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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

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