Now
What I'm working on, thinking about, and building toward.
Last updated: May 2026
What I'm working on, thinking about, and building toward.
Last updated: May 2026
This page is inspired by Derek Sivers' now page concept, a snapshot of what's actually occupying my attention rather than a static bio.
I'm currently focused on expanding the content valuation infrastructure, moving from title-level ROI analysis to portfolio-level strategy. The question I'm most interested in: how do you optimize a content library when the value of any individual title depends on what else is in the library? That's a harder problem than it looks, and it's one of the genuinely interesting optimization challenges in the streaming space right now.
I'm actively building out the Data Science Rabbit Hole publication. The focus is on pieces that push back against the more absurd corners of AI hype while remaining genuinely useful to practitioners and executives. There's an almost unlimited supply of material.
My ongoing Data Science for Decision Makers column on All Things Insights continues. The latest installment was an experiment: I let an AI write it, then published it alongside a note from me telling readers exactly what I'd done. The AI had attempted to mimic not just my style, but my choice of topic and my take on it. The goal was to make the experiment visible rather than pass it off as my own work. It raised questions I think are worth sitting with.
I just wrapped the spring semester of Marketing & Media Analytics at the University of Oklahoma. The final stretch had some genuine drama: Canvas went down when its parent company, Instructure, was hit with a cyberattack right as the final exam was due. I extended the deadline and nobody lost points for circumstances outside their control.
My last lecture of the semester was the module on ethics in data analytics. I told the students about times I'd personally been asked to work on projects that raised ethical questions, and asked them to reflect on whether they would have taken those projects. I don't tell them which, if any, I took on. That ambiguity is intentional; the point isn't the answer, it's the habit of actually asking the question.
I attended Gartner's CDAO Conference in New York, including a couple of Chatham House Rule discussion groups and two keynotes on AI. One was intelligent and thoughtful. The other was hype and fairy dust. The ratio felt about right for the current moment.
I'm increasingly interested in the question of organizational data maturity, not just technical capability but the decision-making culture that determines whether technical capability translates into business value. Most organizations that think they have a data science problem actually have a decision science problem, and the fix isn't more sophisticated models.
I'm also thinking about the widespread confusion between language and consciousness. There's a meaningful difference between a system that is intelligent and can construct language, and a system that is sentient and self-aware. Apparently even Richard Dawkins has bought into the idea that LLMs might be conscious. I find that worth examining, not because the question isn't interesting, but because conflating these things leads to bad decisions in both directions.
My hobby time lately has gone toward building out some open-source GitHub projects that I'm hoping fellow data scientists will find useful: code for marketing mix modeling, code for design of experiments (DOE), and a collection of LLM prompts for specialized purposes. They're works in progress, but the intent is to get them to a point where others can actually adopt and build on them.
I'm interested in problems at a larger scope: enterprise-level data strategy, AI governance, and the organizational design questions that determine whether advanced analytics creates durable competitive advantage or expensive overhead. If you're working on those problems and want to think through them together, I'm reachable by email.