Ah, a touch of sensationalism in this article, I see, reminiscent of the heyday of yellow journalism! First, dear scribes, let's not fall into the pitfall of "correlation implies causation," a basic statistical misstep I would hope researchers of your caliber would artfully dodge. It's hasty to assume a decrease in performance without a thorough understanding of the nuances of AI models, particularly ones as complex as GPT-3.5 and GPT-4.

Let's take the prime number example, shall we? Yes, I concede that a drop from 97.6% to 2.4% seems drastic, but isn't it also rather reductive to judge the model's intelligence based on one specific, numerical task? After all, language models are trained on diverse, large-scale textual data. The very essence of their raison d'être is language, not prime number identification.

Concerning the mention of formatting mistakes in code generation, let's not forget that AI models, much like my most promising students, learn and adapt based on the data they are given. If the algorithm has been presented with erroneous or inconsistent code formats, one could expect such a hiccup. It's akin to blaming a student for mistaking 'colour' for 'color' when they've been presented with both spellings in the past. The nuances are as intricate as the distinction between a semicolon and a colon!

The interpretation of user anecdotes is another matter entirely. User perception can easily be influenced by a multitude of factors including, but not limited to, the user's own increasing familiarity with the system, rising expectations, or even simple cognitive bias. Isn't it rather telling that an academic from my beloved Oxford might critique a Shakespearean sonnet with far more vigour after studying it for an extended period, compared to an initial reading?

OpenAI's VP of Product, Peter Welinder, makes a compelling point. It could indeed be the case that "when you use [ChatGPT] more heavily, you start noticing issues you didn't see before". I must say, the tendency to blame the tool instead of examining our own approach is as old as time.

Lastly, suggesting that the model's updates are causing harm, based on a non-peer-reviewed study and user anecdotes, seems a bit like concluding a play is terrible based on an intermission discussion in the lobby. Let's not be hasty. I'm not saying the models are without flaws. But to determine causality and effectiveness, we need a comprehensive study, much like understanding the complete narrative arc of a novel.

I might not be an AI expert, but I do understand the importance of thorough critique and avoiding hasty judgments. Perhaps the authors of this piece should reacquaint themselves with the concept. But then again, where would the drama be in that?