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06/11/2025

OpenAI Sora 2 Team: How Generative Video Will Unlock Creativity and World Models

The OpenAI Sora 2 team (Bill Peebles, Thomas Dimson, Rohan Sahai) discuss how they compressed filmmaking from months to days, enabling anyone to create compelling video. Bill, who invented the diffusion transformer that powers Sora and most video generation models, explains how space-time tokens enable object permanence and physics understanding in AI-generated video, and why Sora 2 represents a leap for video. Thomas and Rohan share how they're intentionally designing the Sora product against mindless scrolling, optimizing for creative inspiration, and building the infrastructure for IP holders to participate in a new creator economy. The conversation goes beyond video generation into the team’s vision for world simulators that could one day run scientific experiments, their perspective on co-evolving society alongside technology, and how digital simulations in alternate realities may become the future of knowledge work. Hosted by: Konstantine Buhler and Sonya Huang, Sequoia Capital
04/02/2025

Roblox Studio Head Stef Corazza: Using AI to Empower Creators

Stef Corazza leads generative AI development at Roblox after previously building Adobe’s 3D and AR platforms. His technical expertise, combined with Roblox’s unique relationship with its users, has led to the infusion of AI into its creation tools. Roblox has assembled the world’s largest multimodal dataset. Stef previews the Roblox Assistant and the company’s new 3D foundation model, while emphasizing the importance of maintaining positive experiences and civility on the platform. Mentioned in this episode: Driving Empire: A Roblox car racing game Stef particularly enjoys RDC: Roblox Developer Conference Ego.live: Roblox app to create and share synthetic worlds populated with human-like generative agents and simulated communities| PINNs: Physics Informed Neural Networks ControlNet: A model for controlling image diffusion by conditioning on an additional input image that Stef says can be used as a 2.5D approach to 3D generation. Neural rendering: A combination of deep learning with computer graphics principles developed by Nvidia in its RTX platform Hosted by: Konstantine Buhler and Sonya Huang, Sequoia Capital
02/07/2024

Zapier’s Mike Knoop launches ARC Prize to Jumpstart New Ideas for AGI

As impressive as LLMs are, the growing consensus is that language, scale and compute won’t get us to AGI. Although many AI benchmarks have quickly achieved human-level performance, there is one eval that has barely budged since it was created in 2019. Google researcher François Chollet wrote a paper that year defining intelligence as skill-acquisition efficiency—the ability to learn new skills as humans do, from a small number of examples. To make it testable he proposed a new benchmark, the Abstraction and Reasoning Corpus (ARC), designed to be easy for humans, but hard for AI. Notably, it doesn’t rely on language. Zapier co-founder Mike Knoop read Chollet’s paper as the LLM wave was rising. He worked quickly to integrate generative AI into Zapier’s product, but kept coming back to the lack of progress on the ARC benchmark. In June, Knoop and Chollet launched the ARC Prize, a public competition offering more than $1M to beat and open-source a solution to the ARC-AGI eval. In this episode Mike talks about the new ideas required to solve ARC, shares updates from the first two weeks of the competition, and shares why he’s excited for AGI systems that can innovate alongside humans. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital Mentioned: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models: The 2019 paper that first caught Mike’s attention about the capabilities of LLMs On the Measure of Intelligence: 2019 paper by Google researcher François Chollet that introduced the ARC benchmark, which remains unbeaten ARC Prize 2024: The $1M+ competition Mike and François have launched to drive interest in solving the ARC-AGI eval Sequence to Sequence Learning with Neural Networks: Ilya Sutskever paper from 2014 that influenced the direction of machine translation with deep neural networks. Etched: Luke Miles on LessWrong wrote about the first ASIC chip that accelerates transformers on silicon Kaggle: The leading data science competition platform and online community, acquired by Google in 2017 Lab42: Swiss AU lab that hosted ARCathon precursor to ARC Prize Jack Cole: Researcher on team that was #1 on the leaderboard for ARCathon Ryan Greenblatt: Researcher with current high score (50%) on ARC public leaderboard (00:00) Introduction (01:51) AI at Zapier (08:31) What is ARC AGI? (13:25) What does it mean to efficiently acquire a new skill? (19:03) What approaches will succeed? (21:11) A little bit of a different shape (25:59) The role of code generation and program synthesis (29:11) What types of people are working on this? (31:45) Trying to prove you wrong (34:50) Where are the big labs? (38:21) The world post-AGI (42:51) When will we cross 85% on ARC AGI? (46:12) Will LLMs be part of the solution? (50:13) Lightning round
25/03/2025

