Machine Minds

Artificial general intelligence is moving from a research ambition to a mainstream claim. But when industry leaders say AGI has arrived, what exactly are they measuring?

Andrew Kemendo, Executive Director of the AGI Society, joins Greg to explore how we should evaluate increasingly bold claims about artificial intelligence, why benchmarks alone may not capture general intelligence, and what it would actually take for an intelligent system to operate meaningfully in the physical and social world.

Andrew brings an unusually broad perspective shaped by gaming, military intelligence, software development, reinforcement learning, robotics, and decades of interest in human-level machines. His career has taken him from the Air Force Academy and intelligence work in Iraq to helping modernize software delivery inside the military, contributing to early ethical AI standards, building AI products, and now helping lead an organization that has spent nearly two decades bringing together researchers working on AGI.

The conversation examines the difference between believing a technological claim and scientifically demonstrating it. Andrew explains how the AGI Society evaluates public claims by looking at what was actually tested, what evidence is missing, and whether existing benchmarks capture enough of the physical, contextual, and social capabilities we associate with intelligence.

Greg and Andrew also dig into embodiment, the limits of foundation models, why human intelligence cannot be reduced to a single neural system, and Andrew’s proposed upgrade to the Wozniak Coffee Test: putting a robot into an unfamiliar and socially complicated environment where making coffee requires understanding people, incentives, context, and consequences.

Highlights:

• Andrew’s unconventional path from teenage game development and Ray Kurzweil’s The Age of Spiritual Machines to military intelligence, AI, robotics, and the AGI Society.
• How intelligence work trained him to separate narrative from reality by looking for context, incentives, missing information, and evidence.
• What building and shipping modern software inside the military taught him about bureaucracy, end users, institutional resistance, and the rare conditions required for meaningful technological change.
• Why early ethical AI work focused heavily on bias, datasets, and the human activity that organizations fail to measure before automating it.
• How the AGI Society is expanding beyond its long-running conference and journal to create communities where researchers, builders, and the public can participate in defining and understanding AGI.
• Why Andrew believes claims that AGI has arrived should be evaluated against the evidence rather than accepted or rejected based on reputation, hype, or marketing.
• The problem with relying on a narrow collection of benchmarks, especially when physical, real-world tasks remain largely absent from AGI evaluations.
• Why leadership in AI comes with a responsibility to communicate capabilities accurately and avoid steering the public toward a version of reality the technology cannot yet support.
• Andrew’s argument that embodiment matters because intelligence requires interacting with reality, finding resources, making decisions, and experiencing the consequences of those decisions.
• Why a single foundation model is unlikely to represent the entire architecture of intelligence, and what biology can teach robotics engineers about combining computation, mechanics, sensing, and control.
• The “adversarial coffee test,” where a generally intelligent robot would need to navigate an unfamiliar environment, understand social expectations, model human incentives, and still successfully accomplish its task.
• What Andrew finds genuinely exciting today, from Waymo and Unitree to reinforcement learning, curriculum-based training, and increasingly capable AI agents.
• Advice for founders navigating AI and robotics hype: develop a real point of view, understand your technical approach, and resist reshaping the company around whatever narrative is currently attracting funding.
• Andrew’s rapid-fire takes on AGI by 2030, the overuse of the word “agent,” the Wozniak Coffee Test, why self-evaluation is a major red flag, and whether he would trust a robot to make coffee in his kitchen today.

As AI systems become more capable and claims about AGI grow louder, this conversation is a reminder that progress needs more than impressive demonstrations. It needs clear definitions, serious evaluation, real-world evidence, and a willingness to ask what intelligence actually means before declaring that we have built it.

Learn more about the AGI Society: https://agi-society.org/

Learn more about the AGI Conference: https://agi-conference.org/

Learn more about Andrew Kemendo: https://kemendo.com/

Learn more about Direction: https://givedirection.com/

Connect with Andrew Kemendo: https://www.linkedin.com/in/andrewkemendo/

Connect with Greg Toroosian: https://www.linkedin.com/in/gregtoroosian/

As robots, drones, autonomous vehicles, and machines move deeper into the physical world, knowing exactly where they are becomes foundational. Point One Navigation is building the precision location infrastructure designed to make that possible at massive scale.

Aaron Nathan, CEO and founder of Point One Navigation, joins Greg to unpack why traditional GPS is not precise enough for many autonomous systems, how his team can take positioning accuracy from roughly five meters down to just a few centimeters, and why location will become a critical layer of infrastructure for the physical AI economy.

Aaron’s path into autonomy started long before self-driving cars became mainstream. As a student at Cornell, he helped build the university’s DARPA Grand Challenge vehicle during an era when autonomous vehicles required racks of computers, foundational software had to be written from scratch, and the supporting robotics ecosystem barely existed. That experience taught him an enduring lesson: no matter how sophisticated perception or control becomes, a machine that does not reliably know where it is cannot operate safely.

That insight eventually led to Point One Navigation, where Aaron and his team are building the location layer for everything from autonomous lawnmowers and agricultural equipment to drones, vehicles, and large robotic fleets.

Greg and Aaron explore:

• Aaron’s early robotics journey, from soccer-playing robots at Cornell to helping build an autonomous vehicle for the DARPA Grand Challenge
• Why the hardest problems in robotics often live at the intersection of software and hardware, rather than inside either discipline alone
• How lessons from Coherent Navigation, cybersecurity, privacy, and large-scale software platforms shaped the architecture and philosophy behind Point One
• Why Aaron chose to build foundational infrastructure for autonomy instead of creating another autonomous vehicle company
• How Point One makes GPS roughly 100 times more accurate, taking positioning from meters of uncertainty down to centimeter-level precision
• Why high-precision location matters for machines even when conventional GPS works perfectly well for humans
• The economics and outdated infrastructure that kept precision positioning inaccessible for years, despite the underlying technology already existing
• What it takes to build and operate a global corrections network, including thousands of physical stations and a massive real-world sensor infrastructure
• Why Point One is expanding beyond corrections into a broader location platform with positioning engines, hardware such as Atlas, APIs, and sensor fusion
• How sensor fusion allows robots to keep navigating when GPS conditions deteriorate under trees, between buildings, or in other difficult environments
• Why different machines, including cars, drones, and boats, require different approaches to positioning and sensor fusion
• The edge cases that appear when robotics systems move from small pilots to fleets of thousands or even hundreds of thousands of machines
• Why Aaron believes physical AI has an unavoidable problem that software does not: physics is always on
• How global positioning, SLAM, vision, lidar, and other sensing technologies can work together instead of competing for the same role
• Why reliable infrastructure and the "boring" problems of robotics matter more than impressive demos when companies begin deploying at scale
• How large AI models are opening new robotics use cases by allowing machines to operate in increasingly uncertain and dynamic environments
• What needs to happen for millions or billions of robots to safely coordinate across factories, warehouses, roads, ports, yards, and airspace
• Why Point One’s biggest constraint on growth is now hiring across engineering, R&D, customer success, and go-to-market teams
• Aaron’s advice for technical founders: solving the technical problem is only one part of building a successful company, and understanding customer need, value, communication, and willingness to pay is equally important
• Lessons from the DARPA era that still guide Aaron today, especially the value of keeping individual components simple enough to understand when something inevitably fails

Aaron also shares the thinking behind Fuse, Point One’s conference focused on localization and real-world system design. Rather than creating a traditional product conference, the team is bringing together companies that have successfully deployed physical systems to discuss what actually works, what breaks, and what engineers can learn from operating at scale. The next Fuse event takes place November 19 in San Francisco.

For robotics teams moving beyond demos and into real-world deployment, this conversation offers a detailed look at one of the least visible but most important layers of physical AI: making sure every machine knows exactly where it is, even when the environment does not cooperate.

Conference website: https://fuse.pointonenav.com/sf

FUSE discount code: MACHINEMINDS30

Learn more about Point One Navigation: https://www.pointonenav.com/

Connect with Aaron Nathan: https://www.linkedin.com/in/aaron-nathan/

Connect with Greg Toroosian: https://www.linkedin.com/in/gregtoroosian/

Retailers know what enters their stores and what leaves them, but what happens in between has traditionally been a major data blind spot. Caitlin Allen, SVP of Marketing at Simbe Robotics, joins Greg to explore how robots, computer vision, and shelf intelligence are helping retailers finally understand what is happening inside the aisle and turn that visibility into measurable business outcomes.

Caitlin has spent two decades building markets around disruptive technologies, from medical technology and enterprise software to roles at Andreessen Horowitz, Lyft, and now robotics. Across those experiences, one lesson has remained consistent: great technology is not enough. Creating a new category means helping customers understand why they need to change, giving them confidence to act, and building the organizational support required to make that change stick.

At Simbe, that means looking beyond Tally as simply an inventory robot. The company’s platform uses robotics and other sensing technologies to detect what is happening on the shelf, determine what needs attention, direct teams toward action, and confirm that the problem has been resolved. The result is a continuously improving source of shelf data that can support everything from pricing and replenishment to ecommerce fulfillment, demand forecasting, and the broader move toward self-optimizing stores.

