Quantum computing + AI + agents = AGI?

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 dramatically00:00 — “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 Winterroth01:03 The robotics investment landscape02:00 Seth’s background in applied robotics03:01 The five forces accelerating robotics04:04 Are there too many humanoid robot companies?05:00 Creative destruction in robotics06:02 How investors pick the winners06:38 Why robotics companies need velocity07:00 The danger of being too early or too late07:51 How Wayve benefited from entering later08:36 Project Prometheus and the $10 billion seed round09:05 Why Genesis AI is building full stack10:01 Full-stack robotics versus foundation-model platforms11:03 The dirty secret: Where are all the robots?12:03 Reliability, safety, and deployment friction12:39 The challenge of vertical integration14:02 What robotics companies should build themselves15:03 Capital requirements and the robotics J-curve16:01 How robotics companies can scale rapidly16:39 When humanoid robots may reach the market17:04 Figure, 1X NEO, Unitree, and AgiBot18:24 A broader definition of robotics19:04 Are cameras and dishwashers robots?19:39 Autonomous vehicles as embodied AI20:23 Why transportation autonomy could be transformational21:05 Why humanoids still have a long way to go21:44 One general-purpose robot or many specialized machines?22:35 What will make home robots successful?23:39 Finding the minimum viable home robot24:20 Physical and digital products25:04 Why the App Store model matters for robotics26:05 How robots will gain new capabilities over time26:35 The brutal economics of consumer robotics27:12 Consumers buy outcomes, not robots27:48 Robotics beyond humanoids28:18 Kiva Systems and the modern robotics era29:05 Why constrained environments win first30:02 Robotics in automotive manufacturing and logistics31:02 Eclipse’s robotics portfolio31:31 When robots will begin walking among us32:03 The next major robotics inflection point32:42 Escaping the trough of disillusionment33:29 Why autonomous transportation is nearly solved34:04 How self-driving changes cities and society34:36 The future of robotic surgery35:01 Delivery drones and regulatory barriers35:39 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 realityGuestAndrew WootenCo-founder, Rhoda AIWebsite: https://rhoda.ai00:00 Why Humanoid Robots Don’t Have Wheels00:18 Can Robots Learn From Internet Video?00:42 Best Use Cases for Humanoid Robots02:10 Why Warehouses and Factories Come First04:02 The Economic Impact of Robotics05:00 Home Robots vs. Industrial Robots06:05 Why Rhoda AI Chose Wheels08:00 Building a General-Purpose Robot09:55 Why Full-Stack Robotics Companies Have an Advantage10:40 The Evolution of Physical AI12:20 Why Vision-Language-Action Models Fall Short14:05 Training Robots With Internet-Scale Video16:05 How Rhoda AI’s Video-Action Model Works17:25 Edge AI vs. Cloud Computing19:05 How Robots Develop Physical Intuition20:40 Predicting the Near Future in Real Time22:00 Can Robots Build a Subconscious?23:10 Using Language Models to Control Robots25:15 Rhoda AI’s Hardware Strategy26:20 The Biggest Problems With Today’s Humanoids28:20 When Will Robots Truly Learn on the Job?30:05 Training Complex Tasks in 8–10 Hours31:15 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 NEOIf 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.GuestMike ElganTech journalist, columnist, author, and podcasterHost of SuperintelligentAuthor of The Attachment Economy on SubstackSubscribe for more conversations on AI, robots, innovation, and the future of technology:https://techfirst.substack.comChapters:00:00 AI, layoffs, and whether AI is really to blame01:00 Meet Mike Elgan02:00 Why people believe AI will cause mass job loss03:00 AI washing and layoffs as a CEO “fig leaf”05:00 Techno-utopian claims about AI replacing work06:00 Why AI layoffs often don’t pass the logic test08:00 Past tech revolutions and new job creation09:00 Companies that lay off because of AI “lack imagination”11:00 Why new industries can create more jobs13:00 Nobody can predict where AI will lead15:00 Why the speed of AI change feels different16:00 AI, robotics, and fear about the future of work17:00 AI natives and generational change19:00 Why humans treat talking AI like a person20:00 Education when facts are instantly available22:00 Cursive, typing, and speech-to-text24:00 Humanoid robots in the home25:00 Human work, creativity, and future value26:00 Why human connection may become more valuable27:00 Are humanoid robots a dumb idea?29:00 Specialized robots vs. humanoid robots31:00 The attachment economy after the attention economy32:00 AI products designed to create emotional attachment34:00 Relationship AI, robot pets, and illusion35:00 Why chatty AI feels conscious36:00 The human brain, AI illusion, and caution37:00 Closing thoughts with Mike Elgan

