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Robot guard dogs are moving from experiments to serious business. Asylon Robotics has signed a $17 million, five-year deal with security giant Securitas to deploy autonomous robot dogs and aerial drones at critical infrastructure facilities. As far as I can tell, this could be the biggest robot security deal ever. In this episode of NEXT, I chat with Asylon CEO Damon Henry about the big deal, why autonomous security robots are finally proving their value, and what the future of security looks like for mission-critical and national security facilities. Asylon already has more than 50 robots deployed across the US, using Boston Dynamics Spot quadrupeds, autonomous aerial drones that they make themselves, custom security hardware, and a 24/7 robotic security operations center. We discuss: - How Asylon's $17M Securitas deal came together - How robot dogs and aerial drones work together to secure facilities - Why customers are seeing reductions in crime and vandalism - How autonomous robots work alongside human security guards - Why Asylon builds some hardware and buys other components - The potential for humanoid robots in security and inspection - What large-scale deployments mean for the ROI of robotics Damon also shares real-world results from customer deployments, including Mercedes-Benz Stadium in Atlanta, where robot patrols have helped protect human guards while improving security outcomes. #Robotics #RobotDogs #PhysicalAI #AutonomousRobots #SecurityRobots #BostonDynamics #Asylon 00:00 Robot security lands a $17M deal 00:27 Asylon's deal with Securitas 01:00 How the autonomous security system works 02:45 Securing critical infrastructure 03:25 The growing threat to physical security 04:20 How robot dogs and drones work together 05:55 Real-world crime reduction results 06:25 Building vs. buying robot hardware 07:15 Autonomous charging and battery swapping 07:50 Cybersecurity and military compliance 08:40 Could humanoids join security patrols? 10:40 How robots work with human guards 11:40 Mercedes-Benz Stadium deployment 12:35 Why robots improve with experience 13:10 What the $17M deal proves

What does it actually take to put 1,000 humanoid robots to work? Hexagon Robotics President Arnaud Robert joins me to explain how the company is moving its Aeon humanoid from pilots and demonstrations into real industrial production, including a plan with Schaeffler that could eventually scale to 1,000 robots. And the big lesson is that scaling humanoids isn't about taking one robot doing one task and multiplying it by 1,000. It's about building a multipurpose fleet that can move between jobs as demand changes across a factory. We talk about: • Why humanoids need to be multipurpose to justify themselves • How Aeon gathers training data while doing real work • Hexagon's “sim-to-real-to-sim” training loop • Why customer-specific factory data matters • The difference between a successful pilot and a production-ready robot • The two metrics Hexagon cares about most: cycle time and human intervention • What Hexagon learned from early sensor-fusion and actuator challenges • Why Aeon uses wheels instead of legs — and can still climb stairs • How humanoids could eliminate bottlenecks instead of simply replacing workers • Schaeffler's “robot gym” and the train-deploy-scale path to 1,000 humanoids Robert's view of the factory of the future isn't a lights-out facility with no people. It's a highly autonomous factory where humans and robots work together — with robots handling repetitive tasks and shifting between bottlenecks while skilled workers focus on problem-solving. This is what humanoid robotics looks like when the conversation moves beyond prototypes and into production. 00:00 What does it take to deploy 1,000 humanoids? 01:31 Why 1,000 robots changes the problem 03:02 Why humanoids need to do multiple jobs 03:55 The race for physical AI training data 04:51 Simulation, synthetic and real-world data 06:20 Hexagon's sim-to-real-to-sim loop 08:10 What robot-generated training data captures 10:21 Pilot vs. production-ready humanoids 12:14 The industrial experience advantage 13:12 The two metrics that really matter 15:39 What Hexagon learned from failure 17:35 The humanoid actuator problem 18:47 Why Aeon uses wheels instead of legs 19:59 Yes, a wheeled humanoid can climb stairs 21:28 What the factory of the future looks like 23:42 Using humanoids to eliminate bottlenecks 25:17 Automation exposes the hidden 3% 26:07 Schaeffler's path to 1,000 humanoids 27:18 Train, deploy, scale

