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Earley AI Podcast

Earley AI Podcast

Di: Seth Earley
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In this podcast hosts Seth Earley invites a broad array of thought leaders and practitioners to talk about what's possible in artificial intelligence as well as what is practical in the space as we move toward a world where AI is embedded in all aspects of our personal and professional lives. They explore what's emerging in technology, data science, and enterprise applications for artificial intelligence and machine learning and how to get from early-stage AI projects to fully mature applications. Seth is founder & CEO of Earley Information Science and the award-winning author of "The AI Powered Enterprise."

© 2026 Earley AI Podcast
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  • Earley AI Podcast – Episode 101: IoT and OT Security, How AI Erased Security Through Obscurity, and What Organizations Need to Do Now with John Gallagher
    Sep 28 2026

    Why the Physical World Is Now the Biggest Cybersecurity Vulnerability in Most Organizations - and Why AI Changed Everything

    Guest: John Gallagher, VP Viakoo Labs at Viakoo

    Host: Seth Earley, CEO at Earley Information Science

    Published on: September 28, 2026

    In this episode, Seth Earley speaks with John Gallagher, VP of Viakoo Labs at Viakoo, a company that has spent 12 years building automated cyber hygiene for IoT and OT systems across millions of devices. They explore why the physical world - cameras, building automation, water infrastructure, manufacturing equipment - is now the most underprotected attack surface in most organizations, how AI has eliminated the obscurity that passively protected these systems for decades, why ransomware has shifted from stealing data to shutting down operations, why human-in-the-loop is a hard line that can never be crossed in OT remediation, and what it actually takes to build a digital twin of every device in an enterprise environment and use it to match the speed of AI-driven threats.

    Key Takeaways:

    • IT security investments do not transfer to OT and IoT environments - the 150,000 operating systems, device types, and tightly coupled application relationships require a completely separate approach.
    • AI has eliminated security through obscurity - threat actors who previously could not justify the effort to attack niche OT systems can now use AI to read manuals, understand device architectures, and find vulnerabilities at scale.
    • Ransomware has shifted from stealing data to shutting down operations - holding a manufacturing line or energy facility hostage is a fundamentally different and more consequential threat than data exfiltration.
    • Organizations typically have 5 to 20 times more OT and IoT devices than IT systems - the attack surface is orders of magnitude larger than most executives realize.
    • Threat detection in OT is now a solved problem - the unsolved problem is remediation at scale, which requires AI-assisted preparation and autonomous execution with a human in the loop for every deployment decision.
    • A digital twin of every device - tracking make, model, firmware, memory state, configuration, and change over time - is the foundational knowledge layer before any security action can be taken.
    • The defense is not a smarter model - it is knowing what you own, what connects to what, what breaks when you touch it, and who is accountable. That is an inventory problem, a normalization problem, and an information architecture problem.

    Insightful Quotes:

    "AI has brought what we call the inversion. It's taken things like remediation - firmware, password, certificate updates - and said, if you were okay updating even a few hundred devices manually a few times a year, those days are gone. It's a daily occurrence now, and you have to match AI speed." - John Gallagher

    "Will we ever put AI in charge of deploying that firmware update? Never. That is a hard line. There is a strong argument that AI never should be put in a decision-making process in this environment without a human in the loop." - John Gallagher

    "For decades, devices running on the physical side of business were protected by the fact that they were too obscure to be worth an attacker's time. That protection was never a strategy, and AI has erased it. The defense is not a smarter model - it is the knowledge of what you own, the firmware, what connects to it, what breaks when you touch it, who is accountable." - Seth Earley

    Tune in to discover why IoT and OT security is the most consequential and most underestimated cybersecurity challenge in the enterprise today - and what the path from awareness to remediation at scale requires.

    Links

    LinkedIn: https://www.linkedin.com/in/b2bpipelinebuilder/

    Website: https://www.viakoo.com/company/

    Thanks to our sponsors:

