Sutton contends that LLMs are fundamentally limited because they lack the ability to learn from real-world feedback and operate without clear goals. Unlike humans and animals, which continuously adapt through interaction, LLMs rely on fixed datasets and cannot generalize effectively beyond their training distribution. He advocates for a shift toward reinforcement learning systems that learn on-the-fly, grounded in dynamic environments with intrinsic reward mechanisms. Such agents would possess architectures integrating policy, value functions, perception, and environmental modeling, enabling continual adaptation. While current models excel in narrow tasks, they fail at open-ended learning. Sutton believes scalable computation and experiential learning will ultimately surpass human-designed solutions, aligning with his 'Bitter Lesson.' The discussion also touches on post-AGI futures, where AI self-replication and knowledge transfer could redefine progress, emphasizing the need for ethical guidance over control, akin to parenting.