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 expected

We 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.

🎙 Guest

Jan Liphardt

Founder & CEO, OpenMind

Stanford Professor of Bioengineering

Website: https://openmind.com

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00:00 Intro: The promise of humanoid robots at home

00:40 Meet Jan Liphardt and OpenMind’s OM1

01:12 Why your “labor droid” isn’t here yet

01:41 The “hand problem” and what robots can realistically do now

03:07 Why economics matters: $300/hour tasks vs. laundry and dishes

04:19 Robot hands today: reliability, repairability, and washing hands

05:16 LG’s laundry-folding demo and why fabric is still hard

06:16 Hospitals and hygiene: why “robot hand-washing” is unsolved

07:41 Hands as a separate system: compute, sensors, and integration

08:31 Why wheeled humanoids exist: hands first, body second

09:26 The real home use cases today: security, education, companionship

10:08 Aging in place: fall detection and remote nurse escalation

11:30 Real-world stories: parents living alone and why this matters

11:54 Privacy tradeoffs: robots vs. always-on home cameras

12:52 AIBO and why people get attached to mobile robots

13:52 Self-charging and the “my mom won’t plug it in” problem

14:21 Beyond falls: autism support and memory care

15:27 The education use case: “do my homework” vs. teach me

16:26 Personalized learning: what current classrooms miss

17:51 Why robot teachers beat screens for younger kids

18:46 Home security basics: unfamiliar face detection + alerts

19:15 Adding sensors: smoke, fire, sound, and anomaly detection

19:41 Quadrupeds vs. humanoids: cost, simplicity, and mobility

20:01 Safety issue: pinch hazards and kids hugging robots

20:46 What’s next for home labor robots

21:43 Why OM1 must be open source: transparency and trust

23:39 Why ROS 2 isn’t enough for human environments

24:37 OM1 approach: LLM-centric “Lego blocks” for robot behavior

25:43 Open-source humanoids for kids and why ownership matters

27:41 What’s missing: simulation is the bottleneck

28:11 Gazebo/Isaac Sim pain and the need for realistic sims

29:57 Why voice + “digital humans” matter in simulation

30:47 Tipping points: factories, warehouses, robotaxis, and humanoids

35:46 Wrap-up and final thoughts

AI is hitting entertainment like a sledgehammer ... from algorithmic gatekeepers and AI-written scripts to digital actors and entire movies generated from a prompt.

In this episode of TechFirst, host John Koetsier sits down with Larry Namer, founder of E! Entertainment Television and chairman of the World Film Institute, to unpack what AI really means for Hollywood, creators, and the global media economy.

Larry explains why AI is best understood as a productivity amplifier rather than a creativity killer, collapsing months of work into hours while freeing creators to focus on what only humans can do. He shares how AI is lowering barriers to entry, enabling underserved niches, and accelerating new formats like vertical drama, interactive storytelling, and global-first content.

The conversation also dives into:

• Why AI-generated actors still lack true human empathy

• How studios and IP owners will be forced to license their content to AI companies

• The future of deepfakes, guardrails, and regulation

• Why market fragmentation isn’t a threat — it’s an opportunity

• How China, Korea, and global platforms are shaping what comes next • Why writers and storytellers may be entering their best era yet

Larry brings decades of perspective from every major media transition — cable, streaming, global expansion — and makes the case that AI is just the next tool in a long line of transformative technologies.

If you care about the future of movies, television, creators, and culture, this is a conversation you don’t want to miss.

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🎙 Guest

Larry Namer

Founder, E! Entertainment Television

Chairman, World Film Institute

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00:00 – AI, emotion, and the danger of “AI twins”

