Researchers have proposed a novel AI scaling law, dubbed 'inference-time search,' which could potentially enhance performance in models like Google’s Gemini 1.5 Pro. While it suggests generating multiple answers in parallel to select the best, experts express skepticism regarding its practical utility. Critics argue that it relies on clear evaluation functions, which are often lacking in real-world applications. As the AI industry seeks efficient reasoning techniques, the limitations of this approach may hinder progress. The quest for new scaling methods continues amid rising operational costs for reasoning models.