From autonomous vehicles to factory floors, a new wave of vision technology is transforming how manufacturers think about quality. Bucket Robotics is at the center of that shift, bringing simulation-driven inspection systems to an industry long reliant on manual checks and outdated tooling.

Matt Puchalski, founder and CEO of Bucket Robotics, joins Greg to share how his experience in self-driving cars shaped a fundamentally different approach to quality inspection. Instead of relying on expensive hardware or months of data collection, his team is using CAD-based simulation to generate training data instantly, unlocking faster deployment, lower costs, and more scalable automation.

We explore why quality inspection remains one of the most painful bottlenecks in manufacturing, how legacy vision systems have failed to keep up, and what it takes to build robots that actually work outside of polished demos.

Highlights:

• Matt’s journey from Georgia Tech and Michelin to autonomy startups and ultimately founding Bucket Robotics
• Why quality inspection is still one of the most manual, inconsistent, and frustrating parts of manufacturing
• The core insight behind Bucket: applying self-driving car vision systems to factory environments
• How CAD-based simulation replaces months of data collection with minutes of synthetic training data
• The “sim-to-real” challenge and why perception in changing lighting and environments is harder than it looks
• Why most vision systems fail in production and how Bucket is designed for real-world robustness from day one
• Lessons from early market assumptions, including why medical device manufacturing was not the right starting point
• The economics of inspection: balancing cost, speed, and accuracy across high-mix and high-volume environments
• What makes a strong customer fit, from ambiguous defect definitions to expensive rework caught too late
• Common objections from manufacturers burned by legacy vision systems and how simulation changes the equation
• Why labor shortages and supply chain reshoring are accelerating demand for automated quality solutions
• Hiring for empathy in robotics and why understanding the end operator matters more than credentials
• The importance of engineers who ship, not just prototype, and why early adopters beat bleeding edge thinkers
• Hard-earned hiring lessons, especially the need for teams willing to travel and work onsite with customers
• Where robotics is overhyped today, especially around deployment at scale versus polished demos
• Why lightweight, lower-cost robotic systems are unlocking a new wave of practical automation
• Matt’s view on the future of manufacturing: a hybrid human and robotic workforce rather than full autonomy
• Founder reality: why building a company can feel easier than operating autonomous vehicles, but far more isolating
• The long-term vision for Bucket Robotics as the “cloud computing moment” for manufacturing quality systems

Matt's LinkedIn: https://www.linkedin.com/in/matt-puchalski/

Bucket's LinkedIn: https://www.linkedin.com/company/bucketrobotics/

Matt's email: matt@bucketrobotics.com

Bucket's Youtube: https://www.youtube.com/@Bucket_Robotics

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

From disaster response inspiration to reimagining the backbone of global manufacturing, GrayMatter Robotics is tackling one of the largest untapped opportunities in automation: bringing true autonomy to the 90% of factory work still done by hand.

Ariyan Kabir, co-founder and CEO of GrayMatter Robotics, joins Greg to share how a firsthand experience with an earthquake in Bangladesh sparked his mission to build intelligent machines that can take on dangerous, tedious work. What started as a question about why robots were not helping in high-risk environments has evolved into a company building “factory superintelligence,” a full stack physical AI platform designed to transform how goods are made.

In this conversation, Ariyan breaks down why traditional robotics has struggled in high variability environments, how GrayMatter is bridging the gap with multimodal sensing and foundation models for manufacturing, and why solving these challenges is critical not just for productivity, but for economic resilience and national security.

Highlights:

• Ariyan’s journey from aspiring astronaut to robotics founder, and how a real world disaster shaped his mission to build intelligent, helpful machines
• The hidden reality of manufacturing, with nearly 90% of production still manual despite decades of automation
• The core problem GrayMatter is solving, enabling robots to adapt to high variability in materials, environments, and processes
• Why physical AI requires more than vision alone, and how multimodal sensing unlocks real world autonomy
• Starting with sanding as a strategic wedge, then expanding into grinding, painting, blasting, and inspection through transferable learning
• The power of data, building one of the largest manufacturing datasets to train foundation models for materials and processes
• Robot scientists and domain specific AI agents that compress process optimization timelines from months to days
• How optimizing human, robot, and AI workflows can drive massive gains, including tripling throughput without adding robots
• Lessons from early deployment challenges, from consumables to real world variability, and how they shaped more intelligent systems
• The importance of an adoption playbook, and why deploying robotics successfully depends on process and people as much as technology
• Ariyan’s perspective on talent, why high agency and system level thinkers are the most valuable builders in the age of AI
• What is still missing in robotics today, and why domain specific intelligence layers are the next frontier
• A vision for the future, rapidly reconfigurable, fully autonomous factories that can adapt in real time to new products and global needs

