Transition to AI: learning from Tesla and Google

Chris recounts his shift to AI work, learning frameworks like Caffe and TensorFlow, and scaling software for TPUs at Google.

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Chris Lattner is one of the most influential engineers of the past two decades. He created the LLVM compiler infrastructure and the Swift programming language – and Swift opened iOS development to a broader group of engineers. With Mojo, he’s now aiming to do the same for AI, by lowering the barrier to programming AI applications.

I sat down with Chris in San Francisco, to talk language design, lessons on designing Swift and Mojo, and – of course! – compilers. It’s hard to find someone who is as enthusiastic and knowledgeable about compilers as Chris is!

We also discussed why experts often resist change even when current tools slow them down, what he learned about AI and hardware from his time across both large and small engineering teams, and why compiler engineering remains one of the best ways to understand how software really works.

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Timestamps

(00:00) Intro

(02:35) Compilers in the early 2000s

(04:48) Why Chris built LLVM

(08:24) GCC vs. LLVM

(09:47) LLVM at Apple

(19:25) How Chris got support to go open source at Apple

(20:28) The story of Swift

(24:32) The process for designing a language

(31:00) Learnings from launching Swift

(35:48) Swift Playgrounds: making coding accessible