The Wonder-Driven Builder — with Paige Bailey
Paige Bailey is a developer relations engineering lead at Google DeepMind, a geophysicist-turned-AI-engineer who was once told by her professors that building open-source libraries was a waste of time. We talk about her path from planetary science to TensorFlow, why statisticians have a hidden edge in the age of AI, and what it means to be a curious generalist when the cost of building software is approaching zero. Bonus: installing solar-powered silent-film birdhouses as street art in San Francisco.
Episode notes
Paige Bailey (Google DeepMind) takes us from her rescued Apple II to GPU-accelerated geophysics simulations to the quiet thesis running through all of it (that the most interesting things happen at the intersections). We chat about why statisticians’ tolerance for uncertainty might be the most underrated AI skill, and Paige makes the case for getting roasted for your own music taste.
- From planetary science to TensorFlow, before it was GPU-capable
- Geophysicists as early GPU adopters
- The professors who said open-source wasn’t “real science”
- Building silent-film birdhouses as San Francisco street art
- Hiding Gemini API tests inside whimsical side projects
- The right-tool-for-the-job case for mixing AI models
- Why “taste” is the skill that matters when code costs nothing
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