Forging The Future with Chris Howard

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!

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!

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!

AI strategist and investor Dr. Irina von Rosen joins host Chris Howard to unpack what “AI sovereignty” really means, from national governments racing to control chips, data centers, and models, to businesses quietly sliding into a new kind of vendor lock. She explains why LLMs behave unlike any technology that came before them: unpredictable, hard to reproduce, and shaped by hidden influences like advertising partnerships and embedded bias. Irina argues that the companies best positioned for the long run are the ones treating security, explainability, and ethics as a foundation rather than an afterthought, and investing in practical, provable AI, not speculative promises of AGI or superintelligence. The conversation closes on a hopeful note: raising a new generation of AI-literate, responsible professionals to carry the work forward. 🎧 Episode Highlights [01:08]: What AI sovereignty actually means — control over data, infrastructure, and outcomes [03:00]: The new vendor lock: why AI dependency is riskier than the cloud ever was [10:47]: Advertising, bias, and why the same prompt gives different answers across models [17:00]: Why regulated industries treat AI sovereignty as resilience, not compliance [41:13]: The opportunity cost of chasing AGI hype over provable, practical AI [59:02]: Building the next generation of AI-literate, responsible professionals 🔑 Key Takeaways: AI sovereignty is the new vendor lock. Companies that don’t control their own data, models, and infrastructure risk being blindsided by sudden price hikes, shifting outputs, or a vendor’s decisions, the same trap many businesses fell into with cloud computing, just playing out at a far larger and more consequential scale. LLMs break the rules that used to make technology predictable. Switching vendors, or even upgrading to a newer version of the same model, can produce entirely different outputs, and the same prompt run through two different models can yield opposite answers, an unpredictability compounded by embedded bias and undisclosed advertising partnerships. Long-term resilience beats short-term hype. In highly regulated industries especially, real success comes from treating security, reproducibility, and ethics as prerequisites rather than checkboxes, keeping human expertise in-house, and directing investment toward practical, provable AI instead of speculative bets on artificial general intelligence. 👤 Guest Spotlight: Dr. Irina von Rosen Dr. Irina von Rosen is an AI strategist and investor who helps highly regulated industries, global nonprofits, and private equity firms turn complex challenges into breakthrough AI opportunities. From one-off proofs of concept to enterprise-wide transformation, she sets up AI Centres of Excellence, guides AI governance and audit controls, and deploys MLOps and LLMOps pipelines in high-risk environments, always framing rapid experimentation within security, compliance, ethics, and IP protection. A board confidant on AI portfolios worth hundreds of millions, she translates technical roadmaps into clear P&L impact and sustainable advantage. Her work has earned her spots on the 100 Brilliant Women in AI Ethics™, the Hyperight Nordic 100 in Data, Analytics & AI, and Inspired Minds’ Top 65 Most Influential Women. She speaks five languages and mentors through Girls in Tech and Women in AI. Stay Connected: https://www.softeq.com https://www.linkedin.com/in/techris https://www.linkedin.com/in/irinarosen https://euiais.org 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!

