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In Ep #112 of 22Astronauts, Ilir Aliu sits down with Joe Harris, Founder & CEO of Alloy Robotics. Before starting Alloy, Joe helped scale a consumer telehealth platform to a $1B+ acquisition. Now, he is bringing those high-growth scaling principles to physical AI. "Reliability is measured at the action level. If every action is 99% accurate and you multiply that across 10,000 actions a day, your robot's actual uptime degrades incredibly fast." Alloy Robotics builds the "AI Robotics Engineer", a specialized data and agent platform that ingests robot logs, telemetry, and sensor streams to automatically detect, diagnose, and resolve field failures. In this episode, Joe and Ilir break down why general-purpose robotics hype collides with real-world fleet engineering, why hardware teams need SRE and agentic tooling, and how software remains the highest-leverage layer in robotics. 𝐒𝐏𝐎𝐍𝐒𝐎𝐑 This podcast is brought to you by ‪ ⁨@CoreWeaveYT⁩ providing the specialized cloud infrastructure that powers physical AI and large-scale model training. Follow Joe Harris on X: https://x.com/_joe_harris_ Alloy Robotics: https://www.usealloy.ai/ 22Astronauts: https://22astronauts.vc/ Hosted by Ilir Aliu TIMESTAMPS (00:00) Building the First AI Robotics Engineer (04:05) Don't Sell a Platform, Sell a Solution (09:38) Lessons from Scaling a $1B+ Business (14:38) Scar Tissue vs. Intellectual Learning (20:40) Why Hardware Needs SRE & Agentic Tooling (27:58) How Platform Shifts Expand Total Market Cap (34:25) The Hidden Cost of Robotics Log Data (41:10) Global Supply Chains & Re-Industrialization (51:12) The Compound Math of 99.999% Fleet Reliability

Rajat Bhageria is the Founder & CEO of Chef Robotics (San Francisco, CA), the leading physical AI company deploying AI-guided robot arms across food manufacturing and production facilities. Instead of chasing restaurant cooking hype or $15/hr fast-casual makelines, Chef Robotics pivoted to high-volume food factories (including Amy's Kitchen, Chef Bombay, Gate Gourmet, and Sunbasket), recently surpassing 100M+ servings deployed in production across the US, Canada, and Europe. After studying economics at Wharton and robotics at Penn's GRASP Lab, Rajat sold his first computer vision startup (ThirdEye) at age 20, co-founded pre-seed fund Prototype Capital, and launched Chef Robotics in 2019. Backed by $65M+ in funding (from Kleiner Perkins, Avataar Ventures, Construct Capital, and SVB), Chef Robotics uses real-world production data to build domain-specific Food Foundation Models (FFM) and world models for deformable material handling. 𝐒𝐏𝐎𝐍𝐒𝐎𝐑This podcast is brought to you by  CoreWeave  providing the specialized cloud infrastructure that powers physical AI and large-scale model training. Chef Robotics: https://chefrobotics.ai Rajat on LinkedIn: https://www.linkedin.com/in/rajatbhageria/ Ilir on X: https://x.com/IlirAliu_ Ilir on LinkedIn: https://www.linkedin.com/in/ilir-aliu/ Timestamps: 0:00 Why Chef Robotics walked away from restaurant deals to focus on food factories 3:39 Growing up in Cincinnati: Reading biographies of Carnegie, Rockefeller & Edison 8:33 Building computer vision for the visually impaired (ThirdEye) at Penn 12:35 Basic research vs. Product execution: Why shipping matters 15:14 Why cooking robots fail: "Customers don't want cooking robots, they want assembly" 18:05 Expanding beyond prepared meals: Airline catering, meatpacking, and CPG kitting 22:04 Selling before building: Raising seed capital from Kleiner Perkins 25:34 Why every founder's primary job is persuasion and sales 33:23 The "Value Investing" approach in robotics: Finding high-volume factory assembly 36:08 Specialists vs. Humanoids: Why industrial plants need "Home Depot, not Costco" 41:34 Learning from the 2015 robotics graveyard: Why ROI trumps generalizability 46:28 Real-world production data vs. simulation: The Food Foundation Model 52:52 The Goal & Bottleneck Theory: Focusing strictly on operational constraints 59:20 Advice to my younger self: Category creation takes time