00:00 – Welcome to Tech First + the AI disruption of entertainment

00:01 – Chaos in Hollywood: Disney, Netflix, Warner Bros, and consolidation

00:02 – AI as a productivity tool, not a creativity replacement

00:03 – How AI gives creators back their most valuable asset: time

00:04 – Regulation, guardrails, and the need for consequences

00:05 – Fragmentation, niche content, and the future economics of media

00:06 – Why streaming has been a gift to writers and storytellers

00:06 – Disney licensing IP to AI and why it was inevitable

00:07 – Contracts, actors’ rights, and why the law must catch up

00:08 – Deepfakes, AI avatars, and digital celebrities

00:09 – AI actors, empathy gaps, and spotting what isn’t human

00:10 – Using GPT to launch a bestselling book in days

00:11 – Big media M&A in an AI-driven world

00:12 – Jobs AI will eliminate vs. jobs AI will create

00:13 – Miniseries, deep storytelling, and why streaming changed everything

00:14 – Vertical video, short-form drama, and old ideas in new formats

00:15 – China vs. the West: who’s ahead in entertainment tech

00:16 – Global storytelling and Game of Thrones–scale opportunities

00:17 – Why Hollywood could ruin vertical video

00:18 – Interactive, immersive, and branched storytelling

00:19 – The future of screens, platforms, and audience choice

00:20 – Why new media never replaces old media

00:20 – Final thoughts on abundance, choice, and creativity

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 progress

If you’re interested in robotics, embodied AI, or the future of intelligent machines operating alongside humans, this episode is a must-watch.

👤 Guest

Ali Farhadi

CEO, Allen Institute for AI (AI2)

Professor, University of Washington

Former Apple researcher

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00:00 – Plato vs Aristotle… in robotics?

00:55 – What “reasoning” means in the physical world

02:10 – How humans predict actions before they happen

03:45 – Why physical AI is fundamentally different from text AI

04:50 – The next revolution: AI in the real world

05:30 – What is MOMO Act?

06:20 – Chain-of-thought… for robots

07:45 – Trajectories as reasoning and robot transparency

08:55 – Trust, safety, and correcting robots mid-action

10:15 – Why predictability builds trust in machines

11:40 – What’s broken with data-only AI approaches

13:10 – Why reasoning + data isn’t an “either/or”

14:00 – Open sourcing robotics models: why it matters

15:20 – How closed AI slows innovation

16:45 – Global competition and open research

17:40 – What’s next for robotics reasoning models

18:20 – Can these models work across robot types?

19:30 – Temporal and spatial reasoning in MOMO 2

20:40 – Scaling robotics vs scaling LLMs

21:10 – Edge vs cloud AI for robots

22:20 – Specialized models, latency, and privacy

23:00 – Final thoughts on the future of physical AI

Can we really build a $10,000 humanoid robot on open-source AI?

In this episode of TechFirst, John Koetsier talks with Chris Kudla, CEO of Mind Children, about a radically different approach to humanoid robots. Instead of six-figure industrial machines built for factories or war zones, Mind Children is building small, safe, friendly social robots designed for kids, classrooms, and elder care.

Meet Cody (MC-1), their first humanoid prototype.

Cody is built on open-source AI from SingularityNET, combined with modular hardware, low-torque actuators, and a wheeled base designed for safety, affordability, and mass production. And there's some other AI bits and pieces from all the big name companies that you'd recognize.

Mind Children's goal is ambitious: a $10,000 humanoid robot that families, schools, and care facilities can actually afford.

In this conversation we explore:

• Why social robots may be the real gateway to embodied AI

• How Cody is designed for children and elder care instead of factories

• Why wheels beat bipedal legs for safety, cost, and stability

• How open-source AI and modular software stacks enable faster innovation

• The emotional and ethical challenges of building companion robots

• And what it takes to bring a humanoid robot to market at scale

This is not sci-fi. This is the early blueprint of a future where humanoid robots are personal, affordable, and open-source.

00:00 – The $10,000 open-source humanoid question

01:58 – Meet Cody, the MC-1 prototype

04:10 – Why Cody is small, child-sized, and approachable

06:55 – Designing humanoids for kids and elder care

09:45 – Social robots vs industrial humanoids

12:40 – Wheels instead of legs and why that matters

16:05 – Low-torque actuators, safety, and toy-like design

19:20 – Modular hands, arms, and future upgrades

22:10 – Open-source AI and SingularityNET’s role

25:30 – On-robot vs cloud AI and why it matters

28:40 – Vision, LiDAR, and simulated world models

32:10 – Emotional awareness and social intelligence

35:10 – The $10K target and mass-production strategy

38:15 – The risks of attachment to robot companions

40:00 – Final thoughts on Cody and the future of social robots

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 control

From 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:

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👤 Guest

Mark Vange

CEO & Founder, Automate.ly

Former CTO, Electronic Arts

Investor, serial entrepreneur, and builder focused on intent-driven, AI-native software

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⏱️ Chapter Markers

00:00 – Is AI the New UI?

