On the same day I published Part I of this series, OpenAI released GPT-5.3-Codex, a new model that the company claims helped to engineer itself:
The recent rapid Codex improvements build on the fruit of research projects spanning months or years across all of OpenAI. These research projects are being accelerated by Codex, with many researchers and engineers at OpenAI describing their job today as being fundamentally different from what it was just two months ago. Even early versions of GPT‑5.3-Codex demonstrated exceptional capabilities, allowing our team to work with those earlier versions to improve training and support the deployment of later versions.
Codex is useful for a very broad range of tasks, making it difficult to fully enumerate the ways in which it helps our teams. As some examples, the research team used Codex to monitor and debug the training run for this release. It accelerated research beyond debugging infrastructure problems: it helped track patterns throughout the course of training, provided a deep analysis on interaction quality, proposed fixes and built rich applications for human researchers to precisely understand how the model’s behavior differed compared to prior models.
These are the early stages. I expect the scale of automation to have expanded considerably within the coming year.
