Unsupervised Learning

Fill out this short listener survey to help us improve the show: https://forms.gle/bbcRiPTRwKoG2tJx8This week on Unsupervised Learning, Jacob sits down with Nicole Brichtova and Oliver Wang, the Google researchers behind "Nano Banana" - the breakthrough AI image model that achieved unprecedented character consistency and took over social media.The conversation covers how their model fits into creative workflows, why we're still in the early innings of image AI development despite impressive current capabilities, and how image and video generation are converging toward unified models. They also share honest perspectives on current limitations, safety approaches, and why the expectation of going from prompt to production-ready content is fundamentally overhyped.(0:00) Intro(1:42) Early Nano Banana Use Cases and Character Consistency(3:05) Popular Features and User Requests(3:54) Future Frontiers in Image Models(5:26) Personalization and Aesthetic Models(7:39) Model Success and User Engagement(10:59) Product Design for Different Users(19:30) Advanced Use Cases and Future Workflows(23:14) Editing Workflows and Chatbots(25:14) Google's Image Model Applications(27:12) Milestones in Image Generation(29:30) MidJourney's Success(30:54) Future of Image Models(33:55) Image Models vs. Video Models(36:35) 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

Fill out this short listener survey to help us improve the show: https://forms.gle/bbcRiPTRwKoG2tJx8 Tri Dao, Chief Scientist at Together AI and Princeton professor who created Flash Attention and Mamba, discusses how inference optimization has driven costs down 100x since ChatGPT's launch through memory optimization, sparsity advances, and hardware-software co-design. He predicts the AI hardware landscape will shift from Nvidia's current 90% dominance to a more diversified ecosystem within 2-3 years, as specialized chips emerge for distinct workload categories: low-latency agentic systems, high-throughput batch processing, and interactive chatbots. Dao shares his surprise at AI models becoming genuinely useful for expert-level work, making him 1.5x more productive at GPU kernel optimization through tools like Claude Code and O1. The conversation explores whether current transformer architectures can reach expert-level AI performance or if approaches like mixture of experts and state space models are necessary to achieve AGI at reasonable costs. Looking ahead, Dao sees another 10x cost reduction coming from continued hardware specialization, improved kernels, and architectural advances like ultra-sparse models, while emphasizing that the biggest challenge remains generating expert-level training data for domains lacking extensive internet coverage. (0:00) Intro(1:58) Nvidia's Dominance and Competitors(4:01) Challenges in Chip Design(6:26) Innovations in AI Hardware(9:21) The Role of AI in Chip Optimization(11:38) Future of AI and Hardware Abstractions(16:46) Inference Optimization Techniques(33:10) Specialization in AI Inference(35:18) Deep Work Preferences and Low Latency Workloads(38:19) Fleet Level Optimization and Batch Inference(39:34) Evolving AI Workloads and Open Source Tooling(41:15) Future of AI: Agentic Workloads and Real-Time Video Generation(44:35) Architectural Innovations and AI Expert Level(50:10) Robotics and Multi-Resolution Processing(52:26) Balancing Academia and Industry in AI Research(57:37) 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, Jacob sits down with Peter Deng, General Partner at Felicis and former Product Leader at OpenAI, Facebook, and Uber. Peter shares his insider perspective on building ChatGPT Enterprise in just seven weeks and leading voice mode development at OpenAI. The conversation covers everything from why traditional SaaS pricing models are broken for AI products to how evals became the new product specs, the "AI under your fingernails" test for founding teams, and why current agents are massively overhyped.They also explore how consumer AI will fragment across multiple winners rather than consolidate into a single super app, the coming integration between ChatGPT and apps like Uber, and why voice AI will unlock entirely new categories of applications. Plus, insights on the changing dynamics between foundation models and startups, and what it really takes to build defensible AI companies. It's a comprehensive look at AI product strategy from someone who's been at the center of the industry's biggest breakthroughs. (0:00) Intro(1:17) AI Business Models and Pricing Strategies(7:48) Product Development in AI Companies(18:36) The Role of Product Managers in AI(23:06) Voice Interaction and AI(26:43) AI in Education(30:39) Consumer and Enterprise Adoption of AI(33:36) The Impact of AI on Salaries and HR(40:37) The Role of Unique Data in AI Development(49:03) Challenges and Strategies for AI Companies(52:58) The Future of AI and Its Impact on Society(57:31) Reflections on OpenAI(58:38) 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, Jacob sits down with Joshua Meier, co-founder of Chai Discovery and former Chief AI Officer at Absci, to explore the breakthrough moment happening in AI drug discovery. They discuss how the field has evolved through three distinct waves, with the current generation of companies finally achieving success rates that seemed impossible just years ago.  The conversation covers everything from moving drug discovery out of the lab and into computers, to why AI models think differently than human chemists, to the strategic decisions around open sourcing foundational models while keeping design capabilities proprietary. It's an in-depth look at how AI is fundamentally changing pharmaceutical innovation and what it means for the future of medicine. Check out the full Chai-2 Zero-Shot Antibody report linked here: https://www.biorxiv.org/content/10.1101/2025.07.05.663018v1.full.pdf (0:00) Intro(2:10) The Evolution of AI in Drug Discovery(6:09) Current State and Future of AI in Biotech(11:15) Challenges and Modalities in Therapeutics(15:19) Data Generation and Model Training(23:59) Open Source and Model Development at Chai(28:35) Protein Structure Prediction and Diffusion Models(30:57) Open Source Models and Their Impact(35:41) How Should Chai-2 Be Used?(39:34) The Future of AI in Pharma and Biotech(43:51) Key Milestones and Metrics in AI-Driven Drug Discovery(48:24) Critiques and Hesitation(55:06) 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