Why generative interfaces are reigniting the UI conversation

02:10 – The Hidden Cost of Traditional Interfaces

Why one-size-fits-all software limits users

04:20 – When UIs Are Generated on Demand

Adaptive experiences vs fixed screens and buttons

06:15 – Conversational & Multimodal Interfaces

Why voice, audio, and language are all “UI”

08:30 – When Consistency Still Matters

Safety, muscle memory, and shared interface conventions

10:45 – How Generative UIs Change Work

AI as a collaborator, not a replacement

13:05 – Making Every Page an Application

Why “dumb forms” and static sites are disappearing

15:10 – The Browser as the Ultimate Interface

Neutral canvases vs AI-controlled environments

17:10 – AI Agents, Payments, and Control

Why money is the hardest unsolved AI problem

19:25 – The Future of Multimodal UI

Why UI goes far beyond pixels and screensIs AI really the new UI — or is that just another tech buzzphrase?

The web is turning agentic.

And that changes everything from shopping to search to SEO.

In this episode of TechFirst, John Koetsier sits down with Dave Anderson (VP at ContentSquare + host of the “Tech Seeking Human” podcast) to unpack what happens when browsers and AI assistants don’t just answer … they do stuff. For you. On your behalf.

From Atlas and agentic browsing to the growing backlash from retailers (hello, Amazon vs Perplexity), we explore who benefits, who loses, and what the internet becomes when agents are the default user.

You’ll hear why retailers are nervous (security, margins, coupon hunting), why agent-first experiences might create “headless” retailers (like ghost kitchens, but for ecommerce), and why search is shifting from SEO to AI visibility.

Plus: real talk about trusting agents with your credit card, hallucinations, and what it means if your agent can look indistinguishable from you.

Guest

Dave Anderson — VP, ContentSquare

https://contentsquare.com

Podcast: Tech Seeking Human

https://www.techseekinghuman.ai

Links & subscribe

Subscribe for more conversations on tech, AI, and what’s next: https://techfirst.substack.com

Transcripts always available here

https://johnkoetsier.com

00:00 Agentic web: what changes when browsers “do stuff”

00:59 Meet Dave Anderson (VP + podcast host)

01:31 30,000 feet: why “agents” suddenly matter

03:48 The agent future John wanted 10 years ago

04:21 Why Amazon doesn’t want your agent shopping on Amazon

05:07 Ticketmaster, bots, and the security nightmare

06:26 Siri’s original promise vs today’s reality

08:31 Are agents just bots… or something different?

10:04 Retail fears: coupon hunting, margins, returns chaos

11:21 Can you trust an agent with your credit card?

11:59 Why retailers want their own agents (and control)

13:14 Amazon’s agent works… but is it the whole internet?

14:19 Ghost kitchens for retail: “headless” agent-first brands

15:17 Hugo Boss jacket test: agents vs manual search

16:40 Agents should talk to your finance agent

17:14 Kids + deepfakes: what even looks real anymore?

18:04 Is this corrosive to apps… or the web?

19:10 Online identity, anonymity, and agent verification

20:28 Two futures: human-first brands vs agent-first retail

21:19 Agentic browsers on your device: can they “look like you”?

22:51 Baseball vs golf: the best analogy for search now

24:44 Instant shopping problem: returns + missing “services layer”

26:10 AI weirdness: wrong names, wrong locations, shifting behavior

27:37 Agents beyond shopping: support is the sleeper win

29:49 Inventing the future: who adopts agents and who won’t

31:13 Will people get tired of AI and crave humans again?

31:45 Serendipity vs optimization: the restaurant debate

32:36 Wrap: nobody solved agents… but the shift is real

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 intelligence

If you’re building or betting on robotics, smart wearables, AR, or physical AI, this episode explains the infrastructure shift that’s already underway.