Almetra is turning the perception stack from self-driving cars into factory intelligence. I sat down with Silviu Homoceanu, Co-Founder & CTO of Almetra, to talk about growing up with computers in post-communist Romania, eight years inside VW's self-driving programme, and why the data most factories run on doesn't describe what actually happens on the line. Silviu learned programming by translating English menus on his father's work PC, was selling self-built accounting software at 15, and came to Germany for a PhD in machine learning. He joined VW Group Research in 2014 and spent the next eight years on perception for autonomous driving, before a question from a manufacturing colleague sent him into factories for seven months. Today, Almetra (formerly Deltia) combines computer vision, sensors and machine data to show how production actually runs instead of how it was planned. The company raised a $19M Series A led by blisce/ in June 2026 and is opening an office in Boston. We cover why factory data is often wrong, what happens when the MES reports a reality that isn't real, the 100% defect detection problem, why so many robotics startups get stuck in pilot purgatory, what reliability really means on a running line, and Almetra's move from measuring manual work to automating parts of it. Silviu Homoceanu: https://www.linkedin.com/in/silviu-homoceanu-893b7a5 Almetra: https://www.almetra.ai/ 𝐒𝐏𝐎𝐍𝐒𝐎𝐑 - CoreWeave — The Essential Cloud for AI. CoreWeave sponsors this podcast. Join CoreWeave at #FullyConnected: https://coreweave.com/fully-connected-2026/agenda 👉 Free code: FCLR26 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 00:00 Meeting at the AI Campus in Berlin 00:47 Who Silviu is and what Almetra does 02:44 Romania after 1989 and his father's first PC 06:08 A 286 at home, MS-DOS, then Linux 08:04 Running a computer business at 15 10:21 High school nights in the computer lab 14:44 The mentor who said: study abroad 16:15 Flipping burgers to see the MIT campus 18:58 Romania vs Germany: theory against practice 20:23 First job: cluster computing for bridge simulations 26:20 Why computer networks stopped being enough 28:00 The exam answer that led to a PhD offer 30:47 Percentages or factors: how he picks problems 36:20 VW Research comes knocking in 2014 39:05 The meeting with manufacturing that changed everything 39:52 Seven months in factories: the data is wrong 40:30 Merantix, meeting Max, and founding the company 42:08 The Goal, bottlenecks, and life on a shop floor 43:31 The 100% defect detection problem 46:14 When the MES reports a reality that isn't real 48:09 Automating stations and the robotics angle 49:33 99.999% and why factories say no to startups 52:47 What is actually automatable today 55:07 The Series A, blisce/, and the move to Boston 59:21 Closing Follow 22Astronauts: Newsletter: 22astronauts.com LinkedIn: https://www.linkedin.com/company/22astronauts Instagram: https://www.instagram.com/_22astronauts_/

Almond is building robots for the era of physical AI. I sat down with Saba Khalilnaji, Founder & CEO of Almond, inside the company's San Francisco factory to talk about building robots, scaling hardware, factory automation, and why Almond wants to become "America's robot factory." Saba started building robots as a kid, later spent nearly seven years at DoorDash, and eventually left to work on one of the hardest problems in automation: getting robots to operate reliably in factories where the environment and tasks constantly change. Today, Almond is building Axol, a dual-arm robot designed for physical AI, data collection, teleoperation and real-world deployment. We cover why traditional automation struggles in high-mix manufacturing, what Saba learned from DoorDash, the rise of "neo-integrators," why deployments are Almond's North Star, manufacturing robots in San Francisco, and Almond's work on teaching Axol to help build Axol. Saba Khalilnaji: https://www.linkedin.com/in/saba-khalilnaji/ Almond: https://www.almond.bot/ 𝐒𝐏𝐎𝐍𝐒𝐎𝐑 • CoreWeave — The Essential Cloud for AI. Join CoreWeave at Fully Connected in San Francisco, bringing together the people building the next generation of AI infrastructure and applications. Learn more: https://www.coreweave.com/fully-connected-2026 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 00:00 Inside Almond's San Francisco robot factory 01:02 A first look at Axol 02:12 Building robots as a kid 07:50 Science Olympiad and early robotics 11:56 Studying bioengineering at Berkeley 14:58 From robotics to DoorDash 18:19 What DoorDash taught Saba about moving fast 22:37 Why he left to start a company 25:10 How your environment shapes ambition 29:28 Discovering the automation problem inside factories 31:06 Why traditional factory automation struggles 34:49 What Almond is building 36:03 The rise of the "neo-integrator" 38:36 Why deployments are Almond's North Star 40:17 Building America's robot factory 43:09 Why Almond believes in owning the full stack 44:05 Teaching Axol to help build Axol 46:09 Saba's advice to his younger self 47:25 Why it's called Almond 49:25 Closing Follow 22Astronauts: Newsletter: 22astronauts.com LinkedIn: https://www.linkedin.com/company/22astronauts Instagram: https://www.instagram.com/_22astronauts_/

