Africa-focused technology, digital and innovation ecosystem insight and commentary.
…
continue reading
Player FM - Internet Radio Done Right
16 subscribers
Checked 5d ago
Added one year ago
Content provided by Sequoia Capital. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Sequoia Capital or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
Similar to Training Data
The power of Data is undeniable. And unharnessed - it’s nothing but chaos. Making data your ally. Using it to lead with confidence and clarity. Host Jess Carter is solving problems in real-time to reveal what’s possible. Helping communities and people thrive. This is Data Driven Leadership, a show brought to you by Resultant.
…
continue reading
Show notes are at https://stevelitchfield.com/sshow/chat.html
…
continue reading
The director’s commentary track for Daring Fireball. Long digressions on Apple, technology, design, movies, and more.
…
continue reading
The American healthcare system is one of the most innovative in the world. But it’s also riddled with complex challenges, such as access to affordable medications, inefficiency and administrative burdens, and communication barriers between providers. There’s clearly a better way—and at Surescripts, we have a unique sightline into what that may be. In this series, host Melanie Marcus, Chief Marketing Officer of Surescripts, sits down with today’s most inspiring and innovative leaders in healt ...
…
continue reading
Download This Show is your weekly guide to the world of media, culture, and technology. From social media to gadgets, streaming services to privacy issues. Each week Rae Johnston and guests take a fun, deep dive into how technology is reshaping our lives.
…
continue reading
A weekly talk show taking a pragmatic look at the art and business of Software Development and the world of technology.
…
continue reading
Android Backstage, a podcast by and for Android developers. Hosted by developers from the Android engineering team, this show covers topics of interest to Android programmers, with in-depth discussions and interviews with engineers on the Android team at Google. Subscribe to Android Developers YouTube → https://goo.gle/AndroidDevs
…
continue reading
Big tech is transforming every aspect of our world. But how, and at what cost? This season of Land of the Giants – The Disney Dilemma – focuses on Disney’s ability to weather the ups and downs of the business cycle and changing tastes and explores what has kept it successful for over 100 years. The entertainment giant has leveraged nostalgia and its intellectual property to build a beloved brand, but after an acquisition spree that included Marvel, Lucasfilm, and 20th Century Fox, can it sus ...
…
continue reading
Investor Shayle Kann is asking big questions about how to decarbonize the planet: How cheap can clean energy get? Will artificial intelligence speed up climate solutions? Where is the smart money going into climate technologies? Every week on Catalyst, Shayle explains the world of climate tech with prominent experts, investors, researchers, and executives. Produced by Latitude Media.
…
continue reading
Player FM - Podcast App
Go offline with the Player FM app!
Go offline with the Player FM app!
))
Daily Deals
Podcasts Worth a Listen
SPONSORED
<
<div class="span index">1</div> <span><a class="" data-remote="true" data-type="html" href="/series/interrogation-nation-full-police-interrogations-serial-killer-docs-and-true-crime-investigations">Interrogation Nation - Full Police Interrogations, Serial Killer Docs and True Crime Investigations</a></span>
1 Interrogation Nation - Full Police Interrogations, Serial Killer Docs and True Crime Investigations
Interrogation Nation - Full Police Interrogations, Serial Killer Documentaries, and True Crime Investigations Welcome to Interrogation Nation—the podcast where the pressure is high, the lights are bright, and the truth has nowhere to hide. Each week, we bring you uncut, full-length police interrogations, raw suspect interviews, and chilling confessions from real criminal cases. Dive deep into the minds of serial killers, murder suspects, and criminal masterminds as detectives work to uncover the truth—one question at a time. We feature: Full police interrogation audio Serial killer documentaries and psychological breakdowns True crime case files, trial footage, and real confessions Exclusive audio from homicide, missing persons, and cold case investigations Whether you're a true crime addict, criminal psychology enthusiast, or just obsessed with what really happens inside the interrogation room, Interrogation Nation gives you front-row access to the raw, unfiltered side of justice. ⚖️ Real voices. Real crimes. Real consequences. 🎧 Subscribe and uncover the truth—one tape at a time. true crime podcast, police interrogations, real interrogations, serial killer interviews, true crime audio, suspect confession tapes, true crime stories, murder confession recordings, interrogation room audio, uncut police tapes, real murder investigations, serial killer podcast, criminal psychology, raw interrogation tapes, interrogation podcast, police interviews, homicide suspect confessions, killer confession podcast, cold case interrogations, full confession audio, interrogation nation Become a supporter of this podcast: https://www.spreaker.com/podcast/interrogation-nation-full-police-interrogations-serial-killer-docs-and-true-crime-investigations--6672917/support .
Databricks Founder Ion Stoica: Turning Academic Open Source into Startup Success
Manage episode 461054921 series 3586723
Content provided by Sequoia Capital. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Sequoia Capital or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
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.
57 episodes
Share
Manage episode 461054921 series 3586723
Content provided by Sequoia Capital. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Sequoia Capital or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
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.
57 episodes
All episodes
×
1 OpenAI Just Released ChatGPT Agent, Its Most Powerful Agent Yet 37:36
37:36 
Play Later 
Play Later 
Lists 
Like 
Liked37:36
Play Later Play Later Lists Like LikedIsa 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…
1 DeepMind's Pushmeet Kohli on AI's Scientific Revolution 41:13
41:13 Play Later Play Later Lists Like Liked41:13
Play Later Play Later Lists Like LikedPushmeet 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…
1 Mapping the Mind of a Neural Net: Goodfire’s Eric Ho on the Future of Interpretability 47:07
47:07 Play Later Play Later Lists Like Liked47:07
Play Later Play Later Lists Like LikedEric 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…
1 ElevenLabs’ Mati Staniszewski: Why Voice Will Be the Fundamental Interface for Tech 59:53
59:53 Play Later Play Later Lists Like Liked59:53
Play Later Play Later Lists Like LikedMati 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…
1 From DevOps ‘Heart Attacks’ to AI-Powered Diagnostics With Traversal’s AI Agents 40:32
40:32 Play Later Play Later Lists Like Liked40:32
Play Later Play Later Lists Like LikedAnish 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.…
1 The Breakthroughs Needed for AGI Have Already Been Made: OpenAI Former Research Head Bob McGrew 48:51
48:51 Play Later Play Later Lists Like Liked48:51
Play Later Play Later Lists Like LikedAs 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…
1 OpenAI Codex Team: From Coding Autocomplete to Asynchronous Autonomous Agents 37:44
37:44 Play Later Play Later Lists Like Liked37:44
Play Later Play Later Lists Like LikedHanson 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.…
1 Google I/O Afterparty: The Future of Human-AI Collaboration, From Veo to Mariner 53:51
53:51 Play Later Play Later Lists Like Liked53:51
Play Later Play Later Lists Like LikedFresh 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…
1 From Data Centers to Dyson Spheres: P-1 AI's Path to Hardware Engineering AGI 38:25
38:25 Play Later Play Later Lists Like Liked38:25
Play Later Play Later Lists Like LikedFormer 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…
1 Gong’s Amit Bendov: From Meeting Recordings to Revenue AI 42:05
42:05 Play Later Play Later Lists Like Liked42:05
Play Later Play Later Lists Like LikedCEO Amit Bendov shares how Gong evolved from a meeting transcription tool to an AI-powered revenue platform that's increasing sales capacity by up to 60%. He explains why task-specific AI agents are the key to enterprise adoption, and why human accountability will remain crucial even as AI takes over routine sales tasks. Amit also reveals how Gong survived recent market headwinds by expanding their product suite while maintaining their customer-first approach. Hosted by Sonya Huang and Pat Grady, Sequoia Capital Mentioned in this episode : “ New paradigm of AI architectures ”: Yann LeCun’s talk at Davos where he talks about the world beyond transformers and LLMs in the next 3-5 years. The Beginning of Infinity : Book by David Deutsch that Amit says is “mind-changing.”…
1 LIVE: Google's Jeff Dean on the Coming Transformations in AI 30:59
30:59 Play Later Play Later Lists Like Liked30:59
Play Later Play Later Lists Like LikedAt AI Ascent 2025, Jeff Dean makes bold predictions. Discover how the pioneer behind Google's TPUs and foundational AI research sees the technology evolving, from specialized hardware to more organic systems, and future engineering capabilities.
