Unsupervised Learning x Latent Space Crossover Special

03/29/25 • -1 min

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 Authenticatio...

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 Authenticatio...

Previous Episode

The Agent Network — Dharmesh Shah

If you’re in SF: Join us for the Claude Plays Pokemon hackathon this Sunday!

If you’re not: Fill out the 2025 State of AI Eng survey for $250 in Amazon cards!

We are SO excited to share our conversation with Dharmesh Shah, co-founder of HubSpot and creator of Agent.ai.

A particularly compelling concept we discussed is the idea of "hybrid teams" - the next evolution in workplace organization where human workers collaborate with AI agents as team members. Just as we previously saw hybrid teams emerge in terms of full-time vs. contract workers, or in-office vs. remote workers, Dharmesh predicts that the next frontier will be teams composed of both human and AI members. This raises interesting questions about team dynamics, trust, and how to effectively delegate tasks between human and AI team members.

The discussion of business models in AI reveals an important distinction between Work as a Service (WaaS) and Results as a Service (RaaS), something Dharmesh has written extensively about. While RaaS has gained popularity, particularly in customer support applications where outcomes are easily measurable, Dharmesh argues that this model may be over-indexed. Not all AI applications have clearly definable outcomes or consistent economic value per transaction, making WaaS more appropriate in many cases. This insight is particularly relevant for businesses considering how to monetize AI capabilities.

The technical challenges of implementing effective agent systems are also explored, particularly around memory and authentication. Shah emphasizes the importance of cross-agent memory sharing and the need for more granular control over data access. He envisions a future where users can selectively share parts of their data with different agents, similar to how OAuth works but with much finer control. This points to significant opportunities in developing infrastructure for secure and efficient agent-to-agent communication and data sharing.

Other highlights from our conversation

The Evolution of AI-Powered Agents – Exploring how AI agents have evolved from simple chatbots to sophisticated multi-agent systems, and the role of MCPs in enabling that.

Hybrid Digital Teams and the Future of Work – How AI agents are becoming teammates rather than just tools, and what this means for business operations and knowledge work.

Memory in AI Agents – The importance of persistent memory in AI systems and how shared memory across agents could enhance collaboration and efficiency.

Business Models for AI Agents – Exploring the shift from software as a service (SaaS) to work as a service (WaaS) and results as a service (RaaS), and what this means for monetization.

The Role of Standards Like MCP – Why MCP has been widely adopted and how it enables agent collaboration, tool use, and discovery.

The Future of AI Code Generation and Software Engineering – How AI-assisted coding is changing the role of software engineers and what skills will matter most in the future.

Domain Investing and Efficient Markets – Dharmesh’s approach to domain investing and how inefficiencies in digital asset markets create business opportunities.

The Philosophy of Saying No – Lessons from "Sorry, You Must Pass" and how prioritization leads to greater productivity and focus.

Timestamps

00:00 Introduction and Guest Welcome

02:29 Dharmesh Shah's Journey into AI

05:22 Defining AI Agents

06:45 The Evolution and Future of AI Agents

13:53 Graph Theory and Knowledge Representation

20:02 Engineering Practices and Overengineering

25:57 The Role of Junior Engineers in the AI Era

28:20 Multi-Agent Systems and MCP Standards

35:55 LinkedIn's Legal Battles and Data Scraping

37:32 The Future of AI and Hybrid Teams

39:19 Building Agent AI: A Professional Network for Agents

40:43 Challenges and Innovations in Agent AI

45:02 The Evolution of UI in AI Systems

01:00:25 Business Models: Work as a Service vs. Results as a Service

01:09:17 The Future Value of Engineers

01:09:51 Exploring the Role of Agents

01:10:28 The Importance of Memory in AI

01:11:02 Challenges and Opportunities in AI Memory

01:12:41 Selective Memory and...

Next Episode

The Creators of Model Context Protocol

Today’s guests, David Soria Parra and Justin Spahr-Summers, are the creators of Anthropic’s Model Context Protocol (MCP). When we first wrote Why MCP Won, we had no idea how quickly it was about to win.

In the past 4 weeks, OpenAI and now Google have now announced the MCP support, effectively confirming our prediction that MCP was the presumptive winner of the agent standard wars. MCP has now overtaken OpenAPI, the incumbent option and most direct alternative, in GitHub stars (3 months ahead of conservative trendline):

For protocol and history nerds, we also asked David and Justin to tell the origin story of MCP, which we leave to the reader to enjoy (you can also skim the transcripts, or, the changelogs of a certain favored IDE). It’s incredible the impact that individual engineers solving their own problems can have on an entire industry.