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Treść dostarczona przez Sequoia Capital. Cała zawartość podcastów, w tym odcinki, grafika i opisy podcastów, jest przesyłana i udostępniana bezpośrednio przez Sequoia Capital lub jego partnera na platformie podcastów. Jeśli uważasz, że ktoś wykorzystuje Twoje dzieło chronione prawem autorskim bez Twojej zgody, możesz postępować zgodnie z procedurą opisaną tutaj https://pl.player.fm/legal.
Join us as we train our neural nets on the theme of the century: AI. Sonya Huang, Pat Grady and more Sequoia Capital partners host conversations with leading AI builders and researchers to ask critical questions and develop a deeper understanding of the evolving technologies—and their implications for technology, business and society. The content of this podcast does not constitute investment advice, an offer to provide investment advisory services, or an offer to sell or solicitation of an offer to buy an interest in any investment fund.
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Treść dostarczona przez Sequoia Capital. Cała zawartość podcastów, w tym odcinki, grafika i opisy podcastów, jest przesyłana i udostępniana bezpośrednio przez Sequoia Capital lub jego partnera na platformie podcastów. Jeśli uważasz, że ktoś wykorzystuje Twoje dzieło chronione prawem autorskim bez Twojej zgody, możesz postępować zgodnie z procedurą opisaną tutaj https://pl.player.fm/legal.
Join us as we train our neural nets on the theme of the century: AI. Sonya Huang, Pat Grady and more Sequoia Capital partners host conversations with leading AI builders and researchers to ask critical questions and develop a deeper understanding of the evolving technologies—and their implications for technology, business and society. The content of this podcast does not constitute investment advice, an offer to provide investment advisory services, or an offer to sell or solicitation of an offer to buy an interest in any investment fund.
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1 Palo Alto Networks’s Nikesh Arora: AI, Security and the New World Order 1:00:08
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Palo Alto Networks’s CEO Nikesh Arora dispels DeepSeek hype by detailing all of the guardrails enterprises need to have in place to give AI agents “arms and legs.” No matter the model, deploying applications for precision-use cases means superimposing better controls. Arora emphasizes that the real challenge isn’t just blocking threats but matching the accelerated pace of AI-powered attacks, requiring a fundamental shift from prevention-focused to real-time detection and response systems. CISOs are risk managers, but legacy companies competing with more risk-tolerant startups need to move quickly and embrace change. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital Mentioned in this episode: Cortex XSIAM : Security operations and incident remediation platform from Palo Alto Networks…
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1 MongoDB’s Sahir Azam: Vector Databases and the Data Structure of AI 44:26
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MongoDB product leader Sahir Azam explains how vector databases have evolved from semantic search to become the essential memory and state layer for AI applications. He describes his view of how AI is transforming software development generally, and how combining vectors, graphs and traditional data structures enables high-quality retrieval needed for mission-critical enterprise AI use cases. Drawing from MongoDB's successful cloud transformation, Azam shares his vision for democratizing AI development by making sophisticated capabilities accessible to mainstream developers through integrated tools and abstractions. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital Mentioned in this episode: Introducing ambient agents : Blog post by Langchain on a new UX pattern where AI agents can listen to an event stream and act on it Google Gemini Deep Research : Sahir enjoys its amazing product experience Perplexity : AI search app that Sahir admires for its product craft Snipd : AI powered podcast app Sahir likes…
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1 Roblox Studio Head Stef Corazza: Using AI to Empower Creators 54:46
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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…
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1 ReflectionAI Founder Ioannis Antonoglou: From AlphaGo to AGI 52:29
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Ioannis Antonoglou, founding engineer at DeepMind and co-founder of ReflectionAI, has seen the triumphs of reinforcement learning firsthand. From AlphaGo to AlphaZero and MuZero, Ioannis has built the most powerful agents in the world. Ioannis breaks down key moments in AlphaGo's game against Lee Sodol (Moves 37 and 78), the importance of self-play and the impact of scale, reliability, planning and in-context learning as core factors that will unlock the next level of progress in AI. Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital Mentioned in this episode: PPO : Proximal Policy Optimization algorithm developed by DeepMind in game environments. Also used by OpenAI for RLHF in ChatGPT. MuJoCo : Open source physics engine used to develop PPO Monte Carlo Tree Search : Heuristic search algorithm used in AlphaGo as well as video compression for YouTube and the self-driving system at Tesla AlphaZero : The DeepMind model that taught itself from scratch how to master the games of chess, shogi and Go MuZero : The DeepMind follow up to AlphaZero that mastered games without knowing the rules and able to plan winning strategies in unknown environments AlphaChem : Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies DQN : Deep Q-Network, Introduced in 2013 paper, Playing Atari with Deep Reinforcement Learning AlphaFold : DeepMind model for predicting protein structures for which Demis Hassabis, John Jumper and David Baker won the 2024 Nobel Prize in Chemistry…
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1 Kumo’s Hema Raghavan: Turning Graph AI into ROI 52:06
