Resources
Worth your time
A running list of the books, blogs, articles, and videos that have shaped how I think about AI and product — chosen because each one taught me something I still use.
Books
Inspired: How to Create Tech Products Customers Love (opens in a new tab)Marty CaganThe clearest account of how strong product teams discover what to build before they build it. Still the book I hand to new product managers.
AI Engineering: Building Applications with Foundation Models (opens in a new tab)Chip HuyenThe most complete map of what it takes to ship on top of foundation models: evaluation, retrieval, fine-tuning, and the trade-offs between them. The reference I reach for when a team asks whether an idea is buildable.
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications (opens in a new tab)Chip HuyenWhy most ML projects fail after the model works: data, monitoring, and the feedback loops that keep a system honest in production. The prequel to AI Engineering, and still the better book on operating what you ship.
HBR's 10 Must Reads on Managing Yourself (opens in a new tab)Harvard Business Review, with Peter Drucker and Clayton ChristensenDrucker's "Managing Oneself" alone is worth the price: know what you are good at, how you learn, and where you belong. The rest of the collection is the honest answer to what leadership costs and how to pay it deliberately.
Blogs
AI/MLbling About (opens in a new tab)Alex GraberAn AI/ML engineer's working notes on LLMs, generative AI, and the arXiv papers worth reading, written by someone building production AI rather than commenting on it. Check out the AI Treadmill for the latest in AI.
Lenny's Newsletter (opens in a new tab)Lenny RachitskyDeeply researched product, growth, and career advice, drawn from interviews with the people who actually did the work. The closest thing product management has to a shared curriculum.
Chip Huyen's Blog (opens in a new tab)Chip HuyenLong-form essays on AI system design from the author of AI Engineering: how to evaluate, deploy, and operate models in production, written with the rigor of someone who has done it at scale.
SVPG Articles (opens in a new tab)Marty Cagan and the Silicon Valley Product Group partnersStrong opinions, loosely held, on how empowered product teams actually work. The running commentary behind Inspired, and the first place I look when a product organization is drifting toward feature factory.
Anthropic Research (opens in a new tab)AnthropicPrimary sources on interpretability, alignment, and how frontier models actually behave, published by the lab doing the work. Read it to understand the models before deciding what to build on them.
Stratechery (opens in a new tab)Ben ThompsonThe business and strategy of technology, one argument at a time. Aggregation Theory and the rest of his frameworks are the fastest way to understand why platforms win and where AI changes the answer.
Articles
An Alien Mind (opens in a new tab)Jakub Pachocki, OpenAIOpenAI's chief scientist on what it felt like to see reasoning models work, why he expects progress to keep compounding, and why he thinks no one is ready. Unusually candid for a lab, and worth reading before forming a view on where this goes.
An LLM-as-Judge Won't Save the Product. Fixing Your Process Will (opens in a new tab)Eugene YanEvals are not a tool you buy but a practice you run: look at the data, annotate the failures, hypothesize, test, and monitor. The best short argument for why measurement is a product discipline, not a model feature.
Understanding Is the New Bottleneck (opens in a new tab)Geoffrey LittWhen agents write most of the code, the scarce resource is a human who understands the system. Litt's answer is not to read every diff but to build explainer docs, quizzes, and micro-worlds that make understanding cheap. The clearest case I have read for keeping people in the loop on purpose.
AI Evals: Everything You Need to Know (opens in a new tab)Hamel Husain and Shreya ShankarThe FAQ from the most-taught evals course there is: traces, error analysis, LLM-as-judge, and what to do when the judge disagrees with the expert. If a team asks me one question about evals, the answer is usually in here.
How People Are Really Using AI in 2026 (opens in a new tab)Marc Zao-Sanders, Harvard Business ReviewThe third annual look at what people actually do with generative AI, ranked by real use rather than vendor demos. Useful precisely because the year-over-year shifts are small: the durable uses are the ones worth building for.
Videos
3Blue1Brown (opens in a new tab)Grant SandersonThe best visual explanations of the math under modern AI. Start with the neural network series and the episodes on transformers and attention; nothing else makes the mechanics this intuitive.
Two Minute Papers (opens in a new tab)Károly Zsolnai-FehérNew AI and graphics research explained in a few minutes each, with the results shown rather than described. The fastest way to keep a feel for what the labs are shipping without reading every paper.
AI Engineer (opens in a new tab)AI Engineer World's Fair and Summit talksEvery talk from the AI Engineer conferences, free. The practitioners who are actually shipping agents, evals, and retrieval systems explaining what worked, in twenty minutes at a time.
Lenny's Podcast (opens in a new tab)Lenny RachitskyLong interviews with the product leaders and growth experts behind the newsletter, with the tactical detail that never fits in writing. The companion to the newsletter, and often the better version of it.
Andrej Karpathy (opens in a new tab)Andrej KarpathyThe Zero to Hero series builds a language model from scratch in code, one concept at a time, and the long-form lectures on how LLMs are trained are the best on the internet. If you want to actually understand the thing, start here.