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Latest Episodes:

Delphi’s Dara Ladjevardian: How AI Digital Minds Can Scale Human Connection Aug 12, 2025

Show notes

Dara Ladjevardian, founder and CEO of Delphi, is creating digital minds that allow people to scale their thoughts and availability without replacing human connection. Inspired by Ray Kurzweil’s theory of mind as a hierarchy of pattern recognizers, Dara built an adaptive temporal knowledge graph that captures how people think and reason. From helping CEOs train new hires to enabling coaches to monetize their expertise 24/7, Delphi represents a new form of conversational media. Dara explains why authentic human representation matters, how digital minds actually increase desire for real human connection, and why he believes 2026 will be the tipping point for adoption for digital minds.

Hosted by Sonya Huang and Jess Lee, Sequoia Capital

Mentioned in this episode:

How to Create a Mind: 2012 book by Ray Kurzweil that inspired Dara
The Memoirs of Akbar Ladjevardian: 2008 book about Dara’s grandfather, an Iranian industrialist, that led him to create his first “digital mind”
Build: 2022 book by Tony Fadell that refers to itself as “a mentor in a box”; another inspiration for Dara
The 2 Sigma Problem: 1984 paper by Benjamin Bloom about how students that receive one-on-one tutoring perform two standard deviations better than students educated in a classroom environment

Vercel CEO Guillermo Rauch: Building the Generative Web with AI Aug 05, 2025

Show notes

Vercel CEO Guillermo Rauch has spent years obsessing over reducing the friction between having an idea and getting it online. Now with AI, he's achieving something even more ambitious: making software creation accessible to anyone with a keyboard. Guillermo explains how v0 has grown to 3 million users by focusing on reliability and quality, why ChatGPT has become their fastest-growing customer acquisition channel, and how AI is enabling “virtual coworkers” across design, development, and marketing. He shares his contrarian view that the future belongs to ephemeral, generated-on-demand applications rather than traditional installed software, and why he believes we're on the cusp of the biggest transformation to the web in its history.

Hosted by Sonya Huang and Pat Grady, Sequoia Capital

OpenAI’s IMO Team on Why Models Are Finally Solving Elite-Level Math Jul 30, 2025

Show notes

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

OpenAI Just Released ChatGPT Agent, Its Most Powerful Agent Yet Jul 22, 2025

Show notes

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

DeepMind's Pushmeet Kohli on AI's Scientific Revolution Jul 11, 2025

Show notes

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

Mapping the Mind of a Neural Net: Goodfire’s Eric Ho on the Future of Interpretability Jul 08, 2025

Show notes

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

ElevenLabs’ Mati Staniszewski: Why Voice Will Be the Fundamental Interface for Tech Jul 01, 2025

Show notes

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

From DevOps ‘Heart Attacks’ to AI-Powered Diagnostics With Traversal’s AI Agents Jun 24, 2025

Show notes

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:

SRE: Site reliability engineering. The function within engineering teams that monitors and improves the availability and performance of software systems and services.
Golden signals: four key metrics used by Site Reliability Engineers (SREs) to monitor the health and performance of IT systems: latency, traffic, errors and saturation.
MELT data: Metrics, events, log, and traces. A framework for observability.
The Bitter Lesson: Another mention of Nobel Prize winner Rich Sutton’s influential post.

The Breakthroughs Needed for AGI Have Already Been Made: OpenAI Former Research Head Bob McGrew Jun 17, 2025

Show notes

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

OpenAI Codex Team: From Coding Autocomplete to Asynchronous Autonomous Agents Jun 10, 2025

Show notes

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: