According to God of Prompt on Twitter, lowering the model temperature parameter from 0.7 to 0.3 when evaluating with TruthfulQA significantly increases the 'truthful' answer score by 17%, not by improving actual accuracy, but by making models respond more cautiously and hedge with phrases like 'I don't know' (source: twitter.com/godofprompt/status/2011366460321657230). This exposes a key limitation in the TruthfulQA benchmark, as it primarily measures the conservativeness of AI responses rather than genuine accuracy, impacting how AI performance and business trustworthiness are assessed in real-world applications.

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, leaked peer review emails highlight a growing divide in AI safety research, where reviewers prioritize standard benchmarks like TruthfulQA, while some authors focus on real-world harm reduction metrics instead. The emails expose that reviewers often require improvements on recognized benchmarks to recommend publication, potentially sidelining innovative approaches that may not align with traditional metrics. This situation underscores a practical business challenge: AI developers seeking to commercialize safety solutions may face barriers if their results do not show gains on widely-accepted academic benchmarks, even if their methods prove effective in real-world applications (source: God of Prompt on Twitter, Jan 14, 2026).

According to God of Prompt (@godofprompt), a review of 2,847 AI research papers reveals a concerning trend: most efforts are focused on optimizing models for performance on six standardized benchmarks, such as TruthfulQA, rather than addressing critical real-world safety issues. While advanced techniques have improved benchmark scores, there remain significant gaps in tackling model deception, goal misalignment, specification gaming, and harms from real-world deployment. This highlights an industry-wide shift where benchmark optimization has become an end rather than a means to ensure AI safety, raising urgent questions about the practical impact and business value of current AI safety research (source: Twitter @godofprompt, Jan 14, 2026).