Jared Kaplan is an influential American theoretical physicist and a prominent figure in the field of artificial intelligence. He is best known as the co-founder and Chief Science Officer of Anthropic, a leading AI company developing large-scale, safe AI systems. Kaplan's work has significantly advanced the understanding of AI scaling laws, which explore how increasing the size of AI models and datasets can predictably enhance performance. His research has been foundational in the development of large language models, including work on systems like GPT-3 at OpenAI, where he previously contributed before founding Anthropic.
Recent years have seen several notable developments in Jared Kaplan's professional career and his contributions through Anthropic:
Attribute | Information |
---|---|
Full Name | Jared Kaplan |
Born | 22 May 1984, USA |
Nationality | American |
Occupation | Theoretical Physicist, Chief Science Officer |
Known For | Co-founding Anthropic, AI scaling laws |
Net Worth | Estimated at $1.2 billion as of 2025 |
Education | Ph.D. in Physics, Harvard University |
Residence | Pacifica, California |
Jared Kaplan was born and raised in the United States. He pursued his undergraduate studies in physics and mathematics at Stanford University, where he graduated with high honors, before earning his Ph.D. in physics from Harvard University. Kaplan's academic journey was marked by a strong interest in the fundamental aspects of theoretical physics, particularly quantum gravity and cosmology. His education laid the groundwork for his future work in AI, as he transitioned from a purely theoretical focus to applying these principles in machine learning and artificial intelligence contexts.
Kaplan began his career in theoretical physics, focusing on quantum gravity, string theory, and the AdS/CFT correspondence while serving as a professor at Johns Hopkins University. His work in this field was recognized by prestigious fellowships and grants, including the Sloan Research Fellowship and NSF CAREER grant. His pivot to AI was fueled by his interest in the potential of scaling laws to predictably increase AI performance. Notably:
Currently, Kaplan oversees scientific research and strategic direction at Anthropic, focusing on developing AI systems that benefit human society while minimizing risks. Anthropic's work under his leadership has influenced the broader AI industry, providing frameworks for safely deploying advanced AI technologies. The company's approach integrates Constitutional AI principles, ensuring that AI systems operate safely and effectively. As a thought leader in the field, Kaplan is engaged in public discourse on AI ethics and safety, influencing policy and corporate strategies in AI development.
Under Kaplan's leadership, Anthropic has focused on AI advancements that prioritize safety and ethical considerations. Kaplan's contributions involve developing scaling laws that have become fundamental in creating efficient AI models. His research has been instrumental in enabling AI to perform complex tasks with accuracy and reliability, aligning the technology's development with human-centric values.
Before venturing into AI, Kaplan made his mark as a theoretical physicist at Johns Hopkins University (JHU). His work at JHU revolved around quantum gravity and the AdS/CFT correspondence, contributing to an enriched understanding of these complex topics. His academic pursuits there laid the groundwork for his transition into AI, as he leveraged his deep knowledge in theoretical concepts to address practical challenges in AI development.
Jared Kaplan is a pivotal figure in the landscape of artificial intelligence, combining rigorous scientific inquiry with practical applications in AI. Through his work at Anthropic, he is at the forefront of developing AI systems that prioritize safety, reliability, and alignment with human values. His contributions continue to shape the future of AI technologies, ensuring they are developed and deployed with caution and foresight. Kaplan's work exemplifies the integration of physics and computer science to advance human understanding of intelligent systems, ensuring a balanced approach to AI's potential and limitations.