Loading...

PODCAST

Super Data Science: ML & AI Podcast with 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

technology

All Episodes

75:25

1033: Workslop: The Hidden Cost of AI-Generated Work,...

Super Data Science: ML & AI Podcast with Jon Krohn ·

2026/10/06

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

en

27:49

1032: Agents Need 10x More Data Than Humans, with...

Super Data Science: ML & AI Podcast with Jon Krohn ·

2026/10/02

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:

en

75:04

1031: Tokenomics: Why Your Agentic AI Bill Is...

Super Data Science: ML & AI Podcast with Jon Krohn ·

2026/09/29

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

en

22:53

1030: Garbage In, Gospel Out: Why Agents Need Better...

Super Data Science: ML & AI Podcast with Jon Krohn ·

2026/09/25

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:

en

70:05

1029: How AI Brought a Podcast Back From the Dead,...

Super Data Science: ML & AI Podcast with Jon Krohn ·

2026/09/22

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

en

29:39

1028: The Chip Built for Agentic AI Inference, with...

Super Data Science: ML & AI Podcast with Jon Krohn ·

2026/09/18

In Episode #1028, Anton McGonnell (VP of Product at SambaNova) joins Jon Krohn to explain why the chips running most AI inference today were never designed for the job. Agentic AI has changed the computational profile of inference, with much larger inputs and far heavier caches feeding the token generation that follows, and that shift has exposed where GPU architecture struggles. SambaNova has raised over $2 billion to build an alternative, the reconfigurable dataflow unit, which lays a whole model

en

62:45

1027: Building an Always-On AI Agent for Busy...

Super Data Science: ML & AI Podcast with Jon Krohn ·

2026/09/15

In Episode #1027, Dr. Dilani Kahawala (Co-Founder and CEO of Anna) joins Jon Krohn to explain what it takes to build an always-on AI assistant that busy parents will trust with their inboxes. Anna watches the email, school apps, WhatsApp messages and calendars flowing into a family's life and surfaces what matters, over text and voice, with barely any app to speak of. Dilani came to it by way of a Harvard physics PhD, McKinsey, and a decade of product leadership at Etsy, Meta and Atlassian, and says

en

19:37

1026: OpenAI’s GPT-6 Astra

Super Data Science: ML & AI Podcast with Jon Krohn ·

2026/09/11

In Episode #1026, Jon Krohn breaks down GPT-6 Astra, OpenAI’s new flagship that its president has floated as a possible marker of AGI. Jon covers what the model is, what it costs, its state-of-the-art results across computer use, coding, abstract reasoning and science and the safety story, which for this release is unusually intertwined with capability. He weighs the AGI claim against Anthropic’s Fable 5.1 and lands, as ever, in a measured middle.Additional materials:⁠

en

70:34

1025: Word Gravity: How Transformers Bend Space, with...

Super Data Science: ML & AI Podcast with Jon Krohn ·

2026/09/08

In Episode #1025, Dr. Luis Serrano (Founder of Serrano Academy) joins Jon Krohn to explain the paper he co-authored on the curved spacetime of transformer architectures, in which attention stops being a lookup table and becomes something closer to gravity: words bend the space around them, and the embedding of "bank" visibly curves toward "river" as it travels through the layers of the network. In this episode, he recreates Eddington’s 1919 eclipse experiment inside a transformer, draws the line

en

34:23

1024: In Case You Missed It in August 2026

Super Data Science: ML & AI Podcast with Jon Krohn ·

2026/09/04

In ICYMI Episode #1024, Jon Krohn tracks the gap between AI investment and AI return, from the technology side to the people side. Hear from Pete Johnson, Jerry Yurchisin, Priyanka Vergadia and Tristan Handy, discussing why four out of five organizations have the structures for AI success in place while only one in five sees the returns, which decisions should never be handed to a language model however confident it sounds, how to structure Claude skills so that your output stops being slop and why

en

78:05

1023: Agentic AI Skills That Matter Now, with...

Super Data Science: ML & AI Podcast with Jon Krohn ·

2026/09/01

In Episode #1023, Aishwarya Srinivasan (Co-Founder of The Gen Academy) joins Jon Krohn to work out where a competitive moat comes from once anything you can build in ten minutes, somebody else can build in ten minutes too. Ash came to teaching through Illuminate AI, the mentorship community she started in 2020, and now trains senior engineers and leaders to ship agentic AI in production; she is blunt that vibe coding lowers the floor without touching the engineering judgment that production demands

en

17:31

1022: CLAUDE.md, AGENTS.md, Skills, Hooks and...

Super Data Science: ML & AI Podcast with Jon Krohn ·

2026/08/28

In Episode #1022, Jon Krohn tackles the art of steering AI agents, deciding where your instructions should live so they get followed reliably without bloating every request. A sequel to Episode #1020 (where model size and effort set an agent’s horsepower), this one is about direction: the seven ways to deliver instructions, why a hook beats a prompt, the industry-wide agents.md standard, and three practical takeaways you can apply whatever your stack.Additional materials:⁠

en

51:55

1021: How dbt Won Analytics Engineering, with dbt...

Super Data Science: ML & AI Podcast with Jon Krohn ·

2026/08/25

In Episode #1021, Tristan Handy (Founder and CEO of dbt Labs) joins Jon Krohn to explain how a study of about a hundred companies in 2016 became analytics engineering, and then became a tool that over a hundred thousand data teams rely on. Tristan coined the term, chose SQL when Spark was the fashionable answer, and spent a decade turning down acquisition offers because none of them were good for the people using dbt. He is now merging dbt Labs with Fivetran and taking on the presidency of the

en

16:41

1020: How to Choose Model Size and Effort Level: The...

Super Data Science: ML & AI Podcast with Jon Krohn ·

2026/08/21

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

en

59:55

1019: Anyone Can Write Code Now, So What Gets You...

Super Data Science: ML & AI Podcast with Jon Krohn ·

2026/08/18

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

en

12:58

1018: Alibaba's Qwen3.8-Max: Open-Weight Model...

Super Data Science: ML & AI Podcast with Jon Krohn ·

2026/08/14

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

en

57:24

1017: Vector Search, Agentic Memory and Effective...

Super Data Science: ML & AI Podcast with Jon Krohn ·

2026/08/11

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

en

30:27

1016: In Case You Missed It in July 2026

Super Data Science: ML & AI Podcast with Jon Krohn ·

2026/08/07

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

en

78:03

1015: Mathematical Optimization in the Agentic AI...

Super Data Science: ML & AI Podcast with Jon Krohn ·

2026/08/04

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,

en

20:57

1014: OpenAI Agent Breaches Hugging Face: All You...

Super Data Science: ML & AI Podcast with Jon Krohn ·

2026/07/31

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

en

552 results

Similar Podcasts

Lead With AI

Tamara Nall

Two Blokes Talking Tech

Trevor Long and Stephen Fenech

Pioneers of AI

WaitWhat

The Brave Technologist

Brave Software

The Good Tech Companies

HackerNoon