What does it actually look like to work with hundreds of AI agents? In this episode of NEXT with John Koetsier, I chat with Steve Ancheta, founder and CEO of Zig.ai, an AI-native platform for relationship-driven sales. Steve explains why what looks like a single AI agent to a user can actually be a swarm of hundreds of specialized agents working behind the scenes, handling copywriting, follow-ups, CRM updates, next-best actions, orchestration, and more. We also dig into how Steve personally uses AI agents as force multipliers across his work, why he still writes important investor and customer messages himself, and why simply giving an agent access to documents isn’t enough. Steve shares his approach to building an effective agent harness, including two critical pieces many people overlook: governance and grounded truth. We also discuss OpenClaw, NanoClaw, cloud-based agents, agent security, human-in-the-loop workflows, critical thinking, AI productivity, and the shift from reactive agents that wait for instructions to proactive agents that start doing useful work on their own. Topics include: - Why Steve believes many beginners shouldn’t build agents yet - How hundreds of specialized agents can operate as one system - AI agents as force multipliers rather than human replacements - Why human taste and connection become more important as AI improves - How Steve uses agents for planning, operations, scheduling, and analysis - The dangers of AI-generated documents nobody actually reads - Agent governance, guardrails, and permissions - Giving agents a reliable source of truth - Why vector databases alone aren’t a magic solution - Balancing human interaction with AI-assisted work - The move from reactive AI agents to proactive AI agents - Why humans should remain in the loop for external actions Guest: Steve Ancheta Company: Zig.ai 00:00 Why beginners may not be ready for AI agents 00:24 Working with AI agents every day 01:34 Steve Ancheta’s journey into AI 03:00 The early days of agent workflows 04:36 OpenClaw, Hermes, and the agent boom 05:37 The security problem with early AI agents 06:00 Why Steve moved agents to the cloud 07:00 Local agents vs. cloud-based agents 08:00 How quickly agent technology is evolving 09:00 How Zig.ai uses hundreds of AI agents 10:00 Why one “agent” may actually be an entire swarm 11:00 Specialized agents and orchestration 11:30 What Steve personally uses AI agents for 12:00 Why he still writes important emails himself 13:00 AI agents as force multipliers 14:00 Why AI won’t eliminate the human element 15:00 Human connection and taste matter more than ever 15:50 Steve’s favorite uses for AI agents 16:00 The danger of AI-generated documents 17:15 Agents for planning, operations, and forecasting 18:00 Using agents to find problems across a company 18:40 AI as a chief of staff 19:25 Why beginners probably shouldn’t build agents yet 20:00 What an agent harness actually does 21:00 Zig.ai is fundamentally a data company 22:00 Giving AI agents a reliable source of truth 23:00 Governance, guardrails, and agent permissions 24:00 Why vector databases aren’t enough 24:35 How AI agents change the way we work 25:20 Balancing AI time with human interaction 26:00 Why Steve deliberately spends time away from technology 27:00 The next phase: proactive AI agents 28:00 Keeping humans in the loop 29:00 When AI agents start giving us tasks

What will the home of 2035 actually look like: One humanoid robot that does everything, or 10 specialized robots, each handling one specific task extremely well?In this episode of NEXT with John Koetsier, I talk with Amber Atherton, serial entrepreneur and investor at Patron, about a very different vision for the future of home robotics.Instead of assuming that every household will eventually have a general-purpose humanoid assistant, Amber argues that consumer robotics may scale first through highly specialized devices that quietly take over specific routines and chores.Think skincare. Hair. Gardening. Cleaning. Wardrobe management. Kitchen tasks. Pet care.In other words, the future home may be full of robots without looking like it’s full of robots.We discuss why Roomba is still such an important example of successful consumer robotics, why specialization often wins in consumer markets, what people may actually be willing to pay for useful home robots, and how falling hardware costs plus better AI models could create a new wave of robotics startups.We also get into the bigger platform question: if homes eventually contain dozens of physically intelligent devices, who owns the operating system underneath them?Google DeepMind? NVIDIA? A new robotics platform? Or an ecosystem we haven’t seen yet?And, of course, we debate whether humanoids ultimately win anyway.This episode is cross-posted from my Humanoid Daily podcast, where I cover the companies, technologies, investments, and ideas shaping the humanoid robotics boom.Topics include:• Humanoid robots vs. specialized robots • What home robotics could look like by 2035 • Robots for hair, skincare, gardening, and household chores • Why Roomba remains such an important robotics success story • Consumer robotics business models • Pricing home robots • AI foundation models and physical AI • Robotics manufacturing and supply chains • Robot training data • The future operating system for physical AI • Google DeepMind, NVIDIA, 1X, Figure, and Apptronik • Whether the future home needs a humanoid at allNEXT with John Koetsier explores what’s coming next in technology, business, AI, robotics, and the future of the world around us.

