When AI can generate and optimize code at scale, does that make software greener — or just faster to get wrong - or does it cost more to train and run those AI models then the savings we have optimizing our own software? Anne Currie, co-author of O'Reilly's Building Green Software, joins us to untangle the paradox. US data centers are on track to consume over 10% of the national grid by 2030 with a growing part of that energy going to AI workloads, yet it may also be our best tool for writing more efficient software — if the training data is any good (spoiler: for C, it often isn't; for Rust, it's a different story).
In our episode Brian, Andi and Anne get into the fundamentals that most teams are skipping: operational efficiency. Turning off systems you don't need, rightsizing what you do — these unglamorous moves can slash your hosting bill in half, and they're the prerequisite for any code-level green gains to actually matter. Plus: graceful shutdowns, grayouts, and why we'll probably need AI to eventually rewrite itself.
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