Sequoia Capital

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.

The Problem With Testing AI Architectures at Small Scale | Jerry Tworek, Core Automation

Jerry Tworek

The event critiques the industry's conventional practice of validating model architectures on small datasets, arguing that this approach fails to capture the true potential of scalable systems. It specifically highlights that reinforcement learning models require a minimum threshold of compute to exhibit meaningful capabilities, suggesting that lower-scale testing often yields uninteresting results. Consequently, the organization advocates for a paradigm shift toward large-scale initial evaluations to ensure architectural research accurately reflects future performance.

The Most Automated AI Lab Isn't Removing Humans | Jerry Tworek, Core Automation

Jerry Tworek

Aiming to become the world's most autonomous laboratory, the organization is deploying AI-driven coding agents as foundational infrastructure to drastically accelerate research iteration and data gathering. Rather than adapting legacy structures to these tools, the company is building native workflows that prioritize human agency and allow individual researchers to scale their output through automation's force-multiplying effects. This strategic pivot transforms the research environment, enabling single agents to execute significantly larger volumes of work and test ideas with unprecedented speed.

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.

The Philosopher CEO | Clay Co-Founder Kareem Amin

Clay Co-Founder Kareem Amin, Kareem Amin, Varun, Nikolai, Mike Vernal, Pete Caputa, Jack Dorsey, David Solomon, Tarek

Clay CEO Kareem guides his company through a "long strange trip" of pivoting from broad ambitions to the specific niche of Go-to-Market Engineering, where he prioritizes radical transparency and usage-based pricing to align with technical users. By rejecting standard management dogmas in favor of "momentum detective" leadership and "deep work" practices, Kareem fosters a culture of devotion that withstands pricing backlash and drives a unique product philosophy valuing flexibility over simplicity. This strategy successfully established a new market category while navigating a five-year period of ambiguity to achieve product-market fit without relying on external validation.

Every CIO will have to answer for every token | Factory's Matan Grinberg

Matan Grinberg

The factory router introduces a dynamic model selection strategy that optimizes enterprise AI efficiency by routing distinct tasks to specialized, cost-effective models rather than relying on a single frontier solution. This framework enables CIOs to address the critical need for granular token allocation, assigning specific capabilities such as lightweight generation for "vibe coding" or custom fine-tuned models for legacy COBOL maintenance. By matching model performance to precise organizational workflows, the approach allows for complex multi-model validation pipelines while avoiding the inefficiency of blanket usage caps.

90% of AI tokens will be asynchronous | Matan Grinberg, Factory

Matan Grinberg

The event outlines a critical shift in AI consumption from fragile, prompt-dependent synchronous models to robust asynchronous workflows where autonomous agents initiate tasks without human triggers. Industry forecasts predict that within 12 to 24 months, 90% of tokens will be generated by these autonomous systems, marking the transition from current "co-pilot" tools to fully "agent-native" operations. This evolution promises to decouple revenue growth from active human intervention by enabling systems to independently identify customer signals and execute first-pass solutions.

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.

Why "Tokens Aren't Fungible" - Anthropic's Angela Jiang

Angela Jiang

The organization is transitioning its strategic focus from knowledge retrieval to an execution layer powered by Cloud Managed Agents, which enables AI systems to string together tasks and edit files across multiple systems. This infrastructure serves as the foundation for a forthcoming coordination layer that will introduce strategies as a meta harness to orchestrate complex workflows through specialized token roles. The roadmap outlines a sequential evolution moving from the current execution capabilities toward this higher-level abstraction where high-level intent guides composed, autonomous systems.

Anthropic's Katelyn Lesse & Angela Jiang: Building an Ecosystem, not a Walled Garden

Katelyn Lesse, Angela Jiang, Sonya Huang, Lauren Reeder, Caitlin

Anthropic is pivoting its platform strategy from a knowledge-centric foundation to an execution and coordination layer, aiming to democratize custom software development through a unified architecture for both internal and external users. This roadmap introduces specialized primitives for "token-heavy" verticals like coding and finance while enabling flexible model routing and ecosystem interoperability through standards like the Model Context Protocol. By prioritizing cost optimization and advanced workflow orchestration, the company seeks to make the last mile of AI-driven development economically viable for builders ranging from individual developers to large enterprises.

Kalshi's Co-CEOs Disagree by Design — Why Constant Conflict Became Their Pattern

The event details a leadership model within a complex organization where intentional, continuous disagreement serves as a core operating pattern to balance regulatory risk against innovation. This approach relies on designated contrarian voices that act as a necessary check by identifying potential failure points when team enthusiasm peaks. Ultimately, the strategy establishes a delicate, constant mechanism for navigating the tension between advancing new initiatives and adhering to strict regulatory constraints.

Every Startup Has a Hole in the Ship — Kalshi's Tarek Mansour on the Problem You Can't Delegate

Tarek Mansour

The speaker distinguishes between organizations that actively confront critical internal flaws and those that ignore them, asserting that unaddressed defects inevitably lead to failure despite external growth. In January, the industry identified a reputational leak caused by public confusion between regulated platforms like Calci and unregulated offshore operators such as Polymarket. To resolve this, the organization has initiated a strategy to educate stakeholders on the necessity of regulation, aiming to normalize compliant operations and distinguish them from non-compliant models.

Kalshi's Tarek Mansour: Chaos by Design

Tarek Mansour, Luana Lopes Lara, Dharmesh

Kalshi co-founders Tarkan and Luana structured their organization to maintain a flat hierarchy and deliberate strategic disagreement, which fueled a legal battle against the CFTC that resulted in a 2024 victory and a 95% dominance of the U.S. prediction market. The company distinguishes itself through a regulated exchange model that charges fees on volume rather than customer losses, while enforcing strict risk management protocols for younger traders to ensure responsible usage. This victory allowed Kalshi to launch political markets just before the 2024 election, leveraging a founder-led culture of aggressive detail and real-time cultural synchronization to solidify its position against offshore competitors.

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.

Why Hardware-Software Co-Design Is AI's Real 100x: Dylan Patel of SemiAnalysis

Dylan Patel, Shaun Maguire, Sonya Huang

Semi-Analysis, founded by Dylan Patel after a period of homelessness and a stint in quantitative trading, has grown to a 90-person team generating nearly $100 million in revenue by blending engineering expertise with hedge fund economics. The firm leverages Patel's annual attendance at over 40 global semiconductor conferences to gather proprietary supply chain data and launched InferenceX, a living benchmarking platform supported by over $50 million in compute donations from major tech firms. Looking ahead, the company projects critical shifts in the industry driven by power grid limitations, the obsolescence of single-architecture hardware, and a strategic pivot toward software-hardware co-design to navigate the escalating demands of artificial intelligence.