『Forging The Future with Chris Howard』のカバーアート

Forging The Future with Chris Howard

Forging The Future with Chris Howard

著者： Chris Howard
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Join Chris Howard, Founder and CEO of Softeq, as he interviews knowledgeable leaders in the innovation spectrum, including CEOs, CTOs, R&D professionals, and start-up founders. Real conversations, technology, and processes of bringing new ideas to market.© 2022 All rights reserved. "Forging the Future" podcast, content, title, and logo owned by Softeq. Unauthorized use prohibited. Contact: ftf@speakerboxmedia.com. Respect our creativity. 経済学
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  • Andy Lonsberry of Path Robotics on Why Physical AI Is Ready for the Factory Floor

    Andy Lonsberry of Path Robotics on Why Physical AI Is Ready for the Factory Floor
    2026/09/10
    In this episode of Forging the Future, host Chris Howard talks with Andy Lonsberry, CTO of Path Robotics, about how physical AI is finally closing a skilled-labor gap that traditional automation never could. Andy explains why Path focused first on welding the hardest, highest-turnover skilled trade. The conversation covers why this AI moment differs from a decade of past automation attempts, why no single robot form factor (arms, quadrupeds, humanoids) will dominate, and a surprising story about how skeptical floor workers turned into the biggest advocates for more robots, and why Andy sees physical AI as essential to keeping manufacturing viable in the U.S. as the skilled labor shortage grows. 🎧 Episode Highlights [01:38] What Path Robotics actually builds, and why they combined a large neural network with a custom sensor to make robots capable of skilled labor tasks. [02:52] Why welding, specifically — the result of interviewing 100 U.S. manufacturers about their single biggest bottleneck to scaling. [03:55] "Robots are coming to keep your jobs." Why 100% of Path's customers are trying to grow production, not cut headcount. [08:16] Why no single robot form factor wins. Path's traditional six-degree-of-freedom arms handle indoor manufacturing, while its new quadruped, Rove, is built for outdoor work like shipyards and construction sites. [13:54] Teaching a robot to see and learn: the custom sensor built to see through welding smoke and arc glare, and the "Weld World" model that predicts welding physics before a second model learns to become a great welder through reinforcement learning. [24:28] The surprising discovery that production-based pay incentives turned skeptical floor workers into the loudest advocates for getting more robots on the line. [31:00] Why complex, judgment-heavy work will likely always stay human, and how "Mission Control" telemetry lets Path monitor every deployed robot in real time. [36:51] The customer case study: a part that used to take 150 manual welding hours saw a 91% reduction in labor time, unlocking production volume the customer couldn't hit with people alone. [38:48] The labor math that makes this urgent: the American Welding Society estimates 320,000 additional welders are needed over the next four years — a number Andy calls "not even close to being possible" to hire. 🔑 Key Takeaways: Takeaway 1: Physical AI in manufacturing isn't displacing workers, it's filling a gap that traditional hiring can't close. Every one of Path's customers adopted the technology to grow production, not to cut headcount, and the skilled labor shortage (320,000 additional welders needed over four years, by one industry estimate) makes the case starker than a typical automation pitch. Takeaway 2: No single robot form factor will dominate. Traditional arms, quadrupeds, and humanoids each have distinct advantages depending on the environment. The real differentiator is the intelligence layer that can control any of them, not the hardware itself. Takeaway 3: Getting buy-in from the shop floor may matter as much as the technology itself. When one customer tied weekly production bonuses to output, the workers most likely to resist automation became the ones requesting more robots, turning a feared job-loss narrative into aligned incentives for everyone. 👤 Guest Spotlight: Andy Lonsberry, CTO, Path Robotics Andy Lonsberry co-founded Path Robotics with his brother eight years ago with a mission to help rebuild U.S. manufacturing as a source of national strength. As CTO, he leads development of Obsidian, the company's core neural network, and the custom sensors that let robots handle skilled labor tasks. Path's robots are now deployed across the U.S., Canada, and Mexico, spanning shipbuilding, energy infrastructure, AI infrastructure, and heavy industry. In this episode, Andy shares why he believes this moment in physical AI is fundamentally different from a decade of prior automation attempts, and what it will take to keep production growing onshore as the skilled labor shortage deepens. Stay connected ⁠https://www.linkedin.com/showcase/forging-the-future-with-chris-howard⁠ Stay inspired and ahead of the curve by subscribing to Forging the Future. Share your thoughts on this episode with the hashtag #ForgingTheFuture or tag us online!
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    41 分
  • Why 85% of Robot Infrastructure Costs Aren't the Robot: Dan Cummins on Physical AI

    Why 85% of Robot Infrastructure Costs Aren't the Robot: Dan Cummins on Physical AI
    2026/08/27
    In this episode of Forging the Future, host Chris Howard talks with Dan Cummins, Dell Technologies Fellow, about the shift from massive, centralized AI infrastructure to intelligence deployed at the edge — inside factories, hospitals, vehicles, and retail stores. Dan traces his path from building embedded sonar and warfare systems for submarines at the Department of Defense, to leading major storage architecture transformations at EMC and Dell (VNX2, Unity, PowerStore), to his current work building Dell's edge AI strategy — from NativeEdge to what's now called Dell Distributed Private Cloud (DDPC) and the Dell Automation Platform. The conversation covers why enterprise edge deployments so often stall at the pilot stage, the massive opportunity (and risk) around physical AI data capture, intent-based and agentic orchestration, and why systems-level thinking may be the biggest skills gap facing the next generation of AI-first engineers. 🎧 Episode Highlights [00:02:00] Dan's origin story: building embedded warfare, sonar, and radar systems for the Department of Defense before college.[00:03:00] The submarine noise-leak detection system Dan built and deployed across the entire fleet; an early lesson in end-to-end systems thinking.[00:06:00] Joining EMC to modernize the aging, single-threaded Clarion storage stack for the flash era.[00:07:00] The VNX2 project: taking a 16-core architecture from 200,000 IOPS to over a million IOPS in under a millisecond.[00:13:40] The pivot to Dell and Edge AI — solving for the fragmentation of solutions. [00:24:00] Where edge deployments actually fail: Deploying physical AI at enterprise scale.[00:28:00] The physical AI data problem: why 90% of robot trajectory and experience data is mishandled today, and the opportunity for a purpose-built data platform.[00:38:50] The generational gap: why systems-design experience, not just AI fluency, is becoming a critical and increasingly rare skill.[00:44:00] Dan's five-year outlook: physical AI, sovereign AI, and customer-owned training loops. 🔑 Key Takeaways: Takeaway 1: The hardest part of physical AI isn't the model or the pilot, it's deploying and managing it at enterprise scale. Most physical AI today remains siloed and piloted because organizations lack the orchestration platforms needed to provision and manage fleets of endpoints consistently.Takeaway 2: Experience data generated by physical AI (robot trajectories, sensor data, etc.) is quickly becoming valuable intellectual property — and today, roughly 90% of it is mishandled, siloed, or lost, making replay and root-cause analysis nearly impossible when something goes wrong.Takeaway 3: Systems-level thinking has to come first. AI coding tools are only as good as the design requirements you give them. Non-functional requirements (like data reduction or latency targets) that aren't explicitly written into a spec simply won't be honored by an AI agent. 👤 Guest Spotlight: Dan Cummins, Dell Technologies Fellow and VP of Edge Computing and Solution Platforms Dan Cummins is a Dell Technologies Fellow whose career spans embedded defense systems, enterprise storage architecture, and edge AI. He began his career building sonar, radar, and surveillance systems for the U.S. Navy, including a fleet-wide submarine noise-leak detection system. At EMC and later Dell, he led major storage transformations including VNX2 and the ground-up reinvention of PowerStore. He now leads Dell's enterprise edge and physical AI strategy, having helped build NativeEdge. Dan also serves on the board of the FIDO Alliance and as an advisory board member for the University of New Hampshire's computer science program. In this episode, he shares how his systems-engineering background shapes his view on where physical AI is headed next. Stay connected https://www.linkedin.com/in/danielcummins603https://www.linkedin.com/in/techrishttps://www.linkedin.com/showcase/forging-the-future-with-chris-howardhttps://www.softeq.com/forging-the-future-podcast Stay inspired and ahead of the curve by subscribing to Forging the Future. Share your thoughts on this episode with the hashtag #ForgingTheFuture or tag us online!
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    50 分
  • Lights Out: Why Fully Automated Factories Are Closer Than You Think