What happens when you combine frontier AI, agentic systems, and quantum computing? In this episode of NEXT with John Koetsier, we chat with Mykola Maksymenko, co-founder and CTO of Haiqu, about how AI agents are dramatically accelerating quantum research and potentially scientific discovery as a whole. Maksymenko shares how an AI system was able to reconstruct months of his own PhD research in a fraction of the time, even identifying a bug in one of his formulas. He also discusses experiments involving genomics, molecular simulation, quantum chemistry, and condensed matter physics. The bigger question: do we really need to wait for fault-tolerant quantum computers with hundreds or thousands of logical qubits before quantum computing becomes useful? Maksymenko argues that useful quantum applications are already emerging today, particularly when AI agents help scientists discover algorithms, orchestrate workflows, and handle the complexity of working with noisy quantum hardware. We also explore the risks of increasingly capable AI systems, scientific guardrails, quantum utility versus quantum supremacy, and what happens when researchers can test ideas in a weekend that previously might have required months or years. Topics include: • Agentic AI for scientific research • Quantum utility vs. quantum supremacy • AI-assisted genomics • Quantum chemistry and molecular dynamics • Automating quantum software workflows • AI as a scientific collaborator and educator • The risks and guardrails of frontier AI • Why scientific discovery could accelerate dramatically 00:00 — “I think AGI is here” 00:40 — Meet Mykola Maksymenko 01:01 — Why a quantum physicist started experimenting with genomics 02:18 — Reproducing years of research with AI and quantum computing 03:07 — The convergence of AI, agents, and quantum computers 04:26 — Are we entering the singularity? 04:45 — AI reconstructs six months of PhD research 05:28 — The risks of AI-assisted genomics and scientific discovery 06:46 — Pandora’s box and the race for frontier AI 07:02 — What researchers are doing with the technology now 08:29 — Can quantum computers already do useful work? 09:21 — From quantum supremacy to quantum utility 11:06 — Molecular dynamics and quantum chemistry 12:12 — The minimum viable product of quantum computing 13:00 — Why Haiku moved deeper into agentic AI 13:30 — How much faster can AI make scientific research? 15:47 — AI as a scientist’s educator and collaborator 16:51 — Running research experiments over a weekend 17:16 — What happens next for AI + quantum computing 17:38 — How AI agents operate quantum computers 18:59 — Closing thoughts

AI is moving beyond text, images, and code. Now it’s learning to read and write -- shall we say program -- DNA.In this episode of NEXT, John Koetsier talks with Eric Nguyen, co-founder and CEO of Radical Numerics, about the rapidly emerging world of biological AI.Nguyen and his team helped create Evo and Evo 2, generative foundation models for DNA, and are now working toward what they call “general biological intelligence”: AI systems capable of understanding biology across DNA, gene expression, methylation, proteins, and other biological signals.The potential upside is enormous. These systems could help scientists detect cancer earlier, develop treatments for antibiotic-resistant superbugs, understand disease more deeply, and eventually design countermeasures to emerging biological threats on demand.But the same capabilities introduce serious risks.Nguyen explains how AI could potentially generate biological sequences that retain dangerous functions while evading traditional sequence-matching detection systems ... essentially creating biological “deepfakes.” He also discusses why AI labs need to develop biodefense capabilities alongside increasingly powerful biological design tools.The conversation covers DNA foundation models, AI-generated viruses, biosecurity, pathogen detection, wastewater surveillance, attribution of biological threats, antimicrobial resistance, cancer detection, open-source versus closed-source biological AI, and why biology may be the next major frontier for artificial intelligence.00:00 AI-designed DNA and “deepfake” viruses00:21 AI is moving into biology00:55 Meet Eric Nguyen of Radical Numerics01:20 Why DNA is a language01:39 Teaching AI to read and write DNA02:54 What