A team of theoretical physicists from Harvard, Cambridge, the Institute for Advanced Study, and Vanderbilt used OpenAI’s models not just as a tool, but as a collaborator, cracking a problem in particle physics that had stymied them for months. In this Five-Minute Friday, Jon Krohn walks through how GPT-5.2 Pro simplified a 32-variable mathematical expression into a single line, proposed what it called the “obvious generalization” for any number of gluons, and how a more powerful internal model then produced a formal proof after 12 hours of autonomous reasoning. Find out why this may be a template for AI-assisted scientific discovery and what it means for the future of research.


Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/980 [https://www.superdatascience.com/980]⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠



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

980: AI Making Theoretical Physics Breakthroughs

For years, Jon has been quoting the stat that the world's data is roughly doubling every year. His guest today says that’s way too conservative, he’s seeing enterprise data soon growing at close to 10x per year. And most organizations are nowhere near ready for what that means. In this episode, Rohit Choudhary, founder and CEO of Acceldata, explains how the agentic data management platform his team has built helps enterprises make their increasingly vast amounts of data self-aware, self-optimizing, and AI-ready. He breaks down why governance needs to be operational and real-time rather than a one-time compliance exercise, and shares his view on why the most valuable professionals in the age of AI won’t be the best programmers, they’ll be the ones with the clearest thinking and the deepest domain expertise.


Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/979⁠ [https://www.superdatascience.com/979]⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠



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



In this episode you will learn:

 * (03:26) How Rohit coined the term “data observability”

 * (06:04) Agentic data management use cases

 * (12:46) Why fixing data at the point of consumption is 1000x more expensive

 * (30:49) Career paths and skills for the age of AI

 * (42:38) Why enterprise data will soon grow at nearly 10x per year

979: Agentic Data Management and the Future of Enterprise AI, with Rohit Choudhary

A game millions of people solve over morning coffee is exposing a fundamental weakness in today’s most powerful AI models. In this Five-Minute Friday, Jon Krohn breaks down Pathway’s new Sudoku Extreme benchmark, roughly 250,000 of the hardest Sudoku puzzles available and why leading LLMs like o3-mini, DeepSeek-R1, and Claude 3.7 Sonnet scored effectively zero percent, while Pathway’s post-transformer BDH architecture achieved 97.4% accuracy at a fraction of the cost. Listen to the episode to find out what BDH is doing differently, why Sudoku performance matters far beyond puzzles, and what this means for the future of AI reasoning.


Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/978 [https://www.superdatascience.com/978]⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠



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

A Post-Transformer Architecture Crushes Sudoku (Transformers Solve ~0%)

What’s going to be the next big step function that blasts us forward in AI capabilities? To find out, Jon Krohn sits down with Professor Kyunghyun Cho, whose 200,000 citations and co-authorship of the first paper on attention place him among the most influential AI researchers in the world. In this episode, Kyunghyun explains why today’s models have already captured most correlations in passive data, making the real challenge about actively choosing which data to collect. He also weighs in on the open debate around world models, whether AI needs high-fidelity, step-by-step imagination or whether a high-level latent representation that lets it skip ahead is sufficient and shares the surprising discovery that 80% of his 200 computer science students had never installed a coding agent.


Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/977 [https://www.superdatascience.com/977]⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠



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



In this episode you will learn:

 * 
 * (06:43) The story behind the attention mechanism
 * (28:43) Sample efficiency and active data collection
 * (39:04) World models and latent planning
 * (49:52) Teaching undergrads with coding agents
 * (58:21) Reranking, multi-stage ranking, and the foundations of RAG

977: Attention, World Models and the Future of AI, with Prof. Kyunghyun Cho

NVIDIA just dropped Nemotron 3 Super, a 120-billion-parameter open-weight model that only activates 12 billion parameters at a time and it’s built for the agentic AI era. In this Five-Minute Friday, Jon Krohn breaks down the model’s hybrid Mamba-Transformer architecture, its million-token context window, and why its combination of frontier-class reasoning with blazing-fast throughput matters for anyone building multi-agent systems. Find out how Nemotron 3 Super claimed the #1 spot on the DeepResearch Bench leaderboards, which companies are already adopting it, and where you can start using it today.


Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/976⁠⁠ [https://www.superdatascience.com/976]⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠



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

976: NVIDIA’s Nemotron 3 Super: The Perfect LLM for Multi-Agent Systems

Zack Kass speaks to Jon Krohn about his bestselling, tech-positive book, The Next Renaissance, that charts the rapid progress of humanity and the benefits that artificial intelligence will bring to us, as well as why a future where intelligence is a cheap and abundant resource will give humanity an edge. Elsewhere in the show, Zack discusses why it’s important to hold parents, teachers and students accountable for their education, why it is incumbent on us to build a healthier relationship with technology, and his 4 principles for thriving in the age of AI.


