Latest Interviews
Showing 16–30 of 30 transcripts.
Clear all filters- 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 Capital46 min
Pricing in the AI Era: From Inputs to Outcomes, with Paid CEO Manny Medina
Manny Medina, Pat Grady, Lauren Reeder
Manny Medina outlines a strategic pivot for AI success toward narrow, high-utility applications that replace specific Business Process Outsourcing roles rather than general-purpose creative tasks. He details four emerging pricing frameworks, particularly agent-based billing that allows companies to allocate AI costs to human resources budgets, while warning that rising inference costs are compressing margins unless vendors shift to outcome-based revenue models. Medina concludes that sustainable growth requires founders to abandon broad market ambitions in favor of deep vertical expertise, utilizing paid to track unit economics as the industry transitions from trial-based excitement to contract renewals.
- 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.
- Sequoia Capital42 min
ServiceNow ft. Frank Slootman and Fred Luddy - From Starting Over at 50 to Dodging a $150B Mistake
Frank Slootman, Fred Luddy, Roelof Botha, Doug Leone, Pat Grady, Carl Eschenbach, Rolof Huerta
Founded by Fred Luddy in 2004 after a previous bankruptcy, ServiceNow evolved from a free IT help desk tool into a $150 billion enterprise platform through a strategic pivot to broad, seat-based licensing and a complete cloud infrastructure overhaul. In 2011, Luddy ceded the CEO role to Frank Slootman to enforce operational discipline, while investors from Sequoia Capital blocked a $2.5 billion acquisition offer from VMware to force a path toward an IPO. This decision proved prescient as the company's market value surged to $10 billion within two years of going public in 2012, validating the strategy to prioritize long-term disruption over immediate financial stability.
- Sequoia Capital1h 13m
Sierra co-founder Clay Bavor on Making Customer-Facing AI Agents Delightful
Clay Bavor, Ravi Gupta, Pat Grady, Brett Taylor, Karthik Narasimhan, Robbie
Former Google leader Clay Bavore and Brett Taylor founded Sierra in late 2022 to deploy proprietary AgentOS-branded AI agents that replace traditional navigation with natural language interactions for major brands like Weight Watchers and Sonos. The company addresses specific large language model limitations, such as hallucinations and data silos, by utilizing supervisor agents and a declarative SDK to achieve over 70% autonomous resolution rates for complex customer tasks. Sierra's unique resolution-based pricing model and proprietary TAU Bench benchmark underscore a strategic shift toward industrial-grade AI that prioritizes factual accuracy and task reliability over raw model size.
- Sequoia Capital51 min
Phaidra’s Jim Gao on Building the Fourth Industrial Revolution with Reinforcement Learning
Jim Gao, Sonya Huang, Pat Grady
Phaedra CEO Jim Gow leverages reinforcement learning to deploy autonomous "virtual plant operators" that optimize mission-critical industrial facilities like Google's data centers and Merck's vaccine manufacturing plants, achieving up to 40% energy reductions while strictly maintaining safety constraints. By inserting cloud-based intelligence layers over legacy hardware, the system moves beyond simple recommendations to issue direct commands that adapt in real-time to physical changes, effectively solving complex constraint optimization problems without new sensor infrastructure. Looking ahead, Gow targets broader climate impact through AI-driven grid balancing to manage renewable energy volatility, while noting that widespread adoption depends on overcoming historical data storage gaps in the industrial sector.
- Sequoia Capital39 min
Fireworks Founder Lin Qiao on the Power of Small Models to Democratize AI Use Cases
Lin Qiao, Sonya Huang, Pat Grady, Lynn Tiao
Founded in 2022 by former Meta PyTorch leaders Lynn Diao and others, Fireworks is a SaaS platform dedicated to compressing AI model deployment timelines from years to days through a specialized, PyTorch-native infrastructure. The company automates complex optimization tasks like quantization and semantic caching using handwritten CUDA kernels to enable high-performance, low-latency inference for small model stacks and fine-tuned enterprise workloads. By targeting the gap between research and production, Fireworks facilitates the migration of startups and traditional enterprises away from generic experimentation toward scalable, cost-efficient custom models that compete with larger monolithic systems.