Fill out this short listener survey to help us improve the show: https://forms.gle/bbcRiPTRwKoG2tJx8In this episode, Simon Eskildsen, co-founder and CEO of TurboPuffer, lays out a compelling vision for how AI-native infrastructure needs to evolve in an era where every application wants to connect massive amounts of context to large language models. He breaks down why traditional databases and even large context windows fall short—especially at scale—and why object-storage-native search is the inevitable next step. Drawing on his experience from Shopify and Readwise, Simon introduces the SCRAP framework to explain the limits of context stuffing and makes a clear case for why cost, recall, performance, and access control drive the need for smarter retrieval systems. From practical lessons in building highly reliable infra to hard technical problems in vector indexing, this conversation distills the future of AI infra into first principles—with clarity and depth. (0:00) Intro(0:49) The Evolution of AI Context Windows(2:32) Challenges in AI Data Integration(3:56) SCRAP: Scale, Cost, Recall, ACLs, and Performance(9:21) The Rise of Object-Oriented Storage(16:47) Turbo Puffer Use Cases(22:32) Challenges in Vector Search(27:02) Challenges in Query Planning and Data Filtering(27:53) Focusing on Core Problems and Simplicity(28:28) Customer Feedback and Future Directions(29:11) Reliability and Simplicity in Design(30:39) Evaluating Embedding Models and Search Performance(32:17) The Role of Vectors in Search Engines(34:16) Balancing Focus and Expansion(35:57) AI Infrastructure and Market Trends(38:36) The Future of Memory in AI(43:01) Table Stakes for AI in SaaS Applications(45:55) Multimodal Data and Market Observations(46:57) 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

