About this episode
David is an OG in AI who has been at the forefront of many of the major breakthroughs of the past decade. His resume: VP of Engineering at OpenAI, a key contributor to Google Brain, co-founder of Adept, and now leading Amazon’s SF AGI Lab. In this episode we focused on how far test-time compute gets us, the real implications of DeepSeek, what agents milestones he’s looking for and more.[0:00] Intro[1:14] DeepSeek Reactions and Market Implications[2:44] AI Models and Efficiency[4:11] Challenges in Building AGI[7:58] Research Problems in AI Development[11:17] The Future of AI Agents[15:12] Engineering Challenges and Innovations[19:45] The Path to Reliable AI Agents[21:48] Defining AGI and Its Impact[22:47] Challenges and Gating Factors[24:05] Future Human-Computer Interaction[25:00] Specialized Models and Policy[25:58] Technical Challenges and Model Evaluation[28:36] Amazon's Role in AGI Development[30:33] Data Labeling and Team Building[36:37] Reflections on OpenAI[42:12] Quickfire With your co-hosts: @jacobeffron - Partner at Redpoint, Former PM Flatiron Health @patrickachase - Partner at Redpoint, Former ML Engineer LinkedIn @ericabrescia - Former COO Github, Founder Bitnami (acq’d by VMWare) @jordan_segall - Partner at Redpoint
