Michael Lawrence, Director of Sales and Business Development at Anantak Robotics, joins Greg to discuss what it actually takes to bring autonomous material handling systems into warehouses and manufacturing environments. Drawing on a career that spans electrical engineering, entrepreneurship, robotics, and commercial strategy, Michael shares why successful automation is as much about partnerships, process design, and customer education as it is about technology.

At Anantak Robotics, Michael helps bridge the gap between technical capability and operational reality, helping customers deploy autonomous tuggers, pallet jacks, and forklifts that solve real-world material movement challenges without forcing facilities to redesign how they work.

In this conversation, Greg and Michael explore:

• Why many robotics companies underestimate the importance of business ecosystems, service networks, and partnerships when bringing automation to market
• The lessons Michael learned building his first autonomous construction equipment company and how they shaped his view of commercialization
• Why warehouse automation sales cycles often take years, not months, and what separates successful deployments from stalled pilot projects
• How Anantak approaches autonomous material handling with tuggers, pallet jacks, and forklifts designed for existing warehouse and manufacturing environments
• The importance of fitting into customer workflows rather than forcing facilities to adapt to the technology
• Where autonomous material movement delivers the fastest ROI and why clearly defined operating procedures accelerate adoption
• What "practical autonomy" looks like in messy, real-world environments filled with variability, edge cases, and imperfect conditions
• Why customer champions, change management, and operator feedback are critical ingredients for long-term deployment success
• The role of humans in the loop and why robots are best viewed as tools that eliminate repetitive tasks rather than replace people
• Common misconceptions customers have about warehouse automation and how education helps close the expectation gap
• Why Michael believes many robotics companies focus on building technically impressive products instead of solving the problems customers actually care about
• His perspective on humanoid robots, material handling automation, and where the industry is headed over the next decade
• How consolidation, improved capabilities, and growing customer familiarity could drive the next major wave of warehouse automation adoption

For anyone building robotics companies, deploying automation, or trying to understand what separates hype from real-world value creation, this episode offers a grounded look at how practical autonomy is reshaping material handling operations one deployment at a time.

Learn more about Anantak Robotics:

• Website: anantak.com
• Email: sales@anantak.com
• LinkedIn: linkedin.com/company/anantak-robotics

Connect with Michael Lawrence on LinkedIn: https://www.linkedin.com/in/mglaw/

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

Before founding Augmenta, Frio spent years working in computational science, generative design, and advanced AI systems at Autodesk Research. His experience applying artificial intelligence to manufacturing, aerospace, and simulation eventually led him toward one of the hardest design problems imaginable: construction. At Augmenta, he and his team are building AI systems capable of generating detailed construction blueprints for electrical systems and eventually entire buildings, helping contractors and engineers dramatically compress timelines while reducing burnout and coordination overhead.

In this conversation, Greg and Frio explore:

• Why construction presents a fundamentally different AI challenge than software or manufacturing, and why design in the built environment has “no amortization”
• How Augmenta’s AI generates construction-ready electrical blueprints from scratch, similar to how AI coding agents generate software
• Why building design is ultimately a geometry, constructability, and real-world reasoning problem rather than a language problem
• The hidden complexity of “unwritten rules” in construction, and how AI must learn the field knowledge that experienced foremen and electricians develop over decades
• How AI-driven design can reduce weeks of coordination work into days or even hours on hospitals, data centers, and other mission-critical infrastructure projects
• Why contractor burnout, labor shortages, and compressed schedules are accelerating demand for AI-powered workflows
• The challenge of building trust with contractors and field teams, and why deep customer collaboration became essential to Augmenta’s product development
• What Augmenta’s five-year strategic partnership with EJ Electric signals about the broader construction industry’s readiness for advanced AI adoption
• How AI could reshape collaboration between engineers, contractors, owners, and specialty trades by compressing pre-construction timelines and improving coordination
• Why Frio believes the biggest impact of AI in construction will not just be automation, but a fundamental restructuring of how projects are delivered and how companies operate

For anyone interested in AI for physical industries, construction technology, or the future of engineering workflows, this episode offers a deep look into how intelligent systems could transform the way buildings are designed and delivered.

Learn more about Augmenta:

• https://www.augmenta.ai/
• https://www.linkedin.com/company/augmenta-ai/posts/?feedView=all

Connect with Francesco Iorio on LinkedIn: https://www.linkedin.com/in/francescoiorio/

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

Michelle Lo, Director of Customer Strategy and Success at GrayMatter Robotics, joins Greg to discuss what it actually takes to deploy automation in high-mix manufacturing environments. Drawing from her background in electric vehicles and industrial automation, Michelle shares why successful robotics adoption depends just as much on customer alignment, operator trust, and long-term partnership as it does on the robots themselves.

Greg and Michelle explore the realities of manufacturing transformation, from backlog-driven demand and workforce shortages to the nuanced collaboration between humans and robots on the factory floor. They also unpack why configurable automation platforms are enabling faster deployment cycles, how manufacturers evaluate ROI beyond labor replacement, and why “perfect” automation is not always what customers actually want.

Highlights:

• Michelle’s journey from EVs and automotive manufacturing into customer strategy and robotics at GrayMatter Robotics
• Why automation adoption is a long-term transformation journey rather than a one-time deployment
• The hidden labor shortages driving demand for automation across industries like aerospace, specialty vehicles, and industrial manufacturing
• How GrayMatter Robotics partners directly with operators during deployment to improve adoption and long-term success
• The difference between configurable automation platforms and fully custom systems and why deployment speed matters
• Why manufacturers evaluate robotics based on throughput, consistency, quality, and capacity rather than simple labor replacement
• Lessons from real-world production environments where no two parts, surfaces, or workflows are ever exactly the same
• How AI-powered automation systems adapt to high-mix manufacturing environments with constantly changing variables
• Michelle’s perspective on humanoid robots and why purpose-built industrial systems are often better suited for manufacturing tasks
• The surprising reality that some customers intentionally want “imperfect” robotic finishes to preserve the familiar look and feel of legacy products
• How AI and predictive factory intelligence could optimize everything from workflow orchestration to production efficiency in the near future
• What makes a successful automation partnership before, during, and after deployment

If you are building automation for manufacturing, deploying robotics at scale, or navigating the human side of industrial transformation, this episode offers a grounded look at how AI-powered systems are changing the factory floor while keeping people at the center of the process.

Learn more about GrayMatter Robotics:

• https://graymatter-robotics.com/
• https://www.linkedin.com/company/graymatter-robotics/
• https://x.com/GrayMatterRobot

Connect with Michelle on LinkedIn: https://www.linkedin.com/in/michlo/

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

In this conversation, Paige Craig, founder and managing partner of Outlander VC, joins Greg to unpack how his unconventional path through the Marine Corps, intelligence work, and entrepreneurship shaped his philosophy as an investor. Paige shares why he spends more time analyzing founders than products, how his team evaluates leadership under chaos, and why physical AI and robotics will define the next two decades of innovation.

The discussion also dives deep into the realities of robotics deployment, the hidden complexity behind autonomy, and what separates founders who can survive the brutal transition from prototype to real-world scale.

