Advertise on podcast: Unsupervised Learning with Jacob Effron

This episode features Jerry Tworek, a key architect behind OpenAI's breakthrough reasoning models (o1, o3) and Codex, discussing the current state and future of AI. Jerry explores the real limits and promise of scaling pre-training and reinforcement learning, arguing that while these paradigms deliver predictable improvements, they're fundamentally constrained by data availability and struggle with generalization beyond their training objectives. He reveals his updated belief that continual learning—the ability for models to update themselves based on failure and work through problems autonomously—is necessary for AGI, as current models hit walls and become "hopeless" when stuck. Jerry discusses the convergence of major labs toward similar approaches driven by economic forces, the tension between exploration and exploitation in research, and why he left OpenAI to pursue new research directions. He offers candid insights on the competitive dynamics between labs, the focus required to win in specific domains like coding, what makes great AI researchers, and his surprisingly near-term predictions for robotics (2-3 years) while warning about the societal implications of widespread work automation that we're not adequately preparing for.   (0:00) Intro (1:26) Scaling Paradigms in AI (3:36) Challenges in Reinforcement Learning (11:48) AGI Timelines (18:36) Converging Labs (25:05) Jerry’s Departure from OpenAI (31:18) Pivotal Decisions in OpenAI’s Journey (35:06) Balancing Research and Product Development (38:42) The Future of AI Coding (41:33) Specialization vs. Generalization in AI (48:47) Hiring and Building Research Teams (55:21) 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

Ari Morcos and Rob Toews return for their spiciest conversation yet. Fresh from NeurIPS, they debate whether models are truly plateauing or if we're just myopically focused on LLMs while breakthroughs happen in other modalities. They reveal why infinite capital at labs may actually constrain innovation, explain the narrow "Goldilocks zone" where RL actually works, and argue why U.S. chip restrictions may have backfired catastrophically—accelerating China's path to self-sufficiency by a decade. The conversation covers OpenAI's code red moment and structural vulnerabilities, the mystique surrounding SSI and Ilya's "two words," and why the real bottleneck in AI research is compute, not ideas. The episode closes with bold 2026 predictions: Rob forecasts Sam Altman won't be OpenAI's CEO by year-end, while Ari gives 50%+ odds a Chinese open-source model will be the world's best at least once next year.   (0:00) Intro (1:51) Reflections on NeurIPS Conference (5:14) Are AI Models Plateauing? (11:12) Reinforcement Learning and Enterprise Adoption (16:16) Future Research Vectors in AI (28:40) The Role of Neo Labs (39:35) The Myth of the Great Man Theory in Science (41:47) OpenAI's Code Red and Market Position (47:19) Disney and OpenAI's Strategic Partnership (51:28) Meta's Super Intelligence Team Challenges (54:33) US-China AI Chip Dynamics (1:00:54) Amazon's Nova Forge and Enterprise AI (1:03:38) End of Year Reflections and Predictions   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

Edwin Chen is the founder and CEO of Surge AI, the data infrastructure company behind nearly every major frontier model. Surge works with OpenAI, Anthropic, Meta, and Google, providing the high-quality data and evaluation infrastructure that powers their models.    Edwin reveals why optimizing for popular benchmarks like LMArena is "basically optimizing for clickbait," how one frontier lab's models regressed for 6-12 months without anyone knowing, and why the industry's approach to measurement is fundamentally broken. Jacob and Edwin discuss what actually makes elite AI evaluators, why "there's never going to be a one size fits all solution" for AI models, and how frontier labs are taking surprisingly divergent paths to AGI.   (0:00) Intro (0:56) The Pitfalls of Optimizing for LMArena (4:34) Issues with Data Quality and Measurement (9:44) The Importance of Human Evaluations (13:40) The Rise of RL Environments (17:21) Challenges and Lessons in Model Training (19:59) Silicon Valley's Pivot Culture (23:06) Technology-Driven Approach (24:18) Quality Beyond Credentials (27:51) Impact of Scale Acquisition (28:35) Hiring for Research Culture (30:48) Divergence in AI Training Paradigms (34:16) Future of AI Models (39:32) Multimodal AI and Quality (43:44) 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

