Andrej Karpathy

Activities

historical

Andrej Karpathy (born in Bratislava, Czechoslovakia; raised partly in Toronto) is a Slovak–Canadian computer scientist best known for his research and teaching in deep learning and for leading large-scale AI efforts in industry. He completed undergraduate studies in computer science/physics and a PhD in Computer Science at Stanford University under Fei-Fei Li, where his work centered on connecting images and natural language using convolutional and recurrent neural networks.

Career highlights:

• Early research internships at Google Brain/Research and DeepMind focused on large-scale representation learning and reinforcement learning.
• Founding member and research scientist at OpenAI] (2015–2017), contributing to early large-scale model research.
• Sr. Director of AI at Tesla (2017–2022), leading the computer vision team responsible for neural-network-based Autopilot / Full Self-Driving efforts and the associated data and training infrastructure.
• Public educator and content creator: co‑created and taught Stanford’s CS231n deep learning course; produced highly influential lecture videos and tutorials (ConvNetJS, NanoGPT/NanoChat educational material, “Zero to Hero” style series).

present (as of 2025)

• Founder of Eureka Labs (announced 2024), an AI-first education venture focused on creating curriculum and hands-on courses for learning and building with large models.
• Continued public speaking, teaching, and open-source contributions aimed at making machine learning fundamentals accessible to engineers and builders.
• Creator/author of compact, transparent training pipelines and educational projects (commonly referenced as NanoGPT / NanoChat or similar small-scale reproducible model pipelines) to teach model training end-to-end on a modest budget.

Connections to other people and companies

• Fei‑Fei Li (PhD advisor, Stanford) — academic mentorship and collaboration on vision + language research.
• OpenAI — founding member and research scientist; returned for a second tenure in 2023 before departing in 2024.
• Tesla / Elon Musk — recruited Karpathy to lead Tesla’s AI vision team; Karpathy became a public-facing technical leader for Tesla’s Autopilot and AI Day presentations.
• Wider community ties: active in the ML education ecosystem through course materials, talks (YouTube, conference keynotes, podcasts), and many collaborations with researchers across academia and industry.

Expectations for the future

Karpathy’s trajectory suggests continued focus on education, reproducible model engineering, and tooling that lowers the barrier to understanding how large models are built and deployed. Through Eureka Labs and open-source educational pipelines, expect:

• More learning-by-building courses that let engineers train and iterate on compact, interpretable models (teaching first principles).
• Practical guides and transparent tooling for context engineering, model evaluation, and agent scaffolding (bridging research insights to production practices).
• Advocacy for pragmatic, safety-aware progress in agent development — emphasizing human-in-the-loop designs, verification, and incremental autonomy.

Interests

• Deep learning fundamentals (representation learning, computer vision, sequence models).
• Education and pedagogy: making complex topics accessible through clear explanations, minimal reproducible code, and end-to-end pipelines.
• Practical systems and engineering at scale: dataset engineering, training infrastructure, and deploying models to real-world products.
• Research pragmatics: balancing model scale with data quality, interpretability, and robust evaluation.

Sources

• Official site: https://karpathy.ai
• Wikipedia: https://en.wikipedia.org/wiki/Andrej_Karpathy
• Stanford CS231n course materials and lectures: https://cs231n.github.io/
• Coverage and profiles (examples): TIME Magazine AI coverage (2024 list), news articles on Karpathy’s roles at Tesla and OpenAI
• Public talks and videos (searchable on YouTube): “Software Is Changing (Again)”, Tesla AI Day presentations, longer interviews/podcasts (Dylan Patel, Lex Fridman, etc.)
• Community writeups & open-source references: NanoGPT / NanoChat educational threads and repositories (widely referenced in 2024–2025 educational material)