Highlights:

• Paige’s journey from a difficult childhood and military service to building and bootstrapping a multi-hundred-million-dollar intelligence company
• Why Outlander VC invests at the “pre-conception” stage, backing founders before products or customers exist
• The 38-point founder framework Outlander uses to evaluate vision, intelligence, character, and execution
• Why great founders often emerge from hardship, high agency, and an obsession with solving problems
• The loneliness of leadership and why Paige believes the best investors act as true problem-solving partners
• How Outlander structures conviction-driven investing, including single-partner authority to write early checks
• Why physical AI, robotics, and automation are entering a massive growth cycle driven by AI, manufacturing reshoring, and falling hardware costs
• The biggest differences between investing in robotics versus pure software startups
• Why cheap, rapidly deployable robots often outperform “exquisite” high-cost systems in the race toward autonomy
• Lessons from backing Coco Robotics and Havoc AI, including the realities of deploying robots into unpredictable real-world environments
• The overlooked operational challenges of robotics businesses: supply chains, government relations, field operations, and human oversight
• Why many robotics founders underestimate the difficulty of scaling hardware systems outside the lab
• Paige’s perspective on defense tech investing, the influx of “tourist VCs,” and what founders should look for in strategic investors
• The leadership gaps technical founders often face as companies scale, and how mentorship can help engineering leaders grow into organizational leadership roles
• Why AI may fundamentally reshape the future role of engineering leadership and startup team structures

Connect with Paige Craig on LinkedIn: https://www.linkedin.com/in/paigecraig/

Learn more about Outlander VC: https://outlander.vc/

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

Grace Brown, founder and CEO of Andromeda Robotics, joins Greg to share her journey from a STEM-obsessed student in Australia to building one of the most distinctive companies in the humanoid robotics space. What started as a response to isolation during COVID has evolved into Abby, a social companion robot designed to bring meaningful connection into aged care environments.

Rather than optimizing for flashy demos or industrial efficiency, Grace and her team are focused on something far more complex: building robots that people trust, relate to, and genuinely care about. In this conversation, she unpacks why emotional intelligence is the missing layer in robotics, how design and psychology shape adoption, and what it will take for humanoids to scale in human environments.

Highlights:

• Grace’s early path into engineering and how a clear passion for math, physics, and problem-solving led her toward robotics from a young age
• The founding story of Andromeda Robotics and how strict COVID lockdowns in Australia exposed the real-world impact of loneliness
• Why Abby was designed as a character, not a tool, and how Pixar-inspired design principles drive trust and adoption
• The overlooked challenge of social acceptance in robotics and why capability alone is not enough to succeed in human environments
• Real-world deployments of Abby in aged care facilities and what the team has learned from observing how people actually interact with robots
• The importance of personalization in human-robot interaction, from voice tuning to behavioral adaptation for individual users
• Why emotional intelligence and “social awareness” will be critical for all robots working alongside humans, even outside consumer settings
• The interdisciplinary nature of building social robots, combining engineering, animation, healthcare insight, and operations
• How Grace thinks about hiring, from early generalists to later specialists, and why mission alignment is the most important filter
• The concept of “anti-selling” during hiring to attract people who truly want ownership and responsibility in a startup environment
• Using AI agents internally to accelerate iteration speed and rethink how teams build and operate in modern startups
• The broader responsibility of shaping the future of robotics and why who builds this technology will determine its impact on society

Learn more about Andromeda Robotics:

• Youtube: https://www.youtube.com/@AndromedaRobotics
• Website: https://andromedarobotics.ai/
• LinkedIn: https://www.linkedin.com/company/andromedarobotics/posts/?feedView=all

Connect with Grace Brown:

• Instagram: https://www.instagram.com/grace.jbrown/
• LinkedIn: https://www.linkedin.com/in/grace-brown-619b59161/

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

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/

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/

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/

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/

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/

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/

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/

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/

Theo Nash, founder and CEO of Mundane, joins Greg to challenge the hype cycle and refocus the conversation on what robots are actually for: eliminating dull, dirty, and dangerous work while amplifying human capability.

From growing up around London auto garages to studying at Stanford and building teams across Palo Alto, Shenzhen, and Vancouver, Theo shares how his path shaped a mission centered on safety, embodiment, and human–robot collaboration. Mundane is not chasing flashy demos. Instead, the team is rebuilding the robotics stack from first principles, with a bold bet on telepresence, tactile sensing, and intuitive control systems that feel less like a video game and more like becoming the robot.

This is a deep dive into embodiment, reliability, vertical integration, and why scaling too fast could be the biggest mistake in robotics today.

In this conversation, Greg and Theo explore:

• Why most humanoid demos miss the real question: can the robot do economically valuable work reliably?
• Mundane’s founding principle that robots should eliminate drudgery, not replace human fulfillment
• The embodiment gap and why teleoperation must feel like true presence, not remote control
• Proprioception in robotics and what it means to “feel” where a robot’s limbs are in space
• Why wiping a table is harder for a robot than playing chess
• The hidden challenge of reliability, from operator accuracy to hardware consistency between units
• Lessons from China’s advanced manufacturing ecosystem and how speed and supply chains shape hardware startups
• Why scaling slowly, even in a hype cycle, may be the smartest long term strategy
• Telepresence as infrastructure and the vision for a VP of Teleportation
• How robots can reduce workplace injuries and remove humans from hazardous environments like oil rigs and disaster zones
• Why Theo believes AI and robotics are collaborators, not replacements for human workers
• Building a team culture that works hard, plays hard, and debates openly without hierarchy

If you are building physical AI, deploying robots in the field, or thinking about the future of human labor in an automated world, this episode is a thoughtful and ambitious look at what it will actually take to make robots useful at scale.

Connect with Theo Nash: https://www.linkedin.com/in/theo-nash/

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

Sam Fok, co-founder and CEO of femtoAI, joins Greg to explore the journey from studying neurons to designing ultra efficient AI chips for real world devices. Growing up in a household of scientists, Sam was drawn early to questions about intelligence and consciousness. That curiosity eventually led him from neuroscience labs to Stanford’s Brains in Silicon Lab and into the world of neuromorphic engineering.

Today, femtoAI is building brain inspired hardware and software that brings AI out of massive data centers and into everyday products such as wearables, appliances, robotics, and consumer electronics. The goal is simple but ambitious: make AI run efficiently in the real world without straining power grids, supply chains, and balance sheets.

Greg and Sam dive into the technical, commercial, and personal pivots required to turn deep research into a scalable company, including the hard decision to rebuild their core platform from the ground up.

Highlights:

• Sam’s path from neuroscience and recording brain signals to building neuromorphic chips at Stanford
• The founding story of femtoAI and why timing mattered as AI crossed from research curiosity to commercial force
• How femtoAI enables AI to run directly on devices like glasses, earbuds, appliances, and robots instead of relying on the cloud
• Why sparse computation and localized processing, inspired by biology, are key to unlocking efficient edge AI
• The massive pivot from PhD era technology to a commercially viable platform built for manufacturability and scale
• The tension between academic incentives and commercial realities, from benchmarks and novelty to reliability and reproducibility
• Two distinct customer profiles: companies that want turnkey AI features and those that want full stack tools to build their own models
• Why ease of use and integration often matter more than technical elegance
• Lessons on earning credibility in deep tech by delivering real, testable results rather than selling grand visions
• Building highly technical teams with intellectual curiosity and cross stack awareness
• The importance of trust and empowerment when leading engineers across silicon, firmware, compilers, and AI tooling
• Why Sam believes today’s dominant AI approaches alone will not be enough to reach truly general robotics
• What the next three to five years could look like as intelligence spreads across devices and environments

If you are building AI powered hardware, deploying robots, or thinking about how to bring intelligence out of the cloud and into physical products, this conversation offers a grounded look at what it really takes to make AI usable, efficient, and scalable in the real world.

Learn more about femtoAI: https://femto.ai/

Developer portal: https://developer.femto.ai/

Connect with Sam Fok: https://www.linkedin.com/in/sam-fok/

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

Now an AI Specialist at Robotiq, Jennifer works at the intersection of hardware, software, and customer reality. In this conversation, she joins Greg to unpack what it actually takes to deploy robots that deliver ROI, why integration is often harder than the robot itself, and how the industry can avoid getting lost in hype cycles.

