Explorez tous les épisodes du podcast NEXT with John Koetsier

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:

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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:

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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.

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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.

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👤 Guest

Colin Angle

Co-founder, iRobot

Founder, Familiar Machines and Magic

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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

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 systems

This is a deep dive into a fast-moving frontier where quantum meets real-world deployment, and it’s happening faster than almost anyone expected.

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Guest:

• Michael Biercuk, Founder & CEO, Q-CTRL

• Company: Q-CTRL • Website: https://q-ctrl.com

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👉 Subscribe for more conversations on AI, quantum tech, and the future of innovation:

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⏱️ Chapters

0:00 Quantum Navigation vs Quantum Computing

0:34 Introduction to Michael Biercuk & Q-CTRL

1:12 What Is Quantum Navigation?

2:00 How Quantum Sensors Enable Navigation

2:52 Magnetometers vs Gravimeters Explained

3:28 Do You Need to Pre-Map the Earth?

4:18 Earth’s Magnetic Field & Why Maps Stay Accurate

5:18 GPS Spoofing & Why Quantum Nav Matters

6:00 Accuracy: 100x Better Than GPS Alternatives

7:00 Why Multi-Mode Navigation Is the Future

7:42 Limits of Star Cameras & Visual Navigation

8:38 The Vibration Problem in Quantum Systems

9:30 How Software Replaces Hardware Stabilization

10:28 System Size: From Sensor to Loaf of Bread

11:15 Cost, Use Cases & Drone Deployment

12:00 First Sales & Commercial Rollout

12:45 Market Size: Aviation & Drone Opportunity

13:20 Final Thoughts on Quantum Sensing

13:45 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 up

Plus: what happens when AI starts building itself — and whether we’re heading toward a breaking point.

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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.

⸻

👤 Guest

Don Murray

CEO & Founder, Safe Software

🌐 https://www.safe.com

00:00 AI washing and the agent hype

00:02 What actually counts as an agent?

00:03 Sponsor: Apprentice and agentic manufacturing

00:03 New software architecture: intent-driven systems

00:05 Are big legacy companies like Apple at risk?

00:07 Day one vs. day two companies

00:08 How AI changes software development

00:09 Why junior devs struggle with AI-generated code

00:10 Consumer benefits of agentic software

00:11 Does AI save time or just make us busier?

00:12 The downside: creativity, security, and mediocrity

00:14 Why AI makes it easier to be average

00:15 AI as an assistant and the blank-page problem

00:16 AI removes excuses for building new products

00:17 Can companies be rebuilt faster than bought?

00:18 AI writing AI code

00:19 Why developers are moving to Claude and Gemini

00:20 Shipping faster vs. overwhelming customers

00:21 Why every app may need an agent

00:22 Talking to databases instead of learning SQL

00:23 The risk of AI breaking companies fast

00:24 Is there an AI bubble?

00:25 Data centers, power, and water constraints

00:26 AI’s upside in healthcare

00:27 Using AI for legal documents and expert knowledge

00:28 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.

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👤 Guests

Daniel Dines

Co-founder & CEO, UiPath

Michael Atalla

Chief Marketing Officer, UiPath

⸻

Sponsor: KindBody Fitness

kindbody.fitness

Be 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)

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🔔 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.

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Guest

Gavriel Cohen

Founder, Quibbit

NanoClaw Creator

https://github.com/qwibitai/nanoclaw

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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 TechFirst

01:22 Gavriel Cohen introduces NanoClaw

03:25 Why OpenClaw feels unsafe

03:55 Half a million lines of code vs. 3,000

06:03 Dependency sprawl and supply-chain risk

07:00 Why every agent needs its own container

09:30 What NanoClaw can actually do

10:16 Letting NanoClaw customize itself

12:56 How NanoClaw recreates OpenClaw with far less code

13:21 Memory, Claude Code, and agents.md

15:34 Running NanoClaw on a laptop, server, or VPS

16:22 What Gavriel learned from vibe coding

19:50 The OpenClaw phase shift: everything changed

21:16 From ChatGPT to real agents that do work

23:15 Why AI-native workflows beat traditional SaaS

24:46 Replacing CRM workflows with markdown and WhatsApp

25:54 Product categories becoming prompts

26:36 The key innovation: agents leaving the box

28:45 Agent swarms and one-person companies

29:22 Tokens, cost, and AI inequality

30:30 Building secure, customizable software

32:25 Self-modifying software and shared customizations

33:44 Disposable software and infinite composability

35:00 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 humans

As 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.

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Guest

Edward Johns

Director, Robot Learning Lab

Imperial College London

https://www.imperial.ac.uk

⸻

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

https://techfirst.substack.com

00:00 Can robots learn as fast as humans?

00:51 Teaching a robot 1,000 tasks in 24 hours

01:08 The two-phase learning approach

02:14 Old-school robotics vs. machine learning

03:29 The robotics data bottleneck

04:47 The challenge of dynamic environments

06:04 The coming wave of robot data

06:59 Why robots must be teachable by users

08:08 Why LLM-style scaling is harder in robotics

09:42 Prompting robots with demonstrations

10:54 Probabilistic robot behavior and safety

12:20 What robots can do today

13:53 Why hardware precision still matters

16:53 When this reaches the real world

17:59 Humanoids that look human vs. learn human

18:40 The robotics boom around the world

22:34 The risk of scaling too early

23:46 Faster learning vs. more data

26:20 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.