In this episode, Jacob sits down with Karol Hausman (Co-Founder) and Danny Driess (Research Scientist) from Physical Intelligence, two of the minds behind some of the most exciting advances in robotics. They unpack the last decade of progress in AI robotics, from early skepticism to the breakthroughs powering today’s generalist robot models.  The conversation covers everything from folding laundry with robots to building scalable data pipelines, the limits of simulation, and what it’ll take to bring robot assistants into everyday homes. It's a wide-ranging and thoughtful look at where robotics is headed, as well as how fast we might get there. (0:00) Intro(1:31) Early Days in Robotics(2:08) Shift to Learning-Based Robotics(4:50) Challenges and Breakthroughs(8:45) Google's Role and Spin-Out Decision(15:08) Comparing Robotics to Self-Driving Cars(19:18) Hardware and Intelligence(21:05) Future Milestones and Scaling Challenges(33:23) Data Collection and Infrastructure Needs(35:49) Choosing and Tackling Complex Tasks(38:49) Evaluating Model Performance(41:28) The Role of Simulation in Robotics(44:27) Research Strategies and Hiring(48:16) Open Source and Community Impact(52:27) Advancements in Training and Model Efficiency(58:45) Future of Robotics and AI(1:01:16) 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

Ion Stoica helped define the modern data stack. Now he’s coming for AI evaluation. From co-founding Databricks and Anyscale to launching LMArena, Ion has shaped the infrastructure underlying some of the biggest shifts in computing. In this conversation, he unpacks what most people get wrong about model evaluation, the infrastructure challenges ahead for agents and heterogeneous compute, and why he believes the U.S. is structurally disadvantaged in open-source AI compared to China. (0:00) Intro(0:49) Launching a New Startup: LMArena(1:01) The Origin of the Vicuna Model(1:54) Challenges in Model Evaluation(6:33) Becoming a Company(7:47) Expanding Evaluation Capabilities(13:48) The Importance of Human-Based Evaluations(18:56) Open Source vs. Proprietary Models(23:05) Infrastructure and Collaboration Challenges(28:22) China's Strategic Advantages in Technology(29:54) Opportunities in AI Infrastructure(31:50) Challenges in AI Model Optimization(35:49) The Role of Data in AI Enterprises(39:31) Reflections on AI Progress and Predictions(50:40) 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

Brendan Foody is the co-founder and CEO of Mercor, a company building the infrastructure for AI-native labor markets. Mercor’s platform is already used by top AI labs to label data, evaluate human and AI candidates, and make performance-driven hiring decisions.  They’re operating at the intersection of recruiting, evals, and foundation model development—helping companies shift from intuition to measurable prediction. Brendan and his team recently raised $100M and are working with some of the most advanced players in the AI ecosystem today. (0:00) Intro(1:17) State of AI in Talent Evaluation(1:54) Improvements in AI Models(4:07) Mercor Background and Mission(5:09) AI Use Cases in Hiring(13:43) Data Labeling Landscape(16:48) Expanding Beyond Coding(18:39) Company Vision and Market Strategy(21:11) Meeting with xAI(23:47) Does Mercor Use Their Own Product?(25:41) Exploring Multimodal Capabilities(28:03) Skills for the Future: Embracing AI(29:29) The Demand for Software Engineers(34:55) Foundation Model Landscape(38:42) AI Regulations(39:57) 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

Jacob and Logan sit down with Max Junestrand, founder and CEO of Legora - a rapidly growing legal AI platform (and Redpoint portfolio company). After announcing their Series B last week, Max joined the show to discuss why law is uniquely suited for AI, what it takes to scale an enterprise-ready product across global markets, and a few crazy moments from Legora’s journey so far. They dig into product strategy, lessons on evolving alongside foundational models, and how AI is reshaping the future of law firms. Whether you're building in AI or just curious how it’s being applied in complex industries, this one’s packed with practical insights. (0:00) Intro(1:30) The Evolution of AI in Law(2:43) AI's Impact on Legal Processes(8:28) Advantages Over Other Players in the AI Law Space(12:19) Challenges in Educating Users(17:28) The Hardest Part of Building Legora(18:46) Pricing Models and Cost Management(25:42) YC Experience and Commercial Focus(28:11) Being Patient When Releasing Products(30:58) Maintaining a Fast-Paced Work Culture(33:24) Rapid Growth and Market Penetration(36:59) 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