Jennifer brings a research background in robotic manipulation and tactile sensing, along with firsthand experience navigating the fragile complexity of physical systems. Her perspective cuts through buzzwords and focuses on what matters: reliability, safety, usability, and long term value for customers.

In this conversation, Greg and Jennifer explore:

• Jennifer’s journey from mechanical engineering at McGill to a decade deep in robotics and AI
• Why engineers, not just scientists, are the ones who bring theory into the physical world
• The hidden challenge in robotics: integration can account for 50 to 80 percent of total automation cost
• Lessons from building automated pick and place systems for data collection and the real data barriers in physical AI
• What separates shiny robotics demos from systems that actually deploy and scale
• Why vertical focus and application specific reliability win over general purpose promises
• The hard truth about automation: if a human still has to supervise it, you have not automated anything
• How AI meaningfully adds value in unstructured environments, and when it simply adds unnecessary complexity
• The importance of setting the right expectations with customers and practicing real customer empathy
• Why supply chain and support operations become major bottlenecks when robotics companies scale
• Using OKRs to align teams without creating decision making bottlenecks
• Safety and privacy concerns as robots move into unconstrained human environments
• The current hype cycle around humanoids and where Jennifer sees sustainable growth in robotics adoption
• Why she is excited about “weird robots” and unconventional form factors that move beyond human imitation

For founders, engineers, and operators building physical AI systems, this episode offers a grounded look at what it takes to move from prototype to production, and from hype to durable impact.

Learn more about Robotiq: https://robotiq.com/

Connect with Jennifer Kwiatkowski on LinkedIn: https://www.linkedin.com/in/jenniferkwiatkowski/

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

Andrew Singletary, co-founder and CEO, and Amir Sharif, COO of 3Laws Robotics, join Greg to unpack how a decade of academic research in safety critical control evolved into a commercial platform for dynamic safety across robotics, vehicles, and autonomous systems. From warehouse robots and mobile manipulators to aircraft and automobiles, their work aims to make autonomy scalable, certifiable, and trusted in the real world.

Andrew traces his path from high school robotics competitions to nuclear engineering at Georgia Tech, a PhD at Caltech, and ultimately founding 3Laws to productize safety technology that companies repeatedly asked him to integrate. Amir brings the operator’s lens shaped by multiple startups and exits, focusing on product discipline, execution, and building an organization that values getting it right over being right.

Together, they explore what it actually takes to move beyond academic proofs into functional safety that survives broken sensors, real world uncertainty, and regulatory scrutiny.

Highlights:

• The origin of 3Laws Robotics and the influence of Isaac Asimov’s Three Laws on the company’s philosophy
• Why traditional industrial safety equates safety with stopping and why that breaks down for mobile robots, vehicles, and aircraft
• The concept of dynamic safety and how robots can remain in motion while maintaining established safety guarantees
• How safety events, gridlock, and human resets quietly destroy productivity across robot fleets
• The gap between academic definitions of safety and industry expectations for functional safety
• Why safety must be designed in early rather than bolted on at the end of development
• How 3Laws works with both established robot form factors and emerging systems like mobile manipulators and humanoids
• The role of exposure and scale in redefining safety risk as robot fleets grow
• Lessons from hiring early team members and why skepticism is often a signal of deep understanding
• How Andrew and Amir divide responsibilities between vision, product, and operations without rigid boundaries
• Why culture, values, and hiring standards act as long term accelerants rather than speed bumps
• The challenge of focus when a single safety platform can apply to logistics, automotive, aerospace, and beyond
• What founders often get wrong about changing industries versus working within existing frameworks
• Why certification and third party validation are critical to trust in autonomous systems

For founders, operators, and engineers building robots that must operate alongside people, this conversation offers a grounded look at how safety, productivity, and autonomy intersect and why the future of robotics depends on systems that can move intelligently, not just stop.

Learn more about 3Laws Robotics: https://3laws.io

Connect with Andrew Singletary on Linkedin: https://www.linkedin.com/in/andrewsingletary/

Connect with Amir Sharif on Linkedin: https://www.linkedin.com/in/asharif/

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

Nikita Rudin, co-founder and CEO of Flexion Robotics, joins Greg to share his journey from growing up in Switzerland with dreams of space exploration, to pioneering reinforcement learning for robots during his PhD, to helping shape NVIDIA’s simulation tools, and ultimately to founding a company aimed at becoming the “brain” behind general purpose robots.

Nikita brings a rare perspective that spans deep research, real-world demos, and early-stage company building. He explains why hand-engineered behaviors and teleoperation do not scale, how reinforcement learning and simulation unlock robustness, and what it really takes to move robotics from impressive demos to economically viable deployments.

In this conversation, Greg and Nikita explore:

• Nikita’s path from physics to engineering to robotics, and why he has always been drawn toward faster real-world impact
• Early breakthroughs in neural network controlled quadrupeds and why reinforcement learning became the only scalable path forward
• The hidden human effort behind robotics demos, and why that model cannot support general purpose robots
• How Flexion Robotics is building a horizontal AI software platform to serve as the brain across many robot types
• The role of simulation, reinforcement learning, and the sim-to-real gap in training robots safely and at scale
• Why reinforcement learning leads to more robust systems that can recover from failure rather than collapse
• The limits of simulation, where real-world data still matters, and why mixing both is essential
• Why demand for humanoid and general purpose robots is already strong across logistics, manufacturing, and material handling
• The tension between customer engagement and long-term platform development in a frontier technology startup
• What Nikita looks for in exceptional roboticists, including action-oriented thinking, healthy stubbornness, and strong collaboration
• Underappreciated roles in robotics startups, from infrastructure engineers to data and deployment pipelines
• How Nikita’s leadership style has evolved as the team scaled to more than 40 engineers
• Where the robotics industry risks over-promising, especially around consumer and home robots
• Why hype can be healthy if expectations and timelines remain honest
• Advice for founders on underestimating team size, timelines, and the operational realities of company building
• Flexion’s near-term milestones, including moving from demos to real industrial deployments and making the economics work at scale
• How a successful horizontal robotics platform could accelerate the entire ecosystem

If you are building robots, investing in them, or trying to understand what it will take to move from research breakthroughs to real-world impact, this episode offers a grounded, deeply technical, and forward-looking view of where robotics is headed.

Learn more about Flexion Robotics: https://flexion.ai

Follow Flexion Robotics on LinkedIn and X for updates and technical insights

Connect with Nikita Rudin on LinkedIn: https://www.linkedin.com/in/nikita-rudin

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

Joe Harris, founder of Alloy, joins Greg to unpack how his background in electrical engineering, machine learning, and growth teams shaped a product that helps robotics companies move faster, ship more reliably, and avoid rebuilding the same internal tooling over and over again. What started as frustration with inaccessible data and slow feedback loops has become a platform designed to turn robot data into a shared, searchable source of truth across engineering, validation, and commercial teams.

The conversation dives deep into why replay tools break down at scale, how modern LLMs are changing what’s possible with robotics telemetry, and why deciding what not to build is one of the most important skills for early-stage founders.

Highlights:

• Joe’s path from electrical engineering and machine learning research into growth teams at scale, and how feedback loops became a unifying theme across software and robotics
• Why robotics companies are drowning in data but starving for insight, with robots generating gigabytes per minute across video, sensor data, and logs
• How Alloy helps teams move beyond one-off replay by enabling cross-sectional analysis, natural language search, and summarized field test reports
• The validation and verification teams who feel the value first, and how faster analysis turns into faster deployments for customers
• Why most robotics startups should not build their own telemetry and analysis stack, and how the industry is entering a tooling renaissance similar to early cloud software
• The importance of pain times frequency when deciding what features to build and what to cut
• Lessons from early mistakes, including why free pilots often fail and how paid pilots create real commitment on both sides
• Joe’s philosophy on early hiring, small teams, mission alignment, and building culture without unnecessary process
• What the next 12 to 18 months look like for Alloy as robotics fleets scale and foundation models reshape the landscape
• A long-term vision for a world of abundant automation, where robots learn continuously from experience and data interpretation becomes critical infrastructure

If you are building robots, deploying them at scale, or thinking about the unseen infrastructure required to make robotics reliable in the real world, this episode offers a candid and deeply technical look at what it takes to turn raw robot data into real-world progress.

