Thought Leadership
AI, Identity & Strategy
I write from lived experience in strategy, identity, and AI. Generative AI helps refine clarity and validate data , but the voice and perspective are entirely my own.
The Robot Monk and the Illusion of Care
A robot dressed as a Buddhist monk may seem like a harmless curiosity, but it reveals something deeper about the way AI is being introduced into public life. The issue is not AI itself. It is the performance around AI: the borrowed trust, the forced inevitability, and the soft language of care wrapped around systems that often lack accountability. This essay explores the illusion of care in AI, from smiley interfaces and commencement speeches to data centers, automation, and the quiet stripping of human knowledge for parts.
Beyond the Bot, But Not Yet Beyond the Human
A recent McKinsey discussion on agentic AI explores how more adaptive systems could transform customer experience. It highlights an important operational shift as organizations redesign service around intelligent agents. Yet much of what is described reflects empathy expressed through interaction design rather than a deeper understanding of human experience. As AI moves from tools to participants in human interaction, the meaning of empathy may need to evolve as well.
The Lazarus Illusion: How Exception Culture Breaks AI Systems Before Anyone Notices
The world does not end loudly. It ends cleanly.
The Lazarus Project isn’t about time travel. It’s about exception culture, what happens when reasonable people decide the rules apply to everyone else. This piece traces how optimization without dignity quietly hardens into harm, and why AI systems are already walking the same path.
Alternate excerpt, sharper:
The most dangerous systems don’t fail through misuse. They fail when they work exactly as designed and the people inside them stop feeling where the rules cut. The Lazarus Project is a warning about AI we are already ignoring.
Structure Is Dignity & Relief: Why Ambiguity Breaks Teams
This week, I wasn’t reacting or firefighting. I was designing.
Not control, but structure. Not hierarchy, but clarity.
What I learned is simple: when leaders refuse to define the systems they run, ambiguity becomes a shield. People don’t grow. They contract. And the work slowly hollows out.
Structure, when built with intention, does the opposite. It gives people relief, dignity, and the space to do their best work.
Dial-Up Data, Streaming AI
Retailers have been admitting the same truth for years: our data is a mess. This piece is driven by lived experience and a recent post from Clare Kitching that pushed me to go deeper. AI is not a miracle layer that bypasses foundation work. It is a spotlight on what we built in the dial-up era: patched systems, inconsistent definitions, and human impact treated as an afterthought. The good news: this is our first real rebuild window since the internet. I share a practical 12-week approach to make data trustworthy, governable, and built to see people.
The Awareness Layer: How Systems Notice What They Are Doing to People
Organizational harm rarely begins with a dramatic moment. It starts with a shift in tone that no one names. A clipped answer. A softened voice. A message written at the wrong hour. These signals are not small. They are the early warnings of a system beginning to strain.
The Awareness Layer is what allows an organization to sense these changes before they turn into damage. Without it, collapse looks sudden. With it, the truth of what is happening becomes impossible to ignore.
When “AI Employees” Call Humans The Constraint: Why Podium’s Jerry 2.0 Feels So Gross
There is a special kind of nausea that hits when a founder calls people “the biggest constraint” and their new AI “the employee.” Podium’s Jerry 2.0 is sold as 24/7 help for local businesses, but the real pitch is simple: more revenue with fewer humans. This piece pulls apart why that story feels so slick, so scummy and what it reveals about how tech leaders really see the rest of us.
From essay to instrument: Emotional Metadata™ as a composite KPI
Most dashboards only care that the task was completed. Emotional metadata asks what the task completed in the person.
In this essay I introduce Human Layer Integrity, a composite KPI built from four scores: Dignity Experience Score, Legibility and Comprehension Index, Emotional Residue Score, and Edge Case Safety Index. Together they turn the human impact of AI, pricing, and organizational decisions into a structural metric that sits beside NPS and EBITDA, so harm stops hiding in the shadow of your numbers.
The Human Harm Layer: When Organizational AI Turns Outward to The Shopper
Retail AI does not begin at the edge of the app. It begins inside the organization, in the way leaders treat employees who raise concerns about harm.
The Human Harm Layer is the mechanism that carries those internal habits into external decisions about customers. When a worker is told to “go into listen mode” after naming how a pricing model will make life more expensive for poor Black neighborhoods, that is not just a bad meeting. It is Emotional Metadata the system chooses to ignore.
The same logic shows up later in delivery platforms and dynamic pricing schemes that quietly charge the highest tax on convenience to the people with the least slack. This essay maps how that harm travels, how it becomes code and what it will take for retailers to stop exporting workplace violence into customer experience.
Same Cart, Different Power: What Instacart’s AI Pricing Experiments Really Tell Us
Instacart calls it experimentation. Consumer Reports and Groundwork Collaborative call it what it is: an AI pricing system that quietly tests how far it can push your grocery bill. This essay connects their findings to food deserts, price elasticity, and racialized access, and lays out the guardrails any serious retailer or regulator should demand before turning AI loose on the cost of eating.