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TWIML AI Podcast

Sam Charrington

Long-running interview show with ML researchers and practitioners on cutting-edge techniques — the research pipeline behind the compute and infrastructure buildout the space industry is starting to compete for.

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E780Your Ranking Model Is Optimizing the Wrong Thing with Zoe Weil - #780In this episode, Zoe Weil, co-founder and CEO of Sequen, joins us to discuss how ranking systems can use live behavioral signals to optimize for purchases rather than clicks or search relevance alone. Zoe explains why approaches developed aOct 15 · 53 min
E779Why Jev Is Changing How We Build With AI with Diogo Almeida - #779In this episode, Diogo Almeida, co-founder and CEO of TypeSafe, joins us to discuss Jev, TypeSafe’s recently released model for bringing fast, reliable intelligence directly into software. We explore the idea of “machine-native intelligenceOct 6 · 1h 30m
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Source: Episodes via the show's public RSS feed · as of Oct 16, 2026 20:00 UTC

Showing the 50 most recent episodes. Full archive on the show's site.

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From Math Olympiads to Navier-Stokes: How Fast Is AI Progressing? with Greg Burnham - #778AI systems have gone from struggling with grade-school math to helping solve research problems that have resisted mathematicians for decades, including Navier-Stokes. In this episode, Greg Burnham, who leads AI capabilities research at Epoc

Sep 29 · 1h 8m

E777From Voice Agents to AI Avatars with Alexander Smola - #777Voice AI has gotten remarkably good, but natural conversation remains a high bar. Small delays, awkward interruptions, or the wrong tone can quickly break the illusion—and adding vision and visual presence only raises the stakes. In this epSep 17 · 1h 5m

E776Do AI Tokenomics Matter More Than Model Benchmarks? with Chris Potts - #776As reasoning models consume more tokens and AI systems become more expensive to run, understanding what those tokens actually buy is becoming increasingly important. In this episode, Stanford professor and Big Spin co-founder Chris Potts joSep 9 · 59 min

E775World Models and the Future of Spatial AI with Justin Johnson - #775In this episode, Justin Johnson, co-founder of World Labs, joins us to discuss world models and the emerging field of spatial AI. We explore why many researchers see capabilities beyond language as an important frontier for AI, and what it Sep 1 · 1h 6m

E774Why the Next AI Breakthrough May Come from Physics with Max Welling - #774The conventional wisdom in AI is that the next breakthrough will come from more compute, more data, and larger models. But what if the next leap comes from somewhere else? In this episode, Max Welling—co-founder and CTO of CuspAI and profesAug 26 · 58 min

E773Why Image Generation Needs More Than Bigger Models with Fatih Porikli - #773Text-to-image models have become remarkably good at producing realistic images. But realism isn’t the same as correctness. Ask for several distinct people, a specific composition, or a high-resolution image generated locally, and today’s moAug 12 · 57 min

E772Why Models Are AI’s Next Training Dataset with Damian Borth - #772For more than a decade, AI has advanced by training ever-larger models on ever-larger datasets. But as high-quality training data becomes harder to find and pretraining grows increasingly expensive, researchers are looking for new ways to kJul 27 · 47 min

E771How AI Learns to Smell with Alex Wiltschko - #771In this episode, Alex Wiltschko, founder and CEO of Osmo, joins the show to discuss his goal of giving computers a sense of smell and what it takes to build olfactory intelligence. We explore the science behind smell, from the hundreds of oJul 8 · 60 min

E770Why AI Agents Break the GenAI Security Model with Devvret Rishi - #770In this episode, Sam talks with Dev Rishi, GM of AI at Rubrik, about what happens when agents move beyond answering questions and start taking action across tools, systems, and business processes. We explore why the enterprise playbook of sJun 16 · 56 min

E769Is RAG Dead? Lessons from Building AI for Tax Law with Alex Bowcut - #769As context windows grow into the millions of tokens, many AI practitioners are questioning whether retrieval-augmented generation (RAG) is still necessary. If modern models can ingest entire libraries of documents, why bother with retrievalJun 9 · 52 min

E768Relational Foundation Models for Enterprise Data with Jure Leskovec - #768In this episode, Jure Leskovec, co-founder and chief scientist at Kumo and professor of computer science at Stanford, joins us to explore two fronts of his work: AI for science and relational deep learning. We begin with AI Virtual Cell, a May 21 · 1h 6m

E767How to Find the Agent Failures Your Evals Miss with Scott Clark - #767In this episode, Scott Clark, co-founder and CEO of Distributional, joins us to explore how teams can reliably operate and improve complex LLM systems and agents in production. Scott introduces a Maslow’s hierarchy of observability: telemetMay 7 · 53 min