happens when AI can create biological systems?03:15 The promise and risks of programmable DNA04:32 The medical upside of AI-driven biology04:58 Moving beyond single-molecule drug discovery06:30 Can AI model the complexity of the human body?06:59 Biology has more data than we know how to use08:38 Where all the DNA data comes from09:12 The dangerous side of AI-generated biology10:01 What is a “deepfake virus”?12:41 Could AI make pathogens more dangerous?14:36 Putting AI biodefense on the front lines16:56 The three pillars of biodefense18:22 On-demand treatments for new diseases19:11 How close is this future?21:53 Why biodefense capabilities are falling behind24:36 Should powerful DNA models be open source?25:40 How Evo and Evo 2 were made safer26:49 Why Radical Numerics is keeping Omni closed27:55 AI-generated bacteriophages and superbugs29:23 Balancing breakthrough biology with biosecurity30:53 Using AI to detect cancer earlier32:08 Why cancer detection needs multiple biological signals33:49 “Sensor fusion” for biology34:24 Why a holistic view of medicine matters

Why aren’t hundreds of millions of intelligent robots already operating in the physical world? In this episode of NEXT with John Koetsier, John speaks with Seth Winterroth, partner at Eclipse, about the rapidly changing robotics investment landscape, the rise of physical AI, and the race to build the next generation of autonomous machines. They explore whether the world really needs hundreds of humanoid robotics companies, why timing matters as much as technology, and why some robotics startups may need to build the entire stack ... from hardware and embedded software to AI models, evaluation systems, and deployment infrastructure. The conversation also covers Genesis AI, Wayve, Project Prometheus, Apptronik, Figure, 1X, autonomous vehicles, delivery drones, industrial automation, surgical robotics, and the future of robots in the home. Topics include: • Why robots still aren’t widely deployed • The five forces driving robotics investment • Whether 400 humanoid robotics companies are too many • Full-stack versus platform-based robotics strategies • The challenge of achieving reliability and safety • When useful home robots may finally arrive • Why autonomous vehicles are already robots • The industries likely to adopt robotics first • The future of manufacturing, logistics, transportation, and surgery • What the next major robotics inflection point could be Seth Winterroth is a partner at Eclipse, an investment firm focused on companies transforming physical industries. Eclipse has backed robotics and automation companies including Genesis AI, Wayve, MiND Robotics, Foxglove, and Third Wave Automation. Subscribe to NEXT for more conversations about AI, robotics, emerging technology, and the companies shaping the future. 00:00 Why aren’t robots everywhere yet? 00:37 Introducing Seth Winterroth 01:03 The robotics investment landscape 02:00 Seth’s background in applied robotics 03:01 The five forces accelerating robotics 04:04 Are there too many humanoid robot companies? 05:00 Creative destruction in robotics 06:02 How investors pick the winners 06:38 Why robotics companies need velocity 07:00 The danger of being too early or too late 07:51 How Wayve benefited from entering later 08:36 Project Prometheus and the $10 billion seed round 09:05 Why Genesis AI is building full stack 10:01 Full-stack robotics versus foundation-model platforms 11:03 The dirty secret: Where are all the robots? 12:03 Reliability, safety, and deployment friction 12:39 The challenge of vertical integration 14:02 What robotics companies should build themselves 15:03 Capital requirements and the robotics J-curve 16:01 How robotics companies can scale rapidly 16:39 When humanoid robots may reach the market 17:04 Figure, 1X NEO, Unitree, and AgiBot 18:24 A broader definition of robotics 19:04 Are cameras and dishwashers robots? 19:39 Autonomous vehicles as embodied AI 20:23 Why transportation autonomy could be transformational 21:05 Why humanoids still have a long way to go 21:44 One general-purpose robot or many specialized machines? 22:35 What will make home robots successful? 23:39 Finding the minimum viable home robot 24:20 Physical and digital products 25:04 Why the App Store model matters for robotics 26:05 How robots will gain new capabilities over time 26:35 The brutal economics of consumer robotics 27:12 Consumers buy outcomes, not robots 27:48 Robotics beyond humanoids 28:18 Kiva Systems and the modern robotics era 29:05 Why constrained environments win first 30:02 Robotics in automotive manufacturing and logistics 31:02 Eclipse’s robotics portfolio 31:31 When robots will begin walking among us 32:03 The next major robotics inflection point 32:42 Escaping the trough of disillusionment 33:29 Why autonomous transportation is nearly solved 34:04 How self-driving changes cities and society 34:36 The future of robotic surgery 35:01 Delivery drones and regulatory barriers 35:39 Closing thoughts

