The Origins of the Urban Crisis
by Sugrue, Thomas J.
4.2 Black Workers in Selected Detroit-Area Steel Plants, 1965 4.3 Black Enrollment in Apprenticeship Programs in Detroit, 1957–1966 5.1 Automation-Related Job Loss at Detroit-Area Ford Plants, 1951–1953 5.2 Decline in Manufacturing Employment in Detroit, 1947–1977 5.3 Percentage of Men between Ages
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improve working conditions, reduce hours, and improve workplace safety. It was simply “a better way to do the job.”16 Certainly automated production replaced some of the more dangerous and onerous factory jobs. At Ford, automation eliminated “mankilling,” a task that demanded high speed and involved tremendous risk. “Mankilling” required a worker to remove
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quench tank, all within several seconds. In Ford’s stamping plants, new machines loaded and unloaded presses, another relatively slow, unsafe, and physically demanding job before automation. Here automation offered real benefits to workers.17 5.2. When Ford introduced automated assembly lines in its newly opened Lima, Ohio plant in 1954, it
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1953 and 1955, when Ford announced the construction of new engine production facilities at Brookpark Village, Ohio, and in Lima, Ohio.23 The effects of automation on job opportunities in communities like Detroit were a well-guarded corporate secret. Responding to labor union criticism of automation, employers downplayed the possibility of significant
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job loss. When Ford began automating and decentralizing the Rouge plant, John Bugas, Ford’s vice president for industrial relations, told workers that they had nothing to fear. “I
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employees in the Rouge operations resulting from the building of new facilities will be substantial.” Ford labor relations official Manton Cummins dismissed claims that automation led to job loss as a union-led “scare campaign.” Yet the only detailed statistics on automation and its effects on employment, a UAW-sponsored study of
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campaigns, industry publications like Automation magazine argued that automation reduced labor costs, but by the mid-1950s, they seldom raised labor as a rationale, because automation’s effect on jobs had become a sensitive political issue. Instead, the magazine’s editors went on the defensive against charges that
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automation led to job loss. In February 1960, for example, the magazine noted that “lest anyone be deluded into thinking of [the Plymouth Detroit assembly plant] as a workerless
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there would be “no direct way I can imagine to avoid by private means the dislocations that come from technological obsolescence.”26 TABLE 5.1 Automation-Related Job Loss at Detroit-Area Ford Plants, 1951–1953 General Motors Vice President Louis Seaton was even more sanguine than Ford, but more disingenuous. He
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that the number of auto industry jobs nationwide would fall because of automation. Some economists argued that over the long run, the introduction of automated processes would increase jobs nationwide. Aggregate employment statistics, however, masked profound local variation. Local economies in places like Detroit reeled from the consequences of automation-caused plant
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workers. Labor leaders also became staunch advocates of government funding for education and retraining programs to prepare workers for new automated jobs. Contract provisions guaranteed that workers who lost their jobs because of automation would be protected by seniority and transferred to other jobs. Their programs offered remedies for the symptoms of automation, rather
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: Labor and Taxes Automation was not the only force contributing to job loss in Detroit. Smaller firms that did not automate production or suffer automation-related job losses still fled the city in increasing numbers in the 1950s. Labor relations were especially important in motivating firms to relocate outside of Detroit, or
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industries. Thus they were less likely to have accumulated enough seniority to protect their jobs. And because blacks were concentrated in unskilled, dangerous jobs—precisely those affected by automation—they often found that their job classifications had been eliminated altogether.67 By the early 1960s, observers noted that a seemingly permanent class
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and to federal courtrooms. In February 1950, James Simmons, a worker in the Plastics Department at the Rouge, warned that the introduction of automated machinery would put jobs at risk. Simmons saw changes in the Rouge as “part of a pattern that calls for taking these jobs to new unorganized sections of
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cause of unemployment in Detroit. Social service and employment agencies in the city turned their energies toward what they perceived as a growing gap between automated jobs and workers who were so inadequately trained or educated that they could not qualify for those jobs. Black agencies were especially concerned about the effect
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of automation on black job prospects. The Detroit Urban League (DUL) ran the most important black employment agency in the city. From its founding in 1916 through the 1940s
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men held positions as unskilled laborers; three decades later, a mere one in twelve held unskilled jobs. This decline was primarily a consequence of automation: manufacturers eliminated unskilled jobs throughout the period (see Chapter 5). The fraction of blacks employed in the service sector also fell significantly in the postwar period. More