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Hema Raghavan is co-founder of Kumo, a company that makes graph neural networks accessible to enterprises by connecting to their relational data stored in Snowflake and Databricks. Hema talks about how running GNNs on GPUs has led to breakthroughs in performance as well as the query language Kumo developed to help companies predict future data points. Although approachable for non-technical users, the product provides full control for data scientists who use Kumo to automate time-consuming feature engineering pipelines. Mentioned in this episode: Graph Neural Networks : Learning mechanism for data in graph format, the basis of the Kumo product Graph RAG : Popular extension of retrieval-augmented generation using GNNs LiGNN : Graph Neural Networks at LinkedIn paper KDD : Knowledge Discovery and Data Mining Conference Hosted by: Konstantine Buhler and Sonya Huang, Sequoia Capital…
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1 Databricks Founder Ion Stoica: Turning Academic Open Source into Startup Success 1:00:05
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Berkeley professor Ion Stoica, co-founder of Databricks and Anyscale, transformed the open source projects Spark and Ray into successful AI infrastructure companies. He talks about what mattered most for Databricks' success -- the focus on making Spark win and making Databricks the best place to run Spark. He highlights the importance of striking key partnerships -- the Microsoft partnership in particular that accelerated Databricks' growth and contributed to Spark's dominance among data scientists and AI engineers. He also shares his perspective on finding new problems to work on, which holds lessons for aspiring founders and builders: 1) building systems in new areas that, if widely adopted, put you in the best position to understand the new problem space, and 2) focusing on a problem that is more important tomorrow than today. Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital Mentioned in this episode: Spark : The open source platform for data engineering that Databricks was originally based on. Ray : Open source framework to manage, executes and optimizes compute needs across AI workloads, now productized through Anyscale MosaicML : Generative AI startups founded by Naveen Rao that Databricks acquired in 2023. Unity Catalog : Data and AI governance solution from Databricks. CIB Berkeley : Multi-strategy hedge fund at UC Berkeley that commercializes research in the UC system. Hadoop : A long-time leading platform for large scale distributed computing. VLLM and Chatbot Arena : Two of Ion’s students’ projects that he wanted to highlight.…
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1 XBOW CEO and GitHub Copilot Creator Oege de Moor: Cracking the Code on Offensive Security With AI 51:37
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Oege de Moor, the creator of GitHub Copilot, discusses how XBOW’s AI offensive security system matches and even outperforms top human penetration testers, completing security assessments in minutes instead of days. The team’s speed and focus is transforming the niche market of pen testing with an always-on service-as-a-software platform. Oege describes how he is building a large and sustainable business while also creating a product that will “protect all the software in the free world.” XBOW shows how AI is essential for protecting software systems as the amount of AI-generated code increases along with the scale and sophistication of cyber threats. Hosted by: Konstantine Buhler and Sonya Huang, Sequoia Capital Mentioned in this episode: Semmle : Oege’s previous startup, a code analysis tool to secure software, acquired in 2019 by GitHub Nico Waisman : Head of security at XBOW, previously a researcher at Semmle The Bitter Lesson : Highly influential post by Richard Sutton HackerOne : Cybersecurity company that runs one of the largest bug bounty programs Suno : AI songwriting app that Oege loves Machines of Loving Grace : Essay by Anthropic founder, Dario Amodei…
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1 Ramp CEO Eric Glyman: Using AI to Build “Self-Driving Money” 38:48
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When ChatGPT ushered in a new paradigm of AI in everyday use, many companies attempted to adapt to the new paradigm by rushing to add chat interfaces to their products. Eric has a different take—he doesn’t think chatbots are the right form factor for everything. He thinks “zero-touch” automation that works invisibly in the background can be more valuable in many cases. He cites self-driving cars as an analogy—or in this case, “self-driving money.” Ramp is a new kind of finance management company for businesses, offering AI-powered financial tools to help companies handle spending and expense processes. We’ll hear why Eric thinks AI that you never see is one of the most powerful instruments for reducing time spent on drudgery and unlocking more time for meaningful work. Hosted by: Ravi Gupta and Sonya Huang, Sequoia Capital Mentioned in this episode: Paribus : Glyman’s previous company, acquired by Capital One in 2016 Karim Atiyeh : Cofounder and CTO at Ramp and Glyman’s cofounder at Paribus Devin : AI agent product from Cognition Labs and Glyman’s favorite AI app Hit Refresh : Book by Satya Nadella…
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1 Dust’s Gabriel Hubert and Stanislas Polu: Getting the Most From AI With Multiple Custom Agents 1:03:07
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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…
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1 Clay’s Kareem Amin on Building the Sales ‘System of Action’ with AI 51:38
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Clay is leveraging AI to help go-to-market teams unleash creativity and be more effective in their work, powering custom workflows for everything from targeted outreach to personalized landing pages. It’s one of the fastest growing AI-native applications, with over 4,500 customers and 100,000 users. Founder and CEO Kareem Amin describes Clay’s technology, and its approach to balancing imagination and automation in order to help its customers achieve new levels of go-to-market success. Hosted by: Alfred Lin, Sequoia Capital…
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1 Decart’s Dean Leitersdorf on AI-Generated Video Games and Worlds 46:34
