AI Blogs Worth Reading

A curated list of blogs and writers producing genuinely valuable AI content. People doing real thinking about what's happening and what it means.

AI Lab Leaders & Researchers

Dario Amodei

Anthropic's CEO writes long-form, deeply thoughtful essays on AI's transformative potential and existential risks. His piece "Machines of Loving Grace" is a vision of how AI could transform civilization if we get safety right. Combines insider knowledge of frontier AI development with philosophical depth. Doesn't write often, but when he does it's worth reading carefully.

Andrej Karpathy

Former Tesla AI Director and OpenAI founding member. Known for making complex deep learning concepts accessible through clear, practical explanations. His posts become canonical references: "The Unreasonable Effectiveness of Recurrent Neural Networks" and "A Recipe for Training Neural Networks" have guided thousands of practitioners. Also active on YouTube with excellent educational content.

Lilian Weng (Lil'Log)

Head of Applied AI Research at OpenAI. Her posts are comprehensive, well-researched surveys of entire subfields. Each one is essentially a mini-textbook with excellent diagrams and references. "LLM Powered Autonomous Agents" is the definitive guide to building agents. If you need to get up to speed on a topic quickly, check if Lilian has written about it first.

Christopher Olah (colah's blog)

Anthropic co-founder and pioneer of neural network interpretability. His visual explanations of complex concepts are legendary. "Understanding LSTM Networks" is the canonical explanation, cited thousands of times. Co-founded the Distill journal, which set a new standard for clarity in ML research communication.

Independent Researchers & Thinkers

Gwern Branwen

Polymathic pseudonymous researcher who was one of the first to recognize LLM scaling trends. Combines rigorous methodology with speculative thinking. His essays are continuously updated with new research over years. Covers AI scaling, GPT capabilities analysis, alignment, and much more. The writing is scholarly but accessible, often with full datasets, code, and interactive elements.

Scott Alexander (Astral Codex Ten)

Psychiatrist and prolific writer from the rationalist community. Combines exhaustive data analysis with incisive clarity and humor. Deep engagement with AI safety and forecasting. "Meditations On Moloch" is a classic game-theoretic analysis of coordination failures relevant to AI risk. Also runs prediction markets and collaborates on AI forecasting projects.

Simon Willison

Django co-creator who has become the go-to source for practical, hands-on LLM insights. Documents real-world experimentation with AI tools and their sharp edges. If you want to know what actually works when using LLMs for programming (and what doesn't), Simon has probably tried it and written it up. Frequently featured on Hacker News.

AI Safety & Alignment

LessWrong / AI Alignment Forum

The intellectual home of AI safety research. Founded by Eliezer Yudkowsky, it hosts technical discussions from leading alignment researchers including Paul Christiano, Nate Soares, and researchers from MIRI, Redwood Research, and other safety organizations. The Alignment Forum is more technical; LessWrong is broader. Essential reading for understanding the intellectual foundations of AI safety.

Anthropic Alignment Science Blog

Direct research notes from Anthropic's Alignment Science team. Covers cutting-edge work including alignment faking experiments, interpretability research, jailbreak robustness, and dangerous knowledge localization. The Transformer Circuits thread is particularly valuable for understanding how neural networks actually work inside.

AI Lab Research Blogs

Google DeepMind

Publishes groundbreaking research on AI capabilities, safety, and applications. Notable for AlphaFold (protein structure prediction), AlphaProof (mathematical reasoning), and extensive interpretability work. Over 100 papers presented at major conferences annually. Good for tracking the frontier of what's technically possible.

OpenAI Research

Research updates from the lab behind GPT and ChatGPT. Covers breakthroughs, scaling laws, RLHF, safety research, and capability evaluations. The scaling laws papers are foundational for understanding why LLMs keep getting better.

Meta AI (FAIR)

Led by Turing Award winner Yann LeCun. Publishes open research and open-sources major models (LLaMA, etc.). Strong emphasis on open science. LeCun's contrarian takes on AI risk and architecture (world models, I-JEPA) are worth engaging with even if you disagree.

ML Systems & Engineering

Eugene Yan

Principal Applied Scientist at Amazon. Focuses on practical ML systems design, production patterns, and engineering best practices. His "applied-ml" GitHub repo is an influential collection of real-world ML applications. Essential reading if you're building ML systems, not just training models.

Chip Huyen

Author of Designing Machine Learning Systems and AI Engineering. Her MLOps guide is a comprehensive curriculum for ML engineering. Deep analysis of the ML tools landscape. Practical and grounded in real production experience.

Academic & Educational

BAIR Blog (Berkeley AI Research)

Cutting-edge academic research from UC Berkeley grad students and professors. Covers robotics, generative models, reinforcement learning, and foundation models. Good for seeing what's coming out of one of the top academic AI programs.

Jay Alammar

Transformed how developers learn about transformers and attention mechanisms. His Illustrated Transformer is the canonical visual explanation. Custom illustrations make abstract concepts concrete. Start here if you want to understand how modern language models actually work.

Sebastian Raschka

Applied ML tutorials with clean explanations and reproducible code. Maintains a curated collection of 200+ LLM research papers. His architecture comparisons covering models from DeepSeek-V3 to Kimi K2 are useful for tracking the rapidly evolving landscape.

Adjacent but Essential

Paul Graham

Y Combinator co-founder. While not exclusively AI-focused, his essays on startups, technology, and thinking are essential context for understanding how AI will reshape work and creativity. "Writes and Write-Nots" is a recent piece on AI's impact on writing.

Dwarkesh Patel

Podcast interviewer who gets the best out of AI researchers. His conversations with Ilya Sutskever, Dario Amodei, and other leaders are primary sources for understanding how frontier labs think. The written transcripts are valuable in themselves.

AI Native Enterprise

AI strategy for technical leaders. Cuts through the hype to help you evaluate, implement, and scale AI in your organization. Covers practical topics like comparing AI agent platforms and AI coding tools for enterprise development teams.