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

    há 15 h

    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

    1h 36min
  2. 1006: In Case You Missed It in June 2026

    há 4 dias

    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?

    44 min
  3. 1005: People Skills for Analytical Thinkers, with Bestselling Author Gilbert Eijkelenboom

    30 de jun.

    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

    1h 11min
  4. 1004: Recursive Self-Improvement

    26 de jun.

    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.

    10 min
  5. 1003: Building an AI Data Center End to End, with Lightning AI’s Frank Basso

    23 de jun.

    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

    1h 12min
  6. 1002: Fable 5: The Full Story from Capabilities to Drama

    19 de jun.

    1002: Fable 5: The Full Story from Capabilities to Drama

    Anthropic’s Claude Fable 5 was the most capable AI model ever released to the public and it lasted just three days before the US government forced it offline. Jon Krohn unpacks both halves of the story: what makes Fable 5 special, and why it was pulled. Fable 5 and its locked-down sibling Mythos 5 are the same model separated only by safeguards, in a new “Mythos-class” tier above Opus. Jon covers its state-of-the-art benchmarks, premium $10/$50-per-million-token pricing, conservative safety classifiers, and the federal export-control directive, reportedly sparked by an Amazon-flagged “jailbreak” that took it down. Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1002⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

    16 min
  7. 1001: How AI Erased My Career Moat, an Episode #1001 Special: Jon Krohn interviewed by Kirill Eremenko

    16 de jun.

    1001: How AI Erased My Career Moat, an Episode #1001 Special: Jon Krohn interviewed by Kirill Eremenko

    For this episode #1001 special, the tables are turned: SuperDataScience founder Kirill Eremenko takes the host’s chair and Jon Krohn is the guest. They trace Jon Krohn’s path from an Oxford neuroscience PhD to a New York hedge fund to founding the AI consulting firm Y Carrot, why he regrets leaving academia and how tools like Claude Code erased his hard-won technical moat and why that makes skilled engineers more valuable than ever. Along the way: whether AI is a bubble, Jevons paradox and the data-center boom, the RICE framework for choosing AI projects, the single biggest reason AI projects fail and how a well-built AI agent could give anyone “Christopher Nolan–like” focus. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.superdatascience.com/1001⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: (03:42) From an Oxford neuroscience PhD to AI consulting (17:25) Defining AGI and why consciousness isn’t required (30:39) Are we in an AI bubble? Why we benefit either way (46:32) Jevons paradox: why cheaper AI means more data centers (01:08:31) The RICE framework for prioritizing AI projects (01:15:08) The number-one reason AI projects fail in production (01:31:50) AI, attention, and protecting your wellbeing

    1h 56min
  8. 1000: Ten Years of the Super Data Science Podcast, with Jon, Kirill and Special Guests

    12 de jun.

    1000: Ten Years of the Super Data Science Podcast, with Jon, Kirill and Special Guests

    For this landmark 1,000th episode and the show’s 10-year anniversary, host Jon Krohn is joined by SuperDataScience founder Kirill Eremenko, who hosted the podcast for its first 400-plus episodes before handing over the reins. In a first for the show, the episode was recorded live with the audience invited to join on air, alongside surprise appearances from the team, longtime guests, and even Jon’s family. Together, Jon Krohn and Kirill look back on a decade of the podcast and field listener questions on AI’s biggest opportunities, the build-versus-buy dilemma, how to break into the field today, and how to stay grounded amid the relentless pace of AI. Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1000⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

    1h
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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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