Can robots learn from the internet the same way ChatGPT learned from text? In this episode, Andrew Wooten, co-founder of Rhoda AI, explains why his company believes the future of robotics isn’t collecting millions of hours of robot data ... it’s learning from internet-scale video. Instead of relying on traditional vision-language-action (VLA) models that require enormous training datasets, Rhoda’s approach teaches robots physical intuition by predicting the future through video. We also explore why, in Andrew's opinion, warehouses and factories will likely be the first major market for humanoid robots (not homes!), why Rhoda chose a wheel-based humanoid design, how language models fit into physical AI, and how the company’s robots can learn complex tasks with just 8–10 hours of training data instead of 10,000+ hours. If you’re interested in robotics, AI, automation, or the future of manufacturing, this conversation offers a fascinating look at where physical AI is heading. In this episode: * Why warehouses beat homes as the first market for humanoid robots * Why Rhoda chose wheels instead of legs * The biggest limitation of today’s robot AI models * How internet-scale video teaches robots physics * Why predicting the future helps robots manipulate the real world * Edge AI vs. cloud robotics * The role of LLMs in controlling robots * How Rhoda cut robot training from 10,000+ hours to just 8–10 hours * When zero-shot robot learning could become reality Guest Andrew Wooten Co-founder, Rhoda AI Website: https://rhoda.ai 00:00 Why Humanoid Robots Don’t Have Wheels 00:18 Can Robots Learn From Internet Video? 00:42 Best Use Cases for Humanoid Robots 02:10 Why Warehouses and Factories Come First 04:02 The Economic Impact of Robotics 05:00 Home Robots vs. Industrial Robots 06:05 Why Rhoda AI Chose Wheels 08:00 Building a General-Purpose Robot 09:55 Why Full-Stack Robotics Companies Have an Advantage 10:40 The Evolution of Physical AI 12:20 Why Vision-Language-Action Models Fall Short 14:05 Training Robots With Internet-Scale Video 16:05 How Rhoda AI’s Video-Action Model Works 17:25 Edge AI vs. Cloud Computing 19:05 How Robots Develop Physical Intuition 20:40 Predicting the Near Future in Real Time 22:00 Can Robots Build a Subconscious? 23:10 Using Language Models to Control Robots 25:15 Rhoda AI’s Hardware Strategy 26:20 The Biggest Problems With Today’s Humanoids 28:20 When Will Robots Truly Learn on the Job? 30:05 Training Complex Tasks in 8–10 Hours 31:15 Zero-Shot Robot Learning and What Comes Next

My instant first look at 1X’s just-unveiled NEO robot hands. Hot take: they're a massive leap toward truly useful humanoid robots. With 25 degrees of freedom, tendon-driven actuation, tactile sensing, force feedback, and near-human dexterity, these hands can do far more than simple gripping. They can assemble LEGO, pick up tiny screws and coins, plug in a USB-C cable, zip jackets, use tools, sort delicate fruit, and even wash themselves. (Washable robot hands! That's not common.) In this video, I break down what makes these hands fundamentally different from traditional robotic grippers, why backdrivability and force transparency matter, and why hands may be the single most important component of practical humanoid robots. Topics covered: * Why most robot hands are still “numb” * How tendon-driven hands improve dexterity * Force sensing and tactile feedback explained * Precision handling of tiny objects * Strength, durability, and IP68 sealing * Why washable robot hands matter for home robotics * Over-the-air upgrades and the future of NEO If humanoid robots are going to become useful assistants in homes, warehouses, and workplaces, breakthroughs like this are what will make it possible.

