Co-Intelligence versus The Hundred-Page Machine Learning Book.
Both show up on every "best" list. They're not competitors. They're a sequence. Here's which one to read first, and when.
Reviewed by Ashish Sheth · Updated April 2026
Author
Ethan Mollick
Andriy Burkov
Pages
256
160
Published
2024
2019
Publisher
WH Allen (Penguin UK)
Andriy Burkov (self-published)
Level
beginner
beginner to intermediate
Amazon Rating
4.5/5 (3,998)
4.6/5 (1,400)
Goodreads Rating
4.02/5 (1,330)
4.25/5 (1,466)
Co-Intelligence
Strengths
+ Most accessible AI book for non-technical readers — a NYT bestseller
+ Practical examples grounded in actual classroom and workplace experiments
+ Short and readable in a weekend
+ Mollick's framing ('jagged frontier', 'four rules') has shaped how the industry talks about AI
Caveats
− Light on technical depth — software engineers may want more under-the-hood
− Some examples already feel dated as models improve fast
− Mostly observations, less actionable than the title suggests for builders
The Hundred-Page Machine Learning Book
Strengths
+ Distills a massive field into ~150 readable pages
+ Endorsed by Peter Norvig and other ML luminaries
+ Author follows up with companion books for deeper dives
+ Costs less than a textbook chapter and is more useful
Caveats
− Genuinely a survey, not a tutorial — you cannot build models from this alone
− Some math notation moves fast for absolute beginners
− Not enough depth to prepare for ML engineer interviews at top companies
The verdict
Read Co-Intelligence first to build foundations, then move to The Hundred-Page Machine Learning Book for advanced concepts.
Co-Intelligence
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The Hundred-Page Machine Learning Book
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Frequently asked
Which is better, Co-Intelligence or The Hundred-Page Machine Learning Book?
Read Co-Intelligence first to build foundations, then move to The Hundred-Page Machine Learning Book for advanced concepts.
Is Co-Intelligence too non-technical for software developers?
Most developers find it useful precisely because it isn't a coding book. It gives you the mental model for when AI helps and when it doesn't. Read it before AI Engineering, not instead of.
Can I really learn machine learning in 100 pages?
You can learn the vocabulary, the major algorithms, and how to think about ML problems. You cannot learn to build production models. Treat this as a map, not a tutorial.