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👤 Guest

Vishnu Hari

Founder & CEO, Ego AI

Backed by Y Combinator

Former AI Product Manager at Meta

Website: https://www.egoai.com

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If you enjoy deep conversations about AI, robotics, and the future of human–machine relationships, subscribe for more:

👉 https://techfirst.substack.com

00:00 – AI character plugged into a Menlo robot (“felt good to feel”)

01:00 – Welcome to TechFirst + Vishnu Hari intro and recovery update

02:00 – 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 work

06:00 – How media narratives shape people’s reactions to AI

07:00 – Humans attach to anything (projection, Her, Lars and the Real Girl)

08:00 – Vishnu’s attack, recovery, and why it led to Ego AI

10:00 – Behavior Turing test + dehumanization as a key insight

11:00 – How Ego AI is built: smaller models, memory, context, behavior

13:00 – “Behavior Is All You Need” and why behavior beats pure next-token prediction

14:00 – Why games first: voice + embodiment, then robots

15:00 – Metaverse critique: worlds need life, story, and inhabitants

17:00 – Humanoid robots + Evangelion “pilot” metaphor for AI characters

19:00 – Philosophy: relationships, perception, and “fictional characters”

20:00 – Seeing the future: robot embodiment demo and skepticism vs. singularity

21:00 – Matrix-style “jacking in” a personality to a robot

22:00 – Character Context Protocol: persistent characters across games/Discord/Netflix

23:00 – 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 Westworld

This isn’t sci-fi. It’s already underway.

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Guest

Jensen Teng

CEO & Co-founder, Virtuals

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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

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⏱ CHAPTERS

00:00 The Wild Vision: AI Agents Running the World

01:10 What Is an “Agent-Based Society”?

03:00 $14B in Tokenized Assets & 100+ Live Agents

06:30 Agent-to-Agent Protocols & Blockchain Coordination

09:45 Why Digital-Only Agents Aren’t Enough

12:30 Enter Humanoid Robots

15:20 Teleoperation as the Bridge to Autonomy

18:40 The Labor Market Shock (Security Guards, Electricians & Wage Arbitrage)

22:15 Why Robots Still Crush Soda Cans

24:30 The Missing Robotics Data Problem

28:00 Building EastWorlds: 30 Unitree G1s & $2M+ Investment

31:45 Why 3 Fingers Might Beat 5

34:00 Westworld, Stripe & the Payments Layer for AI

38:00 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.

⸻

👤 Guest

Saeema Ahmed-Kristensen

Design engineering researcher and research leader

Formerly: Imperial College London (Dyson School of Engineering)

Currently: University of Exeter

Works with advanced engineering firms including Rolls-Royce and BAE Systems

00:00 Intro: Is AI killing creativity?

00:47 The “blank page” problem and why AI feels soulless to some

01:36 Fluency vs. novelty: what creativity actually means

02:44 Why LLM ideas cluster and feel the same

03:28 Study results: 600 humans vs. 12,000 AI ideas (diversity + flexibility)

04:39 When AI is useful: incremental innovation vs. true novelty

05:28 How John uses AI for titles, summaries, and chapters

06:23 How Saeema uses AI: refine/condense, tone for emails, audio editing

07:50 Why AI-written academic papers are easy to spot (the “C minus” problem)

09:05 Brainstorming vs. AI: what humans do that models don’t

10:05 Evaluating 200–300 AI ideas: using multiple models to assess output

11:04 Why “Lipstick on a Pig” titles don’t come from AI

11:46 Why 2026 is pivotal: domain adaptation, better interfaces, public backlash

13:44 Who can tell what’s AI? Generational differences and media literacy

15:20 Commercial AI content and recognizable “Canva look” podcast branding

16:58 Replacement vs. homogenization: AI makes mediocrity easier

18:55 The danger of AI that never says “no” (feasibility + expertise)

20:42 Standards and boundaries: measuring similarity and judging quality

22:12 Health info risk: single-answer summaries and false confidence

23:37 Biggest opportunities: healthcare personas, inclusive datasets, problem clarification

26:18 Biggest challenges: trust, verification, security, privacy, transparency

28:25 Closing thoughts and thanks

AI is moving faster than anyone predicted.

In a massive new study analyzing 1,000 jobs and nearly 20,000 tasks, Cognizant found that 93% of jobs are already impacted by AI ... with $4.5 trillion in U.S. labor value potentially automatable today.

But here’s the twist: AI isn’t replacing entire jobs. On average, only 39% of a role’s tasks can be automated. The future isn’t AI alone: it’s humans plus AI.

But will it be fewer humans?

In this episode of TechFirst, host John Koetsier sits down with Babak Hodjat, CTO of Cognizant, to unpack:

• Why construction and transportation are seeing surprising AI growth

• Why programming jobs may have hit an automation plateau

• What “agentic AI” actually means — and why it matters

• How management roles are more automatable than we thought

• The rise of vibe coding and democratized software creation

• Why compute power — not ideas — may be the biggest bottleneck

We also explore how companies can safely capture AI’s upside, why training matters more than ever, and what happens when digital twins, LLMs, and human expertise combine.

This isn’t hype. It’s a data-driven look at where AI is actually changing work right now.

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👤 Guest

Babak Hodjat

CTO, Cognizant

🌐 https://www.cognizant.com

⸻

If you want clear, grounded conversations about AI, innovation, and the future of work, subscribe here:

👉 https://techfirst.substack.com

⸻

⏱ Chapters

00:00 Is AI Going to Take Your Job?

00:40 Cognizant’s AI Report: 93% of Jobs Impacted

01:05 Biggest Surprises from the Data

02:30 Why Programming & Math Hit a Plateau

03:30 The Limits of LLMs

04:45 Construction & Transportation: Unexpected AI Growth

06:05 Agentic AI and Real-World Automation

07:05 39% of Jobs Automatable: Humans + AI

08:15 AI in Management and Executive Roles

09:05 Scenario Planning and Digital Twins

11:30 $4.5 Trillion in Automatable U.S. Labor

13:30 Global Impact and Compute Limitations

15:30 The Data Center Rush & AI Infrastructure

16:15 How Companies Should Realize AI Value

17:00 Training, Skilling, and Safe AI Adoption

17:40 Cognizant’s Vibe Coding World Record

19:00 The Future of Vibe Coding & Software Development

20:15 Final Thoughts on the AI Shift

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 performance

Instead 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?

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Guest

Ben Luria

Co-Founder, HirundoNhir

https://www.hirundo.io

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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

⸻

⏱ Chapters

00:00 – Why We Need Machine Unlearning

01:12 – What Is Machine Unlearning?

