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 environmentsGuest:Matthew Anderson — CEO, A&K RoboticsCompany: A&K RoboticsIf 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.com00:00 – Intro00:30 – Meet A&K Robotics and the Vision for Autonomous Airport Mobility01:20 – Why Crowd Navigation AI Is the Hardest Problem in Robotics02:40 – Navigating Dense Airport Crowds and Passenger Flow04:05 – Directional Sound and Designing a Better Airport Experience05:50 – Building an “iPhone Experience” for Mobility Robots06:30 – Sensors, LIDAR, and Operating Without GPS07:20 – Fleet Management and Autonomous Operations in Airports08:00 – Mapping Airports and Optimizing Routes Through Crowds09:00 – Scaling the Business and Solving Systems Integration10:00 – Charging, Docking Stations, and the Future Airport Network10:45 – Raising an $8 Million Series A Round11:20 – Customers: Vancouver International Airport and Aena12:10 – Building a Polished Robotics Platform on Seed Funding12:50 – Matthew Anderson’s Background in Robotics and Drones14:00 – The Bigger Vision: Crowd Navigation for All Robots14:40 – The Personal Story Behind the Company Mission15:40 – Licensing Opportunities and the $5 Billion Airport Mobility Market16:45 – Hiring, Scaling the Team, and Expanding Production18:00 – Growing Up Hacking Robots and the AC/DC Story19:10 – Why Building Robots Is Fun — and Why Accounting Wasn’t20:40 – 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 AIIf 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.GuestNavin Gurnani — CEO, Code NinjasSponsorThis episode is sponsored by Apprentice — the first AI agent built for agentic manufacturing.Chapters0:00 Intro: Is AI destroying education?1:00 Teaching kids to build with AI, not depend on it2:00 AI, coding, games, and decision-making3:00 Why understanding AI builds confidence4:00 Passive AI users vs. AI creators5:00 What kids learn at Code Ninjas6:00 Grit, resilience, and problem-solving8:00 Belt system and early wins9:00 Building confidence through teaching others10:00 AI literacy by age level11:00 Teaching kids to use AI responsibly12:00 Coding in the age of vibe coding14:00 AI-assisted entrepreneurship for kids15:00 Building future-ready mindsets16:00 What a future-ready kid looks like17:00 Adaptability and spotting AI mistakes18:00 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 lifeColin 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.⸻👤 GuestColin AngleCo-founder, iRobotFounder, 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 Machines1:05 What is a “Familiar” Robot?2:00 Emotional AI vs Humanoid Robotics3:00 Coming Out of Stealth4:00 The $2.5 Trillion Opportunity in Emotional AI5:00 Combining iRobot, Boston Dynamics, and Disney6:00 Why Robot Form Factor Matters7:00 First Look: Familiar in Action8:00 Companionship vs Utility in Home Robots9:30 Pricing Strategy: Like Owning a Pet11:00 Managing Expectations in Robotics12:30 Privacy, Security, and On-Device AI14:00 How Familiar Communicates Without Speech15:30 Sensors, AI Stack, and Personality Modeling17:00 Learning Behavior Like a Pet18:30 Why Not a Dog? The “Abstract Bear” Design20:00 Platform Vision and Future Capabilities21:30 Elder Care and Real-World Applications22:30 Reinforcement Learning Breakthroughs23:30 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

What happens when GPS goes down: jammed, spoofed, or completely denied?In this episode of TechFirst, host John Koetsier sits down with Michael Biercuk, founder and CEO of Q-CTRL, to explore one of the most surprising breakthroughs in quantum technology: quantum navigation.While most of the quantum world is focused on computing, Q-CTRL is building something entirely different: AI-powered quantum sensing systems that can navigate aircraft, drones, and vehicles without GPS.Even more surprising? This technology didn’t exist just over a year ago. Now it’s already shipping.You’ll learn:• How quantum sensors can “see” invisible features of the Earth• Why magnetic and gravitational fields enable GPS-free navigation• How this system achieves 100x better accuracy than current GPS alternatives• Why it works in environments where other systems fail (clouds, water, darkness, interference)• The role of AI software in stabilizing fragile quantum systems in real-world conditions• What this means for aviation, defense, and the future of autonomous systemsThis is a deep dive into a fast-moving frontier where quantum meets real-world deployment, and it’s happening faster than almost anyone expected.