For founders, engineers, and operators thinking about the future of manufacturing, this episode offers a deep dive into how physical AI will reshape the industrial world and why the race to build intelligent factories is just getting started.

Learn more about GrayMatter Robotics:

• https://graymatter-robotics.com/
• https://www.linkedin.com/company/graymatter-robotics/posts/?feedView=all
• https://x.com/GrayMatterRobot

Connect with Ariyan Kabir:

• https://x.com/ariyankabir
• https://www.linkedin.com/in/ariyankabir/

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

Warehouse automation is no longer a question of if, but when. As supply chains face growing pressure from labor shortages, unpredictable demand spikes, and rising customer expectations, robotics is becoming a critical lever for speed, accuracy, and resilience.

Kait Peterson, VP and Head of Marketing at Locus Robotics, joins Greg to break down how modern warehouse automation is evolving from rigid, capital-intensive systems into flexible, scalable solutions that can adapt in real time. Drawing on 15 years in supply chain technology, Kait shares how robotics, data, and physical AI are reshaping fulfillment operations and why the next wave of adoption will look very different from the last.

Kait brings a unique perspective at the intersection of marketing, robotics, and human-centered leadership. From making hundreds of cold calls selling warehouse software early in her career to helping scale one of the most recognized brands in warehouse automation, she has seen firsthand how the industry has shifted from skepticism to rapid acceleration. Now at Locus Robotics, she helps translate complex automation systems into clear business value while championing greater inclusion across the tech ecosystem.

In this conversation, Greg and Kait explore:

• Kait’s journey from supply chain SaaS into robotics and how early exposure to warehouse operations shaped her approach to marketing and leadership
• Why flexibility is becoming the defining advantage in warehouse automation, especially for brownfield facilities that cannot afford disruption
• How Locus Robotics differentiates through its Robots as a Service model, combining deployment, maintenance, and continuous optimization into a single offering
• The role of physical AI and why data from billions of robot interactions is becoming a competitive moat in modern automation
• What success looks like for customers, from improved throughput and accuracy to better worker retention and operational scalability
• Why marketing in robotics is fundamentally different from traditional B2C and SaaS, and how understanding customer problems outweighs technical specifications
• The shift from early skepticism to ROI-driven adoption and why automation decisions are now tied to short-term financial performance
• How category creation is shaping the market, including Locus’s push toward a new “robots to goods” paradigm
• The importance of change management and why the most successful robotics deployments focus as much on people as they do on technology
• Why warehouse automation is still in its early innings, with the vast majority of facilities remaining unautomated
• The debate between humanoids and purpose-built robotics, and why solving specific problems may matter more than mimicking human form
• Kait’s leadership philosophy, from building teams rooted in curiosity and collaboration to avoiding common hiring pitfalls
• Her perspective on increasing representation in robotics and why creating inclusive environments is critical to the industry’s future

For anyone building, deploying, or evaluating automation in supply chain operations, this episode offers a practical and forward-looking view of where warehouse robotics is headed and what it takes to succeed in a rapidly evolving market.

Learn more about Locus Robotics: https://locusrobotics.com/

Learn more about The Feminist Exec: https://www.feministexec.com/

Connect with Kait Peterson: https://www.linkedin.com/in/kaitvinson/

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

A rare in-person episode brings together four founders building at the frontier of drones, autonomy, and physical AI. Recorded live from the Drones and Robotics AI Summit in New York, this conversation spans search and rescue, wildlife protection, aerial logistics, and next-generation drone platforms—offering a real-time snapshot of where the industry is heading.