- Sequoia Capital52 min
Klarna CEO Sebastian Siemiatkowski on Getting AI to Do the Work of 700 Customer Service Reps
Sebastian Siemiatkowski, Sonya Huang, Pat Grady
Klarna CEO Sebastian Gunnarsson leveraged a direct partnership with OpenAI to transform the payments platform's dispute resolution from a 14-minute human process to a two-minute autonomous AI system, resulting in a $40 million annual profit increase and the elimination of 700 contracts. Beyond customer service, the company has centralized its operations into a proprietary knowledge graph to power an internal chatbot named Kiki while simultaneously replacing legacy enterprise software to accelerate marketing campaigns from months to days. Gunnarsson frames this strategic shift not as a total replacement of human roles but as a necessary evolution to enforce higher documentation standards and create a "digital financial assistant" that proactively drives savings for consumers.
- Sequoia Capital1h 0m
Microsoft CTO Kevin Scott on How Far Scaling Laws Will Extend | Training Data
Kevin Scott, Pat Grady, Bill Coughran
Microsoft CTO Kevin Scott outlined the company's strategic pivot to a comprehensive AI ecosystem driven by the belief that scaling data and compute will continue to yield exponential capability improvements despite market saturation. Through key partnerships like the one with OpenAI and a focus on hardware efficiency, the organization aims to transition from training-heavy infrastructure to cost-effective inference that augments human cognition across healthcare, education, and scientific research. Scott emphasized that while full autonomy remains a challenge, flexible architectural design and new economic models for data will enable widespread deployment to solve complex societal problems without forcing developers into proprietary traps.
Pat Grady: Sequoia Partner on Investing Lessons from Doug Leone, Roelof Botha and Alfred Lin | E1174
Pat Grady, Doug Leone, Roelof Botha, Alfred Lin, Harry Stebbings
Pat and Doug Leone of Sequoia Capital prioritize founders with deep domain expertise and intense motivation, utilizing a "vector" assessment to select exceptional talent over favorable market conditions alone. The firm distinguishes its portfolio management through active value creation, exemplified by strategic interventions at companies like ServiceNow and HubSpot, while maintaining a "Day One" culture to mitigate internal arrogance. By combining rigorous due diligence with a specialized operator team and long-term harvesting strategies, Sequoia aims to compound returns by backing leaders who can navigate scaling challenges and unlock trillions in market value.
- Sequoia Capital59 min
Factory’s Matan Grinberg and Eno Reyes Unleash the Droids on Software Development | Training Data
Matan Grinberg, Eno Reyes, Sonya Huang, Pat Grady
Factory deploys autonomous software engineering "droids" that leverage existing foundation models to automate unenjoyable enterprise tasks like code review and testing, delivering a 22% increase in engineering cycle speed. The company recently achieved a 19% pass rate on the SWE-Bench benchmark by prioritizing task-specific cognitive architectures over training new models, effectively surpassing previous state-of-the-art performance. Founders Matan Grimberg and Eno Reis position the platform to shift engineering roles toward orchestration, focusing on measurable organizational metrics rather than individual developer replacement.
- Sequoia Capital50 min
LangChain’s Harrison Chase on Building the Orchestration Layer for AI Agents | Training Data
Harrison Chase, Sonya Huang, Pat Grady
Harrison Chase positions Langchain as a critical orchestration layer for the "middle ground" of agent autonomy, prioritizing production-grade reliability over the volatile hype of fully autonomous systems. The company addresses this shift by deploying LangGraph for complex, stateful workflows and LangSmith for observability, enabling organizations to build custom cognitive architectures that balance flexibility with necessary human-in-the-loop controls. As the industry transitions from static chains to dynamic agents, these tools facilitate the move from customer support automation to software development integration while redefining testing and user experience paradigms.