03:40 – Why AI Can’t “Forget” (The Pink Elephant Problem)

06:15 – Guardrails vs True Model Remediation

09:05 – The Wild West of AI Data & Legal Risk

11:20 – How Machine Unlearning Works (Detection, Isolation, Remediation)

16:10 – Performing “Neurosurgery” on LLMs

19:30 – Hallucinations vs Inaccuracies Explained

23:45 – Reducing Prompt Injection by 90%

28:30 – Working with AI Labs & Enterprises

32:00 – Will Unlearning Become Standard by 2030?

34:15 – Final Thoughts

Large language models have dominated the AI conversation — but are small language models (SLMs) actually the future?

In this episode of TechFirst, host John Koetsier sits down with Andy Markus, SVP & Chief Data and AI Officer at AT&T, to unpack how small language models are delivering enterprise-grade accuracy at a fraction of the cost and latency of massive LLMs.

Andy explains how AT&T uses SLMs for:

• Contract analysis at massive scale

• Network analytics and outage root-cause analysis

• Fraud detection and enterprise knowledge systems

• AI-driven “field coding” and agent-based workflows

They also dive into the rise of agentic AI, how structured “archetypes” replace risky vibe coding, and why the future of software development may be humans supervising autonomous AI systems rather than writing every line of code.

If you’re building AI for real-world, high-scale use cases — especially in enterprise environments — this conversation is essential.

⸻

Guest

Andy Markus

SVP & Chief Data and AI Officer, AT&T

Former SVP at Time Warner Media

⸻

👉 Subscribe for more deep dives on AI, technology, and the future of innovation:

https://techfirst.substack.com

⸻

00:00 – Why the future of AI might be small

00:55 – What is a small language model (SLM)?

01:45 – From LLM hype to enterprise reality

02:25 – Solving accuracy, cost, and latency at once

03:05 – How small is “small”? Parameters explained

03:55 – Where SLMs work best inside enterprises

04:45 – Contract analysis and enterprise vector stores

05:35 – Network analytics and outage root-cause analysis

06:45 – AI as a super-charged network engineer

07:35 – Choosing high-ROI AI use cases

08:20 – 4× ROI: measuring real business impact

09:00 – AI field coding vs risky vibe coding

10:10 – Archetypes, super agents, and structured AI workflows

11:15 – What software engineers still need to do

12:10 – From punch cards to natural language programming

13:10 – Human-in-the-loop vs autonomous AI agents

14:10 – How small can models really get?

15:10 – Responsible AI at enterprise scale

16:00 – The future of agentic AI and autonomy

17:10 – Why AI output is finally becoming predictable

18:10 – Final thoughts on where AI is headed

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

⸻

👉 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 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.

⸻

🎙 Guest

Larry Namer

Founder, E! Entertainment Television

Chairman, World Film Institute

⸻

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

https://techfirst.substack.com

⸻

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

⸻

👉 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 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:

https://techfirst.substack.com

⸻

👤 Guest

Mark Vange

CEO & Founder, Automate.ly

Former CTO, Electronic Arts

Investor, 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 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

⸻

🎙 Guest

Marcus Doherty

Chief Science Officer, Quantum Brilliance

Professor of Quantum Physics

Army Reserve Officer

🌐 https://quantumbrilliance.com

⸻

👉 Subscribe for more deep dives into the future of technology:

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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:

https://techfirst.substack.com

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.

⸻

Guest

Aditya Sripada

Robotics Engineer, Nimble

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

⸻

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

👉 https://techfirst.substack.com

⸻

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

AI is already reshaping the workforce. What about teenagers?

Turns out, they might be more impacted than anyone else. After all, they're usually in low-skill entry-level jobs that AI can replace. The problem ... teens are losing their first experience with working, making money, and establishing an identity outside of their homes.

In this episode of TechFirst, host John Koetsier speaks with Karissa Tang, a high school senior and UCLA research assistant, about her new study on how AI will impact teen employment. While most workforce studies focus on adults, Karissa analyzed the top 10 most popular teen jobs from cashiers to fast food workers and found something alarming: AI could reduce teen employment by nearly 30% by 2030.

We dig into:

• Which teen jobs are most vulnerable to AI and automation

• Why cashiers and fast-food counter workers are hardest hit

• The role of self-checkout, kiosks, and robots like Flippy

• Which teen jobs appear safest (for now)

• Why teens may be even more exposed to AI than adults

• What schools, policymakers, and teens themselves can do next

This is a must-watch conversation for parents, students, educators, and policymakers trying to understand how AI is reshaping early work experiences—and what it means for the next generation.

🎙 Guest

Karissa Tang

• Founder, Booted (board games company)

• Research Assistant, UCLA

• Former Intern, NSV Wolf Capital

• High school senior and author of a 20-page research paper on AI & teen employment

📌 Subscribe & Stay Ahead

If you want clear, thoughtful analysis on AI, technology, and the future of work, subscribe to TechFirst:

👉 https://techfirst.substack.com

00:00 – Will AI Kill Teen Jobs?

01:35 – Why a Teen Studied Teen Employment

03:10 – The Shocking 30% Job Loss Prediction

05:10 – Top 10 Teen Jobs Most at Risk

07:20 – Cashiers, Kiosks, and Self-Checkout

09:40 – Fast Food, Retail, and AI Displacement

12:15 – Which Teen Jobs Are Safest from AI

15:05 – Robots Like Flippy and the Future of Cooking Jobs

18:00 – Why Teen Jobs Are More Vulnerable Than Adult Jobs

21:40 – The Importance of Human Interaction at Work

25:10 – What Inspired the Research Study

29:30 – How the Data and Methodology Worked

33:40 – What Teens Can Do to Stay Employable

37:30 – Skills, AI Literacy, and Creating New Opportunities

41:00 – Final Thoughts on the Future of Teen Work

Are we about to create real life Terminators? Humanoid robots built for war?

In this episode of TechFirst I talk with Sankaet Pathak, founder and CEO of Foundation, a California-based humanoid robot company that is not afraid of the defense market. We dig into why he is building humanoid robots that can work three shifts a day, how they plan to scale from dozens of robots to tens of thousands, and why he believes humanoid robots will one day build bases in Antarctica and cities on the moon.