From detecting phones in disaster zones to decoding animal communication, deploying drone delivery networks at city scale, and rethinking the cost-performance curve of aerial systems, each founder shares how they are tackling hard, real-world problems—and what it takes to move from prototype to deployment.

In this conversation, Greg speaks with Manny Cerniglia (Flyhound), Sara Nozkova (Flox Intelligence), Santiago Barrera (Aerialoop), and Justin Call (Modovolo) about:

• How Flyhound is turning everyday devices into life-saving signals by enabling drones to locate and identify phones, even without cell service, for search and rescue and disaster response
• Why radio frequency complexity remains one of the hardest challenges in real-world deployment, and how environmental factors shape system performance
• How Flox Intelligence is using AI to decode animal communication and prevent human-wildlife conflicts across airports, railways, and industrial sites
• The shift from drone-based systems to edge-deployed stationary units, and what it takes to move from research to validated, real-world impact
• Why physical AI startups face unique hurdles in funding, scaling hardware, and bridging the gap between prototype and production
• How Aerialoop built a “metro system in the sky,” operating high-frequency drone logistics networks and moving everything from food to medical samples in dense urban environments
• Lessons from scaling to hundreds of daily drone flights, including what breaks first in operations, manufacturing, and training
• The importance of regulatory collaboration—and how working alongside governments can accelerate deployment instead of slowing it down
• Why finding the right early customers is as critical as finding the right investors when building frontier technology
• How Modovolo is rethinking drone design to dramatically improve performance while reducing cost, unlocking new use cases across defense, public safety, and commercial sectors
• The growing demand for modular, payload-driven drone systems—and why enabling customer innovation is key to long-term adoption

This episode is a fast-moving look at the builders pushing drones and robotics out of the lab and into the real world—one deployment, one partnership, and one hard-earned lesson at a time.

Connect with Manny Cerniglia: https://www.linkedin.com/in/mannyce/

Learn more about Flyhound: https://www.flyhound.com/

Connect with Sara Nozkova: https://www.linkedin.com/in/sára-nožková-91339685/

Learn more about Flox Intelligence: https://floxintelligence.com/

Connect with Santiago Barrera: https://www.linkedin.com/in/santiagobarrerav/

Learn more about Aerialoop: https://www.aerialoop.com/

Connect with Justin Call: https://www.linkedin.com/in/justincall/

Learn more about Modovolo: https://modovolo.com/

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

From early experiments with dismantled electronics to building AI systems that power real-world machines, Ash Saxena has spent decades at the intersection of research, entrepreneurship, and applied intelligence. Now, as Founder & Chief AI Officer of TorqueAGI, he is focused on one of the most ambitious challenges in technology: enabling robots to perform meaningful work in the physical world.

Ash brings a rare depth of experience, from his PhD work at Stanford alongside Andrew Ng to founding and scaling multiple AI-driven companies. His perspective cuts through the noise of today’s AI hype cycle, offering a grounded view on what is actually working, what is misunderstood, and where the real opportunities lie in robotics and embodied intelligence.

We explore how the shift from data-driven AI to reasoning-based systems is reshaping robotics, why most companies are approaching the problem the wrong way, and what it takes to move from impressive demos to reliable deployment in the real world.

Highlights:

• Ash’s journey from building robots as a child to leading AI innovation across academia and industry, including early work on deep learning for robotics
• Key inflection points that led him to found multiple companies, including applying AI to unlock access to credit through Catapult
• Why “technology-first” companies often fail and the importance of aligning AI with real customer demand and ROI
• The evolution of AI from statistical models to deep learning to today’s foundation models and reasoning-based systems
• Why the biggest shift in AI is not better models, but dramatically faster time to deployment from years to days or weeks
• What Torque AGI is actually building: end-to-end robotic “skills” that combine foundation models, agents, and real-time infrastructure
• Why data collection at massive scale may not be the answer and how useful systems can be built with far less data than expected
• The gap between AI demos and real-world deployment, and why most demonstrations fail outside controlled environments
• A pragmatic roadmap for robotics adoption, from simple tasks today to more complex industrial automation over the next decade
• Where Torque AGI fits in the stack as a modular layer that translates AI models into actionable robotic capabilities
• The importance of interpretability, safety, and measurable performance when deploying AI into physical systems
• The core technical bottleneck in robotics today: bridging deep learning with real-world physics and constraints
• Why industrial robotics will see massive value creation in the next 5 to 10 years, while humanoids remain further out
• A contrarian take on general-purpose systems: general AI will matter more than general-purpose robots
• Where the industry is overhyping progress, especially around humanoid demos, and what is actually working today
• Why AI-driven upgrades to existing robots could unlock 10x to 40x increases in productivity without new hardware
• How to stay disciplined as a founder in a hype-driven market by focusing on real customer outcomes instead of funding cycles
• What a successful deployment looks like, from quick demos to full operational integration in messy real-world environments