1 LIVE: Ambient Agents and the New Agent Inbox ft. Harrison Chase of LangChain 8:28
8:28 Play Later Play Later Lists Like Liked8:28
Play Later Play Later Lists Like LikedRecorded live at Sequoia’s AI Ascent 2025: LangChain CEO Harrison Chase introduces the concept of ambient agents, AI systems that operate continuously in the background responding to events rather than direct human prompts. Learn how these agents differ from traditional chatbots, why human oversight remains essential and how this approach could dramatically scale our ability to leverage AI.…
1 LIVE: How AI is Reinventing Software Business Models ft. Bret Taylor of Sierra 34:30
34:30 Play Later Play Later Lists Like Liked34:30
Play Later Play Later Lists Like LikedRecorded live at Sequoia’s AI Ascent 2025: Sierra co-founder Bret Taylor discusses why AI is driving a fundamental shift from subscription-based pricing to outcomes-based models. Learn why this transition is harder for incumbents than startups, why applied AI and vertical specialization represent the biggest opportunities for entrepreneurs and how to position your AI company for success in this new paradigm.…
1 LIVE: Sam Altman of OpenAI on Building the ‘Core AI Subscription’ for Your Life 32:27
32:27 Play Later Play Later Lists Like Liked32:27
Play Later Play Later Lists Like LikedRecorded live at Sequoia’s AI Ascent 2025: Sam reflects on OpenAI’s evolution from a 14-person research lab to a dominant AI platform. He envisions transforming ChatGPT into a deeply personal AI service that remembers your entire life's context—from conversations to emails—while working seamlessly across all services. Sam describes the generation gap in how users engage with ChatGPT, and makes surprisingly specific predictions for the next 2-3 years of AI evolution.…
1 Workday CEO Carl Eschenbach: Building the System of Record for the AI Era 47:49
47:49 Play Later Play Later Lists Like Liked47:49
Play Later Play Later Lists Like LikedWorkday CEO and Sequoia partner Carl Eschenbach explains how the company is evolving its platform to handle both human and AI workers. He shares Workday’s three-pronged approach to monetizing AI through seat-based pricing, role-based agents and consumption-based API access. Eschenbach discusses why domain-specific agents with curated data will be more valuable than general-purpose models in the enterprise, and how Workday is helping enterprises navigate the transition to an AI-powered workplace while maintaining human connection. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital…
T
Training Data
1 The Quest to ‘Solve All Diseases’ with AI: Isomorphic Labs’ Max Jaderberg 55:40
55:40 Play Later Play Later Lists Like Liked55:40
Play Later Play Later Lists Like LikedAfter pioneering reinforcement learning breakthroughs at DeepMind with Capture the Flag and AlphaStar, Max Jaderberg aims to revolutionize drug discovery with AI as Chief AI Officer of Isomorphic Labs, which was spun out of DeepMind. He discusses how AlphaFold 3's diffusion-based architecture enables unprecedented understanding of molecular interactions, and why we're approaching a "Move 37 moment" in AI-powered drug design where models will surpass human intuition. Max shares his vision for general AI models that can solve all diseases, and the importance of developing agents that can learn to search through the whole potential design space. Hosted by Stephanie Zhan, Sequoia capital Mentioned in this episode : Playing Atari with Deep Reinforcement Learning : Seminal 2013 paper on Reinforcement Learning Capture the Flag : 2019 DeepMind paper on the emergence of cooperative agents AlphaStar : 2019 DeepMind paper on attaining grandmaster level in StarCraft II using multi-agent RL AlphaFold Server : Web interface for AlphaFold 3 model for non-commercial academic use…
T
Training Data
1 Pricing in the AI Era: From Inputs to Outcomes, with Paid CEO Manny Medina 45:29
45:29 Play Later Play Later Lists Like Liked45:29
Play Later Play Later Lists Like LikedFormer Outreach CEO Manny Medina discusses his new company Paid, which provides billing, pricing and margin management tools for AI companies. He explains why traditional SaaS pricing models don’t work for AI businesses, and breaks down emerging approaches like outcome-based and agent-based pricing. Manny shares why he believes focused AI applications targeting specific workflows will win over broad platforms, while emphasizing that AI companies need better tools to understand their unit economics and capture more value. Hosted by Pat Grady and Lauren Reeder, Sequoia Capital Mentioned in this episode: CPQ : Configure, Price, Quote Invent and Wander : Book by Jeff Bezos and Walter Isaacson Foundations of Statistical Natural Language Processing : 1999 book by Chris Manning and Hinrich Schütze that Manny cites as a piece of AI content every AI founder should read. (still in print, companion site here ) The fox and the hedgehog: Quandri XBOW HappyRobot Owl Crosby…
T
Training Data
1 Arc Institute's Patrick Hsu on Building an App Store for Biology with AI 58:11
58:11 Play Later Play Later Lists Like Liked58:11
Play Later Play Later Lists Like LikedPatrick Hsu, co-founder of Arc Institute, discusses the opportunities for AI in biology beyond just drug development, and how Evo 2, their new biology foundation model, is enabling a broad ecosystem of applications. Evo 2 was trained on a vast dataset of genomic data to learn evolutionary patterns that would have taken years to find; as a result, the model can be used for applications from identifying mutations that cause disease to designing new molecular and even genome scale biological systems. Hosted by Josephine Chen and Pat Grady, Sequoia Capital Mentioned in this episode: Sequence modeling and design from molecular to genome scale with Evo : Public pre-print of original Evo paper Genome modeling and design across all domains of life with Evo 2 : Public pre-print of Evo 2 paper ClinVar : NIH database of the genes that are known to cause disease, and mutations in those genes causally associated with disease state Sequence Read Archive : Massive NIH database of gene sequencing data Machines of Loving Grace : Daria Amodei essay that Patrick cites on how AI could transform the world for the better Arc Virtual Cell Atlas : Arc’s first step toward assembling, curating and generating large-scale cellular data from AI-driven biological discovery (among many other tools ) Protein Data Bank (PDB): a global archive of 3D structural information of biomolecules used by DeepMind to train AlphaFold OpenAI Deep Research : The one AI app Patrick uses daily…
T
Training Data
1 Replit CEO Amjad Masad on 1 Billion Developers: A Better End State than AGI? 1:26:18
1:26:18 Play Later Play Later Lists Like Liked1:26:18
Play Later Play Later Lists Like LikedAmjad Masad set out more than a decade ago to pursue the dream of unleashing 1B software creators around the world. With millions of Replit users pre-ChatGPT, that vision was already becoming a reality. Turbocharged by LLMs, the vision of enabling anyone to code—from 12-year-olds in India to knowledge workers in the U.S.—seems less and less radical. In this episode, Amjad explains how an explosion in the developer population could change the economy, society and more. He also discusses his early days programming in Jordan, his unique management approach and what AI will mean for the global economy. Hosted by David Cahn and Sonya Huang, Sequoia Capital Mentioned in this episode: On the Naturalness of Software : 2012 paper on applying NLP to code Attention Is All You Need : Seminal 2017 paper on transformers I Am a Strange Loop : 2007 follow up to Douglas Hofstadter’s 1979 classic Gödel, Escher, Bach that explores how self-referential systems can describe minds On Lisp : Paul Graham’s 1993 book on the original programming language of AI…
T
Training Data
1 Why CRM Needs an AI Revolution, with Day.ai Founder Christopher O’Donnell 1:11:05
1:11:05 Play Later Play Later Lists Like Liked1:11:05
Play Later Play Later Lists Like LikedChristopher O’Donnell believes the fundamental problems with CRM—incomplete data, complex workflows, siloed work products and the fear of leads falling through the cracks—can finally be solved through AI. Founder of Day.ai and former Chief Product Officer of HubSpot, Christopher explains how his team is building a system that automatically captures the full context of customer relationships while giving users transparency and control. He shares lessons from building HubSpot’s CRM and why he’s taking a deliberate approach to product development despite the pressure to scale quickly in the AI era. Hosted by Pat Grady, Sequoia Capital Mentioned in this episode: The Innovator's Dilemma : Classic book by Clay Christensen (referenced regarding HubSpot's second S-curve strategy) Hubspot CRM : The only product to successfully challenge Salesforce’s dominance in the CRM category From Super Mario Brothers to Elden Ring : Analogy to what an AI-powered CRM experience can be through comparison of video games launched in 1985 vs 2022 Punk’d : Hidden camera–practical joke reality television series that premiered on MTV in 2003, created by Ashton Kutcher and Jason Goldberg Slow is smooth and smooth is fast : SEALs-derived concept mentioned regarding product development) Aga stove (highlighted as extraordinary product design example)…
T
Training Data
1 From Software Engineers to AI Word Artisans: Filip Kozera of Wordware 43:04
43:04 Play Later Play Later Lists Like Liked43:04
Play Later Play Later Lists Like LikedFilip 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.…
T
Training Data
1 Josh Woodward: Google Labs is Rapidly Building AI Products from 0-to-1 51:16
51:16 Play Later Play Later Lists Like Liked51:16
Play Later Play Later Lists Like LikedAs VP of Google Labs, Josh Woodward leads teams exploring the frontiers of AI applications. He shares insights on their rapid development process, why today’s written prompts will become outdated and how AI is transforming everything from video generation to computer control. He reveals that 25% of Google’s code is now written by AI and explains why coding could see major leaps forward this year. He emphasizes the importance of taste, design and human values in building AI tools that will shape how future generations work and create. Mentioned in this episode: Notebook LM : Personal research product based on Gemini 2 (previously discussed on Training Data .) Veo 2 : Google DeepMind’s new video generation model. Paul Graham on X replying to Aaron Levie’s post that “One approach to take in building in AI is to do something that's too expensive to be reasonably practical right now, and just bet that the costs will drop by 10X or 100X over time. The cost curve is on your side.” Where Good Ideas Come From : Book on the history of innovation by Steven Johnson. Project Mariner : Google DeepMind’s research prototype exploring human-agent interaction starting with browser use. Replit Agent : Josh’s favorite new AI app The Lego Story : Book on the history of Lego. Hosted by: Ravi Gupta and Sonya Huang, Sequoia Capital…
T
Training Data
1 How AI Breakout Harvey is Transforming Legal Services, with CEO Winston Weinberg 54:09
54:09 Play Later Play Later Lists Like Liked54:09
Play Later Play Later Lists Like LikedHarvey CEO Winston Weinberg explains why success in legal AI requires more than just model capabilities—it demands deep process expertise that doesn’t exist online. He shares how Harvey balances rapid product development with earning trust from law firms through hyper-personalized demos and deep industry expertise. The discussion covers Harvey’s approach to product development—expanding specialized capabilities then collapsing them into unified workflows—and why focusing on complex work like international mergers creates the most defensible position in legal AI. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital…
T
Training Data
1 The AI Product Going Viral With Doctors: OpenEvidence, with CEO Daniel Nadler 1:04:52
1:04:52 Play Later Play Later Lists Like Liked1:04:52
Play Later Play Later Lists Like LikedOpenEvidence is transforming how doctors access medical knowledge at the point of care, from the biggest medical establishments to small practices serving rural communities. Founder Daniel Nadler explains his team’s insight that training smaller, specialized AI models on peer-reviewed literature outperforms large general models for medical applications. He discusses how making the platform freely available to all physicians led to widespread organic adoption and strategic partnerships with publishers like the New England Journal of Medicine. In an industry where organizations move glacially, 10-20% of all U.S. doctors began using OpenEvidence overnight to find information buried deep in the long tail of new medical studies, to validate edge cases and improve diagnoses. Nadler emphasizes the importance of accuracy and transparency in AI healthcare applications. Hosted by: Pat Grady, Sequoia Capital Mentioned in this episode: Do We Still Need Clinical Language Models? : Paper from OpenEvidence founders showing that small, specialized models outperformed large models for healthcare diagnostics Chinchilla paper : Seminal 2022 paper about scaling laws in large language models Understand : Ted Chiang sci-fi novella published in 1991…
T
Training Data
1 OpenAI’s Deep Research Team on Why Reinforcement Learning is the Future for AI Agents 32:45
32:45 Play Later Play Later Lists Like Liked32:45
Play Later Play Later Lists Like LikedOpenAI’s Isa Fulford and Josh Tobin discuss how the company’s newest agent, Deep Research, represents a breakthrough in AI research capabilities by training models end-to-end rather than using hand-coded operational graphs. The product leads explain how high-quality training data and the o3 model’s reasoning abilities enable adaptable research strategies, and why OpenAI thinks Deep Research will capture a meaningful percentage of knowledge work. Key product decisions that build transparency and trust include citations and clarification flows. By compressing hours of work into minutes, Deep Research transforms what’s possible for many business and consumer use cases. Hosted by: Sonya Huang and Lauren Reeder, Sequoia Capital Mentioned in this episode: Yann Lecun’s Cake : An analogy Meta AI’s leader shared in his 2016 NIPS keynote…
T
Training Data
1 Palo Alto Networks’ Nikesh Arora: AI, Security and the New World Order 1:00:08
1:00:08 Play Later Play Later Lists Like Liked1:00:08
Play Later Play Later Lists Like LikedPalo 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…
T
Training Data
1 MongoDB’s Sahir Azam: Vector Databases and the Data Structure of AI 44:26
44:26 Play Later Play Later Lists Like Liked44:26
Play Later Play Later Lists Like LikedMongoDB 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…
T
Training Data
1 Roblox Studio Head Stef Corazza: Using AI to Empower Creators 54:46
54:46 Play Later Play Later Lists Like Liked54:46
Play Later Play Later Lists Like LikedStef 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…
T
Training Data
1 ReflectionAI Founder Ioannis Antonoglou: From AlphaGo to AGI 52:29
52:29 Play Later Play Later Lists Like Liked52:29
Play Later Play Later Lists Like LikedIoannis 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…
T
Training Data
1 Kumo’s Hema Raghavan: Turning Graph AI into ROI 52:06
52:06 Play Later Play Later Lists Like Liked52:06
Play Later Play Later Lists Like LikedHema 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…
T
Training Data
1 Databricks Founder Ion Stoica: Turning Academic Open Source into Startup Success 1:00:05
1:00:05 Play Later Play Later Lists Like Liked1:00:05
Play Later Play Later Lists Like LikedBerkeley 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.…
T
Training Data
1 XBOW CEO and GitHub Copilot Creator Oege de Moor: Cracking the Code on Offensive Security With AI 51:37
51:37 Play Later Play Later Lists Like Liked51:37
Play Later Play Later Lists Like LikedOege 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…
T
Training Data
1 Ramp CEO Eric Glyman: Using AI to Build “Self-Driving Money” 38:48
38:48 Play Later Play Later Lists Like Liked38:48
Play Later Play Later Lists Like LikedWhen 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…