In this conversation, Greg and Caitlin explore:

• Caitlin’s unconventional path from medical technology and entrepreneurship to Andreessen Horowitz, Lyft, and eventually retail robotics
• Why category creation starts with showing buyers the cost of standing still before asking them to embrace something new
• The balance between FOMO, the fear of missing out, and the fear of messing up when introducing disruptive technology
• Why the retail shelf remains one of the industry’s biggest data blind spots, and how digitizing it can unlock value across the entire retail ecosystem
• How Tally uses autonomous shelf scanning to identify misplaced products, pricing issues, gaps, and other problems that are difficult to capture consistently with manual processes
• Why 60% of products grocers believe are out of stock may actually be somewhere else inside the store
• The four stages of Simbe’s platform: detecting what is happening, determining what it means, directing the right action, and confirming that the action was completed
• How retailers decide between autonomous robots, fixed cameras, and RFID depending on data fidelity, product type, and how frequently information needs to be captured
• Why one large grocer projected hundreds of millions of dollars in savings from shelf intelligence, and why autonomous mobile robots produced significantly fewer errors than alternative scanning approaches
• How improved computer vision, longer battery life, and edge computing in Tally 4.0 expand what retailers can see and how quickly they can act on that information
• Why public retail environments are uniquely difficult for robotics, with changing displays, spills, shoppers, children, and unpredictable movement all sharing the same space
• The human-centered design thinking behind Tally, including how the team studied what makes a penguin appear friendly when designing a robot people would feel comfortable around
• Why earning trust is a prerequisite to delivering value when robots operate directly alongside shoppers and employees
• The importance of change management in robotics deployments, and why leadership alignment, repeated communication, clear KPIs, and integration into existing workflows can matter as much as the technology itself
• How successful retailers use automation to remove repetitive tasks while giving store employees more time for higher-value, human work
• Why long enterprise sales cycles require robotics companies to build relationships across multiple stakeholders instead of relying on a single internal champion
• How Caitlin thinks about events, category creation, strategic advisory boards, and building a go-to-market engine that can compete with companies that have much larger budgets
• Why robotics companies should make the customer the hero of their storytelling rather than leading with their own technology
• Caitlin’s advice for physical AI founders: own the architecture of the problem, choose investors who believe in the long-term vision, and find early customers willing to build alongside you

For robotics founders, marketers, retail leaders, and anyone trying to bring an unfamiliar technology into a traditional industry, this conversation is a practical look at what it takes to turn sophisticated robotics into adoption, measurable outcomes, and ultimately a new market category.

Learn more about Simbe Robotics: https://www.simberobotics.com/

Connect with Caitlin Allen on LinkedIn: https://www.linkedin.com/in/caitlinrallen/

Connect with Greg Toroosian: https://www.linkedin.com/in/gregtoroosian/

The next frontier of AI will not be confined to screens. It will live in trucks, warehouses, construction sites, factories, roads, and the countless physical operations that keep the world moving.

Johan Land, Chief Product Officer at Samsara, joins Greg to explore what happens when AI meets the messy reality of the physical world. Drawing on a career spanning Google, YouTube, Waymo, TomTom, Kobalt Music, and now Samsara, Johan shares why learning has been the thread connecting every stage of his journey, and why staying relevant in the current AI era requires continuously testing the limits of what new models can actually do.

At Samsara, that challenge means connecting cameras, sensors, vehicles, machines, and workers, turning enormous amounts of real-world data into insights, then using AI agents to act on those insights. Johan explains why the final step, autonomous action, is also the hardest, especially when AI systems are making judgment calls involving driver safety, industrial operations, and potentially life-or-death situations.

In this conversation, Greg and Johan explore:

• Why Johan believes most people underestimate how capable AI models already are, and why even a few months can leave your understanding out of date
• How constant experimentation and building custom model harnesses help him understand where the frontier of AI actually is
• The lessons Johan took from helping transform Waymo from a research project into an operating service, particularly why the long tail of edge cases determines whether AI works in the real world
• Why physical AI starts with data, and how Samsara uses cameras, sensors, machines, vehicles, and human inputs to build a real-time picture of physical operations
• How Samsara layers analytics and AI agents on top of that data to identify problems, guide decisions, and increasingly take action on behalf of operators
• Why autonomous agents are harder than analytics, especially when AI must make judgment calls around safety, escalation, compliance, and human behavior
• How Samsara detects driver drowsiness over time and uses conversational AI to intervene before fatigue becomes an accident
• The balance between edge and cloud AI, including why millisecond safety decisions must happen locally while more complex reasoning can happen in the cloud
• How massive amounts of video and sensor data unlock better models, richer edge-case coverage, asset tracking, and an increasingly real-time view of the physical world
• Why voice and two-way audio could become one of the most powerful interfaces for AI in jobs where workers need their hands free
• The engineering challenges behind putting 360-degree cameras, significant compute, and edge AI into equipment operating in harsh physical environments
• How customers continually discover unexpected applications for Samsara technology, from tracking high-value inventory to monitoring assets throughout supply chains
• Why Johan believes mixed autonomy will arrive incrementally, with human workers, traditional machines, autonomous vehicles, and eventually humanoid robots operating together
• The case for a separate orchestration layer that can normalize data and workflows across vehicles and machines from many different manufacturers
• Why Johan pushes back on the idea that hardware is becoming a commodity, and why tightly integrating hardware, software, optics, and AI models can produce better performance and clearer accountability
• What he believes physical AI companies often get wrong: impressive demos are not the same as reliable, repeatable value in real-world operations
• How Samsara organizes hardware, software, and AI teams around fundamentally different development cycles while keeping them tightly connected
• What Johan looks for in product leaders, including deep customer understanding, commercial instinct, high agency, relentless ownership, and a willingness to spend time inside the operations they are building for
• His advice for physical AI founders: relationships, credibility, trust, financial stability, and customer connection matter far more than many technology-first startups initially expect
• Why Johan believes humanoids will be working on real job sites by 2030, even if human intuition struggles to keep pace with exponential technological progress

