NEXT with John Koetsier

Waymo and Tesla get the headlines. But Avride has quietly completed more than 100,000 commercial autonomous rides through Uber in Dallas, driven over 2 million autonomous miles, and built a fleet of more than 200 vehicles.

So why haven't you heard more about it?

In this episode of NEXT with John Koetsier, I talk with Avride chief commercial officer Sergei Kirillov about what it takes to build a robotaxi business, why the economics don't work until safety drivers disappear, and how autonomous transportation could reshape a trillion-dollar mobility market.

We discuss:

- 100,000+ commercial robotaxi rides and 92% positive rider feedback

- 2 million autonomous miles and a fleet of 200+ vehicles

- Why passenger pickup and drop-off are surprisingly difficult

- Avride's plans for fully driverless operations in 2027

- The economics of robotaxis versus human-driven ride-hailing

- Expansion plans across 10 potential new markets

- Autonomous delivery robots operating in 30+ markets

- Why Hyundai's IONIQ 5 is Avride's current platform

- Whether robotaxis will reduce car ownership and transportation costs

Plus: Why Avride believes there's room for multiple winners in a mobility market worth more than $1 trillion.

Subscribe for more conversations about AI, robotics, autonomous vehicles, and the technologies transforming our world.

00:00 The trillion-dollar robotaxi opportunity

00:20 Meet Avride: 100,000 autonomous rides

01:00 What Avride has learned from real passengers

03:00 Robotaxi safety and accident rates

04:20 200 vehicles and 2 million autonomous miles

05:20 Why pickups and drop-offs are so difficult

06:15 Where Avride plans to expand next

07:00 Delivery robots and the future of autonomous delivery

09:00 Do competing robotaxi fleets need to communicate?

10:00 When will Avride remove safety drivers?

11:00 Why robotaxi economics don't work with drivers

12:00 Regulatory challenges and expansion

12:30 Why Avride uses the Hyundai IONIQ 5

13:15 The future of autonomous vehicle design

14:30 Do robotaxis need steering wheels?

15:15 Will robotaxis replace personal car ownership?

16:30 How big will autonomous ride-hailing get?

#Robotaxi #AutonomousVehicles #Avride #Waymo #Tesla #SelfDrivingCars

Robot guard dogs are moving from experiments to serious business. Asylon Robotics has signed a $17 million, five-year deal with security giant Securitas to deploy autonomous robot dogs and aerial drones at critical infrastructure facilities.

As far as I can tell, this could be the biggest robot security deal ever.

In this episode of NEXT, I chat with Asylon CEO Damon Henry about the big deal, why autonomous security robots are finally proving their value, and what the future of security looks like for mission-critical and national security facilities.

Asylon already has more than 50 robots deployed across the US, using Boston Dynamics Spot quadrupeds, autonomous aerial drones that they make themselves, custom security hardware, and a 24/7 robotic security operations center.

We discuss:

- How Asylon's $17M Securitas deal came together

- How robot dogs and aerial drones work together to secure facilities

- Why customers are seeing reductions in crime and vandalism

- How autonomous robots work alongside human security guards

- Why Asylon builds some hardware and buys other components

- The potential for humanoid robots in security and inspection

- What large-scale deployments mean for the ROI of robotics

Damon also shares real-world results from customer deployments, including Mercedes-Benz Stadium in Atlanta, where robot patrols have helped protect human guards while improving security outcomes.

#Robotics #RobotDogs #PhysicalAI #AutonomousRobots #SecurityRobots #BostonDynamics #Asylon

00:00 Robot security lands a $17M deal

00:27 Asylon's deal with Securitas

01:00 How the autonomous security system works

02:45 Securing critical infrastructure

03:25 The growing threat to physical security

04:20 How robot dogs and drones work together

05:55 Real-world crime reduction results

06:25 Building vs. buying robot hardware

07:15 Autonomous charging and battery swapping

07:50 Cybersecurity and military compliance

08:40 Could humanoids join security patrols?

10:40 How robots work with human guards

11:40 Mercedes-Benz Stadium deployment

12:35 Why robots improve with experience

13:10 What the $17M deal proves

What does it actually take to put 1,000 humanoid robots to work?

Hexagon Robotics President Arnaud Robert joins me to explain how the company is moving its Aeon humanoid from pilots and demonstrations into real industrial production, including a plan with Schaeffler that could eventually scale to 1,000 robots.

And the big lesson is that scaling humanoids isn't about taking one robot doing one task and multiplying it by 1,000.

It's about building a multipurpose fleet that can move between jobs as demand changes across a factory.