This episode features Olivier Godement, Head of Product for Business Products at OpenAI, discussing the current state and future of AI adoption in enterprises, with a particular focus on the recent releases of GPT 5.1 and Codex. The conversation explores how these models are achieving meaningful automation in specific domains like coding, customer support, and life sciences: where companies like Amgen are using AI to accelerate drug development timelines from months to weeks through automated regulatory documentation. Olivier reveals that while complete job automation remains challenging and requires substantial scaffolding, harnesses, and evaluation frameworks, certain use cases like coding are reaching a tipping point where engineers would "riot" if AI tools were taken away. The discussion covers the importance of cost reduction in unlocking new use cases, the emerging significance of reinforcement fine-tuning (RFT) for frontier customers, and OpenAI's philosophy of providing not just models but reference architectures and harnesses to maximize developer success.   (0:00) Intro (1:46) Discussing GPT-5.1 (2:57) Adoption and Impact of Codex (4:09) Scientific Community's Use of GPT-5.1 (6:37) Challenges in AI Automation (8:19) AI in Life Sciences and Pharma (11:48) Enterprise AI Adoption and Ecosystem (16:04) Future of AI Models and Continuous Learning (24:20) Cost and Efficiency in AI Deployment (27:10) Reinforcement Learning and Enterprise Use Cases (31:17) Key Factors Influencing Model Choice (34:21) Challenges in Model Deployment and Adaptation (38:29) Voice Technology: The Next Frontier (41:08) The Rise of AI in Software Engineering (52:09) 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

This week on Unsupervised Learning, Jacob Effron is joined by Jordan Schneider, host of China Talk, who challenges widespread assumptions about US-China AI competition. China's AI development is driven by private capital and market competition—not central government planning—with companies like DeepSeek, Alibaba, and ByteDance operating more like Silicon Valley startups than state projects. The critical bottleneck is compute: the West maintains a 10-15x advantage in advanced chips, and US export controls implemented one month before ChatGPT created a structural edge favoring America for years. Chinese companies aggressively open-source models from strategic necessity—they couldn't establish a quality gap justifying paid access like OpenAI. Jordan explains why the "Goldilocks strategy" of controlled chip dependency fails, why expert consensus opposes selling advanced semiconductors to China despite Nvidia's lobbying, and how Taiwan's invasion risk is driven more by domestic politics than AGI scenarios. China's real advantage may emerge in robotics manufacturing at scale, where they're already deploying while the US debates strategy.   Inside the Politburo's AI Study Session: https://www.chinatalk.media/p/xi-takes-an-ai-masterclass Submit your questions to Jacob here: https://docs.google.com/forms/d/1vHBYv0bTT_EgFWTjbKnLr_sn3pZnFmcFGWYVTltKEco/edit   (0:00) Intro (1:45) The Chinese AI Ecosystem: Pre and Post ChatGPT (3:45) Government Influence and Private Sector Dynamics (6:40) Venture Funding and Major Players (8:36) Talent and International Collaboration (11:25) Open Source Models and Market Dynamics (15:24) What Role Does The Chinese Government Play? (31:17) US-China AI Policy and Strategic Competition (36:18) The Argument for Selling AI Accelerators (37:02) Risks of Not Selling to China (43:34) Technological Constraints and Huawei's Challenges (51:18) US-China Relations and Taiwan (1:02:46) 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