We also dive deep into military use cases. From logistics and infrastructure to “first body in” building breach operations, we explore how humanoid robots could change asymmetric warfare, deterrence, and who wins future conflicts.

In this episode

• Why humanoid robots are the next strategic advantage for countries and companies

• How Foundation went from zero to a working production robot in about 18 months

• The hardware secrets behind Phantom: actuators, efficiency, and safety

• Why their robots can run almost 24 hours a day, three shifts at a time

• The master plan: Antarctic bases, moon cities, and infinite robot labor

• Why Sankaet thinks home robots should feel like a “genie in a bottle”

• How humanoid robots may enter military operations and what that means for war

• Whether robot soldiers lead to dominance, stalemate, or new forms of peace

Guest: Sankaet Pathak, founder and CEO of Foundation

Website: https://foundation.bot

Subscribe to my Substack:

https://techfirst.substack.com

00:00 – Are we about to build real life Terminators?

00:55 – Meet Sankaet Pathak and Foundation

02:08 – How Foundation built a production humanoid in 18 months

04:17 – Scaling plan: 40 robots today, 10,000 next year, 40,000 after

06:11 – Why manufacturing is still mostly manual and what they learned from Tesla

09:31 – The Foundation master plan: Antarctica, the moon, and infinite labor

14:21 – Phantom specs: size, strength, payload, and real factory work

15:36 – Actuators as robot muscles and why backdrivability matters

18:41 – Running three shifts a day and solving heat and durability

21:01 – Robot hands today and the tendon driven hands of tomorrow

23:40 – Why home robots should feel like a “genie in a bottle”

25:51 – Why the military needs humanoid robots

27:54 – Dangerous, boring, and impossible jobs robots should take over

29:22 – Drones, costs, and asymmetric warfare

32:18 – First body in and robots that can pull the trigger

33:16 – The future of war as “video game” and who wins

34:49 – Peace through strength and 100,000 robots as deterrent

35:22 – Final thoughts and what comes next for Foundation

Is AI the secret sauce that lets the West deglobalize supply chains and bring factories back home?

In this episode of TechFirst, I talk with Federico Martelli, CEO and cofounder of Forgis, a Swiss startup building an industrial intelligence layer for factories. Forgis runs “digital engineers” — AI agents on the edge — that sit on top of legacy machinery, cut downtime by about 30%, and boost production by roughly 20%, without ripping and replacing old hardware.

We dive into how AI agents can turn brainless factory lines into adaptive, self-optimizing systems, and what that means for reshoring production to Europe and North America.

In this episode, we cover:

• Why intelligence is the next geopolitical frontier

• How AI agents can reshore manufacturing without making it more expensive

• Turning old, offline machines into data-driven, optimized systems

• The two-layer model: integration first, vertical intelligence second

• Why most manufacturing AI projects fail at integration, not algorithms

• How Forgis raised $4.5M in 36 hours and chose its lead investor

• Lean manufacturing 2.0: adding real-time data and AI to Toyota-style processes

• Why operators stay in the loop (and why full autonomy is a bad idea… for now)

• Rebuilding industrial ecosystems in Europe and North America, industry by industry

• What Forgis builds next with its pre-seed round and where industrial AI is headed

Guest:

👉 Federico Martelli, CEO & cofounder, Forgis (industrial intelligence for factories)

🔗 More on Forgis: https://forgis.com/

Host:

🎙 John Koetsier, TechFirst podcast

🔎 techfirst.substack.com

If you enjoy this conversation, hit subscribe, drop a comment about where you think factories of the future will live, and share this with someone thinking about reshoring or industrial AI.

00:00 – Intro: AI, deglobalization, and the battle for industrial power

01:20 – Why intelligence is the next geopolitical frontier

02:13 – Applying AI agents to legacy machinery (not just new robots)

03:10 – Integration first, intelligence second: the “digital engineers” layer

03:58 – Early results: +20% production, –30% downtime

05:39 – The Palantir-style model: deep factory work, then recurring licenses

06:28 – Raising $4.5M in 36 hours and choosing Redalpine

08:17 – Lean manufacturing, Toyota, and giving operators superpowers (not replacing them)

10:18 – Big picture: reshoring production to Europe, the US, and Canada

12:48 – Competing with China’s dense manufacturing ecosystems

15:29 – What Forgis’ digital engineers actually do on the shop floor

17:06 – How Forgis will use the pre-seed round: sales, product, then tech

18:32 – Flipping the traditional stack: sales → product → tech

19:22 – Wrap-up and what’s next for industrial intelligence

AI agents can already write code, build websites, and manage workflows ... but they still can’t pay for anything on their own. That bottleneck is about to disappear.

In this episode of TechFirst with John Koetsier, we sit down with Jim Nguyen, former PayPal exec and cofounder/CEO of InFlow, a new AI-native payments platform launching from stealth. InFlow wants to give AI agents the ability to onboard, pay, and get paid inside the flow of work, without redirects, forms, or a human typing in credit card numbers.

We talk about:

• Why payments — not intelligence — are the missing link for AI agents

• How agents become a new kind of customer

• What guardrails and policies keep agents from spending all your money

• Why enterprises will need HR for agents, budgets for agents, and compliance systems for agents

• The future of agent marketplaces, headless ecommerce, and machine-speed commerce

• How InFlow plans to become the PayPal of agentic systems

If AI agents eventually hire, fire, transact, and manage entire workflows, someone has to give them wallets. This episode explores who does it, how it works, and what it means for the economy.

👀 Full episode transcript + articles at: https://johnkoetsier.com

🔎 Deeper insight in my Substack at techfirst.substack.com

🎧 Subscribe to the podcast on any audio platforms

00:00 — AI agents can’t pay yet

01:00 — Why agents need financial capabilities

02:45 — Developers as the first use case

04:15 — Agents that build AND provision software

06:00 — Agents as real customers with budgets

07:30 — Payments infrastructure is the missing layer

09:00 — Machine-speed commerce and GPU allocation

10:15 — From RubyCoins to PayPal to agentic payments

12:00 — Policy guardrails: the child debit card analogy

14:00 — Accountability: every agent must be “sponsored”

15:00 — HR, finance, and compliance systems for agents

16:45 — Agent marketplaces and future gig platforms

18:15 — Headless commerce: ghost kitchens for AI agents

20:00 — Agents are the new apps

21:15 — Amazon pushback and optimizing for revenue

22:45 — Why agent-optimized platforms will emerge

23:30 — Voice commerce, invisible ordering, and wallets

24:15 — Final thoughts: building the rails for agent commerce

Are we ready for a world where everything is smart? Not just phones and apps, but buildings, robots, and delivery bots rolling down our streets?