Surviving AI: The Promise and Peril of Artificial Intelligence
by Calum Chace · 28 Jul 2015 · 144pp · 43,356 words
is transforming so many industries will evolve over the next thirty years. We don’t know whether technological unemployment will be the result of the automation of jobs by AI, or whether humans will find new jobs in the way we have done since the start of the industrial revolution. What is
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analysis to make us more efficient and more effective. But this improvement means change, and change is usually uncomfortable. There are concerns that it is automating our jobs out of existence, and that this will increase rapidly in the coming few years. There are concerns that AI is de-humanising war, and
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people and policy makers with an even bigger concern than digital disruption. It may render most of us unemployed, and indeed unemployable, because our jobs have been automated. Automation Automation has been a feature of human civilisation since at least the early industrial revolution. In the 15th century, Dutch workers threw their shoes into
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individual who was dismissed from a particular job, there was generally the chance to retrain, or find new work elsewhere. The idea that each job lost to automation equates to a person rendered permanently unemployed is known as the Luddite Fallacy. This is unfair to the Luddites, who weren’t advancing a
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were simply protesting about the very real danger of starvation in the short term. It is also not true that Maynard Keynes argued that automation would destroy jobs any time soon. The essay quoted above goes on to say, “But this is only a temporary phase of maladjustment. All this means in
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daily bread, leisure is a longed-for sweet – until they get it.” This time it’s different? Some people argue that soon, people automated out of a job may not find new employment, thanks to the rapid advances in machine learning, and the availability of increasingly powerful and increasingly portable computers. MIT
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job churn or economic singularity If computers steal our old jobs, perhaps we can invent lots of new ones? In the past, people whose jobs were automated turned their hands to more value-adding activity, and the net result was higher overall productivity. The children of people who did back-breaking farm
The Information State: Politics in the Age of Total Control
by Jacob Siegel · 24 Mar 2026 · 348pp · 103,246 words
control. In the early stages of projects like SAGE and Igloo White, they often faced opposition from military officers who questioned their effectiveness. Automation not only threatened their jobs; it cheapened their bravery and honor. But a pattern of reversal developed in such cases. Once the technological system failed as an instrument
We Are as Gods: A Survival Guide for the Age of Abundance
by Peter H. Diamandis and Steven Kotler · 13 Apr 2026 · 225pp · 76,418 words
we evolve? Every burst in speciation brings with it extinction. The new wipes out the old, and studies show that 47 percent of US jobs could be automated by 2030, which sure sounds like a lot of extinction. The opportunity is hidden in the challenge. If robots are taking on the dull
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draws on what is most distinctly human. Ben Lamm and the Adjacent Possible The 47 percent of US jobs that could be automated by 2030 sounds like a terrifying prediction. Yet, historically, automation doesn’t eliminate jobs; it reshapes them and often into careers that sound like science fiction. Consider this reframe: “By 2030
Iain M Banks - The Culture complete works
by Iain M. Banks · 5,095pp · 1,429,463 words
processes, with human labour restricted to something indistinguishable from play, or a hobby. No machine is exploited, either; the idea here being that any job can be automated in such a way as to ensure that it can be done by a machine well below the level of potential consciousness; what to
The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence
by Sebastian Mallaby; · 30 Mar 2026 · 607pp · 161,998 words
more powerful digital minds that no one—not even their creators—can understand, predict, or reliably control,” Bengio and his cosignatories declared. “Should we automate away all the jobs, including the fulfilling ones?” they demanded. “Should we develop nonhuman minds that might eventually outnumber, outsmart, obsolete and replace us?” The letter also
After the Spike: Population, Progress, and the Case for People
by Dean Spears and Michael Geruso · 7 Jul 2025 · 264pp · 96,174 words
being adopted. That’s because babies are freeloaders. They won’t become the workers who could balance society’s books for decades. Liberalized immigration and job automation are fast solutions to the problem of too few workers. Increased funding for scientific research is a fast solution to boosting innovation. Restructuring public benefit
Only Humans Need Apply: Winners and Losers in the Age of Smart Machines
by Thomas H. Davenport and Julia Kirby · 23 May 2016 · 347pp · 97,721 words
voice mail, word processing, online travel sites, and Internet search applications have been chipping away the rest of what used to be a secretarial job. Era Two automation doesn’t only affect office workers. It washes across the entire services-based economy that arose after massive productivity gains wiped out jobs in
