Python’s popularity in data science and backend engineering has made it the default language for building AI infrastructure. However, with the rapid growth of AI applications, developers are increasingly looking for tools that combine Python’s flexibility with the rigor of production-ready systems.

Pydantic began as a library for type-safe data validation in Python and has become one of the language’s most widely adopted projects. More recently, the Pydantic team created Pydantic AI, a type-safe agent framework for building reliable AI systems in Python.

Samuel Colvin is the creator of Pydantic and Pydantic AI. In this episode, he joins the podcast with Gregor Vand to discuss the origins of Pydantic, the design principles behind type safety in AI applications, the evolution of Pydantic AI, the LogFire observability platform, and how open-source sustainability and engineering discipline are shaping the next generation of AI tooling.

Please click here to see the transcript of this episode.

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