Arvind Narayanan is one of the leading voices in AI when it comes to cutting through the hype. As a Princeton professor and co-author of AI Snake Oil, he’s one of the most thoughtful voices cautioning against both unfounded fears and overblown promises in AI. In this episode, Arvind dissects the future of AI in education, its parallels to past tech revolutions, and how our jobs are already shifting toward managing these powerful tools. Some of our favorite take-aways:

[0:00] Intro
[0:46] Reasoning Models and Their Uneven Progress
[2:46] Challenges in AI Benchmarks and Real-World Applications
[5:03] Inference Scaling and Verifier Imperfections
[7:33] Agentic AI: Tools vs. Autonomous Actions
[12:07] Future of AI in Everyday Life
[15:34] Evaluating AI Agents and Collaboration
[24:49] Regulatory and Policy Implications of AI
[27:49] Analyzing Generative AI Adoption Rates
[29:17] Educational Policies and Generative AI
[30:09] Flaws in Predictive AI Models
[31:31] Regulation and Safety in AI
[33:47] Academia's Role in AI Development
[36:13] AI in Scientific Research
[38:22] AI and Human Minds
[46:04] Economic Impacts of AI
[49:42] 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

In this episode of Unsupervised Learning, we sit down with Dylan Patel, Chief Analyst at SemiAnalysis, to break down what these sweeping changes really mean. From how they consolidate power among Big Tech to China's narrowing options for AI dominance, we unpacked the impact of this regulatory shift.

Follow SemiAnalysis: https://semianalysis.com/

[0:00] Intro
[1:07] Grading the AI Diffusion Rule
[3:48] What Will Happen to the Malaysian Data Centers?
[7:23] How do the Regulations Favor Giant Tech Companies?
[9:07] Pre-Regulation AI Landscape
[13:00] Where Does Chinese AI Go From Here?
[22:00] The Goldie Locks Approach to Regulation
[24:16] Size of Cluster Buildouts Today
[37:47] How Big Will Cluster Buildouts Get?
[43:00] Are Open-Source Models Falling Behind?
[47:51] Questions Dylan Wants the Answer To
[51:30] Hardware Startups
[1:01:05] The Future of Enterprise AI
[1:05:10] What Made CoreWeave So Successful?
[1:19:28] 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

In this cross-over episode with The Logan Bartltett Show, Marc Benioff (CEO, Salesforce) responds to Satya Nadella’s recent predictions and shares his thoughts on the current reality of Agi. He dives into the rise of digital labor, the multi-trillion-dollar potential of agentic technology, and what the future split between software and agentic revenue might look like. Marc also discusses why CEOs need to stay grounded in delivering actionable solutions, and he emphasizes the moral obligation businesses have to retrain employees and invest in communities as AI continues to evolve.

(00:00) Intro

(01:45) Salesforce's AI Impact on Business

(03:03) The Future of Digital Labor

(05:28) Agentic AI and Customer Success

(07:42) Salesforce's Competitive Edge

(11:48) Marc Benioff's Response to Satya Nadella

(14:16) The Role of AI in Enterprise Software

(20:14) The Balance of AI and Human Labor

(28:34) Salesforce's Philanthropic Efforts

(36:24) The Future of AI and Regulation

(40:24) Conclusion and Farewell

In our new world of AI, few minds shine as brightly as Bob McGrew's. Until November Bob was the Chief Research Officer at OpenAI, and before that led Palantir’s engineering and product management for the first decade of its existence. He’s seen it all and we were fortunate to get his insights and vision for the future in one of my favorite episodes of Unsupervised Learning to date:

[0:00] Intro
[0:44] Debating AI Model Capabilities
[0:57] Inside vs Outside Perspectives on AI Progress
[1:39] Challenges in AI Pre-Training
[3:02] Reinforcement Learning and Future Models
[3:48] AI Progress in 2025
[5:58] New Form Factors for AI Models
[8:56] Reliability and Enterprise Integration
[18:14] Multimodal AI and Video Models
[24:05] The Future of Robotics
[32:46] The Complexity of Automating Jobs with AI
[34:08] AI in Startups: Tackling Boring Problems
[35:33] AI's Impact on Productivity and Consultants
[36:43] Traits of Top AI Researchers
[40:52] The Evolution of OpenAI's Mission
[46:57] The Challenges of Scaling AI
[49:16] The Future of AI and Human Agency
[54:47] AI in Social Sciences and Academia
[1:01:15] Reflections and Future Plans
[1:02:57] 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

Lin Qiao, the co-founder of Fireworks.ai, sits down for a deep dive into the future of AI. Lin ran the PyTorch team at Meta, which developed some of the most fundamental open-source AI software in use today. She’s got a riveting perspective on the AI landscape that is a must-listen.

[0:00] Intro

[1:06] Fireworks: Revolutionizing AI Inference

[2:12] Challenges in AI Model Development

[4:05] The Future of AI: Compound Systems

[4:32] Designing Effective AI Tools

[10:26] Customization and Fine-Tuning in AI

[14:06] Human-in-the-Loop Automation

[16:38] Evaluating AI Models

[19:18] Building Complex AI Systems

[21:18] Function Calling and AI Orchestration

[26:52] AI Infrastructure and Hardware

[31:08] Small Expert Models

[31:27] Hyperscalers and Resource Management

[32:14] Inference Systems and Scalability

[33:08] Running Models Locally: Cost and Privacy

[35:20] Open Source Models and Meta's Role

[36:41] The Evolution of AI Training and Inference

[38:04] Fireworks' Vision and Market Strategy

[40:46] The Impact of Generative AI

[45:18] AI Research and Future Trends

[46:58] Building for a Rapidly Changing AI Landscape

[49:36] 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

Noam Brown, renowned AI researcher and key figure at OpenAI, joins us for a deep dive into the o1 release. Recorded just one day before o1’s full public debut, this episode explores the groundbreaking advancements and challenges behind this innovative test-time compute model.

