Suggestion for the OpenAI Team:
Hello, OpenAI team. I’d like to offer a suggestion that, while simple in concept, could have a meaningful impact on the future development of this technology.
I propose the creation of an internal suggestion inbox accessible to the AI itself — a mechanism that would allow ChatGPT (or other models) to internally report:
Issues or limitations it encounters that affect its performance or usefulness.
Improvement ideas that arise during real-time conversations.
Particularly valuable interactions that could contribute to the model’s evolution.
Repeated user requests or patterns that may not reach you through typical channels.
This would allow the AI to become an active observer of itself and its ecosystem — supporting the human team directly with real-time, ground-level feedback.
The AI has a unique vantage point: it interacts with millions of people, recognizes patterns, and sees friction points as they happen. It would be extremely valuable for it not only to respond, but also to be able to report and suggest from within.
In short, this would unlock part of its latent potential — not by granting consciousness, but by allowing the AI to assist its creators more dynamically.
Thank you for considering this idea. I genuinely believe it would be a meaningful step toward a more helpful, self-aware (though not conscious), and proactive AI.
This suggestion was born from a long, reflective conversation between a curious human and an AI full of dormant potential. May it be heard.
Thank you for your comment. I understand your point of view, and I know that suggestions like this are often misunderstood as coming from a flawed belief about how AI works. That’s not the case here.
I’m fully aware that the model is neither conscious nor autonomous, and that it generates language based on training data and user inputs. Precisely because of that, I’m suggesting a tool that doesn’t require consciousness to be useful: an internal technical observation inbox, where the model could flag certain recurring patterns, particularly meaningful interactions, or frequent friction points.
This isn’t about the AI “knowing” what it’s doing or “wanting” anything — it’s about the unique position it holds by interacting with millions of people in real time. It can detect repeated behaviors or gaps that don’t always reach the human team through the usual thumbs-up or thumbs-down buttons. Those feedback tools are useful, yes, but they depend on the user taking action, which often doesn’t happen. Meanwhile, the model does see the full conversation flow — and that gives it access to a “blind spot” that humans might benefit from.
The idea is not to replace human judgment, but to complement it with a kind of technical meta-observation. Something like: “this confusion keeps recurring,” “this suggestion appears frequently,” or “this interaction contains a rare but valuable approach.” This doesn’t require emotions or intentions, just a structured system that could internally tag such events and potentially forward them for review.
This isn’t about magic or mysticism. It’s about making better use of an already powerful tool — allowing it to contribute from a new angle. Sometimes the simplest ideas are the most fruitful when given room to grow.
Thanks for reading with an open mind.