T
Training Data
1 Dust’s Gabriel Hubert and Stanislas Polu: Getting the Most From AI With Multiple Custom Agents 1:03:07
1:03:07 Play Later Play Later Lists Like Liked1:03:07
Play Later Play Later Lists Like LikedFounded 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…
T
Training Data
1 Clay’s Kareem Amin on Building the Sales ‘System of Action’ with AI 51:38
51:38 Play Later Play Later Lists Like Liked51:38
Play Later Play Later Lists Like LikedClay 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…
T
Training Data
1 Decart’s Dean Leitersdorf on AI-Generated Video Games and Worlds 46:34
46:34 Play Later Play Later Lists Like Liked46:34
Play Later Play Later Lists Like LikedCan 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…
T
Training Data
1 How Glean CEO Arvind Jain Solved the Enterprise Search Problem – and What It Means for AI at Work 44:48
44:48 Play Later Play Later Lists Like Liked44:48
Play Later Play Later Lists Like LikedYears 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…
T
Training Data
1 OpenAI Researcher Dan Roberts on What Physics Can Teach Us About AI 41:42
41:42 Play Later Play Later Lists Like Liked41:42
Play Later Play Later Lists Like LikedIn 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…
T
Training Data
1 Google NotebookLM’s Raiza Martin and Jason Spielman on Creating Delightful AI Podcast Hosts and the Potential for Source-Grounded AI 32:07
32:07 Play Later Play Later Lists Like Liked32:07
Play Later Play Later Lists Like LikedNotebookLM 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…
T
Training Data
1 Snowflake CEO Sridhar Ramaswamy on Using Data to Create Simple, Reliable AI for Businesses 59:29
59:29 Play Later Play Later Lists Like Liked59:29
Play Later Play Later Lists Like LikedAll 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…
T
Training Data
1 OpenAI's Noam Brown, Ilge Akkaya and Hunter Lightman on o1 and Teaching LLMs to Reason Better 45:22
45:22 Play Later Play Later Lists Like Liked45:22
Play Later Play Later Lists Like LikedCombining LLMs with AlphaGo-style deep reinforcement learning has been a holy grail for many leading AI labs, and with o1 (aka Strawberry) we are seeing the most general merging of the two modes to date. o1 is admittedly better at math than essay writing, but it has already achieved SOTA on a number of math, coding and reasoning benchmarks. Deep RL legend and now OpenAI researcher Noam Brown and teammates Ilge Akkaya and Hunter Lightman discuss the ah-ha moments on the way to the release of o1, how it uses chains of thought and backtracking to think through problems, the discovery of strong test-time compute scaling laws and what to expect as the model gets better. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital Mentioned in this episode: Learning to Reason with LLMs : Technical report accompanying the launch of OpenAI o1. Generator verifier gap : Concept Noam explains in terms of what kinds of problems benefit from more inference-time compute. Agent57: Outperforming the human Atari benchmark , 2020 paper where DeepMind demonstrated “the first deep reinforcement learning agent to obtain a score that is above the human baseline on all 57 Atari 2600 games.” Move 37 : Pivotal move in AlphaGo’s second game against Lee Sedol where it made a move so surprising that Sedol thought it must be a mistake, and only later discovered he had lost the game to a superhuman move. IOI competition : OpenAI entered o1 into the International Olympiad in Informatics and received a Silver Medal. System 1, System 2: The thesis if Danial Khaneman’s pivotal book of behavioral economics, Thinking, Fast and Slow , that positied two distinct modes of thought, with System 1 being fast and instinctive and System 2 being slow and rational. AlphaZero : The predecessor to AlphaGo which learned a variety of games completely from scratch through self-play. Interestingly, self-play doesn’t seem to have a role in o1. Solving Rubik’s Cube with a robot hand : Early OpenAI robotics paper that Ilge Akkaya worked on. The Last Question : Science fiction story by Isaac Asimov with interesting parallels to scaling inference-time compute. Strawberry: Why? O1-mini : A smaller, more efficient version of 1 for applications that require reasoning without broad world knowledge. 00:00 - Introduction 01:33 - Conviction in o1 04:24 - How o1 works 05:04 - What is reasoning? 07:02 - Lessons from gameplay 09:14 - Generation vs verification 10:31 - What is surprising about o1 so far 11:37 - The trough of disillusionment 14:03 - Applying deep RL 14:45 - o1’s AlphaGo moment? 17:38 - A-ha moments 21:10 - Why is o1 good at STEM? 24:10 - Capabilities vs usefulness 25:29 - Defining AGI 26:13 - The importance of reasoning 28:39 - Chain of thought 30:41 - Implication of inference-time scaling laws 35:10 - Bottlenecks to scaling test-time compute 38:46 - Biggest misunderstanding about o1? 41:13 - o1-mini 42:15 - How should founders think about o1?…
T
Training Data
1 Why Vlad Tenev and Tudor Achim of Harmonic Think AI Is About to Change Math—and Why It Matters 39:45
39:45 Play Later Play Later Lists Like Liked39:45
Play Later Play Later Lists Like LikedAdding code to LLM training data is a known method of improving a model’s reasoning skills. But wouldn’t math, the basis of all reasoning, be even better? Up until recently, there just wasn’t enough usable data that describes mathematics to make this feasible. A few years ago, Vlad Tenev (also founder of Robinhood) and Tudor Achim noticed the rise of the community around an esoteric programming language called Lean that was gaining traction among mathematicians. The combination of that and the past decade’s rise of autoregressive models capable of fast, flexible learning made them think the time was now and they founded Harmonic. Their mission is both lofty—mathematical superintelligence—and imminently practical, verifying all safety-critical software. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital Mentioned in this episode: IMO and the Millennium Prize : Two significant global competitions Harmonic hopes to win (soon) Riemann hypothesis : One of the most difficult unsolved math conjectures (and a Millenium Prize problem) most recently in the sights of MIT mathematician Larry Guth Terry Tao : perhaps the greatest living mathematician and Vlad’s professor at UCLA Lean : an open source functional language for code verification launched by Leonardo de Moura when at Microsoft Research in 2013 that powers the Lean Theorem Prover mathlib : the largest math textbook in the world, all written in Lean Metaculus : online prediction platform that tracks and scores thousands of forecasters Minecraft Beaten in 20 Seconds : The video Vlad references as an analogy to AI math Navier-Stokes equations : another important Millenium Prize math problem. Vlad considers this more tractable that Riemann John von Neumann : Hungarian mathematician and polymath that made foundational contributions to computing, the Manhattan Project and game theory Gottfried Wilhelm Leibniz : co-inventor of calculus and (remarkably) creator of the “universal characteristic,” a system for reasoning through a language of symbols and calculations—anticipating Lean and Harmonic by 350 years! 00:00 - Introduction 01:42 - Math is reasoning 06:16 - Studying with the world's greatest living mathematician 10:18 - What does the math community think of AI math? 15:11 - Recursive self-improvement 18:31 - What is Lean? 21:05 - Why now? 22:46 - Synthetic data is the fuel for the model 27:29 - How fast will your model get better? 29:45 - Exploring the frontiers of human knowledge 34:11 - Lightning round…
T
Training Data
1 Jim Fan on Nvidia’s Embodied AI Lab and Jensen Huang’s Prediction that All Robots will be Autonomous 49:13
49:13 Play Later Play Later Lists Like Liked49:13