From Software Engineers to AI Word Artisans: Filip Kozera of Wordware

Filip Kozera sees parallels between Excel’s democratization of data analytics and Wordware’s mission to put AI development in the hands of knowledge workers. Drawing inspiration from Excel’s 750 million users (compared to 30 million software developers), Wordware is creating tools that balance the rigid structure of programming with the fuzziness of natural language. Filip explains why effective AI development requires working across multiple abstraction layers—from high-level concepts to detailed implementation—while preserving human creative control. He shares his vision for “word artisans” who will use AI to amplify their creative impact. Hosted by Sonya Huang, Sequoia Capital Mentioned in this episode: Lovable: Generative AI app that builds UIs and web apps Her: 2013 Spike Jonze film that Filip uses as an example of how voice will not be the best modality to express knowledge work. Descript: AI video editing app that Filip uses a lot. Granola: AI notetaking app Filip uses every day.. Gemini 2.0 Pro: Google’s newest long context model that can handle 6000 page pdfs. Limitless pendant: Wearable device for collecting personal conversational context to drive AI experiences that Filip can’t wait for to ship. DeepLearning.AI: Andrew Ng’s amazing resource for learning about AI 3Blue1Brown: Grant Sanderson’s incredible channel on YouTube that explains math and AI visually.
26/11/2024

Dust’s Gabriel Hubert and Stanislas Polu: Getting the Most From AI With Multiple Custom Agents

Founded in early 2023 after spending years at Stripe and OpenAI, Gabriel Hubert and Stanislas Polu started Dust with the view that one model will not rule them all, and that multi-model integration will be key to getting the most value out of AI assistants. In this episode we’ll hear why they believe the proprietary data you have in silos will be key to unlocking the full power of AI, get their perspective on the evolving model landscape, and how AI can augment rather than replace human capabilities. Hosted by: Konstantine Buhler and Pat Grady, Sequoia Capital 00:00 - Introduction 02:16 - One model will not rule them all 07:15 - Reasoning breakthroughs 11:15 - Trends in AI models 13:32 - The future of the open source ecosystem 16:16 - Model quality and performance 21:44 - “No GPUs before PMF” 27:24 - Dust in action 37:40 - How do you find “the makers” 42:36 - The beliefs Dust lives by 50:03 - Keeping the human in the loop 52:33 - Second time founders 56:15 - Lightning round
08/07/2025

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

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
05/12/2024

YouTube ft. Steve Chen - 18 Months That Changed the Internet

This episode takes us back to the earliest days of YouTube, as the founders explain why it was a longshot that succeeded against all odds. When cofounders Steve Chen, Chad Hurley and Jawed Karim left PayPal to start YouTube, it wasn’t even clear that the nascent broadband infrastructure could support playing video in a browser. In a brief period until its acquisition by Google—from its first incarnation as a video dating site to confronting daunting technical and legal challenges—the early story of YouTube is an underdog tale of scrappy upstarts who ended up changing the world. Host: Roelof Botha, Sequoia Capital Featuring: Steve Chen, Jawed Karim, Zahavah Levine, Colin Corbett, Yu Pan
7月7日

Inside Zipline's Autonomous System: 140M Miles, Zero Incidents

The largest commercial autonomous system on earth isn't a robotaxi fleet — it's Zipline, which has flown 140 million autonomous miles with zero safety incidents. Co-founder Keller Rinaudo Cliffton and Eric Watson, who leads systems engineering and safety, explain why the drone itself is only 15% of the solution. The rest spans inventory management, air traffic integration, and engineering systems such as a dual flight computer failover protocol that recently saved a delivery mid-flight. They trace Zipline's path from launching blood delivery in Rwanda in 2016 (when drone delivery was illegal in the US) to a 51% reduction in maternal mortality in that country, a $550 million commercial diplomacy partnership with the State Department, and a cost curve that fell from $300 per delivery to $12. Zipline is now racing toward a million deliveries a day, and a quiet inflection point when autonomous delivery becomes cheaper than sending a car. Hosted by Alfred Lin and Pat Grady, Sequoia Capital
11/07/2025

DeepMind's Pushmeet Kohli on AI's Scientific Revolution

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
6月2日

Knowing What Your Customers Want, All the Time: Listen Labs' Alfred Wahlforss

Alfred Wahlforss, co-founder and CEO of Listen Labs, is building an AI agent that interviews your customers at a scale no focus group ever could—thousands of voice conversations at once, drawn from an audience of 30 million people. A year after launch, Listen serves hundreds of Fortune 100s to Startups including Microsoft, Google, NBC Universal, P&G, Anthropic, Cursor, and Cognition. Alfred explains the counterintuitive finding underneath it all: people are often more honest with an AI than a human interviewer, opening up to a non-judgmental entity that costs less and never makes them feel rushed. He walks through why interview transcripts—not credit card data or behavioral logs—turn out to be the richest fuel for predicting how customers will behave, how Listen back-tests its simulations to know which questions it can and can't answer, and why 80% of the company's engineering goes into building the right audience. As AGI makes building trivial, Alfred argues the scarce resource becomes knowing what to build. That's the loop Listen wants to own.

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