Sholto Douglas, a Member of Technical Staff at Anthropic, joined Unsupervised Learning to break down why coding is the clearest early signal of model progress, how AI agents are already accelerating research, and what it’ll take to unlock real-world breakthroughs in fields like biology and robotics. (0:00) Intro(0:48) Claude 4(1:30) Capabilities and Improvements(2:29) Practical Applications and Advice(3:04) Future of AI in Coding(4:38) Managing Multiple AI Models(11:20) The Barrier to Agents is Reliability(16:35) Agents Conducting Research(19:54) Impact of Models on World GDP(25:14) Most Important Metrics in Model Improvement(29:53) Stories of Model Creativity(32:45) How Often Will New Models Be Shipped in the Future?(39:51) Day-to-Day Work of AI Researchers(46:46) The Future of AI and Society(51:26) 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

The recent AI 2027 report sparked widespread discussion with its stark warnings about the near-term risks of unaligned AI.Authors @Daniel Kokotajlo (former OpenAI researcher now focused full-time on alignment through his nonprofit, @AI Futures, and one of TIME’s 100 most influential people in AI) and @Thomas Larsen joined the show to unpack their findings.We talk through the key takeaways from the report, its policy implications, and what they believe it will take to build safer, more aligned models. (0:00) Intro(1:15) Overview of AI 2027(2:32) AI Development Timeline(4:10) Race and Slowdown Branches(12:52) US vs China(18:09) Potential AI Misalignment(31:06) Getting Serious About the Threat of AI(47:23) Predictions for AI Development by 2027(48:33) Public and Government Reactions to AI Concerns(49:27) Policy Recommendations for AI Safety(52:22) Diverging Views on AI Alignment Timelines(1:01:30) The Role of Public Awareness in AI Safety(1:02:38) Reflections on Insider vs. Outsider Strategies(1:10:53) Future Research and Scenario Planning(1:14:01) Best and Worst Case Outcomes for AI(1:17:02) Final Thoughts and Hopes for the Future 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, I sit down with Michelle Pokrass, who leads a research team at OpenAI within post-training focused on improving models for power users: developers using OpenAI models in the API and power users in ChatGPT. We unpack how OpenAI prioritized instruction-following and long context, why evals have a 3-month shelf life, what separates successful AI startups, and how the best teams are fine-tuning to push past the current frontier.If you’ve ever wondered how OpenAI really decides what to build, and how it affects what you should build, this one’s for you. (0:00) Intro(1:03) Deep Dive into GPT-4.1 Development(2:23) User Feedback and Model Evaluation(4:01) Challenges and Improvements in Model Training(5:54) Advancements in AI Coding Capabilities(9:11) Future of AI Models and Fine-Tuning(20:44) Multimodal Capabilities(22:59) Deep Tech Applications and Data Efficiency(24:14) Preference Fine Tuning vs. RFT(26:29) Choosing the Right Model for Your Needs(28:18) Prompting Techniques and Model Improvements(32:10) Future Research and Model Enhancements(39:14) Power Users and Personalization(40:22) Personal Journey and Organizational Growth(43:37) 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

When Khan Academy launched Khanmigo, Salman Khan thought they might reach 100k users by 2025. Today, they’re at 1.4 million. 🎧 Sal joined us on Unsupervised Learning to talk about AI's role in education— from the vantage point of someone deploying it at scale. As founder of Khan Academy, he’s overseen the rollout of AI tools to over a million teachers and students, giving him a front-row seat to what’s actually working in classrooms. (0:00) Intro(1:17) The Vision for Future Classrooms(4:28) Khan Academy's AI Initiatives(7:06) Proactive AI and Engagement(10:33) Teacher and Student Experiences(18:24) District-Level Adoption and Policy(25:56) Gamification and Engagement(27:36) Evaluating AI Models(31:03) Global Impact and Future Prospects(37:43) Challenges and Innovations in AI Education(44:19) 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

