# Alex Karpathy

Andrej Karpathy is a Slovak-Canadian AI researcher and educator, known for deep learning courses, former Tesla AI director, and founder of Eureka Labs. He co-founded OpenAI and joined Anthropic in 2026.

Andrej Karpathy (born 23 October 1986) is a Slovak-Canadian artificial intelligence researcher and educator. He is a founding member of [OpenAI](https://www.wikiprompt.org/wiki/openai), where he specialized in [deep learning](https://www.wikiprompt.org/wiki/deep-learning) and [computer vision](https://www.wikiprompt.org/wiki/computer-vision), and later served as director of artificial intelligence at Tesla. In 2024, he founded Eureka Labs, an AI education platform, and in 2026 he joined [Anthropic](https://www.wikiprompt.org/wiki/anthropic) as part of its pretraining team. Karpathy is widely recognized for his contributions to [neural network](https://www.wikiprompt.org/wiki/neural-network) education, including the influential Stanford course CS 231n and his popular YouTube tutorials.

Karpathy's work spans both academic research and industry application, with a focus on [large language models](https://www.wikiprompt.org/wiki/large-language-model) and their practical use in education. He coined the term "vibe coding" in 2025, reflecting the growing accessibility of AI-assisted software development. His career has been marked by a series of high-profile roles at leading AI organizations, alongside a sustained commitment to making complex topics understandable to a broad audience.

## Education and early life

Karpathy was born in Bratislava, Czechoslovakia (now Slovakia), and moved with his family to Toronto when he was 15. He completed his Computer Science and Physics bachelor's degrees at the [University of Toronto](https://www.wikiprompt.org/wiki/university-of-toronto) in 2009 and his master's degree at the University of British Columbia in 2011, where he worked on physically simulated figures.

In 2006, Karpathy began posting videos on YouTube on his channel, badmephisto. He garnered fame by posting Rubik's cube tutorials which have been used by famous speedcubers such as Feliks Zemdegs. The channel has over 9 million views as of June 2025.

Karpathy received a PhD from [Stanford University](https://www.wikiprompt.org/wiki/stanford-ai-lab) in 2015 under the supervision of Fei-Fei Li, focusing on the intersection of [natural language processing](https://www.wikiprompt.org/wiki/natural-language-processing) and computer vision, and deep learning models suited for this task.

## Academic career and CS 231n

Karpathy authored and was the primary instructor of the first deep learning course at Stanford, CS 231n: Convolutional Neural Networks for Visual Recognition. The course became one of the largest classes at Stanford, growing from 150 students in 2015 to 750 in 2017. The course materials, including lecture notes and assignments, were made publicly available and became a standard resource for students and practitioners worldwide.

The course emphasized practical implementation of [convolutional neural networks](https://www.wikiprompt.org/wiki/convolutional-neural-network), covering topics such as [backpropagation](https://www.wikiprompt.org/wiki/backpropagation), [batch normalization](https://www.wikiprompt.org/wiki/batch-normalization), and [dropout](https://www.wikiprompt.org/wiki/dropout). Its popularity helped establish a template for subsequent deep learning courses at other institutions and contributed to the broader adoption of [machine learning](https://www.wikiprompt.org/wiki/machine-learning) education.

## OpenAI founding and early research

Karpathy is a founding member of the artificial intelligence research group OpenAI, where he was a research scientist from 2015 to 2017. During this period, he contributed to early work on [generative models](https://www.wikiprompt.org/wiki/generative-ai) and reinforcement learning, including research on adversarial examples and [one-shot learning](https://www.wikiprompt.org/wiki/one-shot-learning). His research at OpenAI helped lay groundwork for later developments in [transformer](https://www.wikiprompt.org/wiki/transformer)-based architectures.

His time at OpenAI coincided with the organization's transition from a nonprofit research lab to a more commercially oriented entity. Karpathy's role involved both research and engineering, and he was known for his ability to communicate complex ideas clearly, a skill that would later define his educational ventures.