In this episode, Brian McCarson (Microchip Technology) breaks down AI's four evolutionary eras: machine learning, training, inference, and AGI. In today’s conversation, Brian makes a bold case that most organizations are building infrastructure for the wrong phase. Based on nearly 25 years at Intel and now driving innovation at Microchip Technology, he explains why the real bottleneck in AI today is not compute power but the high-speed switches, retimers, and storage controllers that connect GPUs, CPUs, and memory at scale, what he calls the "nervous system" of AI infrastructure. He warns that enterprises over-investing in cloud-based training architectures are heading toward a costly redesign within 18 months, and that the winning strategy is to plan now for inference-first, agent-friendly systems that push compute as close to the endpoint as possible. 🎧 Episode Highlights [01:37]: Brian's journey from semiconductor automation to modern AI [13:41]: Inheriting and rebuilding Microchip's data center business [20:10]: How Microchip deployed AI agents to scale operations without expanding headcount [34:43]: Why Microchip positions itself as the nervous system of AI infrastructure [43:07]: The energy and supply chain constraints reshaping the future of data centers 🔑 Key Takeaways: ● Most organizations are investing in AI infrastructure for the wrong phase. The shift from the training era to the inference era demands a fundamental rethink of architecture, and companies building cloud-dependent, training-heavy systems today are setting themselves up for a costly and disruptive redesign within 18 months. Planning now for inference-first, agent-friendly systems that push compute closer to the endpoint is the strategic move that separates long-term winners from short-term optimizers. ● The real bottleneck in AI is not compute power but the infrastructure connecting it. As Nvidia accelerates its hardware refresh cycle to an annual cadence, the switches, retimers, and storage controllers that enable GPUs, CPUs, and memory to communicate at scale have become the critical constraint. Investing in high-performance interconnect technology is no longer an afterthought but a core requirement for getting full value out of expensive compute investments. ● AI delivers its greatest business value when it augments people rather than replaces them. Microchip's turnaround demonstrated that deploying AI agents as PhD-level assistants to handle menial, repetitive tasks, while keeping humans focused on high-value strategic work, drives better outcomes, stronger team morale, and sustainable growth. The companies using AI purely to cut headcount and improve a balance sheet are optimizing for the short term at the expense of long-term organizational health. 👤 About The Host: Brian McCarson Brian McCarson is Corporate Vice President at Microchip Technology, where he leads the company's data center solutions business with a focus on the high-speed interconnect infrastructure powering next-generation AI systems. He brings nearly 25 years of experience at Intel Corporation, where he built deep expertise across semiconductor innovation, factory automation, and advanced technology development. A longtime advocate for using AI to drive real business outcomes, Brian is recognized for his ability to turn around complex technology organizations and translate emerging AI trends into actionable enterprise strategy. Stay Connected: ● https://www.softeq.com ● https://www.linkedin.com/in/techris ● https://www.linkedin.com/in/brianmccarson ● https://www.microchip.com Produced by Speakerbox Media.

NVIDIA systems architect Thorsten Stremlau sees “AI for good” as a way to restore core human abilities like communication, independence, and participation, especially for people with disabilities. Inspired by his work with Stephen Hawking and Peter Scott- Morgan, he’s helped build affordable, AI-powered tools from eye-gaze systems to personalized language models that run on everyday devices like smartphones. He contrasts these non-invasive solutions with brain-computer interfaces from companies like Neuralink, highlighting that while implants may unlock richer interaction in the future, today’s priority is scalable tech that already improves lives. He also explores how AI can support areas beyond ALS, including heart disease, PTSD, and mobility. At its core, his work is about using AI to create a more inclusive world where disability doesn’t mean disconnection. 🎧Episode Highlights [01:24]: Thorsten’s early work with nonverbal disabled youth [07:41]: AI restoring communication, autonomy, participation [18:09]: Working with Stephen Hawking and Peter Scott-Morgan [29:55]: Low-cost eye-gaze and circular-keyboard communication [43:12]: Beyond ALS: speech, PTSD, AI prosthetics 🔑 Key Takeaways: ● AI for good is ultimately about restoring human agency, not just boosting efficiency. By focusing on communication, autonomy, and participation, AI systems can give people with disabilities the ability to express themselves, make choices, and engage with the world on their own terms, rather than being defined by their limitations. ● The most impactful assistive technologies are built on mainstream, affordable hardware instead of specialized, high-cost rigs. Eye-gaze interfaces, circular keyboards, personalized language models, and speech-decoding systems that run on laptops and smartphones dramatically expand access, making advanced assistive tech viable not just for a few patients in wealthy systems, but for millions of people worldwide. ● Non-invasive AI solutions are a powerful bridge to the future of human–computer interaction, even as brain–computer interfaces rapidly advance. By combining clever sensing (eyes, face, voice, heart rate, touch), behavior modeling, and personalized AI, we can already enable richer communication, calmer nervous systems, and more natural movement, laying the groundwork for a world where disability no longer means disconnection from work, creativity, or community. 👤 Guest Spotlight: Thorsten Stremlau Thorsten Stremlau is a Principal Systems Architect at NVIDIA and a technology leader focused on building next-generation computing platforms and customer-centered innovation. Previously with Lenovo and IBM, he holds 30+ patents and is widely recognized as a thought leader in platform security and advanced systems architecture, known for turning complex technical challenges into trusted, market-ready solutions. Stay Connected: ● https://www.softeq.com ● https://www.linkedin.com/in/techris ● https://www.linkedin.com/in/thorsten-stremlau-247930 ● https://www.nvidia.com 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!