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’s also the corollary: If it can be automated in an economical fashion, it will be. Already we’re seeing a rapid decomposition of jobs and automation of the most codifiable parts—which are sometimes the parts that have required the greatest education and experience. Take the job of “physician advisor,” a
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is such a prime example of it. The term, first coined by the Marxist sociologist Harry Braverman, is commonly used to describe both what automation does to jobs and what it does to the labor force. The jobs are deskilled when technologies are introduced that no longer require workers to have formerly
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on the path to automation. 1. There are automated systems available today to do some of its core tasks. The strongest evidence that automation will increasingly threaten a job is the existence of an automated system today that performs all or part of its core function. If we were radiologists or pathologists
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’s getting. Rather than running for the hills, the better strategy for a financial advisor might be to focus on that part of his current job that automation isn’t threatening: the hand-holding he does with clients who, for example, know they could be earning higher returns but have trouble stomaching
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few of them.) The other widely discussed option is to somehow convince a cash-strapped government to guarantee you an income if you lose your job to automation. We don’t deny that it’s important for governments at every level to address this pressing issue. But government bureaucracies have always been
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steps. The remainder of this chapter will show this in a few illustrative professions. First, because it is such a clear example of a job threatened by automation, we’ll look at insurance underwriting. Then, for greater context, we’ll look briefly at how teachers and financial advisors are stepping up, aside
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make small, repetitive decisions, those who step up make larger, more sweeping ones. The step-up role may not preserve a large number of jobs as automation advances—most organizations have only a few people in such roles—but it is important out of proportion to the numbers. It’s at the
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it depending on how it reconciles.”3 In marketing, a step-up job might involve coordination and pursuit of the many opportunities for automating marketing decisions. A LinkedIn job description featured such a role for a “Sales/Marketing Automation Specialist,” who would “manage and execute marketing campaigns utilizing integrated automation and CRM
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categories that had been previously neglected. Ferrara was widely quoted as saying—and he repeated it internally—that human reporters would not lose their jobs because of the automated reporting. “The type of work we want to automate is not at the core of what we do,” he noted. “We’re automating
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that role. Shane Herrell, the Global Search Program manager at the software company SAS, works with (using them, not developing them) digital marketing automation programs extensively in his job. He said that he spends a lot of time working with automation software vendors. He described the challenge of working with the mind
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. Since I’m not looking at every single load anymore, I can focus on the bigger picture now.” As with Torrence’s job at Schneider, even though automated systems are automated—supposedly working without human intervention—humans are still involved in almost every case of such systems. Automated radiology systems exist, but so
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we hinted at when we discussed insurance underwriting. Many of those in the step-in role were chosen because they were experts in the jobs that were automated. They were experienced and knowledgeable underwriters, marketers, or accountants. They put in their time making nonautomated decisions, and because they did that often and
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you need to conduct this profession, somebody might well turn it into software. Finally, if someone has already created even an experimental project to automate your target job or key tasks within it, you might want to go back to the drawing board and choose another. Building on Your Narrowness The magic
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Prolog. More recently, programming in business rule engines (IBM’s ILOG and Fair Isaac’s Blaze, for example) was a common approach to automated systems development. While such jobs still exist, they are not the preferred tools in which most software development in the automation space is done. Most development is done
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other program. If you have a background in one of these (slightly) ancillary aspects of software, you can probably find the same kind of job related to automated software. Data Scientists —A couple of years ago, Tom and D. J. Patil, now chief data scientist of the White House Office of Science
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. Sandro Catanzaro, a native of Peru, is the cofounder and senior vice president of analytics and innovation at DataXu, a Boston-based marketing automation software company. In that job he does data science himself as well as managing that activity for the company (though he does more of the latter now). DataXu
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are highly technical in nature. Who knows how many it will add up to? The step-forward category probably won’t replace all the jobs eliminated because of automation, but it will be a fast-growing and important segment of the tech economy. Gil Press, a columnist for Forbes, makes the optimistic
