If you can’t maintain 4o stable for paid users, then why not fix GPT5

Addressing the Stability and Development of AI Models: A Call for Consistency and Improvement

In the rapidly evolving landscape of artificial intelligence, user experience and system stability are paramount. Recently, concerns have been raised regarding the inconsistency of AI model performance, particularly with GPT-4. If maintaining a stable, reliable version of GPT-4 for paid users proves challenging, it prompts an important question: why not redirect focus towards enhancing GPT-5 to prevent similar issues across both free and paid platforms?

This discussion is not about dismissing critiques with derogatory remarks; instead, it emphasizes constructive dialogue around the technical and ethical responsibilities of AI developers. Transparency about the challenges in maintaining stable AI systems is crucial to advancing both user trust and technological development.

Stakeholders often speculate about the core reasons behind performance issues—are they primarily due to costs, business interests, or safety considerations? For instance, safety measures aimed at protecting minors might restrict certain functionalities, but should these limitations come at the expense of user experience for paying customers? Striking a balance between safety, usability, and innovation is essential.

The anticipation surrounding GPT-5 has been high, fueled by marketing promises of a superior model. However, initial impressions suggest that the release has not met expectations, with some users perceiving it as a downgrade compared to GPT-4. Such discrepancies can erode user trust and highlight the importance of delivering on promises through rigorous testing and transparent communication.

Moreover, the evolution of AI models is often intertwined with external factors such as legal challenges and societal concerns. For example, some developers and companies have faced lawsuits related to AI deployment, which can impact the models’ development and deployment strategies, including modifications or restrictions that inadvertently affect user experience.

In conclusion, for companies like OpenAI, maintaining high-quality, reliable AI systems should be a continuous priority. Addressing stability issues proactively and ensuring that upcoming models like GPT-5 improve upon their predecessors can help rebuild user confidence and foster responsible innovation. It is essential to focus on creating AI that is not only powerful but also consistent, safe, and aligned with user expectations.