PodcastEconomiaTraining Data

Training Data

Sequoia Capital
Training Data
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84 episodi

  • Training Data

    How Autonomous Labs Will Transform Scientific Research: Ginkgo Bioworks’ Jason Kelly

    24/03/2026 | 58 min
    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
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    Greetings, Earthlings: Philip Johnston of Starcloud on Data Centers in Space

    17/03/2026 | 44 min
    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.
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    Physics Gets a Vote: Nominal Cofounders on Hardware Development in an AI World

    10/03/2026 | 40 min
    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
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    Building the GitHub for RL Environments: Prime Intellect's Will Brown & Johannes Hagemann

    10/02/2026 | 44 min
    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
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    What’s the Future of Vertical SaaS in an AGI World? Jamie Cuffe, CEO of Pace

    03/02/2026 | 51 min
    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

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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.
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