Give People Money: How a Universal Basic Income Would End Poverty, Revolutionize Work, and Remake the World

Annie Lowrey

A New York Times Book Review Editors' Choice Shortlisted for the 2018 FT & McKinsey Business Book of the Year Award A brilliantly reported, global look at universal basic income—a stipend given to every citizen—and why it might be necessary in an age of rising inequality, persistent poverty, and dazzling technology. Imagine if every month the government deposited $1,000 into your bank account, with nothing expected in return. It sounds crazy. But it has become one of the most influential and hotly debated policy ideas of our time. Futurists, radicals, libertarians, socialists, union representatives, feminists, conservatives, Bernie supporters, development economists, child-care workers, welfare recipients, and politicians from India to Finland to Canada to Mexico—all are talking about UBI. In this sparkling and provocative book, economics writer Annie Lowrey examines the UBI movement from many angles. She travels to Kenya to see how a UBI is lifting the poorest people on earth out of destitution, India to see how inefficient government programs are failing the poor, South Korea to interrogate UBI’s intellectual pedigree, and Silicon Valley to meet the tech titans financing UBI pilots in expectation of a world with advanced artificial intelligence and little need for human labor.

Politics & GovernmentPolitics & Government · medium confidencePolitics & Government81 signalsLlm Classifier: Politics & Government -> Politics & GovernmentGoogle Books: Political Science -> ScienceGoogle Books: Political Science -> Politics & GovernmentTechnology, Computing & AIBusiness, Capitalism & CorporationsClass, Poverty & InequalityHousing, Cities & Urban Life
Experimental book profileGenerated by GPT-5.4 nano · may contain inaccuracies

This is an unverified interpretation of the catalog description, offered as an opt-in discovery experiment—not as bibliographic fact.

  1. 01 Central figures

    None extracted with sufficient confidence.

  2. 02 Central places

    None extracted with sufficient confidence.

    These suggestions did not meet the normal display threshold and are more likely to be wrong.

    • Kenya55%
    • India55%
    • Silicon Valley45%
  3. 03 Suggested argument

    No argument inferred with sufficient confidence.

  4. 04 Reading orientation

    38 / 100 academic

    Serious trade

    An estimate of intended readership and scholarly apparatus—not quality or importance.

Confidence percentages are the model's own estimates. Profile confidence: 62%.