Recommended reading, listening, references and credits

We read, listen, watch and discuss with many. It’s simply impossible to include every person, podcast, video, book, paper, article we’ve touched, but we would like to acknowledge some specific expertise as well as include reading follow up for those who want to go deeper. 

This is a living document/blog so expect it to be added to over time.

Recommended books

This reading list contains books that encompass the broader scope of AI, Big Data and an algorithmic society. Most are non-technical and instead draw on the human side of AI; how we make decisions, are ruled by our emotions and how AI and technology are changing us. Consider this list an intersection of psychology, neuroscience, AI, economics and business problem solving.

Yes, we’ve read (or listened to) them all!


The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Pedro Domingos. 

Probably the best book written about the various tribes (styles) of machine learning.

Superintelligence: Paths, Dangers, Strategies. Nick Bostrom

A philosopher takes on AI and goes into the weeds on what happens when AI surpasses human intelligence.

Machine, Platform, Crowd: Harnessing our Digital Future. Andrew McAfee and Erik Brynjolfsson. 

Perhaps the best known book on AI and digital transformation.

Design Sprints

Sprint: Solve Big Problems and Test New Ideas in Just Five Days. Jake Knapp

What it’s all based on.

Human and societal impact of AI

Winners Take All: The Elite Charade of Changing the World. Anand Giridharadas

While not directly related to AI, this book covers some related territory if you accept that AI is (or could be) an important contributor to income inequality and non-democratic power.

Ghost Work: How To Stop Silicon Valley from Building a New Global Underclass. Mary L. Gray and Siddharth Suri.

Not directly AI related but contains interesting discussion and ideas around scale and algorithmic supremacy. 

The Fourth Age: smart robots, conscious computers, and the future of humanity. Byron Reese.

World Without Mind: The Existential Threat of Big Tech. Franklin Foer.

This fascinating book makes you question what happens when AI, owned by a few, does all the thinking for us.

Irresistible: The Rise of Addictive Technology and the Business of Keeping Us Hooked. Adam Alter.

Great read on the link between behavioral addiction and the power of AI-enabled product design.

The Attention Merchants: The Epic Scramble to Get Inside Our Heads. Tim Wu. 

Terrific book on the attention economy and how AI enables this.

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Cathy O’Neil.

One of the seminal works in this area.

AI, work, competition and commerce

Virtual Competition: the Promise and Perils of the Algorithm-Driven Economy. Ariel Ezrachi and Maurice E. Stucke.

A must-read for those who want the detail on how AI will change competition.

Prediction Machines: the Simple Economics of Artificial Intelligence. Ajay Agrawal, Joshue Gans, Avi Goldfarb

Excellent outline of how an era of cheap prediction can enable new business models.

The Efficiency Paradox: What Big Data Can’t Do. Edward Tenner.

A super-interesting take on AI and big data failures in a big picture way.

The Four: the Hidden DNA of Amazon, Facebook and Google. Scott Galloway

Iconic NYU Prof Galloway on the role that The Four play in our economy.

Humans are Underrated: What High Achievers Know that Brilliant Machines Never Will. Geoff Colvin.

Great examples of where human talents matter.

The Future of the Professionals: How Technology Will Transform the Work of Human Professionals. Richard Susskind and Daniel Susskind.

Thorough consideration of how professional work will be delaminated and transformed with AI.

Small Data: the Tiny Clues that Uncover Huge Trends. Martin Lindstrom

A counterpoint to all the big data talk, where an observant human with a sense of design savvy and honed intuition can discover something big.

Rise of the Robots: Technology and the Threat of a Jobless Future. Martin Ford.

One of the first books to tackle the subject and contains some important and relevant ideas today.

The Industries of the Future. Alec Ross.

A bit too all-encompassing at certain points but conceptually covers a wide scope of innovation and AI.

Human psychology and intelligence

Misbehaving: the Making of Behavioral Economics. Richard Thaler

Nudge. Richard Thaler

Both important works for understanding how humans make decisions so that you can understand how AI can be complimentary.

Thinking Fast and Slow. Daniel Kahneman

Benchmark book on human decision making by Nobel Prize winner Daniel Kahneman. 

Farsighted: How We Make the Decisions that Matter the Most. Steven Johnson.

Excellent read on the latest research about human decision making and how to improve your own.

Algorithms to Live By: The Computer Science of Human Decisions. Tom Griffiths, Brian Christian.

Great read on some of the ways we can use to think like algorithms and make better decisions as a result.

Sensemaking: The Power of the Humanities in the Age of the Algorithm. Christian Madsbjerg.

Fast read but pointed on the importance of learning more than just STEM.

Behave: The Biology of Humans at their Best and Worst. Robert Sapolsky. 

This book has absolutely nothing to do with AI. It is absolutely brilliant and is perhaps the one book to read about humans. (Thinking Fast and Slow a very, very close second).

References specifically for masterclass:

Human-centered design and AI

Josh Lovejoy’s work

Listen to What does human-centered AI even mean? A very meta conversation with Josh Lovejoy. From Innovation For All in Podcasts.

Microsoft’s guides

Google’s guides

Mark Riedl

Recent work from Harvard on how humans prefer machine advice:

Work on mental models and voice assistants

Fascinating new work on explainability and what people like and don’t like

How Machines Learn and technical aspects

• The “giants” of AI whose work has both informed us and been instrumental in making this primer both accurate, yet accessible

• Celeste Knudsen for her skilled and entertaining illustrations of complex machine learning concepts

• Pedro Domingos, whose book “The Master Algorithm” we highly recommend. The development of the “Five Tribes” structure has made machine learning far more accessible for everyone.

• BigML for their ideas on feature engineering in particular

• Jason@machinelearningmastery for helping us step through the basics • Zachary Lipton at UCSD for explaining how time works in a neural net • Bob Van den Hoek for his Quora answers

• Norm Jouppi, Google

• Alex Gray, Skytree

• Yann LeCun, Director of AI Research at Facebook

• Kalid Azad, BetterExplained, for perhaps the best ever Bayes explanation (and we studied many)

Deep learning revolution, Sejnowski, MIT Press 2018

Special shoutout to Cassie Kozyrkov, Chief Decision Scientist at Google. Her explanations of ML and data science are terrific and we were inspired to experiment with her ideas with our dog, Fanta.

Work, automation and augmentation

David Autor, MIT, for numerous papers, videos and insights into how automation works in the world of work.

The Oxford report on automation and jobs

MGI report on the same

Our Quartz article, a deep dive comparing our research with these two studies.

Recent study on what kind of automation/augmentation startups sell:

Recent work by MIT on automation and jobs:

Recent work on loss aversion in work with robots

Recent work on AI and “so-so” automation


Recommended follow-on reading for governance section

Google’s AI principles and ideas on governance:

AI Now

Discrimination in AI

Regulating AI

Amazon Anti-trust

Antitrust and virtual competition

Explainable machines

Proxy discrimination in AI

Superstar firms

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