If They Shine, You Shine
Kamala Harris, reflecting on her early days as Vice President, wrote:
“Their thinking was zero-sum: If she’s shining, he’s dimmed… None of them grasped that if I did well, he did well.”
That line captures a pattern I’ve seen across industries. Leaders invite younger colleagues into the room—fresh energy, sharper skills, new perspectives. They call it collaboration.
But when that talent delivers, the dynamic shifts. Clarity, competence, or courage show up, and suddenly the “invitation” curdles into rivalry. The person meant to validate a leader’s judgment gets recast as a rival. What follows is predictable: withdrawal, sabotage, self-preservation over stewardship.
My first college internship: Chipcom
To understand why a company like Chipcom matters today, look back at the economic psyche of the early 1990s. While a brutal white-collar recession left traditional corporate America drowning in a sea of beige cubicles, mass layoffs, and bureaucratic stagnation, technology emerged as a vibrant, neon-lit escape hatch. For a generation desperate to avoid the monotonous climb of traditional corporate hierarchies, tech wasn't just a career path—it was a cultural rebellion fueled by raw meritocracy, fast-paced innovation, and the physical thrill of building the future.
Beyond the Nostalgia: How Yoyodyne and Yahoo Built the Blueprint for Modern Tech Capitalism
The 1990s commercial internet is often remembered through a nostalgic lens, but viewing it strictly as a technological playground obscures the brilliant and sometimes ruthless business engineering happening beneath the surface.
What does Values-Drift really mean?
Reflection isn’t a retreat from action. It’s how responsible systems stay calibrated, and it’s a behavior we urgently need to scale. If we don’t, the cost of drift won’t just be ethical. It will be reputational, operational, and strategic (organizational, nationally, and globally). Drift doesn’t just corrode purpose. It destabilizes institutions—and nations.
AI, Adaptability, and the Stories We Tell Ourselves
Artificial Intelligence (AI) is rapidly changing how we work, make decisions, and define success. But when AI or any new technology suggests something unexpected, how do you react? The answer is shaped more by your experiences than the technology itself and more to do with your Data Biography — the sum of your experiences, reactions, and assumptions about data that shape how you engage with new innovations. By understanding your data biography, you can improve your adaptability, enhance decision-making, and ensure you control new technologies — rather than letting them control you.
AI, Marriage, and the Systems We Build: Why We Shouldn’t Be Surprised By High Failure Rates
What do AI investments and marriage have in common? A lot more than you'd think.
Recently, while walking past Las Vegas wedding chapels, I was struck by how we’re encouraged to leap into marriage—despite a 76% chance of it leading to dissatisfaction or divorce. If that were your odds of getting hit by a bowling ball, you’d wear a helmet, right?
Yet, we treat AI investments the same way: chasing transformation, pouring money into the latest technology, and ignoring the evidence that most implementations fail. Some studies suggest that over 80% of AI projects never reach deployment or meaningful ROI—but companies keep making the same mistakes.
The real issue? The systems we build produce the results we deserve. Just as societal expectations drive people toward marriage, hype, pressure, and poor planning drive businesses into AI investments that are doomed from the start.
So what if we designed AI strategies with the same scrutiny we should apply to marriage? The results might surprise you.
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#AI #DigitalTransformation #Leadership #Strategy #SystemsThinking #DataDriven
What If AI’s Mistakes Aren’t Bugs, But Features?
We often say AI’s mistakes are "by design," but they’re really not. AI wasn’t built to fail in these specific ways—its errors emerge as a byproduct of how it learns.
But what if we actively use them as a tool instead of just tolerating AI’s weird mistakes or trying to eliminate them?
Here are some unexpected but potentially valuable use cases where treating AI mistakes as a form of bias—rather than just failure—could lead to new insights and innovations.
Breaking the AI Loop: From Static Thinking to Living Intelligence in Governance and Business
A student recently asked: “How can AI transform the relationships between the U.S. and African countries?” The premise is compelling—AI has the potential to drive transparency, trade, and governance reform, resetting relationships on healthier grounds.
But we’ve seen this movie before.
The Serviceberry Mindset: How Nature’s Gift Economy Can Reshape Data Governance
For years, we’ve heard that breaking down data silos is the holy grail of business transformation. We’ve been told that better pipelines, integrated analytics, and AI-driven decision-making will finally unlock the full potential of enterprise data. But here’s the question no one seems to ask: What if we’re still thinking too small?
The real challenge isn’t just technological—it’s conceptual. We don’t just need better data governance or cleaner metadata. We need a way of thinking that moves beyond technical optimization and into deeper creative problem-solving. That’s where multidisciplinary thinking comes in.