Is AI really causing mass layoffs or are CEOs just using AI as a convenient excuse? In this episode, John Koetsier talks with longtime tech journalist, columnist, author, and podcaster Mike Elgan about why the “AI is killing jobs” narrative may be overblown. Elgan argues that many companies are engaging in AI washing: blaming layoffs on AI to make cost-cutting look like innovation. The conversation goes deep into the future of work, why every major technology shift creates fear before new opportunities emerge, how AI will change education and human skills, and why humanoid robots may be more hype than practical reality. They also explore Elgan’s concept of the attachment economy: a future where AI products don’t just compete for our attention, but for our emotional bonds. Guest Mike Elgan Tech journalist, columnist, author, and podcaster Host of Superintelligent Author of The Attachment Economy on Substack Subscribe for more conversations on AI, robots, innovation, and the future of technology: https://techfirst.substack.com Chapters:00:00 AI, layoffs, and whether AI is really to blame01:00 Meet Mike Elgan02:00 Why people believe AI will cause mass job loss03:00 AI washing and layoffs as a CEO “fig leaf”05:00 Techno-utopian claims about AI replacing work06:00 Why AI layoffs often don’t pass the logic test08:00 Past tech revolutions and new job creation09:00 Companies that lay off because of AI “lack imagination”11:00 Why new industries can create more jobs13:00 Nobody can predict where AI will lead15:00 Why the speed of AI change feels different16:00 AI, robotics, and fear about the future of work17:00 AI natives and generational change19:00 Why humans treat talking AI like a person20:00 Education when facts are instantly available22:00 Cursive, typing, and speech-to-text24:00 Humanoid robots in the home25:00 Human work, creativity, and future value26:00 Why human connection may become more valuable27:00 Are humanoid robots a dumb idea?29:00 Specialized robots vs. humanoid robots31:00 The attachment economy after the attention economy32:00 AI products designed to create emotional attachment34:00 Relationship AI, robot pets, and illusion35:00 Why chatty AI feels conscious36:00 The human brain, AI illusion, and caution37:00 Closing thoughts with Mike Elgan

Humanoid robots are often pitched as factory workers, warehouse assistants, or home helpers. But what if education becomes their biggest opportunity? In this episode, Faraday Future co-CEO Chris Chen explains why K-12 schools, STEM programs, and university research labs could be among the first large-scale adopters of humanoid robots and robot dogs. Chris shares why Faraday Future believes we’re at the beginning of an “iPhone moment” for robotics, how the company plans to deliver nearly 1,000 robots this year, and why physical AI represents the next major evolution beyond today’s large language models. We also discuss: • Why humanoid robot adoption is accelerating worldwide • The transition from digital AI to physical AI • How robots could help teach coding, STEM, and AI literacy • Security, hospitality, and inspection use cases already being deployed • Why Chris believes robotics could become a much larger market than automobiles • Building a robotics ecosystem powered by data, developers, and AI If you’re interested in AI, robotics, education, automation, or the future of work, this conversation offers a fascinating look at where the industry is headed next. Guest: Chris Chen Co-CEO, Faraday Future Nasdaq: FFAI Subscribe for more conversations with the leaders shaping the future of technology: https://techfirst.substack.com Chapters: 00:00 Introduction: Humanoid Robots in Education 00:31 Faraday Future’s Vision for Physical AI Infrastructure 01:42 The Goal of 1,000 Robot Deliveries 02:22 Why Humanoid Robot Manufacturing Is Accelerating 03:37 The Starting Point of the Humanoid Robotics Industry 04:14 From Digital AI to Physical AI 06:04 Why Schools Are a Key Robotics Market 06:52 The Three Factors Driving Robotics Adoption 07:15 K-12 Education, STEM Training, and Robotics Institutes 08:12 Getting Kids Interested in AI Instead of Games 09:04 The Future Demand for Robotics Technicians 09:43 Humanoids vs. Robot Dogs in Education 09:59 Will Every Student Have an AI Tutor? 10:30 Beyond Education: Security, Inspection, and Hospitality 11:14 Robot Dogs for Autonomous Security Patrols 11:50 The Coming Ecosystem for Robot Maintenance 12:06 Will Humanoid Robots Become Bigger Than Cars? 12:57 How Robots Could Impact Global GDP 13:28 Competing in the Exploding Robotics Industry 13:56 Building a Robotics Flywheel Through Data 15:01 The Team Behind Faraday Future Robotics 15:44 Where Faraday Future Will Be in One Year 16:03 Faraday Future, Robotics, EVs, and Web3 17:00 Closing Thoughts

