I had the exact opposite reaction. I am in no way an AI expert (or novice for that matter), but I generally have an understanding of how tokenization works and how LLMs parse text strings into a series of tokens. Thus, I thought this paragraph was particularly well-written in a manner that explained pretty clearly what happened, but in a manner accessible to a layperson like me:

> In this case, the bug was in the step where the model chooses these numbers. Akin to being lost in translation, the model chose slightly wrong numbers, which produced word sequences that made no sense.

I liked this because when I first saw the example word salads I was so impressed by them - they look to be syntactically correct, but semantically they're gibberish. But knowing the basics of how LLMs choose the next token let me imagine some bugs where the "lookup table" if you will of word-to-token or vice versa (and I realize that may not be exactly the best analogy) was slightly offset.

Further, this sentence, "More technically, inference kernels produced incorrect results when used in certain GPU configurations." led me to understand how this could make it out into production - I'm sure ChatGPT has to be using tons of different GPUs in different configs to handle their volume, so it's understandable to me that their was a new matrix of config settings + code that made this bug show up.

I don't say any of the above to "excuse" OpenAI, but I also don't think they need any "excusing" to begin with. I don't think this was an unforgivable bug, and I appreciate them being so quick to explain what happened.