Learn more about Alloy: www.usealloy.ai

Connect with Joe Harris: https://x.com/_joe_harris_

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

Edward Mehr, co-founder and CEO of Machina Labs, joins Greg to unpack his journey from early software obsessions to SpaceX, and ultimately to founding a company focused on rethinking how the world makes metal parts. Drawing from hands-on experience across software, robotics, and aerospace manufacturing, Edward shares why factories themselves are the real product, and how breaking the link between tooling and design unlocks speed, resilience, and creativity.

The conversation explores how Machina Labs’ robotic “Robocraftsman” systems combine dexterity, AI-driven learning, and modular deployment to form metal without molds or dies. The result is manufacturing that adapts as fast as software, enabling rapid iteration, distributed production, and entirely new business models.

Highlights from the conversation include:

• Edward’s early fascination with computers and making things, and how hands-on craft and coding shaped his view of the physical and digital worlds
• Lessons from SpaceX on why manufacturing speed, not engineering ambition, is often the true constraint in hardware innovation
• Why traditional factories are locked to specific designs and materials, and how Machina Labs is building product-agnostic, software-defined factories
• The concept of the Robocraftsman, a robotic system that learns like a human craftsperson and adapts processes in real time using data and AI
• How Machina Labs captures data from both physical forming and simulation to train models that optimize force, tooling, and process parameters
• Early traction in aerospace and defense, including dramatically reducing lead times for legacy aircraft parts that once took years to replace
• Expanding into automotive manufacturing and enabling mass customization directly from OEMs without expensive tooling
• A major partnership in the UAE focused on rapidly deployable, distributed manufacturing for defense and commercial resilience
• The strategic importance of factories as national security assets in an era of fragile global supply chains
• How portable, containerized manufacturing systems open the door to off-world production on the Moon, Mars, and beyond
• The challenges of building multidisciplinary teams across robotics, AI, and materials science, and how leadership evolves as companies scale
• Edward’s vision for the future of manufacturing, where physical expression becomes as fast, personal, and iterative as software development

If you are building hardware, scaling robotics, or rethinking how physical products get made, this episode offers a deep look at what it takes to bring software-speed thinking into the world of atoms.

Learn more about Machina Labs: https://machinalabs.ai/

Machina Labs Advances Custom Automotive Manufacturing with AI and Robotics: https://machinalabs.ai/resources/machina-labs-advances-custom-automotive-manufacturing-with-ai-and-robotics

Strategic Development Fund Announces Investment and Initial Agreement with Machina Labs: https://machinalabs.ai/resources/uae-strategic-development-fund-announces-investment-and-initial-partnership-with-machina-labs

Connect with Edward Mehr on LinkedIn: https://www.linkedin.com/in/edward-mehr/

Peter’s path into venture capital started early, influenced by entrepreneurship, real estate investing, and a shift from wanting to be an engineer to seeing business itself as a tool for solving problems at scale. After helping rebuild and operate one of the largest student-run venture funds in the country, he went on to co-found University Growth Fund, a diversified Series A and beyond firm with a mission that blends strong returns, student development, and economic impact.

The conversation spans what makes founders investable beyond pitch decks, why fundraising ability is often underestimated as a CEO skill, and how venture dynamics change when markets tighten. Peter also shares how AI is rapidly reshaping creation, distribution, and labor, and why ownership of assets may matter more than ever in the decade ahead.

Topics covered include:

• Peter’s journey from student venture investor to Partner at University Growth Fund and the lessons learned rebuilding a fund from the ground up
• How living through multiple market cycles changes how investors evaluate risk, founders, and timing
• Why a CEO’s ability to raise capital in both good and bad markets can determine a company’s survival
• The concept of grit in founders and why simply outlasting competitors can be a decisive advantage
• What Peter looks for beyond resumes including earned secrets, founder insight, and lived experience
• How University Growth Fund balances real venture execution with training the next generation of investors
• Why most businesses should not raise venture capital and the control trade-offs founders must accept if they do
• How AI is driving the cost of creation toward zero and shifting competitive advantage toward distribution, sales, and brand
• The implications of AI on labor markets and why asset ownership may become increasingly critical
• Common mistakes founders make when pitching VCs and how to think more clearly about what capital is actually needed for

For founders, operators, and anyone trying to understand how venture capital is evolving in an AI-driven world, this episode offers a grounded and experience-backed look at what really matters when building companies that last.

Connect with Peter Harris on LinkedIn: https://www.linkedin.com/in/vcpete/

Learn more about University Growth Fund: https://www.ugrowthfund.com/

Listen to Peter’s podcast, VC.fm: https://vc.fm/

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

Adrian’s path spans early work in payments and crypto, a formative chapter at Coinbase, and several pivotal years at Cruise during the early rise of self driving cars. At Cruise, he saw firsthand how much bespoke infrastructure was required to build, debug, and scale autonomy and how every leading AV company was quietly reinventing the same internal tooling. That realization became the foundation for Foxglove: a data and visualization platform designed to give robotics teams the same off the shelf leverage that software startups take for granted.

In this conversation, Greg and Adrian unpack:

• Adrian’s journey from early programming curiosity to building infrastructure at Coinbase and Cruise, and why autonomous vehicles made the value of robotics instantly tangible
• Why robotics development is dominated by custom tooling, siloed data formats, and painful debugging workflows, and how that slows the entire industry
• The origin of Foxglove and its mission to provide a shared data platform for robotics and physical AI, from logging and visualization to debugging and analysis
• What makes robotics data fundamentally different, including multimodal sensors, massive data volumes, limited bandwidth, and edge-first constraints
• The creation of MCAP as an open data format, and why interoperability is a prerequisite for robotics to scale beyond a handful of well funded teams
• How Foxglove acts as a single pane of glass for understanding robot behavior across simulations, incidents, and real world deployments
• Why robotics startups face “death by a thousand paper cuts,” from hardware and autonomy to go to market, pricing, and reliability expectations
• Lessons from fundraising in a non consensus market, and why finding investors who already believe your thesis matters more than convincing skeptics
• The parallels between today’s humanoid robotics hype and the early days of self driving cars, including the long tail of real world deployment
• What Foxglove looks for when hiring, and why proactive ownership is the mindset Adrian would clone across the entire company
• A ten year vision where starting a robotics company feels more like starting a SaaS company, with off the shelf infrastructure enabling founders to focus on real customer problems

If you care about the future of robotics, autonomy, and physical AI, and want to understand what actually needs to change for the industry to scale, this episode is a grounded and deeply informed look at the infrastructure beneath the hype.

Learn more about Foxglove: https://foxglove.dev

Connect with Adrian Macneil on LinkedIn: https://www.linkedin.com/in/adrianmacneil

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

With a background spanning visual arts, motion design, and product leadership at brands like Nike, Shakir brings a rare design-first lens to robotics and automation. After years in traditional design and marketing, he made a deliberate pivot into product design—drawn by the ability to tie design decisions directly to real user problems, business outcomes, and measurable impact. That shift ultimately led him to robotics, where he saw a massive gap between technical capability and human usability.

