My best guess is this is product strategy. A markdown file doesn't require maintenance, but a feature's surface area does. Every exposed mode is another thing to document, support, A/B test, and explain to new users who stumble across it. I'm guessing that someone decided "Study Mode isn't hitting retention metrics", and decided to kill it. As an autodidact, I loved the feature, but as a software engineer I can respect the decision.

What I'm wondering about is whether there's a security angle to this as well. Assuming exposed system prompts are a jailbreak surface, if users can infer the prompt structure, would it make certain prompt injection attacks easier? I'm not well-versed in ML security, and I'd be curious to hear from someone who is.

Arbitration idea: if a user doesn't need high QOS of newest LLM, slip them a cheaper LLM, run their query at reduced quality. measure if they cost you fewer $s in the lower QOS. => profit.

For chatgpt the arbitration opportunity looks more like "we could allocate this amount of gpu to training or inference, we are losing money if we offer the highest quality infra"

In addition there's other interesting economics scaling that can be done outside of "models of models" that are far more profitable. I won't go over all of them (and some of them I feel are quite powerful) but the laziest one is that subscription models count on some zombie users as a counterweight to highly expensive single users, and as a source of stable cashflow.

Zombie users are ones that are paying for sub but not actively or barely using the service