Welcome to my AI Lab Notebook
This is where I study AI not as a product, but as a system shaping human life.
Over time, three themes have defined my work:
1. AI Governance as Architecture: I build frameworks like the AI OSI Stack, persona architecture, and semantic version control because AI needs scaffolding, not slogans.
2. The Human Meaning Crisis in Machine Time: I explore how AI destabilizes identity, trust, and authenticity as machine speed outpaces human comprehension.
3. Power, Distribution, and Responsibility: I examine who benefits from AI, who is displaced, and how governance, economics, and control shape outcomes.
These pillars guide everything I write here. AI’s future won’t be determined by capability alone, it will be determined by the structures, meanings, and power dynamics we build around it.
Thanks for reading.
Update — The AI OSI Stack: A Governance Blueprint for Scalable and Trusted AI
Following my September 9, 2025 post on the AI OSI Stack, this update expands the conversation with the release of the AI OSI Stack’s canonical specification and GitHub repo. It marks a shift from concept to infrastructure: transforming the Stack into a working blueprint for accountable intelligence. Each layer, spanning civic mandate, compute, data stewardship, and reasoning integrity, turns trust into something structural and verifiable.
The Shadow Filter: Language, Power, and the Algorithmic Struggle for Authenticity
In an earlier piece, I wrote about Semantic Version Control — the quiet ways language gets updated, corrected, or erased. The Shadow Filter is its larger frame: language as a site of power. From Qin China’s script reforms to Cold War propaganda, rulers have shaped words to shape thought. Today, algorithms act as new gatekeepers: ATS systems demand keywords, social platforms enforce algospeak, and generative AI flattens voices into statistical averages. The cost is authenticity, as fluency itself becomes suspect. but its effects are not inevitable.
Epistemology by Design: My Work with Custom GPTs and the Ethics of Engineered Knowledge
Custom GPTs do more than execute instructions. They shape the conditions of knowledge itself. Every persona encodes assumptions about what counts as truth and whose voice carries weight. I call this epistemology by design. Done poorly, such systems erase alternatives and limit inquiry. Done well, they scaffold pluralism while still providing direction. The opportunity is to build epistemic partners that expand agency. The risk is dependence on voices that sound objective but are not. When I design these systems, I ask a simple question: what kind of world am I training myself, and others, to inhabit?
Why You Should Care About AI
AI is already part of daily life. It screens job applications, shapes news feeds, and powers therapy tools. The question is not whether AI matters but whether it is trustworthy. Trust rests on four loops. How AI reasons. How it treats people. How it is governed. How it shapes meaning. When these loops are weak, AI becomes invisible yet unaccountable. When they are strong, AI can become infrastructure we rely on. Caring about AI is not optional. It is already shaping choices that define who we are.
How People Are Using ChatGPT: Insights from the Largest Consumer Study to Date
A large study confirmed what many sensed. ChatGPT has moved from novelty to daily habit. People use it to write, to clarify, to think aloud. Yet adoption does not guarantee truth. Fluency can mask error. Repetition can bend meaning. The real lesson is not only that AI is widely used. It is that trust is fragile. Authority is not earned through scale but through reliability. AI is already in the room. What matters now is whether we learn to question its answers with the same intensity that we welcome its speed.
When Everything Sounds Like a Bot: On Authenticity in the Age of AI
Online discourse increasingly feels synthetic. Smooth, fluent, yet strangely hollow. Authenticity signals are disappearing. This matters. Without messiness, trust weakens and outsider voices vanish. Governance becomes distorted. The response cannot be more optimization. It must be design that restores character, imperfection, and diversity. AI may flood the conversation with fluent text, but legitimacy will come from spaces that preserve the unpredictable texture of human speech.
The AI OSI Stack: A Governance Blueprint for Scalable and Trusted AI
AI is often spoken of as a single entity, a black box that contains everything. This collapse hides differences and invites monopoly. The AI OSI Stack provides a layered alternative. Like the OSI model did for the internet, it separates hardware, models, APIs, and governance. The result is interoperability, clarity, and embedded trust. The point is not only technical soundness but institutional stability. AI should not be a monolith. It should be a system of layers that can be trusted piece by piece.
Why We Need Outsider Voices in the AI Conversation
The AI conversation is dominated by insiders. Corporate and academic voices hold the microphone. That dominance creates blind spots and weakens public trust. Outsiders bring the sharp questions insiders avoid. They bring lived experience and values such as fairness, usability, and dignity. If AI is to become legitimate, these voices cannot be invited late. They must be part of design from the beginning. True trust in AI will not be built by insiders alone.
From Frameworks to Chaos: Testing AI in a Crisis Scenario
What happens when AI is dropped into a boardroom crisis with fractured alliances and incomplete data? I tested this by simulating a mutiny scenario. Traditional frameworks collapsed under the weight of uncertainty. Yet Solomon adapted, not with formula but with improvisation. One method stood out. By forcing adversaries to steel-man each other’s arguments, conflict transformed into structured dialogue. The exercise revealed AI’s potential as a crisis partner. It does not simply repeat frameworks. It improvises, centering on trust, legitimacy, and power dynamics. In unpredictable conditions, this kind of adaptability matters more than perfection.
A Pivotal Conversation: Learning from Dominique Shelton Leipzig on AI Governance
I had the privilege of a long conversation with Dominique Shelton Leipzig, a leading authority on privacy and AI governance. The exchange offered insights, resources, and guidance that I could not have accessed otherwise. It marked a turning point in my work, clarifying how governance must blend law, ethics, and lived context. For me, it underscored the importance of mentorship in a field that too often moves faster than reflection. Progress is not only technical. It is also relational.