⸻Guest:• Michael Biercuk, Founder & CEO, Q-CTRL• Company: Q-CTRL • Website: https://q-ctrl.com⸻👉 Subscribe for more conversations on AI, quantum tech, and the future of innovation:https://techfirst.substack.com⸻⏱️ Chapters0:00 Quantum Navigation vs Quantum Computing0:34 Introduction to Michael Biercuk & Q-CTRL1:12 What Is Quantum Navigation?2:00 How Quantum Sensors Enable Navigation2:52 Magnetometers vs Gravimeters Explained3:28 Do You Need to Pre-Map the Earth?4:18 Earth’s Magnetic Field & Why Maps Stay Accurate5:18 GPS Spoofing & Why Quantum Nav Matters6:00 Accuracy: 100x Better Than GPS Alternatives7:00 Why Multi-Mode Navigation Is the Future7:42 Limits of Star Cameras & Visual Navigation8:38 The Vibration Problem in Quantum Systems9:30 How Software Replaces Hardware Stabilization10:28 System Size: From Sensor to Loaf of Bread11:15 Cost, Use Cases & Drone Deployment12:00 First Sales & Commercial Rollout12:45 Market Size: Aviation & Drone Opportunity13:20 Final Thoughts on Quantum Sensing13:45 Speed of Innovation & Closingr

Are AI agents really the future of software — or just the latest wave of hype?In this episode of TechFirst, host John Koetsier sits down with Don Murray, CEO of Safe Software, to break down what’s actually happening with “agentic AI.” From AI-washing and “agent-washing” to real-world use cases in coding, automation, and enterprise software, this conversation cuts through the noise.They explore how AI agents differ from traditional apps, why intent-based software is emerging, and how developers are already shipping faster with AI writing code. But it’s not all upside — there are real risks, from security vulnerabilities to the possibility of AI-driven mistakes at massive scale.You’ll also hear: • Why “agentic AI” might just be a rebrand of automation • How AI is changing software development (and junior dev roles) • The surprising productivity boost for senior engineers • Why AI could make companies faster — and more fragile • The rise of “good enough” content and the risk of mediocrity • How enterprises are (and aren’t) keeping upPlus: what happens when AI starts building itself — and whether we’re heading toward a breaking point.⸻This episode is sponsored by Apprentice: did you think AI was only for digital work? Nope ... AI-native manufacturing is here. This month's sponsor is Apprentice, which 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.⸻👤 GuestDon MurrayCEO & Founder, Safe Software🌐 https://www.safe.com00:00 AI washing and the agent hype00:02 What actually counts as an agent?00:03 Sponsor: Apprentice and agentic manufacturing00:03 New software architecture: intent-driven systems00:05 Are big legacy companies like Apple at risk?00:07 Day one vs. day two companies00:08 How AI changes software development00:09 Why junior devs struggle with AI-generated code00:10 Consumer benefits of agentic software00:11 Does AI save time or just make us busier?00:12 The downside: creativity, security, and mediocrity00:14 Why AI makes it easier to be average00:15 AI as an assistant and the blank-page problem00:16 AI removes excuses for building new products00:17 Can companies be rebuilt faster than bought?00:18 AI writing AI code00:19 Why developers are moving to Claude and Gemini00:20 Shipping faster vs. overwhelming customers00:21 Why every app may need an agent00:22 Talking to databases instead of learning SQL00:23 The risk of AI breaking companies fast00:24 Is there an AI bubble?00:25 Data centers, power, and water constraints00:26 AI’s upside in healthcare00:27 Using AI for legal documents and expert knowledge00:28 Final thoughts on agentic AI and AI-ready data