Learn more about TorqueAGI: LinkedIn | Twitter | Website

Connect with Ash Saxena: LinkedIn | Stanford

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

Hardware has long lagged behind software in speed, accessibility, and iteration. But that gap is starting to close.

Matthias Wagner, founder and CEO of Flux, joins Greg to unpack how AI is transforming electronics design from a slow, manual, and fragmented process into something far more collaborative, automated, and accessible. After years at Facebook and a deep frustration with legacy hardware tooling, Matthias set out to build what he calls the first AI hardware engineer. A system that can help anyone design, iterate, and manufacture electronics with the speed and flexibility of modern software.

From rethinking PCB design workflows to enabling entirely new classes of builders around the world, this conversation explores what happens when hardware finally gets its GitHub moment.

In this conversation, Greg and Matthias explore:

• Matthias’s journey from early software engineering to Facebook and ultimately founding Flux to tackle stagnant hardware design tooling
• Why hardware has lagged decades behind software in collaboration, automation, and developer experience
• How Flux acts as an AI hardware engineer, guiding users from concept to schematic to manufacturing-ready design
• The inefficiencies of traditional PCB design and how AI can consolidate complex systems into single, optimized boards
• Why building in the browser unlocks real-time collaboration, faster iteration cycles, and continuous product improvement
• How Flux integrates supply chain data directly into the design process to avoid costly delays and redesigns
• The shift from waterfall hardware development to more agile, software-like workflows
• Why democratizing hardware will unlock millions of new builders, not just make existing engineers more productive
• Real-world examples of non-traditional users building hardware, including farmers creating custom automation systems
• Where AI fits across the hardware stack, from component selection to simulation and layout optimization
• The reality of building a deep tech startup, including five years with no revenue and multiple near-death moments
• Lessons on fundraising for long-horizon products and why operator investors matter early on
• How AI is reshaping team structure, hiring, and what it means to be an effective engineer today
• Why tooling is the most underestimated lever in accelerating robotics and hardware innovation
• Matthias’s vision for the future where building hardware becomes so easy that “hardware is hard” disappears as a concept

If you are building in robotics, hardware, or just thinking about how AI will reshape the physical world, this episode offers a compelling look at the tools and mindset shifts required to unlock the next wave of innovation.

Website: https://www.flux.ai/

Job site: https://jobs.ashbyhq.com/flux

Connect with Flux on LinkedIn: https://www.linkedin.com/company/buildwithflux/posts/?feedView=all

Connect with Matthias Wagner on LinkedIn: https://www.linkedin.com/in/matthias-wagner-5220b047/

Flux X: https://x.com/BuildWithFlux

Matthias' X: https://x.com/MatthiasWagner

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

From climbing robots inspecting boiler tubes to AI-powered platforms optimizing the world’s most critical assets, Gecko Robotics is redefining how we understand and maintain the infrastructure that powers modern society.

Ed Bryner, Chief Technology Officer at Gecko Robotics, joins Greg to unpack how his journey from hands-on engineer to technical leader has been shaped by a deep focus on applied engineering, mission-driven teams, and building technology directly in the real world. With roots in robotics competitions, defense work, and industrial systems, Ed brings a uniquely grounded perspective on what it takes to move from prototype to production in some of the harshest environments on earth.

We explore how Gecko is building a vertically integrated stack from robot to cloud, why infrastructure health data is the missing layer in industrial decision-making, and how continuous inspection is unlocking entirely new ways to operate, maintain, and extend the life of critical assets.

