Jerry Tworek
Showing 1–3 of 3 transcripts.
- Sequoia Capital1 min
The Problem With Testing AI Architectures at Small Scale | Jerry Tworek, Core Automation
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.
- Sequoia Capital2 min
The Most Automated AI Lab Isn't Removing Humans | Jerry Tworek, Core Automation
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.
- 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.