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
