Super Data Science: ML & AI Podcast with Jon Krohn

In Episode #1020, Jon Krohn unpacks the two dials that increasingly decide what you get out of a large language model: which model size you pick and how much effort you tell it to spend. Using a July Anthropic blog post by Claude Code’s Lydia Holly as a jumping-off point, with guidance that generalizes to any model family, Jon explains what each setting actually does under the hood. Model size swaps which frozen weights handle your request (roughly, how capable), while effort sets how thorough and certain the model must be before calling a task done, not a simple “thinking-time slider.” He offers a clean diagnostic for when to raise effort versus move to a bigger model, shows why cheaper-per-token isn’t always cheaper-per-task and surveys how OpenAI, Google and open-weight labs have all converged on these same two dials.

Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1020⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

In this episode you will learn:

(00:56) What the model-size dial actually does

(05:29) Why effort isn’t a thinking-time slider

(13:25) Three practical takeaways for using both dials

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

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.

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

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