Evan Beard is the co-founder and CEO of Standard Bots, building AI-trained industrial robot arms designed to automate real factory work, not demos:In this episode, Evan shares a founder path that started in software startups and Y Combinator long before robotics. Seeing engineers at YC building giant robots convinced him this was a “cheat code on life” and he taught himself mechanical and electrical engineering to make it possible 

We talk about the early years spent building prototypes in a small apartment, running more than one hundred calls with manufacturers, and discovering the same problem every time. Companies wanted automation, but robots were too expensive and too difficult to program 

Evan explains why Standard Bots focuses on usable automation instead of chasing hype. The goal was not a slightly better robot but something ten times easier to deploy, trained through demonstration and physical AI rather than complex programming 

We also discuss surviving the near-death phase, raising funding weeks before running out of money, rebuilding electronics during the chip shortage, and learning electrical engineering while debugging exploding circuit boards remotely 

A conversation about persistence, customer obsession, and why the future of robotics will be decided by real ROI on factory floors, not humanoid spectacle.

Grace Brown is the founder and CEO of Andromeda, building social companion robots designed for aged care and healthcare environments:

In this episode, Grace shares a founder journey that started long before a company existed. She had been building robots since her teenage years, but the real motivation appeared during COVID lockdowns. People in care facilities were physically supported, yet emotionally isolated. The goal became simple. Build a robot people actually want around them.

We talk about learning outside the curriculum, reaching out to mentors early, and running long real-world pilots before the product was ready. Instead of waiting for perfection, Andromeda deployed unfinished systems into nursing homes to understand behavior, trust, and human reaction.

Grace explains why the hardest problem in social robotics is not intelligence but comfort. A robot can function technically and still fail if people feel uneasy around it. Design, personality, and interaction determine adoption more than raw capability.

We also discuss building a company as a young founder, hiring commercial leadership early, and how moving into the US startup ecosystem changed the pace of decisions and iteration.

You don't want to miss this one!

Camilla Mazzoleni is the co-founder and Chief Product Officer of FORGIS, a Zurich-based startup building an AI operating layer for industrial automation:

In this episode, Camilla shares a founder journey shaped long before robotics.

She grew up as a competitive alpine skier, leaving home early to train at elite level. Discipline, pressure, and repetition defined her daily life. Losing was normal. Learning how to reset and keep going became second nature.

After an injury ended her professional sports career, that same intensity moved into engineering. Camilla talks about discovering robotics through building, not theory. Working on factory floors, programming robots across vendors, and seeing firsthand how slow and fragmented industrial automation really is.

We talk about how Forgis came to life at ETH Zurich. Why hardware is not the bottleneck in factories. Why software fragmentation is. And how Forgis sits on top of existing systems as a hardware-agnostic, edge-based AI layer that upgrades how factories operate instead of tearing them apart.

A great conversation about discipline, switching paths, and why Europe’s manufacturing future depends on intelligence, not replacement.

Stephen James is the founder and CEO of Neuracore, and Assistant Professor of Robot Learning at Imperial College London:

In this episode, Stephen shares his path from growing up in Wales to spending a decade at Imperial, a postdoc at Berkeley, and eventually founding Neuracore. Not because he wanted to be a startup founder, but because he kept running into the same problem again and again: every robotics team rebuilding the same infrastructure from scratch.

We talk about what actually slows robotics teams down, why data pipelines matter more than clever algorithms, and how Neuracore aims to become the infrastructure layer that lets teams focus on deployment instead of plumbing.

Stephen also reflects on imposter syndrome, work ethic, moving between academia and industry, and why Europe can and should build its own robotics infrastructure instead of copying Silicon Valley playbooks.

A very honest conversation about building foundations, not hype, and why scaling robotics is mostly about removing friction.

Steve Xie is the founder and CEO of Lightwheel AI, building the simulation and synthetic data layer powering the next generation of embodied AI and humanoid robotics.

In this episode, Steve shares a rare founder journey that starts far from robotics. From studying physics at Peking University, struggling to stand out, and rebuilding confidence through sheer consistency, to a PhD at Columbia and an early failed startup built out of love for his dog. A detour that taught him the cost of building without a business model.

We talk about his path through Cruise, NVIDIA, and NIO, where he led large-scale simulation efforts for autonomous driving. Steve explains how those years shaped his conviction that simulation, data quality, and evaluation are the real bottlenecks in physical AI.

He then breaks down how Lightwheel AI came to life. Why sim-ready assets matter more than solvers. How synthetic data actually closes the sim-to-real gap. And why robotics teams hit a ceiling without proper evaluation and scaling infrastructure.