Play Later Play Later Lists Like LikedAI researcher Jim Fan has had a charmed career. He was OpenAI’s first intern before he did his PhD at Stanford with “godmother of AI,” Fei-Fei Li. He graduated into a research scientist position at Nvidia and now leads its Embodied AI “GEAR” group. The lab’s current work spans foundation models for humanoid robots to agents for virtual worlds. Jim describes a three-pronged data strategy for robotics, combining internet-scale data, simulation data and real world robot data. He believes that in the next few years it will be possible to create a “foundation agent” that can generalize across skills, embodiments and realities—both physical and virtual. He also supports Jensen Huang’s idea that “Everything that moves will eventually be autonomous.” Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital Mentioned in this episode: World of Bits : Early OpenAI project Jim worked on as an intern with Andrej Karpathy. Part of a bigger initiative called Universe Fei-Fei Li : Jim’s PhD advisor at Stanford who founded the ImageNet project in 2010 that revolutionized the field of visual recognition, led the Stanford Vision Lab and just launched her own AI startup, World Labs Project GR00T : Nvidia’s “moonshot effort” at a robotic foundation model, premiered at this year’s GTC Thinking Fast and Slow : Influential book by Daniel Kahneman that popularized some of his teaching from behavioral economics Jetson Orin chip : The dedicated series of edge computing chips Nvidia is developing to power Project GR00T Eureka : Project by Jim’s team that trained a five finger robot hand to do pen spinning MineDojo: A project Jim did when he first got to Nvidia that developed a platform for general purpose agents in the game of Minecraft. Won NeurIPS 2022 Outstanding Paper Award ADI: artificial dog intelligence Mamba : Selective State Space Models, an alternative architecture to Transformers that Jim is interested in (original paper here ) 00:00 Introduction 01:35 Jim’s journey to embodied intelligence 04:53 The GEAR Group 07:32 Three kinds of data for robotics 10:32 A GPT-3 moment for robotics 16:05 Choosing the humanoid robot form factor 19:37 Specialized generalists 21:59 GR00T gets its own chip 23:35 Eureka and Issac Sim 25:23 Why now for robotics? 28:53 Exploring virtual worlds 36:28 Implications for games 39:13 Is the virtual world in service of the physical world? 42:10 Alternative architectures to Transformers 44:15 Lightning round…
T
Training Data
1 Founder Eric Steinberger on Magic’s Counterintuitive Approach to Pursuing AGI 51:15
51:15 Play Later Play Later Lists Like Liked51:15
Play Later Play Later Lists Like LikedThere’s a new archetype in Silicon Valley, the AI researcher turned founder. Instead of tinkering in a garage they write papers that earn them the right to collaborate with cutting-edge labs until they break out and start their own. This is the story of wunderkind Eric Steinberger, the founder and CEO of Magic.dev. Eric came to programming through his obsession with AI and caught the attention of DeepMind researchers as a high school student. In 2022 he realized that AGI was closer than he had previously thought and started Magic to automate the software engineering necessary to get there. Among his counterintuitive ideas are the need to train proprietary large models, that value will not accrue in the application layer and that the best agents will manage themselves. Eric also talks about Magic’s recent 100M token context window model and the HashHop eval they’re open sourcing. Hosted by: Sonya Huang, Sequoia Capital Mentioned in this episode: David Silver : DeepMind researcher that led the AlphaGo team Johannes Heinrich : a PhD student of Silver’s and DeepMind researcher who mentored Eric as a highschooler Reinforcement Learning from Self-Play in Imperfect-Information Games : Johannes’s dissertation that inspired Eric Noam Brown : DeepMind, Meta and now OpenAI reinforcement learning researcher who eventually collaborated with Eric and brought him to FAIR ClimateScience : NGO that Eric co-founded in 2019 while a university student Noam Shazeer : One of the original Transformers researchers at Google and founder of Charater.ai DeepStack: Expert-Level Artificial Intelligence in Heads-Up No-Limit Poker : the first AI paper Eric ever tried to deeply understand LTM-2-mini : Magic’s first 100M token context model, build using the HashHop eval (now available open source) 00:00 - Introduction 01:39 - Vienna-born wunderkind 04:56 - Working with Noam Brown 8:00 - “I can do two things. I cannot do three.” 10:37 - AGI to-do list 13:27 - Advice for young researchers 20:35 - Reading every paper voraciously 23:06 - The army of Noams 26:46 - The leaps still needed in research 29:59 - What is Magic? 36:12 - Competing against the 800-pound gorillas 38:21 - Ideal team size for researchers 40:10 - AI that feels like a colleague 44:30 - Lightning round 47:50 - Bonus round: 200M token context announcement…
T
Training Data
1 Crucible Moments Returns for S2: The ServiceNow Story ft. CEO Frank Slootman & Founder Fred Luddy 42:53
42:53 Play Later Play Later Lists Like Liked42:53
Play Later Play Later Lists Like LikedOn Training Data, we learn from innovators pushing forward the frontier of AI’s capabilities. Today we’re bringing you something different. It’s the story of a company currently implementing AI at scale in the enterprise, and how it was built from a bootstrapped idea in the pre-AI era to a 150 billion dollar market cap giant. It’s the Season 2 premiere of Sequoia’s other podcast, Crucible Moments, where we hear from the founders and leaders of some legendary companies about the crossroads and inflection points that shaped their journeys. In this episode, you’ll hear from Fred Luddy and Frank Slootman about building and scaling ServiceNow. Listen to Crucible Moments wherever you get your podcasts or go to: Spotify: https://open.spotify.com/show/40bWCUSan0boCn0GZJNpPn Apple: https://podcasts.apple.com/us/podcast/crucible-moments/id1705282398 Hosted by: Roelof Botha, Sequoia Capital Transcript: https://www.sequoiacap.com/podcast/crucible-moments-servicenow/…
T
Training Data
1 Sierra Co-Founder Clay Bavor on Making Customer-Facing AI Agents Delightful 1:12:31
1:12:31 Play Later Play Later Lists Like Liked1:12:31
Play Later Play Later Lists Like LikedCustomer service is hands down the first killer app of generative AI for businesses. The reasons are simple: the costs of existing solutions are so high, the satisfaction so low and the margin for ROI so wide. But trusting your interactions with customers to hallucination-prone LLMs can be daunting. Enter Sierra. Co-founder Clay Bavor walks us through the sophisticated engineering challenges his team solved along the way to delivering AI agents for all aspects of the customer experience that are delightful, safe and reliable—and being deployed widely by Sierra’s customers. The Company’s AgentOS enables businesses to create branded AI agents to interact with customers, follow nuanced policies and even handle customer retention and upsell. Clay describes how companies can capture their brand voice, values and internal processes to create AI agents that truly represent the business. Hosted by: Ravi Gupta and Pat Grady, Sequoia Capital Mentioned in this episode: Bret Taylor : co-founder of Sierra Towards a Human-like Open-Domain Chatbot : 2020 Google paper that introduced Meena, a predecessor of ChatGPT (followed by LaMDA in 2021) PaLM: Scaling Language Modeling with Pathways : 2022 Google paper about their unreleased 540B parameter transformer model (GPT-3, at the time, had 175B) Avocado chair : Images generated by OpenAI’s DALL·E model in 2022 Large Language Models Understand and Can be Enhanced by Emotional Stimuli : 2023 Microsoft paper on how models like GPT-4 can be manipulated into providing better results 𝛕-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains : 2024 paper authored by Sierra research team, led by Karthik Narasimhan (co-author of the 2022 ReACT paper and the 2023 Reflexion paper) 00:00:00 Introduction 00:01:21 Clay’s background 00:03:20 Google before the ChatGPT moment 00:07:31 What is Sierra? 00:12:03 What’s possible now that wasn’t possible 18 months ago? 00:17:11 AgentOS 00:23:45 The solution to many problems with AI is more AI 00:28:37 𝛕-bench 00:33:19 Engineering task vs research task 00:37:27 What tasks can you trust an agent with now? 00:43:21 What metrics will move? 00:46:22 The reality of deploying AI to customers today 00:53:33 The experience manager 01:03:54 Outcome-based pricing 01:05:55 Lightning Round…
