In just two months, a scrappy three-person team at OpenAI sprinted to fulfill what the entire AI field has been chasing for years—gold-level performance on the International Mathematical Olympiad problems. Alex Wei, Sheryl Hsu and Noam Brown discuss their unique approach using general-purpose reinforcement learning techniques on hard-to-verify tasks rather than formal verification tools. The model showed surprising self-awareness by admitting it couldn’t solve problem six, and revealed the humbling gap between solving competition problems and genuine mathematical research breakthroughs.

Hosted by Sonya Huang, Sequoia Capital

Isa Fulford, Casey Chu, and Edward Sun from OpenAI's ChatGPT agent team reveal how they combined Deep Research and Operator into a single, powerful AI agent that can perform complex, multi-step tasks lasting up to an hour. By giving the model access to a virtual computer with text browsing, visual browsing, terminal access, and API integrations—all with shared state—they've created what may be the first truly embodied AI assistant. The team discusses their reinforcement learning approach, safety mitigations for real-world actions, and how small teams can build transformative AI products through close research-applied collaboration.

Hosted by Sonya Huang and Lauren Reeder, Sequoia Capital

Pushmeet Kohli leads AI for Science at DeepMind, where his team has created AlphaEvolve, an AI system that discovers entirely new algorithms and proves mathematical results that have eluded researchers for decades. From improving 50-year-old matrix multiplication algorithms to generating interpretable code for complex problems like data center scheduling, AlphaEvolve represents a new paradigm where LLMs coupled with evolutionary search can outperform human experts. Pushmeet explains the technical architecture behind these breakthroughs and shares insights from collaborations with mathematicians like Terence Tao, while discussing how AI is accelerating scientific discovery across domains from chip design to materials science.

Hosted by Sonya Huang and Pat Grady, Sequoia Capital

Eric Ho is building Goodfire to solve one of AI’s most critical challenges: understanding what’s actually happening inside neural networks. His team is developing techniques to understand, audit and edit neural networks at the feature level. Eric discusses breakthrough results in resolving superposition through sparse autoencoders, successful model editing demonstrations and real-world applications in genomics with Arc Institute's DNA foundation models. He argues that interpretability will be critical as AI systems become more powerful and take on mission-critical roles in society.

Hosted by Sonya Huang and Roelof Botha, Sequoia Capital

Mentioned in this episode:

Mech interp: Mechanistic interpretability, list of important papers here

Phineas Gage: 19th century railway engineer who lost most of his brain’s left frontal lobe in an accident. Became a famous case study in neuroscience.

Human Genome Project: Effort from 1990-2003 to generate the first sequence of the human genome which accelerated the study of human biology

Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs

Zoom In: An Introduction to Circuits: First important mechanistic interpretability paper from OpenAI in 2020

Superposition: Concept from physics applied to interpretability that allows neural networks to simulate larger networks (e.g. more concepts than neurons)

Apollo Research: AI safety company that designs AI model evaluations and conducts interpretability research

Towards Monosemanticity: Decomposing Language Models With Dictionary Learning. 2023 Anthropic paper that uses a sparse autoencoder to extract interpretable features; followed by Scaling Monosemanticity

Under the Hood of a Reasoning Model: 2025 Goodfire paper that interprets DeepSeek’s reasoning model R1

Auto-interpretability: The ability to use LLMs to automatically write explanations for the behavior of neurons in LLMs

Interpreting Evo 2: Arc Institute's Next-Generation Genomic Foundation Model. (see episode with Arc co-founder Patrick Hsu)

Paint with Ember: Canvas interface from Goodfire that lets you steer an LLM’s visual output  in real time (paper here)

Model diffing: Interpreting how a model differs from checkpoint to checkpoint during finetuning

Feature steering: The ability to change the style of LLM output by up or down weighting features (e.g. talking like a pirate vs factual information about the Andromeda Galaxy)

Weight based interpretability: Method for directly decomposing neural network parameters into mechanistic components, instead of using features

The Urgency of Interpretability: Essay by Anthropic founder Dario Amodei

On the Biology of a Large Language Model: Goodfire collaboration with Anthropic

Mati Staniszewski, co-founder and CEO of ElevenLabs, explains how staying laser-focused on audio innovation has allowed his company to thrive despite the push into multimodality from foundation models. From a high school friendship in Poland to building one of the fastest-growing AI companies, Mati shares how ElevenLabs transformed text-to-speech with contextual understanding and emotional delivery. He discusses the company's viral moments (from Harry Potter by Balenciaga to powering Darth Vader in Fortnite), and explains how ElevenLabs is creating the infrastructure for voice agents and real-time translation that could eliminate language barriers worldwide.

Hosted by: Pat Grady, Sequoia Capital

Mentioned in this episode:

Attention Is All You Need: The original Transformers paper

Tortoise-tts: Open source text to speech model that was a starting point for ElevenLabs (which now maintains a v2)

Harry Potter by Balenciaga: ElevenLabs’ first big viral moment from 2023

The first AI that can laugh: 2022 blog post backing up ElevenLab’s claim of laughter (it got better in v3)

Darth Vader's voice in Fortnite: ElevenLabs used actual voice clips provided by James Earl Jones before he died

Lex Fridman interviews Prime Minister Modi: ElevenLabs enabled Fridman to speak in Hindi and Modi to speak in English.