Prof. Mario Krenn is the Scientific Director & Co-Founder of Feyer GmbH and Professor for Machine Learning in Science at the University of Tübingen, where he leads the Artificial Scientist Lab. For over a decade, Mario’s research has focused on using AI not just to analyze physics data, but at a conceptual level; building AI explorers that discover completely novel experimental setups, optics, and quantum hardware. In July 2026, alongside CEO Jonathan Klimesch and CTO Sören Arlt, he co-founded Feyer GmbH: a frontier AI lab for automated physical invention. As one of only 10 European teams selected for SPRIND's €125M Next Frontier AI Challenge (€3M non-dilutive seed phase), Feyer couples neural explorers with ultra-fast, differentiable physics simulators. Their mission is to autonomously invent next-generation industrial hardware (from advanced microscopy and lasers to lithography and quantum sensors) scaling Europe's high-tech manufacturing strengths. Feyer GmbH: https://feyer.ai Mario Krenn: https://mariokrenn.wordpress.com Ilir on X: https://x.com/IlirAliu_ Ilir on LinkedIn: https://www.linkedin.com/in/ilir-aliu/ Timestamps: 0:00 "AI that invents industrial hardware": What is Feyer GmbH? 3:08 How the Cyber Valley ecosystem turned an academic researcher into a founder 5:43 Growing up in Austria: Programming operating systems at age 10 11:31 Stephen Hawking, mysteries of space, and choosing physics over engineering 15:01 Joining Anton Zeilinger's lab in Vienna: "What is the next to the next step?" 18:33 March 2014: Writing a program to design a quantum experiment humans couldn't solve 21:58 Finding solution.txt & the realization that computers can be creatively scientific 28:28 Moving to Toronto & Vector Institute: Learning AI material discovery from Alán Aspuru-Guzik 34:49 Returning to Europe & building the Artificial Scientist Lab in Erlangen & Tübingen 37:47 Asking questions to the universe: Why physics experiments are fundamental 43:35 Breaking resolution limits in microscopy & applying AI to industrial high-tech 45:49 Winning SPRIND's Next Frontier AI Challenge & founding Feyer GmbH 47:25 Scaling European hardware strengths: Semiconductor, photonics, and optics leadership.

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

Nic Radford is the Co-Founder & CEO of Persona AI (Houston, TX) and former Founder/CEO of Nauticus Robotics (NASDAQ: KITT) & Lead of NASA’s Robonaut 2 and Valkyrie humanoid programs. With nearly 30 years of experience building humanoid robotics (from human-rated space manipulation at NASA Johnson Space Center to subsea autonomous robots) Nic brings a veteran, unfiltered perspective to the commercial humanoid market. Together with Co-Founders Dr. Jerry Pratt (CTO, ex-IHMC & Figure AI) and Jide Akinyode (COO, ex-NASA & Nauticus), Persona AI is building modular industrial humanoids for heavy, labor-constrained industries. Rather than chasing general-purpose home helpers or $15/hr warehouse sorting, Persona targets skilled industrial trades—starting with autonomous welding humanoids for shipbuilding in partnership with HD Hyundai and POSCO. Persona AI: https://persona.ai/ Nic on LinkedIn: https://www.linkedin.com/in/nicolaus-radford/ Ilir on X: https://x.com/IlirAliu_ Ilir on LinkedIn: https://www.linkedin.com/in/ilir-aliu/ Timestamps: 0:00 Why the commercial humanoid robotics market has it completely wrong 3:17 Growing up in rural Indiana & fixing mechanics out of necessity 5:31 Decathlon, severe arthritis & learning "tenacity squared" as a founder 8:16 CB radios, 286/386 PCs, and entering the early Internet at Purdue 12:35 The 2000 Wired cover that sparked a 30-year humanoid obsession 15:23 Getting lost at NASA JSC and stumbling upon Robonaut 1 23:18 Camping in the Texas desert: Building amateur rockets during the early SpaceX era 25:25 Why networking is the lifeblood of deep tech partnerships 28:00 Storytelling for VCs: Why presentation skills are learned, not innate 30:50 From 15 years at NASA (Robonaut 2 & Valkyrie) to becoming an entrepreneur 35:47 Taking space tech underwater: Building Aquanaut & taking Nauticus public on NASDAQ 36:41 Partnering with Jerry Pratt to start Persona AI 37:36 Why general-purpose "Me-Too" robots face an ROI dead end 38:37 Targeting skilled trades: Why shipbuilding and heavy industry need humanoid welders 41:46 GM assembly lines & the brutal reality of automotive cycle times 44:42 The home humanoid myth: "Can we all just stop this? There is zero ROI." 48:34 First-time founders vs. Veteran founders: Tech obsession vs. Cash flow & GTM 49:50 Raising a $42M pre-seed & closing a $100M+ seed round 51:20 Delivering humanoid welders to HD Hyundai in 24 months 52:35 "Look what we built in 90 days": Calling out fake startup timelines

