Latest Interviews
Showing 1–15 of 23 transcripts.
Clear all filters- Sequoia Capital47 min
Chai Discovery's Bitter Lesson: Drug Design Is Another Scaling Problem
Josh Meier, Matt McPartlon, Pat Grady, Sonali Singh
Chai Discovery is industrializing drug discovery by deploying a simplified, AI-driven infrastructure that replaces traditional trial-and-error with scalable molecule design, partnering with major pharmaceutical firms like Eli Lilly and Pfizer rather than managing a full internal pipeline. The company's second-generation model has achieved a 15% binding success rate through diffusion-based generation and a continuous feedback loop from wet-lab experiments, effectively targeting historically undruggable biological structures. By shifting the industry toward computational "last in class" solutions, Chai aims to accelerate development timelines from months to days while ensuring extreme safety and manufacturability at the molecular generation stage.
- Sequoia Capital49 min
Building the Automated AGI Lab: Core Automation's Jerry Tworek and Rohan Anil
Jerry Tworek, Rohan Anil, Sonya Huang, Pat Grady
Founded by Jerry Liu and Rohan Ramachandran, Core Automation aims to replace static Transformer models with a new class of AI systems capable of continual, test-time learning. The organization is building an automated research lab designed to overcome architectural bottlenecks by developing hardware-efficient kernels and enabling models to autonomously optimize their own code. Success for the venture is defined by the system's ability to self-improve without human intervention, effectively extending the team's operations while bypassing the diminishing returns of current scaling methods.
- Sequoia Capital52 min
Factory's Matan Grinberg: The Coming ‘Dark Factory’ Where Software Builds Itself
Matan Grinberg, Sonya Huang, Pat Grady
Factory CEO Matan Cohen refunded nearly $2 million in early revenue to pivot the company from premature autonomous agents to a model-agnostic architecture that prioritizes customer obsession over vendor lock-in. By launching the Droid CLI and implementing a "Model Router" that dynamically allocates tasks between open-weight and frontier models, the firm has shifted its business model toward outcome-based pricing that replaces token consumption with result-oriented billing. This strategy positions Factory to navigate an impending market correction while transitioning enterprises from synchronous tool usage to asynchronous "dark factories" that autonomously resolve complex issues.
- Sequoia Capital55 min
Inside Zipline's Autonomous System: 140M Miles, Zero Incidents
Keller Rinaudo Cliffton, Eric Watson, Alfred Lin, Pat Grady
Since its 2016 launch in Rwanda, Zipline has evolved from a struggling medical logistics provider into a global autonomous infrastructure operator serving 5,000 hospitals across eight countries. The company now achieves a safety record of zero incidents over 140 million commercial miles while leveraging a vertical integration strategy to reduce delivery costs below those of human-driven vehicles. This operational success recently facilitated a $550 million partnership with the US State Department aimed at deploying similar life-saving services under a new commercial diplomacy framework.
- Sequoia Capital42 min
How End-to-End Learning Created Autonomous Driving 2.0: Wayve CEO Alex Kendall
Alex Kendall, Pat Grady, Sonya Huang, Sonia
Wave positions itself as a strategic partner for global manufacturers, deploying an embodied AI foundation model that replaces traditional hand-engineered stacks with a singular end-to-end neural network. By combining diverse multi-sensor data, generative world models, and OEM partnerships with companies like Nissan, the company aims to scale superhuman safety and "eyes-off" autonomy across 90 million annual vehicles without relying on proprietary fleets. This approach leverages unsupervised learning and Lingo vision-language integration to rapidly generalize across global cities, enabling rapid adaptation to edge cases while reducing the need for exhaustive real-world data collection.
- Sequoia Capital42 min
Nvidia CTO Michael Kagan: Scaling Beyond Moore's Law to Million-GPU Clusters
Michael Kagan, Sonya Huang, Pat Grady
Following NVIDIA's strategic acquisition of Mellanox in 2019, the combined entity transformed the AI landscape by shifting industry reliance from Moore's Law to a "scale-out" architecture that unifies thousands of GPUs through ultra-low latency interconnects. This integration enabled the development of specialized hardware, including Bluefield DPUs and Spectrum X switches, to overcome physical energy constraints and optimize distinct training versus inference workloads across gigawatt-scale data centers. Consequently, the partnership has driven a 45-fold increase in market capitalization while establishing a new philosophy where software-hardware co-design allows AI to function as a foundational utility for simulating complex scientific and historical phenomena.
