According to God of Prompt (@godofprompt), the AI research industry faces a systematic problem of benchmark overfitting, with 94% of studies testing on the same six benchmarks. Analysis of code repositories shows that researchers often run over 40 configurations, publish only the configuration with the highest benchmark score, and fail to disclose unsuccessful runs. This practice, referred to as p-hacking, is normalized as 'tuning' and raises concerns about the real-world reliability, safety, and generalizability of AI models. The trend highlights an urgent business opportunity for developing more robust, diverse, and transparent AI evaluation methods that can improve model safety and trustworthiness in enterprise and consumer applications (Source: @godofprompt, Jan 14, 2026).