I think their principal mistake was in conflating the introduction of GPT-5 with the model-selection heuristics they started using at the same time. Whatever empirical hacks they came up with to determine how much thinking should be applied to a given prompt are not working well. Then there's the immediate-but-not-really deprecation of the other models. It should have been very clear that the image-based tests that the CNN reporter referred to were not running on GPT-5 at all. But it wasn't, and that's a big marketing communications failure on OpenAI's part.
One of several, for anyone who sat through their presentation.
I done a couple of experiments now and I can get an LLM to make not horrible and mostly functional code with effort. (I’ve been trying to create algorithms from CS papers that don’t link to code) I’ve observed once you discover the magic words the LLM wants and give sufficient background in the history, it can do ok.
But, for me anyway, the process of uncovering the magic words is slower than just writing the code myself. Although that could be because I’m targeting toy examples that aren’t very large code bases and aren’t what is in the typical internet coding demo.