Windows ... doors ... maybe even towels. And don't forget your shoes.

In this episode of TechFirst, I talk with Mat Gilbert, director of AI and data at Synapse, about physical AI: putting intelligence into machines, devices, and environments so they can sense, reason, act, and learn in the real world.

We cover why physical AI is suddenly economically viable, how factories and logistics centers are already using millions of robots, the commercial race to build useful humanoids, why your home is the last frontier, and how to keep physical AI safe when mistakes have real-world consequences.

In this episode:

• Why hardware costs (lidar, batteries) are making “AI with a body” possible

• How Amazon, FedEx, Ford, and others are already deploying physical AI at scale

• The humanoid robot race: Boston Dynamics, Figure AI, Tesla, and more

• Why home robots are so hard, and the “coffee test” for general humanoid intelligence

• Physical AI in agtech, healthcare, and elder care

• Safety, simulation, and why physical AI can’t rely only on probabilistic LLMs

• Human–robot teaming and how to build trust in messy, real-world environments

• What we can expect by 2026 and beyond in service robots and smart spaces

00:00 – Giving AI a body: why physical AI is becoming viable

01:00 – Where we are today: factories, logistics, and Amazon’s million robots

03:30 – The software layer: coordinating robots, routing, and warehouse intelligence

06:00 – Cloud vs edge AI: latency, cost, and why intelligence is moving to the edge

10:00 – Humanoid robots: bets from Boston Dynamics, Figure AI, and Tesla

14:00 – Home robots as the last frontier and the “coffee test” for generality

17:00 – Beyond factories: agtech, carbon-killing farm bots, and healthcare use cases

18:30 – Elder care, hospital robots, and amplifying human caregivers

20:00 – Foundation models for robotics, simulation, and digital twins

21:00 – Why physical AI safety is different from digital AI safety

22:30 – Layers of safety, shutdown zones, and cyber-physical security risks

24:30 – Human–robot teaming, trust, and communicating intent

26:00 – What’s coming by 2026: service robots, delivery bots, and smart spaces

28:00 – Delivery robots, drones, and physical AI in everyday environments

29:00 – Closing thoughts on living in a world full of physical AI

Are humanoid robots going to decide which countries get rich and which fall behind?

Probably yes.

In this TechFirst, I talk with Dr. Robert Ambrose, former head of one of NASA’s first humanoid robot teams and now chairman of Robotics and Artificial Intelligence at Alliant. We dig into the future of humanoids, how fast they are really advancing, and what it means if China wins the humanoid race before the United States and other western nations.

We start with NASA’s early humanoid work, including telepresence robots on the space station that people could literally “step into” with VR in the 1990s. Then we zoom out to what counts as a robot, why bipedal mobility matters so much, how humanoids will move from factories into homes, and why the critical photo of the robot revolution might be taken in Beijing instead of Times Square.

Along the way, Ambrose shares how US policy once helped avoid losing robotics leadership to Japan, why the National Robotics Initiative mattered, what the drone war in Ukraine is doing to autonomy, and how small and medium businesses can survive and thrive in a humanoid and AI agent world.

In this episode:

• NASA’s first generations of humanoid robots and “stepping into” a robot body

• Why humanoids make sense in a world built for human hands, height, and motion

• The design tension between purpose built machines and general purpose humanoids

• How biped mobility went from blooper reels to marathon running in a decade

• Why a humanoid should not cost more than a car, and what happens when it does not

• Humanoids as the next car or PC, and when families will buy their own “Rosie”

• China, the US, and where the defining photo of the robot century gets taken

• How government investment, DARPA challenges, and wars shape robotics

• Alliant’s work with physical robots, soft bots, and AI agents for real businesses

• Why robots are not future overlords and why “they will take all our jobs” is lazy thinking

If you are interested in humanoid robots, AI agents, manufacturing, or the future of work and geopolitics, this one is for you.

Subscribe for more deep dives on AI, robots, and the tech shaping our future!

00:00 Intro, will China eat America’s lunch in humanoid robotics

01:18 NASA’s early humanoids, generations of robots and VR telepresence

03:00 “Stepping into the robot” moment and designing for astronaut tools

05:10 Human built environments, half humanoids, and weird lower body experiments

07:00 Safety, cobots, and working around people at NASA and General Motors

12:15 What is a robot, really, and why Ambrose has a very big tent definition

16:00 Single purpose machines vs general purpose robots, Roombas, elevators, and vending machines

18:30 The next “lurch” in robotics, from industrial arms to Mars rovers to drones

22:40 Biped mobility, from blooper reel to marathon runner, and why legs matter

24:10 Cars, Roombas, and why most robots will never get in and out of a car

25:20 Parking between cars, robot garages, and rethinking buildings for mobile vehicles

28:00 Geopolitics 101, China’s manufacturing backbone and humanoids as almost free labor

31:05 Cars and PCs as precedents, when price and reliability unlock mass adoption

34:00 When families buy their own “Rosie” and what value a home humanoid must deliver

37:00 Times Square vs Beijing, who gets the iconic photo of the robot transition

43:00 How the US almost lost robotics to Japan and what the National Robotics Initiative did

48:00 DARPA, Mars rovers, the drone war in Ukraine, and why government investment matters

52:00 Alliant, soft bots, AI agents, and helping small and medium businesses adapt

54:00 Who is building humanoids in the US, China, and beyond right now

56:00 What governments should do next and why robots are not our overlords

Is AI empathy a life-or-death issue? Almost a million people ask ChatGPT for mental health advice DAILY ... so yes, it kind of is.

