『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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  • 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
  • AI Sovereignty: Why Owning Your Data and Models Will Define Who Wins the AI Era

    AI Sovereignty: Why Owning Your Data and Models Will Define Who Wins the AI Era
    2026/07/24
    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!
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    1 時間 2 分
  • How Microchip's Brian McCarson Is Building the Data Center Infrastructure Powering AGI

    How Microchip's Brian McCarson Is Building the Data Center Infrastructure Powering AGI
    2026/06/04
    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.
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    59 分
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