Machine Minds

Autonomous forklifts and pallet jacks may generate plenty of headlines, but the real challenge isn't building robots that can move. It's building solutions that fit seamlessly into existing operations, deliver measurable ROI, and earn customer trust over years of deployment.

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: [email protected]
• 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/

Construction is one of the world’s largest industries, yet much of the design process still depends on manual coordination, long hours, and workflows that struggle to keep pace with growing project complexity. Francesco “Frio” Iorio, co-founder and CEO of Augmenta, joins Greg to explore how AI-native design tools could fundamentally reshape the future of construction, engineering, and collaboration across the built environment.

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/

As robotics and AI reshape manufacturing, the hardest challenge is often not the technology itself. It is helping people, processes, and entire organizations successfully adapt to it. GrayMatter Robotics is tackling that challenge head-on by building AI-powered automation systems designed for real-world manufacturing environments where variability, human expertise, and operational complexity are everywhere.

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/

From bootstrapping a defense intelligence startup with five credit cards to backing some of the most ambitious robotics and autonomy companies in the world, Paige Craig has built his career around one core belief: exceptional people matter more than polished ideas.

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/

From engineering-first robots to emotionally intelligent companions, Andromeda Robotics is redefining what human-robot interaction can look like in the real world.

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/

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center of that shift, bringing simulation-driven inspection systems to an industry long reliant on manual checks and outdated tooling.

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

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

Highlights:

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

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

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

Matt's email: [email protected]

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

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

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

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

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

Highlights:

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

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

Learn more about GrayMatter Robotics:

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

Connect with Ariyan Kabir:

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

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

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

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

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

In this conversation, Greg and Kait explore:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Highlights:

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

Learn more about TorqueAGI: LinkedIn | Twitter | Website

Connect with Ash Saxena: LinkedIn | Stanford

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