Scaling "Thinking": Gemini 2.5 Tech Lead Jack Rae on Reasoning, Long Context, & the Path to AGI | "The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis | Podwise
This episode explores the recent advancements in large language models (LLMs), particularly focusing on Google DeepMind's Gemini 2.5 Pro. The interview centers around the surprising effectiveness of reinforcement learning from correctness signals in improving reasoning capabilities, a phenomenon attributed to a culmination of incremental progress rather than a singular breakthrough. More significantly, the discussion delves into the concurrent release of similar reasoning models by various frontier model developers, suggesting a confluence of obvious next steps in the field and rapid knowledge dissemination. For instance, the guest highlights the rapid internal progress within Google DeepMind's Gemini team, emphasizing the iterative nature of model improvements and the role of reinforcement learning in shaping cognitive behaviors. As the discussion pivoted to the relationship between reasoning and agency, the guest notes the tight coupling between these concepts, while acknowledging that the path to true AGI likely requires further breakthroughs in memory systems and multimodal integration. In contrast to concerns about the potential for obfuscated reward hacking, the guest emphasizes the importance of interpretability and the ongoing exploration of different approaches to surfacing model "thoughts." Ultimately, the conversation concludes with a perspective on the roadmap to AGI, highlighting the continued importance of scaling context windows, improving reasoning capabilities, and integrating more modalities for a more holistic and human-like AI experience.
