Runway
A menu-bar app that tracks Claude usage in real time, surfacing burn rate against rate limits before you hit them. Built to scratch my own itch — then built properly because the itch wasn’t just mine.
Twelve years leading product, from early-stage to scale. I lead by staying close to the work — writing specs, sketching flows, sometimes shipping code. That habit became more useful, not less, once AI started reshaping how products actually get built.
The shape of product leadership has shifted in twenty years — more strategy meetings, more dashboards, more layers between the executive and what actually ships. For me, the work gets sharper when I stay close to it. I write specs. I sketch flows. I prototype features. I’ll ship code when the situation calls for it — because that’s how I stay calibrated to what teams need, and how I notice the small things that turn into big things later.
The AI shift is making this matter in a new way. The teams figuring out where AI actually fits aren’t doing it from reports — they’re doing it by using the tools, hitting the limits, and forming opinions from the inside. When the executive shares that experience, decisions get sharper. When they don’t, strategy drifts from how the work is actually changing.
A menu-bar app that tracks Claude usage in real time, surfacing burn rate against rate limits before you hit them. Built to scratch my own itch — then built properly because the itch wasn’t just mine.
A working diagnostic from my dissertation — the operating-model and decision-rights factors that determine whether an organization can move AI from experimentation into production. Frameworks and case studies coming Q3 2026.
How do organizations actually become AI-ready — not at the tools layer, but at the level of operating model, decision rights, and how executives relate to the work itself?