Unsupervised Learning is a podcast that interviews the sharpest minds in AI about what’s real today, what will be real in the future and what it means for businesses and the world - helping builders, researchers and founders deconstruct and understand the biggest breakthroughs.

Top guests: Noam Shazeer, Bob McGrew, Noam Brown, Dylan Patel, Percy Liang, David Luan

https://www.latent.space/p/unsupervised-learning

Timestamps

00:00 Introduction and Excitement for Collaboration

00:27 Reflecting on Surprises in AI Over the Past Year

01:44 Open Source Models and Their Adoption

06:01 The Rise of GPT Wrappers

06:55 AI Builders and Low-Code Platforms

09:35 Overhyped and Underhyped AI Trends

22:17 Product Market Fit in AI

28:23 Google's Current Momentum

28:33 Customer Support and AI

29:54 AI's Impact on Cost and Growth

31:05 Voice AI and Scheduling

32:59 Emerging AI Applications

34:12 Education and AI

36:34 Defensibility in AI Applications

40:10 Infrastructure and AI

47:08 Challenges and Future of AI

52:15 Quick Fire Round and Closing Remarks

Chapters

• 00:00:00 Introduction and Collab Excitement
• 00:00:58 Open Source and Model Adoption
• 00:01:58 Enterprise Use of Open Source Models
• 00:02:57 The Competitive Edge of Closed Source Models
• 00:03:56 DeepSea and Open Source Model Releases
• 00:04:54 Market Narrative and DeepSea Impact
• 00:05:53 AI Engineering and GPT Wrappers
• 00:06:53 AI Builders and Low-Code Platforms
• 00:07:50 Innovating Beyond Existing Paradigms
• 00:08:50 Apple and AI Product Development
• 00:09:48 Overhyped and Underhyped AI Trends
• 00:10:46 Frameworks and Protocols in AI Development
• 00:11:45 Emerging Opportunities in AI
• 00:12:44 Stateful AI and Memory Innovation
• 00:13:44 Challenges with Memory in AI Agents
• 00:14:44 The Future of Model Training Companies
• 00:15:44 Specialized Use Cases for AI Models
• 00:16:44 Vertical Models vs General Purpose Models
• 00:17:42 General Purpose vs Domain-Specific Models
• 00:18:42 Reflections on Model Companies
• 00:19:39 Model Companies Entering Product Space
• 00:20:38 Competition in AI Model and Product Sectors
• 00:21:35 Coding Agents and Market Dynamics
• 00:22:35 Defensibility in AI Applications
• 00:23:35 Investing in Underappreciated AI Ventures
• 00:24:32 Analyzing Market Fit in AI
• 00:25:31 AI Applications with Product Market Fit
• 00:26:31 OpenAI's Impact on the Market
• 00:27:31 Google and OpenAI Competition
• 00:28:31 Exploring Google's Advancements
• 00:29:29 Customer Support and AI Applications
• 00:30:27 The Future of AI in Customer Support
• 00:31:26 Cost-Cutting vs Growth in AI
• 00:32:23 Voice AI and Real-World Applications
• 00:33:23 Scaling AI Applications for Demand
• 00:34:22 Summarization and Conversational AI
• 00:35:20 Future AI Use Cases and Market Fit
• 00:36:20 AI Education and Model Capabilities
• 00:37:17 Reforming Education with AI
• 00:38:15 Defensibility in AI Apps
• 00:39:13 Network Effects and AI
• 00:40:12 AI Brand and Market Positioning
• 00:41:11 AI Application Defensibility
• 00:42:09 LLM OS and AI Infrastructure
• 00:43:06 Security and AI Application
• 00:44:06 OpenAI's Role in AI Infrastructure
• 00:45:02 The Balance of AI Applications and Infrastructure
• 00:46:02 Capital Efficiency in AI Infrastructure
• 00:47:01 Challenges in AI DevOps and Infrastructure
• 00:47:59 AI SRE and Monitoring
• 00:48:59 Scaling AI and Hardware Challenges
• 00:49:58 Reliability and Compute in AI
• 00:50:57 Nvidia's Dominance and AI Hardware
• 00:51:57 Emerging Competition in AI Silicon
• 00:52:54 Agent Authentication Challenges
• 00:53:53 Dream Podcast Guests
• 00:54:51 Favorite News Sources and Startups
• 00:55:50 The Value of In-Person Conversations
• 00:56:50 Private vs Public AI Discourse
• 00:57:48 Latent Space and Podcasting
• 00:58:46 Conclusion and Final Thoughts