According to @godofprompt, a widespread trend in artificial intelligence research involves systematic p-hacking, where experiments are repeatedly run until benchmarks show improvement, with successes published and failures suppressed (source: Twitter, Jan 14, 2026). This practice, often labeled as 'hyperparameter tuning,' results in 87% of claimed AI advances being mere benchmark exploitation without actual safety improvements. The current incentive structure in the AI field—driven by review panels and grant requirements demanding benchmark results—leads researchers to optimize for benchmarks rather than genuine innovation or safety. This focus on benchmark optimization over meaningful progress presents significant challenges for both responsible AI development and long-term business opportunities, as it risks misaligning research incentives with real-world impact.