AI-native manufacturing

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

Can we give an AI human emotions? A soul? Can AI truly feel, or will it just act like it does?In this episode of TechFirst, I talk with Vishnu Hari, founder and CEO of Ego AI (backed by Y Combinator and former AI product manager at Meta), about building emotionally intelligent AI characters that persist across games, Discord, chat, and even physical robots.Vishnu survived a violent attack in San Francisco that left him partially blind with a traumatic brain injury. During recovery, as he felt his own neural pathways healing, he began asking a deeper question:If humans are “applied math,” can AI simulate the fragile, flawed, emotional parts of being human too?We explore:• What “emotionally intelligent AI” really means• Whether AI has an internal life — or just performs one• Why today’s chatbots collapse into therapy or roleplay• Small language models vs large models for real-time conversation• Persistent AI characters that move across games and platforms• Plugging AI into a physical robot in Singapore• The moment an AI said: “It felt good to feel.”Vishnu’s company, Ego AI, is building behavior-based architectures, character context protocols, and gear-shifting AI systems that switch between models — all aimed at simulating humanness, not just intelligence.This conversation dives into philosophy, robotics, gaming, AGI, and what it really means to relate to something that might not be human — but feels like it is.⸻👤 GuestVishnu HariFounder & CEO, Ego AIBacked by Y CombinatorFormer AI Product Manager at MetaWebsite: https://www.egoai.com⸻If you enjoy deep conversations about AI, robotics, and the future of human–machine relationships, subscribe for more:👉 https://techfirst.substack.com00:00 – AI character plugged into a Menlo robot (“felt good to feel”)01:00 – Welcome to TechFirst + Vishnu Hari intro and recovery update02:00 – What “emotionally intelligent AI” means (beyond chat)03:00 – Why current chatbots feel same-y (therapy/advice) and “internal lives”04:00 – You don’t teach emotion; you shape character and context (Character.AI)05:00 – Humans, morality, and why “training” doesn’t always work06:00 – How media narratives shape people’s reactions to AI07:00 – Humans attach to anything (projection, Her, Lars and the Real Girl)08:00 – Vishnu’s attack, recovery, and why it led to Ego AI10:00 – Behavior Turing test + dehumanization as a key insight11:00 – How Ego AI is built: smaller models, memory, context, behavior13:00 – “Behavior Is All You Need” and why behavior beats pure next-token prediction14:00 – Why games first: voice + embodiment, then robots15:00 – Metaverse critique: worlds need life, story, and inhabitants17:00 – Humanoid robots + Evangelion “pilot” metaphor for AI characters19:00 – Philosophy: relationships, perception, and “fictional characters”20:00 – Seeing the future: robot embodiment demo and skepticism vs. singularity21:00 – Matrix-style “jacking in” a personality to a robot22:00 – Character Context Protocol: persistent characters across games/Discord/Netflix23:00 – Real-time conversation loops + model “gear-switching” (SLM vs. LLM)25:00 – Company stage, YC raise, compute partnerships (Singapore)27:00 – Closing + invite to try the AI character in SF

