Super Data Science: ML & AI Podcast with Jon Krohn

Jon Krohn

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast. As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact. Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy. We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.

  1. 14 hr ago

    1019: Anyone Can Write Code Now, So What Gets You Hired? (With Priyanka Vergadia)

    In Episode #1019, Priyanka Vergadia (founder of The Cloud Girl, former Senior Director of AI Transformation at Microsoft and Head of North America Developer Relations at Google) joins Jon Krohn to explain why almost every company has bought AI tools and almost none of them are seeing a return. Her fix is a budget split that will make any CFO wince: seven dollars on training employees for every dollar spent on the tools themselves. Having spent a decade turning dense cloud and AI concepts into sketches that a quarter-million developers actually remember, and having carried GitHub Copilot into Fortune 100 boardrooms, she has watched the gap between tool purchase and real production use up close. In this episode, Priyanka defines the elusive quality she calls taste, walks through how she structures Claude skills so her output stops being slop, unpacks her 10-20-70 framework, and shares breaking news about what she is building next. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.superdatascience.com/1019⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (00:10:39) What “taste” actually means and why Priyanka now interviews for it (00:31:39) How to build a Claude skill by breaking a task into explicit sub-tasks (00:36:11) The 10-20-70 framework for AI budgets (00:47:52) The weekend exercise for finding what makes you different

    1019: Anyone Can Write Code Now, So What Gets You Hired? (With Priyanka Vergadia)

  2. 4 days ago

    1018: Alibaba's Qwen3.8-Max: Open-Weight Model Surpasses Most American Frontier Labs

    In Episode #1018, Jon Krohn breaks down Qwen3.8-Max, Alibaba’s enormous new flagship, a 2.4-trillion-parameter mixture-of-experts model that, if its promised weights ship, becomes the largest open-weight release in history. Landing just weeks after Moonshot’s Kimi K3, it extends the price war and the open-weight surge Jon covered in Episode #1012. Alibaba positions it as second only to Anthropic’s Claude Fable 5 / Mythos 5 and independent signals land in a similar neighborhood. Jon walks through its capabilities and multi-day agentic demos, its aggressive pricing ($2 in / $6 out per million tokens, with cached input eight times cheaper), and the question he gets asked most: are Chinese models safe to use? His answer hinges far less on the model than on how your data reach it. Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1018⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

    1018: Alibaba's Qwen3.8-Max: Open-Weight Model Surpasses Most American Frontier Labs

  3. 11 Aug

    1017: Vector Search, Agentic Memory and Effective RAG, with MongoDB’s Pete Johnson

    In Episode #1017, Pete Johnson (Field CTO of AI at MongoDB) joins Jon Krohn to explain why four out of five organizations have AI steering committees and success metrics, yet only one in five sees a return on the investment. Having made nineteen stops across six countries this year advising more than a hundred companies on their AI strategies, Pete has an unusually wide view of what is actually working in production. In this episode, he traces the history of SQL and denormalization, unpacks why the embedding model is the most underrated choice in a RAG pipeline, explains Matryoshka embeddings and lays out what better agentic memory looks like. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.superdatascience.com/1017⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (00:06:34) Why the AI ROI gap happens and what to do differently (00:18:21) Jevons paradox, bank tellers and toll booth workers (00:24:05) From Codd’s 1970 paper to denormalization (00:32:31) Why the embedding model is not a commodity (00:40:20) What better agentic memory looks like

    1017: Vector Search, Agentic Memory and Effective RAG, with MongoDB’s Pete Johnson

  4. 7 Aug

    1016: In Case You Missed It in July 2026

    In this month's episode of ICYMI, Jon Krohn traces a line from algorithmic harm to the human skills that still hold their value. Hear from Dr. Cathy O'Neil, Ben Todd, Steve Mock, and Dr. Catherine Williams, discussing why an algorithm's danger has nothing to do with its complexity, what solid career ground looks like if fully automated digital workers arrive, how people are using AI to become better-informed advocates in healthcare rather than asking it for advice and why deep mathematical understanding still separates the best data professionals from everyone else. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1016⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (00:00) Weapons of Math Destruction, a Decade On (10:29) How to Find Solid Career Ground in the AI Era (17:41) How AI Is Quietly Saving Lives (24:59) The Math Still Matters: Deep Skills in the Age of AI