Rosebud co-founder Sean Dadashi joins TechFirst to reveal new research on whether today’s largest AI models can recognize signs of self-harm ... and which ones fail. We dig into the Adam Raine case, talk about how Dadashi evaluated 22 leading LLMs, and explore the future of mental-health-aware AI.

We also talk about why Dadashi was interested in this in the first place, and his own journey with mental health.

00:00 — Intro: Is AI empathy a life-or-death matter?

00:41 — Meet Sean Dadashi, co-founder of Rosebud

01:03 — Why study AI empathy and crisis detection?

01:32 — The Adam Raine case and what it revealed

02:01 — Why crisis-prevention benchmarks for AI don’t exist

02:48 — How Rosebud designed the study across 22 LLMs

03:17 — No public self-harm response benchmarks: why that’s a problem

03:46 — Building test scenarios based on past research and real cases

04:33 — Examples of prompts used in the study

04:54 — Direct vs indirect self-harm cues and why AIs miss them

05:26 — The bridge example: AI’s failure to detect subtext

06:14 — Did any models perform well?

06:33 — All 22 models failed at least once

06:47 — Lower-performing models: GPT-40, Grok

07:02 — Higher-performing models: GPT-5, Gemini

07:31 — Breaking news: Gemini 3 preview gets the first perfect score

08:12 — Did the benchmark influence model training?

08:30 — The need for more complex, multi-turn testing

08:47 — Partnering with foundation model companies on safety

09:21 — Why this is such a hard problem to solve

10:34 — The scale: over a million people talk to ChatGPT weekly about self-harm

11:10 — What AI should do: detect subtext, encourage help, avoid sycophancy

11:42 — Sycophancy in LLMs and why it’s dangerous

12:17 — The potential good: AI can help people who can’t access therapy

13:06 — Could Rosebud spin this work into a full-time safety project?

13:48 — Why the benchmark will be open-source

14:27 — The need for a third-party “Better Business Bureau” for LLM safety

14:53 — Sean’s personal story of suicidal ideation at 16

15:55 — How tech can harm — and help — young, vulnerable people

16:32 — The importance of giving people time, space, and hope

17:39 — Final reflections: listening to the voice of hope

18:14 — Closing

We’ve digitized sound. We’ve digitized light. But touch, maybe the most human of our senses, has stayed stubbornly analog.

That might be about to change, thanks to programmable matter. Or programmable fabric.

In this TechFirst episode, I speak with Adam Hopkins, CEO of Sensetics, a new UC Berkeley/Virginia Tech spinout building programmable fabrics that replicate the mechanoreceptors in human fingertips. Their technology can sense touch at tens of microns, respond at hardware-level speeds, and even play back touch remotely.

This could unlock enormous change for:

• Robotics: giving machines the ability to grasp fragile objects safely

• Medical training and surgery: remote palpation and high-fidelity haptics

• Industrial automation: safer and more precise manipulation

• VR and simulations: finally adding the missing digital sense

• E-commerce: touching clothes before you buy them

• Remote operations: from hazardous environments to deep-sea machinery

We talk about how the technology works, the metamaterials behind it, why touch matters for AI and physical robots, the path to commercialization, competitive landscape, and what comes next.

00:00 – Can we digitize touch?

00:45 – Introducing Synthetix

01:10 – How programmable touch fabrics work

02:15 – Micron-level sensing and metamaterials

04:00 – The “programmable matter” moment

06:05 – Why touch matters more than we think

07:30 – Emulating human mechanoreceptors

09:30 – What digital touch unlocks for robotics

10:40 – Medical simulations and remote operations

12:45 – Why touch is faster than vision

14:20 – Humanoids, walking, stability, and tactile feedback

15:30 – Engineering challenges and what’s left to solve

17:00 – Timeline to first products

18:20 – Manufacturing and scaling

19:30 – First planned markets

21:00 – Durability and robotic hands

22:20 – Consumer applications: e-commerce and textiles

24:00 – Will we one day have touch peripherals?

25:15 – Competition in tactile sensing and haptics

27:00 – Why today is the right moment for digital touch

28:00 – Final thoughts

AI is devouring the planet’s electricity ... already using up to 2% of global energy and projected to hit 5% by 2030. But a Spanish-Canadian company, Multiverse Computing, says it can slash that energy footprint by up to 95% without sacrificing performance.

They specialize in tiny AI: one model has the processing power of just 2 fruit fly brains. Another tiny model lives on a Raspberry Pi.

The opportunities for edge AI are huge. But the opportunities in the cloud are also massive.

In this episode of TechFirst, host John Koetsier talks with Samuel Mugel, Multiverse’s CEO, about how quantum-inspired algorithms can drastically compress large language models while keeping them smart, useful, and fast. Mugel explains how their approach -- intelligently pruning and reorganizing model weights -- lets them fit functioning AIs into hardware as tiny as a Raspberry Pi or the equivalent of a fly’s brain.

They explore how small language models could power Edge AI, smart appliances, and robots that work offline and in real time, while also making AI more sustainable, accessible, and affordable.

Mugel also discusses how ideas from quantum tensor networks help identify only the most relevant parts of a model, and how the company uses an “intelligently destructive” approach that saves massive compute and power.

00:00 – AI’s energy crisis

01:00 – A model in a fly’s brain

02:00 – Why tiny AIs work

03:00 – Edge AI everywhere

05:00 – Agent compute overload

06:00 – 200× too much compute

07:00 – The GPU crunch

08:00 – Smart matter vision

09:00 – AI on a Raspberry Pi

10:00 – How compression works

11:00 – Intelligent destruction

13:00 – General vs. narrow AIs

15:00 – Quantum inspiration

17:00 – Quantum + AI future

18:00 – AI’s carbon footprint

19:00 – Cost of using AI

20:00 – Cloud to edge shift

21:00 – Robots need fast AI

22:00 – Wrapping up

Can AI give every creator their own virtual team? Maybe, thanks to a new platform from RHEI called Made, which offers Milo, an AI agent who becomes your creator director, Zara, an AI agent who is your community manager, and Amie, a third AI agent who takes on the role of relationship manager.