Guest

Kirin Sinha

Founder & CEO, Illumix

https://www.illumix.com

👉 Subscribe for more deep conversations on technology, AI, and the future:

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00:00 Raising the Bar on “Smart” Devices

01:07 Meet Kirin, Founder & CEO of Illumix

01:21 What Is a World Model — and Why It Matters

02:23 Why LLMs Alone Won’t Lead to AGI

03:46 From AR & the Metaverse to Physical AI

05:18 AR vs VR vs the Metaverse — Different Problems, Different Futures

06:32 Spatial Perception, Scene Understanding, and Contextual Intelligence

07:39 Why Continuous Video Is So Hard for Machines

08:39 The Camera Flip: From Selfie AI to World-Facing AI

09:58 Why Cameras Beat LiDAR for Wearables and Robots

10:27 Inside the Perception Stack

11:20 Edge vs Cloud Compute in Physical AI

12:37 Why On-Device Intelligence Matters for UX

13:52 SLMs, Efficiency, and the Limits of “Bigger Is Better”

15:11 Knowing What to Run — and When

16:06 Intent, Memory, and Real-Time AI Decisions

17:32 Physical Intelligence vs Digital Intelligence

18:39 Memory Palaces, Spatial Brains, and Human AI

19:39 Do We Need New Chips for Humanoid Robots?

20:26 How Chip Architectures Will Evolve for Physical AI

21:47 Privacy, On-Device Processing, and Trust

22:48 Final Thoughts on the Future of World-Aware AI

Quantum computers usually mean massive machines, cryogenic temperatures, and isolated data centers. But what if quantum computing could run at room temperature, fit inside a server rack — or even a satellite?

In this episode of TechFirst, host John Koetsier sits down with Marcus Doherty, Chief Science Officer of Quantum Brilliance, to explore how diamond-based quantum computers work — and why they could unlock scalable, edge-deployed quantum systems.

Marcus explains how nitrogen-vacancy (NV) centers in diamond act like atomic-scale qubits, enabling long coherence times without extreme cooling. We dive into quantum sensing, quantum machine learning, and why diamond fabrication — including the world’s first commercial quantum diamond foundry — could be the key to manufacturing quantum hardware at scale.

You’ll also hear how diamond quantum systems are already being deployed in data centers, how they could operate in vehicles and satellites, and what the realistic roadmap looks like for logical qubits and real-world impact over the next decade.

Topics include:

• Why diamonds are uniquely suited for quantum computing

• How NV centers work at room temperature

• Quantum sensing vs. quantum computing

• Manufacturing challenges and timelines

• Quantum computing at the edge (satellites, vehicles, sensors)

• The future of hybrid classical-quantum systems

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🎙 Guest

Marcus Doherty

Chief Science Officer, Quantum Brilliance

Professor of Quantum Physics

Army Reserve Officer

🌐 https://quantumbrilliance.com

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00:00 Diamonds and the next wave of quantum computing

01:20 Why diamond qubits work at room temperature

03:20 NV centers explained: defects that behave like atoms

05:05 How diamonds replace massive quantum isolation systems

06:40 Building the world’s first quantum diamond foundry

08:30 Defect-free diamonds, isotopes, and qubit engineering

10:15 Quantum sensing vs. quantum computing with diamonds

12:40 From desktop quantum systems to millions of qubits

14:25 Roadmap: logical qubits, timelines, and scale

16:10 Quantum computers at the edge: vehicles and satellites

18:10 Quantum machine learning and real-world deployments

19:50 The long game: why diamond quantum computing scales

Will AI kill your job?

What happens to your job as AI gets smarter and companies keep laying people off even while profits rise? Will you still have a job? Will the job you have change beyond recognition?

Scary questions, no?

In this episode of TechFirst, host John Koetsier sits down with Nikki Barua, co-founder of Footwork and longtime founder, executive, and resiliency expert, to unpack what work really looks like in the age of AI.

Layoffs are no longer just about economic downturns. Companies are growing, innovating, and still cutting staff, often because AI is enabling more output with less capacity.

So what does that mean for you?

Nikki argues the future doesn’t belong to those who simply “learn AI tools,” but to agentic humans: people who lead with uniquely human strengths and use AI to amplify their impact. This conversation explores:

• Why today’s layoffs are different from past cycles

• How AI is compressing jobs before creating new ones

• What it means to move from doing work to directing outcomes

• Why identity, curiosity, and agency matter more than certifications

• How to rethink workflows instead of chasing shiny AI tools

• The FLIP framework: Focus, Leverage, Influence, and Power

This episode isn’t about fear.

