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

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1. 

   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…

2. 

   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…

3. 

   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…

4. 

   27 Apr 2026 · 58 min

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

   The data field has changed shape faster than almost any other. The role that used to be a statistician became a data scientist, became an ML engineer, and is now morphing into AI engineer. Consulting firms are hiring fewer entry-level analysts and more vibe-coders who can ship AI systems to production. For data and AI professionals, this raises immediate questions. Which parts of the work are most exposed to automation, and which are not? Where should you invest your time? And which backgrounds are now producing the strongest hires, whether you are building a team or trying to join one? Ben…

5. 

   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…

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…

7. 

   30 Mar 2026 · 50 min

   #353 The Data Team's Agentic Future with Ketan Karkhanis, CEO at ThoughtSpot

   Data and AI platforms are racing toward agentic and even autonomous analytics. But the bottleneck is rarely the model—it’s data readiness: governed metrics, clear metadata, and a semantic layer machines can read. For data engineers and analysts, this shifts work from hand-built SQL and dashboard tweaks to designing meaning and trust. If an agent can draft column descriptions, propose a model for a new business question, and build the first dashboard layout, where do you add the most value? What do you measure to prove ROI in 30 days? How do you prevent “shiny demos” from driving strategy too…

8. 

   23 Mar 2026 · 56 min

   #352 AI Agents at Work: What Actually Breaks (and How to Fix It) with Danielle Crop, EVP Digital Strategy & Alliances at WNS

   AI agents are spreading across the data and AI industry, promising to automate everything from research to outreach. At the same time, teams are learning that these tools can hallucinate, leak data, or act in surprising ways. In day-to-day work, the challenge is deciding which tasks to hand off, what data to share, and how to keep the output trustworthy. Do your agents actually add value, or just add noise? Are they running in a secured, ring-fenced environment? How do you balance playful experimentation with critical checking when an agent confidently gets a key fact wrong? Danielle leads…

9. 

   16 Mar 2026 · 1 hr 4 min

   #351 Will World Models Bring us AGI? with Eric Xing, President & Professor at MBZUAI

   World models are emerging as the next step after large language models, pushing AI from book knowledge toward systems that can simulate the physical and social world. Instead of just generating text or short videos, the goal is steerable simulation with long-horizon consistency and planning. For practitioners, this raises practical choices: what data and representations do you need, and when do you mix symbolic reasoning with generative models? How do you test whether a model can follow actions over minutes, not seconds? And where do you start—robotics, driving safety, or synthetic data…

10. 

    9 Mar 2026 · 1 hr 10 min

    #350 How to Make Hard Choices in AI with Atay Kozlovski, Researcher at the University of Zurich

    Across the AI industry, high-stakes tools are being deployed in places where errors can harm people: sepsis alerts in hospitals, identity checks, welfare fraud detection, immigration enforcement, and recommendation systems that shape life outcomes. The pattern is familiar: scale and speed go up, while human review becomes rushed, shallow, or punished for disagreeing. In daily work, that can look like a nurse forced to act on false alarms, or a team using an LLM summary in ways the designers never planned. When should you slow down deployment? How do you detect new “wild” use cases early? And…