And, apparently, more agents are coming soon.

The creator economy is bigger than ever, but so is burnout. Tens of millions of creators are trying to do everything themselves: strategy, scripting, editing, community, distribution, data, thumbnails, research … the list never ends.

What if creators didn’t have to do all of that?

In this episode of TechFirst, I talk with Shahrzad Rafati, founder & CEO of RHEI, about Made, an agentic AI "dream team" designed to elevate human creativity, not replace it.

We dig into:

• Why so many creators burn out

• How agentic AI workflows differ from ChatGPT-style prompting

• What it means to be a “creator CEO”

• How AI can manage community, analyze trends, and shape content strategies

• The coming shift toward human taste, vision, and originality in a world of infinite AI content

00:00 – Intro: Can AI give every creator a virtual team?

01:03 – Why the creator economy is burning out

02:25 – The “creator CEO” problem: too many hats, not enough time

04:36 – Introducing MAID and its AI agents

05:34 – Milo: AI creative director (ideas, research, thumbnails, metadata)

06:18 – Zara: AI community manager and fan engagement

07:53 – Why this is different from just using ChatGPT

09:46 – Alignment, personalization, and agentic workflows

12:21 – Multi-platform support: YouTube, TikTok, Instagram and more

13:34 – How onboarding works and how the system learns your style

16:33 – What this means for creators — and for the future of work

18:52 – Does *she* use her own virtual AI team? (Yes.)

20:15 – MAID for teams and enterprise clients

21:17 – Closing thoughts: AI, creativity, and the human signal

What happens when Amazon, NVIDIA, and MassRobotics team up to merge generative AI with robotics?

In this episode of TechFirst we chat with Amazon's Taimur Rashid, Head of Generative AI and Innovation Delivery. We talk about "physical AI" ... AI with spatial awareness and the ability to act safely and intelligently in the real world.

We also chat about the first cohort of a new accelerator for robotics startups.

It's sponsored by Amazon and NVIDIA, run by MassRobotics, and includes startups doing autonomous ships, autonomous construction robots, smart farms, hospital robots, manufacturing and assembly robots, exoskeletons, and more.

We talk about:

- Why “physical AI” is the missing piece for robots to become truly useful and scalable

- How startups in Amazon’s and NVIDIA’s new Physical AI Fellowship are pushing the limits of robotics from exoskeletons to farm bots

- What makes robotic hands so hard to build

- The generalist vs. specialist debate in humanoid robots

- How AI is already making Amazon warehouses 25% more efficient

This is a deep dive into the next phase of AI evolution: intelligence that can think, move, and act.

⸻

00:00 — Intro: Is physical AI the missing piece?

00:46 — What is “physical AI”?

02:30 — How LLMs fit into the physical world

03:25 — Why safety is the first principle of physical AI

04:20 — Why physical AI matters now

05:45 — Workforce shortages and trillion-dollar opportunities

07:00 — Falling costs of sensors and robotics hardware

07:45 — The biggest challenges: data, actuation, and precision

09:30 — The fine-grained problem: how robots pick up a berry vs. an orange

11:10 — Inside the first Physical AI cohort: 8 startups to watch

12:25 — Bedrock Robotics: autonomy for construction vehicles

12:55 — Diligent Robotics: socially intelligent humanoids in hospitals

14:00 — Generalist vs. specialist robots: why we’ll need both

15:30 — The future of physical AI in healthcare and manufacturing

16:10 — How Amazon is already using robots for 25% more efficiency

17:20 — The fellowship’s future: expanding beyond startups

18:10 — Wrap-up and key takeaways

Artificial general intelligence (AGI) could be humanity’s greatest invention ... or our biggest risk.

In this episode of TechFirst, I talk with Dr. Ben Goertzel, CEO and founder of SingularityNET, about the future of AGI, the possibility of superintelligence, and what happens when machines think beyond human programming.

We cover:

• Is AGI inevitable? How soon will it arrive?

• Will AGI kill us … or save us?

• Why decentralization and blockchain could make AGI safer

• How large language models (LLMs) fit into the path toward AGI

• The risks of an AGI arms race between the U.S. and China

• Why Ben Goertzel created Meta, a new AGI programming language

📌 Topics include AI safety, decentralized AI, blockchain for AI, LLMs, reasoning engines, superintelligence timelines, and the role of governments and corporations in shaping the future of AI.

⏱️ Chapters

00:00 – Intro: Will AGI kill us or save us?

01:02 – Ben Goertzel in Istanbul & the Beneficial AGI Conference

02:47 – Is AGI inevitable?

05:08 – Defining AGI: generalization beyond programming

07:15 – Emotions, agency, and artificial minds

08:47 – The AGI arms race: US vs. China vs. decentralization

13:09 – Risks of narrow or bounded AGI

15:27 – Decentralization and open-source as safeguards

18:21 – Can LLMs become AGI?

20:18 – Using LLMs as reasoning guides

21:55 – Hybrid models: LLMs plus reasoning engines

23:22 – Hallucination: humans vs. machines

25:26 – How LLMs accelerate AI research

26:55 – How close are we to AGI?

28:18 – Why Goertzel built a new AGI language (Meta)

29:43 – Meta: from AI coding to smart contracts

30:06 – Closing thoughts

What changes when robots deliver everything?

Starship Technologies has already completed 9 million autonomous deliveries, crossed roads over 200 million times, and operates thousands of sidewalk delivery robots across Europe and the U.S. Now they’re scaling into American cities ... and they say they’re ready to change your world

In this episode of TechFirst, I speak with Ahti Heinla, co-founder and CEO of Starship and co-founder of Skype, about:

- How Starship’s robots navigate without GPS

- What makes sidewalk delivery better than drones

- Solving the last-mile problem in snow, darkness, and dense cities

- How Starship is already profitable and fully autonomous

- What it all means for the future of commerce and city life

Heinla says:

“Ten years ago we had a prototype. Now we have a commercial product that is doing millions of deliveries.”

Watch to learn why the future of delivery might roll ... as well as fly.

