
Show overview
Training Data has been publishing since 2024, and across the 2 years since has built a catalogue of 99 episodes, alongside 4 trailers or bonus episodes. That works out to roughly 75 hours of audio in total. Releases follow a weekly cadence.
Episodes typically run thirty-five to sixty minutes — most land between 39 min and 53 min — and the run-time is fairly consistent across the catalogue. It is catalogued as a EN-language Technology show.
The show is actively publishing — the most recent episode landed 1 months ago, with 24 episodes already out so far this year. The busiest year was 2025, with 49 episodes published. Published by Sequoia Capital.
From the publisher
Join us as we train our neural nets on the theme of the century: AI. Sonya Huang, Pat Grady and more Sequoia Capital partners host conversations with leading AI builders and researchers to ask critical questions and develop a deeper understanding of the evolving technologies—and their implications for technology, business and society. The content of this podcast does not constitute investment advice, an offer to provide investment advisory services, or an offer to sell or solicitation of an offer to buy an interest in any investment fund.
Latest Episodes
View all 99 episodesMemory and Continual Learning: Engram's Dan Biderman and Jessy Lin
Simulating Humans at Scale: Simile's Joon Sung Park
Google DeepMind's Logan Kilpatrick: Why the Model Eats the Harness
LIVE: Jensen Huang on Building the Dynamo of the Intelligence Age
Knowing What Your Customers Want, All the Time: Listen Labs' Alfred Wahlforss
How Cursor Trained Composer on Fireworks: Distributed Infrastructure for High-Performance RL
Rebuilding IT From the Ground Up for the AI Age: Serval's Jake Stauch
Suno's Mikey Shulman: Everyone Can Make Music Now
ElevenLabs' Mati Staniszewski: How Voice Becomes the Interface for Everything
Anthropic's Boris Cherny: Coding's Printing Press Moment
Waymo's Dmitri Dolgov: 20 Million Rides and the Road to Full Autonomy
OpenAI's Greg Brockman: Why Human Attention Is the New Bottleneck
Demis Hassabis on Building DeepMind, AlphaFold, and the Final Stretch to AGI
Andrej Karpathy: From Vibe Coding to Agentic Engineering
From SEO to Agent-Led Growth: Profound’s James Cadwallader

How Autonomous Labs Will Transform Scientific Research: Ginkgo Bioworks’ Jason Kelly
Jason Kelly founded Ginkgo Bioworks in 2008 with a simple but radical idea: DNA is code, and cells are programmable. Sixteen years later, AI is finally making that vision real in ways that could reshape science itself. Jason describes a landmark collaboration with OpenAI in which a reasoning model with access to a robotic lab beat the state of the art in biochemistry by 40% - not by being smarter than scientists, but by running experiments 24 hours a day and sharing data across a hundred parallel hypotheses simultaneously. He argues that the biggest inefficiency in science isn't intelligence, it's manual labor. Once AI helps scale research, the cost of discovery collapses and breakthroughs follow, with profound implications for biopharma, national competitiveness, and human health. Hosted by Sonya Huang and Pat Grady, Sequoia Capital

Greetings, Earthlings: Philip Johnston of Starcloud on Data Centers in Space
Philip Johnston, founder and CEO of Starcloud, explains why space will become the primary location for AI compute infrastructure within the next decade. After witnessing SpaceX's massive manufacturing scale at Starbase, Philip realized that declining launch costs would make space-based data centers cheaper than terrestrial ones. He breaks down the physics of heat dissipation in vacuum, the economics of solar power without atmosphere, and why the marginal cost of space infrastructure decreases while Earth-based costs increase. Philip previews a future where close to a trillion dollars per year in CapEx flows to space compute. And, yes, we get his take on aliens. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital.

Physics Gets a Vote: Nominal Cofounders on Hardware Development in an AI World
Nominal’s cofounders (Cameron McCord, Jason Hoch and Bryce Strauss) realized that the new age of reindustrialization requires a new approach to hardware engineering and testing that’s closer to how software is developed. They founded Nominal with the insight that while SpaceX, Tesla, and Anduril built proprietary internal platforms for hardware testing, the thousands of new hardware entrants can't afford to replicate that work. Nominal serves as the system of record for hardware testing, helping companies move from PDF-based workflows to modern data infrastructure that catalogs telemetry from sensors producing millions of data points per second. The platform enables engineers to author validation logic that follows hardware systems from initial testing through manufacturing and field deployment. We discuss their belief that all hardware companies will become physical AI companies, and why they think Nominal's role as the verification layer will be critical - because unlike a video game, physical products require rigorous validation before they enter the real world. Hosted by: Alfred Lin and Sonya Huang, Sequoia Capital

Building the GitHub for RL Environments: Prime Intellect's Will Brown & Johannes Hagemann
Will Brown and Johannes Hagemann of Prime Intellect discuss the shift from static prompting to "environment-based" AI development, and their Environments Hub, a platform designed to democratize frontier-level training. The conversation highlights a major shift: AI progress is moving toward Recursive Language Models that manage their own context and agentic RL that scales through trial and error. Will and Johannes describe their vision for the future in which every company will become an AI research lab. By leveraging institutional knowledge as training data, businesses can build models with decades of experience that far outperform generic, off-the-shelf systems.Hosted by Sonya Huang, Sequoia Capital

What’s the Future of Vertical SaaS in an AGI World? Jamie Cuffe, CEO of Pace
Jamie Cuffe is solving one of AI's hardest problems: getting conservative, regulated industries to trust autonomous agents with mission-critical work. At Pace, he's building AI that replaces traditional BPOs in insurance, handling everything from email triage to claims processing with 50-75% cost savings. Drawing on his experience at Retool, Jamie emphasizes the importance of "closing the distance" with customers through forward-deployed engineering and being "the rock" that clients can rely on. He shares how focusing on top-tier insurance carriers and maintaining exceptionally high standards is enabling Pace to capture a meaningful share of the $400 billion BPO market while building a durable business model - at AI-native velocity. Hosted by Lauren Reeder and Pat Grady, Sequoia Capital