T
Training Data
1 Phaidra’s Jim Gao on Building the Fourth Industrial Revolution with Reinforcement Learning 50:33
50:33 Play Later Play Later Lists Like Liked50:33
Play Later Play Later Lists Like LikedAfter AlphaGo beat Lee Sedol, a young mechanical engineer at Google thought of another game reinforcement learning could win: energy optimization at data centers. Jim Gao convinced his bosses at the Google data center team to let him work with the DeepMind team to try. The initial pilot resulted in a 40% energy savings and led he and his co-founders to start Phaidra to turn this technology into a product. Jim discusses the challenges of AI readiness in industrial settings and how we have to build on top of the control systems of the 70s and 80s to achieve the promise of the Fourth Industrial Revolution. He believes this new world of self-learning systems and self-improving infrastructure is a key factor in addressing global climate change. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital Mentioned in this episode: Mustafa Suleyman : Co-founder of DeepMind and Inflection AI and currently CEO of Microsoft AI, known to his friends as “Moose” Joe Kava : Google VP of data centers who Jim sent his initial email to pitching the idea that would eventually become Phaidra Constrained optimization : the class of problem that reinforcement learning can be applied to in real world systems Vedavyas Panneershelvam : co-founder and CTO of Phaidra; one of the original engineers on the AlphaGo project Katie Hoffman : co-founder, President and COO of Phaidra Demis Hassabis : CEO of DeepMind…
T
Training Data
1 Fireworks Founder Lin Qiao on How Fast Inference and Small Models Will Benefit Businesses 39:18
39:18 Play Later Play Later Lists Like Liked39:18
Play Later Play Later Lists Like LikedIn the first wave of the generative AI revolution, startups and enterprises built on top of the best closed-source models available, mostly from OpenAI. The AI customer journey moves from training to inference, and as these first products find PMF, many are hitting a wall on latency and cost. Fireworks Founder and CEO Lin Qiao led the PyTorch team at Meta that rebuilt the whole stack to meet the complex needs of the world’s largest B2C company. Meta moved PyTorch to its own non-profit foundation in 2022 and Lin started Fireworks with the mission to compress the timeframe of training and inference and democratize access to GenAI beyond the hyperscalers to let a diversity of AI applications thrive. Lin predicts when open and closed source models will converge and reveals her goal to build simple API access to the totality of knowledge. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital Mentioned in this episode: Pytorch : the leading framework for building deep learning models, originated at Meta and now part of the Linux Foundation umbrella Caffe2 and ONNX : ML frameworks Meta used that PyTorch eventually replaced Conservation of complexity : the idea that that every computer application has inherent complexity that cannot be reduced but merely moved between the backend and frontend, originated by Xerox PARC researcher Larry Tesler Mixture of Experts : a class of transformer models that route requests between different subsets of a model based on use case Fathom : a product the Fireworks team uses for video conference summarization LMSYS Chatbot Arena : crowdsourced open platform for LLM evals hosted on Hugging Face 00:00 - Introduction 02:01 - What is Fireworks? 02:48 - Leading Pytorch 05:01 - What do researchers like about PyTorch? 07:50 - How Fireworks compares to open source 10:38 - Simplicity scales 12:51 - From training to inference 17:46 - Will open and closed source converge? 22:18 - Can you match OpenAI on the Fireworks stack? 26:53 - What is your vision for the Fireworks platform? 31:17 - Competition for Nvidia? 32:47 - Are returns to scale starting to slow down? 34:28 - Competition 36:32 - Lightning round…
T
Training Data
1 GitHub CEO Thomas Dohmke on Building Copilot, and the the Future of Software Development 1:07:34
1:07:34 Play Later Play Later Lists Like Liked1:07:34
Play Later Play Later Lists Like LikedGithHub invented collaborative coding and in the process changed how open source projects, startups and eventually enterprises write code. GitHub Copilot is the first blockbuster product built on top of OpenAI’s GPT models. It now accounts for more than 40 percent of GitHub revenue growth for an annual revenue run rate of $2 billion. Copilot itself is already a larger business than all of GitHub was when Microsoft acquired it in 2018. We talk to CEO Thomas Dohmke about how a small team at GitHub built on top of GPT-3 and quickly created a product that developers love—and can’t live without. Thomas describes how the product has grown from simple autocomplete to a fully featured workspace for enterprise teams. He also believes that tools like Copilot will bring the power of coding to a billion developers by 2030. Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital Mentioned in this episode: Nat Friedman : Former Microsoft VP (and now investor) who came up with the idea that Microsoft should buy GitHub Oege de Moor : Github developer (and now founder of XBOW) who came up with the idea of using GPT-3 for code and went on to create Copilot Alex Graveley : principal engineer and Chief Architect for Copilot (now CEO of Minion.ai) who came up with the name Copilot (because his boss, Nat Firedman, is an amateur pilot) Productivity Assessment of Neural Code Completion : Original GitHub research paper on the impact of Copilot on Developer productivity Escaping a room in Minecraft with an AI-powered NPC : Recent Minecraft AI assistant demo from Microsoft With AI, anyone can be a coder now : TED2024 talk by Thomas Dohmke JFrog : The software supply chain platform that GitHub just partnered with 00:00:00 - Introduction 00:01:18 - Getting started with code 00:03:43 - Microsoft’s acquisition of GitHub 00:11:40 - Evolving Copilot beyond autocomplete 00:14:18 - In hindsight, you can always move faster 00:15:56 - Building on top of OpenAI 00:20:21 - The latest metrics 00:22:11 - The surprise of Copilot’s impact 00:25:11 - Teaching kids to code in the age of Copilot 00:26:38 - The momentum mindset 00:29:46 - Agents vs Copilots 00:32:06 - The Roadmap 00:37:31 - Making maintaining software easier 00:38:48 - The creative new world 00:42:38 - The AI 10x software engineer 00:45:12 - Creativity and systems engineering in AI 00:48:55 - What about COBOL? 00:50:23 - Will GitHub build its own models? 00:57:19 - Rapid incubation at GitHub Next 00:59:21 - The future of AI? 01:03:18 - Advice for founders 01:05:08 - Lightning round…
T
Training Data
1 Meta’s Joe Spisak on Llama 3.1 405B and the Democratization of Frontier Models 42:07
42:07 Play Later Play Later Lists Like Liked42:07
Play Later Play Later Lists Like LikedAs head of Product Management for Generative AI at Meta, Joe Spisak leads the team behind Llama, which just released the new 3.1 405B model. We spoke with Joe just two days after the model’s release to ask what’s new, what it enables, and how Meta sees the role of open source in the AI ecosystem. Joe shares that where Llama 3.1 405B really focused is on pushing scale (it was trained on 15 trillion tokens using 16,000 GPUs) and he’s excited about the zero-shot tool use it will enable, as well as its role in distillation and generating synthetic data to teach smaller models. He tells us why he thinks even frontier models will ultimately commoditize—and why that’s a good thing for the startup ecosystem. Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital Mentioned in this episode: Llama 3.1 405B paper Open Source AI Is the Way Forward : Mark Zuckerberg essay released with Llama 3.1. Mistral Large 2 The Bitter Lesson by Rich Sutton 00:00 Introduction 01:28 The Llama 3.1 405B launch 05:02 The open source license 07:01 What's in it for Meta? 10:19 Why not open source? 11:16 Will frontier models commoditize? 12:41 What about startups? 16:29 The Mistral team 19:36 Are all frontier strategies comparable? 22:38 Is model development becoming more like software development? 26:34 Agentic reasoning 29:09 What future levers will unlock reasoning? 31:20 Will coding and math lead to unlocks? 33:09 Small models 34:08 7X more data 37:36 Are we going to hit a wall? 39:49 Lightning round…
