This episode features a deep dive into the current state of AI model progress with Ari Morcos (CEO of Datalogy AI and former DeepMind/Meta researcher) and Rob Toews (partner at Radical Ventures). The conversation tackles whether model progress is genuinely slowing down or simply shifting into new paradigms, exploring the role of reinforcement learning in scaling capabilities beyond traditional pre-training. They examine the talent wars reshaping AI labs, Google's resurgence with Gemini, the sustainability of massive valuations for companies like OpenAI and Anthropic, and the infrastructure ecosystem supporting this rapid evolution. The discussion weaves together technical insights on data quality, synthetic data generation, and RL environments with strategic perspectives on acquisitions, regulatory challenges, and the future intersection of AI with physical robotics and brain-computer interfaces.

(0:00) Intro
(2:59) Debate on Model Progress
(8:03) Challenges in AI Generalization and RL Environments
(15:44) Enterprise AI and Custom Models
(20:27) Google's AI Ascent and Market Impact
(24:30) Valuations and Future of AI Companies
(27:55) Evaluating xAI's Position in the AI Landscape
(30:31) The Talent War in AI Research
(35:45) The Impact of Acquihires on Startups
(42:35) The Future of AI Infrastructure
(48:28) The Potential of Brain-Computer Interfaces
(54:45) The Evolution of AI and Robotics
(1:00:50) The Importance of Data in AI Research

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

This 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/bbcRiPTRwKoG2tJx8

In 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