- Sequoia Capital36 min
Building the Universal AI Automation Layer ft n8n CEO Jan Oberhauser
Jan Oberhauser, George Robson, Pat Grady, Henry Suryawirawan
Following a strategic pivot to an orchestration layer for AI agents, N8N achieved a fourfold revenue increase in eight months by shifting from lead-generation tactics to a community-driven adoption model. Led by founder Jan Oberhauser, the company expanded its US operations and redefined its role as a "Excel of AI," supporting a diverse technology stack that allows users to build and manage agents without locking into specific models. This approach positions N8N to capture the market from the bottom up, targeting non-technical builders while anticipating a future acceleration in foundation model innovation driven by capital influx and enterprise demand.
- Sequoia Capital1h 1m
Building the Generative Web with AI ft Vercel CEO Guillermo Rauch
Guillermo Rauch, Sonya Huang, Pat Grady
Vercel is leveraging its V0 AI platform to democratize software creation by enabling designers and marketers to build functional applications through natural language, thereby expanding the developer ecosystem to millions. This strategic shift has driven a doubling of the user base without paid acquisition, as the tool automates code generation and security protocols to replace traditional pitch decks with working prototypes. Looking ahead, the company predicts a "generational leap" where AI agents and dynamic web interfaces transform the industry, forcing legacy enterprises to adapt or risk obsolescence within the next five years.
- Sequoia Capital1h 0m
Why Voice Will Be the Fundamental Interface for Tech ft ElevenLabs’ Mati Staniszewski
Founded in late 2021 by co-founders Piotr and Maddie Staniszewski, ElevenLabs distinguishes itself through a specialized architecture that predicts acoustic output to capture nuanced emotion and tone. The remote company leverages a global team of research engineers and specialized voice coaches to deliver high-fidelity speech synthesis used in healthcare, customer support, and interactive education platforms like Chess.com. With a strategic focus on achieving human-level interaction by 2025, the firm prioritizes low-latency infrastructure and proactive safety measures to enable universal translation and ambient computing.
- Sequoia Capital38 min
From Data Centers to Dyson Spheres: P-1 AI's Path to Hardware Engineering AGI
Paul Eremenko, Sonya Huang, Pat Grady
P1.ai is launching its AI engineering agent "Archie," which addresses the scarcity of physical design data by synthesizing massive, physics-informed datasets to train specialized models for hardware creation. Initially targeting data center cooling applications in 2025, the system operates as a collaborative team member that orchestrates existing CAD and simulation tools to perform complex design synthesis and error correction at entry-level engineer proficiency. With angel investment from Jeff Dean, the company aims to scale the agent's capabilities from residential cooling to aerospace domains over the next four years, ultimately striving for Engineering AGI defined by self-reflection and domain generalization.
- Sequoia Capital48 min
Building the System of Record for the AI Era ft Workday CEO Carl Eschenbach
Carl Eschenbach, Sonya Huang, Pat Grady
Workday CEO Carl Eschenbach is redefining the platform as a unified "Agent System of Record" to govern both human employees and AI agents across its database of over 70 million users. To monetize this shift, the company is deploying a multi-faceted pricing model involving seat-based uplifts, role-based agent fees, and consumption-based API access while utilizing strategic acquisitions like HiredScore to drive measurable productivity gains. Despite an 8% workforce restructuring to fund reinvestment, Eschenbach emphasizes that AI will serve as a growth engine rather than a cost-cutting tool, maintaining a human-in-the-loop approach for critical decisions to preserve the company's core values.
- Sequoia Capital1h 3m
Getting the Most From AI With Multiple Custom Agents ft Dust’s Gabriel Hubert and Stanislas Polu
Gabriel Hubert, Stanislas Polu, Konstantine Buhler, Pat Grady
Dust positions itself as a horizontal platform for AI adoption, predicting a bimodal future where enterprise users seamlessly switch between frontier APIs and local models to navigate varying technological plateaus. By prioritizing product-market fit over proprietary model training and leveraging Retrieval-Augmented Generation to unlock data silos, the company enables diverse teams to build specialized agents that augment human work rather than replace it. This strategy targets a demographic of young power users and aims to scale from isolated pilots to organization-wide adoption, facilitating everything from cross-functional translation to global expansion despite current limitations in reasoning breakthroughs.
- 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 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.