Latest Interviews
Showing 31–45 of 59 transcripts.
Clear all filters- Sequoia Capital33 min
OpenAI’s Deep Research Team on Why Reinforcement Learning is the Future for AI Agents
Isa Fulford, Josh Tobin, Sonya Huang, Lauren Reeder
Launched three weeks ago, OpenAI's Deep Research is an agentic system powered by a fine-tuned O3 model that executes complex, multi-hour tasks like market analysis and medical research in 5 to 30 minutes. Utilizing reinforcement learning to optimize browsing and coding strategies, the tool distinguishes itself through a pre-research clarification flow that refines user prompts for higher-quality synthesis. As part of a broader 2025 shift toward agent-driven workflows, this technology aims to amplify knowledge workers by automating information-intensive processes previously deemed too time-consuming.
- Sequoia Capital1h 0m
AI, Security and the New World Order ft. Palo Alto Networks’s Nikesh Arora
Nikesh Arora, Sonya Huang, Pat Grady, Jim Goetz
Nikesh Arora outlines a shifting AI landscape where falling development costs enable specialized models while warning that premature agency requires rigorous "AI firewalls" to mitigate real-time cyber threats and hallucinations. He details Palo Alto Networks' strategy of acquiring only category leaders under strict co-authorship agreements, a tactic designed to preserve agility amidst a predicted five-year battle between autonomous agents. Additionally, Arora forecasts a regulatory bifurcation for critical infrastructure alongside a market split between enterprise-grade specialized systems and consumer-focused general-purpose AI.
- Sequoia Capital44 min
Vector Databases and the Data Structure of AI ft. MongoDB’s Sahir Azam
Sahir Azam, Sonya Huang, Pat Grady, Amy Quinton
This session explores the evolution of quality engineering for probabilistic software, highlighting how traditional deterministic models are being replaced by RAG architectures and vector databases to achieve 99.99% reliability in enterprise environments. It details concrete ROI from the automotive and pharmaceutical sectors, where embedding models and large language models have drastically reduced diagnosis times and automated complex clinical reporting while preserving data sovereignty. The discussion concludes by framing databases as the essential memory layer for AI agents, emphasizing MongoDB's strategy to unify structured, unstructured, and vector data into a single system that supports the next generation of agent-driven workflows.
- Sequoia Capital55 min
Using AI to Empower Creators fr Roblox Studio Head Stef Corazza
Stef Corazza, Konstantine Buhler, Sonya Huang
Roblox is leveraging its $29 billion market-cap infrastructure and a multimodal dataset authorized by its creator community to deploy generative AI tools that increase developer output by up to 180%. These features, including code assistants and 3D material generators, are currently driving a 30% higher game publication rate while maintaining safety through strict moderation protocols. The company's roadmap aims to transition game creation from pixel-level control to intent-based design, utilizing 3D foundational models to enable dynamic storytelling and real-time visual personalization.
- Sequoia Capital53 min
From AlphaGo to AGI ft ReflectionAI Founder Ioannis Antonoglou
Ioannis Antonoglou, Stephanie Zhan, Sonya Huang, Giannis Antinoglou
DeepMind founders Demis Hassabis and Shane Legg pioneered Artificial General Intelligence research by utilizing video games as controlled testbeds, evolving from AlphaGo's human-supervised neural networks to the self-learning AlphaZero and MuZero architectures. This strategic shift addressed critical limitations like hallucination and the "data wall" by prioritizing reinforcement learning and planning over static data pre-training, a methodology now considered essential for future AGI development. Looking ahead, experts predict that within five years, increased compute will directly yield higher intelligence in autonomous agents, marking a transition toward systems capable of independent reasoning and novel scientific discovery.
- Sequoia Capital52 min
Turning Graph AI into ROI ft Kumo’s Hema Raghavan
Hema Raghavan, Konstantine Buhler, Sonya Huang, Constantine, Sonia
Kumo AI co-founder Hema Raghavan presents a GPU-accelerated AutoML platform that automatically constructs graph structures from relational data to enable predictive SQL-like queries without manual engineering. The system delivers rapid four-week proofs of concept for sectors ranging from fintech fraud detection to healthcare demand forecasting by integrating directly with Snowflake and Databricks while maintaining strict data residency. By combining optimized cost architectures with explainability features and LLM synergies, Kumo lowers the barrier to graph learning for diverse enterprises requiring immediate, accurate model insights.
- Sequoia Capital1h 0m
Turning Academic Open Source into Startup Success ft Databricks Founder Ion Stoica
Ion Stoica, Stephanie Zhan, Sonya Huang, Jan Stojka, Matej
Databricks addresses the critical gap between AI experimentation and production by promoting Compound AI Systems and launching the open-weight Dbricks model to ensure enterprise data privacy and compliance. Under founder Jan Stojka's guidance, the company leverages strategic partnerships with rivals like Microsoft while prioritizing robust data infrastructure over base model capabilities to drive accuracy and security. Looking ahead, the organization forecasts a shift toward commoditized inference costs and autonomous agents, urging founders to focus on verifiable, production-ready solutions rather than technical demonstrations.
- Sequoia Capital52 min
Cracking the Code on Offensive Security With AI ft XBOW CEO and GitHub Copilot Creator Oege de Moor
Oege de Moor, Konstantine Buhler, Sonya Huang
Former Oxford professor and GitHub Copilot creator Uge Demore's company Expo deploys an autonomous AI system to automate offensive security testing, achieving an 85% success rate on proprietary benchmarks while matching top human penetration testers in 28 minutes instead of 40 hours. The platform continuously identifies critical vulnerabilities in major financial institutions and replaces traditional $18,000 manual tests with a scalable, subscription-based service designed to outpace AI-assisted cyber threats. Operating on a foundation of proprietary training data and strict cloud-based guardrails, Expo aims to transform web security standards by making continuous, automated offensive testing accessible to organizations of all sizes.