HelloRobo now operates as a specialized product design partner for robotics and automation companies, helping them build market-ready interfaces for robot operations, fleet management, and human–machine collaboration. Rather than chasing flashy MVPs or over-designed “vision concepts,” Shakir and his team focus on interfaces that can actually ship, scale, and be adopted by operators in the real world.

In this conversation, Greg and Shakir dive into:

• Shakir’s journey from art direction and brand design to building a robotics-focused product design firm
• Why robotics companies struggle with UX—and why established design patterns often don’t exist yet
• The decision to narrow HelloRobo’s focus exclusively to robotics and automation
• How contributing to Open Robotics helped establish credibility before commercial traction
• What makes UX in robotics fundamentally different from consumer software
• Designing operator interfaces and fleet management systems for complex, safety-critical environments
• Lessons from working with Bedrock Robotics, including designing new interaction patterns from scratch
• Why talking directly to operators beats copying existing UI patterns
• The three traits HelloRobo looks for when hiring designers: product thinking, visual clarity, and the ability to embrace chaos
• How design teams can stay grounded in business metrics instead of aesthetics alone
• The difference between MVPs, overbuilt “vision” products, and what Shakir calls market-ready software
• Why onboarding and education are still missing pieces in most robotics products
• Building culture in a creative, distributed team—and why HelloRobo is opening its first New York office
• Founder lessons on risk, playfulness, and learning to build without over-controlling outcomes
• What excites Shakir most about the future of robotics: mobility, prosthetics, and technologies that extend human capability

For founders building robotic systems, leaders scaling hardware companies, or anyone thinking about how humans actually interact with autonomous machines, this episode is a reminder that great robotics isn’t just engineered—it’s designed.

Learn more about HelloRobo: https://hellorobo.co

Connect with Shakir Dzheyranov on LinkedIn: https://www.linkedin.com/in/shakir-works

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

Jacob’s journey into AI began early, fueled by science fiction, programming, and a fascination with building systems that could do meaningful work autonomously. From studying and conducting research at Carnegie Mellon to working at Google Brain, he watched deep learning unify once-fragmented AI subfields—vision, language, speech—under a single scalable framework. That unification shaped his conviction that the next breakthrough wouldn’t come from incremental tuning, but from rethinking a fundamental architectural bottleneck.

At Manifest AI, Jacob and his team are tackling what they believe is the missing piece: scalable long-context intelligence. Their work centers on replacing transformer attention with a new family of architectures called retention models, designed to compress and retain relevant information over time—rather than repeatedly replaying massive histories. The goal: AI systems that can reason, learn, and work continuously over hours, days, or longer.

In this conversation, Greg and Jacob explore:

• Jacob’s path from aspiring scientist to AI researcher and founder—and why curiosity plus first principles thinking matter more than trends
• Why today’s large language models excel at short tasks but break down over long horizons
• The core limitation of transformer attention—and why “attention is all you need” may no longer hold
• How retention architectures unify the strengths of transformers and recurrent neural networks
• What it means for an AI system to compress knowledge instead of endlessly appending memory
• Why long-term reasoning, iterative problem solving, and true autonomy require architectural change—not orchestration hacks
• The misconception that agent orchestration can substitute for unified, persistent intelligence
• How long-context models could reshape agents from short-lived “consultants” into persistent, personalized collaborators
• The technical challenge of translating theoretical breakthroughs into high-performance GPU kernels
• Why Manifest AI is open source—and how their work aims to move the entire field forward
• Lessons from unifying AI subfields, the “bitter lesson” of scale, and avoiding ad-hoc solutions that won’t last
• Jacob’s view on cost, intelligence density, and why better architectures will increase—not reduce—investment in AI
• Advice for founders and researchers: focus relentlessly on the single bottleneck that matters most

If you’re building AI systems, researching foundations of intelligence, or trying to understand what comes after today’s models, this episode offers a rare, deeply reasoned look at where the field may be heading—and why architectural simplicity could unlock far more than brute force scale.

Learn more about Manifest AI: https://manifestai.com

Explore the open-source retention models: pip install retention

Connect with Jacob Buckman on LinkedIn: https://www.linkedin.com/in/jacobbuckman

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

Chris brings a rare blend of firsthand operator insight and deep robotics experience. From continuous improvement roles at Cummins, to factory-scale automation at Tesla, to building heavy-payload robots at a previous startup, his career repeatedly exposed the same inefficiency: trucks spending hours being loaded and unloaded for trips that last minutes. Slip Robotics was born from the realization that this problem wasn’t unsolved because it was hard—it was unsolved because no one had redesigned the workflow end-to-end.

Slip’s solution is deceptively simple: a large, flat autonomous robot that becomes the staging lane, drives directly into trailers, stays with the freight during transport, and unloads autonomously on the other side. The result is five-minute loads, five-minute unloads, reduced labor, faster throughput, and dramatically improved dock utilization—without modifying trailers or docks.

In this conversation, Greg and Chris explore:

• Chris’s path from risk-averse engineer to founder—and why betting on yourself can be the most rational risk
• The real-world pain that inspired Slip Robotics, observed firsthand across manufacturing, warehousing, and factory logistics
• Why docks function as the “physical API” of logistics—and how Slip leverages that instead of fighting it
• The technical challenges of moving 12,000-pound payloads up dock levelers and trailer slopes
• Why Slip’s robots generate massive ROI despite moving only ~5% of the time
• Early customer deployments, including automotive OEMs cutting lead times in half within weeks
• How close, on-site collaboration with early customers shaped a scalable, commercial product
• Why Slip operates on a Robotics-as-a-Service (RaaS) model—and how it aligns incentives around uptime and performance
• Hiring for early-stage robotics startups: why the first 20–30 people must be both specialists and adaptable
• Slip’s core cultural principles, including “rapidly deploying elegantly simple solutions” and delivering disproportionate value to stakeholders
• Where logistics automation is headed—and why dock efficiency may be the biggest unlock in transportation networks
• Chris’s take on general-purpose robots vs. highly specialized form factors, and where humanoids may (or may not) fit

If you’re building robots, deploying automation, or thinking about how physical systems actually scale in real-world environments, this episode offers a clear-eyed look at how simplicity, speed, and workflow-first thinking can unlock massive value.

Learn more about Slip Robotics: https://sliprobotics.com

Connect with Chris Smith on LinkedIn: https://www.linkedin.com/in/christopher-r-smith

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

Nicholas brings a rare dual perspective: a mechanical engineer turned software leader who spent years in Pittsburgh’s startup ecosystem building mobile manipulation systems, AMRs, and government-funded robotics programs before shifting into healthcare tech. His experience—from garage-stage startups to acquisitions and rebrands—gives him a clear lens on what it takes to scale robots from impressive demos to mission-critical reliability. At Omnicell, he now helps drive software for medication-picking systems, IV-compounding robots, and the next wave of automation designed to return pharmacists and clinicians to the work they trained for: caring for patients.

In this conversation, Greg and Nicholas explore:

• Why hospital pharmacies operate like 24/7 logistics centers—and why automation is overdue
• The long, largely unseen history of medication-picking robots (30 years and counting)
• What the autonomous pharmacy roadmap looks like, and why most hospitals are still at level 1 or 2
• The hard truth about robotics in healthcare: reliability isn’t a target, it’s a requirement
• How systems like Omnicell’s IV compounding and XR2 picking platforms reduce waste, increase traceability, and free clinicians from manual labor
• Lessons from Nicholas’s journey through multiple robotics companies, acquisitions, and pivots—and how software talent evolves within physical systems
• What he looks for when hiring software engineers in mission-critical environments, including curiosity, culture fit, and growth mindset over rigid credentials
• The promise (and limits) of AI in physical automation, and why general physical intelligence will unlock far more than humanoids

For anyone building automation in regulated environments—or simply trying to understand how robotics can meaningfully improve patient care—this episode offers a grounded, insightful look at the future of healthcare efficiency.

