#368 AI Agents Are Now Your Database's Main User | Reynold Xin, Co-Founder at Databricks

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   3 Aug 2026 · 40 min

   #371 The Real Reason Your Product Team Needs a Feedback Loop with Todd Olson, CEO at Pendo

   Software teams are shipping faster than ever, but speed hasn't solved the oldest problem in the industry: most software still isn't very good. AI coding tools have lowered the barrier to building something, yet they haven't lowered the barrier to building something worth using. As more people who aren't trained software creators start shipping products, a new question is forming across product, design, and engineering teams: if AI can build almost anything, how do you make sure it builds the right thing, and builds it well? Todd Olson is co-founder and CEO of Pendo, the product experience…

2. 

   27 Jul 2026 · 44 min

   #370 Failure is Data (and Other Career Advice) | Todd Dewett, Leadership Author & Speaker

   As AI takes over more technical and routine work, the skills that set data and AI professionals apart are shifting. Raw technical ability and a high IQ still matter, but they are becoming table stakes as tools get more capable and teams get smarter. What increasingly separates people is harder to automate: communication, self-awareness, authenticity, and the ability to keep learning through failure. For anyone building a career in this space, that raises real questions. Which skills are actually worth investing in now? What holds up as AI advances? And how do you keep growing once you have…

3. 

   20 Jul 2026 · 49 min

   #369 How to Become a Top Business Intelligence Analyst | Helen Wall, Founder at Helen Data Design & Microsoft Influencer

   Business intelligence has never been only about building charts and writing queries. Most of the work that makes a report trustworthy happens below the surface — in the data models, documentation, and stakeholder conversations that users never see. For analysts, technical skill is just the starting point; understanding what the business actually needs, and why a number exists, matters just as much. So what really separates a competent analyst from a great one? How do you build something that answers the right question, not just any question? And which skills are worth investing in first?…

4. 

   6 Jul 2026 · 52 min

   #367 Don't Build on Jell-O: How to Make Agentic AI Reliable with Dan Klein, CTO at Scaled Cognition

   Across the AI industry, capability has exploded while trustworthiness has lagged badly behind. The same technology that writes fluent prose can invent a refund policy that was never real, and most of those errors are subtle enough that no one notices. As more teams hand high-stakes work to AI — in banking, healthcare, customer service — the cost of confident mistakes adds up fast. So how common are hallucinations, really? Can chaining models together or adding humans to the loop fix it? And is reliability something you can design into a system from the start? Dan Klein is the CTO and…

5. 

   29 Jun 2026 · 49 min

   #366 Can AI Agents Outperform a Data Scientist? | James Zou, Professor at Stanford University

   AI agents are no longer limited to automating routine tasks like customer support or report generation. Research labs and pharmaceutical companies are beginning to deploy teams of specialist AI agents capable of designing experiments, analyzing data, and proposing new hypotheses — in some cases producing results that outperform human experts. For data scientists and researchers, this raises urgent questions: Where do AI agents excel in scientific workflows today, and where do they fall short? How do you build an agent that can genuinely innovate rather than just replicate what's already been…

6. 

   22 Jun 2026 · 53 min

   #365 Your 90 Day Blueprint for AI Success with Charlene Li, Author of Winning with AI

   Most organizations know AI matters, but few have turned that conviction into a written plan. Ambition and hope are everywhere; a clear roadmap tied to business strategy is rare. For teams on the ground, this gap shows up as scattered initiatives, tools nobody fully uses, and a lot of activity that never adds up to real value. So where do you actually start? How do you move from a long list of use cases to a focused plan you can execute? And who in the organization should own the job of turning AI into business results? Charlene Li is a New York Times bestselling author and strategic advisor…

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   7 Oct 2026 · 48 min

   #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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