What if airports had self-driving mobility pods that could safely navigate through crowds, just like something out of The Jetsons? Or the Pixar movie Wall-E? In this episode, John Koetsier sits down with Matthew Anderson, CEO of A&K Robotics, to explore the future of autonomous mobility. A&K Robotics is building AI-powered self-driving pods designed to help people navigate airports independently without relying on wheelchairs or staff assistance. But the real breakthrough isn’t just autonomy. It’s crowd navigation. Matthew explains why navigating dense, unpredictable crowds is one of the hardest problems in robotics, and how A&K’s “crowd-centric AI” could become foundational technology for airports, stadiums, smart cities, conferences, and even humanoid robots in the future. They also discuss: * Why airports are the perfect proving ground for robotics * The AI and sensor stack powering autonomous mobility * Directional sound systems inspired by The Sphere in Las Vegas * Scaling robotics startups from prototype to deployment * Raising an $8M Series A round * The personal story that inspired Matthew to build the company * Why the future of robotics depends on moving safely through human environments Guest: Matthew Anderson — CEO, A&K Robotics Company: A&K Robotics If you enjoy conversations about AI, robotics, startups, and the future of technology, subscribe for more interviews with founders and innovators shaping what’s next. Subscribe here: https://techfirst.substack.com 00:00 – Intro 00:30 – Meet A&K Robotics and the Vision for Autonomous Airport Mobility 01:20 – Why Crowd Navigation AI Is the Hardest Problem in Robotics 02:40 – Navigating Dense Airport Crowds and Passenger Flow 04:05 – Directional Sound and Designing a Better Airport Experience 05:50 – Building an “iPhone Experience” for Mobility Robots 06:30 – Sensors, LIDAR, and Operating Without GPS 07:20 – Fleet Management and Autonomous Operations in Airports 08:00 – Mapping Airports and Optimizing Routes Through Crowds 09:00 – Scaling the Business and Solving Systems Integration 10:00 – Charging, Docking Stations, and the Future Airport Network 10:45 – Raising an $8 Million Series A Round 11:20 – Customers: Vancouver International Airport and Aena 12:10 – Building a Polished Robotics Platform on Seed Funding 12:50 – Matthew Anderson’s Background in Robotics and Drones 14:00 – The Bigger Vision: Crowd Navigation for All Robots 14:40 – The Personal Story Behind the Company Mission 15:40 – Licensing Opportunities and the $5 Billion Airport Mobility Market 16:45 – Hiring, Scaling the Team, and Expanding Production 18:00 – Growing Up Hacking Robots and the AC/DC Story 19:10 – Why Building Robots Is Fun — and Why Accounting Wasn’t 20:40 – Final Thoughts and the Future of Autonomous Mobility

Is AI in education a disaster ... or inevitable. We can easily see that AI is already changing education ... but is it making kids smarter, or just more dependent? In this episode of TechFirst, John Koetsier talks with Navin Gurnani, CEO of Code Ninjas, about how kids can learn to build with AI instead of simply asking ChatGPT for answers. They discuss why coding still matters in the age of vibe coding, how AI can actually strengthen creativity and critical thinking, and the foundational skills kids need to thrive in a future shaped by artificial intelligence. Navin explains how Code Ninjas teaches children as young as 8 to understand AI “behind the curtain,” develop grit and resilience, and gain the confidence to create games, apps, and even entrepreneurial projects powered by AI. The conversation also dives into: * Why passive AI use puts kids at a disadvantage * The mindset future-ready kids need * AI literacy for parents and children * How coding builds confidence and problem-solving skills * Why adaptability may become the most important human skill * The difference between using AI and leading with AI If you’re a parent, educator, entrepreneur, or simply curious about the future of learning, this episode is packed with practical insights about preparing kids for an AI-driven world. Guest Navin Gurnani — CEO, Code Ninjas Sponsor This episode is sponsored by Apprentice — the first AI agent built for agentic manufacturing. Chapters 0:00 Intro: Is AI destroying education? 1:00 Teaching kids to build with AI, not depend on it 2:00 AI, coding, games, and decision-making 3:00 Why understanding AI builds confidence 4:00 Passive AI users vs. AI creators 5:00 What kids learn at Code Ninjas 6:00 Grit, resilience, and problem-solving 8:00 Belt system and early wins 9:00 Building confidence through teaching others 10:00 AI literacy by age level 11:00 Teaching kids to use AI responsibly 12:00 Coding in the age of vibe coding 14:00 AI-assisted entrepreneurship for kids 15:00 Building future-ready mindsets 16:00 What a future-ready kid looks like 17:00 Adaptability and spotting AI mistakes 18:00 One thing parents should do now