This episode features Dianne Na Penn, a senior product leader at Anthropic, discussing the launch of Claude Opus 4.5 and the evolution of frontier AI models. The conversation explores how Anthropic approaches model development—balancing ambitious capability roadmaps with user feedback, making strategic bets on areas like agentic coding and computer use while deliberately avoiding others like image generation. Dianne shares insights on the shifting nature of AI evaluation (moving beyond saturated benchmarks like SWE-bench toward more open-ended measures), the evolution of scaffolding from "training wheels" to intelligence amplifiers, and why she believes we're closer to transformative long-running AI than most people think. She also discusses Anthropic's distinctive culture of authenticity, the under appreciated benefits of model alignment for producing independent-thinking AI, and why the real bottleneck to AI agents isn't model capability anymore but product innovation.   (0:00) Intro (0:57) Starting the Work on Opus 4.5 (2:04) Model Capabilities and Surprises (5:59) Computer Use and Practical Applications (7:21) Pricing and Positioning (10:02) Customer Feedback and Early Access (16:44) The Reality of Enterprise Agents (18:47) Future of AI and Long-Running Intelligence (28:06) Anthropic's Culture and Decision Making (30:31) Key Decisions and Fun Moments (33:45) 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

This episode features the core team behind Sora, OpenAI's groundbreaking video generation platform that became the #1 app in the App Store. Bill Peebles (research lead), Rohan Sahai (product lead), and Thomas Dimson (engineering/product lead with Instagram background) discuss the unexpected viral success of Sora's launch, the product journey that led to the breakthrough "cameo" feature (putting yourself in AI-generated videos), and their philosophy of building a creator-first social network that prioritizes human creativity over passive consumption. They reveal the technical milestones in video generation, their small team size (under 50 people total at launch), navigation of content moderation challenges, early monetization strategy, and their ambitious vision for video models as world simulators that could eventually contribute to scientific breakthroughs by 2028. The conversation captures both the tactical product decisions and strategic philosophy that made Sora a cultural phenomenon.   (0:00) Intro (1:35) Unexpected Success of ChatGPT and Sora (3:55) Sora as an Independent App (5:38) Sora Prototypes and Evolution (8:07) User Creativity and Surprising Use Cases (14:46) Celebrity Engagement and Rights Management (17:58) Competition and Future of AI Video Models (25:42) Empowering Creators (31:21) The Evolution of Image Generation (33:36) How Do Models Need to Improve? (42:10) Monetization of Sora (45:54) Global Reach and Cultural Impact (48:38) Moderation and Safety Challenges (50:09) Integration with Other OpenAI Products (52:07) How do Models Learn Physics? (55:16) 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

This episode features Rob Toews from Radical Ventures and Ari Morcos, Head of Research at Datology AI, reacting to Andrej Karpathy's recent statement that AGI is at least a decade away and that current AI capabilities are "slop." The discussion explores whether we're in an AI bubble, with both guests pushing back on overly bearish narratives while acknowledging legitimate concerns about hype and excessive CapEx spending. They debate the sustainability of AI scaling, examining whether continued progress will come from massive compute increases or from efficiency gains through better data quality, architectural innovations, and post-training techniques like reinforcement learning. The conversation also tackles which companies truly need frontier models versus those that can succeed with slightly-behind-the-curve alternatives, the surprisingly static landscape of AI application categories (coding, healthcare, and legal remain dominant), and emerging opportunities from brain-computer interfaces to more efficient scaling methods.   (0:00) Intro (1:04) Debating the AI Bubble (1:50) Over-Hyping AI: Realities and Misconceptions (3:21) Enterprise AI and Data Center Investments (7:46) Consumer Adoption and Monetization Challenges (8:55) AI in Browsers and the Future of Internet Use (14:37) Deepfakes and Ethical Concerns (26:29) AI's Impact on Job Markets and Training (31:38) Google and Anthropic: Strategic Partnerships (34:51) OpenAI's Strategic Deals and Future Prospects (37:12) The Evolution of Vibe Coding (44:35) AI Outside of San Francisco (48:09) Data Moats in AI Startups (50:38) Comparing AI to the Human Brain (56:07) The Role of Physical Infrastructure in AI (56:55) The Potential of Chinese AI Models (1:03:15) Apple's AI Strategy (1:12:35) The Future of AI Applications   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

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