Connect with Nicholas on Linkedin: https://www.linkedin.com/in/nicholaskirsch

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

Richard brings an unusually layered background to robotics: he’s a third-generation biochemist who grew up watching labs evolve from manual rabbit and frog testing to fully automated, 10,000-test-per-day facilities. That firsthand view of how healthcare automated over decades now informs how he thinks about robotics support, standards, and scale. After building one of Argentina’s largest labs and co-founding a successful software company, he spent two years doing nothing but researching robotics before launching Robotic Crew, a nearshore “human in the loop” partner for robot fleets.

In this conversation, Greg and Richard get into:

• Why robotics is entering its “lab automation moment” – and what other industries can learn from decades of healthcare and manufacturing automation
• How Robotic Crew supports pioneers deploying robots in uncharted territory, from first unit to scaled fleets
• The logic of nearshore support for robots: Latin American talent, daylight operations, and why robotics can’t be treated like asynchronous software outsourcing
• “Robotears,” tiered support (Tier 1–3 and beyond), and how to build career paths for people who keep robots productive in the field
• The trade-offs founders face around uptime, ownership of incident data, and when to bring in a partner for support
• What it feels like when a startup hits real momentum—not hype—and why Richard believes robotics companies will run out of engineers long before they run out of demand
• A real-world case study improving response and resolution times for autonomous robot kitchens, and what that reveals about process, telemetry, and customer success in robotics

If you’re deploying robots, building them, or thinking about how to support a growing fleet without burning out your core team, this episode is a deep dive into the future of robotic workforce enablement—and the humans who will keep that future running.

Connect with Richard on Linkedin: https://www.linkedin.com/in/rpetrazzini

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

Milt brings a rare blend of hands-on engineering, ecosystem building, and strategic thinking. From early days fixing XT and 386 machines in a small-town computer shop, through field roles and leadership at Intel and Sick, to helping seed the Atlanta robotics community with RoboGeorgia, he’s consistently sat at the intersection of technology and partnerships. Now at Next Cobot, he focuses on giving robot builders the safety-critical and control platforms they need so they can innovate faster on what makes their systems unique.

In this conversation, Greg and Milt explore:

• Why “alignment” is Milt’s non-negotiable in partnerships—and how it changes the BD conversation
• The shift from customization to configuration as a path to real scalability
• How mid-market manufacturers and smaller warehouses can approach automation differently from the top 10% of global players
• The growing importance of safety platforms and standards as humanoids and advanced AI move closer to the factory floor
• What it actually looks like to build bridges between Taiwan and US robotics ecosystems
• Milt’s moonshot fascination with nano-robotics and “bottom-up” fabrication
• Practical career advice for engineers and technical leaders who want to move into business development and strategy roles in robotics

If you’re building robots, selling them, or betting your operations on them, this episode will sharpen how you think about partners, platforms, and the long game of automation.

Connect with Milt Walker on Linkedin: https://www.linkedin.com/in/mwalkeriii/
Connect with Greg Toroosian on Linkedin: https://www.linkedin.com/in/gregtoroosian/

Chapters:
2:34 Essential Technology
3:31 Early Career Insights
4:42 Robotics Passion
7:38 Business Development Path
9:58 Atlanta's Robotics Ecosystem
12:37 Joining NexCobot
15:19 Partnership Strategies
16:14 Market Opportunities
18:33 Barriers to Adoption
21:35 Finding the Right Partners
24:15 Successful Collaborations
27:07 Customization vs. Scalability
30:44 Technological Trends
37:07 Standards and Regulations
45:41 Moonshot Ideas
47:06 Advice for Aspiring Professionals
49:21 Reflections on Career Growth

Highlights:

• The Webvan lesson: How a $800M IPO collapsed because marketing never asked customers what they actually wanted... and how Kiva Systems learned from that failure to build the robots that became Amazon Robotics.
• The 500-customer rule: Why Oliver insists founders speak to hundreds of potential customers before building, and how CIV Robotics pivoted from drones to terrestrial robots by listening to construction giants like Bechtel.
• Five rules for automation startups: High utilization, 99.9% reliability, clear payback periods, defensible IP, and knowing your low-hanging fruit—Oliver breaks down what separates successful hardware companies from the graveyard.
• Culture as a competitive moat: Drawing from Netflix's "keeper test" and Pixar's collaborative creative process, Oliver explains why hiring one superstar beats five mediocre players - and why arrogance has no place in a startup.
• Cap table strategy: Why raising at an inflated valuation can doom your next round, how to price for competitive interest, and when to choose government grants, corporate VCs, or traditional funds.
• Defense tech is heating up: Oliver's new focus on Israeli technologies emerging from elite military units, the Golden Dome initiative, and why Apptronik's humanoid robots are built contract-first for shipbuilding - not household chores.

Oliver's book "A Startup Field Guide in the Age of Robotics and AI" is available on Amazon or at routledge.com (20% off with discount code 25ESA3 - valid 1 July 2025 - 31 December 2025)

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

https://www.linkedin.com/in/gregtoroosian/

Connect with Peter:

https://www.linkedin.com/in/peterhaas-robotics/

In this episode, Ayman Labib, co-founder and CEO of SIMPL Automation, shares how his 25+ years in manufacturing and integration led him to build a company that’s challenging the traditional timelines, costs, and risks of warehouse automation.

We explore how adaptive ASRS (Automated Storage and Retrieval Systems) can bolt onto existing racking, why simplicity and speed are key to driving adoption, and how his team is bringing “Lego-like” modularity and 4G-enabled plug-and-play design into the world of logistics technology.

Highlights:

• Ayman’s path from engineer and automation consultant to co-founding Simpl Automation — and the pivotal lessons from years in manufacturing and integration.
• How Simpl’s adaptive ASRS technology “snaps on” to existing storage systems, minimizing disruption and implementation time.
• The company’s mission to de-risk automation for customers — allowing one-aisle-at-a-time pilots that prove value before full rollout.
• Solving for space, scalability, and ROI - enabling customers to extend the life of their facilities and improve density without rebuilding infrastructure.
• The “Simpl-in-a-Box” concept: deploying full systems with private 4G networks that stay outside customer firewalls for faster, safer deployment.
• Lessons from early engineering challenges — and how localization, vibration, and racking variation shaped the system’s evolution.
• Why focus, speed, and incremental improvement drive the company’s leadership philosophy and product development culture.
• Navigating the early startup grind — from cash flow and customer acquisition to attracting top talent and leveraging long-term industry networks.
• Ayman’s take on the future of warehouse automation — faster implementation cycles, increased consolidation, and humanoids entering the workforce.
• How AI fits in: optimizing routes, handling “non-happy path” recoveries, and becoming the adaptive brain of warehouse operations.
• Candid advice for founders: know your total addressable market, plan for double the time and capital, and aim for commercial viability before perfection.

Learn more about Simpl Automation: https://simplautomation.com (Simpl without the “e”)

Connect with Ayman Labib: https://www.linkedin.com/in/ayman-l-45a3ba4/

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

Learn more about Craitor: https://www.craitor.com/

Connect with Eric: https://www.linkedin.com/in/ericshnell/

Connect with me: https://www.linkedin.com/in/gregtoroosian/

We broke down what Toyota’s backing really changes, how to balance moonshots with near-term wins, and the operational details factories care about beyond pure throughput.

