How is a GPT implementation “extremely out of distribution” but “planet scale infra” isn’t? That’s got me totally confused about your point that I was taking seriously.
Planet scale infra often requires some novel ideas at the architectural layer (the engineers can input those) and then it’s mostly in-distribution C++ / Rust / Go; most of the hyperscalers open-source their stacks, for one. But for two, a ring buffer, look-aside cache, deterministic hash, b-tree, lsm-tree, etc. are all well-known patterns.
Hyperscaling is also often a very clear objective function; i need this code path to run in this many microseconds/nanoseconds, so i can hit the scale numbers I need. Claude/Sol can extract prod logs and build a representative micro-benchmark, and then hill-climb on it autonomously. Thats straight up the fairway for the training set, even if it often has a high bar for finishing and requires sophisticated Workflows or lots of tokens to explore the search space.
E2A: sorry sorry, typo, I meant microGPT. I can see why this would be confusing.
So despite its importance much of it is actually pretty in-distribution.