Ep 107 | "We Don't Retrain Models, We Teach Them Cause & Effect" (w/ Johannes Haux)

Johannes Haux is the Co-Founder & CEO of kausable (Heidelberg, Germany), a deep-tech AI startup building reasoning-first causal foundation models. Instead of memorizing internet-scale text patterns or relying on millions of trial-and-error attempts, kausable’s models (based on the Prior-data Fitted Network / PFN approach) learn abstract cause-and-effect structures from synthetic data—enabling them to adapt to entirely new physical environments in-context from just a handful of examples. After working in computer vision under Prof. Björn Ommer at Heidelberg University (alongside the future Black Forest Labs founders) and serving as Head of AI at AskUI, Johannes co-founded kausable with Dr. Benjamin Herdeanu (CTO) and Gregor Ramien (COO). The company recently raised a €12M seed round led by UVC Partners and Entourage, backed by top-tier angels from OpenAI, DeepMind, Black Forest Labs, and Neura Robotics. kausable: https://kausable.ai Johannes on LinkedIn: https://www.linkedin.com/in/jhaux/ Ilir on X: https://x.com/IlirAliu_ Ilir on LinkedIn: https://www.linkedin.com/in/ilir-aliu/ Timestamps: 0:00 Building foundation models in the Cyber Valley deep-tech ecosystem 1:25 Working in Prof. Ommer’s lab & meeting the future Black Forest Labs team 3:48 Early startup lessons at Sysmagine & AskUI 4:09 Why internet-scale LLMs aren't enough for general-purpose physical intelligence 4:52 Discovering Prior-data Fitted Networks (PFNs) & founding kausable 7:00 "I was a bad student": Why structure and habits matter more than raw talent 12:19 Wanting to be a movie director before choosing physics at Heidelberg 18:37 Quitting academia right before COVID lockdowns & taking the entrepreneurial leap 27:38 Why community & ecosystem matter for European deep-tech startups 29:26 How humans actually learn vs. brute-force reinforcement learning in robotics 32:01 In-context learning: Adapting to distribution drift & new sensors without retraining 33:47 TipPFN & predicting critical transitions: Seizures, blackout risks, and physical dynamics 37:37 Michael Black's perspective: Why Europe is a strong launchpad for disruptive AI 41:37 Exploratory tech vs. concrete customer problems: Making the "faster horses" bet 47:17 Moving from a research lab to early design partners 50:36 "Don't fake it, but think big": Advice for European deep-tech founders