Aidan joined this week’s Unsupervised Learning for a wide-ranging conversation on model architectures, enterprise adoption, and what’s breaking in the foundation model stack. If you’re building or investing in AI infrastructure, Aidan is worth listening to. He co-authored the original Transformer paper, leads one of the most advanced model labs outside of the hyperscalers, and is now building for real-world enterprise deployment with Cohere’s agent platform, North. Cohere serves thousands of customers across sectors like finance, telco, and healthcare — and they’ve made a name for themselves by staying model-agnostic, privacy-forward, and deeply international (with major bets in Japan and Korea) (0:00) Intro(0:32) Enterprise AI(3:23) Custom Integrations and Future of AI Agents(4:33) Enterprise Use Cases for Gen AI(7:02) The Importance of Reasoning in AI Models(10:38) Custom Models and Synthetic Data(17:48) Cohere's Approach to AI Applications(23:24) Future Use Cases and Market Fit(27:11) Building a Unified Automation Platform(27:34) Strategic Decisions in the AI Journey(29:19) International Partnerships and Language Models(31:05) Future of Foundation Models(32:27) AI in Specialized Domains(34:40) Challenges in Data Integration(35:06) Emerging Foundation Model Companies(35:31) Technological Frontiers and Architectures(37:29) Scaling Hypothesis and Model Capabilities(42:26) AI Research Culture and Team Building(44:39) Future of AI and Societal Impact(48:31) Addressing AI Risks 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

At Redpoint’s annual meeting with investors, Redpoint partners Scott Raney, Alex Bard, Patrick Chase and Jacob Effron shared unfiltered thoughts on some of the most topical questions in AI today, where value will accrue, which industries are best positioned for defensibility at the application layer, and more. (0:00) Intro(0:39) AI Investment Landscape(1:30) Market Projections(3:25) Strategic Imperatives in AI(6:04) AI Model Layer Insights(10:06) Infrastructure Layer(11:46) Application Layer(15:04) Vertical AI SaaS Opportunities(21:45) Evaluating Early-Stage Founders(23:39) First Mover Advantage and Competitive Dynamics(25:28) Domain Expertise vs. AI Expertise(26:58) Why AI Startups Fail(29:41) Startups vs. Incumbents(35:15) Navigating High AI Valuations 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

To unpack some of the most topical questions in AI, I’m joined by two fellow AI podcasters: Swyx and Alessio Fanelli, co-hosts of the Latent Space podcast. We’ve been wanting to do a cross-over episode for a while and finally made it happen.Swyx brings deep experience from his time at AWS, Temporal, and Airbyte, and is now focused on AI agents and dev tools. Alessio is an investor at Decibel, where he’s been backing early technical teams pushing the boundaries of infrastructure and applied AI. Together they run Latent Space, a technical newsletter and podcast by and for AI engineers.To subscribe or learn more about Latent Space, click here: https://www.latent.space/ (0:00) Intro(1:08) Reflecting on AI Surprises of the Past Year(2:24) Open Source Models and Their Adoption(6:48) The Rise of GPT Wrappers(7:49) Challenges in AI Model Training(10:33) Over-hyped and Under-hyped AI Trends(24:00) The Future of AI Product Market Fit(30:27) Google's Momentum and Customer Support Insights(33:16) Emerging AI Applications and Market Trends(35:13) Challenges and Opportunities in AI Development(39:02) Defensibility in AI Applications(42:42) Infrastructure and Security in AI(50:04) Future of AI and Unanswered Questions(55:34) 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