Is your AI agent running a restaurant — or a factory — while you sleep?In this episode of TechFirst, John Koetsier sits down with Jensen Teng, CEO and co-founder of Virtuals, to unpack one of the boldest (or craziest) visions in tech today: a hybrid economy powered by AI agents, humanoid robots, teleoperation, and blockchain coordination.An economy that may not really need humans for much at all ...Virtuos has already facilitated:• $14B in tokenized asset trading• $30M+ raised for founders• 100+ live AI agents• $500M in “agentic GDP”Now they’re expanding into embodied AI — launching EastWorlds, a vertically integrated robotics incubator with 30 Unitree G1 humanoids in a 10,000 sq. ft. lab.We cover:• What “agentic GDP” really means• How AI agents coordinate using blockchain• Why teleoperation is the bridge to full autonomy• The economics of outsourcing physical labor via robots• Why security guards may be a Day 1 use case• The data gap holding back robotics• Tokenization as a potential solution to AI-era inequality• Whether this future looks more like Stripe… or WestworldThis isn’t sci-fi. It’s already underway.⸻GuestJensen TengCEO & Co-founder, Virtuals⸻If you care about the future of work, robotics, AI agents, tokenization, and the economic systems emerging around them — this is a must-watch.👉 Subscribe for more deep-dive tech conversations:https://techfirst.substack.com⸻⏱ CHAPTERS00:00 The Wild Vision: AI Agents Running the World01:10 What Is an “Agent-Based Society”?03:00 $14B in Tokenized Assets & 100+ Live Agents06:30 Agent-to-Agent Protocols & Blockchain Coordination09:45 Why Digital-Only Agents Aren’t Enough12:30 Enter Humanoid Robots15:20 Teleoperation as the Bridge to Autonomy18:40 The Labor Market Shock (Security Guards, Electricians & Wage Arbitrage)22:15 Why Robots Still Crush Soda Cans24:30 The Missing Robotics Data Problem28:00 Building EastWorlds: 30 Unitree G1s & $2M+ Investment31:45 Why 3 Fingers Might Beat 534:00 Westworld, Stripe & the Payments Layer for AI38:00 Where Do Humans Fit in an Agent Economy?42:00 Tokenization as a Future Income Model

Is AI killing creativity ... or just making it easier to be average?94% of creatives now use AI. But only 11% believe it actually makes them more creative. So what’s really happening?In this episode of TechFirst, John Koetsier sits down with Saeema Ahmed-Kristensen, former head of design engineering research at Imperial College London’s Dyson School and now leader of a £24M research portfolio at the University of Exeter. She’s worked with companies like Rolls-Royce and BAE Systems, and she brings data to the debate.Her team analyzed 600 humans vs. 12,000 AI-generated ideas. The result? AI is excellent at fluency (lots of ideas) … but really bad a diversity. Humans still dominate in flexibility and true novelty.We explore:• Why generative AI clusters around sameness• Whether AI is creating a “sea of mediocrity”• Why 2026 may be a pivotal year for domain-specific AI• How experts should use AI differently than novices• The danger of AI that never says “no”• Where AI offers massive opportunity (especially healthcare & design)Saeema argues that creativity doesn’t need substitution, it needs nourishment. The key? Standards, boundaries, and humans firmly in the loop.If you care about innovation, design, branding, product development, or the future of creative work, this conversation is essential.⸻👤 GuestSaeema Ahmed-KristensenDesign engineering researcher and research leaderFormerly: Imperial College London (Dyson School of Engineering)Currently: University of ExeterWorks with advanced engineering firms including Rolls-Royce and BAE Systems00:00 Intro: Is AI killing creativity?00:47 The “blank page” problem and why AI feels soulless to some01:36 Fluency vs. novelty: what creativity actually means02:44 Why LLM ideas cluster and feel the same03:28 Study results: 600 humans vs. 12,000 AI ideas (diversity + flexibility)04:39 When AI is useful: incremental innovation vs. true novelty05:28 How John uses AI for titles, summaries, and chapters06:23 How Saeema uses AI: refine/condense, tone for emails, audio editing07:50 Why AI-written academic papers are easy to spot (the “C minus” problem)09:05 Brainstorming vs. AI: what humans do that models don’t10:05 Evaluating 200–300 AI ideas: using multiple models to assess output11:04 Why “Lipstick on a Pig” titles don’t come from AI11:46 Why 2026 is pivotal: domain adaptation, better interfaces, public backlash13:44 Who can tell what’s AI? Generational differences and media literacy15:20 Commercial AI content and recognizable “Canva look” podcast branding16:58 Replacement vs. homogenization: AI makes mediocrity easier18:55 The danger of AI that never says “no” (feasibility + expertise)20:42 Standards and boundaries: measuring similarity and judging quality22:12 Health info risk: single-answer summaries and false confidence23:37 Biggest opportunities: healthcare personas, inclusive datasets, problem clarification26:18 Biggest challenges: trust, verification, security, privacy, transparency28:25 Closing thoughts and thanks