TechDrive Zurich

Does moving AI data centres into space solve the problem, or change the engineering challenge?Bilge Demirköz joins Justinas Mišeikis on TechDrive Zürich for a conversation about cooling, crowded orbits and the choices we make as AI becomes part of everyday life.Bilge is a Professor of Physics at METU and a member of the UN AI Panel. Her perspective comes from working with AI in research while questioning where it is useful, what it consumes and how it affects people.We discuss why solar energy does not make heat removal automatic in orbit, how satellite debris can create further collision risks, and why large infrastructure plans need a broader environmental assessment. Back on Earth, Bilge reflects on children's motivation to learn, chatbots that tell us what we want to hear, and the difficulty of building evidence while the technology keeps changing.There is enthusiasm here too: analysing International Space Station data in the search for dark matter, an AI-assisted filter design she says worked despite looking unfamiliar, and student projects exploring the stratosphere. She also explains the local Qwen model in her lab and acknowledges the hallucinations the team still catches.In this episode:Physics, mathematics, music and learning across disciplinesPower versus cooling in orbital AI infrastructureSatellite collisions and shared environmental risksAI as fire: useful, powerful and in need of responsible useEducation, curiosity and chatbot behaviourScientific evidence and the UN AI Panel's roleHer lab's local-model exceptionAI-assisted simulation and engineering designStratospheric balloons and experiments closer to EarthThe case for keeping an efficient solution that already worksJoin the TechDrive Zürich WhatsApp group for new episodes and behind the scenes:https://chat.whatsapp.com/JgGidD8SqsuFzohXvZmo5zABOUT THE GUESTBilge Demirköz is a Professor of Physics at Middle East Technical University (METU) and a member of the UN's Independent International Scientific Panel on Artificial Intelligence. This episode shares her personal perspectives and research experiences.ABOUT THE SHOWTechDrive Zürich is an English-language series of candid, in-car conversations with founders, researchers, investors and operators shaping technology. Hosted by Justinas Mišeikis in Zürich, Switzerland.Video episodes on YouTube: https://www.youtube.com/@TechDrive-ZurichAll episodes and platform links: https://zurich.techdrive.tv/Follow on LinkedIn: https://www.linkedin.com/company/techdrivezurich/Instagram: https://www.instagram.com/techdrivezurichTikTok: https://www.tiktok.com/@techdrive.zurichFollow TechDrive Zürich on your podcast app for the next drive.

How do you turn a robotics breakthrough into something customers actually use?Max Vogt of Swisscom Ventures joins Justinas Mišeikis for a drive through Zürich to explore the gap between great research, a promising startup and technology that becomes part of everyday life.Join the TechDrive Zürich WhatsApp community for new episodes and behind the scenes:https://chat.whatsapp.com/JgGidD8SqsuFzohXvZmo5zMax’s route into venture capital began with law, took him to Hong Kong, and included helping students find their feet at St John’s College. That experience still shapes how he thinks about backing people.We talk about what he looks for beyond a founder’s pitch deck, why technical teams need to stay flexible about their solution, and what seeing technology deployed in Shenzhen made him reconsider about Switzerland. We also discuss robotics and physical AI, the timing of a startup’s market, and why the outcome of a venture investment takes years to judge.In this episode:From law and Hong Kong to venture capitalUnderstanding the person behind the pitchFalling in love with the problemRobotics, physical AI and whether customers are readyBringing Swiss technology into everyday useThe work, relationships and patience behind a venture fundTechDrive Zürich is a series of candid, in-car conversations with the people building technology in and around Zürich, hosted by Justinas Mišeikis.Watch TechDrive Zürich on YouTube:https://www.youtube.com/@TechDrive-ZurichConnect with Max:https://www.linkedin.com/in/maximilianvogt/All episodes and listening platforms:https://zurich.techdrive.tv/LISTEN & FOLLOW TECHDRIVE ZÜRICHAll episodes and platforms: https://zurich.techdrive.tv/Spotify: https://open.spotify.com/show/033uReCOJSJ3CFbj8W5gDXApple Podcasts: https://podcasts.apple.com/us/podcast/techdrive-zurich/id1896934782LinkedIn: https://www.linkedin.com/company/techdrivezurich/Instagram: https://www.instagram.com/techdrivezurichTikTok: https://www.tiktok.com/@techdrive.zurichFacebook: https://www.facebook.com/profile.php?id=61591083008421

A robot can land a backflip, yet struggle to pull keys out of a pocket. Enzo Wälchli, founder of Operator Circle and former Chief Commercial Officer at ANYbotics, joins Justinas Mišeikis for a drive through Zürich to explain the gap between an impressive demo and a dependable product. From selling drilling machines at Hilti to building a robotics company’s commercial organisation, his experience shapes how he thinks about customers, hardware and investment. We discuss why deployments take time, what years of field data are worth, and where Switzerland has an opportunity in physical AI.For first insights and behind the scenes, join our WhatsApp group: https://chat.whatsapp.com/JgGidD8SqsuFzohXvZmo5zIn this episode:From a 23rd-floor strategy office to meeting customers on construction sitesWhy robotics companies need different expectations from SaaS businessesWhat industrial pilots reveal that laboratory demonstrations cannotWhether robot software can succeed without close hardware partnershipsHumanoids, useful dexterity and the role of spectacular demosSwitzerland’s engineering strengths and the thinking behind Operator CircleTechDrive Zürich features candid, in-car conversations with the people building technology in and around Zürich, hosted by Justinas Mišeikis.Subscribe on LinkedIn: https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7475433043554598912Join our WhatsApp group: https://chat.whatsapp.com/JgGidD8SqsuFzohXvZmo5zWatch the video on YouTube: https://youtu.be/1ZWaSGfw--MConnect with Enzo: https://www.linkedin.com/in/ewaelchli/TechDrive Zürich website: https://techdrive.tv/

