TechFirst with John Koetsier

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.GuestMike ElganTech journalist, columnist, author, and podcasterHost of SuperintelligentAuthor of The Attachment Economy on SubstackSubscribe for more conversations on AI, robots, innovation, and the future of technology:https://techfirst.substack.comChapters: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

AI models are powerful, but they don’t forget. And that's a problem.They hallucinate. They inherit bias. They absorb sensitive data. And once they’re trained, fixing those issues is painfully expensive. Retraining takes weeks and maybe tens of millions of dollars. And any guardrails the AI company puts up are brittle.What if you could perform surgery on the model itself?In this episode of TechFirst, John Koetsier sits down with Ben Luria, co-founder of Hirundo, to explore machine unlearning, a new approach that selectively removes unwanted data, behaviors, and vulnerabilities from trained AI systems.Hirundo claims it can:• Cut hallucinations in half• Massively reduce bias• Reduce successful prompt injection attacks by over 90%• Do it in under an hour on a single GPU• Preserve benchmark performanceInstead of adding more guardrails, machine unlearning works inside the model, identifying problematic weights, isolating behavioral vectors, and surgically removing risks without degrading quality.If AI is going mainstream in enterprises, it needs a remediation layer. Is machine unlearning the missing piece?⸻GuestBen LuriaCo-Founder, HirundoNhirhttps://www.hirundo.io⸻Topics Covered• Why AI models “can’t forget”• The difference between hallucinations and inaccuracies• Why guardrails aren’t enough• How prompt injection works — and how to reduce it• Removing PII and noncompliant training data• AI security at the model level• Why machine unlearning could become standard by 2030⸻If you’re building, deploying, or investing in AI, this is a conversation you can’t miss.👉 Subscribe for more deep dives into AI, innovation, and the future of tech:https://techfirst.substack.com⸻⏱ Chapters00:00 – Why We Need Machine Unlearning01:12 – What Is Machine Unlearning?03:40 – Why AI Can’t “Forget” (The Pink Elephant Problem)06:15 – Guardrails vs True Model Remediation09:05 – The Wild West of AI Data & Legal Risk11:20 – How Machine Unlearning Works (Detection, Isolation, Remediation)16:10 – Performing “Neurosurgery” on LLMs19:30 – Hallucinations vs Inaccuracies Explained23:45 – Reducing Prompt Injection by 90%28:30 – Working with AI Labs & Enterprises32:00 – Will Unlearning Become Standard by 2030?34:15 – Final Thoughts

Humanoid robots are coming into our homes, but they probably won’t be doing your laundry anytime soon.In this episode of TechFirst, host John Koetsier sits down with Jan Liphardt, founder & CEO of OpenMind and Stanford bioengineering professor, to unpack what home robots will actually do in the near future ... and why the “labor-free home” vision is mostly a myth (for now).Jan explains why hands are still one of the hardest unsolved problems in robotics, why folding laundry is far harder than it looks, and why the most valuable early use cases for home robots aren’t chores at all. Instead, we explore where robots are already delivering real value today:• Health companionship and fall detection for aging parents• Personalized education for kids, beyond screens• Home security that respects privacy• And why people form emotional bonds with robots faster than expectedWe also dive into OM1, OpenMind’s open-source, AI-native operating system for robots, and why openness, transparency, and configurability will matter deeply as robots move from factories into our living rooms.If you’re curious about the real future of humanoid robots — what’s hype, what’s possible today, and what’s coming next — this conversation is for you.🎙 GuestJan LiphardtFounder & CEO, OpenMindStanford Professor of BioengineeringWebsite: https://openmind.com⸻👉 Subscribe for more conversations on AI, robotics, and the future of technology:https://techfirst.substack.com⸻00:00 Intro: The promise of humanoid robots at home00:40 Meet Jan Liphardt and OpenMind’s OM101:12 Why your “labor droid” isn’t here yet01:41 The “hand problem” and what robots can realistically do now03:07 Why economics matters: $300/hour tasks vs. laundry and dishes04:19 Robot hands today: reliability, repairability, and washing hands05:16 LG’s laundry-folding demo and why fabric is still hard06:16 Hospitals and hygiene: why “robot hand-washing” is unsolved07:41 Hands as a separate system: compute, sensors, and integration08:31 Why wheeled humanoids exist: hands first, body second09:26 The real home use cases today: security, education, companionship10:08 Aging in place: fall detection and remote nurse escalation11:30 Real-world stories: parents living alone and why this matters11:54 Privacy tradeoffs: robots vs. always-on home cameras12:52 AIBO and why people get attached to mobile robots13:52 Self-charging and the “my mom won’t plug it in” problem14:21 Beyond falls: autism support and memory care15:27 The education use case: “do my homework” vs. teach me16:26 Personalized learning: what current classrooms miss17:51 Why robot teachers beat screens for younger kids18:46 Home security basics: unfamiliar face detection + alerts19:15 Adding sensors: smoke, fire, sound, and anomaly detection19:41 Quadrupeds vs. humanoids: cost, simplicity, and mobility20:01 Safety issue: pinch hazards and kids hugging robots20:46 What’s next for home labor robots21:43 Why OM1 must be open source: transparency and trust23:39 Why ROS 2 isn’t enough for human environments24:37 OM1 approach: LLM-centric “Lego blocks” for robot behavior25:43 Open-source humanoids for kids and why ownership matters27:41 What’s missing: simulation is the bottleneck28:11 Gazebo/Isaac Sim pain and the need for realistic sims29:57 Why voice + “digital humans” matter in simulation30:47 Tipping points: factories, warehouses, robotaxis, and humanoids35:46 Wrap-up and final thoughts

