Ethan Mollick is Associate Professor at the Wharton School of the University of Pennsylvania. His Substack, One Useful Thing, is the most consistently practical writing on working with AI. Several ideas from this course — models / apps / harnesses, cyborg vs. centaur, "you aren't prompting, you are managing" — come directly from his work.
Getting started / foundations
October 19, 2025
Which tools to use, which to skip, and why the gap between marketing and actual capability matters. Practical selection criteria for journalists evaluating new tools.
November 24, 2024
Argues that context matters more than prompt perfection; recommends 10 hours of hands-on practice over studying theory. Directly relevant to understanding why context files (CLAUDE.md, GEMINI.md, AGENTS.md) work.
April 16, 2023
The classic Mollick prompting guide. No magic prompts exist; what works is giving AI context (persona, audience, constraints, examples) and iterating. This is the conceptual foundation for Module 2.
December 2024
AI is most useful for tasks you could do but shouldn't waste time on. Identifies where AI genuinely helps vs. where it undermines judgment or quality. Read before designing any automation.
Working methods: cyborg vs. centaur
March 4, 2024
Different prompt framings (Star Trek, political thriller, etc.) dramatically change AI accuracy. Context isn't just about facts — it's about establishing a frame for how AI should think. Directly relevant to how you write CLAUDE.md files.
April 22, 2024
Argues that prompts function as "programs in prose" that non-technical experts can author. The conceptual basis for custom skills: you don't need to write code to encode domain expertise.
January 27, 2026
Managing AI agents requires the same skills as managing people: clear instructions, feedback, and evaluation criteria. As development shifts from writing code to directing AI, management skills become the bottleneck.
November 12, 2025
How to test and customize AI models to find what works for your specific tasks. Useful for evaluating whether a tool fits a particular reporting workflow before committing to it.
Agents and automation
September 29, 2025
AI agents that can plan and use tools to accomplish multi-step tasks are now practical, not theoretical. Small accuracy improvements lead to large gains in what agents can complete. Read before Module 4.
September 11, 2025
Working with AI systems that produce impressive results but operate in opaque ways. Covers trust, verification, and workflow design — critical for journalism, where you can't just accept output.
November 18, 2025
Chronicles how fast model capabilities have changed; demonstrates Gemini 3 building complete interactive applications. Useful context for understanding what agent-based workflows can now do.
AI's capabilities and limits
April 2025
Introduces "Jagged AGI": AI with superhuman ability at narrow tasks but obvious gaps elsewhere. Explains why AI fails in unpredictable ways — and why retrieval systems (RAG) matter: they fill the gaps the model can't fill from memory.
August 28, 2025
We're entering an era where powerful AI is as accessible as Google Search. Every institution built for scarce intelligence — schools, hospitals, newsrooms — now has to operate with abundant AI. Raises the stakes for figuring out what human editorial judgment is actually for.
August 7, 2025
Newer models automatically select computation depth and handle complex tasks with minimal instruction. Relevant for understanding why describing a workflow in plain English can produce a working pipeline.
Education and learning
August 30, 2024
Addresses what education looks like when AI can complete most traditional assignments. The parallel to journalism is direct: if AI can generate copy, what is a journalist's editorial judgment actually for?
Book
Penguin Random House, 2024
NYT bestseller; named best book of 2024 by The Economist and Financial Times. Develops the cyborg/centaur framework for human-AI collaboration. The conceptual foundation for everything in this course.
Already assigned in the course
These Mollick pieces appear as required readings in specific modules:
INTRODUCTION MODULE + MODULE 01
Models / apps / harnesses framework — the foundation for how the course talks about AI tools.
NEXT: Head back to Module 1 or browse the full syllabus.