Product observations, written down.
This is where I share my thinking and perspective on product leadership, strategy, operations, and go-to-market, with a clear focus on experimentation and measurement. Everything here is drawn from building products and the operating systems behind them. I explore the principles, tradeoffs, and organizational mechanisms that help teams move more efficiently while making better-informed decisions.
Featured
All writing →- Four Gates Before You Build AI Article / AI Cheap prototyping removed the filter that used to kill weak AI ideas before they shipped. Four gates put it back - each one far cheaper to check now than to regret later.
- AI-Enabled, or AI-Native, That Is the Question Article / AI The model is the most replaceable part of what you build. The decision that actually holds is made earlier, and rarely on purpose: whether what you're building can survive the model being wrong.
- Portfolio Rationalization Is an Operating System Problem Article / Product Operations Healthy portfolios aren't the product of periodic cleanups but of operating systems - funding, governance, cadence, ownership, and incentives - that question continuation as rigorously as they question entry.
- Learning Is the Product Article / Experimentation & Measurement Experiment volume looks like a staffing number. Take it apart and most of it is knowledge the program produced and lost.
- Experimentation Platforms Are Becoming Organizational Memory Article / Experimentation & Measurement Documentation keeps conclusions and loses the reasoning behind them. Experimentation platforms keep the reasoning, which quietly makes them the place an organization stores what it knows.
- Product Management in the Age of Fast. The Expectation Problem. Article / AI Organizations are taking the cases where building got easier and generalizing the feeling across all deliveries.
Explore the thinking
All ideas →- AI Foundations for Product Leaders The hard part of building with AI is rarely the model - it is everything the model is wired into. This idea gathers the foundations product leaders need to make AI architecture decisions on purpose: what changes when software stops being deterministic, and what those choices commit a team to long after launch.
- Portfolio Dynamics Product portfolios are not designed as much as they are accumulated. They are the visible result of thousands of individually rational decisions made over time. This idea explores why that accumulation becomes difficult to reverse - and why healthier portfolios begin with better decision systems, not better cleanup.
- Product Management in the Age of Fast AI made building dramatically faster and cheaper - but it did not make governing what gets built any easier, and the gap between the two is where organizations now fail.