    • VKTR
    • Earley Information Science
    • AI Powered Enterprise Book
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    47 min
  • Earley AI Podcast - Episode 100: Running a Business on AI, the Three Loop Framework, and Why Organizations Need to Think Bigger with Bryan McAnulty
    Sep 24 2026
    Why the Organizations Getting the Most from AI Are the Ones That Stopped Treating It as a Tool and Started Treating It as a Collaborative PartnerGuest: Bryan McAnulty, Founder and Product Director at Heights Platform and LatchLoopHost: Seth Earley, CEO at Earley Information SciencePublished on: September 22, 2026In this episode, Seth Earley speaks with Bryan McAnulty, Founder and Product Director at Heights Platform - a platform that has helped over 10,000 creators build online knowledge businesses - and LatchLoop, an AI agent platform he built to run his own company and is now launching for other teams. They explore why most organizations are dramatically underestimating what AI can do right now, how the gap between idea and execution has collapsed to near zero, why unambiguous outcomes are the difference between an agent that delivers and one that invents its own problems, and what a three-loop framework for automation, goals, and feedback changes about how teams work. Bryan shares candid and specific insights from years of running AI-first operations - including what happens when you give agents too much autonomy and the cognitive load lessons that changed how LatchLoop was designed.Key Takeaways:Most organizations are treating AI as a better search engine - the ones that will win are the ones treating it as a collaborative partner capable of long-running autonomous work.The gap between idea and execution has collapsed to near zero - what previously took months of engineering can now be handed off to a model with a well-defined plan and verified in hours.Three loops structure how AI agents should work: automation loops for recurring well-defined tasks, goal loops for longer autonomous work with verifiable outcomes, and feedback loops for iterative back-and-forth work.Unambiguous outcomes are the most important input to any agent - without them, models will invent tasks, solve problems that do not exist, and add unrequested changes that seemed aligned with the goal.Agent guardrails should be structural, not aspirational - not please do not break production but forcing the agent to operate only in the branch, environment, or scope it is authorized to touch.Agent memory and institutional knowledge should be portable - if it lives only inside a vendor's platform, you are building organizational intelligence you cannot take with you.Your ideas are better than the AI's ideas - the implementation belongs to the agent, but the vision, the why, and the how-it-actually-feels-to-a-human belongs to you and always will.Insightful Quotes:"The companies who are gonna win realize that there's a fundamentally different and better thing we can now offer that we could have never done before. And part of the challenge is we're all looking at the same little chat text input that we had in 2023, but the capabilities of the models and the tools behind them are just so much different now." - Bryan McAnulty"You can't say make me the best website. Because make me the best website will give you the most average website. You need to define what best means to you. And if you don't know how to define that to the model, then ask it - what could we do that could make this verifiably true for what I'm looking for?" - Bryan McAnulty"We have a requirements gap now. Because the tools can build these outputs and perform these tasks, it's really about being crisp and precise about what those inputs should be - defining requirements in as much granularity as possible. That is where the human has to show up." - Seth EarleyTune in to discover why running a business on AI requires a fundamentally different way of thinking about work - and what the organizations getting it right have built that most teams have not.LinkedIn: https://www.linkedin.com/in/bryanmcanulty/Heights Platform: https://www.heightsplatform.comLatchLoop: https://www.latchloop.com Ways to Tune In:Earley AI Podcast: https://www.earley.com/earley-ai-podcast-homedLogos: https://dlogos.xyz/podcasts/earley-ai-podcast-271271ceApple Podcast: https://podcasts.apple.com/podcast/id1586654770Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbEiHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/Stitcher: https://www.stitcher.com/show/earley-ai-podcastAmazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcastBuzzsprout: https://earleyai.buzzsprout.com/ Thanks to our sponsors:VKTREarley Information ScienceAI Powered Enterprise Book
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    36 min
  • Earley AI Podcast - Episode 99 Data Governance, Business Context, and Why AI Makes the Old Problems Worse with Zoher Karu
    Sep 15 2026
    Why the Same Data Problems That Existed Before AI Still Exist - They Just Get Expressed Faster, With More ConfidenceGuest: Zoher Karu, Founder and President at ZiZi AdvisorsHost: Seth Earley, CEO at Earley Information SciencePublished on: September 14, 2026In this episode, Seth Earley speaks with Zoher Karu, Founder and President of ZiZi Advisors, who has spent his career building enterprise data and analytics programs at Sears Holdings, eBay, Citibank, and Blue Shield of California - and building personalization systems before personalization was something a large language model could attempt. They explore why data governance has become the most important discipline in the AI era, why giving an LLM clean data is still not enough if it does not understand your business, why the differentiating factor between organizations will not be the model but the context, and what executives most consistently get wrong when they point powerful new tools at the same old data problems.Key Takeaways:Data governance has become sexy again not because AI demands new governance, but because the cost of skipping the old kind now shows up faster, with more confidence behind the wrong answer.Pointing a more powerful AI engine at ungoverned data does not produce better answers - it produces bad decisions faster, with AI's characteristic knack for sounding right even when it is wrong.Multiple definitions of the same metric across the same organization - different versions of active customer, different versions of sales - are not AI problems, they are governance problems that AI amplifies.Cleaning data is necessary but not sufficient - the model also needs to understand the context of your business, the rules, the exceptions, and the institutional knowledge that lives in people's heads.The AI models themselves are moving toward commoditization; the differentiating factor will be how well organizations have captured and made available their own business context and knowledge.Start with productivity improvements to demonstrate early value, but the real value of AI is business process change - asking not just how to automate the notes after a phone call, but why you are taking phone calls at all.Governance is not internal bureaucracy - it is the brakes in the car. The reason you can go fast around a curve is that you know you have brakes. Controls let you operate at the limit rather than inching along out of fear.Insightful Quotes:"Just because you point powerful AI tools at your data doesn't mean it can figure out exactly what's what. There might be four columns called sales. How does it know which one you actually meant? And the classic problems - data silos, multiple sources of truth, ambiguity about how things connect together - they always existed, and they still exist." - Zoher Karu"You can give an LLM all the data you want, and it can be pristine, but if you don't tell it the context around the way to use that data, that's going to be the next wave of problems to solve. The way you run your business is also your asset - and that is typically captured loosely in documents, Slack messages, emails, or not captured anywhere at all." - Zoher Karu"The organizations that treat AI like magic are the ones that are getting burned. The same old problems - the data silos, the multiple sources of truth, the missing business context - do not disappear. They just get expressed faster, with more confidence." - Seth EarleyTune in to discover why the discipline that seemed least exciting in the AI era turns out to be the most consequential - and what it takes to build an AI foundation that actually reflects how your organization runs.LinksLinkedIn: https://www.linkedin.com/in/zzkaru/Ways to Tune In:Earley AI Podcast: https://www.earley.com/earley-ai-podcast-homedLogos: https://dlogos.xyz/podcasts/earley-ai-podcast-271271ceApple Podcast: https://podcasts.apple.com/podcast/id1586654770Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbEiHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/Stitcher: https://www.stitcher.com/show/earley-ai-podcastAmazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcastBuzzsprout: https://earleyai.buzzsprout.com/Thanks to our sponsors:VKTREarley Information ScienceAI Powered Enterprise Book
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    43 min
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