Jerry Tworek describes Core Automation as part of a new generation of AI labs built after the emergence of capable reasoning models and agents. Instead of adding automation to an established organization, the company is designed around AI systems that can execute research workflows from the beginning.
The immediate goal is not to replace researchers who form hypotheses and decide which directions matter. Tworek says current agents are strongest when a task is stated precisely, so the lab uses them to implement experiments, collect results and shorten the cycle between an idea and reliable evidence. Reducing an experiment from a month to a day can materially increase the rate of useful insight.
He also traces how code generation and verifiable programming tasks helped reinforcement learning scale into modern reasoning systems. Core Automation aims to turn the operating methods it develops into a blueprint for highly automated companies, with a shared company-level AI coordinating knowledge and execution. Sponsor messages and investment promotions at the end are omitted.
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