Robots aren’t just software. They’re AI in the physical world. And that changes everything.In this episode of TechFirst, host John Koetsier sits down with Ali Farhadi, CEO of Allen Institute for AI, to unpack one of the biggest debates in robotics today: Is data enough, or do robots need structured reasoning to truly understand the world?Ali explains why physical AI demands more than massive datasets, how concepts like reasoning in space and time differ from language-based chain-of-thought, and why transparency is essential for safety, trust, and human–robot collaboration. We dive deep into MOMO Act, an open model designed to make robot decision-making visible, steerable, and auditable, and talk about why open research may be the fastest path to scalable robotics.This conversation also explores:• Why reasoning looks different in the physical world• How robots can project intent before acting• The limits of “data-only” approaches• Trust, safety, and transparency in real-world robotics• Edge vs cloud AI for physical systems• Why open-source models matter for global AI progressIf you’re interested in robotics, embodied AI, or the future of intelligent machines operating alongside humans, this episode is a must-watch.👤 GuestAli FarhadiCEO, Allen Institute for AI (AI2)Professor, University of WashingtonFormer Apple researcher⸻👉 Subscribe for more conversations like this: https://techfirst.substack.com⸻00:00 – Plato vs Aristotle… in robotics?00:55 – What “reasoning” means in the physical world02:10 – How humans predict actions before they happen03:45 – Why physical AI is fundamentally different from text AI04:50 – The next revolution: AI in the real world05:30 – What is MOMO Act?06:20 – Chain-of-thought… for robots07:45 – Trajectories as reasoning and robot transparency08:55 – Trust, safety, and correcting robots mid-action10:15 – Why predictability builds trust in machines11:40 – What’s broken with data-only AI approaches13:10 – Why reasoning + data isn’t an “either/or”14:00 – Open sourcing robotics models: why it matters15:20 – How closed AI slows innovation16:45 – Global competition and open research17:40 – What’s next for robotics reasoning models18:20 – Can these models work across robot types?19:30 – Temporal and spatial reasoning in MOMO 220:40 – Scaling robotics vs scaling LLMs21:10 – Edge vs cloud AI for robots22:20 – Specialized models, latency, and privacy23:00 – Final thoughts on the future of physical AI

