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  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. Sequoia Capital1h 0m

    Why Voice Will Be the Fundamental Interface for Tech ft ElevenLabs’ Mati Staniszewski

    Mati Staniszewski, Pat Grady

    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.

  7. 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.

  8. Sequoia Capital58 min

    Arc Institute's Patrick Hsu on Building an App Store for Biology with AI

    Patrick Hsu, Josephine Chen, Pat Grady

    The ARC Institute, led by Patrick Hsu, has launched EVO2, a generative foundation model trained on genomic sequences across all domains of life to predict the functional consequences of genetic variants with state-of-the-art accuracy. By reclassifying Variants of Unknown Significance, this open-source tool aims to transform genetic testing outcomes and prevent unnecessary medical interventions while establishing a roadmap toward simulating virtual cells by 2030. This initiative reflects a strategic shift within ARC to merge academic rigor with industrial application, utilizing evolutionary data to build a unifying theory of biology rather than focusing solely on traditional drug design.

  9. Sequoia Capital1h 11m

    Why CRM Needs an AI Revolution, with Day.ai Founder Christopher O’Donnell

    Christopher O'Donnell, Pat Grady

    Christopher O'Donnell, the former architect behind HubSpot's billion-dollar CRM growth, is launching Day.ai to replace manual data entry with an AI-native system that decompresses customer interactions into high-fidelity natural language records. By balancing autonomous automation with user oversight, the company targets the enterprise CRM market's $10 billion ARR potential while avoiding the risks of fully self-driving data models. The product's development strategy relies on direct customer engagement and a "slow is smooth" philosophy to ensure the system of record remains robust before scaling.

  10. Sequoia Capital54 min

    How AI Breakout Harvey is Transforming Legal Services, with CEO Winston Weinberg

    Winston Weinberg, Sonya Huang, Pat Grady

    Founded in July 2022 by Winston Weinberg and Gabe, Harvey targets the $400 billion U.S. legal market by deploying specialized agentic workflows that prioritize citation accuracy over generic chat interfaces. The company differentiates itself through a hybrid revenue-share model and a rigorous technical architecture utilizing OpenAI's O-series models alongside proprietary process data to eliminate hallucinations in high-stakes legal tasks. By collaborating with top-tier law firms as design partners and transitioning toward automating commoditized work, Harvey aims to fundamentally shift industry economics from billable hours to fixed fees while expanding access to justice.

  11. Sequoia Capital1h 5m

    The AI Product Going Viral With Doctors: OpenEvidence, with CEO Daniel Nadler

    Daniel Nadler, Pat Grady

    Open Evidence currently scales to over 400,000 global monthly active physicians through a direct-to-pro model that bypasses traditional enterprise sales by relying on word-of-mouth adoption. The platform distinguishes itself by training exclusively on peer-reviewed literature, including a strategic partnership with the New England Journal of Medicine, to prevent hallucinations while enabling doctors to resolve complex comorbidities that standard search engines cannot address. Co-founder Daniel Nadler projects this technology could prevent 300,000 to 800,000 deaths annually within five years, with a long-term vision of achieving hyper-personalized medicine to extend human life expectancy toward 120 to 130 years.

  12. 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.

  13. 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.

  14. 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.

  15. 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.