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Can GenAI allow us to connect our imagination to what we see on our screens? Decart’s Dean Leitersdorf believes it can. In this episode, Dean Leitersdorf breaks down how Decart is pushing the boundaries of compute in order to create AI-generated consumer experiences, from fully playable video games to immersive worlds. From achieving real-time video inference on existing hardware to building a fully vertically integrated stack, Dean explains why solving fundamental limitations rather than specific problems could lead to the next trillion-dollar company. Hosted by: Sonya Huang and Shaun Maguire, Sequoia Capital 00:00 Introduction 03:22 About Oasis 05:25 Solving a problem vs overcoming a limitation 08:42 The role of game engines 11:15 How video real-time inference works 14:10 World model vs pixel representation 17:17 Vertical integration 34:20 Building a moat 41:35 The future of consumer entertainment 43:17 Rapid fire questions…
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1 How Glean CEO Arvind Jain Solved the Enterprise Search Problem – and What It Means for AI at Work 44:48
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Years before co-founding Glean, Arvind was an early Google employee who helped design the search algorithm. Today, Glean is building search and work assistants inside the enterprise, which is arguably an even harder problem. One of the reasons enterprise search is so difficult is that each individual at the company has different permissions and access to different documents and information, meaning that every search needs to be fully personalized. Solving this difficult ingestion and ranking problem also unlocks a key problem for AI: feeding the right context into LLMs to make them useful for your enterprise context. Arvind and his team are harnessing generative AI to synthesize, make connections, and turbo-change knowledge work. Hear Arvind’s vision for what kind of work we’ll do when work AI assistants reach their potential. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital 00:00 - Introduction 08:35 - Search rankings 11:30 - Retrieval-Augmented Generation 15:52 - Where enterprise search meets RAG 19:13 - How is Glean changing work? 26:08 - Agentic reasoning 31:18 - Act 2: application platform 33:36 - Developers building on Glean 35:54 - 5 years into the future 38:48 - Advice for founders…
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1 OpenAI Researcher Dan Roberts on What Physics Can Teach Us About AI 41:42
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In recent years there’s been an influx of theoretical physicists into the leading AI labs. Do they have unique capabilities suited to studying large models or is it just herd behavior? To find out, we talked to our former AI Fellow (and now OpenAI researcher) Dan Roberts. Roberts, co-author of The Principles of Deep Learning Theory , is at the forefront of research that applies the tools of theoretical physics to another type of large complex system, deep neural networks. Dan believes that DLLs, and eventually LLMs, are interpretable in the same way a large collection of atoms is—at the system level. He also thinks that emphasis on scaling laws will balance with new ideas and architectures over time as scaling asymptotes economically. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital Mentioned in this episode: The Principles of Deep Learning Theory : An Effective Theory Approach to Understanding Neural Networks , by Daniel A. Roberts, Sho Yaida, Boris Hanin Black Holes and the Intelligence Explosion : Extreme scenarios of AI focus on what is logically possible rather than what is physically possible. What does physics have to say about AI risk? Yang-Mills & The Mass Gap : An unsolved Millennium Prize problem AI Math Olympiad : Dan is on the prize committee…
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1 Google NotebookLM’s Raiza Martin and Jason Spielman on Creating Delightful AI Podcast Hosts and the Potential for Source-Grounded AI 32:07
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NotebookLM from Google Labs has become the breakout viral AI product of the year. The feature that catapulted it to viral fame is Audio Overview, which generates eerily realistic two-host podcast audio from any input you upload—written doc, audio or video file, or even a PDF. But to describe NotebookLM as a “podcast generator” is to vastly undersell it. The real magic of the product is in offering multi-modal dimensions to explore your own content in new ways—with context that’s surprisingly additive. 200-page training manuals become synthesized into digestible chapters, turned into a 10-minute podcast—or both—and shared with the sales team, just to cite one example. Raiza Martin and Jason Speilman join us to discuss how the magic happens, and what’s next for source-grounded AI. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital…
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1 Snowflake CEO Sridhar Ramaswamy on Using Data to Create Simple, Reliable AI for Businesses 59:29
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All of us as consumers have felt the magic of ChatGPT—but also the occasional errors and hallucinations that make off-the-shelf language models problematic for business use cases with no tolerance for errors. Case in point: A model deployed to help create a summary for this episode stated that Sridhar Ramaswamy previously led PyTorch at Meta. He did not. He spent years running Google’s ads business and now serves as CEO of Snowflake, which he describes as the data cloud for the AI era. Ramaswamy discusses how smart systems design helped Snowflake create reliable "talk-to-your-data" applications with over 90% accuracy, compared to around 45% for out-of-the-box solutions using off the shelf LLMs. He describes Snowflake's commitment to making reliable AI simple for their customers, turning complex software engineering projects into straightforward tasks. Finally, he stresses that even as frontier models progress, there is significant value to be unlocked from current models by applying them more effectively across various domains. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital Mentioned in this episode: Cortex Analyst : Snowflake’s talk-to-your-data API Document AI : Snowflake feature that extracts in structured information from documents…
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