According to @godofprompt, innovative AI safety approaches often fail to get published because there are no established benchmarks to evaluate their effectiveness. For example, when researchers propose new ways to measure real-world AI harm, peer reviewers typically demand results on standard tests like TruthfulQA, even if those benchmarks are not relevant to the new approach. As a result, research that does not align with existing quantitative comparisons is frequently rejected, leading to slow progress and a field stuck in a local optimum (source: @godofprompt, Jan 14, 2026). This highlights a critical business opportunity for developing new, widely accepted AI safety benchmarks, which could unlock innovation and drive industry adoption.

According to God of Prompt, the AI industry requires institutional reform at three levels to address real safety concerns and prevent the gaming of benchmarks: publishing should accept novel metrics without benchmark comparison, funding agencies should reserve 30% of resources for research creating new evaluation methods, and peer reviewers must be trained to assess work without relying on standard baselines (source: God of Prompt, Jan 14, 2026). This approach could drive practical improvements in AI safety evaluation, open new business opportunities in developing innovative metrics tools, and encourage a broader range of AI risk assessment solutions.