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 AIIf 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 ChenCo-CEO, Faraday FutureNasdaq: FFAISubscribe for more conversations with the leaders shaping the future of technology:https://techfirst.substack.comChapters:00:00 Introduction: Humanoid Robots in Education00:31 Faraday Future’s Vision for Physical AI Infrastructure01:42 The Goal of 1,000 Robot Deliveries02:22 Why Humanoid Robot Manufacturing Is Accelerating03:37 The Starting Point of the Humanoid Robotics Industry04:14 From Digital AI to Physical AI06:04 Why Schools Are a Key Robotics Market06:52 The Three Factors Driving Robotics Adoption07:15 K-12 Education, STEM Training, and Robotics Institutes08:12 Getting Kids Interested in AI Instead of Games09:04 The Future Demand for Robotics Technicians09:43 Humanoids vs. Robot Dogs in Education09:59 Will Every Student Have an AI Tutor?10:30 Beyond Education: Security, Inspection, and Hospitality11:14 Robot Dogs for Autonomous Security Patrols11:50 The Coming Ecosystem for Robot Maintenance12:06 Will Humanoid Robots Become Bigger Than Cars?12:57 How Robots Could Impact Global GDP13:28 Competing in the Exploding Robotics Industry13:56 Building a Robotics Flywheel Through Data15:01 The Team Behind Faraday Future Robotics15:44 Where Faraday Future Will Be in One Year16:03 Faraday Future, Robotics, EVs, and Web317:00 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 environmentsGuest:Matthew Anderson — CEO, A&K RoboticsCompany: A&K RoboticsIf you enjoy conversations about AI, robotics, startups, and the future of technology, subscribe for more interviews with founders and innovators shaping what’s next.Subscribe here:https://techfirst.substack.com00:00 – Intro00:30 – Meet A&K Robotics and the Vision for Autonomous Airport Mobility01:20 – Why Crowd Navigation AI Is the Hardest Problem in Robotics02:40 – Navigating Dense Airport Crowds and Passenger Flow04:05 – Directional Sound and Designing a Better Airport Experience05:50 – Building an “iPhone Experience” for Mobility Robots06:30 – Sensors, LIDAR, and Operating Without GPS07:20 – Fleet Management and Autonomous Operations in Airports08:00 – Mapping Airports and Optimizing Routes Through Crowds09:00 – Scaling the Business and Solving Systems Integration10:00 – Charging, Docking Stations, and the Future Airport Network10:45 – Raising an $8 Million Series A Round11:20 – Customers: Vancouver International Airport and Aena12:10 – Building a Polished Robotics Platform on Seed Funding12:50 – Matthew Anderson’s Background in Robotics and Drones14:00 – The Bigger Vision: Crowd Navigation for All Robots14:40 – The Personal Story Behind the Company Mission15:40 – Licensing Opportunities and the $5 Billion Airport Mobility Market16:45 – Hiring, Scaling the Team, and Expanding Production18:00 – Growing Up Hacking Robots and the AC/DC Story19:10 – Why Building Robots Is Fun — and Why Accounting Wasn’t20:40 – Final Thoughts and the Future of Autonomous Mobility

Is AI in education a disaster ... or inevitable. We can easily see that AI is already changing education ... but is it making kids smarter, or just more dependent?In this episode of TechFirst, John Koetsier talks with Navin Gurnani, CEO of Code Ninjas, about how kids can learn to build with AI instead of simply asking ChatGPT for answers.They discuss why coding still matters in the age of vibe coding, how AI can actually strengthen creativity and critical thinking, and the foundational skills kids need to thrive in a future shaped by artificial intelligence.Navin explains how Code Ninjas teaches children as young as 8 to understand AI “behind the curtain,” develop grit and resilience, and gain the confidence to create games, apps, and even entrepreneurial projects powered by AI.The conversation also dives into:* Why passive AI use puts kids at a disadvantage* The mindset future-ready kids need* AI literacy for parents and children* How coding builds confidence and problem-solving skills* Why adaptability may become the most important human skill* The difference between using AI and leading with AIIf you’re a parent, educator, entrepreneur, or simply curious about the future of learning, this episode is packed with practical insights about preparing kids for an AI-driven world.GuestNavin Gurnani — CEO, Code NinjasSponsorThis episode is sponsored by Apprentice — the first AI agent built for agentic manufacturing.Chapters0:00 Intro: Is AI destroying education?1:00 Teaching kids to build with AI, not depend on it2:00 AI, coding, games, and decision-making3:00 Why understanding AI builds confidence4:00 Passive AI users vs. AI creators5:00 What kids learn at Code Ninjas6:00 Grit, resilience, and problem-solving8:00 Belt system and early wins9:00 Building confidence through teaching others10:00 AI literacy by age level11:00 Teaching kids to use AI responsibly12:00 Coding in the age of vibe coding14:00 AI-assisted entrepreneurship for kids15:00 Building future-ready mindsets16:00 What a future-ready kid looks like17:00 Adaptability and spotting AI mistakes18:00 One thing parents should do now