Time Person of the Year 2024: ElevenLabs-powered experiment with “conversational journalism”

Iconic Voices: Richard Feynman, Deepak Chopra, Maya Angelou and more available in ElevenLabs reader app

SIP trunking: a method of delivering voice, video, and other unified communications over the internet using the Session Initiation Protocol (SIP)

Genesys: Leading enterprise CX platform for agentic AI

Hitchhiker’s Guide to the Galaxy: Comedy/science-fiction series by Douglas Adams that contains the concept of the Babel Fish instantaneous translator, cited by Mati

FYI: communication and productivity app for creatives that Mati uses, founded by will.i.am

Lovable: prototyping app that Mati loves

Anish Agarwal and Raj Agrawal, co-founders of Traversal, are transforming how enterprises handle critical system failures. Their AI agents can perform root cause analysis in 2-4 minutes instead of the hours typically spent by teams of engineers scrambling in Slack channels. Drawing from their academic research in causal inference and gene regulatory networks, they’ve built agents that systematically traverse complex dependency maps to identify the smoking gun logs and problematic code changes. As AI-generated code becomes more prevalent, Traversal addresses a growing challenge: debugging systems where humans didn’t write the original code, making AI-powered troubleshooting essential for maintaining reliable software at scale.

Hosted by Sonya Huang and Bogomil Balkansky, Sequoia Capital

Mentioned in this episode:

As OpenAI's former Head of Research, Bob McGrew witnessed the company's evolution from GPT-3’s breakthrough to today's reasoning models. He argues that there are three legs of the stool for AGI—Transformers, scaled pre-training, and reasoning—and that the fundamentals that will shape the next decade-plus are already in place. He thinks 2025 will be defined by reasoning while pre-training hits diminishing returns. Bob discusses why the agent economy will price services at compute costs due to near-infinite supply, fundamentally disrupting industries like law and medicine, and how his children use ChatGPT to spark curiosity and agency. From robotics breakthroughs to managing brilliant researchers, Bob offers a unique perspective on AI’s trajectory and where startups can still find defensible opportunities.

Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital

Mentioned in this episode: 

Solving Rubik’s Cube with a robot hand: OpenAI’s original robotics research

Computer Use and Operator: Anthropic and OpenAI reasoning breakthroughs that originated with OpenAi researchers

Skild and Physical Intelligence: Robotics-oriented companies Bob sees as well-positioned now

Distyl: AI company founded by ex-Palintir alums to create enterprise workflows driven by proprietary data

Member of the technical staff: Title at OpenAI designed to break down barriers between AI researchers and engineers

Howie.ai: Scheduling app that Bob uses

Hanson Wang and Alexander Embiricos from OpenAI's Codex team discuss their latest AI coding agent that works independently in its own environment for up to 30 minutes, generating full pull requests from simple task descriptions. They explain how they trained the model beyond competitive programming to match real-world software engineering needs, the shift from pairing with AI to delegating to autonomous agents, and their vision for a future where the majority of code is written by agents working on their own computers. The conversation covers the technical challenges of long-running inference, the importance of creating realistic training environments, and how developers are already using Codex to fix bugs and implement features at OpenAI.

Hosted by Sonya Huang and Lauren Reeder, Sequoia Capital 

Mentioned in this episode: 

The Culture: Sci-Fi series by Iain Banks portraying an optimistic view of AI

The Bitter Lesson: Influential paper by Rich Sutton on the importance of scale as a strategic unlock for AI.

Fresh off impressive releases at Google’s I/O event, three Google Labs leaders explain how they’re reimagining creative tools and productivity workflows. Thomas Iljic details how video generation is merging filmmaking with gaming through generative AI cameras and world-building interfaces in Whisk and Veo. Jaclyn Konzelmann demonstrates how Project Mariner evolved from a disruptive browser takeover to an intelligent background assistant that remembers context across multiple tasks. Simon Tokumine reveals NotebookLM’s expansion beyond viral audio overviews into a comprehensive platform for transforming information into personalized formats. The conversation explores the shift from prompting to showing and telling, the economics of AI-powered e-commerce, and why being “too early” has become Google Labs’ biggest challenge and advantage.

Hosted by Sonya Huang, Sequoia Capital

00:00 Introduction

02:12 Google's AI models and public perception

04:18 Google's history in image and video generation

06:45 Where Whisk and Flow fit

10:30 How close are we to having the ideal tool for the craft?

13:05 Where do the movie and game worlds start to merge?

16:25 Introduction to Project Mariner

17:15 How Mariner works

22:34 Mariner user behaviors

27:07 Temporary tattoos and URL memory

27:53 Project Mariner's future

29:26 Agent capabilities and use cases

31:09 E-commerce and agent interaction

35:03 Notebook LM evolution

48:26 Predictions and future of AI

Mentioned in this episode: 

Whisk: Image and video generation app for consumers

Flow: AI-powered filmmaking with new Veo 3 model

Project Mariner: research prototype exploring the future of human-agent interaction, starting with browsers

NotebookLM: tool for understanding and engaging with complex information including Audio Overviews and now a mobile app

Shop with AI Mode: Shopping app with a virtual try-on tool based on your own photos

Stitch: New prompt-based interface to design UI for mobile and web applications.

ControlNet paper: Outlined an architecture for adding conditional language to direct the outputs of image generation with diffusion models

Former Airbus CTO Paul Eremenko shares his vision for bringing AI to physical engineering, starting with Archie—an AI agent that works alongside human engineers. P-1 AI is tackling the challenge of generating synthetic training data to teach AI systems about complex physical systems, from data center cooling to aircraft design and beyond. Eremenko explains how Archie breaks down engineering tasks into primitive operations and uses a federated approach combining multiple AI models. The goal is to progress from entry-level engineering capabilities to eventually achieving engineering AGI that can design things humans cannot.

Hosted by Sonya Huang and Pat Grady, Sequoia Capital