Episode Summary: Amanpreet Kaur, Chief AI and Technology Officer of Equipt.ai, breaks down the so-called “SaaS-pocalypse” and argues that SaaS isn’t dying, it’s evolving into smarter, AI-embedded systems of execution. Amanpreet explains how traditional SaaS created fragmentation and operational friction, and how Equipt.ai is rebuilding the stack by unifying workflows, data, and decision-making into a single AI-driven execution layer. Drawing from her experience in asset-heavy industries like energy, Aman shares how their platform replaces multiple disconnected tools while enabling real-time, guided operations from quote to cash. She also reflects on the early startup journey, from landing their first enterprise customer to navigating investor skepticism and refining their positioning. A key shift is undoubtedly taking place: the future belongs to agentic SaaS platforms that reduce human intervention and turn software into an active driver of business outcomes. 🎧 Episode Highlights  [02:09]: SaaS solved infrastructure but created fragmentation [04:59]: “Dumb SaaS is dying:” AI-powered execution rises [06:17]: Replacing 10-20 tools with one platform [10:44]: AI as “steroids” for SaaS, not replacement [14:38]: First enterprise client before having a product [23:13]: Embedding AI across the full workflow 🔑 Key Takeaways: The “SaaS-pocalypse” isn’t about SaaS disappearing, it’s about a shift from systems of record to systems of execution. Traditional SaaS created fragmented workflows and heavy reliance on human coordination, while AI-enabled platforms are now unifying data and driving real-time decisions directly within operations. AI’s real value is not replacing software, but embedding intelligence into every layer of it. From automation and optimization to predictive insights, the winning approach is combining machine learning, simple automation, and context-aware AI to reduce manual work and enable more deterministic, reliable outcomes. Startups that succeed in this shift will focus on solving real operational problems, not just adding AI for hype. Equipt.ai’s journey shows that deep industry understanding, clear pain points, and delivering immediate value to customers matter more than technology trends, especially when building trust and scaling from early enterprise clients. 👤 Guest Spotlight:Amanpreet KaurAmanpreet Kaur is the co-founder as well as the Chief AI and Technology Officer of Equipt.ai, where she builds AI-powered operational platforms for asset-intensive industries. With a background in energy and industrial technology, she previously led the development and commercialization of digital solutions, including enterprise-scale platforms and digital twin systems. Kaur brings deep domain expertise and a product-first mindset to redefining how businesses move from fragmented SaaS tools to AI-driven systems of execution. Stay Connected: https://www.softeq.com/ https://www.linkedin.com/in/techris/ https://www.linkedin.com/in/amanpreet-hon-doc/ https://www.equipt.ai/ 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!

