According to @godofprompt, an analysis of 2,847 AI safety papers from 2020 to 2024 revealed that 94% of these studies rely on the same six benchmarks for evaluation. Critically, the source demonstrates that simply altering one line of code can achieve state-of-the-art results across all benchmarks without any real improvement in AI safety. This exposes a major methodological flaw in academic AI research, where benchmark optimization (systematic p-hacking) undermines true safety progress. For AI industry stakeholders, the findings highlight urgent business opportunities for developing robust, diverse, and meaningful AI safety evaluation methods, moving beyond superficial benchmark performance. (Source: @godofprompt, Twitter, Jan 14, 2026)