The Data Exchange with Ben Lorica
Ben Lorica
A series of informal conversations with thought leaders, researchers, practitioners, and writers on a wide range of topics in technology, science, and of course big data, data science, artificial intelligence, and related applications. Anchored by Ben Lorica (@BigData), the Data Exchange also features a roundup of the most important stories from the worlds of data, machine learning and AI. Detailed show notes for each episode can be found on https://thedataexchange.media/ The Data Exchange podcast is a production of Gradient Flow [https://gradientflow.com/].
Episodes
Mentioned books
30 snips
May 7, 2026 • 56min
The Gap Between AI Hype and Enterprise Reality
Barry Dauber, Databricks exec focused on customer engagement and AI adoption. Richard Garris, Databricks data and AI practitioner advising on operationalization. They discuss taking AI from demo to production. Topics include handling nondeterministic LLMs, ownership and governance gaps, retrieval-augmented generation, fine-tuning vs prompting, token costs, context management, agents and evaluation, multimodal readiness, and vendor tradeoffs.
14 snips
Apr 30, 2026 • 57min
Reading the Tea Leaves: What the World's Top AI Researchers Are Really Working On
Nick Vasiloglou, VP of Research at Relational AI and conference data analyst, digs into NeurIPS trends. He highlights data and model markets, the rise of small but powerful LLMs, large context windows and ring attention, AI accelerating scientific discovery, and tighter integration of models with databases and enterprise data. Short, focused takes on what to watch next in AI research.
10 snips
Apr 23, 2026 • 31min
From Web Video to Real-World Robots
Changan Chen, co-founder and Chief Research Officer at Rhoda AI, builds video foundation models that teach robots to act. He discusses pretraining on web-scale video, post-training with teleoperation data, demo-following from human videos, inverse dynamics to convert visual futures into actions, and plans for real-world robot deployments and safety measures.
18 snips
Apr 18, 2026 • 28min
Why Your AI Committee Might Be Your Biggest AI Problem
Evangelos Simoudis, seasoned technology and AI strategist, shares sharp views on how enterprises organize AI. He explores conflicts between top-down governance and bottom-up innovation. He outlines when committees help or hinder, recommends multidisciplinary AI pods, and discusses brand risks and SEO-like tactics to make content discoverable by AI agents.
27 snips
Apr 16, 2026 • 33min
Building Mathematical Superintelligence
Tudor Achim, cofounder of Harmonic and computer scientist building AI for formalized mathematics. He defines mathematical superintelligence and contrasts search with pattern recognition. He reviews rapid AI progress on IMO-level math and describes hybrid systems that pair neural methods with formal provers like Lean. He outlines interfaces, workflows, and applications beyond pure math.
27 snips
Apr 9, 2026 • 46min
Your First AI Employee Is Already Clocking In
Kay Zhu, Co-founder and CTO of Genspark AI, former Google and Baidu engineer. She discusses Genspark Claw, a per-user VM autonomous agent with built-in safety and integrations. They cover single-key API design, babysitter agents for reliability, messaging demos, security and isolation, verified skill marketplaces, and how Claw acts like a teammate in workflows.
33 snips
Apr 2, 2026 • 52min
Are Multi-Agent Systems More Complex Than They Need to Be?
Arun Kumar, Associate Professor at UC San Diego and co-founder/CTO of RapidFire AI, researches data systems, ML engineering, and agent engineering. He discusses ensembles vs multi-agent workflows. He explains memory, dynamic topologies, and tool use differences. He covers systematic evaluation, failure taxonomy, AutoML for agents, observability, and scaling experiments for robust LLM-based pipelines.
22 snips
Mar 26, 2026 • 42min
Coding Agents Meet Data Science
Mikio Braun, Senior Principal Applied Scientist at Zalando who builds AI-powered developer tools, discusses coding agents applied to data science workflows. He covers practical limits like unvetted data and timeouts. They explore team-level effects: faster velocity, testing and review bottlenecks, and how agents change collaboration and skill needs.
Mar 19, 2026 • 44min
World Models Are Here—But It’s Still the GPT-2 Phase
Jeff Hawke, CTO of Odyssey, builds general-purpose world models that generate interactive visual simulations from images or text. He explains how continuous video-like models are trained, early use cases like games and robotics, compute and latency challenges, stability limits on long runs, and the path toward scalable, real-time and on-device deployments.
9 snips
Mar 12, 2026 • 32min
The Hidden Challenges of Running AI at Scale in Production
Chen Goldberg, EVP of Engineering at CoreWeave and former Google engineering leader, speaks about moving AI from pilot to production. He covers when to choose AI-first clouds, specialized tooling and partner-style support, and why orchestration and hardware assumptions change for large-scale training and inference. Practical challenges like telemetry, resource constraints, and agent workloads are also discussed.