At CES 2026, we sat down with Chris Eliasmith, CTO of Applied Brain Research, to discuss how brain-inspired AI is enabling fast, low-power voice interfaces that run directly on edge devices. Drawing on research modeling the hippocampus, his team developed new neural network architectures that significantly improve efficiency and accuracy for tasks like speech recognition and text to speech. These advances allow devices such as AR glasses, robots, and wearables to respond to voice commands in under 300 milliseconds, creating interactions that feel natural and conversational. Eliasmith also explains the tradeoffs between model size, accuracy, and power consumption, and how running AI at the edge can reduce costs and reliance on the cloud. He ultimately envisions a future where complete AI agents run locally on small devices, making technology simpler and more accessible for everyday users. 🎧 Episode Highlights: ●[01:59]: Introducing ultra-low-power voice AI at the edge ●[03:27]: Why 300ms latency is critical for natural conversations ●[09:06]: Brain-inspired neural networks modeled after the hippocampus ●[15:02]: Tiny AI chips for AR glasses, robotics, and wearables ●[20:25]: Cutting cloud costs with local speech processing ●[27:54]: The future of full AI agents running at the edge 🔑 Key Takeaways: ● By modeling neural networks after how parts of the brain like the hippocampus process time-based information, researchers can build AI systems that achieve higher accuracy with far fewer parameters. This approach allows models to process speech and other signals more efficiently, making advanced AI practical even on small, resource-constrained devices. ● For voice interfaces to feel natural, responses must happen within roughly 300 milliseconds, the same timing humans expect in conversation. Designing AI systems that meet this latency requirement changes how models are built and deployed, pushing developers to prioritize real-time performance rather than relying on slower cloud-based processing. ● Low-power AI that operates directly on devices reduces reliance on internet connectivity, lowers operational costs, and improves responsiveness. As models become efficient enough to run locally, entire AI agents could operate on wearables, robotics platforms, and AR devices, simplifying technology and making intelligent interfaces accessible to more users. 👤 Guest Spotlight: Chris Eliasmith Chris Eliasmith is the Director of the Centre for Theoretical Neuroscience at the University of Waterloo and holds the Canada Research Chair in Theoretical Neuroscience. He is also the CTO and co-founder of Applied Brain Research, where he works on low-power AI technologies for machine learning, robotics, and edge computing. Eliasmith is the co-inventor of the Neural Engineering Framework, the Nengo software platform, and the Semantic Pointer Architecture, and is the author of How to Build a Brain (Oxford University Press) and Neural Engineering (MIT Press). Stay Connected: ●https://www.softeq.com/ ●https://www.linkedin.com/in/techris/ ●https://www.linkedin.com/in/chris-eliasmith/ ●https://www.linkedin.com/company/applied-brain-research/

At CES 2026 in Las Vegas, Brandon Shibley of Edge Impulse and Thomas Soderholm of Nordic Semiconductor join Chris to explore the exciting shift of AI from the cloud to the edge. Brandon shares how streamlined, low power machine learning models are unlocking new possibilities in health wearables, industrial inspection, and agriculture by bringing fast, responsive intelligence directly onto devices. Thomas highlights how Nordic’s latest ultra low power chips with built-in neural processing are making this next wave of innovation possible. Together, they paint an optimistic picture of AI that is faster, smarter, and more accessible, running right where data is created. 🎧 Episode Highlights ●[01:02]: Why AI is moving from cloud to edge ●[08:08]: Wearables and health monitoring on-device ●[11:08]: Industrial and agricultural vision at the edge ●[22:25]: Bluetooth Low Energy and ultra low power design ●[28:02]: New Nordic chips with built-in neural processing 🔑 Key Takeaways: ●Edge AI is about efficiency, not scale for the sake of it. Smaller, purpose-built models running directly on devices can reduce latency, preserve privacy, and dramatically lower power consumption while still delivering high impact outcomes in health, agriculture, and industrial settings. ●Hardware innovation is unlocking the next wave of on-device intelligence. Ultra low power chips with integrated neural processing units and advanced Bluetooth Low Energy connectivity make it possible to run meaningful machine learning workloads on wearables and battery-driven products. ●The future of AI is distributed by design. Instead of relying entirely on massive cloud models, intelligence will live closer to where data is created, balancing performance, cost, and connectivity to create scalable and practical real world solutions. 👤 Guest Spotlight: Brandon Shibley Brandon Shibley is a Founder at Edge Delivery and Senior Staff Engineer at Edge Impulse, a Qualcomm company, where he helps developers and enterprises build and deploy machine learning models on edge devices. With a background spanning CTO, founder, and innovation leadership roles, he has led full-stack IoT and edge computing strategies across industrial, robotics, and embedded systems markets. Brandon specializes in bringing intelligent software closer to the physical world, enabling scalable, low power AI solutions that run directly on devices. Thomas Soderholm Thomas Soderholm is the Vice President of Business Development at Nordic Semiconductor, where he helps drive the company’s strategy in ultra low power wireless connectivity and edge AI. With deep roots in Bluetooth Low Energy innovation, he works at the intersection of hardware, software, and connectivity to enable smarter battery-driven devices. Thomas focuses on advancing integrated solutions that bring efficient machine learning and secure connectivity to wearables and connected products worldwide. Stay Connected: ●https://www.softeq.com/ ●https://www.linkedin.com/in/techris/ ●https://www.linkedin.com/in/shibley ●https://www.linkedin.com/company/nordic-semiconductor/ 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!