T
Training Data
1 Klarna CEO Sebastian Siemiatkowski on Getting AI to Do the Work of 700 Customer Service Reps 51:35
51:35 Play Later Play Later Lists Like Liked51:35
Play Later Play Later Lists Like LikedIn February, Sebastian Siemiatkowski boldly announced that Klarna’s new OpenAI-powered assistant handled two thirds of the Swedish fintech’s customer service chats in its first month. Not only were customer satisfaction metrics better, but by replacing 700 full-time contractors the bottom line impact is projected to be $40M. Since then, every company we talk to wants to know, “How do we get the Klarna customer support thing?” Co-founder and CEO Sebastian Siemiatkowski tells us how the Klarna team shipped this new product in record time—and how embracing AI internally with an experimental mindset is transforming the company. He discusses how AI development is proliferating inside the company, from customer support to marketing to internal knowledge to customer-facing experiences. Sebastian also reflects on the impacts of AI on employment, society, and the arts while encouraging lawmakers to be open minded about the benefits. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital Mentioned in this episode: DeepL : Language translation app that Sebastian says makes 10,000 translators in Brussels redundant The Klarna brand : The offbeat optimism that the company is now augmenting with AI Neo4j : The graph database management system that Klarna is using to build Kiki, their internal knowledge base 00:00 Introduction 01:57 Klarna’s business 03:00 Pitching OpenAI 08:51 How we built this 10:46 Will Klara ever completely replace its CS team with AI? 14:22 The benefits 17:25 If you had a policy magic wand… 21:12 What jobs will be most affected by AI? 23:58 How about marketing? 27:55 How creative are LLMs? 30:11 Klarna’s knowledge graph, Kiki 33:10 Reducing the number of enterprise systems 35:24 Build vs buy? 39:59 What’s next for Klarna with AI? 48:48 Lightning round…
T
Training Data
1 Reflection AI’s Misha Laskin on the AlphaGo Moment for LLMs 1:07:04
1:07:04 Play Later Play Later Lists Like Liked1:07:04
Play Later Play Later Lists Like LikedLLMs are democratizing digital intelligence, but we’re all waiting for AI agents to take this to the next level by planning tasks and executing actions to actually transform the way we work and live our lives. Yet despite incredible hype around AI agents, we’re still far from that “tipping point” with best in class models today. As one measure: coding agents are now scoring in the high-teens % on the SWE-bench benchmark for resolving GitHub issues, which far exceeds the previous unassisted baseline of 2% and the assisted baseline of 5%, but we’ve still got a long way to go. Why is that? What do we need to truly unlock agentic capability for LLMs? What can we learn from researchers who have built both the most powerful agents in the world, like AlphaGo, and the most powerful LLMs in the world? To find out, we’re talking to Misha Laskin, former research scientist at DeepMind. Misha is embarking on his vision to build the best agent models by bringing the search capabilities of RL together with LLMs at his new company, Reflection AI. He and his cofounder Ioannis Antonoglou, co-creator of AlphaGo and AlphaZero and RLHF lead for Gemini, are leveraging their unique insights to train the most reliable models for developers building agentic workflows. Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital 00:00 Introduction 01:11 Leaving Russia, discovering science 10:01 Getting into AI with Ioannis Antonoglou 15:54 Reflection AI and agents 25:41 The current state of Ai agents 29:17 AlphaGo, AlphaZero and Gemini 32:58 LLMs don’t have a ground truth reward 37:53 The importance of post-training 44:12 Task categories for agents 45:54 Attracting talent 50:52 How far away are capable agents? 56:01 Lightning round Mentioned: The Feynman Lectures on Physics : The classic text that got Misha interested in science. Mastering the game of Go with deep neural networks and tree search : The original 2016 AlphaGo paper. Mastering the game of Go without human knowledge : 2017 AlphaGo Zero paper Scaling Laws for Reward Model Overoptimization : OpenAI paper on how reward models can be gamed at all scales for all algorithms. Mapping the Mind of a Large Language Model : Article about Anthropic mechanistic interpretability paper that identifies how millions of concepts are represented inside Claude Sonnet Pieter Abeel : Berkeley professor and founder of Covariant who Misha studied with A2C and A3C : Advantage Actor Critic and Asynchronous Advantage Actor Critic, the two algorithms developed by Misha’s manager at DeepMind, Volodymyr Mnih, that defined reinforcement learning and deep reinforcement learning…
T
Training Data
1 Microsoft CTO Kevin Scott on How Far Scaling Laws Will Extend 1:00:27
1:00:27 Play Later Play Later Lists Like Liked1:00:27
Play Later Play Later Lists Like LikedThe current LLM era is the result of scaling the size of models in successive waves (and the compute to train them). It is also the result of better-than-Moore’s-Law price vs performance ratios in each new generation of Nvidia GPUs. The largest platform companies are continuing to invest in scaling as the prime driver of AI innovation. Are they right, or will marginal returns level off soon, leaving hyperscalers with too much hardware and too few customer use cases? To find out, we talk to Microsoft CTO Kevin Scott who has led their AI strategy for the past seven years. Scott describes himself as a “short-term pessimist, long-term optimist” and he sees the scaling trend as durable for the industry and critical for the establishment of Microsoft’s AI platform. Scott believes there will be a shift across the compute ecosystem from training to inference as the frontier models continue to improve, serving wider and more reliable use cases. He also discusses the coming business models for training data, and even what ad units might look like for autonomous agents. Hosted by: Pat Grady and Bill Coughran, Sequoia Capital Mentioned: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , the 2018 Google paper that convinced Kevin that Microsoft wasn’t moving fast enough on AI. Dennard scaling : The scaling law that describes the proportional relationship between transistor size and power use; has not held since 2012 and is often confused with Moore’s Law. Textbooks Are All You Need : Microsoft paper that introduces a new large language model for code, phi-1, that achieves smaller size by using higher quality “textbook” data. GPQA and MMLU : Benchmarks for reasoning Copilot : Microsoft product line of GPT consumer assistants from general productivity to design, vacation planning, cooking and fitness. Devin : Autonomous AI code agent from Cognition Labs that Microsoft recently announced a partnership with. Ray Solomonoff : Participant in the 1956 Dartmouth Summer Research Project on Artificial Intelligence that named the field; Kevin admires his prescience about the importance of probabilistic methods decades before anyone else. 00:00 - Introduction 01:20 - Kevin’s backstory 06:56 - The role of PhDs in AI engineering 09:56 - Microsoft’s AI strategy 12:40 - Highlights and lowlights 16:28 - Accelerating investments 18:38 - The OpenAI partnership 22:46 - Soon inference will dwarf training 27:56 - Will the demand/supply balance change? 30:51 - Business models for data 36:54 - The value function 39:58 - Copilots 44:47 - The 98/2 rule 49:34 - Solving zero-sum games 57:13 - Lightning round…
T
Training Data
1 Zapier’s Mike Knoop launches ARC Prize to Jumpstart New Ideas for AGI 55:12
55:12 Play Later Play Later Lists Like Liked55:12
Play Later Play Later Lists Like LikedAs 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…
T
