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.