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

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“What did the council decide on item 42?” — answered with the speaker and the moment.

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