This is a new talk of mine going over how platform engineers can support AI. Well, it’s more about how we don’t exactly know, but we can speculate based on a handful of early use cases. Here’s the slides if you’re into that kind of thing.

Here’s the 🤖 on my key points:

• Platform engineering for AI is mostly running another middleware service - same infrastructure tasks as always, plus model registries and figuring out who handles AI safety evaluations.
• Put a gateway/proxy in the middle immediately - the eternal computing lesson we relearn every five years: never let developers talk directly to services or you’re stuck sending angry emails.
• Three new types of customers for platform teams - developers who want AI coding help, “normals” using internal ChatGPT for business tasks, and the mythical “AI embedded in applications” unicorn nobody can actually find.
• Platform-as-a-product thinking applies here too - make something developers actually want to use, get lawyers and security on your approval board to avoid the old “center of killing dreams” problem.
• Experiment rapidly when you don’t know what you’re doing - which is right now for everyone, so build fast feedback cycles rather than one perfect thing that takes forever and turns out wrong.

Check it out, and tell me if you have any things you’ve learned, heard, done’d, etc. on the topic.