Ep 72: Co-Founder of Chai Discovery Joshua Meier on 99% Faster Drug Discovery, BioTech’s AlphaGo Moment, Building Photoshop for Molecules
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
--------
57:15
--------
57:15
Ep 71: CEO of TurboPuffer Simon Eskildsen on Building Smarter Retrieval, AI App Must-Have Features & Current State of Vector DBs
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
--------
51:08
--------
51:08
Ep 70: Karol Hausman and Danny Driess (Physical Intelligence) Unpack the Most Recent Breakthroughs & Path to Generalist Robots
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
--------
1:09:57
--------
1:09:57
Ep 69: Co-Founder of Databricks & LMArena on Current Eval Limitations, Why China is Winning Open Source and Future of AI Infrastructure
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
--------
54:57
--------
54:57
Ep 68: CEO of Mercor Brendan Foody on Evals Replacing Knowledge Work, AI x Hiring Today & the Future of Data Labeling
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
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.
Listen to Unsupervised Learning, The AI Daily Brief (Formerly The AI Breakdown): Artificial Intelligence News and Analysis and many other podcasts from around the world with the radio.net app
UK