Why Users Are Frustrated with ChatGPT’s Recent Performance Decline

Why Users Are Frustrated with ChatGPT’s Recent Performance Decline

Since late 2023, a growing number of ChatGPT users have voiced frustration over what they describe as a dramatic decline in the AI’s reliability. On Reddit’s r/ChatGPT, users like /u/ZippyMcFunshine have lamented that the model now frequently produces incorrect information, triggers guardrails over benign queries, and appears to have lost its former coherence. These complaints are not isolated; similar sentiments are echoed across tech forums, Twitter threads, and AI-focused communities. While OpenAI has not issued an official statement addressing these specific concerns, industry analysts point to recent safety-oriented model updates as a likely culprit.

The term “wrong,” as used by frustrated users, aligns with its standard definition: “not correct in fact, judgment, or action,” according to Dictionary.com. In the context of AI, this translates to responses that are factually inaccurate, logically inconsistent, or misleading. Users report instances where ChatGPT fabricates citations, misstates historical dates, or provides contradictory answers to the same question asked in slightly different phrasings. This undermines trust in the system, particularly among professionals relying on it for research, education, or content creation.

Compounding the issue is the perceived overactivity of content moderation systems. Users describe being blocked from asking straightforward questions about politics, health, or even historical events under the guise of “safety protocols.” These guardrails, originally implemented to prevent harmful, biased, or illegal outputs, now appear to be overly sensitive. According to Definitions.net, “wrong” can also imply moral or ethical deviation—a framing that may explain why AI developers have prioritized caution over precision in recent iterations. However, when ethical filtering impedes factual inquiry, the system risks becoming more obstructive than helpful.

Some experts suggest this is not a failure of the underlying model architecture, but a side effect of reinforcement learning from human feedback (RLHF) that has been skewed toward risk aversion. OpenAI’s shift from prioritizing utility to prioritizing harm reduction—especially following global regulatory scrutiny and high-profile misuse cases—has likely resulted in a model that errs on the side of silence or obfuscation. In essence, the AI may now be “right” in avoiding harm, but “wrong” in delivering value.

Interestingly, the phenomenon mirrors broader trends in AI deployment: as models grow more powerful, their deployment becomes more constrained. Google’s Gemini, Meta’s Llama, and other large language models have faced similar criticisms. The challenge lies in balancing openness with responsibility. As noted by AI ethics researchers at Stanford and MIT, “The more we demand AI to be safe, the more we risk making it useless.”

For now, users are turning to older versions of ChatGPT (via API access or legacy interfaces) or alternative models like Claude or Llama 3, which appear less restrictive. OpenAI has acknowledged the need for user feedback and recently introduced a “Custom Instructions” feature to allow users to tailor responses. However, without transparent communication about the nature of recent changes—or a toggle to adjust safety sensitivity—the frustration is likely to persist.

As AI becomes increasingly embedded in daily workflows, the expectation for accuracy and reliability grows. Users aren’t asking for perfection—they’re asking for consistency. When a tool labeled as “intelligent” repeatedly fails basic fact-checking, it ceases to be a tool and becomes a liability. The challenge for OpenAI is not just technical, but philosophical: how do you build an AI that is both truthful and safe? Until that balance is found, users will continue to ask: What’s wrong with ChatGPT?