- Sequoia Capital39 min
Using AI to Build “Self-Driving Money” ft Ramp CEO Eric Glyman
Eric Glyman, Ravi Gupta, Sonya Huang
Ramp CEO Eric Gleiman champions a "zero-touch automation" strategy that utilizes large language models to execute invisible financial tasks, allowing over 25,000 companies to automate expense reporting and achieve average annual savings of 5%. This approach replaces traditional chatbot interfaces with self-driving money systems that automatically classify vendors and complete reports, marking a shift from routine data entry to high-value strategic work for finance leaders. By prioritizing enduring customer pains over temporary tech trends, the company positions itself to disrupt legacy financial institutions through non-bank innovation that mimics the operational efficiency of "self-driving" transformations in other industries.
- Sequoia Capital47 min
Decart’s Dean Leitersdorf on AI-Generated Video Games and Worlds
Dean Leitersdorf, Sonya Huang, Shaun Maguire, Sean McGuire
Descartes has unveiled Oasis, a fully playable AI game engine that executes real-time video model inference on standard H100 hardware without requiring specialized Blackwell chips or traditional game engines. By leveraging a vertically integrated architecture and a custom prompt-to-pixels approach, the company converges training in 20 hours compared to the industry standard of two weeks, positioning the technology to transition users from static interfaces to dynamically generated experiences. Founding team members Dean Leiterstorff and Sean McGuire argue that this low-level systems mastery and the rapid convergence of transformer and pixel-based models will establish a durable competitive moat as the firm moves toward a "generated experience" future.
- Sequoia Capital45 min
How Glean CEO Arvind Jain Solved the Enterprise Search Problem – and What It Means for AI at Work
Arvind Jain, Sonya Huang, Pat Grady
Glean CEO Arvind Jain's company has evolved from an enterprise search provider into an AI application platform, leveraging a five-year vision to automate 80% of knowledge worker tasks through a unique RAG architecture that grounds responses in private data. The platform differentiates itself by prioritizing data governance, semantic knowledge graphs, and fine-grained access controls before layering on Large Language Models, which has enabled year-over-year revenue quadrupling. By abstracting complex infrastructure for developers and focusing on agentic workflows, Glean aims to shift the market from reactive querying to proactive, autonomous assistance that doubles productivity for engineering, sales, and support teams.
- Sequoia Capital42 min
OpenAI Researcher Dan Roberts on What Physics Can Teach Us About AI
Dan Roberts, Sonya Huang, Pat Grady
Former Sequoia AI Fellow and MIT PhD Dan Roberts discusses his transition to OpenAI to contribute to the o1 model, framing the current AI landscape as a modern Manhattan Project that requires a physics-inspired approach to understanding complex systems. Roberts argues that economic constraints on scaling will soon necessitate a shift from brute-force compute to architectural innovation, predicting that significant capability gains will depend on whether the next five months of model development can overcome these bottlenecks. He further details his optimistic outlook for AI's impact on mathematics and his advocacy for informal scientific communication to accelerate the adoption of new ideas in the field.
- Sequoia Capital32 min
Google NotebookLM’s Raiza Martin and Jason Spielman on the Potential for Source-Grounded AI
Raiza Martin, Jason Spielman, Sonya Huang, Pat Grady
Google's Notebook LM, developed by a lean team within Google Labs, is an AI-powered research tool that utilizes the Gemini 1.5 Pro model to generate realistic, two-host podcast-style audio summaries grounded strictly in user-uploaded documents. This source-grounded approach has driven viral adoption across educational and corporate sectors, with early users reporting up to a tenfold increase in efficiency when digesting complex materials like investment memorandums or training manuals. Currently in an experimental preview phase, the product aims to evolve from familiar audio formats into broader writing and code generation capabilities while addressing gaps in native collaboration features.
- Sequoia Capital1h 0m
Snowflake CEO Sridhar Ramaswamy on Using Data to Create Simple, Reliable AI for Businesses
Sridhar Ramaswamy, Sonya Huang, Pat Grady, Sonia
Snowflake CEO Sridhar Ramaswamy is driving the company's transformation into an "AI data cloud" that integrates acquired search technology from Neva to serve over 10,000 enterprise customers. The organization addresses reliability concerns in generative AI by prioritizing context engineering and managed governance, enabling business users to access data through grounded chatbots without extensive custom software development. This strategic pivot aims to democratize software creation by embedding AI directly into data workflows, positioning Snowflake to capitalize on the shift toward interoperable cloud storage and controlled mobile ecosystems.
- Sequoia Capital45 min
OpenAI's Noam Brown, Ilge Akkaya and Hunter Lightman on o1 and Teaching LLMs to Reason Better
Noam Brown, Ilge Akkaya, Hunter Lightman, Sonya Huang, Pat Grady
OpenAI's O1 model, internally codenamed Project Strawberry, introduces a paradigm shift by employing "inference time compute" to enable systems to engage in extended, self-correcting reasoning processes akin to human System 2 thinking. This architecture has delivered unprecedented capabilities in STEM domains, allowing the AI to solve complex Olympiad-level programming problems, pass research engineer interviews, and assist in scientific discovery by bridging the gap between difficulty in generation versus verification. While the project faces limitations in speed and creative tasks compared to predecessors like GPT-4, its demonstrated ability to scale performance through increased thinking time marks a significant advancement toward the operational goal of Artificial General Intelligence.