Building Deep Tech with Ilir Aliu

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.