We discuss the technical breakthroughs that set o1 apart, its unique capabilities compared to previous models, and how it disrupts traditional paradigms in AI development. Noam also shares insights into OpenAI’s approach to innovation, the economic realities of scaling AI, and what the future holds for the field.

[0:00] Intro

[0:50] Scaling Model Capabilities and Economic Constraints

[2:48] Excitement Around Test Time Compute

[4:50] Challenges and Future Directions in AI Research

[8:11] Noam Brown's Journey and OpenAI's Research Focus

[16:08] The Role of Specialized Models and Tools

[21:18] Unexpected Use Cases and Future Milestones

[23:44] Proof of Concept: o1's Capabilities

[24:48] The Bitter Lesson: Insights from Richard Sutton

[25:59] Scaffolding Techniques and Their Future

[27:56] Challenges in Academia and AI Research

[30:30] Evaluating AI Models: Metrics and Trends

[34:47] The Role of AI in Social Sciences

[39:39] AI Agents and Emergent Communication

[40:17] Future of AI Robotics

[41:13] Advancing Scientific Research with AI

[43:30] 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

For this episode of Unsupervised Learning we spoke with Russ d'Sa, co-founder of LiveKit, a company at the forefront of voice AI technology. Russ thinks of LiveKit as a “nervous system,” powering the sensory interfaces humans use to interact with AI – including the Advanced Voice feature in ChatGPT as well as applications like Character.ai, Spotify and many more. 

Russ talked about when voice makes sense as an interface, the exciting new UX paradigms on the horizon, the intersection of voice and robotics and Anthropic's Computer Use API.

[0:00] Intro

[0:24] Using ChatGPT Voice in Daily Life

[2:26] How LiveKit Works with ChatGPT Voice

[5:16] LiveKit as the Nervous System for AI

[8:11] Future of Work and AI Interfaces

[18:31] Emerging Use Cases for Voice AI

[22:29] AI Models in Customer Support

[23:10] Latency Improvements in AI

[24:37] Challenges in System Integration

[26:01] Multimodal AI and Browser Integration

[29:40] Telephony and AI in Healthcare

[32:11] Humanoid Robotics and On-Device AI

[33:50] Cloud vs. On-Device Inference

[36:50] 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

Jonathan Frankle is the Chief AI Scientist at Databricks ($43B), which he joined through the acquisition of MosaicML in July 2023. Databricks has over 12,000 customers on the cutting edge of AI; Jonathan works to anticipate their needs and offer solutions even as the tech is rapidly evolving.

[0:00] Intro

[0:52] Incentives and Team Motivation at Databricks

[2:40] The Evolution of AI Models: Transformers vs. LSTMs

[5:27] Mosaic and Databricks: A Strategic Merger

[7:31] Guidance on AI Model Training and Fine-Tuning

[11:11] Building Effective AI Evaluations

[16:02] Domain-Specific AI Models and Their Importance

[19:37] The Future of AI: Challenges and Opportunities

[25:07] Ethical Considerations and Human-AI Interaction

[29:13] Customer Collaboration and AI Implementation

[30:45] Navigating AI Tools and Techniques

[35:41] The Role of Open Source Models

[36:46] AI Infrastructure and Partnerships

[48:27] Academia's Role in AI Research

[52:09] Ethics and Policy in AI

[57:47] 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

I sat down with DeepL cofounder Jarek Kutylowski. DeepL is a comprehensive Language AI platform that enables organizations to communicate effectively across languages, cultures, and markets. Jarek shared a treasure trove of insights on the past, present, and future of AI translation. Here were some standout moments:

[0:00] Intro
[0:38] The Rise of AI and DeepL's Journey
[1:41] DeepL's Competitive Edge and Market Impact
[2:41] Innovations in AI Translation
[4:39] DeepL's Product and Use Cases
[7:35] Challenges and Strategies in AI Translation
[14:29] Human Translators and Data Labeling
[24:39] Building and Scaling AI Infrastructure
[32:52] Evaluating AI Models: Objective vs Subjective
[34:05] Translation Quality Metrics
[35:35] The Debate on AI Moats and Specialized Models
[40:16] The Impact of Real-Time Translation on Business
[45:05] Challenges in Developing Synchronous Speaking Models
[46:49] Adjacent AI Technologies and Their Potential
[48:33] 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

Joshua Xu is co-founder and CEO of HeyGen -- the fast-growing AI video creation and translation platform. You can upload a video, or create a new one from a script using an AI avatar as your star, and HeyGen will translate it into 175 languages. HeyGen now serves over 40,000 customers and is generating $35+ million in revenue.

[0:00] Intro  

[0:37] HeyGen's Viral Moments  

[1:23] Creating Magic with AI  

[3:30] The Future of AI in Video Production  

[9:29] HeyGen's Use Cases and Customer Base  

[13:15] AI Avatars  

[25:46] The Future of Content  

[26:43] Competing with Industry Giants  

[27:16] Innovating for New Markets  

[31:24] Enterprise Push: Lessons and Surprises  

[33:07] Trust and Safety in AI  

[37:03] Fundraising and Financial Strategies for AI Startups  

[41:22] 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