A deep conversation about resilience, delayed gratification, and why the hardest part of building is often unlearning what made you successful before.

I talk with Animesh Garg,Assistant Professor at Georgia Tech and one of the leading voices in robot learning today:

We talk about growing up in India, building his first autonomous vehicle on a $280 budget after being rejected from a CMU program, and how that failure pushed him toward Berkeley, Stanford, and eventually NVIDIA Research. Animesh shares why he avoided computer science early on, what drew him to mechatronics, and how curiosity rather than planning shaped his entire career.

ORBIT and Isaac Lab, why simulation is now the backbone of robot learning, and how world models, reinforcement learning, and foundation models are lowering the barrier for people outside robotics to build real systems.

Animesh explains why he believes the most important robotics breakthroughs will come from people who are still in high school today.

A deeply personal conversation about grit, risk, redefining success, and why chasing interesting problems beats chasing money.

In this episode, I talk with Prof. Dr. Marco Huber, Professor for Cognitive Production Systems at the University of Stuttgart and Scientific Director for AI at Fraunhofer IPA.

Marco shares his journey from a middle-class upbringing with no academic role models to becoming a leading figure in applied AI for manufacturing. We talk about discovering computer science through a single physics teacher, why he almost went to vocational school, and how a mix of personal drive and mentors shaped his path.

We spend some time on his years between academia and industry, what he learned working in high-pressure startups, and why real innovation happens when theory meets factory floors. Marco explains how Germany still leads in fundamental AI research but is at risk of losing the race when it comes to turning research into scalable industrial products.

A conversation about explainable AI, robotics in production, and why Europe has only a small window left to turn Physical AI into a competitive advantage.

Stefan Dörr-Laukien is the co-founder and CEO of NODE Robotics, a Stuttgart-based company building the software layer behind scalable mobile robot fleets.

In this episode, Stefan shares how his path into robotics did not start with founding ambitions, but with curiosity for how machines work. From studying mechanical engineering at TUM to hands-on autonomous driving research at BMW and years at Fraunhofer IPA, his motivation was always the same: building systems that work in the real world, not just on paper.

We talk about the transition from research to entrepreneurship, why understanding the market matters more than perfect technology, and how NODE Robotics emerged as a Fraunhofer spin-off with real customers from day one. Stefan explains why many mobile robot projects fail at scale, how software complexity is the real bottleneck, and why modular, hardware-agnostic autonomy is critical for industrial adoption.

The conversation also looks into decision-making under uncertainty, the risks of staying too technology-driven, and why taking action beats waiting for perfect information. An honest reflection at building deep tech companies in Europe, navigating industrial customers, and turning applied research into a global product.

A grounded conversation about ambition, uncertainty, and what it really takes to scale robotics beyond pilots.

I talked with Stephan van den Brink, founder and CEO of MANUS™, the company behind some of the most advanced data gloves used in robotics:

Not only robotics: teleoperation, motion capture, and embodied AI. Manus started as a small student project and grew into a deep tech company trusted across the robotics world.

We talk about Stephan's path from studying law and economics to discovering he was meant to build things, not file documents. He explains how the first Manus glove was built in evenings and weekends, how an early Kickstarter failure opened better doors, and how an accelerator program became the turning point for the company.

Stephan shares how Manus shifted from VR gaming to B2B simulation, then to motion capture, and now to robotics. He explains why EMF tracking became their core technology and why precise hand data is suddenly in huge demand as humanoids and AI driven robots take off.

We also talk about building a company for ten years, staying alive through hype cycles, making hard calls, and focusing on what real customers need.

In this episode, I talk with Harald Schäfer, CTO at comma.ai, where he is leading one of the most interesting autonomy efforts in the world:

They work on end to end driving and generative world models is changing how small teams can compete with billion dollar labs.

We talk about his path from electrical engineering in Belgium and Santa Barbara to joining comma as one of the earliest engineers. Harald explains how he helped turn a hacker project into a focused engineering team that ships reliable autonomy to thousands of real users.

He walks me through comma’s move to a single neural network that controls the car from video input, why deleting code is often more powerful than adding more, and how his team uses world models to train on billions of synthetic miles that never existed on real roads.

Harald also shares what it is like to build inside a company with no CEO, why simplicity beats complexity in autonomy systems, and how the new comma 4 and Body 2 signal a move beyond cars into general robotics.

If you work in robotics, autonomy, or AI systems, this conversation is packed with lessons about engineering clarity, avoiding brittle stacks, and shipping real products with small teams.