Forget the needle in the haystack—can your AI actually sculpt an answer from a mountain of data? This episode explores the "Michelangelo" framework, a new evaluation that challenges models to "chisel away" irrelevant noise to reveal the latent structure hidden within massive contexts. Discover how frontier models like Gemini, GPT-4o, and Claude 3.5 Sonnet stack up in these grueling reasoning tasks and why even the "smartest" models face a sharp performance drop long before reaching the million-token mark.