Every self-driving car has cameras or a lidar system. It knows what's around it before it decides where to go. Now picture a factory built the same way, except nobody ever built the camera or the lidar. That's process manufacturing today: the reactors, pipes, and stainless steel equipment behind your protein bar, your prescription, the chips in your laptop. Half of everything the world manufactures runs through equipment like this, and almost nobody measures what's actually happening inside it in real time. That's the gap Annie Lu and her brother David set out to close with Laminar. In episode 8 of Code and Cobblestones, Annie joins us, Matt Crane and Will Lehmann, to walk through what physical AI looks like when it's not a robotics story. No sensing the room, deciding, moving an arm. Here the thing being sensed is a chemical reaction, not a room, so Laminar had to build its own version of that camera and lidar from scratch: proprietary sensors first, then closed foundation models trained on the chemistry data, then a direct line into the factory's own control systems. They didn't start with the biggest possible use case. They started with cleaning. Every plant has to shut down between batches and wash everything out, so one product doesn't contaminate the next. Right now, almost every factory in the world runs that cleaning on a fixed schedule that never adjusts, and burns water, energy, chemicals, and downtime doing it. Laminar figured out how to make that process dynamic instead of fixed, and calls it the wedge that earns them the right to expand into everything else: fermentation, filtration, distillation, and beyond. It's why they're now running across six continents, inside seven of the top ten food and beverage companies in the world. What you'll learn: Why Annie treats robotics as the wrong template for physical AI. Chemistry intelligence, not spatial intelligence, is the actual gap How Laminar's stack works end to end: sensors on the equipment, foundation models reading the chemistry, automation acting on it, with zero human in the loop Why clean-in-place became the wedge instead of something bigger or flashier, and how one use case earns the right to expand across a whole plant The real reason legacy manufacturing tech hasn't changed in 60 years, per Annie. It's not that factories resist AI. Nobody built the camera and lidar for chemistry What happened when a competitor told a room of plant managers their model was a black box they couldn't explain, and watched the room turn Her advice for founders on choosing investors: find the ones who already match your DNA, before you ever pitch them Why she thinks Boston's biggest advantage is culture, not capital, and what that means for a physical AI company scaling globally TIMESTAMPS: 00:00 Welcome 01:05 Why Physical AI Now 03:38 Laminar and Process Manufacturing 06:24 The Chemistry Intelligence Stack 09:51 Laminar's Origin Story 12:32 First Principles on the Shop Floor 17:40 Co-Founding With Her Brother 20:32 Winning Trust From Manufacturers 26:57 The Clean-in-Place Wedge 31:57 Advice for Physical AI Founders 35:24 Fundraising and Investor Fit 38:46 Rapid Fire With Annie Lu 40:07 Boston's Manufacturing Culture 42:17 How to Work With Laminar Guest: Annie Lu, Co-founder, CEO at Laminar Hosts: Matt Crane, Founder of MGMT Boston https://mgmtboston.com/ https://www.linkedin.com/in/matthewhcrane/ Will Lehmann Founder of Step Function VC https://stepfunction.vc/ https://www.linkedin.com/in/wlehmann/ Watch/listen/Subscribe: Spotify: https://open.spotify.com/show/033HueFNlE1LPnEFPvVscs?si=e2351085b40b4334 Apple Podcasts: https://podcasts.apple.com/us/podcast/code-and-cobblestones/id6785977680 Code and Cobblestones is a love letter to Boston tech founders. New episodes spotlight the people building the city's next generation of companies. ▶ Subscribe for the rest of the tour. New episodes spotlight the founders and builders putting Boston on the map. Produced in partnership with OBEY Creative, building podcast-driven content engines for B2B founders. One great conversation per week becomes 15-20 assets: clips, LinkedIn posts, blog content, and newsletter material. Learn more at https://obeycreative.com/