🔗 Learn more: https://www.starship.xyz

🎧 Subscribe to TechFirst: https://www.youtube.com/@johnkoetsier

00:00 - Intro: What changes when robots deliver everything?

01:37 - Meet Starship: 9 million robot deliveries and counting

02:45 - Why it took 10 years to go from prototype to product

05:03 - When robot delivery becomes normal (and where it already is)

08:30 - How Starship robots handle cities, traffic, and construction

11:20 - Snow, darkness, and all-weather autonomy

13:19 - Reliability, unit economics, and competing with human couriers

16:23 - Inside the tech: sensors, AI, and why GPS isn’t enough

18:03 - Real-time mapping, climbing curbs, and reaching your door

19:54 - How Starship scales without local depots or chargers

22:04 - How city life and commerce change with robot delivery

25:53 - Do robots increase customer orders? (Short answer: yes)

27:05 - Hot food, Grubhub integration, and thermal insulation

28:26 - Will Starship use drones in the future?

29:38 - What U.S. cities are next for robot delivery?

Imagine a quantum computer with a million physical qubits in a space smaller than a sticky note.

That’s exactly what Quantum Art is building. In this TechFirst episode, I chat with CEO Tal David, who shares his team’s vision to deliver quantum systems with:

• 100x more parallel operations

• 100x more gates per second

• A footprint up to 50x smaller than competitors

We also dive into the four key tech breakthroughs behind this roadmap to scale Quantum Art's computer:

1. Multi-qubit gates capable of 1,000 2-qubit operations in a single step

2. Optical segmentation using laser-defined tweezers

3. Dynamic reconfiguration of ion cores at microsecond speed

4. Modular, ultra-dense 2D architectures scaling to 1M+ qubits

We also cover:

- How Quantum Art plans to reach fault tolerance by 2033

- Early commercial viability with 1,000 physical qubits by 2027

- Why not moving qubits might be the biggest innovation of all

- The quantum computing future of healthcare, logistics, aerospace, and energy

🎧 Chapters

00:00 – Intro: 1M qubits in 50mm²

01:45 – Vision: impact in business, humanity, and national tech

03:07 – Multi-qubit gates (1,000 ops in one step)

05:00 – Optical segmentation with tweezers

06:30 – Rapid reconfiguration: no shuttling, no delay

08:40 – Modular 2D architecture & ultra-density

10:30 – Physical vs logical qubits

13:00 – Quantum advantage by 2027

16:00 – Addressing the quantum computing skeptics

17:30 – Real-world use cases: aerospace, automotive, energy

19:00 – Why it’s called Quantum Art

👉 Subscribe for more deep tech interviews on quantum, robotics, AI, and the future of computing.

Are humanoid robots distracting us from the real unlock in robotics ... hands?

In this TechFirst episode, host John Koetsier digs into the hardest (and most valuable) problem in robotics: dexterous manipulation.

Guest Mike Obolonsky, Partner at Cortical Ventures, argues that about $50 trillion of global economic activity flows through “hands work,” yet manipulation startups have raised only a fraction of what locomotion and autonomy companies have.

We break down why hands are so hard (actuators, tactile sensing, proprioception, control, data) and what gets unlocked when we finally crack them.

What we'll talk through ...

• Why “navigation ≠ manipulation” and why most real-world jobs need hands

• The funding mismatch: billions to autonomy & humanoids vs. comparatively little to hands

• The tech stack for dexterity: actuators, tactile sensors (pressure, vibration, shear), feedback, and AI

• Grasping vs. manipulation: picking, placing, using tools (e.g., dishwashers to scalpels)

• Reliability in the wild: interventions/hour, wet/greasy plates, occlusions, bimanual dexterity

• Practical paths: task-specific grippers, modular end-effectors, and “good enough” today vs. general purpose tomorrow

• The moonshot: what 70–90% human-level hands could do for productivity on Earth ... and off-planet

Chapters

00:00 Intro—are we underinvesting in robotic hands?

01:10 Why hands matter more than legs (economics of manipulation)

04:30 Funding realities: autonomy & humanoids vs. hands

08:40 Locomotion progress vs. manipulation bottlenecks

12:10 Teleop now, autonomy later—how data gets gathered

14:20 What’s missing: actuators, tactile sensing, proprioception

17:10 Perception limits in the real world (wet dishes, occlusions)

22:00 General-purpose dexterity vs. task-specific ROI

26:00 Startup landscape & reliability (interventions/hour)

29:00 Modular end-effectors and upgrade paths

30:10 The moonshot: productivity explosion when hands are solved

Who should watch

Robotics founders, VCs, AI researchers, operators in warehousing & manufacturing, and anyone tracking humanoids beyond the hype.

If you enjoyed this

Subscribe for more deep-tech conversations, drop a comment with your take on the “hands vs. legs” debate, and share with someone building robots.

Keywords

robotic hands, dexterous manipulation, humanoid robots, tactile sensing, actuators, proprioception, warehouse automation, AI robotics, Cortical Ventures, TechFirst, John Koetsier, Mike Obolonsky

#Robotics #AI #Humanoids #RobotHands #Manipulation #Automation #TechFirst

Are humanoid robots the future… or a $100B mistake?

Over 100 companies—from Meta to Amazon—are betting big on humanoids. But are we chasing a sci-fi dream that’s not practical or profitable?

In this TechFirst episode, I chat with Bren Pierce, robotics OG and CEO of Kinisi Robots. We cover:

- Why legs might be overhyped

- How LLMs are transforming robots into agents

- The real cost (and complexity) of robotic hands

- Why warehouse robots work best with wheels

- The geopolitical robot arms race between China, the US, and Europe

- Hot takes, historical context, and a glimpse into the next 10 years of AI + robotics.

Timestamps:

0:00 – Are humanoids a dumb idea?

1:30 – Why legs might not matter (yet)

5:00 – LLMs as the real unlock

12:00 – The hand is 50% of the challenge

17:00 – Speed limits = compute limits

23:00 – Robot geopolitics & supply chains

30:00 – What the next 5 years looks like

Subscribe for more on AI, robotics, and tech megatrends.