Dwarkesh Patel argues that AGI is not imminent due to key limitations in current LLMs, such as their inability to learn continuously or adapt organically. While he remains skeptical about near-term breakthroughs, he sees vast potential in the long term if challenges like multimodal learning and algorithmic efficiency are addressed. Patel outlines milestones—like AI handling small business taxes by 2028 and white-collar work by 2032—that could signal major progress. However, after 2030, he expects advancements to hinge more on algorithmic innovation than computational scale, with uncertain but potentially transformative outcomes.