For anyone building AI, robotics, industrial technology, or products that must work outside controlled environments, this conversation offers a practical look at the gap between technological capability and real-world impact, and what it takes to close it.

Learn more about Samsara: https://www.samsara.com/

Connect with Johan Land on LinkedIn: https://www.linkedin.com/in/johanland/

Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregtoroosian/

Having the best technology does not guarantee you win. Customers do not buy engineering. They buy outcomes.

Melissa Swisher, Chief Revenue Officer at SkySafe, joins Greg to explore what it takes to turn sophisticated technology into a repeatable business, and why that challenge is becoming increasingly important as drones become a routine part of the world around us.

Melissa brings decades of experience building and scaling go-to-market organizations across enterprise software, healthcare technology, AI, and now airspace intelligence. Her career has included high-growth companies, IPOs, founding and successfully exiting Socrates AI, and learning firsthand how culture, focus, and customer understanding can determine whether a technically impressive company actually succeeds.

At SkySafe, Melissa has helped move the business from experimentation toward a focused, scalable go-to-market model. Since joining, the company has quadrupled revenue and grown from a handful of customers to roughly 50 by narrowing its focus, creating clearer buying motions, and building around federal, public safety, law enforcement, and critical infrastructure customers.

The conversation also explores the rapidly changing world above us. As drones become cheaper, more capable, and more common, organizations increasingly need visibility beyond the traditional fence line. SkySafe is building an intelligence network designed not simply to detect a drone, but to understand where it came from, whether it has appeared before, how it behaves, and what patterns emerge across locations over time.

Greg and Melissa get into:

• Why having the best technology is not enough to win, and why customers ultimately buy outcomes rather than engineering
• Melissa’s journey from enterprise sales and leadership to founding Socrates AI and eventually joining SkySafe
• How mentors shaped her career and why she now pushes people into bigger opportunities before they necessarily feel ready
• What dramatically different company cultures taught her about accountability, collaboration, customer focus, and why culture directly drives performance
• Why SkySafe needed to move from experimentation to scale by creating greater focus, clearer qualification, and repeatable buying motions
• The power of saying no, and why startups can die from having too many good ideas
• How SkySafe detects drones and turns individual detections into a broader airspace intelligence network
• Why the company focuses on persistent detection and intelligence rather than treating every unidentified drone as a threat
• How networked RF sensing can reveal patterns, history, and behavior that a standalone sensor cannot
• Why drone forensics can turn airspace data from a monitoring tool into an investigative asset for law enforcement
• The emerging opportunity around critical infrastructure, including energy, utilities, oil and gas, and data centers
• How disciplined execution helped SkySafe quadruple revenue, and why growth often comes from doing the boring, repeatable things consistently well
• What Melissa looks for when hiring go-to-market talent, including curiosity, hunger, adaptability, and comfort with ambiguity
• Her advice to technical founders whose go-to-market strategy is struggling: stop selling to everybody, identify who has an urgent problem they will pay to solve now, and build repeatability before expanding
• Why a use case is not the same thing as a market, and why technical founders should bring commercial leadership into the business earlier
• What nationwide airspace awareness could eventually look like as cities, critical infrastructure, public safety, and federal organizations become part of a broader intelligence network
• Why Melissa believes meaningful career decisions often feel uncomfortable before they feel obvious

For robotics, hard tech, and AI founders trying to turn technical capability into a scalable business, this conversation is a practical look at focus, category creation, go-to-market discipline, and the importance of knowing which opportunities to pursue and which ones to leave behind.

Learn more about SkySafe:

• https://www.skysafe.io/
• https://www.linkedin.com/company/skysafe/

Connect with Melissa on LinkedIn: https://www.linkedin.com/in/melissa-swisher-00480a/

Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregtoroosian/

There’s a huge difference between making a robot work in a lab and making it reliable enough to survive the chaos of a real warehouse. For Michael Bearman, Chief Legal and Safety Officer at Vecna Robotics, closing that gap has been a 23-year journey spanning legal, operations, customer deployments, fundraising, safety, and industry standards.