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the financial system and not due to the automation of work that was proceeding apace in the nation’s factories. Still, the potential threat to jobs that automation also posed had certainly been noted. John Maynard Keynes, most famously, diagnosed in his 1930 essay “Economic Possibilities for Our Grandchildren” what he called
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Forward with, 176–200 Stepping In with, 134–52 Stepping Up and, 91–95 strategy of, as self-defeating, 204–8 strongest evidence of job threat, 19 Automation Anywhere, 48, 216 automotive sector, 1 Autor, David, 70–71 Balaporia, Zahir, 189–91 Bankrate.com, 96 Bathgate, Alastair, 156, 157 Baylor College of
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, 12, 224 income inequality and, 228–29 jobs added by artisanal jobs, 119–21 jobs at risk, 2, 5–6, 30, 78, 86 jobs less likely to be automated, 19 jobs sent offshore, 217 jobs with nonprogrammable skills, 71–73, 109–12 mean income of college graduates, 159 niche market (see Stepping
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, 171 Fidelity, 198 Financial Engines, 213 financial sector. See also Vanguard Group ATMs, 14 augmentation in, 86–88 automated decision-making (robo-advisors) and other automated jobs, 11–12, 18, 20, 22, 25, 29, 48, 86, 87, 88, 92, 100, 105, 156–57, 198–99, 213, 214 bank failure, 90 Cathcart and
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cost of AI programs, 155–56 cost of U.S., 155–56 Dr. Doris Day and Melafind, 137–38 insurance underwriting in, 83–84 jobs at risk of automation, 19 medical researchers, 181–82 radiology, 16–18, 19, 24, 25, 41, 166 robotic surgery, 40, 51 Stepping Narrowly in, 157 therapy and
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planners and brokers, 88 focusing on behavioral finance and economics, 198–99 how to build skills for, 200 in insurance underwriting, 83–84 internal automation leaders, 189–91 jobs, technical and nontechnical, 177–91 marketers, 183–85 number of jobs, 191–92 product managers, 182–83 programmers and IT professionals, 178 reporting
The Economic Singularity: Artificial Intelligence and the Death of Capitalism
by Calum Chace · 17 Jul 2016 · 477pp · 75,408 words
social system suited to the inevitable world of connected intelligent systems. It’s a failure of imagination to debate whether there will be jobs for humans in the automated world, Chace argues - we must look farther and ask how we will organize society when labor is not necessary to provide for the
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mechanisation of agriculture The particular aspect of the industrial and information revolutions which concerns us in this book is automation. Perhaps the clearest example of automation destroying jobs is the mechanisation of agriculture, a sector which accounted for 41% of US employment in 1900, and only 4% by 1970[xi]. (The corresponding
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digital computers which allow far more flexibility in the way an electrochemical process operates, and eventually general-purpose computers were applied to the job. The advantages of process automation are clear: it can make an operation faster, cheaper, and more consistent, and it can raise quality. The disadvantages are the initial investment
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to comprehending the scale of the changes that are coming our way. The book argues that AI systems are on the verge of wholesale automation of white collar jobs – jobs involving cognitive skill such as pattern recognition and the acquisition, processing and transmission of information. In fact it argues that the process is
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that is exactly what he did in a speech at the Trades Union Congress in November 2015[xlvi]. He wondered whether the displacement effect of automation, whereby jobs are destroyed, might start to outweigh the compensation effect, whereby automation raises productivity sufficiently to generate more demand and thus work. In his speech
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he raised many of the concerns explored in this book. He presented an estimate prepared by the Bank of England of the likelihood of automation of the jobs in a range of economic sectors in the UK, adapted from the estimates produced for the US by Frey and Osborne of the Oxford
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technology market research and advisory consultancy. At its annual conference in October 2014, its research director Peter Sondergaard declared that one in three human jobs would be automated by 2025.[l] "New digital businesses require less labor; machines will make sense of data faster than humans can." He described smart machines as
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will rise from 45% to 58%. At the time of publication, the authors concluded that only 5% of jobs were capable of being fully automated, but 60% of jobs could have 30% of their constituent activities automated. But rather than leading to a 30% headcount reduction, with the other 70% of activities
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machine translation systems, IBM's Watson and so on. Hanson is less impressed by these demonstrations of rapidly improving AI: “We do expect automation to take most jobs eventually, so we should work to better track the situation. But for now, Ford's reading of the omens seems to me little better
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do Jobs and tasks As we saw in chapter 3.2, consultants from McKinsey pointed out that machines often don't acquire the ability to automate entire jobs in one fell swoop. Instead they become able to automate certain of the tasks which people in those jobs perform. So what are these