At a rooftop barbecue at CERN, a group of researchers asked each other what any of their work changed for ordinary people. One answer that came out of those evenings involved body bags, and the packaging used to protect particle detectors, and giving forensic teams enough time to return names to families.Daniel Dobos trained as a particle physicist and spent most of his career at CERN building silicon and diamond detectors. The data rates forced him into machine learning long before the current wave: the ATLAS experiment produced 80 terabytes a second, far too much to store, so the analysis had to happen in real time. He went on to co-found THE PORT, a set of humanitarian hackathons run with challenges from the United Nations and the International Red Cross.The technical heart of this conversation is a claim about understanding rather than cost. Daniel argues that we do not know how learning works, so we substitute scale for the missing knowledge, and that scale turns out to work surprisingly well. Good enough for fluent text, not good enough to call the problem solved. He points at reasoning benchmarks where a ten year old still beats the frontier models, and at the human brain running on 20 to 50 watts, as evidence that a far more efficient answer exists and we have not found it yet.In this episode:Why 80 terabytes a second forced CERN into real time inference on FPGAs, not GPUsForty million inferences a second, years before the AI hypeThe rooftop barbecue that moved him from pure research into the ecosystemTHE PORT, and how detector packaging ended up giving families back their deadWhy a hackathon team working on encryption recruited a magicianThe Harvard study suggesting one generation may not be smarter than the lastWhat social media should have taught us about scaling a technologyWhy he let his children play with AI image generators before saying anythingThe question they arrived at on their own, and why that mattered more than a warningFire as the oldest example of a technology that runs both waysRadiation in orbit, and why a flipped bit is worse for a transformer than for deterministic codeThe brain on 20 to 50 watts, and what neuromorphic and energy based approaches might changeLearning overnight, and what dreaming could mean for a language modelFifteen minutes in a robotaxi in Shanghai, and how quickly an eleven year old stopped noticingAbout the guest: Daniel Dobos is a particle physicist who spent most of his career at CERN working on silicon and diamond detectors and on real time machine learning for the ATLAS experiment. He is a co-founder of THE PORT humanitarian hackathons and a father of three.About the show: TechDrive Zürich is a series of candid, in-car conversations with the people building technology in and around Zürich, hosted by Justinas Mišeikis.🔗 ConnectGuest: Daniel Dobos - https://www.linkedin.com/in/danieldobos/TechDrive Zürich, hosted by Justinas Mišeikis - https://techdrive.tv/WhatsApp Group: https://chat.whatsapp.com/JgGidD8SqsuFzohXvZmo5zFollow the LinkedIn newsletter for the whole series: https://www.linkedin.com/newsletters/techdrive-z%C3%BCrich-7475433043554598912/TechDrive Zurich website: https://techdrive.tv/YouTube channel: https://www.youtube.com/@TechDrive-Zurich

Cleaning robots are real. Greeter robots are real. Anything with an arm is not, at least not as a product you can buy and trust to work unsupervised.Prof. Dr. Yulia Sandamirskaya said it precisely: nobody in the world has yet dared to leave a robot with a manipulator doing real work in a care facility and then gone to bed. That is the line between a demo and a product, and the industry has not crossed it.She is a professor at ZHAW, the Zurich University of Applied Sciences, where she leads the Centre for Cognitive Computing in Life Sciences, and she is spinning a company out of her lab to bring robots into elderly care. So this is not scepticism from the sidelines.We get into neuromorphic computing and why a fruit fly performs acrobatics on roughly 100,000 neurons and almost no power. Then the line that reframes everything: your eye is sharp across only about two degrees and jumps three times a second, so the steady scene in front of you is 95% imagination. Intelligence, she argues, is closer to how many things you can imagine at once than to how much data you collected.She is blunt about AGI. It has been almost here since the Dartmouth summer school in 1956, and bigger static models do not close the gap. She makes the case against humanoids on legs, because a robot on stairs is a hazard to everyone near it, and describes a Neuraverse where robot skills work like apps.Her team toured a care home, ended up in the staff room, and she asked a caregiving team leader almost as a joke why he needed robots at all. He paused, then started listing, and she had to get her notebook out. The people who will work beside the machine know where the real bottlenecks are.She also explains why the business model has to be robot as a service rather than a purchase, and tells the story of her grandmother in Belarus, living alone and refusing help, which is the actual reason she is building this.We just started a TechDrive Zürich WhatsApp group. Every episode the moment it lands, behind the scenes, the bloopers that did not make the cut, and updates straight from the guests themselves.WhatsApp Group: https://chat.whatsapp.com/JgGidD8SqsuFzohXvZmo5zWatch the full episode on YouTube: https://www.youtube.com/@TechDriveZurichAnd follow the LinkedIn newsletter for the whole series: https://www.linkedin.com/newsletters/techdrive-z%C3%BCrich-7475433043554598912/TechDrive Zurich website: https://techdrive.tv/