This episode is brought to you by the⁠ ⁠⁠ [https://agntcy.org/]Cisco⁠, by ⁠Acceldata⁠ [https://www.acceldata.io/free-trial] and by ⁠⁠ODSC, the Open Data Science Conference⁠ [https://odsc.ai/]⁠.



Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/975⁠⁠ [https://www.superdatascience.com/975]⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠



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



In this episode you will learn:

 * (03:14) About Zack Kass’ book, The Next Renaissance

 * (20:18) The importance of literacy skills in the age of AI

 * (28:01) AI in education

 * (41:01) Principles for living in the era of AI

975: Unmetered Intelligence is Heralding the Next Renaissance, with Zack Kass

In this week’s Five-Minute Friday, Jon Krohn holds the AI bubble up to the light. He points to the deep greyzone found in AI startups like Cluely that are established on dubious ideas (Cluely’s tagline was “cheat on everything”) and funding bluster, as well as the staggering spending by companies on infrastructure and researcher salaries. Listen to the episode to hear about the historical precedents to the AI bubble that go all the way back to the invention of the railway, what to make of current investments in AI, and what you can do about these changes as an AI practitioner.



Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/974⁠ [https://www.superdatascience.com/974]⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠



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

974: When Will The AI Bubble Burst? How Bad Will It Be?

No one should be manually writing code in 2026, thinks Chris Fregly, Jon Krohn’s guest on this week’s episode. In this interview about Chris’ latest book, AI Systems Performance Engineering, he explains why it’s so important to consider memory bandwidth when evaluating GPU performance, that understanding the full hardware software stack is the most valuable skill for anyone working in AI development, and which shortcuts we still shouldn’t ever take when writing code, even though we might be outsourcing a great deal to generative AI.


This episode is brought to you by the ⁠⁠Cisco [https://agntcy.org/], by Acceldata [https://www.acceldata.io/free-trial] and by ⁠ODSC, the Open Data Science Conference [https://odsc.ai/]⁠.



Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/973⁠ [https://www.superdatascience.com/973]⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠



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



In this episode you will learn:

 * (03:39) Why Chris wrote AI Systems Performance Engineering 

 * (21:39) Essential coding metrics 

 * (37:24) The importance of inference when coding

 * (42:11) How to manage workflows while using AI agents

 * (51:37) Where and how to invest in the AI market

973: AI Systems Performance Engineering, with Chris Fregly

Jon Krohn recaps the month of February in this episode of In Case You Missed It. Across four interviews with Will Falcon (Episode 965), Tom Griffiths (Episode 969), Antje Barth (Episode 963), and Praveen Murugesan (Episode 967), Jon questions the brains behind some of the AI industry’s most innovative companies about launching a startup, developing a popular product, what artificial intelligence can still learn from human intelligence, and how AI might finally start to think on its own.



Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/972 [https://www.superdatascience.com/972]⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠



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

972: In Case You Missed It in February 2026

Lin Qiao, CEO of Fireworks AI, talks to Jon Krohn about how she builds effective models quickly, why coding agents can perform at the level of a junior engineer, and what she attributes to the success of Fireworks AI: True to its name, the company exploded into the AI industry with over $300 million secured in venture capital, as well as netting a further $250 million Series C funding. For Lin, many enterprises miss out by not being familiar with open models. Open models give a lot of control to the user, offering customizability and at a much lower price point. Listen to hear how Fireworks AI helps companies continue to save money through AI.





This episode is brought to you by the⁠ ⁠ [https://aws.amazon.com/ai/machine-learning/trainium/]⁠⁠⁠Dell⁠⁠⁠ [http://www.dell.com/dellproaistudio], by⁠ ⁠⁠Intel⁠⁠⁠ [https://www.dell.com/en-us/shop/dell-laptops/scr/laptops/appref=all-intel-processors-processor-brand], by ⁠Cisco⁠ [https://agntcy.org/] and by ⁠Acceldata⁠ [https://www.acceldata.io/free-trial].



Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/971 [https://www.superdatascience.com/971]⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠



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



In this episode you will learn:

 * (01:19) All about Fireworks AI

 * (24:16) Why companies need to take notice of open models

 * (33:05) The commercial viability of slow-reasoning models

 * (38:51) Fireworks AI’s approach to model performance evaluations

971: 90% of The World’s Data is Private; Lin Qiao’s Fireworks AI is Unlocking It

The Super Data Science podcast with Jon Krohn brings you the latest and most
important machine learning, artificial intelligence, and broader data-world
topics from across both academia and industry. As the quantity of data on our
planet doubles every couple of years and this trend is set to continue for
decades to come, there's an unprecedented opportunity for you to make an
enormous impact in your lifetime. 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, and commercialization − everything you
need to crush it with data science.

Jon Krohn

Shows