Michael joins Greg to share how he went from outside counsel to becoming one of Vecna’s longest-serving leaders, taking on everything from contracts and customer implementations to financial modeling and even an all-night soldering session before a robot demonstration at Congress. That willingness to jump into unfamiliar problems gave him a front-row seat as Vecna evolved from experimental robotics projects into industrial automation deployed across warehouses and manufacturing facilities.

They explore why impressive robotics demos are very different from commercially viable products, how an early rescue robot helped Vecna learn that lesson firsthand, and why a major FedEx deployment pushed the company toward industrial mobile robots. Michael also breaks down Vecna’s approach to automated forklifts, tuggers, and CaseFlow, including how one customer doubled throughput with half the staff by rethinking the traditional case-picking workflow.

In this conversation, Greg and Michael explore:

• Michael’s unconventional journey from outside legal counsel to Chief Legal and Safety Officer, and the many operational, financial, customer, and leadership roles he took on along the way
• Why there is such a large gap between a successful robotics demonstration and a system that customers can rely on in real-world industrial environments
• How Vecna’s early Bear rescue robot helped build the technology, team, and relationships that eventually supported its pivot into industrial robotics
• The FedEx deployment that put more robots into a single site than Vecna’s entire existing fleet and helped reshape the company’s direction
• How automated tuggers, forklifts, and CaseFlow tackle different material-handling workflows across manufacturing, automotive, and third-party logistics
• Why close collaboration with an early 3PL customer helped Vecna build CaseFlow around the realities of warehouse case picking, ultimately doubling throughput with half the staff at that customer
• How flexible Robots as a Service models allow 3PL customers to scale fleets up and down around seasonal peaks instead of paying for idle robots year-round
• What Michael looks for during on-site safety risk assessments, from pedestrian traffic and crosswalks to routes that may look good in CAD but fail in the real warehouse
• Why interoperability matters as warehouses deploy robots from multiple vendors, and how Michael helped shape the MassRobotics AMR interoperability standard that has now progressed into an international ISO standard
• The challenges of writing safety standards for everything from multi-ton industrial robots to dynamically balancing humanoid and legged systems
• Why spending time inside real customer facilities has made Michael a better lawyer, safety leader, and contributor to robotics standards
• What Vecna looks for when hiring in a startup environment, including intelligence, flexibility, curiosity, and a willingness to figure things out rather than staying inside a narrowly defined role
• Why customer trust depends on transparency when something goes wrong, and why hiding information is one of the fastest ways for a robotics company to lose that trust
• Michael’s view that warehouses will continue to need people for edge cases, even as automation expands, with human workers increasingly supporting fleets rather than performing every task manually
• Why robotics founders cannot afford to cut corners on safety, especially when a single serious incident can have enormous consequences for a young company

For robotics founders, operators, engineers, and anyone trying to move automation from a controlled demo into messy real-world environments, this conversation offers a practical look at what reliability, safety, interoperability, and customer trust actually require.

Learn more about Vecna Robotics: https://www.vecnarobotics.com/

Connect with Vecna Robotics: https://www.linkedin.com/company/vecna-robotics/posts/

Connect with Michael Bearman: https://www.linkedin.com/in/bearman/

Connect with Greg Toroosian: https://www.linkedin.com/in/gregtoroosian/

What happens when AI agents stop living entirely on screens and start controlling cameras, wheels, motors, and eventually full robotic bodies?

Chris Matthieu, VP of Developer Ecosystem at RealSense, joins Greg to explore the rapidly converging worlds of AI agents, computer vision, robotics, and physical intelligence. A lifelong hacker who started programming on a Commodore VIC-20 as a kid, Chris has spent more than four decades building technology, including five startup exits, before joining Intel as an entrepreneur in residence and eventually helping grow the RealSense developer ecosystem.

Chris shares how his fascination with connected machines evolved from IoT platforms and decentralized computing into his latest project, AgenticROS, an experimental system that gives AI agents direct access to ROS and allows them to perceive and control physical robots. His robot Bob can already use camera data to identify where Chris is, calculate his position, control its wheels, and follow him through a room without Chris explicitly programming every behavior.

Greg and Chris also dig into RealSense’s evolution from an Intel incubator project into an independent company, why depth perception is becoming foundational infrastructure for physical AI, and how open source software, cheaper hardware, and AI coding tools are dramatically lowering the barrier to building real robotic systems.