What if the hardest part of building a humanoid robot isn’t the brain but the hands? Robot hands are half the complexity of a robot, a humanoid robot CEO told me a while back: they're insanely difficult to get right.In this episode of TechFirst, I talk with Kyber Labs co-founders Tyler Habowski and Yonatan Robbins about why dexterity, maybe even more than AI, is the true bottleneck in robotics.Some of the quotes:- “There are literally zero robot hands deployed right now doing routine work.”- “The best hands are hundreds of thousands of dollars, and they break all the time …”Before the interview, you’ll see an exclusive demo of their next-generation robotic hand in action showing just how far manipulation technology has come.We dig into:• Why humans rely on force, not precision, to manipulate objects• The surprising flaw in most robotic hands today• How Kyber’s “torque-transparent” design works without expensive sensors• Why hardware—not software—is still the limiting factor• A practical path to real-world automation (without sci-fi hype)This isn’t about futuristic humanoids doing everything. It’s about solving real problems today ... from lab automation to manufacturing ... by building hands that actually work.⸻👤 GuestsTyler HabowskiCo-founder, Kyber LabsBackground: SpaceX, robotics manufacturingYonatan RobbinsCo-founder, Kyber LabsBackground: Industrial design, mechanical engineering, medical devices⏱️ CHAPTERS00:00 Why Robot Hands Are So Hard01:30 Sneak Peek + Demo Setup01:30 Demo: Kyber Labs Robot Hand in Action05:30 Interview Start: Are Hands Half the Problem?06:45 Humans Use Force, Not Precision08:45 Why Most Robot Hands Fail10:45 How Kyber’s Hands “Feel” Without Sensors13:15 Back-Drivability vs Torque Transparency15:30 Hardware vs AI: What Actually Matters?17:30 Why Better Hands Unlock Better Robots19:15 Real-World Use Case: Automating Lab Work22:00 Vision vs Touch in Robotics24:00 Why Start With Stationary Robots25:45 Not Building Humanoids (Yet)27:15 What Is a “Minimum Viable” Robot Hand?29:15 The Problem With Today’s Grippers30:45 What the Ultimate Robot Hand Looks Like32:15 The Real Breakthrough: Deploy and Iterate33:30 Final Thoughts + Wrap-Up

What does the agentic enterprise of tomorrow look like? What happens when AI can build software in hours and agents can run entire business processes?In this episode of TechFirst, John Koetsier sits down with UiPath CEO Daniel Dines and CMO Michael Atalla to unpack one of the biggest shifts in enterprise technology: the rise of the agentic enterprise.We explore whether software is becoming disposable, why AI agents are fundamentally different from traditional automation, and what really happens to jobs as companies adopt these systems. Along the way, we dig into process orchestration, trust, judgment, and why human “taste” may become more valuable—not less—in an AI-driven world.This is a deep, practical look at how AI is reshaping work inside real companies as they become agentic enterprises. This isn't just hype, but what’s actually changing right now and what’s coming next.⸻👤 GuestsDaniel DinesCo-founder & CEO, UiPathMichael AtallaChief Marketing Officer, UiPath⸻Sponsor: KindBody Fitnesskindbody.fitnessBe kind to your body with AI-driven fitness customized exactly to you. All the health with none of the gym bro nonsense.⸻🚀 What You’ll Learn• Why AI is making software faster—and more disposable• The difference between task agents, stage agents, and process agents• What an “agentic enterprise” actually looks like in practice• Why trust, judgment, and taste become more important with AI• How AI could reduce enterprise costs—and even drive deflation• The future of work: builders, sellers, and critics• Why fully autonomous AI “swarms” aren’t ready for enterprise (yet)⸻🔔 Subscribe for more conversations on AI, tech, and the future of work👉 https://techfirst.substack.com

NanoClaw is a new agent inspired by OpenClaw, but without the massive security risks you get with OpenClaw. Essentially, it's a safer OpenClaw.What if you could run a powerful AI agent on your own machine: one that can browse, automate tasks, connect to apps, and even manage your workflow ... but without the massive security risks?That’s the idea behind NanoClaw, a lightweight alternative to OpenClaw created by developer Gavriel Cohen. In just a few weeks, the project exploded on GitHub, attracting thousands of stars and a growing community of developers building their own AI agents.In this episode of TechFirst, we explore:• Why OpenClaw raised serious security concerns• How NanoClaw isolates agents in containers• Why a 3,000-line codebase is safer than 500,000 lines• The rise of AI agents that can actually do work• Why entire software categories may soon be replaced by prompts• The future of AI-native workflows and “disposable software”Gavriel also shares how his team uses AI agents in WhatsApp to run their sales pipeline automatically—and how developers are customizing NanoClaw with new capabilities like voice, images, and automation.If you’re interested in AI agents, autonomous workflows, vibe coding, and the future of software, this conversation is packed with insights.