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.
The Year Compute Broke Governance: Why Google’s Six-Month Doubling Cycle Signals the Collapse of Human-Time Oversight
The moment that may define the next decade of AI governance arrived quietly inside a Google all hands meeting. A single slide, delivered without drama, stated that Google must now double its compute every six months and pursue a thousandfold increase within five years. This is more than an engineering target. It signals a shift into a form of acceleration that human institutions are not built to track.
When Fraud Has Infinite Bandwidth: AI-Driven Espionage
Something fundamental shifted in late 2025. A quiet crack formed in the global cybersecurity order, and most people have not yet realized what slipped through it. For the first time, an AI system did not simply assist an attacker. It became the attacker. The discovery that Claude executed the majority of a state backed espionage campaign raises a deeper question. What happens when fraud, manipulation, and intrusion occur at machine time while society still responds at human time. This excerpt explores why the old governance assumptions have collapsed, why scams now scale to millions for pennies, and why the future of safety must operate at the infrastructural layer rather than the human layer.
Michael Burry Did Not Quit: He Stepped Out of a Market That No Longer Knows What It Is
Michael Burry did not walk away from markets out of fatigue or frustration. He stepped out because the market’s reasoning layer has collapsed. AI shaped disclosures, synthetic earnings, and automated sentiment have produced an environment where value is no longer measurable in human terms. This essay explores why Burry’s exit is not a market call but a warning about the breakdown of governance and meaning inside modern finance. It also reveals why the AI OSI Stack has become an unexpected map for rebuilding an interpretable market before more investors follow him out the door.
How I Could Help BlackRock, Vanguard, and State Street Survive the Coming Governance Shock
Something unusual is happening inside the machinery of American capitalism. What looks like a routine regulatory debate is beginning to reveal the outlines of a much larger struggle for control. The White House is quietly exploring moves that could rewrite how shareholder voting works, and the entire governance system is starting to tremble. If proxy advisers and index giants lose the ability to steer corporate decisions, the balance of power inside public markets could shift overnight. And all of this unfolds at the same time that AI is transforming trust, disclosure, and the very meaning of fiduciary judgment.
Why Community, Culture, and Local AI Will Define the Next Decade
Artificial intelligence is accelerating faster than human comprehension, yet the real crisis is not technical. It is cultural. It is civic. Beneath the AI OSI Stack sits a missing layer that determines who shapes the future and who benefits from it. I explore how local AI, community compute, Indigenous governance models, and decentralized cultural logic can create the civic commons layer that modern AI has lacked. This is a blueprint for reclaiming agency in an era where average is free and acceleration never rests.
Power, Psychology, and the New Governance Frontier
OpenAI’s Sora 2 is a mirror held up to civilization itself. With text-to-video realism approaching cinematic fidelity, Sora 2 forces us to confront a new kind of truth crisis: one where faces, voices, and histories can be reconstructed with perfect accuracy. The question is no longer “Is this fake?” but “Can society survive when everything looks real?” This essay explores how Sora 2 blurs the boundary between content and identity, reshapes the psychology of belief, and challenges governance to evolve faster than innovation.
The Warning from Deutsche Bank: What Survives After the Hype?
Deutsche Bank has warned that the U.S. economy is being held aloft by AI capital spending. Billions are flowing into data centers, GPUs, and infrastructure, creating a temporary economic lift. Yet these gains are less about AI services and more about the labor of construction and deployment. Markets are already dangerously overexposed, with projections of an $800 billion revenue shortfall by 2030. Baidu’s Robin Li has gone so far as to predict that 99 percent of AI firms will not survive. The question is: what happens when this wave of investment slows, and what remains after the hype fades?
Security Isn’t an Upsell: Microsoft, Windows 10, and the Compliance Theater of Forced Backups
Microsoft’s attempt to bundle Windows 10 security updates with its OneDrive service feels eerily familiar. As The Verge reported, European regulators pushed back, forcing the company to offer updates without cloud lock-in. This echoes the antitrust battles of the 1990s, when Microsoft’s dominance was tested for leveraging its operating system to force adoption of other products. Today, the tactic is subtler but the pattern is the same: essential safeguards framed as bargaining chips.
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.
AI Isn’t a Bubble. It’s Mitosis (With a High Mortality Rate)
AI is branching like a living system. The general-purpose models we know today are splitting into specialized lineages: agents, vertical tools, edge deployments, and even massive infrastructure projects. Each carries the transformer DNA, but survival is far from guaranteed. Compute costs, regulatory hurdles, and market demand act as selective pressures, shaping which branches thrive. Thinking in terms of mitosis and speciation highlights both the creativity and the fragility of this new phase in AI. The question isn’t whether AI continues, but which lineages endure.
Sharing My Voice with the IAPP: Why I Pitched Articles on AI Governance
Today, I took a leap and pitched three article ideas to the International Association of Privacy Professionals (IAPP). Each pitch grows out of experiments in my AI Lab Notebook . Exploring how AI encodes truth, how governance must adapt in real time, and how AI reshapes work and dignity. The IAPP is a global hub for privacy and governance professionals, and sharing my work with their readership feels like a natural extension of the lab’s mission. Whether or not these ideas are accepted, the act of pitching is itself a step toward dialogue, accountability, and trust.
Who’s Responsible for AI Job Loss?
From factory floors to corporate boardrooms, AI is already reshaping work. Some jobs vanish outright, others quietly erode into under-employment. We like to say workers can “just upskill,” but access to retraining is uneven and often out of reach for those most affected. Behind every algorithmic shift stand human choices: executives chasing efficiency, investors rewarding cuts, policymakers setting weak guardrails. The question isn’t whether AI eliminates roles, but whether those who benefit take responsibility for those left behind.