Is AI really the new UI, or is that just another tech buzzphrase? Or ... is AI actually EVERY user interface now?In this episode of TechFirst, host John Koetsier sits down with Mark Vange, CEO & founder of Automate.ly and former CTO at Electronic Arts, to unpack what happens when interfaces stop being fixed and start being generated on the fly.They explore:• Why generative AI makes it cheaper to create custom interfaces per user• How conversational, auditory, and adaptive experiences redefine “UI”• When consistency still matters (cars, safety systems, frontline work)• Why AI doesn’t replace workers — but radically reshapes workflows• Whether browsers should become AI-native or stay neutral canvases• The unresolved risks around AI agents, payments, and controlFrom hospitals using AI to speak Haitian Creole, to compliance forms that drop from hours to minutes, this conversation shows how every experience can become intelligent, contextual, and helpful.👉 If you care about product design, AI, UX, or the future of software, this episode is for you.Subscribe for more conversations like this:https://techfirst.substack.com⸻👤 GuestMark VangeCEO & Founder, Automate.lyFormer CTO, Electronic ArtsInvestor, serial entrepreneur, and builder focused on intent-driven, AI-native software⸻⏱️ Chapter Markers 00:00 – Is AI the New UI?Why generative interfaces are reigniting the UI conversation02:10 – The Hidden Cost of Traditional InterfacesWhy one-size-fits-all software limits users04:20 – When UIs Are Generated on DemandAdaptive experiences vs fixed screens and buttons06:15 – Conversational & Multimodal InterfacesWhy voice, audio, and language are all “UI”08:30 – When Consistency Still MattersSafety, muscle memory, and shared interface conventions10:45 – How Generative UIs Change WorkAI as a collaborator, not a replacement13:05 – Making Every Page an ApplicationWhy “dumb forms” and static sites are disappearing15:10 – The Browser as the Ultimate InterfaceNeutral canvases vs AI-controlled environments17:10 – AI Agents, Payments, and ControlWhy money is the hardest unsolved AI problem19:25 – The Future of Multimodal UIWhy UI goes far beyond pixels and screensIs AI really the new UI — or is that just another tech buzzphrase?

AI has mastered language, sort of. But the real world is way messier.In this episode of TechFirst, John Koetsier sits down with Kirin Sinha, founder and CEO of Illumix, to explore what comes after large language models: world models, spatial intelligence, and physical AI.They unpack why LLMs alone won’t get us to human-level intelligence, what it actually takes for machines to understand physical space, and how technologies born in augmented reality are now powering robotics, wearables, and real-world AI systems.This conversation goes deep on: • What “world models” really are — and why everyone from Fei-Fei Li to Jeff Bezos is betting on them • Why continuous video and outward-facing cameras are so hard for AI • The perception stack behind robots and smart glasses • Edge vs cloud compute — and why latency and privacy matter more than ever • How AR laid the groundwork for the next generation of physical intelligenceIf you’re building or betting on robotics, smart wearables, AR, or physical AI, this episode explains the infrastructure shift that’s already underway.GuestKirin SinhaFounder & CEO, Illumixhttps://www.illumix.com👉 Subscribe for more deep conversations on technology, AI, and the future:https://techfirst.substack.com00:00 Raising the Bar on “Smart” Devices01:07 Meet Kirin, Founder & CEO of Illumix01:21 What Is a World Model — and Why It Matters02:23 Why LLMs Alone Won’t Lead to AGI03:46 From AR & the Metaverse to Physical AI05:18 AR vs VR vs the Metaverse — Different Problems, Different Futures06:32 Spatial Perception, Scene Understanding, and Contextual Intelligence07:39 Why Continuous Video Is So Hard for Machines08:39 The Camera Flip: From Selfie AI to World-Facing AI09:58 Why Cameras Beat LiDAR for Wearables and Robots10:27 Inside the Perception Stack11:20 Edge vs Cloud Compute in Physical AI12:37 Why On-Device Intelligence Matters for UX13:52 SLMs, Efficiency, and the Limits of “Bigger Is Better”15:11 Knowing What to Run — and When16:06 Intent, Memory, and Real-Time AI Decisions17:32 Physical Intelligence vs Digital Intelligence18:39 Memory Palaces, Spatial Brains, and Human AI19:39 Do We Need New Chips for Humanoid Robots?20:26 How Chip Architectures Will Evolve for Physical AI21:47 Privacy, On-Device Processing, and Trust22:48 Final Thoughts on the Future of World-Aware AI