Super Data Science: ML & AI Podcast with Jon Krohn

Hosted by Jon Krohn

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1013

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Jul 2026

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About the show

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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60 recent

July 24, 202612 min

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.

July 21, 20261 hr 9 min

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

July 17, 202614 min

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.

July 14, 20261 hr 7 min

1009: How AI Is Quietly Saving Lives, with Steve Mock

In Episode #1009, Steve Mock (investor at Blumberg Capital, five-time entrepreneur and creator of aisavedme.org), joins Jon Krohn to explore the quiet layer of everyday AI adoption that rarely gets documented. After his 84-year-old father asked a deceptively simple question, “How does one use AI?”, Steve built a place for people to share how AI is actually helping them. The stories that came in surprised him: they’re rarely about the technology and almost always about human outcomes, caregiving, communication, learning, confidence and connection.  Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.superdatascience.com/1009⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (02:02) Where “AI Saved Me” came from, an 84-year-old dad’s simple question (14:27) The healthcare pattern, using AI to become your own advocate (22:37) The education pattern, personalized study and simulated office hours (27:33) The fulfillment pattern, offloading grunt work to focus on what matters (43:07) Building the whole site as a non-programmer (50:14) The investor’s lens, vertical AI and the “data flywheel” moat

July 10, 202615 min

1008: The AI-Native Startup Playbook

In Episode #1008, Jon Krohn digs into Anthropic's 35-page Founder's Playbook and pulls out the practical guidance for each of its four startup stages: Idea, MVP, Launch and Scale. AI has erased the three bottlenecks that historically gated company-building — capital, headcount and technical skill — turning the founder from individual contributor into an "orchestrator of agents." Along the way, Jon covers the trap of mistaking building for validating, using AI as a structured devil's advocate against your own idea, the compounding danger of "agentic technical debt," two litmus tests for real product-market fit, and the three-layer moat that keeps a well-funded incumbent from copying you. His takeaway: this is classic lean-startup discipline, updated for an era where execution is cheap and judgment is the scarce resource. Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1008⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

July 7, 20261 hr 36 min

1007: How to Find Solid Career Ground in the AI Era, with 80,000 Hours Founder Ben Todd

Benjamin Todd, co-founder and President of 80,000 Hours and author of the new Penguin Random House book 80,000 Hours: How to Have a Fulfilling Career That Does Good, joins Jon Krohn for a major update on career strategy in the AI era, his first appearance since before ChatGPT existed. Ben explains why “follow your passion” is backwards and why rare, valuable skills used to help others are what actually generate lasting fulfillment, the ABZ framework for planning under deep uncertainty, why the only durable move is to keep shifting onto whatever bottleneck AI can’t yet clear, and how a human-level digital worker becomes superhuman almost immediately. He and Jon also map the risk landscape, power-seeking AI, extreme power concentration, engineered pandemics, gradual disempowerment, and S-risks, before landing on a hopeful, actionable note: your career is a bigger lever than ever. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.superdatascience.com/1007⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (06:44) The ABZ framework for career planning under deep uncertainty (14:30) Why “follow your passion” is backwards and what builds fulfillment instead (20:52) The moving bottleneck: how to stay valuable as AI keeps improving (29:54) Why a human-level digital worker becomes superhuman almost immediately (51:11) Power-seeking AI and extreme power concentration (1:16:11) Why your career is a bigger lever than ever

July 3, 202644 min

1006: In Case You Missed It in June 2026

In this month's episode of ICYMI, hear from Chip Huyen, Andrey Kurenkov, Frank Basso and Gilbert Eijkelenboom, discussing why moats are shifting toward physical systems and accumulated product intuition, how Astrocade built vibe coding before the term existed, what it's really like inside a deafeningly loud AI data center, why only 15% of people are technically self-aware and whether AGI requires anything like consciousness. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1006⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (00:00) The Cost of Building Software Is Going to Zero — Now What? (10:18) We Built Vibe Coding Before Anyone Called It That (21:08) AI Data Centers Are Louder Than a Rock Concert (28:39) Why 85% of Data Scientists Can't Communicate Their Work (33:46) Are Humans Also Just Predicting the Next Token?

June 30, 20261 hr 11 min

1005: People Skills for Analytical Thinkers, with Bestselling Author Gilbert Eijkelenboom

Gilbert Eijkelenboom, bestselling author of People Skills for Analytical Thinkers and founder of the training firm MindSpeaking joins Jon Krohn to make the case that communication is a core data skill, not an optional extra. Gilbert shares the “And, But, Therefore” framework for turning dense analysis into a story stakeholders act on, the research suggesting only around 15% of people are genuinely self-aware (and how journaling, meditation, and exercise help close that gap), how childhood experiences install behavioral “algorithms” we carry into the workplace and why behavior change precedes attitude change, so doing small, uncomfortable things for 30 days can rewire how you see yourself. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.superdatascience.com/1005⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (02:54) Why your analysis only creates value once people actually use it (24:53) What it really means that only ~15% of people are self-aware and how to close the gap (34:01) The “And, But, Therefore” framework for data storytelling (37:44) How childhood installs personal “algorithms” and the keep/stop/start question to surface them (46:55) Why behavior change comes before attitude change (the 30-day practice) (50:33) Defusing the trigger between data teams and pushy stakeholders

June 26, 202610 min

1004: Recursive Self-Improvement

Could an AI get good enough at AI research to build its own, more capable successor and kick off a compounding loop? That’s recursive self-improvement (RSI) and it surged into the conversation after Anthropic revealed that, as of May 2026, Claude wrote more than 80% of the code merged into its production codebase. In this Five-Minute Friday, Jon Krohn separates today’s AI-assisted coding from true RSI, walks through the accelerating evidence - METR’s shrinking task “time horizon,” Google DeepMind’s AlphaEvolve, Andrej Karpathy’s overnight training-tuner, weighs Jack Clark’s 60% bet that AI builds its own successor by 2028 against the compute, data and “marketing” skeptics. As ever, Jon lands in the optimistic middle. Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1004⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

June 23, 20261 hr 12 min

1003: Building an AI Data Center End to End, with Lightning AI’s Frank Basso

Frank Basso, VP of Infrastructure at Lightning AI, joins Jon Krohn for a rare ground-level tour of the one layer of the AI stack the show had never covered in over a thousand episodes: the physical data center. Frank explains how Lightning AI provisions its 35,000-plus GPUs through hyperscale co-location, why everything new is liquid-to-chip cooled, how GPUs talk to each other over ultra-fast east-west networks, and what it’s actually like to stand inside a 110-decibel AI data hall. He also debunks the most persistent myths about data-center water and electricity use, and makes the case for fuel cells, nuclear power, and 800-volt DC distribution as the path forward. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.superdatascience.com/1003⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (02:47) What actually makes an AI data center different from a traditional one (06:04) How Lightning AI provisions its 35,000+ GPUs through hyperscale co-location (24:01) Why liquid cooling doesn’t waste water, debunking the biggest data-center myth (29:46) East-west vs. north-south networks, explained (43:47) “Screaming banshees”: why AI data halls run at 105–110 decibels (51:52) Why data centers don’t actually drive up your power bill