3 sources
One of the clearest anchors for understanding why scaling laws became such a central planning tool for frontier-model research and training strategy.
Start HereScaling Laws for Neural Language Models
Researcher Profile
FeaturedAndrej Karpathy
Deep learning engineering, LLM education
Founder at Eureka Labs
Important not only for his direct research contributions, but for translating frontier deep-learning ideas into builder intuition that spreads across the industry.
Organizations
Eureka Labs
About This Page
This profile is meant to help you get oriented quickly: why this researcher matters, what to read first, and where to explore next.
Last reviewed
March 18, 2026
Official And External Links
Known For
The ideas, systems, and research directions that make this person worth knowing.
01
Generative pretraining and practical deep-learning intuition
02
Bridging frontier research and hands-on engineering
03
Clear explanations that shape how builders learn modern AI systems
04
Deep learning engineering, LLM education
05
Andrej Karpathy (website)
06
Industry
Start Here
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Signature Works
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Important because several of the modern foundation-model playbooks trace back to work he helped drive, especially around generative pretraining and multimodal transfer.
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One of the clearest researchers to study for the GPT-3 era, especially around few-shot learning, scaling behavior, and what larger language models started making possible in practice.
Start HereLanguage models are few-shot learners
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A high-signal researcher for understanding how DeepMind approaches generality, especially in areas where reinforcement learning, multimodality, and large-scale systems meet.