Highlights:

• Chris’s journey from hacking on Commodore computers as a child to building and exiting five technology startups before moving into Intel and RealSense. - Why builders who avoid ROS can end up recreating infrastructure that already exists, and why the broader ROS ecosystem can dramatically accelerate robotics development.
• How AgenticROS connects tools such as Claude, Codex, Gemini, and other AI agents directly to ROS, giving them access to cameras, perception data, and robot controls.
• Why Chris believes increasingly capable AI models may eventually control robotic bodies without developers having to explicitly program every movement or create a predefined skill for every task.
• The startup lesson Chris learned at Intel: stop building technology in search of a market and instead start with painful customer problems people are willing to pay to solve.
• Why timing played such an important role in spinning RealSense out of Intel, especially as generative AI, physical AI, and robotics began accelerating simultaneously.
• How RealSense depth cameras give robots an RGBD view of the world by calculating depth at every pixel, while increasingly moving AI and perception processing directly onto the camera itself.
• How open source SDKs and AI code generation are shrinking prototype timelines from weeks or months to hours, allowing developers to validate ideas far more quickly.
• Why side projects, GitHub activity, curiosity, and building in public can be some of the strongest signals for engineers looking to break into robotics and physical AI.
• How one experimental LinkedIn post about Chris’s robot generated roughly 180,000 impressions and led to NVIDIA webinars, conference talks, and unexpected opportunities.
• Why individual developers can sometimes validate ideas faster than large companies, especially when AI coding tools, inexpensive cameras, Raspberry Pis, and NVIDIA Jetsons make functional robotics prototypes accessible for hundreds rather than tens of thousands of dollars.
• Chris’s philosophy of failing fast, testing the market early, and recognizing when either the technology or the customer demand is not ready yet.
• Why he believes the enormous funding rounds flowing into physical AI may reflect the scale of the potential market, even while competing approaches such as VLAs, world models, and skill-based systems are still evolving.
• His advice for anyone entering robotics: start with an inexpensive desktop robot, experiment with it, and remember that complex robots are ultimately systems of smaller components working together.
• Where Chris hopes physical AI ultimately leads: robots that understand the people around them, safely operate in human environments, and perhaps finally bring him coffee in the morning and a beer in the evening.

For developers, founders, students, and anyone curious about where AI goes after the chatbot, this conversation offers a hands-on look at how rapidly robotics is becoming easier to prototype, program, and experiment with, and why the next wave of AI may increasingly have cameras, wheels, arms, and a physical presence.

Learn more about RealSense: https://www.realsenseai.com/

Learn more about AgenticROS: https://agenticros.com/

Connect with Chris Matthieu on LinkedIn: https://www.linkedin.com/in/chrismatthieu/

Connect with Greg Toroosian: https://www.linkedin.com/in/gregtoroosian/

What if the biggest bottleneck in defense technology is not designing better systems, but actually figuring out how to manufacture them at the speed, cost, quality, and scale modern warfare demands?

Eric Hostetler, CEO and co-founder of Volund Manufacturing, joins Greg to unpack why America’s defense industrial base needs more than individual manufacturing breakthroughs. Drawing on a career that spans Vans, Oakley, Fox Racing, Apple, Anduril, and high-end defense manufacturing, Eric brings a consumer goods mindset to one of the country’s most urgent industrial challenges: producing reliable defense hardware at scale.

At Volund, that philosophy is being applied to air-breathing propulsion systems for low-cost, attritable drones and other unmanned systems. Rather than treating the engine as the only innovation, Eric and his team are building a vertically integrated factory where manufacturing, quality control, software, automation, and production planning are designed as one system from the beginning.

In this conversation, Greg and Eric explore:

• Eric’s unconventional path from BMX bikes and learning to weld to nearly 100 trips to China while building consumer products for companies including Vans, Oakley, Fox Racing, and Apple
• How working on rifles for U.S. special operations helped push Eric from consumer products into defense, eventually leading him to join Anduril as one of its earliest employees
• Why consumer goods taught him that cost, quality, production volume, and speed cannot be treated as problems to solve after a product has already been designed
• The problem with designing defense hardware for the demo first and planning to “optimize for manufacturing” later
• Why Eric believes manufacturers need to optimize the whole cost and complexity equation instead of focusing too heavily on individual technologies like AI, robotics, or automation
• How Volund is building vertically integrated propulsion manufacturing for low-cost, medium-to-long-range unmanned systems
• Why the factory itself is part of the product, with CNC machining, sheet metal processing, welding, heat treatment, balancing, quality systems, and other capabilities brought under one roof
• How self-contained manufacturing cells can handle production tracking, metrology, quality control, and scrap decisions without creating downstream bottlenecks
• Why factory software, IoT, MES integration, and automated quality systems may deliver greater gains than simply replacing human labor with robots
• The propulsion supply chain challenges hiding behind bearings, investment castings, rare earth materials, chemical precursors, and other critical components
• Why Eric believes parts of the existing defense industrial base may need to be replaced rather than simply funded to produce more
• How aging machine shops, fragmented suppliers, cybersecurity requirements, and limited scaling expertise make rapid expansion especially difficult
• Why vertically integrated, product-focused factories could become a stronger model for rebuilding domestic manufacturing capacity
• What Volund’s $12 million seed round unlocks as the company works toward low-rate initial production of its first two engine sizes
• Why COVID exposed the fragility of global supply chains and helped reignite investor interest in factories and domestic industrial capacity
• Volund ’s approach to hiring experienced operators and engineers who understand legacy systems but are motivated to build faster, less bureaucratic alternatives
• The Apple principle of “brief by exception,” and what defense companies can learn from eliminating meetings about things that are already going according to plan
• Why investment casting deserves far more attention as a critical, skilled-labor-intensive foundation for propulsion and turbomachinery

