Anthropic Appoints Ex-OpenAI Co-Founder Andrej Karpathy

Anthropic has appointed Andrej Karpathy to its pretraining research team, adding one of the artificial intelligence sector’s most recognised researchers as competition intensifies among leading AI companies for frontier talent.

Karpathy announced the move publicly, stating that he was excited to return to research and development as the next phase of large language model advancement unfolds.

At Anthropic, he will focus on building and leading initiatives aimed at accelerating pretraining research for Claude models, helping improve the systems’ foundational knowledge and core capabilities.

The appointment comes as AI companies increasingly compete for specialised talent in large language models, autonomous systems, and AI infrastructure. Anthropic has been aggressively expanding its research and engineering teams amid growing rivalry with OpenAI, xAI, and other frontier AI labs.

Karpathy is widely known for co-founding OpenAI before leaving in 2017 to join Tesla as Director of AI. At Tesla, he led the company’s computer vision and Autopilot teams, playing a key role in the development of AI systems powering Tesla’s autonomous driving initiatives.

His transition from OpenAI to Tesla had drawn attention within the industry, particularly because Elon Musk was associated with both organisations at the time. Karpathy’s role at both companies later resurfaced during the high-profile Musk versus Altman legal proceedings related to OpenAI.

After leaving Tesla in 2022, Karpathy briefly returned to OpenAI before launching Eureka Labs, an AI-focused education startup aimed at making advanced machine learning concepts more accessible.

Over the course of his career, Karpathy has become one of the most influential figures in applied AI research, particularly in deep learning, computer vision, and large language models. He holds a PhD in computer science from Stanford University.

Anthropic said Karpathy will begin work immediately and help strengthen the company’s long-term research capabilities as it continues investing heavily in model training, AI infrastructure, and next-generation reasoning systems.

The hiring also reflects the broader battle for elite AI researchers as companies race to build increasingly advanced and commercially viable AI systems. With investments in foundational AI models accelerating globally, experienced researchers with expertise across both research and large-scale product deployment are becoming central to competitive strategy in the sector.