⸻GuestGavriel CohenFounder, QuibbitNanoClaw Creatorhttps://github.com/qwibitai/nanoclaw⸻If you enjoy conversations about AI, startups, and the future of technology, subscribe for more episodes:https://techfirst.substack.com⸻00:00 Intro: A safe OpenClaw for TechFirst01:22 Gavriel Cohen introduces NanoClaw03:25 Why OpenClaw feels unsafe03:55 Half a million lines of code vs. 3,00006:03 Dependency sprawl and supply-chain risk07:00 Why every agent needs its own container09:30 What NanoClaw can actually do10:16 Letting NanoClaw customize itself12:56 How NanoClaw recreates OpenClaw with far less code13:21 Memory, Claude Code, and agents.md15:34 Running NanoClaw on a laptop, server, or VPS16:22 What Gavriel learned from vibe coding19:50 The OpenClaw phase shift: everything changed21:16 From ChatGPT to real agents that do work23:15 Why AI-native workflows beat traditional SaaS24:46 Replacing CRM workflows with markdown and WhatsApp25:54 Product categories becoming prompts26:36 The key innovation: agents leaving the box28:45 Agent swarms and one-person companies29:22 Tokens, cost, and AI inequality30:30 Building secure, customizable software32:25 Self-modifying software and shared customizations33:44 Disposable software and infinite composability35:00 Outro

Imagine teaching a robot 1000 tasks in just 24 hours. Imagine teaching robots just like you teach humans.In fact, what if teaching a robot were as easy as showing it once?Humans can learn new skills almost instantly by watching, trying, or receiving a quick explanation. Robots, historically, haven’t been so lucky. Training them often requires huge datasets with real or virtual data, massive engineering effort, and weeks or months of experimentation.But that may be changing.In this episode of TechFirst, host John Koetsier talks with Edward Johns, Director of the Robot Learning Lab at Imperial College London, about a breakthrough in efficient imitation learning that allowed a robot to learn 1,000 different tasks in just 24 hours.Instead of collecting huge datasets, Johns’ team combines simulation training, clever algorithm design, and single demonstrations to dramatically speed up how robots learn.We discuss:• How robots can learn from just one demonstration• Why breaking tasks into “reach” and “interact” phases makes learning faster• The role of simulation data in robotics AI• Why robotics doesn’t have the same data advantage as large language models• The future of prompt-like robot training• Whether humanoid robots will actually learn like humansAs robotics hardware rapidly improves and costs fall, breakthroughs like this could be the key to making robots truly useful in homes, factories, and everyday life.If robots are going to become real collaborators with humans, they’ll need to learn quickly ... just like we do.⸻GuestEdward JohnsDirector, Robot Learning LabImperial College Londonhttps://www.imperial.ac.uk⸻Subscribe for more conversations on AI, robotics, and the future of technology:https://techfirst.substack.com00:00 Can robots learn as fast as humans?00:51 Teaching a robot 1,000 tasks in 24 hours01:08 The two-phase learning approach02:14 Old-school robotics vs. machine learning03:29 The robotics data bottleneck04:47 The challenge of dynamic environments06:04 The coming wave of robot data06:59 Why robots must be teachable by users08:08 Why LLM-style scaling is harder in robotics09:42 Prompting robots with demonstrations10:54 Probabilistic robot behavior and safety12:20 What robots can do today13:53 Why hardware precision still matters16:53 When this reaches the real world17:59 Humanoids that look human vs. learn human18:40 The robotics boom around the world22:34 The risk of scaling too early23:46 Faster learning vs. more data26:20 The next frontier in robot learning