Machine Minds

From satellite missions and drone startups to international development work in Haiti, Peter Haas has carved one of the most unconventional and globally minded paths in robotics. Today, he’s combining decades of experience across academia, entrepreneurship, government, and humanitarian work to answer a pivotal question:How can robotics and AI help the half of the world living on less than $5.50 a day?In this episode, Peter shares his journey - from working on NASA’s Gravity Probe B, to co-founding a drone company, to running robotics research at Brown University, to helping grow the Massachusetts robotics ecosystem. Now, as he embarks on a new chapter consulting from a sailboat in the Caribbean, Peter reveals what’s next for robotics, where the biggest opportunities lie, and why building for real-world problems matters more than ever.Highlights:- A career shaped by exploration: Peter’s early days ranged from being a park ranger and attempting a novel in Paris to working on Gravity Probe B, where robot-made gyroscopes sparked his fascination with precision hardware.  - International development meets robotics: A decade in Haiti showed Peter the limitations of traditional manufacturing models — inspiring his mission to use robotics to uplift underserved populations globally.  - The spark moment: Riding in an early Google self-driving car at TED convinced Peter to fully transition back into tech, eventually co-founding a drone company and entering the robotics ecosystem.  - From research to ecosystem building: Peter shares insights from leading Brown University’s Humanities-Centric Robotics Initiative and helping scale Massachusetts’ 500-company robotics cluster.  What’s exciting him now:- Lightweight, non-transformer models (Liquid AI, etc.) enabling powerful AI on resource-constrained robots.- Better teleoperation interfaces and the rise of “robot call centers” that decouple physical labor from geography.  - Ethical & technical challenges: Why cybersecurity is a looming crisis in robotics, and how insecure firmware and exposed ROS systems create real-world risk.  Lessons from the field:- Failure: building a $50K LiDAR drone just before DJI commoditized photogrammetry — and how that mirrors today’s AI landscape.- Success: why startups like SIMPL Automation win by partnering early and commercializing quickly.- Advice for builders & graduates:- Solve real problems for real customers — not abstract robotics challenges.- The integrator gap is massive: new grads with hands-on skills can build careers serving manufacturers who desperately need automation.  Connect with Greg: https://www.linkedin.com/in/gregtoroosian/Connect with Peter:https://www.linkedin.com/in/peterhaas-robotics/

From retrofitting automation into existing warehouses to redefining how fast robotics can be deployed, SIMPL Automation is pioneering a more flexible, less disruptive path toward warehouse transformation.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/

Florian shares his journey from cryptography researcher to big tech product leader — at companies like Microsoft, Adobe, and Facebook — and how that experience inspired him to tackle one of robotics’ biggest challenges: making robots work together seamlessly across diverse fleets and environments.From interoperability and observability to performance and safety, this episode explores how InOrbit.AI is building the connective tissue of the robotics world — a “Google Translate for robots” that enables smarter, safer, and more efficient automation at scale.Highlights:- Florian’s journey from research in cryptography to leadership roles in major tech companies, and what inspired him to build something from the ground up.- Explaining robot orchestration — helping organizations connect, monitor, and manage fleets of diverse robots across the world.- Florian breaks down InOrbit’s approachObservability – Know what’s happeningOperation – Manage and act on itOrchestration – Coordinate across systemsOptimization – Continuously improve performance- Letting robots “speak” any language rather than forcing one universal standard.- Florian’s philosophy of continuous improvement — for both humans and machines — and how AI powers real-time insights in robotics.Learn more about InOrbit.AI: https://www.inorbit.ai/Connect with Florian Pestoni: https://www.linkedin.com/in/florianpestoni/Connect with me: https://www.linkedin.com/in/gregtoroosian/

From printing parts in combat zones to creating a rugged, mobile printer that works on land, sea, and air — Crater’s mission is transforming logistics and supply chains for the military. Eric shares how the company was born from collaboration with the Marine Corps, what it means to innovate under pressure, and why continuous, in-motion 3D printing could change how we build and repair in the most extreme conditions.Highlights:- Eric’s early days in the rep-rap era of DIY 3D printing and how tinkering with drones led him into advanced manufacturing.-Introducing Crater’s flagship ruggedized 3D printer — capable of printing while in transport, under harsh conditions from -40°F to 120°F, on land, sea, or air.-Eric shares how Crater’s mission goes beyond business — to protect those who protect us, and build technologies that strengthen national security.-The hands-on ethos that drives Crater’s team: test, break, rebuild — all in service of rapid, real-world performance.-Eric’s long-term vision: make mobile 3D printing ubiquitous across industries like construction, mining, and healthcare — enabling resilience wherever it’s needed.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/

Kevin shares his 20-year journey from building self-driving cars and moon landers to leading autonomy at Waymo and now building machines that can literally move mountains. From lessons learned in scaling robotics startups to his philosophy on product milestones and company culture, this conversation dives deep into the realities of building technology that transforms the physical world.Highlights:- Why the best companies balance short-term sustainable business models with long-term transformative missions.-The power of milestones, pragmatic launches, and “walking your way up” to massive impact.-How Bedrock filters 10,000+ applicants to find low-ego, mission-driven builders who care more about impact than titles.-Kevin’s view on company culture, personal growth, and why success in robotics means building something real together.-Kevin’s childhood in a physics lab, his brief dream of being an astronaut, and how curiosity led him from psychology to engineering.Learn more about Bedrock: https://bedrockrobotics.com/Connect with Kevin: https://www.linkedin.com/in/kevin-peterson-a783612/Connect with me: https://www.linkedin.com/in/gregtoroosian/

