Jerry Tworek, Core Automation CEO on AI Reasoning, Agents and Research

Core Automation aims to transcend the current transformer-based AI paradigm by developing more adaptive, efficient neural network architectures. Rather than relying on massive, static fine-tuning, these models learn continuously from specific user data, enabling them to perform complex tasks with greater precision and autonomy. This shift signals a transition toward a post-labor society where AI-driven companies function as resource-allocation engines, with human oversight focused on high-level objectives. By leveraging modern automation, small, high-agency teams can achieve output levels previously requiring large organizations. The business strategy prioritizes on-premise, rack-based deployments, ensuring data privacy while providing highly customized, persistent memory for enterprise clients. This approach challenges the existing reliance on general-purpose models, favoring instead specialized systems that evolve alongside the user's specific operational needs.