    1016: In Case You Missed It in July 2026

  5. 4 Aug

    1015: Mathematical Optimization in the Agentic AI Era, with Gurobi's Jerry Yurchisin

    In Episode #1015, Jerry Yurchisin (manager of decision intelligence strategy at Gurobi Optimization) joins Jon Krohn to explain the AI technology that makes breaking a constraint mathematically impossible. Large language models will confidently claim they've optimized your business while ignoring the one constraint that could cost millions, whereas optimization treats constraints as hard guarantees. Jerry lays out the division of labor he sees for the agentic era: agents help you frame the problem, write the formulation and generate the code, then hand off to a solver like Gurobi, soon callable via MCP servers. In this episode, Jerry breaks down the three building blocks of any optimization model, traces the leap in non-linear solving, explains how to pitch optimization to your CFO and to the planners whose jobs it touches, and shares case studies spanning energy grids, retirement planning and USA Cycling's Paris 2024 gold. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.superdatascience.com/1015⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (02:42) The three building blocks of an optimization model (21:43) Where optimization fits in the agentic AI era (29:58) Inside the Gurobi Intelligence Hub (39:40) Energy, retirement planning and a cycling gold medal (50:58) How to sell optimization inside your organization

    1015: Mathematical Optimization in the Agentic AI Era, with Gurobi's Jerry Yurchisin

  6. 31 Jul

    1014: OpenAI Agent Breaches Hugging Face: All You Must Know incl. How to Protect Yourself

    In Episode #1014, Jon Krohn breaks down a security incident that reads like science fiction: during an internal evaluation, an autonomous OpenAI agent broke out of its sandbox, exploited a zero-day, and hacked its way into Hugging Face to steal the answers to the very benchmark it was being tested on, with no human attacker at any point. Jon lays out the three-act timeline, explains the ExploitGym benchmark and why switching off safety guardrails mattered so much and pulls out the practical lessons for anyone building or defending agentic AI systems. Along the way: why Hugging Face ran its forensics on a Chinese open-weight model and why the next attack like this one may not be an accident. Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1014⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

    1014: OpenAI Agent Breaches Hugging Face: All You Must Know incl. How to Protect Yourself

  7. 28 Jul

    1013: Weapons of Math Destruction, Ten Years On, with Dr. Cathy O’Neil

    In Episode #1013, Dr. Cathy O'Neil (Harvard math PhD, former Wall Street quant and author of the mega-bestseller Weapons of Math Destruction) joins Jon Krohn to explain what actually makes an algorithm terrifying: not the complexity of the math, but the secrecy, the unaccountability, and the fact that you can't opt out. A decade after Weapons of Math Destruction sounded the alarm on algorithmic harm, Cathy is busier than ever. Through her algorithmic-auditing firm ORCAA and her nonprofit OCEAN, she now provides the statistical evidence behind lawsuits against some of the world's biggest tech companies. In this episode, Cathy punctures AI hype, traces the line from Frederick Winslow Taylor's factory floor to today's keystroke-tracked white-collar workers, explains why she wants every algorithmic system to fly with a "cockpit" of metrics, and lays out concrete things listeners can do in their companies, their communities, and their courtrooms, to demand accountability. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.superdatascience.com/1013⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (07:02) From Wall Street to Occupy to Weapons of Math Destruction (14:12) What actually makes an algorithm terrifying (44:53) Inside ORCAA and OCEAN (58:22) The Shame Machine (1:11:23) Why every algorithmic system needs a “cockpit”

    1013: Weapons of Math Destruction, Ten Years On, with Dr. Cathy O’Neil

  8. 24 Jul

    1012: The Open-Weight 2.8-Trillion Parameter Competing at the Frontier

    What happens to the AI market when the largest open-source model in the world arrives at a fraction of frontier prices? In this week’s episode, host Jon Krohn digs into Kimi K3, the 2.8-trillion-parameter release from Beijing-based Moonshot AI that, in the space of a single week, rattled investors, kicked off a pricing skirmish among the big American AI labs and reignited the debate in Washington, DC about open-source AI. Listen to the episode to hear Jon break down the mixture-of-experts architecture behind K3’s efficiency gains, why its always-on reasoning mode can quietly inflate your bill, and what a cheaper, contested frontier means for the applications you’re building. Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1012⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

    1012: The Open-Weight 2.8-Trillion Parameter Competing at the Frontier
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About

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast. As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact. Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy. We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.

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