Unsupervised Learning with Jacob Effron

Max Jungestål, CEO of Legora, joins Jacob Effron and Logan Bartlett to discuss the company's $550M Series D and share a candid account of what building an AI-native company at speed actually looks like from the inside.

Max argues that the AI application layer requires a fundamentally different operating model than traditional SaaS, one built on low ego, constant reinvention, and a willingness to watch nine months of work get washed away by a model update. He walks through how step-function improvements in the underlying models, particularly Opus 4.5 and 4.6, have repeatedly forced Legora to rebuild core product features from scratch, and why he sees that as a feature, not a bug.

On the legal industry, Max offers a ground-level view of how AI is actually diffusing through law firms, less through top-down mandates and more through competitive pressure between firms and, increasingly, from enterprise clients demanding efficiency from their outside counsel. He pushes back on the viability of AI-native law firms, dismisses outcome-based pricing as harder than it looks, and makes the case for why foundation model competition creates tailwinds rather than threats for a company with Legora's depth.

The episode closes with a detailed look at the US expansion strategy, including the deliberate cultural decisions, like flying all New York hires to Stockholm for onboarding, that Max believes are the real source of Legora's compounding advantage.

 

[0:00] Intro

[1:16] Legora's Series D Story

[3:24] Why You Need Low Ego to Build in AI

[5:58] From 60% to 100% Accuracy in One Summer

[7:04] Law Firm Economics Shift

[14:09] Pricing Seats Vs Outcomes

[18:31] Why Foundation Models Entering Legal Helps Legora

[30:10] Convincing a 75-Year-Old Partner to Go All In

[33:02] Hiring Legal Engineers

[34:32] Running an AI-Native Company

[35:57] The Opus 4.5 Christmas Breakthrough

[40:02] Building With Customers

[44:01] All In On US Expansion

[51:22] Stockholm Startup DNA

 

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 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