[theptip]

Agreed, all of the staff/principal engineers I work with are ~99%+ AI generated code, and increasing business value delivered as a result. This is on planet scale infra not CRUD apps. (And yes, you do need to carefully review the output and give steers/corrections. It’s still faster.)

At this point if you can’t get the agent to write good code then either I) you are in a very specific niche (like Karpathy trying to write NanoGPT) that is extremely out-of-distribution, or II) skill issue, you need to learn how to prompt better.

It’s fine to have a skill gap! Just don’t delude yourself that the tools are bad and everyone claiming they are good is wrong.

[dbingham]

> Agreed, all of the staff/principal engineers I work with are ~99%+ AI generated code, and increasing business value delivered as a result. This is on planet scale infra not CRUD apps.

This has been the pro-"write with LLMs" argument the whole time. "If you're not getting good results, you're not prompting well. FAANG are doing x% of their development with AI."

Yet all of the companies that I can think of that build on planet scale infra and have gone all in on LLM development have seen a marked drop in quality and stability since adopting LLM driven development.

So yeah, I don't buy it. When I hear that engineers are writing their with LLMs and I see quality improve or stabilize, then maybe I will start to question whether its a prompting issue.

[piker]

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.

[theptip]

Go read Karpathy on this, his telling will be more nuanced than mine for the specific example. But the whole point of NanoGPT is that it’s distilling all of his wisdom and taste into a very dense and maximally educational version that fits on a single screen. It’s simply not something that has been done before in the corpus.

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.

https://karpathy.github.io/2026/02/12/microgpt/

[potatolicious]

A lot of stuff that underpins planet-scale infra is open source and has been for a long time. The general approaches and architectures for it have also been widely discussed and litigated in the commons so it's not just the code - the specific why is also very well documented.

So despite its importance much of it is actually pretty in-distribution.

[piker]

Okay, but then that does not square with calling a GPT implementation out-of-distribution, let alone "extremely" so.

[tripleee]

it's really cool how in the past measuring developer productivity was incredibly difficult, but now its become easy if someone's making a point about LLMs

[bigstrat2003]

No, the tools really are bad, and everyone claiming they are good really is wrong. Or deliberately lying.

[gamblor956]

They're both wrong and deliberately lying.

[disgruntledphd2]

LLm generation of code is an absurdly high dimensional process, and incredibly sensitive to the starting conditions and any other interactions so in some sense it's expected that there will be wildly different outcomes.

Personally, I'm more on the LLM code is pretty bad from a design point of view, particularly when much of the code is already LLM generated.

[oasisaimlessly]

btw, your last paragraph makes you sound like an ass.