Ep 55: Head of Amazon AGI Lab David Luan on DeepSeek’s Significance, What’s Next for Agents & Lessons from OpenAI

Listen to this episode from Unsupervised Learning on Spotify. 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