For anyone building in defense tech, hard tech, robotics, or advanced manufacturing, this conversation offers a systems-level look at what it will actually take to rebuild American production capacity. The future of defense manufacturing may depend less on finding a single breakthrough technology and more on redesigning the entire factory around speed, scale, reliability, and manufacturability from day one.

Learn more about Volund Manufacturing: https://www.volundmfg.com/

Connect with Eric Hostetler on LinkedIn: https://www.linkedin.com/in/eric-hostetler-4421964/

Connect with Greg Toroosian: https://www.linkedin.com/in/gregtoroosian/

For decades, manufacturers have struggled with the same fundamental problem: there simply are not enough people to fill the jobs that need to be done. Matt Bush believes solving that problem requires changing the way manufacturers think about robotics entirely.

Matt joins Greg to share how nearly two decades running manufacturing plants shaped his approach to automation, from deploying some of the earliest collaborative robots on production lines to building robotic systems designed to feel less like engineering projects and more like everyday tools. His goal is simple: make automation easy enough that the people already working on the shop floor can deploy and use it themselves.

That philosophy led to Beacon, a unified software platform that gives manufacturers the same interface across robotic welding, plasma cutting, painting, and other applications, regardless of the underlying robot hardware. By combining standardized systems, cloud connectivity, fleet-wide software updates, and highly responsive support, Matt and his team are working to remove the friction that has historically kept robotics out of smaller and more flexible manufacturing environments.

Greg and Matt explore:

• How nearly two decades running manufacturing plants taught Matt the importance of producing closer to the point of consumption, and why global supply chain fragility has reinforced that lesson
• The story of "Waldo," an early collaborative robot that moved between jobs throughout a Tennessee factory and showed employees how flexible automation could become
• Why employee attitudes toward robots changed once workers realized automation was taking over repetitive, uncomfortable, and undesirable tasks rather than eliminating their jobs
• How labor shortages pushed Matt toward robotics as a service, including no upfront capital, limited commitment, and a model designed to reduce the financial and operational risk of adopting automation
• Why decades of efforts to solve manufacturing labor shortages through education and recruiting have not been enough, and how demographic changes are making automation increasingly unavoidable
• Matt's belief that robots should be viewed as tools, no differently than drills, grinders, computers, or other technologies that allow people to work more effectively
• How the Beacon platform creates one consistent interface across welding, cutting, painting, and multiple robot brands while allowing manufacturers to manage entire fleets from a single system
• What it takes to make industrial robotics feel "iPhone-esque," from shipping systems largely assembled to continuously removing friction from setup, programming, and operation
• How customer demand has shaped the company's expansion from welding into plasma cutting, liquid painting, and powder coating
• The engineering challenges of deploying robots inside potentially explosive paint environments, including intrinsically safe electronics, air-purged robot arms, and specialized operator controls
• Why standardization allows robotic systems to ship in days rather than the weeks or months traditionally associated with custom automation projects
• How cloud-connected robots enable fleet-wide software updates, production analytics, remote support, and fixes that can be deployed across every connected system at once
• How an AI-powered support system now resolves roughly half of customer inquiries before a human needs to step in, while keeping engineers directly involved when more complex support is required
• Why Matt believes robotics companies should be careful about becoming an "everything platform" and instead build deep expertise around a clearly defined set of customer problems
• What Matt looks for when hiring, including attitude, curiosity, adaptability, and the willingness to ask questions rather than simply following instructions
• Why engineers at the company work trade shows, interact directly with operators, and observe customers using the product so that real-world behavior can shape future software and hardware
• Matt's concern that engineering education has become too theoretical, and why the next generation of robotics engineers needs far more hands-on experience
• Why the biggest misconception about collaborative robots remains the idea that they are coming for people's jobs when, in Matt's view, they are increasingly filling the jobs manufacturers cannot fill at all

For manufacturers still thinking of robotics as a complicated capital project reserved for high-volume production, this conversation offers a different vision: automation that arrives in a box, works alongside the existing workforce, and eventually becomes just another tool on the shop floor.