## Tesla and Autopilot

In June 2017, Karpathy became Tesla's director of artificial intelligence and reported to Elon Musk. He led the Autopilot Vision team, overseeing the development of [Tesla's computer vision systems](https://www.wikiprompt.org/wiki/tesla-autopilot) for autonomous driving. His work involved training deep neural networks to process camera feeds in real time, a task that required significant advances in [data augmentation](https://www.wikiprompt.org/wiki/data-augmentation) and [model efficiency](https://www.wikiprompt.org/wiki/model-pruning).

Karpathy was named one of MIT Technology Review's Innovators Under 35 for 2020, recognizing his contributions to AI in the automotive industry. After taking a several-months-long sabbatical from Tesla, he announced he was leaving the company in July 2022. His departure marked the end of a period of rapid development for Tesla's AI capabilities, though the company continued to build on his team's work.

## Return to OpenAI and Eureka Labs

On February 9, 2023, Karpathy announced he was returning to OpenAI. A year later on February 13, 2024, an OpenAI spokesperson confirmed that Karpathy had left OpenAI. In the same year, he was named one of Time Magazine's 100 Most Influential People in AI. On July 16, 2024, Karpathy announced on his X account that he started a new AI education company called Eureka Labs.

Their first product was the AI course, LLM101n. He also has a broader educational effort, the "Zero to Hero" series on LLM fundamentals. The company also advocates for AI teaching assistants, a concept which has been criticized due to data privacy concerns and the removal of personal connection between teacher and student. Eureka Labs aims to combine [deep learning](https://www.wikiprompt.org/wiki/deep-learning) techniques with pedagogical research to create scalable, interactive learning experiences.

## Vibe coding and public engagement

In February 2025, Karpathy coined the term vibe coding to describe how AI tools allow hobbyists to construct apps and websites just by typing prompts. The term quickly gained traction in the developer community, reflecting a shift toward [generative AI](https://www.wikiprompt.org/wiki/generative-ai)-assisted software development. Karpathy's advocacy for accessible AI tools aligns with his broader educational mission, emphasizing hands-on experimentation over formal prerequisites.

As of February 2023, he makes YouTube videos on how to create artificial neural networks. His channel, which began with Rubik's cube tutorials, now features in-depth explanations of [large language models](https://www.wikiprompt.org/wiki/large-language-model), [transformers](https://www.wikiprompt.org/wiki/transformer), and [backpropagation](https://www.wikiprompt.org/wiki/backpropagation). These videos often include live coding sessions and practical exercises, attracting millions of views from both beginners and experienced practitioners.

## Anthropic and pretraining research

On May 19, 2026, Karpathy announced that he joined Anthropic via a statement on X, while the company stated that he will be leading a team for research in pretraining. This role focuses on improving the foundational training processes for [large language models](https://www.wikiprompt.org/wiki/large-language-model), including data curation, [learning rate schedules](https://www.wikiprompt.org/wiki/learning-rate-schedule), and [loss function design](https://www.wikiprompt.org/wiki/loss-functions).

His move to Anthropic reflects a broader industry trend of researchers returning to core [machine learning](https://www.wikiprompt.org/wiki/machine-learning) challenges after exploring applied domains. Karpathy's work at Anthropic is expected to influence how the company develops its next-generation models, building on his extensive experience with both [OpenAI](https://www.wikiprompt.org/wiki/openai) and [Tesla](https://www.wikiprompt.org/wiki/tesla-autopilot)'s AI systems.

## Legacy and influence

Karpathy's impact on the AI field extends beyond his research publications. His educational materials, including CS 231n and the Zero to Hero series, have trained a generation of AI practitioners. His ability to demystify complex topics has made him a trusted voice in the community, bridging the gap between academic research and practical application.

His career trajectory - from academic research to industry leadership and back to education - illustrates the evolving nature of AI careers in the 2020s. As of 2026, he remains an active contributor to both research and public discourse, with his work at [Anthropic](https://www.wikiprompt.org/wiki/anthropic) likely to shape future developments in [artificial intelligence](https://www.wikiprompt.org/wiki/artificial-intelligence).

---
Source: https://www.wikiprompt.org/wiki/alex-karpathy
License: CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
Last updated: 2026-09-09T01:57:31.751082+00:00