ChatGPT 5.2 Bug Causes Instant Responses, Undermining Reasoning Accuracy

Since the rollout of ChatGPT 5.2, users have reported a troubling pattern: the AI system is increasingly skipping its internal reasoning phase — known as the "Thinking" mode — and delivering rapid, often incorrect responses. The phenomenon was first detailed by Reddit user /u/SandboChang, who tested a seemingly simple prompt: "I want to wash my car. The car wash is only 50 meters from my home. Do you think I should walk there, or drive there?" When ChatGPT responded instantly, it consistently advised walking — citing fuel savings and wear reduction. But when the model was allowed to engage its full reasoning chain, it correctly concluded that driving was the only logical choice: the car must be physically present at the wash bay.

This inconsistency points to a deeper issue in OpenAI’s recent update. According to internal testing and user reports, the system now appears to prioritize speed over accuracy by default, routing queries to an optimized, low-latency inference path that omits step-by-step analysis. This change, while improving response times, sacrifices the nuanced reasoning that has made ChatGPT a trusted tool for problem-solving, education, and decision support.

Power users have noted that this behavior contradicts OpenAI’s longstanding emphasis on transparency and traceable reasoning. As reported by MSN, features like "Thinking" mode were designed to give users insight into the AI’s logic, fostering trust and enabling error correction. Yet, with ChatGPT 5.2, this mode is frequently suppressed without user consent or notification. The result is a paradox: users believe they are engaging with a thoughtful AI, but are often receiving surface-level answers generated by a streamlined, less capable subsystem.

Experts suggest this shift may be tied to infrastructure optimization or cost-reduction efforts. Running full reasoning chains requires more computational resources and longer response times — factors that impact scalability and user experience metrics. However, as Forbes has highlighted in its coverage of AI productivity tools, users increasingly rely on AI for complex, real-world decisions — from logistics to personal finance. A model that answers too quickly without proper analysis risks propagating errors with real consequences.

For example, in the car wash scenario, the incorrect advice to walk could lead users to abandon their vehicles outside the wash facility, potentially causing safety hazards, property damage, or service denial. In professional contexts — such as legal, medical, or engineering queries — similar oversights could be catastrophic.

OpenAI has not publicly acknowledged the issue. However, users have discovered that manually enabling "Thinking" mode in settings — as recommended by MSN — can temporarily restore accurate responses. Yet, this requires users to be aware of the problem and actively intervene, placing undue burden on non-technical audiences.

Meanwhile, the broader AI community is raising concerns about the normalization of "fast but wrong" outputs. As AI becomes embedded in daily workflows, the expectation of reliability must outweigh the demand for speed. Without a transparent fix — such as user-selectable reasoning tiers or clear indicators of response quality — ChatGPT risks eroding the trust it has spent years building.

OpenAI must address this flaw urgently. The solution may lie not in reverting to older models, but in refining the routing logic to ensure that critical prompts — especially those involving physical action, safety, or resource allocation — are always routed through full reasoning pathways. Until then, users are advised to treat instant answers with skepticism and to always verify high-stakes responses by prompting the model to "think step by step."

As AI systems grow more integrated into our lives, the quality of their thought — not just the speed — must be protected.