Build Your AI Literacy

You don't need a computer science degree to become fluent in AI. You need consistent exposure to good information and a willingness to experiment. Here are the resources that helped me most, organized by how much time you have.

Podcasts

This is where I spend six to eight hours a week. Podcasts let you learn while commuting, exercising, or doing tasks that don't require deep focus.

  • the Artificial Intelligence Show (Marketing AI Institute) — Mike Kaput and Paul Roetzer break down AI news weekly with a practical, business-focused lens. Great for staying current.
  • Everyday AI — Approachable daily episodes focused on how regular professionals can use AI tools. Good starting point if you're new.
  • AI Daily Brief — Quick news updates to stay on top of what's happening in the field.
  • Dwarkesh Patel — Long-form interviews with researchers and founders. Goes deeper into the technical and philosophical questions. Not beginner-level, but incredibly valuable once you have some foundation.

Books

  • Co-Intelligence by Ethan Mollick — The book that gave me "three sleepless nights." Mollick is a Wharton professor who writes about AI with rigor and practicality. This is the best single resource for understanding how to think about AI as a professional. Start here.

Substacks and Blogs

These are writers I follow regularly. Most offer free tiers.

  • One Useful Thing (Ethan Mollick) — Practical, thoughtful posts on AI in education and work. Essential reading.
  • Noahpinion (Noah Smith) — Economics and technology commentary. Not AI-specific but often covers the broader implications.
  • The Algorithmic Bridge — About AI, for the people, with common sense.
  • Simon Willison's Newsletter — Cover's AI from a developer's perspective, focusing on the tools, techniques and practical experimentation.
  • Nate Jones — Deep dives on AI strategy and practical implementation for professionals. His work on the AI Skills Stack framework heavily influenced my thinking on leveling up AI fluency.

People to Follow

These are researchers, builders, and thinkers whose work I find consistently valuable. Following them helps you stay connected to where the field is actually heading.

  • Andrej Karpathy — Computer scientist, former OpenAI co-founder and Tesla AI Director. His YouTube videos explaining how LLMs work are the best technical explainers available for non-engineers.
  • Ilya Sutskever — PhD Student of Geoffrey Hinton, Co-Founder of Open AI, now co-Founder of SuperSafe Intelligence. One of the pioneers of deep learning.
  • Dario Amodei — CEO of Anthropic (the company behind Claude). Thoughtful on AI safety and capabilities.
  • Demis Hassabis — CEO of Google DeepMind, 2024 Nobel Prize in Chemistry. Foundational figure in modern AI.
  • Geoffrey Hinton — Professor Emeritus at University of Toronto, 2024 Nobel Prize in Physics. One of the pioneers of deep learning.

Courses and Structured Learning

  • AI Mastery Academy (SmarterX) — The program I've been enrolled in since December 2024. Practical, updated regularly, focused on professional application rather than theory.
  • OpenAI Academy — Free resources directly from OpenAI on using their tools effectively.

Going Deeper: Understanding How LLMs Work

You don't need to understand the technical details to use AI well. But having a mental model of how these systems actually work helps you use them more effectively and spot their limitations.

My Advice

Start with Ethan Mollick's book and one podcast. Give yourself a few weeks of consistent exposure before trying to build anything. Let the concepts settle. Then pick a small workflow and experiment.

Literacy comes from repetition, not intensity. Thirty minutes a day beats a weekend crash course every time.