    Lights Out: Why Fully Automated Factories Are Closer Than You Think
    2026/08/13
    Product Management Director at Intel Ben Tan joins host Chris Howard to explore why some of the most important AI innovation isn’t happening in the cloud, but at the edge, on factory floors, in warehouses, and inside the machines that need to make decisions instantly. Ben unpacks the “pendulum effect” between cloud and edge computing, and why data sovereignty, tokenomics, and latency are now driving more workloads back to the edge. He walks through what a true “lights-out” factory looks like, and why early pilots suggest fully automated, human-free production is closer than most people think, even as the most common mistakes organizations make when launching AI initiatives continue to slow adoption. Ben explains why the industry is shifting from massive frontier models toward smaller, domain-specific “big brain to small brain” architectures built for performance-per-watt. The conversation also covers unexpected industrial use cases for generative AI, from PLC code generation to synthetic defect imagery, a first look at Intel’s upcoming SuperClaw initiative, and why the organizations that succeed with AI are the ones that define success in business terms, not technology terms. 🎧 Episode Highlights [00:02:32]: Why edge AI is having a moment — sovereignty, tokenomics, and latency [00:05:08]: What a “lights-out” factory actually looks like, and how close early pilots already are [00:08:33]: The biggest mistakes organizations make when starting an AI initiative [00:16:00]: Reframing performance vs. cost — and why “good enough” often wins [00:19:18]: The shift to smaller, domain-specific models and performance-per-watt [00:26:51]: Unexpected generative AI use cases on the factory floor, from PLC code to synthetic defects [00:35:15]: A first look at SuperClaw, Intel’s hybrid edge-to-cloud token strategy [00:38:04]: Why defining success for the business — not the technology — is what most people miss 🔑 Key Takeaways: - Edge AI is being pulled forward by more than latency. Data sovereignty concerns and the economics of cloud tokens (“tokenomics”) are now just as influential as real-time performance needs in pushing workloads back to the edge, especially in manufacturing and industrial settings. - The biggest AI failures are organizational, not technical. Top-down rollouts that leave factory-floor workers out of the loop, ungoverned pilot sprawl across departments, and unresolved friction between IT and OT security policies are far more likely to sink an AI initiative than the technology itself. - Bigger isn’t always better. The industry is shifting from massive, general-purpose frontier models toward smaller, domain-specific models — Ben calls it “big brain to small brain” — that deliver higher performance-per-watt and only need to know the one job they’re built to do. 👤 Guest Spotlight: Ben Tan Ben Tan is a Product and Business Development leader with a track record of launching new consumer and enterprise products and services in a world of choices, identifying growth markets, and forging alliances that drive broad adoption. He currently serves as Product Management Director, Industrial and Supply Chain AI at Intel, where he leads a product-led growth strategy through the application of easy-to-use, deployable AI/ML solutions, addressing the needs of both end users and developers. Across his 8+ years at Intel, Ben has also served as Market Development Director for Health and Life Sciences, developing new partnerships and go-to-market routes in healthcare for the rapidly growing remote patient monitoring segment. He builds teams around the belief that measurable small wins strengthen the fabric of development toward the long-term goal, and that success depends on crafting win-win strategies for internal and external partners, an approach that has helped him grow beachheads into sustaining, double-digit-growth businesses, always starting with a deep understanding of user experience and the key value exchange points. Stay Connected: - https://www.linkedin.com/in/ben-tan-3907 - https://www.linkedin.com/company/intel-corporation/home - https://www.linkedin.com/in/techris Stay inspired and ahead of the curve by subscribing to Forging the Future. Share your thoughts on this episode with the hashtag #ForgingTheFuture or tag us online!
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    42 分
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