Are OpenAI and Google intentionally downgrading their models?

The text examines the phenomenon of "LLM drift," where large language models (LLMs) like GPT and Gemini exhibit changes in behavior without explicit version updates, leading to unexpected performance degradation and inconsistencies in outputs. Researchers have observed significant drops in accuracy and reliability in models like GPT-4, raising questions about whether these changes are due to shifts in user interactions or silent updates from developers. Both OpenAI and Google have faced criticism from developers for unannounced changes that affect the stability of software that relies on their models. Despite claims of continuous improvement, the lack of transparency and communication about these updates has led to a loss of trust among users. Studies show that while some capabilities remain stable, others degrade over time, often related to task complexity and context requirements. The industry's current lack of formal obligations or accountability means developers are left without reliable methods to track or verify when and why these changes occur, highlighting a need for policy frameworks to ensure model consistency and transparency.

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