What we cover

• How growth investors read “proof points,” set milestones, and think about $50M–$100M revenue paths and exits
• What Toyota’s network of engineers means for diligence, pilots, and real BD—plus how to enter Japan the right way
• Robotics ROI today: safety, uptime, battery swapping, and why throughput alone won’t close a deal
• Funding moonshots with discipline: using “tech unlocks” between rounds when revenue isn’t the right yardstick yet
• Traits that stand out at Series A–C: self-awareness, honest plans to profitability, and crisp operating cadence
• Where the next wave might come from: embodied AI on the factory floor and “unsexy” sectors now enabled by agents

Guest
 Manas Punhani — Senior Associate, Woven Capital

More about Woven Capital
 https://www.woven.vc

Connect with Manas
 https://www.linkedin.com/in/sdi-academy-manas-punhani/

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

 In this episode, I sit down with Tara Javidi (UCSD professor and AI researcher) and Sam Bigdeli (repeat founder & former semiconductor supply‑chain exec), co-founders of Kav AI, to talk about “active physical intelligence”—hypothesis‑driven, curiosity‑led AI that hunts for the signals that matter in physical systems.

We cover:

• Why passive, data-soaks-everything AI hits a wall in the physical world
• Hypothesis-driven learning: letting models ask “what should I look at next?”
• From oil & gas spills to structural failures—predicting the next “leak” like a language model predicts the next word
• Handling massive, messy, multimodal sensor streams in real time (volume of context, not just length)
• Interpretability when your model is deciding which sensor to query and why
• What academia gets wrong (and right) about startups—and vice versa
• The hardest part of moving from novelty-driven research to problem-driven product
• How (and when) to disagree productively as co-founders

Links mentioned

• Website: www.kavai.com
• Company LinkedIn: https://www.linkedin.com/company/kav-artificial-intelligence/
• Sam Bigdeli: https://www.linkedin.com/in/sam-bigdeli-5310b923/
• Tara Javidi: https://www.linkedin.com/in/tara-javidi-28b450155/

🎙 Connect with me
LinkedIn – https://www.linkedin.com/in/greg-toroosian

 I sat down with Anthony Jules, co-founder & CEO of Robust AI, to unpack how Carter—the cart-shaped AMR that workers can literally steer—delivers a 60 % productivity boost for DHL without changing the warehouse layout.

Talking points

1. Why “agency” (humans grabbing the robot’s handle) was the missing piece in previous AMRs.
2. Vision-only navigation, no LiDAR: the leap to 4th-gen mobile robotics.
3. Human-centric UX as the real adoption moat.
4. Hiring for close-to-the-metal engineers and culture-first teams.
5. What 10 years of collaborative automation might look like across logistics, healthcare, and retail.

Links & resources

• Robust AI — https://www.robust.ai/
• Robust AI on LinkedIn — https://www.linkedin.com/company/robust-ai/
• DHL × Robust AI partnership (60 % productivity) — https://www.dhl.com/us-en/home/press/press-archive/2024/dhl-supply-chain-partners-with-robust-ai-to-drive-the-future-of-warehouse-automation.html
• “People + Physical AI” demo (YouTube) — https://www.youtube.com/watch?v=IFO_B43EDOc
• Anthony's LinkedIn: https://www.linkedin.com/in/anthonyjules/

Connect with me — https://www.linkedin.com/in/gregtoroosian/

Thanks for riding with us through a full century of episodes—here’s to the next hundred!

• The moment Zac realised a recruiter could build a full-stack product in three weeks for < $1 k.
• Tactical first steps for any team: discovery sprint → 30/60/90 quick-win plan → agentic workflows.
• The trifurcation of work (AI-only, human-in-loop, human-only) and how to spot the right bucket.
• Why vertical, hyper-niche agents will beat one-size-fits-all models.
• What an 18-month adoption window really means—and how to avoid being “automated chaos.”

Links:

Bodhi AI – https://bodhiai.io

Forbes feature: “AI’s Trifurcation of Work” – https://bit.ly/ZEForbes-AITrifurcation

Turning On Machines (Zac’s forthcoming book) – https://www.zacengler.com/buy-book

MN AI Tools directory – https://mnaitools.com

Connect with Zac: https://www.linkedin.com/in/zacengler/

Connect with me: https://www.linkedin.com/in/gregtoroosian/

• Why Exotec’s Skypod robots store, climb and travel like no other goods-to-person system.
• The shift from legacy conveyor projects to agile, software-defined warehouses.
• What “mobile AS/RS” really means for densification, throughput and expansion.
• Andy’s lessons on selling eight-figure automation to Fortune-500 operators.
• Where AI vision and data will push fulfilment over the next five years.

Recent news
• New Oxford Industries flagship installation → https://www.exotec.com/news/exotec-announces-partnership-with-oxford-industries-to-implement-next-generation-skypod-system-in-new-multi-brand-distribution-center/
• Next-gen Skypod launch → https://www.exotec.com/news/exotec-launches-next-generation-of-skypod-system-an-all-in-one-robot-based-as-rs-that-addresses-the-majority-of-processes-within-a-warehouse/

Guest info
Andy Williams – EVP Sales, North America, Exotec
LinkedIn: https://www.linkedin.com/in/andrewtaylorwilliams/

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

SVB’s Frontier Tech Solutions:
https://www.svb.com/industry-solutions/hardware-frontier-technology/

Connect with Julian Nash:
https://www.linkedin.com/in/julian-nash-43389334/

Connect with me:
https://www.linkedin.com/in/gregtoroosian/

Engineered Arts Website: https://engineeredarts.co.uk

LinkedIn: https://www.linkedin.com/in/leoofchen/

Company LinkedIn: https://www.linkedin.com/company/engineered-arts-ltd/

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

Link to the study mentioned: https://link.springer.com/article/10.1007/s43441-024-00666-x

Guest Info:
Patrick Leung, CTO at Faro Health  
Website: https://farohealth.com/  
LinkedIn: https://www.linkedin.com/in/puiwah/  

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

We covered:

• How Ember Robotics helps robotics companies quickly find and fix hardware issues
• The importance of real-time, on-device diagnostics for field-deployed robots
• Lessons from their experiences at Tesla and Y Combinator
• Key advice on selecting the right co-founder and building resilient startup teams
• Trends they're watching, including neuromorphic imaging and advances in humanoid robotics

Connect with Shivani: https://www.linkedin.com/in/shivanicm/

Connect with Ritika: https://www.linkedin.com/in/ritikashrivastava/

Ember Robotics Website: https://www.emberrobotics.com

Connect with me: https://www.linkedin.com/in/gregtoroosian/

We discussed:

- The challenges of managing BOMs and why traditional PLM tools are outdated  

- How Bomello simplifies hardware workflows for small and mid-sized companies  

- Lessons from Sam’s journey through engineering, consulting, and entrepreneurship  

- The shift toward cloud-based development and its impact on collaboration  

- How AI and automation are influencing hardware design and supply chain management  

Learn more about Bomello:

Website: https://www.bomello.com

Email: sam@bomello.com

LinkedIn: https://www.linkedin.com/in/sfatoo/

Connect with me:

LinkedIn: https://www.linkedin.com/in/gregtoroosian/

Throughout the conversation, Greg and Chris discuss the unique features of Citadel North's drones, which are designed to remain idle until activated, ensuring they do not interfere with daily life. Chris elaborates on the various activation mechanisms available, highlighting the flexibility and customization options that cater to different customer needs. This segment provides a fascinating look at how technology can be seamlessly integrated into our environments while maintaining a focus on safety and efficiency.

The episode also touches on the future of robotics and hard tech, with Chris sharing his thoughts on the evolving landscape of the industry. He emphasizes the importance of staying ahead of the curve and the need for continuous innovation to meet the demands of an ever-changing market. Listeners will find inspiration in Chris's insights on entrepreneurship and the qualities necessary for building a successful team in the tech sector.

Join Greg and Chris for an engaging discussion that not only highlights the groundbreaking work being done at Citadel North but also offers valuable advice for aspiring entrepreneurs. Whether you're a tech enthusiast or simply curious about the future of robotics, this episode is packed with thought-provoking ideas and practical insights that you won't want to miss.

