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. 3h ago

    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

  2. 4d ago

    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

  3. Aug 4

    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

  4. Jul 31

    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

  5. Jul 28

    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

  6. Jul 24

    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

  7. Jul 21

    1011: The Math Still Matters: Deep Skills in the Age of AI, with Dr. Catherine Williams

    Dr. Catherine Williams, Chief Data Officer at the nonprofit Candid, was solving black-hole equations with pen and paper before she ever wrote a line of code. She earned a PhD in math researching general relativity and black holes, did postdocs at Stanford and Columbia and then became one of the very first data scientists, joining AppNexus back in 2012, around the same time “data scientist” became a job title at all. In this episode, she traces the field’s evolution from Bayesian models to BERT to today’s LLMs, and makes a compelling case that going deep on the underlying math matters more than ever, even now that AI can do the math for you.  Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.superdatascience.com/1011⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (02:40) Catherine’s black hole and general relativity research (13:18) The intellectual habits that carried from math into leadership (16:14) Whether deep math still matters in the age of LLMs (44:10) The BERT moment and the embeddings revolution (48:22) Why frontier capability keeps getting cheaper (57:54) Catherine’s leadership advice: think one level up

    1011: The Math Still Matters: Deep Skills in the Age of AI, with Dr. Catherine Williams

  8. Jul 17

    1010: Fable 5 as Advisor: Anthropic's Two-Model Pattern for Smarter, Cheaper Agents

    In Episode #1010, Jon Krohn digs into “the advisor strategy”, a clever pattern that pairs a fast, cheap executor model with a frontier-class advisor it can consult mid-task, all inside a single API call. Every agent builder faces the same tension: frontier models plan best but cost too much to run on every turn, while small models fumble the decisions that matter. Anthropic’s advisor tool resolves it with roughly a one-line code change, and the benchmarks are startling: Sonnet with an Opus advisor scored higher than Sonnet alone while costing 11.9% less, and Haiku’s BrowseComp score more than doubled at 85% lower cost than Sonnet solo. Jon covers the newest Fable 5 numbers, the practical gotchas, how it differs from OpenAI’s router and why AI progress is now as much about composing models as training bigger ones. Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1010⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

    1010: Fable 5 as Advisor: Anthropic's Two-Model Pattern for Smarter, Cheaper Agents
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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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