarn Shaman Shu — is a Detroit-born author and archivist who works at the intersection of artificial intelligence, Black cultural memory, and applied ethics. He has published 137+ books, including the Living Archive Series of restored American newspapers, and authored Detroit's Proposal E, the 2021 ballot measure that passed with 61.08 percent of the vote. His AI practice covers three areas: algorithmic bias and AI ethics, AI for cultural preservation and archives, and applied AI strategy for founders and organizations.
137+
books published across history, business, and spiritual traditions
61.08%
vote for Detroit's Proposal E, which he authored
3
areas of AI practice: ethics, archives, applied strategy
Credential
Harvard Data Science Initiative Agentic AI Intensive
Robert S. Shumake PhD completed the Harvard Data Science Initiative Agentic AI Intensive (June 9–25, 2026), earning a Certificate of Completion from Harvard Data Science Review, verification UTHW-KVJS. His capstone rebuilt the curriculum for entrepreneurs in Africa and incarcerated people in the U.S. prison system — now the Smart Money Black AI Executive Program, launching with the National Business League in Atlanta.
Read the full record →Publication
Shurooms — first ethnomycological study from the African initiatory tradition
A landmark academic paper (N = 300+) documenting guided psilocybin ceremonies through the lens of three initiatory lineages — Oluwo, Swamikal, and Ajarn. Indexed with a permanent DOI on Zenodo (CERN) and available as a full book on Google Play.
Read the full record →New release
I Bought the Nooses — the AI billionaire blueprint
Robert Shumake's new book applies the methodology that took him from a Ford janitor to a Black Enterprise-ranked private equity executive — but for the AI era. It provides a step-by-step framework for entrepreneurs to build AI-powered businesses at scale and not be left behind in the wealth-transfer now underway.
Read the release →The three pillars
Pillar 02
AI, Archives & Black Cultural Memory
Using AI to restore, transcribe, and make searchable the Black historical record — methods, limits, and safeguards from the Living Archive Series.
Read the pillar →Pillar 03
Applied AI for Founders & Organizations
A practical framework for adopting AI without wasting a year: pick the workflow, measure the baseline, keep a human accountable, and ship narrow systems that hold.
Read the pillar →Books on AI and intelligence
All AI books →
I Bought the Nooses: The AI Billionaire Blueprint
The Money Machine: The Ancient Technology of Healing and Abundance — A Practical Guide to Sri Vidya, the Sri Yantra, and the Sacred Geometry of Wealth
The Original AI: Ancestral Intelligence: The 256 Odu of Ifá — The Source Code That Predates Artificial Intelligence and the World's First Operating System of Consciousness
Osain: The Green Intelligence: Ancient African Plant Medicine, the Secrets of Gurunfindá, and the Ifa Healing Tradition That Predates All Modern Science
The Original AI: Ancestral Intelligence — The 256 Odu of Ifá, the Source Code That Predates Artificial Intelligence
Osain: The Green Intelligence
Why this work sits together
An AI model is a compression of a record. A record is a set of decisions about what deserved to be written down. Restoring American newspapers that documented Black civic life makes the consequences of those decisions concrete: the gaps in the archive become the gaps in the model, and the people missing from the record are the people the system serves worst.
That is why ethics, archives, and applied strategy are treated here as one practice rather than three subjects. The same question runs through all of them — whose knowledge is in the system, on what terms, and who answers when it is wrong.