Beacon platform: https://www.hirebotics.com/solutions/beacon-platform/

Cobot Welder: https://www.hirebotics.com/solutions/cobot-welder/

NEW Cobot Painter: https://www.hirebotics.com/solutions/cobot-painter/

New Tate case study: https://www.hirebotics.com/case-studies/tate-inc/

Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregtoroosian/

Industrial companies apply extraordinary rigor to manufacturing, quality, and operations, yet their approach to revenue growth is often far less disciplined. Founder and principal Ed Marsh joins Greg to explore what changes when manufacturers start treating marketing and sales like an integrated system that can be measured, optimized, and continuously improved.

Ed brings decades of experience across engineering, the Army, industrial sales and marketing, international business, capital equipment, and consulting. Today, through Ed Marsh Consulting, he works primarily with middle-market industrial companies and private equity-backed manufacturers on organic revenue growth, spanning strategy, marketing, sales, technology, talent, and executive leadership.

A central idea behind Ed’s work is Overall Revenue Effectiveness, a framework inspired by Overall Equipment Effectiveness in manufacturing. Instead of accepting leakage throughout the customer journey as inevitable, Ed argues that companies should examine every stage of the revenue engine, from understanding the buyer and generating demand to qualifying opportunities, coaching salespeople, and closing business.

In this conversation, Greg and Ed explore:

• Why industrial buyers care far more about operational outcomes than product features, and why sellers need to understand how a solution affects KPIs, staffing, downtime, shipments, capacity, and the buyer’s day-to-day reality
• Why identifying customer pain is only the beginning of a sales conversation, and why the real focus should be the business outcome a customer is trying to achieve
• How Ed developed the Overall Revenue Effectiveness framework by treating organic revenue growth as an integrated system similar to a manufacturing operation
• Why small inefficiencies across marketing and sales compound into extremely low overall revenue effectiveness, even when individual parts of the system appear to be performing reasonably well
• Why manufacturers would never tolerate the level of yield loss in production that they routinely accept in sales
• The mindset shift required for leadership teams to bring the same accountability, rigor, process, and measurement to go-to-market functions that they already apply to operations
• Why private equity-backed industrial companies often provide a fertile environment for transformation, while family-owned businesses may have completely valid reasons to prioritize margins, stability, or lifestyle over aggressive growth
• What changes when Ed advises startups building AI-enabled software for manufacturers, and what remains constant about focusing on customer outcomes rather than product capabilities
• Why a startup’s first salesperson can shape the company’s entire perception of sales, either reinforcing negative assumptions or establishing a powerful go-to-market culture
• What founders should look for in an early sales hire, including business acumen, creativity, consultative selling skills, entrepreneurial instincts, and the ability to build systems while actively selling
• Why hiring based primarily on resumes and interviews creates problems, and how a more carefully engineered and empirically informed hiring process can improve sales talent decisions
• How founders should navigate the difficult transition when the person who helped build the early sales motion is no longer the right person to lead the next stage of growth
• Why honesty, clear expectations, coaching, and a distinction between being "nice" and being "kind" matter when making difficult leadership decisions
• How AI-first CRM platforms are transforming CRM from a manually maintained database into a memory, context, and orchestration layer that can automatically capture emails, meetings, messages, buying signals, and opportunity information
• How AI can help sales managers identify coaching opportunities, prepare targeted role-playing exercises, improve forecasts, and spend more meaningful time developing their teams
• Why sales coaching and role playing have declined despite their importance, and why companies often expect salespeople to perform without the structured onboarding and training provided to other functions
• How manufacturers can begin experimenting with AI by building Claude skills, connecting tools through MCPs, and automating research and preparation rather than simply producing larger volumes of generic outreach
• Ed’s own experiment building an AI-powered prospecting workflow that identifies ideal accounts, finds relevant contacts, enriches data, conducts research, populates CRM, and prepares prospecting blocks automatically
• Why Ed believes sales is currently the bigger bottleneck than marketing, driven by fundamental changes in how buyers research, assess risk, and make purchasing decisions
• The danger of vague claims around "value" in industrial marketing, and why companies need to communicate specific, differentiated outcomes
• Why ethical guardrails such as the Foreign Corrupt Practices Act matter even when commercial pressure is intense, and the responsibility leadership teams have to reinforce the reasoning behind those standards

For industrial leaders, founders, sales teams, and investors trying to improve organic growth, this conversation offers a practical look at what happens when go-to-market performance is treated with the same seriousness as the factory floor, and how AI could make that revenue engine dramatically more intelligent.

Ed Marsh Consulting Website: https://www.edmarshconsulting.com/

Ed Marsh’s Youtube Channel: www.youtube.com/@EdMarsh

Connect with Ed on LinkedIn: https://www.linkedin.com/in/edwardbmarsh/

Connect with Ed on Twitter/X: https://www.linkedin.com/in/edwardbmarsh/

Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregtoroosian/