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.

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.