
In ICYMI Episode #1024, @JonKrohnLearns tracks the gap between AI investment and AI return, from the technology side to the people side. Hear from Pete Johnson, Jerry Yurchisin, Priyanka Vergadia and Tristan Handy, discussing why four out of five organizations have the structures for AI success in place while only one in five sees the returns, which decisions should never be handed to a language model however confident it sounds, how to structure Claude skills so that your output stops being slop and why the semantic layer matters more, not less, now that analytics agents are the ones asking the questions. In this episode you will learn: • (00:56) Vector Search, Agentic Memory and Effective RAG • (09:20) Mathematical Optimization in the Agentic AI Era • (17:30) Anyone Can Write Code Now, So What Gets You Hired? • (27:14) How dbt Won Analytics Engineering Additional materials: https://www.superdatascience.com/1024 Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

The 12 KB File That Replaces Weeks of Training (Ep. 1021 with Tristan Handy)

The Reason Your Claude Output Still Looks Like Slop (Ep.1019 with Priyanka Vergadia)

The RAG Mistake Almost Every Team Is Making (with Pete Johnson)

The Constraint Your LLM Will Quietly Ignore (with Gurobi's Jerry Yurchisin)

931: Boost Your Profits with Mathematical Optimization, feat. Jerry Yurchisin

813: Solving Business Problems Optimally with Data — with Jerry Yurchisin

The RAG Mistake Almost Every Team Is Making (with Pete Johnson)

The 12 KB File That Replaces Weeks of Training (Ep. 1021 with Tristan Handy)

China’s CXMT IPO Surged on Its Opening Day

What Happens When an AI Agent Confidently Act with Wrong Data?

Southeast Asia Joins the Data Centre Race, Alibaba Goes All-In on AI

Why Top Founders Are Racing Into AI Infrastructure