On Unsupervised Learning we probe the sharpest minds in AI in search for the truth about what’s real today, what will be real in the future and what it all mean...
Ep 55: Head of Amazon AGI Lab David Luan on DeepSeek’s Significance, What’s Next for Agents & Lessons from OpenAI
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
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43:49
Ep 54: Princeton Researcher Arvind Narayanan on the Limitations of Agent Evals, AI’s Societal Impact & Important Lessons from History
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
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57:09
Ep 53: SemiAnalysis Founder Dylan Patel on New AI Regulations, Future of Chinese AI & xAI’s Scrappy Surge to Hyperscale
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
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1:24:15
Ep 52: Marc Benioff Responds to Satya and Unpacks the Agentic Era
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
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39:57
Ep 51: Former Chief Research Officer of OpenAI Bob McGrew - What Comes Next for AI?
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
On Unsupervised Learning we probe the sharpest minds in AI in search for the truth about what’s real today, what will be real in the future and what it all means for businesses and the world. If you’re a builder, researcher or investor navigating the AI world, this podcast will help you deconstruct and understand the most important breakthroughs and see a clearer picture of reality. Follow this show and consider enabling notifications to stay up to date on our latest episodes.
Unsupervised Learning is a podcast by Redpoint Ventures, an early-stage venture capital fund that has invested in companies like Snowflake, Stripe, and Mistral.
Cohosted by Redpoint investors Jacob Effron, Patrick Chase, Jordan Segall and Erica Brescia.
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