Jared Kaplan
TL;DR Jared Kaplan is a leading AI researcher whose work on scaling laws, transformer theory, and large model training has shaped the foundation of modern generative AI.
Jared Kaplan is one of the most influential theoretical minds behind modern AI. Known for his groundbreaking work uncovering the scaling laws that govern large neural networks, Kaplan helped lay the scientific framework that enabled the rise of today’s frontier models. His work bridges physics, deep learning, and mathematical insight, making him a central figure in understanding why large models work—and how to build them effectively.
Originally trained as a theoretical physicist, Jared Kaplan earned recognition in high-energy physics before shifting into the machine-learning world. His background in mathematical modeling and complex systems positioned him perfectly for tackling one of AI’s most important questions: how do neural networks improve as they grow?
At OpenAI, Kaplan co-authored the landmark paper on scaling laws, demonstrating that model performance predictably increases with more data, larger models, and greater compute resources. This discovery became a foundational principle for the development of advanced language models, influencing research agendas across the AI industry.
Kaplan later co-founded Anthropic, where he continued shaping the field of large-model training, safety-focused architectures, and research methodologies that push the boundaries of what AI systems can do. His work emphasizes rigor, clarity, and the scientific method applied to deep learning—traits that have made him a respected voice among both theorists and practitioners.
Today, Kaplan is seen as a key architect of the modern AI era, bridging academic depth with industry-shaping contributions.
Co-author of the scaling laws for neural networks, a foundational discovery in modern AI
Co-founder and early scientist at Anthropic, influencing model design and safety-focused research
Former OpenAI researcher, contributing to large-scale model development
Theoretical physicist turned AI theorist, applying mathematical insight to deep learning
Influential work on large-model training, guiding how frontier models are built and optimized
Thought leader in the science of deep learning, known for formalizing principles that shape the AI ecosystem