⁠01:34 Chris’s journey

04:01 The initial idea for Citadel North

09:25 Activating drones 

18:40 Future of  the space?

27:28 Planning and staying ahead

35:57 Is there anyone else trying to work on something similar?

40:34 The future of robotics and hard tech in general

41:49 Qualities to look for when you're building the team?

48:13 Advice to future entrepreneurs

More about Citadel North

https://www.linkedin.com/company/citadelnorth/

More about our guest 

https://www.linkedin.com/in/chris-zahuranec-27636b21/

Machine Minds:

Apple Podcast:

https://podcasts.apple.com/us/podcast..

Spotify:

 https://open.spotify.com/show/1apGh7x... 

Google Podcast: 

Throughout the episode, Greg and Dave delve into the challenges and opportunities within the AI landscape, particularly focusing on the innovative machine learning techniques employed by MBUE. They explore the importance of understanding industry-specific metrics for success and the necessity of adapting to the rapidly changing technological environment.

Dave emphasizes the significance of defining problems in a way that allows for diverse technological solutions, advocating for a flexible approach to problem-solving that leverages both cutting-edge AI models and traditional methods. The discussion also touches on the current state of the AI industry, addressing the hype surrounding AI technologies and the reality of over-promising and under-delivering.

Listeners will gain valuable insights into the future of AI, the importance of team dynamics in tech startups, and the strategic considerations for building a successful technology company in a competitive landscape. This episode is a must-watch for entrepreneurs, tech enthusiasts, and anyone interested in the transformative power of AI.

Join us as we explore how machine minds are shaping our future and the exciting developments on the horizon for MBUE and the broader AI industry.

00:01:18 - Dave’s journey and pivotal moments.

00:07:53 - Past Roles and insights.

00:09:17 - Advice for founders.

00:19:13 - The idea of creating startups with a view of being acquired by a big player.

 00:20:36 - The core tech behind MBU.

00:25:51 - Team dynamics.

00:31:52 - Internships.

00:40:02 - Over promising and under delivering.

00:46:37 – Where is AI and the robotics industry heading?

More about Mbue 

https://www.linkedin.com/company/mbue/

More about our guest

https://www.linkedin.com/in/davedecaprio/

Machine Minds:

Apple Podcast:

https://podcasts.apple.com/us/podcast..

Spotify:

 https://open.spotify.com/show/1apGh7x... 

Google Podcast: 

https://podcasts.google.com/feed/aHR0...

YouTube:    / @machinemindsshow   

Let’s connect on social media:

 IG:   / machinemindsshow   

LinkedIn:   / gregtoroosian

Arrow AI was founded on two key insights: the need for small businesses to enhance their online presence and the challenges they face in navigating the complex advertising ecosystem. Rolan explains how the COVID-19 pandemic accelerated the demand for businesses to establish a credible online persona, which is now as crucial as their offline presence. Arrow AI addresses this need by automating content creation for social media, blogs, and Google business profiles, allowing businesses to tell their stories and engage with potential customers effectively.

Throughout the episode, Rolan shares success stories, including how Arrow AI helped a veteran-owned toy company significantly increase its online engagement and revenue by automating their social media presence. He emphasizes the importance of combining AI technology with human expertise, ensuring that all content is reviewed by a team of content specialists before being published.

Listeners will gain insights into the future of AI in business, the role of freelancers in the evolving workforce, and the ethical considerations surrounding AI technology. Rolan also discusses Arrow AI's upcoming self-serve tool for freelancers and the company's commitment to building a supportive community for content creators.

Join us for an enlightening discussion on how Arrow AI is transforming the way small businesses approach content marketing and the exciting future of AI-driven solutions.

01:29 Rolan’s journey

04:04 Experiences that influenced Rolan's path?

09:16 Arrow AI function 

13:01 Thoughts on the ethical side of the use of AI?

19:11 Overcoming hurdles

24:10 Significant milestones to share

31:12 Teamwork

36:03 Future trends

More about Arrow AI

https://www.linkedin.com/company/get-arrow-ai/

More about our guest

https://www.linkedin.com/in/rolanreichel/

Machine Minds:

Apple Podcast:

https://podcasts.apple.com/us/podcast..

Spotify:

 https://open.spotify.com/show/1apGh7x... 

Google Podcast: 

https://podcasts.google.com/feed/aHR0...

Alex shares his unique career journey, detailing the challenges and triumphs of launching a startup in a traditionally slow-to-adopt sector. We discuss the cutting-edge technology behind Concrete AI, its integration with existing systems, and how it empowers companies of all sizes to optimize their operations.

Throughout the conversation, Greg and Alex tackle pressing issues such as job displacement due to automation, the importance of sustainability in construction, and the future of leadership in tech-driven environments. Whether you're a tech enthusiast, a construction professional, or simply curious about the intersection of AI and industry, this episode offers valuable insights and thought-provoking discussions.

Tune in to discover how Concrete AI is paving the way for a smarter, more efficient future in construction! Don't forget to like, subscribe, and share your thoughts in the comments!

01:15 Alex’s career path, leading to his role as CEO of Concrete AI.

04:43 Concrete AI and how it evolved.

10:18 Role of AI in optimizing concrete mixtures, and how it can help reduce carbon emissions.

11:40 Challenges of starting a company in the construction industry.

12:00 Convincing traditional construction firms to adopt new technologies.

17:47 Avoiding pitfalls from over-promising and under-delivering.

19:07 Handling the data-rich environment of concrete technology.

25:19 Technology behind Concrete AI and how it integrates with existing systems.

29:00 - Is Concrete AI accessible for all companies sizes? 

30:44 - Communicating and collaborating effectively?

36:19 - Balancing constant technological innovation with business milestones and customer needs.

39:31 - Importance of environmental product declarations.

46:04 - Concerns about job displacement due to AI and automation in the construction industry.

More about Concrete.ai

https://www.concrete.ai/

Machine Minds:

Apple Podcast:

https://podcasts.apple.com/us/podcast..

Spotify:

 https://open.spotify.com/show/1apGh7x... 

Google Podcast: 

https://podcasts.google.com/feed/aHR0...

YouTube:    / @machinemindsshow   

Let’s con

He also delves into his early experiences in Japan working with a venture fund, which ignited his passion for the startup ecosystem. Throughout the conversation, Brian highlights his preference for working with early-stage companies and discusses his current role at BMW I Ventures, where he manages the investment process, including due diligence and negotiations. He offers valuable insights into the firm’s investment philosophy, which revolves around experimentation, conviction, and strong partnerships with entrepreneurs.

Tune in for an engaging discussion on venture capital, startup growth, and the evolving world of high-performance businesses.

01:19 Brian’s journey.

04:10 What drew Brian into industrial automation?

05:12 Key experiences.

09:15 Insights on the current trends in the AI and robotics space.

12:30 Startup valuation.

15:45 Common pitfalls that entrepreneurs encounter when pitching to investors.

20:00 Recent investment that you are excited about and why?

25:10 Role of AI in the next few years?

30:00 Advice to someone looking to break into the venture capital space?

35:20 Biggest misconception regarding working in venture capital.

More about BMW I ventures

https://www.linkedin.com/company/bmw-i-ventures/

https://www.bmwiventures.com/

More about our guest

https://www.linkedin.com/in/b-wei/

Machine Minds:

Apple Podcast:

https://podcasts.apple.com/us/podcast..

Spotify:

 https://open.spotify.com/show/1apGh7x... 

Google Podcast: 

https://podcasts.google.com/feed/aHR0...

YouTube:    / @machinemindsshow   

Let’s connect on social media:

 IG: