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Jan Stojka

Showing 1–3 of 3 transcripts.

  1. Sequoia Capital1h 0m

    Turning Academic Open Source into Startup Success ft Databricks Founder Ion Stoica

    Ion Stoica, Stephanie Zhan, Sonya Huang, Jan Stojka, Matej

    Databricks addresses the critical gap between AI experimentation and production by promoting Compound AI Systems and launching the open-weight Dbricks model to ensure enterprise data privacy and compliance. Under founder Jan Stojka's guidance, the company leverages strategic partnerships with rivals like Microsoft while prioritizing robust data infrastructure over base model capabilities to drive accuracy and security. Looking ahead, the organization forecasts a shift toward commoditized inference costs and autonomous agents, urging founders to focus on verifiable, production-ready solutions rather than technical demonstrations.

  2. a16z10 min

    a16z Podcast | On Data and Data Scientists in the Age of AI

    Ion Stoica, Scott Clark, Frank Chen, Jan Stojka

    Enterprises successfully operationalize AI by progressing through a data foundation, operationalization, and integration phase while avoiding pitfalls like data integrity issues and contextual misalignment. Strategic success relies on an "all-in" approach that prioritizes portfolio hedging, shares artifacts for productivity, and leverages modern tooling to reduce time-to-market by an order of magnitude. As infrastructure barriers vanish, the data science role shifts from algorithm construction to defining business context, ensuring that commoditized tools optimize verified objectives rather than merely accelerating incorrect outcomes.

  3. a16z29 min

    a16z Podcast | A New Lab Rises

    Sonal, Peter Levine, Jan Stojka

    The RISE Lab at Berkeley succeeds its AMP Lab by shifting focus from big data analytics to real-time, intelligent, and secure decision-making, with key projects including the Ray framework, Clipper model serving platform, and Opaque security system. This evolution is driven by a culture of open-source innovation where PhD students intern at major tech firms to align academic research with production-scale challenges. By prioritizing robustness, explainability, and the cloud-edge continuum, the lab collaborates with industry partners like Microsoft and Google to transform open-source software into a mainstream engine for AI development.