Prof. Dr. Jan Peters is Full Professor of Intelligent Autonomous Systems at TU Darmstadt, Department Head of Systems AI for Robot Learning (SAIROL) at DFKI, and a Founding Research Faculty Member of hessian.AI. With over 54,000 Google Scholar citations, Jan is one of the world’s most influential robot-learning pioneers; having co-developed foundational algorithms like Natural Actor-Critic, REPS, and Probabilistic Movement Primitives (ProMPs). IAS Lab: https://ias.informatik.tu-darmstadt.de Jan's Website: https://www.jan-peters.net Ilir on X: https://x.com/IlirAliu_ Ilir on LinkedIn: https://www.linkedin.com/in/ilir-aliu/ Timestamps: 0:00 Combining machine learning with physical robotics before it was cool 4:05 Studying 4 Master’s degrees & dodging the German military draft 8:27 Why DLR and ATR in Japan were lightyears ahead of German universities 12:37 Meeting Kawato, Chris Atkeson, and Stefan Schaal in a bamboo forest 17:10 Turning down Stanford and CMU for a smaller lab at USC 21:46 How double-blind reviews leveled the academic playing field 23:44 Injecting physical mechanics into reinforcement learning 26:01 How 9/11 permanently shifted American culture and optimism 29:40 Returning to Max Planck: The "monastery" era of AI research 35:14 Why being authentic & speaking your mind wins long-term respect 44:01 Surviving hostile German hiring commissions in the 2000s 47:55 Why TU Darmstadt became Europe’s secret powerhouse for physical AI 57:39 High-speed table tennis, juggling, and the limits of robot perception 58:32 Raising €2M for humanoids and fighting 4 years of European bureaucracy 65:49 German red tape vs. American risk-taking: What's holding Europe back? 68:33 Why TU Darmstadt rivals top US universities in AI breadth

Igor Kulakov is the Co-Founder and CEO of MicroFactory (SimpleAutomation, Inc.), a San Francisco-based robotics startup building autonomous "factories-in-a-box" for precision electronic assembly. Backing their bet that tabletop manufacturing cells beat humanoids for real-world reliability, MicroFactory is backed by Naval Ravikant and Hugging Face CEO Clément Delangue, using continuous human teleoperation feedback loops to reach near-100% precision. MicroFactory: https://microfactory.com Igor on X: https://x.com/ihorbeaver Ilir on X: https://x.com/IlirAliu_ Ilir on LinkedIn: https://www.linkedin.com/in/ilir-aliu/ Timestamps: 0:00 Why we don't need humanoid robots for tabletop assembly 2:24 Inspired by "Back to the Future": Building pinball machines and telescopes at 15 6:30 Creating indie games and viral social media apps for 1M daily users 11:40 The pain of physical hardware: Running a wedding light startup in Ukraine for 9 years 21:16 Moving to San Francisco to be at the frontier of physical AI 32:06 Discovering the problem: Why hardware startups struggle with assembly 36:18 The viral photo frame demo that launched MicroFactory at Founders, Inc. 39:38 Why big AGI labs ignore precision and 99.9% reliability 43:07 Beyond pure data collection: Why reinforcement learning damages hardware 45:00 Human-in-the-loop DAgger: Correcting errors on Jetson Nano for $5 per retraining 52:15 Positive ROI vs. perpetual demos: Why shipping matters 62:41 Advice to founders: Change your environment if your experiments aren't working

