Links
Learning Resources
- Large Language Models Explained Briefly — 3Blue1Brown’s accessible explanation of LLM fundamentals: how transformers work, attention mechanisms, and what makes these models tick
- Machine Learning: How Did We Get Here? — Historical foundations and evolution of machine learning: understanding the conceptual and technical developments that created modern ML
- Stanford LLM Fundamentals Course (9 Lectures) — Free comprehensive course: Lecture 1 (Transformers), Lecture 2 (Transformer Models), Lecture 3 (LLMs), Lecture 4 (Training), Lecture 5 (Tuning), Lecture 6 (Reasoning), Lecture 7 (Agentic LLMs), Lecture 8 (LLM Evaluation), Lecture 9 (Recap & Trends). Lectures 7-8 critical for enterprise architecture decisions
- MIT 9-Lecture Series on GenAI + LLMs — Comprehensive introduction to generative AI and large language models by Rickard Brüel Gabrielsson
- Andrew Ng’s Bite-Sized Courses — Practical courses on machine learning and AI fundamentals, designed for rapid skill acquisition
- HuggingFace Learn — Free courses on NLP, transformers, and diffusion models with hands-on examples
- OpenAI Cookbook — Real production patterns and examples for building with language models
- Karpathy Zero to Hero — Build a GPT from scratch: implementing backprop, tokenization, and attention mechanisms
- Fast.ai — Practical deep learning for coders without requiring a PhD; top-down approach to learning
- Stanford CS336: Language Modeling from Scratch I 2025 — Building language models from first principles: data preprocessing, scaling laws, evaluation, and reasoning
Analysis & Critique
- I’m Sorry to Burst Your Bubble: You’re Living in an AI Hype Bubble — David William Silva on AI industry hype, the technical limitations of LLMs as text-prediction engines, and how industry figures conflate marketing with technical truth
Economics & Labor
Simply put, because demand for most of the things that humans produce is much more elastic than we recognize today; and as long as humans are complementary to the production process, it won’t be rare for efficiency gains to get swallowed up by demand growth. This is the famous Jevons paradox, the tendency for the more efficient use of a resource to increase total consumption of that resource, rather than decrease it. Energy is the classic Jevons case: we find over and over again that as energy becomes more efficient to produce, people respond not by consuming the same amount of it, but by increasing their consumption—such that overall energy use tends to rise. (Thus the “paradox” part: energy efficiency increases energy consumption!)
Take software. Software simply means “the things that a computer can do”: and because software is so broad and so capable, we should expect that it’s energy-like and that there’s an enormous latent demand for more software in the world. For that reason we’ve found that every increase in the efficiency of software programming—the move from lower-level to higher-level languages, the emergence of frameworks and libraries, the endless move away from bare metal as compute becomes ever-cheaper and more abundant—has resulted in a dramatic increase in demand for software engineering labor.
All of this makes me suspect that, as long as we are in the cyborg era of human-AI complementarity, we should be quite optimistic about human labor. The world is governed by bottlenecks; as long as those bottlenecks are real, there will be complementarity between humans and AI; and if the human-AI combination can make human labor vastly more productive, we should expect that to be a very good thing: for consumers of course, who will benefit from an enormous consumer surplus, but also for workers.