It’s about reinvention.

If you’re wondering how to stay relevant, valuable, and resilient as AI reshapes work, this is the place to start.

Guest

Nikki Barua

Co-founder, Footwork

(Reinventing organizations with agentic AI)

👉 Subscribe for more conversations on AI, work, and the future of technology:

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Chapters:

00:00 — Work in the AI Age: what happens to your job?

01:05 — Layoffs, AI, and why this cycle feels different

02:55 — “Don’t let AI have the last laugh”

04:45 — Profitable companies cutting jobs: what’s really happening

06:40 — The next 18–24 months: compression before reinvention

08:30 — AI’s impact on young workers and early careers

10:00 — What should you be doing right now?

11:20 — Why surface-level AI use won’t save your job

12:40 — The rise of the “agentic human”

14:20 — From doing to directing: humans + machines as partners

15:55 — Why certifications and training aren’t enough

17:10 — High-agency people win in the AI age

18:35 — The FLIP framework: Focus and identity

20:00 — Leverage: compounding capacity beyond automation

21:20 — Influence: trust, authenticity, and scaled impact

22:25 — Power: upgrading your personal operating system

23:40 — Two shifts that make this AI revolution different

25:05 — Tools vs workflows: where most people get it wrong

26:25 — The real blocker: old identities and fear of change

27:40 — Three steps to stay relevant in the AI age

28:40 — Final thoughts + wrap-up

What if someone actually built TARS from Interstellar—and discovered it really could work?

In this episode of TechFirst, host John Koetsier sits down with Aditya Sripada, a robotics engineer at Nimble, who turned a late-night hobby into a serious research project: a real, working mini-version of TARS, the iconic robot from Interstellar.

Aditya walks through why TARS’s strange, flat form factor isn’t just cinematic flair—and how it enables both walking and rolling, one of the most energy-efficient ways for robots to move. We dive into leg-length modulation, passive dynamics, rimless wheel theory, and why science fiction quietly shapes real robotics more than most engineers admit.

Along the way, Aditya explains what he learned by challenging his own assumptions, how the project connects to modern humanoid and warehouse robots, and why reliability—not flash—is the hardest problem in robotics today. He also previews his next ambitious project: building a real-world version of Baymax, exploring soft robotics and safer human-robot interaction.

This is a deep, accessible conversation at the intersection of science fiction, physics, and real-world robotics—and a reminder that sometimes the ideas we dismiss as “impossible” just haven’t been built yet.

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Guest

Aditya Sripada

Robotics Engineer, Nimble

Researcher in legged locomotion, humanoids, and unconventional robot form factors

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If you enjoyed this episode, subscribe for more deep dives into technology, robotics, and innovation:

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Chapters:

00:00 – TARS in Real Life: Why Interstellar’s Robot Still Fascinates Us

01:00 – Why Building TARS Seemed Physically Impossible

02:00 – From Weekend Hobby to Serious Robotics Research

03:00 – How Science Fiction Quietly Shapes Real Robot Design

04:00 – Walking vs Rolling: Why TARS Uses Both

05:00 – Why Simple Robots Can Beat Complex Humanoids

06:00 – Turning Legs into a Wheel: The Rolling Mechanism Explained

07:00 – Leg-Length Modulation and Passive Dynamics

08:00 – Inside the Actuators: Degrees of Freedom and Compact Design

09:00 – Why TARS’s Arms Don’t Really Make Sense

10:30 – Lessons Learned: Never Dismiss “Impossible” Ideas

12:00 – Rimless Wheels, Gaits, and Robotics Theory

13:00 – What This Project Taught Him at Nimble

14:00 – What “Super-Humanoid” Robots Actually Mean

15:30 – Why Reliability Matters More Than Flashy Demos

16:30 – TARS as a Research Platform, Not a Product

17:30 – From TARS to Baymax: Exploring Soft Robotics

19:00 – Can We Build Safer, Friendlier Humanoid Robots?

20:30 – What’s Next: Recreating Baymax in Real Life

21:30 – Final Thoughts and Wrap-Up