Daniela Amodei, president and co-founder of Anthropic, has suggested that the concept of artificial general intelligence (AGI) may no longer be a useful framework for understanding the current trajectory of AI development. Speaking in a recent interview with CNBC, Amodei argued that the term—once a guiding vision for when machines might match human cognitive abilities—has become outdated given how quickly AI systems are evolving. “AGI is such a funny term,” she said. “Many years ago, it was kind of a useful concept to say, ‘When will artificial intelligence be as capable as a human?’” But today, she noted, that definition no longer holds. “By some definitions of that, we've already surpassed that,” she added, pointing to areas like software development, where Anthropic’s Claude model can now write code at a level comparable to many professional engineers, including those within the company. Despite these advances, Amodei acknowledged that AI still struggles with tasks that humans perform effortlessly—such as navigating complex social situations, understanding nuanced emotions, or making judgment calls in ambiguous real-world contexts. “Claude still can't do a lot of things that humans can do,” she said, highlighting the gap between specialized performance and broad, adaptable intelligence. This contradiction, she explained, is why the AGI concept may no longer be relevant. “I think maybe the construct itself is now wrong—or maybe not wrong, but just outdated,” she said. The idea of a single, universal benchmark for intelligence no longer captures the reality of AI’s current capabilities, which are both impressive and deeply limited in different ways. Amodei’s comments come as Anthropic and other leading AI companies invest heavily in more powerful models and the massive data centers needed to run them. While some experts question whether current large language models can ever achieve true general intelligence without fundamental breakthroughs, Amodei remains open to the possibility, saying, “We don't know. Nothing slows down until it does.” Rather than fixating on whether AI will one day reach AGI, she believes the more urgent questions are about how these systems are being adopted in real-world settings. “The future of AI won’t hinge on whether it meets a textbook definition of AGI,” she said. “It’s about what these systems can do, where they fall short, and how society chooses to deploy them.” She emphasized that progress in AI is not just a technical challenge, but also a social and organizational one. Even if models continue to improve, their real-world impact depends on how quickly institutions can adapt—overcoming hurdles like change management, procurement processes, and identifying where AI actually adds value. The real test, in her view, is not a milestone of intelligence, but the ability to integrate increasingly capable systems into everyday work and life.