In this conversation, Greg and Ed get into:

• Ed’s path into robotics, from a family of engineers to high school competitions that sparked a passion for building at the intersection of hardware and software
• The founding story of Gecko Robotics, starting with a wall-climbing robot designed to inspect boiler tubes and eliminate dangerous manual inspections
• How Gecko evolved from a robotics company into a data and AI platform creating “health records” for industrial infrastructure
• Why infrastructure inspection has historically been so challenging, from scale and complexity to reliance on manual, high-risk human labor
• The power of multimodal data collection, combining ultrasound, LiDAR, and visual data to create high-fidelity digital twins of critical assets
• What it means to build and deploy robots in extreme environments like power plants, submarines, and refineries and why lab-only development fails in the real world
• How continuous data collection, even while assets are operating, is transforming maintenance cycles, planning, and operational availability
• A real-world example of reducing unplanned outages from 12 to zero using Gecko’s inspection and analytics platform
• The shift from static reports to interactive, software-driven decision tools that connect operators, engineers, and executives around a shared source of truth
• The challenge of reliability in robotics and what it takes to build systems that survive dirty, high-risk industrial environments
• How Gecko structures its teams around vertical integration, bringing hardware, software, and domain experts together to accelerate innovation
• Why “orientation” and getting engineers into the field is critical to shortening development cycles and building products that actually work
• The balance between experimentation and scaling and Gecko’s philosophy of proving value with a few customers before expanding broadly
• How advances in AI and developer tools are accelerating experimentation and enabling engineers to work across the full stack
• Ed’s long-term vision of improving the health, lifespan, and sustainability of the world’s built infrastructure

For anyone building in robotics, industrial automation, or physical AI, this episode is a deep dive into what it really takes to deploy technology in the real world and create lasting impact on the systems society depends on every day.

Learn more about Gecko Robotics: https://www.geckorobotics.com/

Connect with Ed Bryner: https://www.linkedin.com/in/edwardbryner/

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

Mobile robots are rapidly spreading across warehouses, hospitals, factories, and beyond. But as fleets grow and companies deploy robots from multiple vendors, a new challenge has emerged. The robots often cannot communicate with each other. Founder and CEO Aldus von der Burg joins Greg to discuss the “interoperability gap” in robotics and why solving it could unlock the next wave of automation.

Aldus shares the unconventional journey that led him into robotics. After studying automotive engineering and working at startups in Denmark, he explored drone delivery before regulatory hurdles forced a pivot. That experience led to the founding of Meili Robots in 2019, and eventually to a realization that the biggest barrier to scaling robotics was not hardware capability, but the software infrastructure needed to coordinate diverse robot fleets.

Today, Meili Robots is building a universal fleet management platform that allows robots from different manufacturers to operate together seamlessly. By taking a hardware-agnostic approach, the company aims to remove friction for operators, integrators, and manufacturers deploying robots across industries.

In this conversation, Greg and Aldus explore:

• Aldus’s path from automotive engineering and drone startups to founding Meili Robots
• The “interoperability gap” preventing robots from different manufacturers from collaborating effectively
• Real-world examples of robot gridlock and how poor coordination creates downtime, safety risks, and lost productivity
• Why many robotics companies build great hardware but ship weak or outdated software stacks
• How Meili’s platform enables vendor-agnostic fleet management across industries like warehousing, healthcare, agriculture, and mining
• The importance of operator independence through configurable tools and no code interfaces
• Why lab demonstrations of autonomy rarely survive real-world deployment environments
• Lessons learned selling into enterprise and industrial automation markets, including the slow pace of procurement and compliance
• Aldus’s hiring philosophy for early stage robotics teams, focusing on personality, curiosity, and strong engineering culture
• His candid take on the robotics hype cycle, including why humanoids may be overhyped compared to practical automation solutions

This episode is a deep dive into the invisible infrastructure layer that will determine whether robots remain isolated tools or become collaborative systems that scale across entire facilities and industries.

Learn more about Meili Robots: https://www.meilirobots.com

Connect with Meili Robots on Linkedin: https://www.linkedin.com/company/meilirobots/

Connect with Aldus on Linkedin: https://www.linkedin.com/in/aldusvdb/

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