The questions keep coming.
How do we decide what matters? Tracing one line of curiosity across three decades.
t first glance, the chapters of my career and the books on my shelf seemed to have very little in common.
My library grew alongside my roles: college introduced psychology, trading introduced markets, media introduced consumer behavior, and Bloomberg introduced technology. Each era left behind another shelf of unanswered questions.
From the outside, these transitions looked completely disconnected—like a random collection of isolated events. In reality, they were tributaries. Each industry operated on its own distinct surface mechanics, yet beneath the noise, they were all feeding into the exact same underlying river of inquiry: how people observe the world, how they assign meaning to what they observe, and what happens when those interpretations begin to scale.
A
When did this line of inquiry actually begin?
Probably long before I realized it, my line of inquiry likely began with a simple college psychology experiment: approaching strangers to analyze the social interaction. While it seemed like an exercise in breaking the ice, the true intent was to expose our underlying implicit biases. This assignment illustrated the awareness fallacy—the cognitive blind spot where humans falsely believe that intellectual awareness of a bias equals behavioral control over it.
We assume that because we can name our prejudices, we can prevent them from influencing our choices. In reality, awareness is just a spectator; the bias continues to dictate our metrics of judgment.
I’ve always been fascinated with the published works that overturned intuition and assumptions and revealed the consistently observable patterns of human behavior.
What does Psychology have to do with financial markets?
Turns out, absolutely everything. When I joined a trading desk in the early 1990s, I was suddenly embedded in a massive global machine designed to translate human behavior into asset pricing. Markets moved instantly on block trades, media rumors, and analyst ratings, sometimes erasing billions in corporate value before lunch. When founders called trying to decipher why their stock was plummeting, I realized they were looking for a narrative, not just numbers.
This is the root of the phrase “buy on the rumor, sell on the news.” Markets do not wait for cold, objective facts; they trade entirely on interpretation.
By the time an official news story breaks, collective judgment is already baked into the price.
A massive block trade is rarely just a transaction; it is a psychological signal that triggers an immediate information cascade.
In this environment, the meaning assigned to a signal always matters far more than the underlying asset itself, laying the exact structural groundwork for the automated attention markets that would later reshape digital media.
Tributary I: High-Velocity Feedback Loops and Market Interpretation
Why did digital media make that question even bigger?
Because the same groupthink dynamics were beginning to play out across all humanity. At Scripps, I watched advertising become increasingly automated. Electronic trading mechanics were already functioning in the financial markets, and now similar systems were reshaping the digital media landscape. While both markets trade a specific asset according to variables of value, time, and price, ad tech platforms made way for much more sophisticated, nuanced transactions. Unlike a share of stock, an options contract, or a fixed income instrument, the asset being traded in the ad marketplace was the attention (or better yet, the engagement of) a single persuadable human.
Facebook was originally considered a “social network” to connect people; however, it was becoming something way more. It had scale, user-generated content, and the tools to meet advertisers’ demand to micro-target an audience. It quickly became a system for evaluating and classifying people, predicting their behaviors, and deciding what individual would see what message and when it should be seen.
This shift represented a massive scaling of the awareness fallacy. Automated algorithms began aligning with our implicit biases at an unprecedented scale, quietly shaping our digital experiences even as we assumed our choices remained entirely independent. Decisions that had once depended on relationships, negotiations, and human judgment were increasingly being made by interconnected systems operating in milliseconds. I became fascinated by how these machines assign meaning to human behavior, sparking a deeper inquiry into the trajectory of human agency as attention became the new currency being assigned a market price.
Tributary II: Automated Systems and the Scaling of Attention
Why did Bloomberg feel different?
Bloomberg rewarded cross-disciplinary curiosity. In that environment, conversations about financial markets, emerging technology, organizational shifts, and global politics weren't isolated silos—they were interconnected ways of understanding a volatile world. My bookshelf exploded. A single hallway conversation would lure me into a question that sent a new volume to my Amazon cart. One week it was behavioral economics, the next it was leadership psychology, organizational behavior, or algorithmic design.
From the outside, my library looked completely random. If Amazon's recommendation engine was trying to figure me out, I suspect I was one of its ultimate edge cases. But to me, each book picked up a thread I’d already been pulling. Over time, these disparate fields began to converge around shared questions of judgment, authority, and decision-making. Though none of these authors were describing the same industry, they were all pointing toward the exact same underlying structure.
It took years to articulate the question hiding underneath all those pages, but it finally became clear: How do humans decide what matters before they decide what to do?
This hidden mechanism appears nearly everywhere. It is a trader deciding if a market tick is meaningful, a recruiter scanning a résumé, a physician interpreting ambiguous symptoms, or a journalist choosing a homepage lead. Before any major decision is executed, a quieter, secondary process happens first: someone must assign meaning to the data. Only then does raw information become an actionable signal.
Tributary III: Structural Convergence and Complex Scaling Systems.
Why does that matter now?
Because increasingly, those judgments about meaning are being programmed into (or often delegated to) machines.
People often say AI makes decisions. Sometimes it does. More often, though, it helps determine which information reaches the human decision-maker in the first place.
That may be even more consequential.
Every organization already has a decision architecture. Some of it is formal. Approval chains. Escalation paths. Policies. Defined authority.
Some of it isn't. Institutional memory. Recurring meetings. Relationships. The person everyone knows to call before an important decision gets made.
AI exposes that architecture. It reveals where decision authority is clear, where it is ambiguous, and where different parts of an organization are interpreting the same signals completely differently.
Tributary IV: The Hidden Topography of Organizational Decision Architecture.
How do humans decide what matters before they decide what to do?
Tributary V: System Optimization and the Blueprint of Choice.
Why has this become my work?
Because I realized that my interest in artificial intelligence was revealing my interest in organizational judgment.
AI simply makes it impossible to ignore.
If systems increasingly participate in decisions, then organizations have to become much more explicit about how signals are interpreted, how authority is assigned, and when human judgment should remain decisive.
Those aren't technical questions. They're organizational ones.
What am I still trying to understand?
Probably the same thing I've been trying to understand all along.
How people observe the world. How they assign meaning to what they observe. How those interpretations become signals. How signals become decisions.
And how thousands of decisions, repeated over time, become organizations, markets, institutions, and cultures.
The books in my library aren't universal recommendations; they are simply artifacts along my journey of sedimentary learning. When you look closely at markets, cults, political movements, and social media platforms, you realize they are all built on the exact same psychological architecture. They are all deeply vulnerable to conformity, identity signaling, authority bias, rapid feedback loops, and chaotic information cascades.
For three decades, I’ve been trying to understand what these complex, automated systems are trying to show us about ourselves. Every book I read and every system I analyze sharpens my core inquiry a little more. While a complete answer to how we delegate our judgment may not be possible, I am certain of one thing: making better decisions always begins with asking better questions.