Super Data Science: ML & AI Podcast with Jon Krohn

In Episode #1038, Jon Krohn unpacks the remarkable and remarkably contentious run of mathematical results AI has produced in recent weeks, led by OpenAI’s claim to have cracked part of a million-dollar Millennium Prize problem. He lays out what was actually proved, the three big caveats that temper it, the chaotic bulk release of 722 manuscripts that followed, and why 25 Fields medallists are alarmed. His throughline for anyone building with AI: when generation gets cheap, verification becomes the bottleneck.

Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1038⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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In Episode #1037, Dr. Richard Socher (CEO and Co-Founder of Recursive) joins Jon Krohn to make the case for automating the scientific method itself. Richard’s startup came out of stealth this year with over half a billion dollars raised to build recursively self-improving superintelligence, and his new book, The Eureka Machine, is already a national bestseller. He also co-founded You.com and the venture firm AIX Ventures, was chief scientist at Salesforce, taught the first university course anywhere on neural networks for NLP, and helped build the ImageNet dataset. In this episode, he traces how AI keeps replacing hand-crafted expertise with scale, lays out the four pillars of his Eureka Machine, argues that hallucinations can be a feature for science, explains why recursive self-improvement within a year or two still rules out an overnight takeoff, and names the software and jobs he expects to endure.

Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1037⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

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In Episode #1036, Jon Krohn digs into the question parents and teachers keep asking: does AI stop children from learning? Drawing on a wave of new studies, he shows the answer turns entirely on how AI is used, as a shortcut that outsources the thinking, or as a tutor that explains and gives feedback. Along the way: a 30,000-student natural experiment in China, a striking tutoring result from Turkey, and three practical takeaways that apply just as much to adults using AI at work.

Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1036⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

In Episode #1035, Carly Taylor (Founder of Rebel Data Science) returns to join Jon Krohn for a wide-ranging conversation about leading engineering teams when the ground keeps moving. Carly shipped nine Call of Duty titles in roughly five years at Activision, where she led game security, and has since been a director of engineering at Microsoft and Field CTO for gaming at Databricks. She is also one of the most entertaining people in data on LinkedIn, where more than 200,000 people follow posts she cheerfully calls unhinged. In this episode, she explains why obsessing over the future produces analysis paralysis, which fundamentals never change, how to tell AI slop from AI used well, what the Buddhist practice of inviting Mara to tea has to do with public speaking, and what the glass cliff means for women being promoted into leadership.

Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1035⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

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In ICYMI Episode #1034, Jon Krohn moves from what sits underneath AI systems to what it takes to live with them every day. Hear from Aishwarya Srinivasan, Luis Serrano, Katie Malone, Ish Shah and Dilani Kahawala, discussing why the bottleneck in shipping software has moved from execution to judgment, how a physicist recast attention in transformers as words bending space, why years of managing people may be the best preparation for managing AI agents, how one weekend side project burned through billions of tokens and which three problems are hardest to solve when a product has no user interface and never stops running.

Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1034⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

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In Episode #1033, Prof. Jeff Hancock (Professor of Communication at Stanford) and Dr. Kate Niederhoffer (Chief Scientist at BetterUp) join Jon Krohn to explain the hidden cost of AI-generated work. A year ago they coined "workslop" in a Harvard Business Review article that went viral and landed the term among Merriam-Webster’s words of the year: content that masquerades as real work but quietly shifts the burden onto whoever receives it. Their research finds that 40% of workers have been sent workslop and 53% admit to producing it, at a cost running to millions of dollars a year for a large organisation. In this episode, they separate workslop from ordinary sloppy work, name the organisational conditions that produce it, introduce their newer concept of relation slipping, and make the case that augmenting people with AI beats automating them away.

Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.superdatascience.com/1033⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

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During their #sponsored discussion, Chief Data Officer at Salesforce Michael Andrew talks to Jon Krohn about what changes for a data team when its customers are AI agents as well as people. Listen to the episode to hear Michael Andrew talk about why agents need ten times more trusted data than humans, how Salesforce untrapped its own customer data with Data 360 and what practitioners should be learning to stay effective in the agentic era!

Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1032⁠⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.⁠⁠⁠

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In Episode #1031, Ish Shah and Tyler Cox (Distinguished Engineers in the Office of the CTO for Dell Technologies' client group) join Jon Krohn to work out why agentic AI bills are exploding and what can be done about it. Over one weekend Ish burned roughly two billion tokens on a side project, and that is the ordinary shape of agentic work now: agents spawn sub-agents, the pie of work grows, and cheaper tokens only invite more ambitious projects. Tyler runs a small Dell lab that pushes hundreds of millions of tokens a day through local hardware instead.

In this episode, they define what makes a system agentic, explain how to read a Pareto curve when choosing models, work through the jagged frontier and why most tasks do not need a frontier model, and lay out what moving agentic workloads onto your own hardware does to the economics.

Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.superdatascience.com/1031⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

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During their #sponsored discussion, Senior Vice President Product Management AI and Metadata at Salesforce, Gaurav Pathak talks to Jon Krohn about why AI agents need well-labeled, high-quality data to deliver reliable answers in the enterprise. Listen to the episode to hear Gaurav Pathak talk about the difference between a “data brawl” and “garbage in, gospel out”, who the “sin eaters” of enterprise AI are and the three skills that matter most for AI engineers today!

Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1030⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.⁠⁠⁠

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In Episode #1029, Dr. Katie Malone (Host of Linear Digressions) joins Jon Krohn to explain how AI brought her podcast back from the dead. After nearly 300 episodes, Katie shut down Linear Digressions due to burnout, but better tools helped her relaunch it six years later. Along the way she has taught machine learning at Udacity and the University of Chicago and led the development of agentic AI platforms inside a company of tens of thousands of people. In this episode, she argues that people management and agent management are the same skill in different clothing, works through what AI slop and process slop are doing to organisations, describes the agent that now produces her show, and takes a pop quiz on three of her favourite data paradoxes.

Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.superdatascience.com/1027⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.

In this episode you will learn: