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Earley AI Podcast

Earley AI Podcast

著者： 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 経済学
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  • Earley AI Podcast - Episode 99 Data Governance, Business Context, and Why AI Makes the Old Problems Worse with Zoher Karu

    Earley AI Podcast - Episode 99 Data Governance, Business Context, and Why AI Makes the Old Problems Worse with Zoher Karu
    2026/09/15
    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 分
  • Earley AI Podcast - Episode 98 Agentic AI in Finance, the Trusted Advisor Advantage, and the Pricing Model Reckoning with Nikita Komarov

    Earley AI Podcast - Episode 98 Agentic AI in Finance, the Trusted Advisor Advantage, and the Pricing Model Reckoning with Nikita Komarov
    2026/09/15
    What It Takes to Build AI That Is Accurate Enough, Traceable Enough, and Trustworthy Enough for High-Stakes Financial WorkGuest: Nikita Komarov, CEO and Founder at Dobs.AIHost: Seth Earley, CEO at Earley Information SciencePublished on: September 9, 2026In this episode, Seth Earley speaks with Nikita Komarov, CEO and Founder of Dobs.AI, who spent seven years at McKinsey advising Fortune 1000 executives before founding a company that is rebuilding financial due diligence, internal audit, and vendor overpayment recovery from the ground up as agentic AI systems. They explore why financial professionals are the most resistant to AI adoption and why that resistance is rational, how orchestrating teams of AI agents with financial controls built in produces outputs that are deterministic enough for audit, why the difference between an efficiency tool and a production-ready AI system is enormous, and how the trusted advisor status accountants have built over decades becomes a platform for entirely new services in the AI era.Key Takeaways:Financial professionals are among the most resistant to AI adoption for a rational reason - LLMs are non-deterministic by nature, and accounting requires numbers that are 100% accurate and traceable.Building production-grade financial AI requires three levers working together: orchestrating teams of agents with defined roles, building financial controls and guardrails into the pipeline, and solving for data extraction accuracy before any analysis begins.The difference between an efficiency tool like Claude or ChatGPT and a production-ready AI system is not the model - it is the architecture, the controls, and the product thinking required to get from unstructured input to a final output a human can take to a client.DOBS AI compresses financial due diligence from a six-week engagement to 72 hours for the management meeting - cutting the cycle from week and a half to three days on that critical milestone alone.Accounting firms have more trust with clients than management consultants or lawyers, and that trust combined with recurring access creates a platform for expanding into advisory services that AI now makes possible.The pricing model reckoning is real - time and materials no longer makes sense when AI does the work in hours, and firms need to shift to value-based pricing anchored to the outcome delivered, not the hours spent.The long-term trajectory is positive, but the mid-term transition is the risk - AI is compressing decades of technological change into five to ten years, and organizations and individuals who are not adapting will be left behind.Insightful Quotes:"Large language models, they predict the next word. That's why these systems are non-deterministic. You can't say what the output will be next. That's the problem in financial services - you need 100% accuracy, but you don't know what the system is going to tell you." - Nikita Komarov"That's exactly the difference between an efficiency tool and a production-ready solution. When people say we use AI, they most likely mean Copilot or ChatGPT - and that's 5 to 10% of what's actually possible." - Nikita Komarov"You can't automate what you don't understand. The first thing you have to do is say, what is the expected output and the outcome, and then how do I verify that I actually get there?" - Seth EarleyTune in to discover why financial AI is one of the most demanding and highest-stakes applications in the enterprise - and what it actually takes to build systems that are accurate and auditable enough to trust.LinksLinkedIn: https://www.linkedin.com/in/nikita-komarov/Website: https://dobs.aiWays to Tune In:Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home Apple Podcast: https://podcasts.apple.com/podcast/id1586654770 Spotify: 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-podcast Buzzsprout: https://earleyai.buzzsprout.com/Thanks to our sponsors:VKTREarley Information ScienceAI Powered Enterprise Book
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    35 分
  • Earley AI Podcast - Episode 97: Biological Computing, Brain-Derived Algorithms, and the Future of AI Efficiency with Alex Ksendzovsky

    Earley AI Podcast - Episode 97: Biological Computing, Brain-Derived Algorithms, and the Future of AI Efficiency with Alex Ksendzovsky
    2026/08/14

    Why Making AI More Biological May Be the Most Consequential Development in the History of Computing

    Guest: Alex Ksendzovsky, CEO and Co-Founder at The Biological Computing Company

    Host: Seth Earley, CEO at Earley Information Science

    Published on: August 14, 2026

    In this episode, Seth Earley speaks with Alex Ksendzovsky, CEO and Co-Founder of The Biological Computing Company, a neurosurgeon and neuroscientist who spent nearly two decades studying how the brain processes information - including implanting electrodes into human brains to understand epilepsy and growing neurons in a dish to study them at the molecular level. They explore why the AI field diverged sharply from biology in the 1980s and what was left behind, how TBC grows real brain cells on electrode arrays to derive mathematical principles that improve AI algorithms, what a 13-20% improvement in video generation quality and a 4-5x efficiency gain means against an industry where 1-2% counts as significant, and where biological computing is headed in the next decade and beyond. This is one of the most technically ambitious and genuinely novel conversations the podcast has had.

    Key Takeaways:

    • AI diverged sharply from biology in the 1980s when backpropagation was introduced - it produced highly performant systems but at the cost of massive energy inefficiency that the brain solved hundreds of millions of years ago.
    • TBC grows hundreds of thousands of neurons on electrode arrays with 4,096 electrodes, encodes information as electrical patterns, and derives mathematical principles from how those neurons actually process and represent that information.
    • The adapter products built from these biological principles plug into existing transformer architectures and produce 13-20% improvements in video quality metrics where a 1-2% improvement is considered publication-worthy.
    • On efficiency, TBC's adapters currently produce a 4-5x improvement in frames per second - and when combined with existing optimization strategies, the two approaches are synergistic rather than conflicting.
    • The catastrophic forgetting problem - AI's inability to learn continuously without losing what was previously learned - is one TBC is directly attacking by studying how biological synapses change during closed-loop learning and deriving new learning rules from that process.
    • The brain is millions of times more efficient than silicon; even capturing a minuscule portion of that through biologically-derived principles has produced gains that suggest the ceiling for this approach is enormous.
    • The ethical framework is clear: the cultured neurons used in TBC's experiments are fundamentally different from a brain - lacking the three-dimensional structure, scale, and emergent properties associated with sentience - and TBC actively works with bioethicists to maintain those guardrails.

    Insightful Quotes:

    "Moving forward past the 1980s into 2026, you have extremely performant AI systems, but they're being trained with brute force and they're extremely inefficient. At TBC, we think the reason for this is because they became extremely non-biological." - Alex Ksendzovsky

    "Just making it a tiny, tiny bit more biological reached these massive gains. It's a testament to the complexity of how the brain operates, and the more of these principles and primitives we can derive and apply, the more improvements we'll get in terms of performance and efficiency." - Alex Ksendzovsky

    "The gap is not a coincidence. It's a result of hundreds of millions of years of evolution solving the same problems that we are now trying to solve in silicon." - Seth Earley

    Tune in to discover why biological computing may be the most consequential and least-understood frontier in AI infrastructure today - and what it means for the energy crisis that is already shaping every data center investment being made.

    Links

    LinkedIn: https://www.linkedin.com/in/alexander-ksendzovsky-31732711/

    Website: https://www.tbc.co

    Blog: https://www.tbc.co/blog


    Thanks to our sponsors:

    • VKTR
    • Earley Information Science
    • AI Powered Enterprise Book
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    42 分
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