What if the next big wave of AI isn’t about robots doing your chores but about robots that understand you? In this episode, we sit down with Colin Angle, co-founder of iRobot and the creator of the Roomba, to explore his bold new venture: Familiar Machines and Magic. After putting over 50 million robots into homes, Angle is now betting on something radically different: a quadruped AI companion designed not for work, but for connection. This isn’t a humanoid. It’s not a vacuum. It’s something entirely new. Powered by on-device multimodal AI, this “familiar” can follow you around your home, learn your routines, encourage healthier habits, and even develop a kind of relationship with you, all while keeping your data private. We dive into: * Why the humanoid robot race might be overhyped * The massive untapped “emotional AI” market * How this robot learns, adapts, and interacts like a pet * Privacy-first AI design (no cloud streaming) * Why form factor matters more than you think * The future of robots in everyday life Colin also shares why now is the perfect moment for physical AI—and how advances in reinforcement learning and edge computing are making this possible. If you thought AI robots were just about automation, this conversation will change your perspective. ⸻ 👤 Guest Colin Angle Co-founder, iRobot Founder, Familiar Machines and Magic ⸻ Sponsor: this episode is sponsored by Apprentice. AI-native manufacturing is here. Apprentice offers the first AI Agent built from the ground up for agentic manufacturing. Connects to all your systems, monitors everything, automates all your processes … but keeps a human in the loop. Check it out at apprentice.io. ⸻ Chapters: 0:00 Introduction to Colin Angle & Familiar Machines 1:05 What is a “Familiar” Robot? 2:00 Emotional AI vs Humanoid Robotics 3:00 Coming Out of Stealth 4:00 The $2.5 Trillion Opportunity in Emotional AI 5:00 Combining iRobot, Boston Dynamics, and Disney 6:00 Why Robot Form Factor Matters 7:00 First Look: Familiar in Action 8:00 Companionship vs Utility in Home Robots 9:30 Pricing Strategy: Like Owning a Pet 11:00 Managing Expectations in Robotics 12:30 Privacy, Security, and On-Device AI 14:00 How Familiar Communicates Without Speech 15:30 Sensors, AI Stack, and Personality Modeling 17:00 Learning Behavior Like a Pet 18:30 Why Not a Dog? The “Abstract Bear” Design 20:00 Platform Vision and Future Capabilities 21:30 Elder Care and Real-World Applications 22:30 Reinforcement Learning Breakthroughs 23:30 Launch Timeline and Closing Thoughts

AI is everywhere ... except the factory. What does AI-native manufacturing look like? Is it possible? Can AI agents help manufacturers produce more product at better quality?And, maybe also enable onshoring or re-shoring?In this episode, host John Koetsier sits down with Apprentice CEO and founder Angelo Stracquatanio to explore what AI-native manufacturing really means, and why traditional AI models fall short in production environments.Instead of chatbots, this new approach uses event-driven AI agents that respond to real-time manufacturing signals: alarms, equipment data, quality issues, and more. The result? Faster troubleshooting, reduced costs, and entirely new levels of automation.Angelo breaks down how their system combines:* Specialized AI models trained on real manufacturing data* Role-specific agents (for operators, quality teams, engineers, and leadership)* Workflow automation that goes far beyond simple promptsThey also dive into:* Why general-purpose AI struggles in manufacturing* How to eliminate hallucinations with guardrails and workflows* Real-world ROI: faster investigations, lower cost of goods, improved throughput* The future of adaptive factories and personalized production* Why humans remain critical, even in highly automated environmentsIf you’re in manufacturing, operations, or industrial innovation, this is a deep look at how AI is actually being deployed ...and where it’s headed next.This month's TechFirst sponsor is also Apprentice. Check out their AI-native solutions for manufacturing at Apprentice.io.👤 GuestAngelo StracquatanioCo-founder & CEO, Apprentice⏱️ Chapters00:00 AI-native manufacturing explained01:00 Why manufacturing needs specialized AI02:00 Building Apprentice 4.104:00 AI for every role in a factory05:00 Why sub-agents beat one general agent06:00 Troubleshooting and quality investigations07:00 Compressing triage time with AI08:00 Does your factory need more data?09:00 Digital maturity in manufacturing10:00 A practical path to AI adoption11:00 Preventing AI hallucinations12:00 Trust and consistency in production13:00 Constraining AI with workflows15:00 The human-in-the-loop model16:00 Guardrails and source traceability17:00 AI supports, not replaces, humans19:00 How autonomous can factories get?20:00 The adaptive plant future21:00 AI as a new automation layer22:00 Adapting to new products and variants23:00 Why flexibility is the future24:00 Manufacturing for personalization25:00 Personalized medicine use case27:00 Customer results and benefits28:00 AI across MES, ERP, QMS, and IoT29:00 ROI from quality and troubleshooting30:00 Alarm triage at scale31:00 Manufacturing and geopolitics32:00 Onshoring with AI33:00 Throughput, labor, and margins34:00 Let humans do the highest-value work35:00 Reducing COGS with AI36:00 Closing thoughts