About this episode

Scott Hanselman interviews Chris Lattner about designing the Mojo programming language for AI applications.

What does it take to design a programming language from scratch when the target isn’t just CPUs, but GPUs, accelerators, and the entire AI stack? In this episode, I sit down with legendary language architect Chris Lattner to talk about Mojo — his ambitious attempt to rethink systems programming for the machine learning era. We trace the arc from LLVM and Clang to Swift and now Mojo, unpacking the lessons Chris has carried forward into this new language. Mojo aims to combine Python’s ergonomics with C-level performance, but the real story is deeper: memory ownership, heterogeneous compute, compile-time metaprogramming, and giving developers precise control over how AI workloads hit silicon. Chris shares the motivation behind Modular, why today’s AI infrastructure demands new abstractions, and how Mojo fits into a rapidly evolving ecosystem of ML frameworks and hardware backends. We also dig into developer experience, safety vs performance tradeoffs, and what it means to build a language that spans research notebooks all the way down to kernel-level execution.

People in this episode

Host: Scott Hanselman

Guest: Chris Lattner

Topics covered

• programming languages
• AI infrastructure
• machine learning
• developer experience
• performance optimization
• metaprogramming

Keywords

• Mojo
• programming language
• AI
• LLVM
• Clang
• Swift
• metaprogramming
• performance
• developer experience

Mentioned in this episode

Organizations: LLVM, Clang, Swift, Modular

Products: Mojo

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