Dr. Jiafei Duan is a Presidential Young Professor at the ⁨National University of Singapore (NUS), Director of the MAGIC Lab (Manipulation and General Intelligence Control), and Research Scientist at A*STAR. Formerly a researcher at UW and the Allen Institute for AI (Ai2), Jiafei co-led the open-source "MolmoAct" and "MolmoAct2" robotics foundation models; action reasoning models designed to provide a transparent, fully reproducible alternative to proprietary "black box" physical AI systems. MAGIC Lab: https://magic-ailab.github.io/ RoboPapers Podcast: https://www.youtube.com/@RoboPapers Jiafei on X: https://x.com/DJiafei Ilir on X: https://x.com/IlirAliu_ Ilir on LinkedIn: https://www.linkedin.com/in/ilir-aliu/ Timestamps: 0:00 Why true robotics progress requires radically open science 1:45 The RoboPapers podcast: Asking the hard research questions 3:34 Building my first mobile manipulator from PVC pipes at 16 7:19 The Leap Motion epiphany: Controlling plywood with Arduino 10:15 Writing the survey that introduced "Embodied AI" to Singapore 14:55 Why Singapore is investing heavily in physical intelligence 21:36 Moving from cognitive science to robotics at UW with Dieter Fox 24:51 AR2-D2: Collecting robot manipulation data using AR & inpainting 28:41 The PhD shift: Learning how to think and communicate science 32:49 Why robotics isn't ready for industry to consolidate yet 39:11 Vision researchers are migrating to ICRA: The field is shifting 41:26 Running open VLA models on a $200 SO-100 hobbyist robot 46:38 Leaving Ai2: The philosophy behind fully open data and weights 50:52 Launching the MAGIC Lab at NUS: Finding efficient model architectures 54:36 Why robotics won't have its ChatGPT moment without open source

My conversation with Prof. Dr. Majid Khadiv, Head of the ATARI LAB (Applied and Theoretical Aspects of Robot Intelligence) and Assistant Professor at TUM (the Technical University of Munich). The ATARI Lab focuses on AI planning in dynamic environments, bridging optimal control with modern foundation models to make general-purpose humanoid robots safe, adaptable, and truly autonomous. ATARI Lab: https://ce.cit.tum.de/en/aipd/ Majid on LinkedIn: https://www.linkedin.com/in/majid-khadiv-a92a2a62/ Ilir on X: https://x.com/IlirAliu_ Ilir on LinkedIn: https://www.linkedin.com/in/ilir-aliu/ Timestamps: 0:00 Building national humanoids in Iran without resources 2:11 Why playing soccer beats being a tech kid 3:03 How my sister proved you can break through the entrance exam 5:10 Thinking deeply about a single math problem for five hours 7:02 Visualizing physics: Why Newtonian mechanics just clicked 8:38 Why traditional schooling fails curious minds 10:50 Should AI tutors replace the classroom experience? 18:48 Discovering the intersection of control and dynamics in robotics 20:30 Building the Surena III humanoid from scratch with PhD students 23:44 Honda's Asimo vs our 98kg robot: The engineering reality 26:01 How the 2015 DARPA Robotics Challenge humbled everyone 28:55 Why grades don't reflect true deep-thinking ability 34:01 How Ludovic Righetti transformed my research mindset at Max Planck 37:09 The price to pay for staying at the forefront of academia 52:19 Why general purpose humanoids are the holy grail 58:26 The hand is the pinnacle of human intelligence 62:03 Fusing language models with evolutionary search for physics planning 64:27 Why safety and OOD cases will keep humanoids out of homes for years 70:19 Advice to my younger self: Have confidence in your curiosity

Andrew Kang is the Co-Founder and CEO of ⁨RoboStrategy⁩ and Managing Partner of Mechanism Capital. RoboStrategy already holds concentrated positions in industry-defining companies including ⁨Figure AI, ⁨Apptronik, ⁨Standard Bots⁩, and Dyna Robotics. It is the first publicly listed vehicle purpose-built to give investors liquid exposure to private robotics and physical AI giants, applying the MicroStrategy capital-markets playbook to automation. RoboStrategy: https://www.robostrategy.ai Andrew on X: https://x.com/Rewkang Andrew on LinkedIn: https://www.linkedin.com/in/rewkang/ Ilir on X: https://x.com/IlirAliu_ Ilir on LinkedIn: https://www.linkedin.com/in/ilir-aliu/ Timestamps 0:00 Why we built a public fund for private robotics 0:35 Building the MicroStrategy for robotics 1:22 Escaping the tiger parent trap 2:11 Video games are elite training for game theory and critical thinking 6:30 Learning arbitrage in RuneScape and Neopets 7:59 Don't believe experts just because of their titles 10:24 Why investing is just a video game with real money 12:40 Harvard nutrition professors don't know what they're talking about 18:48 Recreating global finance from first principles 19:57 Why robotics is the early days of crypto all over again 21:50 Realizing humanoids are no longer sci-fi 22:58 Why I wrote a $19M check when every VC told me not to 27:24 How SPVs are democratizing venture investing 30:55 Bet on the founder and their execution speed, not the pitch deck 36:39 The math behind a $50 trillion physical labor market 41:44 Applying the MicroStrategy playbook to private markets 45:48 Humanoids are the GPUs of physical labor 47:48 Building a new SoftBank for the robotics era 50:51 Why robotics startups need engineers for attention 54:35 Don't be satisfied with surface level answers

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.