tl;dr: Becoming an AI researcher comes down to combining reading with building, focusing on fundamentals (cross-entropy, SVD, policy gradients) rather than chasing trendy 2026 concepts or benchmark scores, and embracing the grunt work and long stretches of obscurity behind most breakthroughs. Cultivate equanimity toward results—be skeptical of good outcomes (often bugs) and learn equally from bad ones—and design workflows for fast iteration while resisting the temptation to outsource understanding to coding agents. Ultimately, temperament, paranoid attention to detail, and persistence matter more than raw talent.