Cover of Co-Intelligence: Living and Working with AI

Co-Intelligence: Living and Working with AI

Ethan Mollick

I keep this at #33 because the clearest practical bridge between generative AI and everyday knowledge work. I still need its pressure.

The long version

Ethan Mollick presents generative AI as a new kind of collaborative technology: capable, uneven, persuasive, and rapidly changing. Rather than offering a static tool manual, he proposes practical habits for inviting AI into work, maintaining human judgment, experimenting across tasks, and recognizing where confident output exceeds dependable knowledge. It reminds me that the way intelligence is becoming a tool, a product, and a decision I still have to own does not happen only to other people.

Why it is here

It is the quickest high-quality orientation before the more technical AI books. The enduring value lies in its behavioral principles—experiment, supervise, verify, and redesign work—not in any one model feature that may age quickly. I do not want the lesson to become another sentence I agree with and leave behind.

How to read it

Choose three recurring tasks and maintain a prompt-and-result lab notebook for two weeks. Record where AI expands options, where it saves time, and where verification costs erase the apparent gain. Revisit product-specific details after major model changes. I will stop long enough to write what the chapter changes, if anything.

What it taught me

  1. 01

    AI can be treated as a varied collaborator, not merely a search box.

  2. 02

    Human expertise remains essential for verification, framing, and accountability.

  3. 03

    Organizations learn faster when experimentation is broad but risk controls are explicit.

Before and after

Before

What makes it easier

Available in Black Shelf

AI for Everyday IT: Accelerate Workplace Productivity

Chrissy LeMaire; Brandon Abshire

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Ajay Agrawal; Joshua Gans; Avi Goldfarb

More about the author

Ethan Mollick is a professor at the Wharton School whose research examines innovation, entrepreneurship, games, and the effects of AI on work and education. He is known for combining early experimentation with evidence-conscious practical guidance.

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