Ryan and I dig into the legal backbone founders skip until it hurts—how to build an IP strategy that actually matches your business, when patents vs. trade secrets make sense, and why a “north star” patent plan should track where you’re going in 5–10 years, not just what you’re shipping today. We also get into the investor lens on patents (signal, diligence transparency, and moat), what happens to IP if the company pivots—or even shuts down—and why a fractional or early GC can save you 10x the pain later.Highlights:- IP isn’t one-size-fits-all: robotics ≠ SaaS. Start from your future state, not a template.-Don’t only patent the v1 widget—protect the core tech that will power versions 2, 3, and 10.- Patents can be marketing and diligence assets, not just litigation shields.- Regulatory fog ahead: safety (robots), privacy and training data (AI). Plan under uncertainty.- Practical hiring tip: bring legal in early (even part-time) so the CEO/CFO aren’t playing quarterback.Learn more about Wilson Sonsini: https://www.wsgr.com/Connect with Ryan: https://www.linkedin.com/in/ryan-smith-9874176/Connect with me: https://www.linkedin.com/in/gregtoroosian/

I sat down with Manas Punhani, Senior Associate at Woven Capital (Toyota’s growth fund), to dig into how growth investors judge traction, price risk, and help founders scale in industrial tech, robotics, and “physical AI.”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 coverHow growth investors read “proof points,” set milestones, and think about $50M–$100M revenue paths and exitsWhat Toyota’s network of engineers means for diligence, pilots, and real BD—plus how to enter Japan the right wayRobotics ROI today: safety, uptime, battery swapping, and why throughput alone won’t close a dealFunding moonshots with discipline: using “tech unlocks” between rounds when revenue isn’t the right yardstick yetTraits that stand out at Series A–C: self-awareness, honest plans to profitability, and crisp operating cadenceWhere the next wave might come from: embodied AI on the factory floor and “unsexy” sectors now enabled by agentsGuest Manas Punhani — Senior Associate, Woven CapitalMore about Woven Capital https://www.woven.vcConnect with Manas https://www.linkedin.com/in/sdi-academy-manas-punhani/Connect with me Greg Toroosian https://www.linkedin.com/in/gregtoroosian/

Episode 104 | AI & Automation Unlocking Materials Discovery | Joseph KrauseIn this episode of Machine Minds, I sit down with Joseph Krause, co-founder and CEO of Radical AI.We talk about Joseph’s path from Rice University and the Army National Guard to building Radical AI. He explains how AI and self-driving labs can speed up materials discovery, why failed experiments are critical data, and where this will first change the world—across aerospace, defense, semiconductors, and energy.Key topics:• The founding story of Radical AI• How AI + autonomous labs form a “materials flywheel”• Why negative results matter in science• Culture and hiring at Radical AI• Working with the White House on the 2025 National AI & R&D Strategy• Where early real-world impact will show upLinks:• To learn more about Radical AI, please visit https://www.radical-ai.com/• Radical AI is hiring! View open roles at: https://jobs.lever.co/RadicalAI• Connect with Joseph on LinkedIn: https://www.linkedin.com/in/josephfkrause/• Follow Joseph on X: https://x.com/josephfkrause?lang=enConnect with me:Greg Toroosian, Samson RoseLinkedIn: https://www.linkedin.com/in/gregtoroosian/

I sat down with Nick Snoad, VP at Marlinspike, to talk about the surge of capital into defense and dual-use tech—and how he actually evaluates teams, markets, and milestones at the earliest stages.What we get into:- Why dual-use matters: bigger markets, more paths to scale- How stage changes the bar: from “prove the tech and market” at seed to “execute and expand” post-A- Using non-dilutive routes (SBIRs, DIU, OTAs, TACFI/STRATFI) without losing the product roadmap- Where adoption is slow and where it’s breaking open: autonomy, simulation-driven wargaming, and the factory OS- Sectors on his radar now: energy (incl. nuclear and geothermal) and data-enabled manufacturing- What “value-add” looks like in defense VC (it’s not just a check)- Team signals he trusts: balanced founding groups, clear hiring plans, self-awareness on when to bring in a scaler- Advice to vets breaking in: go learn inside a startup, then decide if investing is your lanehttps://marlinspike.substack.com/ https://marlinspike.us/ Connect with Nick: https://www.linkedin.com/in/nick-snoad-340389157/ Connect with me: https://www.linkedin.com/in/gregtoroosian/

How do you get AI to seek the right data in the real world instead of drowning in all of it? 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 worldHypothesis-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 wordHandling 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 whyWhat academia gets wrong (and right) about startups—and vice versaThe hardest part of moving from novelty-driven research to problem-driven productHow (and when) to disagree productively as co-foundersLinks mentionedWebsite: www.kavai.comCompany 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 meLinkedIn – https://www.linkedin.com/in/greg-toroosian