Two weeks ago, OpenAI released its set of tools to help developers build agentic systems. Today on Unsupervised Learning, Nikunj Handa (Product Lead) and Steve Coffey (Eng Lead) answer some of the biggest questions around how developers should be thinking about building in the agentic paradigm in 2025. (0:00) Intro(0:53) OpenAI’s Vision for Consumer Interaction(4:51) Building Multi-Agent Systems for Business Solutions(6:53) Challenges and Innovations in AI Fine-Tuning(13:20) Exploring Computer Use Cases and Applications(17:20) Advanced Use Cases and Developer Insights(25:29) Challenges with Context Storage and Chat Completions(26:09) Introducing the Responses API and MCP(27:16) AI Infrastructure Companies and Their Role(29:35) Building the Tools Ecosystem(30:17) Exploring Computer Use Models(31:47) The Future of AI and Developer Tools(38:36) 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

On the latest episode of Unsupervised Learning, Jacob is joined by two of the most influential minds in AI today. 🔹 Noam Shazeer, co-inventor of the Transformer🔹 Jack Rae, Research Director at DeepMind and one of the leads behind Gemini’s Flash ThinkingWe got to ask them all of the top-of-mind questions in AI today about where we are, where we’re headed and what it means for businesses and the world. Some key take-aways: (0:00) Intro(1:30) Exploring Gemini 2.0(4:04) Challenges with Evals and Benchmarks(6:14) Reinforcement Loops and AI Productivity(8:15) Agentic Coding and AI in Development(13:02) Multimodal Capabilities and Applications(15:21) Future of AI: Complexity and Reliability(19:02) Test Time Compute and Data Efficiency(31:20) AI Research Culture and Breakthroughs(38:59) Reflecting on Large Language Models(39:37) The Rise of Vision-Based Models(41:01) Native Image Generation and General Purpose Models(41:35) AI in Healthcare and Specialized Models(43:32) Shifting Timelines and Rapid Adoption(46:48) Open Source Models and Competitive Edge(49:30) AI's Impact on Education and Personal Lives(55:10) AGI Risks and Safety Considerations(57:33) Future of AI Companions(1:02:17) 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 today’s Unsupervised Learning, Mike Schroepfer (ex-CTO of Meta and founder of Gigascale Capital) reveals why energy is a key bottleneck holding AI progress back. Mike discusses how we can scale energy production to democratize AI globally and explores AI’s role in climate change. He also reflects on a decade as Meta’s CTO and how AI coding is transforming the CTO role. Finally, he offers predictions on the future of AI developer tools, VR, and open-source models. (0:00) Intro(0:43) AI's Role in Energy and Climate Change(4:32) Innovative Energy Solutions(14:50) Open Source and AI Development(22:35) Challenges in Chip Design(24:04) Balancing Data Center Capacity(25:55) The Future of VR and AI Integration(29:41) AI's Role in Climate Solutions(31:41) AI in Material Science and Beyond(34:31) Personal AI Assistants and Their Impact(38:47) Reflections on AI and Future Predictions(41:23) 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

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. The company operates a 24/7 public ride-hail service and provides over 150,000 trips each week across San Francisco, Los Angeles, Phoenix, and Austin, making mobility more accessible, sustainable, and safer for everyone.In this week’s episode of Unsupervised Learning, we dive deep into the frontier where AI meets hardware — and there’s no better guide than Vincent Vanhoucke, Distinguished Engineer at Waymo and former Head of Robotics at DeepMind. [0:00] Intro[0:50] Waymo's Technological Evolution[2:40] The Role of LLMs in Autonomous Driving[6:02] Vincent's Journey to Waymo[9:17] Challenges in Autonomous Driving[11:58] Simulation and World Models[27:44] Future Milestones and Expansion[30:10] Broader Robotics and AI[36:12] Future of General Robotics Models[38:14] Hardware vs. Software Approaches in Robotics[40:19] Challenges in Robotic Data Acquisition[40:38] Simulation vs. Real-World Data in Robotics[43:02] Human-Robot Interaction for Data Collection[45:03] Advancements in Multimodal Models[47:08] Unanswered Questions in Robotics[52:02] Reasoning Capabilities in AI[54:57] Future of Robotics and AI[1:00:51] 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