This book provides a novel framework for understanding and revising labor markets and education
policies in an era of machine learning. It posits that while learning and knowing both require
thinking learning is fundamentally different than knowing because it results in cognitive
processes that change over time. Learning in contrast to knowing requires time and agency.
Therefore learning algorithms-that enable machines to modify their actions based on real-world
experiences-are a fundamentally new form of artificial intelligence that have potential to be
even more disruptive to labor markets than prior introductions of digital technology. To
explore the difference between knowing and learning Turing's Imitation Game -that he proposed
as a test for machine thinking-is expanded to include time dependence. The arguments presented
in the book introduce three novel concepts: (1) Comparative learning advantage: This is a
concept analogous to comparative labor advantage but arises from the disparate times required
to learn new knowledge bases skillsets. It is argued that in the future comparative learning
advantages between humans and machines will determine their division of labor. (2) Two
dimensions of job performance-expertise and interpersonal: Job tasks can be sorted into two
broad categories. Tasks that require expertise have stable endpoints which makes these tasks
inherently repetitive and subject to automation. Tasks that are interpersonal are highly
context-dependent and lack stable endpoints which makes these tasks inherently non-routine.
Humans compared to machines have a comparative learning advantage along the interpersonal
dimension which is increasing in value economically. (3) The Learning Game is a time-dependent
version of Turing's Imitation Game. It is more than a thought experiment. The Learning Game
provides a mathematical framework with quantitative criteria for training and assessing
comparative learning advantages. The book is highly interdisciplinary-presenting philosophical
arguments in economics artificial intelligence and education. It also provides data
mathematical analysis and testable criteria that researchers in these fields will find of
practical use. The book calls for a rethinking of how labor markets operate and how the
education system should prepare students for future jobs. It concludes with a list of
counterintuitive recommendations for future education and labor policies that all
stakeholders-employers employees educators students and political leaders-should heed.