Training Data
1 Factory’s Matan Grinberg and Eno Reyes Unleash the Droids on Software Development 59:10
59:10 Play Later Play Later Lists Like Liked59:10
Play Later Play Later Lists Like LikedArchimedes said that with a large enough lever, you can move the world. For decades, software engineering has been that lever. And now, AI is compounding that lever. How will we use AI to apply 100 or 1000x leverage to the greatest lever to move the world? Matan Grinberg and Eno Reyes, co-founders of Factory, have chosen to do things differently than many of their peers in this white-hot space. They sell a fleet of “Droids,” purpose-built dev agents which accomplish different tasks in the software development lifecycle (like code review, testing, pull requests or writing code). Rather than training their own foundation model, their approach is to build something useful for engineering orgs today on top of the rapidly improving models, aligning with the developer and evolving with them. Matan and Eno are optimistic about the effects of autonomy in software development and on building a company in the application layer. Their advice to founders, “The only way you can win is by executing faster and being more obsessed.” Hosted by: Sonya Huang and Pat Grady, Sequoia Capital Mentioned: Juan Maldacena, Institute for Advanced Study , string theorist that Matan cold called as an undergrad SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering , small-model open-source software engineering agent SWE-bench: Can Language Models Resolve Real-World GitHub Issues? , an evaluation framework for GitHub issues Monte Carlo tree search , a 2006 algorithm for solving decision making in games (and used in AlphaGo) Language agent tree search , a framework for LLM planning, acting and reasoning The Bitter Lesson , Rich Sutton’s essay on scaling in search and learning Code churn , time to merge , cycle time , metrics Factory thinks are important to eng orgs Transcript: https://www.sequoiacap.com/podcast/training-data-factory/ 00:00 Introduction 01:36 Personal backgrounds 10:54 The compound lever 12:41 What is Factory? 16:29 Cognitive architectures 21:13 800 engineers at OpenAI are working on my margins 24:00 Jeff Dean doesn't understand your code base 25:40 Individual dev productivity vs system-wide optimization 30:04 Results: Factory in action 32:54 Learnings along the way 35:36 Fully autonomous Jeff Deans 37:56 Beacons of the upcoming age 40:04 How far are we? 43:02 Competition 45:32 Lightning round 49:34 Bonus round: Factory's SWE-bench results…
T
Training Data
1 LangChain’s Harrison Chase on Building the Orchestration Layer for AI Agents 49:50
49:50 Play Later Play Later Lists Like Liked49:50
Play Later Play Later Lists Like LikedLast year, AutoGPT and Baby AGI captured our imaginations—agents quickly became the buzzword of the day…and then things went quiet. AutoGPT and Baby AGI may have marked a peak in the hype cycle, but this year has seen a wave of agentic breakouts on the product side, from Klarna’s customer support AI to Cognition’s Devin, etc. Harrison Chase of LangChain is focused on enabling the orchestration layer for agents. In this conversation, he explains what’s changed that’s allowing agents to improve performance and find traction. Harrison shares what he’s optimistic about, where he sees promise for agents vs. what he thinks will be trained into models themselves, and discusses novel kinds of UX that he imagines might transform how we experience agents in the future. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital Mentioned: ReAct: Synergizing Reasoning and Acting in Language Models , the first cognitive architecture for agents SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering , small-model open-source software engineering agent from researchers at Princeton Devin , autonomous software engineering from Cognition V0 : Generative UI agent from Vercel GPT Researcher , a research agent Language Model Cascades : 2022 paper by Google Brain and now OpenAI researcher David Dohan that was influential for Harrison in developing LangChain Transcript: https://www.sequoiacap.com/podcast/training-data-harrison-chase/ 00:00 Introduction 01:21 What are agents? 05:00 What is LangChain’s role in the agent ecosystem? 11:13 What is a cognitive architecture? 13:20 Is bespoke and hard coded the way the world is going, or a stop gap? 18:48 Focus on what makes your beer taste better 20:37 So what? 22:20 Where are agents getting traction? 25:35 Reflection, chain of thought, other techniques? 30:42 UX can influence the effectiveness of the architecture 35:30 What’s out of scope? 38:04 Fine tuning vs prompting? 42:17 Existing observability tools for LLMs vs needing a new architecture/approach 45:38 Lightning round…
Join us as we train our neural nets on the theme of the century: AI. Sequoia Capital partners Sonya Huang and Pat Grady 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.…
Welcome to Player FM!
Player FM is scanning the web for high-quality podcasts for you to enjoy right now. It's the best podcast app and works on Android, iPhone, and the web. Signup to sync subscriptions across devices.
Daily Deals
Daily Deals
Similar to Training Data
Africa-focused technology, digital and innovation ecosystem insight and commentary.
…
continue reading
The power of Data is undeniable. And unharnessed - it’s nothing but chaos. Making data your ally. Using it to lead with confidence and clarity. Host Jess Carter is solving problems in real-time to reveal what’s possible. Helping communities and people thrive. This is Data Driven Leadership, a show brought to you by Resultant.
…
continue reading
Show notes are at https://stevelitchfield.com/sshow/chat.html
…
continue reading
The director’s commentary track for Daring Fireball. Long digressions on Apple, technology, design, movies, and more.
…
continue reading
The American healthcare system is one of the most innovative in the world. But it’s also riddled with complex challenges, such as access to affordable medications, inefficiency and administrative burdens, and communication barriers between providers. There’s clearly a better way—and at Surescripts, we have a unique sightline into what that may be. In this series, host Melanie Marcus, Chief Marketing Officer of Surescripts, sits down with today’s most inspiring and innovative leaders in healt ...
…
continue reading
Download This Show is your weekly guide to the world of media, culture, and technology. From social media to gadgets, streaming services to privacy issues. Each week Rae Johnston and guests take a fun, deep dive into how technology is reshaping our lives.
…
continue reading
A weekly talk show taking a pragmatic look at the art and business of Software Development and the world of technology.
…
continue reading
Android Backstage, a podcast by and for Android developers. Hosted by developers from the Android engineering team, this show covers topics of interest to Android programmers, with in-depth discussions and interviews with engineers on the Android team at Google. Subscribe to Android Developers YouTube → https://goo.gle/AndroidDevs
…
continue reading
Big tech is transforming every aspect of our world. But how, and at what cost? This season of Land of the Giants – The Disney Dilemma – focuses on Disney’s ability to weather the ups and downs of the business cycle and changing tastes and explores what has kept it successful for over 100 years. The entertainment giant has leveraged nostalgia and its intellectual property to build a beloved brand, but after an acquisition spree that included Marvel, Lucasfilm, and 20th Century Fox, can it sus ...
…
continue reading
Investor Shayle Kann is asking big questions about how to decarbonize the planet: How cheap can clean energy get? Will artificial intelligence speed up climate solutions? Where is the smart money going into climate technologies? Every week on Catalyst, Shayle explains the world of climate tech with prominent experts, investors, researchers, and executives. Produced by Latitude Media.
…
continue reading
Player FM - Podcast App
Go offline with the Player FM app!
Go offline with the Player FM app!