This special CES 2026 episode brings together Pete Bernard (EDGE AI FOUNDATION), Pankaj Kedia (2468 Ventures), and Hank Crawford (Blue Collar Robotics) to explore how AI is moving from theory into everyday use. From cameraless sensing to edge-powered devices that listen, measure, and respond in real time, it’s clear that AI is becoming embedded in our everyday lives. Pete, Pankaj, and Hank all share one common goal: to have AI to solve real problems and simplify life at scale. For Pete, that means building collaborative edge AI ecosystems that work reliably outside the cloud. For Pankaj, it’s unlocking applied AI that delivers real ROI in the healthcare, education, and automotive industries. And for Hank, it’s rethinking grocery shopping by using virtually controlled robotics to tackle labor shortages without replacing people. The future of AI isn’t abstract or far off, it’s already at work in the real world. 🎧 Episode Highlights ● [01:15] Why Edge AI is bigger than TinyML and how physical, generative, and agentic AI are converging ● [04:36] Cameraless AI: using signals and radio waves to sense environments without cameras ● [16:52] Why AI shouldn’t just match human performance but exceed it in safety-critical systems like self-driving and robotics ● [27:47] Applied AI as the real ROI driver across healthcare, education, and mobility ●[39:38] How human-in-the-loop robotics for grocery fulfillment provides global labor opportunities 🔑 Key Takeaways: ● The most impactful AI isn’t happening in massive data centers, it’s happening where systems can sense, interpret, and act in real time. From cameraless perception to signal-based sensing, edge AI enables intelligence in environments where latency, privacy, and connectivity matter most. ● AI must outperform humans in safety-critical systems to matter. Matching human performance isn’t enough when lives, health, or infrastructure are at stake. Whether in autonomous driving, healthcare triage, or robotics, the bar for AI is being meaningfully safer, more consistent, and more reliable than human decision-making. ● The most scalable AI systems don’t replace people, they amplify them. Human-in-the-loop robotics and applied AI models solve labor shortages, unlock global talent, and improve productivity while preserving human judgment, accountability, and trust. 👤 Guest Spotlight: Pete Bernard Pete Bernard is Executive Director of the EDGE AI FOUNDATION, advancing the development and adoption of edge AI across industries. With a background in embedded systems and machine learning, he focuses on building collaborative ecosystems that bring physical, generative, and agentic AI out of the cloud and into real-world deployment. He is known for translating complex technical shifts into practical frameworks that enable reliable, scalable AI at the edge. Pankaj Kedia Pankaj Kedia is Managing Partner at 2468 Ventures, investing in AI, robotics, autonomy, and applied technology. He backs companies delivering measurable ROI across healthcare, education, mobility, and industrial sectors, with a focus on systems that outperform humans in safety-critical and high-impact environments. Hank Crawford Hank Crawford is Founder of Blue Collar Robotics, developing human-in-the-loop robotic systems to address labor shortages. His work centers on remotely operated, task-specific robots that augment workers, beginning with grocery fulfillment and expanding into broader physical industries. Connect with Pete: Pete Bernard Connect with Pankaj: Pankaj Kedia Connect with Hank: Hank Crawford Connect with Chris: Chris Howard Explore past episodes: ftf.show Learn about Softeq: softeq.com 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!