# Andrej Karpathy

Andrej Karpathy is a computer scientist and educator, a founding member of OpenAI and former director of AI at Tesla, credited with popularizing the term vibe coding.

Andrej Karpathy is a Slovak-Canadian computer scientist known for his research contributions, his leadership of Tesla's Autopilot program, and his influential public AI education work. He completed his PhD at Stanford under [fei-fei-li](https://www.wikiprompt.org/wiki/fei-fei-li), focusing on connections between [computer-vision](https://www.wikiprompt.org/wiki/computer-vision) and [natural-language-processing](https://www.wikiprompt.org/wiki/natural-language-processing), and taught Stanford's CS231n convolutional neural networks course, whose lecture notes and materials became widely used self-study resources for a generation of [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) practitioners.

## OpenAI and Tesla

Karpathy was a founding member of [openai](https://www.wikiprompt.org/wiki/openai) in 2015, working on early [reinforcement-learning](https://www.wikiprompt.org/wiki/reinforcement-learning) and [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) research. In 2017 he left to join Tesla as senior director of AI, where he led the Autopilot computer vision and neural network team, restructuring Tesla's self-driving software around large [neural networks](https://www.wikiprompt.org/wiki/neural-network) trained on fleet-collected data rather than heavily hand-engineered rules, part of the broader industry arc around [self-driving cars](https://www.wikiprompt.org/wiki/self-driving-car). He left Tesla in 2022 and briefly returned to [openai](https://www.wikiprompt.org/wiki/openai) in 2023 before departing again in 2024 to focus on independent education projects.

## Education and public writing

Throughout his career, Karpathy has maintained an unusually high public profile as an educator, publishing the widely read 2015 blog post "The Unreasonable Effectiveness of Recurrent Neural Networks," building the "Neural Networks: Zero to Hero" video course series, and creating minimal, readable reference implementations of core [deep-learning](https://www.wikiprompt.org/wiki/deep-learning) and [language model](https://www.wikiprompt.org/wiki/large-language-model) training code intended to demystify how systems like [gpt-2](https://www.wikiprompt.org/wiki/gpt-2) work internally. In 2024, he founded Eureka Labs, an AI-native education startup aiming to combine expert-created course content with AI tutoring.

## Coining "vibe coding"

In February 2025, Karpathy popularized the term "vibe coding" in a widely shared social media post, describing a style of software development in which a programmer describes what they want in natural language, accepts and iterates on code generated by an [AI coding assistant](https://www.wikiprompt.org/wiki/ai-coding-assistant) or agent, and does not closely read or fully understand the generated code, "forgetting that the code even exists." The term was quickly adopted across the industry to describe a broader shift in how developers used tools such as [Claude Code](https://www.wikiprompt.org/wiki/claude-code), [cursor](https://www.wikiprompt.org/wiki/cursor), and [GitHub Copilot](https://www.wikiprompt.org/wiki/github-copilot), while also drawing criticism from some engineers who argued it normalized poor code review and technical debt; the debate over its costs and benefits has continued as agentic coding tools became more capable through 2025 and 2026.

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Source: https://www.wikiprompt.org/wiki/andrej-karpathy
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-02T20:31:58.759824+00:00