Super Data Science: ML & AI Podcast with Jon Krohn

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, by Acceldata and by ⁠ODSC, the Open Data Science Conference⁠.

Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/973⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] 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

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

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

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 ⁠⁠⁠⁠Dell⁠⁠⁠, by⁠ ⁠⁠Intel⁠⁠⁠, by ⁠Cisco⁠ and by ⁠Acceldata⁠.

Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/971⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] 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

Working with code-gen models and Claude Code: In this Five-Minute Friday, Jon Krohn addresses how AI superstars like Andrej Karpathy are using AI agents in their coding work, the outlook for code-gen in 2026, and how you can get started. Hear about Karpathy’s work as well as the soaring success of Peter Steinberger and how he managed to surpass the GitHub commit rate of teams as an individual working with AI agents.

Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/970⁠⁠⁠⁠⁠⁠⁠⁠⁠

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

Princeton Professor Tom Griffiths talks to Jon Krohn about his new book, The Laws of Thought, which grapples with the mathematical models behind biological and artificial intelligence, and what makes the human brain so fascinating for psychologists and computer scientists to study. In this episode, he details how the mathematical principles governing the external world can also be used to explore cognitive science, or “the internal world.” 

This episode is brought to you by the ⁠⁠Dell⁠⁠, by ⁠⁠Intel⁠⁠, by Cisco and by Acceldata.

Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/969⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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

In this episode you will learn:


(01:18) Tom Griffiths’ current research


(21:23) On mathematical inference in LLMs


(35:19) How to engineer inductive bias


(52:00) How to model curiosity into AI systems