We talk about:

• Why humanoids need to be multipurpose to justify themselves

• How Aeon gathers training data while doing real work

• Hexagon's “sim-to-real-to-sim” training loop

• Why customer-specific factory data matters

• The difference between a successful pilot and a production-ready robot

• The two metrics Hexagon cares about most: cycle time and human intervention

• What Hexagon learned from early sensor-fusion and actuator challenges

• Why Aeon uses wheels instead of legs — and can still climb stairs

• How humanoids could eliminate bottlenecks instead of simply replacing workers

• Schaeffler's “robot gym” and the train-deploy-scale path to 1,000 humanoids

Robert's view of the factory of the future isn't a lights-out facility with no people. It's a highly autonomous factory where humans and robots work together — with robots handling repetitive tasks and shifting between bottlenecks while skilled workers focus on problem-solving.

This is what humanoid robotics looks like when the conversation moves beyond prototypes and into production.

00:00 What does it take to deploy 1,000 humanoids?

01:31 Why 1,000 robots changes the problem

03:02 Why humanoids need to do multiple jobs

03:55 The race for physical AI training data

04:51 Simulation, synthetic and real-world data

06:20 Hexagon's sim-to-real-to-sim loop

08:10 What robot-generated training data captures

10:21 Pilot vs. production-ready humanoids

12:14 The industrial experience advantage

13:12 The two metrics that really matter

15:39 What Hexagon learned from failure

17:35 The humanoid actuator problem

18:47 Why Aeon uses wheels instead of legs

19:59 Yes, a wheeled humanoid can climb stairs

21:28 What the factory of the future looks like

23:42 Using humanoids to eliminate bottlenecks

25:17 Automation exposes the hidden 3%

26:07 Schaeffler's path to 1,000 humanoids

27:18 Train, deploy, scale

What does it actually look like to work with hundreds of AI agents?

In this episode of NEXT with John Koetsier, I chat with Steve Ancheta, founder and CEO of Zig.ai, an AI-native platform for relationship-driven sales.

Steve explains why what looks like a single AI agent to a user can actually be a swarm of hundreds of specialized agents working behind the scenes, handling copywriting, follow-ups, CRM updates, next-best actions, orchestration, and more.

We also dig into how Steve personally uses AI agents as force multipliers across his work, why he still writes important investor and customer messages himself, and why simply giving an agent access to documents isn’t enough. Steve shares his approach to building an effective agent harness, including two critical pieces many people overlook: governance and grounded truth.

We also discuss OpenClaw, NanoClaw, cloud-based agents, agent security, human-in-the-loop workflows, critical thinking, AI productivity, and the shift from reactive agents that wait for instructions to proactive agents that start doing useful work on their own.

Topics include:

- Why Steve believes many beginners shouldn’t build agents yet

- How hundreds of specialized agents can operate as one system

- AI agents as force multipliers rather than human replacements

- Why human taste and connection become more important as AI improves

- How Steve uses agents for planning, operations, scheduling, and analysis

- The dangers of AI-generated documents nobody actually reads

- Agent governance, guardrails, and permissions

- Giving agents a reliable source of truth

- Why vector databases alone aren’t a magic solution

- Balancing human interaction with AI-assisted work

- The move from reactive AI agents to proactive AI agents

- Why humans should remain in the loop for external actions

Guest: Steve Ancheta

Company: Zig.ai

00:00 Why beginners may not be ready for AI agents

00:24 Working with AI agents every day

01:34 Steve Ancheta’s journey into AI

03:00 The early days of agent workflows

04:36 OpenClaw, Hermes, and the agent boom

05:37 The security problem with early AI agents

06:00 Why Steve moved agents to the cloud

07:00 Local agents vs. cloud-based agents

08:00 How quickly agent technology is evolving

09:00 How Zig.ai uses hundreds of AI agents

10:00 Why one “agent” may actually be an entire swarm

11:00 Specialized agents and orchestration

11:30 What Steve personally uses AI agents for

12:00 Why he still writes important emails himself

13:00 AI agents as force multipliers

14:00 Why AI won’t eliminate the human element

15:00 Human connection and taste matter more than ever

15:50 Steve’s favorite uses for AI agents

16:00 The danger of AI-generated documents

17:15 Agents for planning, operations, and forecasting

18:00 Using agents to find problems across a company

18:40 AI as a chief of staff

19:25 Why beginners probably shouldn’t build agents yet

20:00 What an agent harness actually does

21:00 Zig.ai is fundamentally a data company

22:00 Giving AI agents a reliable source of truth

23:00 Governance, guardrails, and agent permissions

24:00 Why vector databases aren’t enough

24:35 How AI agents change the way we work

25:20 Balancing AI time with human interaction

26:00 Why Steve deliberately spends time away from technology

27:00 The next phase: proactive AI agents

28:00 Keeping humans in the loop

29:00 When AI agents start giving us tasks

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.