Frameworks, product decisions, and from-the-trenches lessons from a decade shipping robotics products into regulated industries. For the product managers, engineering leaders, and fleet operators doing the actual work of getting robots into the world.
Decision models I've pressure-tested leading a robotics product through compliance, customer development, and international rollout — the 4D framework, nail-it-then-scale-it, dev-lead / QA-lead split. For PMs and engineering leaders figuring out how to bring AI into their org without breaking what already works.
30 posts a month on LinkedIn — lessons, hot takes, and product-leadership patterns from a decade building robotics products. No prompts. No fluff. Just what I learned yesterday.
Everything I'm exploring at the seam between AI and robotics product leadership — how I use it for customer discovery, cross-functional coordination, and product decisions. Plus ML coursework at Georgia Tech, shared as I go.
Delegation, Description, Discernment, Diligence. The framework every product leader should force their team through before their next AI-related decision. Domain knowledge is the real bottleneck — and robotics teams have it in spades.
Every robotics product goes through the same three stages: prototype, pilot, production. Most orgs try to skip stages. Here's how I think about which AI bets belong at which stage — and why the order matters more than the tool.
100+ hours with AI agents. The obvious thing almost nobody says: output is limited by your skill with the tool, not the tool. Same curve as CAD 30 years ago. Most leaders quit their AI pilots right at the "gimmick" stage.
Pick the channel you already live in. I read everything, reply to most.
