#357 Data-Driven Workforce Analytics with Ben Zweig, CEO at Revelio Labs

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   18 May 2026 · 57 min

   #360 What's Your Biggest AI Ethical Nightmare? | Reid Blackman, CEO at Virtue Consultants

   Most AI ethics conversations sound the same: be fair, be transparent, be accountable. The values are right, but in practice they don't get teams out of bed in the morning. Executives nod along, employees take the compliance training, and meanwhile real risks like hallucinations, cascading failures, and autonomous agents acting at scale slip through. So what shifts when teams stop chasing an ethical ideal and start naming the specific disasters they want to avoid? Who needs to be in the room to spot them? And what kind of training actually changes how people use AI day to day? Reid Blackman…

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   12 May 2026 · 44 min

   #359 My Best Friend is AI with Valerie Tiberius, Professor of Philosophy at University of Minnesota

   Valerie Tiberius is the Paul W. Frenzel Chair in Liberal Arts and Professor of Philosophy at the University of Minnesota. She is an expert in ethics, moral psychology, and well-being, and the author of five books including What Do You Want Out of Life? and the forthcoming Artificially Yours: Real Friendship in a World of Chatbots (Princeton University Press, May 2026). She previously served as President of the Central Division of the American Philosophical Association. In the episode, Richie and Valerie explore the purpose of friendship and whether AI can replicate it, the benefits and risks…

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   4 May 2026 · 58 min

   #358 How AI Agents Will Work While You Sleep | Ruslan Salakhutdinov, Professor at Carnegie Mellon

   Almost every AI agent demo lands in roughly the same place: it works most of the time, looks remarkable, and then fails in a way no one anticipated. Self-driving cars hit this wall a decade ago, and agents are running into it now. For data and AI teams, the question is no longer whether agents can complete a task — it's whether they can complete it reliably enough to remove the human reviewer. Which categories of work tolerate a 90% success rate? Which absolutely don't? And where should the next layer of guardrails sit? Ruslan Salakhutdinov is a UPMC Professor of Computer Science at Carnegie…

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   20 Apr 2026 · 54 min

   #356 The Forecast for Time Series Forecasts with Rami Krispin, Senior Manager of Data Science at Apple

   Time series data is everywhere — from inventory systems and energy grids to financial planning and product demand. As data volumes grow, the old ways of building individual forecasting models simply don't scale. How do you forecast hundreds of thousands of products without spending months on manual modeling? How do you know when to trust automation and when to step in? And what does it actually take to produce forecasts that business stakeholders will act on? Rami Krispin is Senior Director of Data Science and Engineering at Apple Finance, where he leads teams working at the intersection of…

5. 

   13 Apr 2026 · 53 min

   #355 AI's Impact on Databases with Shireesh Thota, CVP of Databases at Microsoft

   Cloud data platforms now offer hundreds of services, plus a growing menu of SQL, NoSQL, and open source options. Unified environments promise a simpler path, but the hard trade-offs—consistency versus scale, single-writer versus sharded, RPO/RTO targets—still matter. In daily work, you may be deciding between SQL Server, Postgres, and a globally distributed JSON store, while also asking AI tools to draft queries and spot issues. Should you still learn SQL if an agent can write it? How do you validate the intent, performance, and security of generated queries? And can monitoring agents…

6. 

   6 Apr 2026 · 46 min

   #354 Beyond BI: Decision Intelligence with Graphs with Jamie Hutton, CTO at Quantexa

   Decision intelligence is showing up across data and AI teams as companies move beyond dashboards to decisions made with context. Graphs, entity resolution, and better data products are becoming core tools as messy, siloed data meets stricter risk and compliance needs. In day-to-day work, this means linking “James,” “Jim,” and “Jamie” across systems, enriching records with third‑party sources, and pushing models where the data already lives in your lakehouse. How do you trust your customer counts? Which links in a graph matter, and which are noise? Can graph-based context reduce LLM…

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   7 Oct 2026 · 48 minNew

   #380 The Best Moments from RADAR 11x: AI, Jobs, Agents and Skills

   A job title can stay the same while the work underneath it changes completely. That idea ran through RADAR 11x, our online conference, and this recap keeps the best moments from four of the day's eight sessions. You will hear how to sort your tasks between you and AI, which durable skills hold their value, why pilots stall before production, and how software teams work with coding agents. Missed RADAR? Start here. Which tasks should AI take over? Which skills deserve your next hour of learning? Featured in this recap: Ben Zweig, CEO at Revelio Labs; Nicole Immorlica, Professor at Yale…

8. 

   28 Sep 2026 · 48 min

   #379 The Secrets of Deploying AI in Production | Sumti Jairath, Chief Architect at SambaNova

   AI agents are moving from demos into production, but many teams run into the same wall: responses that take too long and cost too much. As models grow bigger and reason through more steps before answering, every chained agent adds delay, and that adds up fast at scale. For data and AI professionals, this raises a practical question: how do you pick a model and an inference platform that keeps a multi-agent workflow fast without blowing the budget? And underneath that choice sits a bigger one — how much of the AI stack, from chips to software, should a company actually control? Sumti Jairath…

9. 

   21 Sep 2026 · 51 min

   #378 The Data Engine for AI with Ledion Bitincka, CTO at Cribl & Nikhil Mungel, Head of AI R&D at Cribl

   As agents take over more of the actual coding, the nature of technical work is shifting from producing software to judging it. Engineers increasingly spend their time specifying what should be built and then checking whether an agent's output actually solved the problem, rather than writing every line themselves. That changes what skills matter for a career in data and software: problem-solving and evaluation start to outweigh knowing today's specific tools or syntax. It also raises a harder question for teams — if agents can generate work this fast, how do you know which of it is actually…

10. 

    14 Sep 2026 · 38 min

    #377 The Algorithm for Hypergrowth with Jon McNeill, CEO at DVx Ventures & Former President at Tesla

    Hypergrowth companies rarely scale on strategy alone — they scale on how fast decisions get made and how much risk employees feel safe taking. A recurring idea in high-growth environments is treating decisions differently depending on whether they're reversible, and rewarding people for making an impact rather than simply avoiding mistakes. For managers and individual contributors alike, that shift changes what "good work" looks like day to day. Which of your team's decisions actually need a leader's sign-off, and which ones would move faster if people just tried something and adjusted? Jon…

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