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drives and will be affected by. We have considered what people do at work. Now it is time to think about the kinds of jobs which will be automated by these technologies. We’ll start with driving. 3.8 – The poster child for technological unemployment: self-driving vehicles Why? The case for
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transfers today, it will surely be able to handle mortgage applications before long. Manual work Occupations requiring physical labour will take longer to automate than clerical and administrative jobs because getting robots to be dextrous and flexible is surprisingly hard. As we saw in chapter 3.7, progress is rapid, but much
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has been much slower. But the robots are improving fast.[ccxxii] The professions It is certainly not only low-paid, relatively low-prestige service jobs that will be automated. The professions are vulnerable too: lawyers, doctors, architects and journalists. Sometimes accused of being conspiracies against lay people, these are protected occupations, with
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is enormous scope to better align legal resources through technology rather than fear losing jobs.”[ccxxx] So in the short and medium term, machine automation of white-collar jobs opens up vast new areas of work that can be undertaken, and doesn't throw the incumbent humans out of work. They are
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explosion of work which happens as the iceberg of latent demand is revealed can give us a false sense of security. The phenomenon of automation leading to job creation is sometimes called the automation paradox.[ccxxxi] But the paradox may turn out to be short-lived. Forms Another fairly basic form of
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are ready to address head-on the first great question posed by machine intelligence automation, which is this: Is it different this time? Will the automation of jobs by machine intelligence lead to widespread, lasting unemployment? For the answer to be negative, we will have to dramatically increase the supply of jobs
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which are for some reason immune to automation by machine intelligence. Jobs, not work It is important at this point to distinguish between jobs and work. Physicists define work as the expenditure of energy to
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, the on-demand economy, the peer-to-peer economy, the platform economy, and the bottom-up economy. Is this a way to escape the automation of jobs by machine intelligence? To break jobs down into as many component tasks as possible, and preserve for humans those tasks which they can do better
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involved in it.[cclxv] But our concern here is not whether the gig economy is a fair one. It is whether it can prevent the automation of jobs by machine intelligence leading to widespread unemployment. The answer to that is surely No: as time goes by, however finely we slice and dice
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is Over”. Some people believe this phenomenon of humans teaming up with computers to form centaurs is a metaphor for how we can avoid most jobs being automated by machine intelligence. The computer will take care of those aspects of the job (or task) which are routine, logical and dull, and the
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.12 – Conclusion: yes, it’s different this time It's time to answer the question: is it really different this time? Will machine intelligence automate most human jobs within the next few decades, and leave a large minority of people – perhaps a majority – unable to gain paid employment? It seems to me
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intended to serve two functions. First, as I said above, they are a rhetorical device. The arguments in chapter 3.10 that machines will automate our jobs away have been either abstract or fragmentary, and as such, some readers may find them implausible. I'm hoping that the timelines will help make
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to see the state balloon (and risk massive capital flight), and those who can’t add up.[cccxxvi] But if and when machines have permanently automated most jobs, we will need to implement some form of UBI. The fears about it being politically unacceptable will probably prove exaggerated, with attitudes changing as
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some skin in the forecasting game. Offering people the opportunity to bet real money on when they see their own jobs or other peoples’ jobs being automated may be an effective way to improve our forecasting.[cccliv] Planning We don’t have sufficient information to draw up detailed plans for the way
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#axzz3zEmSvuZs [cclix] https://itunes.apple.com/gb/podcast/exchanges-at-goldman-sachs/id948913991?mt=2&i=361020299 [cclx] http://uk.businessinsider.com/high-salary-jobs-will-be-automated-2016-3 [cclxi] http://www.fiercefinanceit.com/story/will-regulatory-compliance-drive-artificial-intelligence-adoption/2016-01-05 [cclxii] http://www.liverpoolecho.co.uk
New Laws of Robotics: Defending Human Expertise in the Age of AI
by Frank Pasquale · 14 May 2020 · 1,172pp · 114,305 words
endeavor in “workforce preparation,” they help extinguish the very values necessary to recognize economism’s shortcomings.22 RETOOLING TAX POLICY IN AN AGE OF AUTOMATION To ensure robust job opportunities, there is a kaleidoscopic array of policy options, all capable of subtle adjustment to balance the many social objectives in play. For
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must be righted.69 The “robot question” is as urgent today as it was in the 1960s.70 Back then, worry focused on the automation of manufacturing jobs. Now, the computerization of services is top of mind.71 At present, economists and engineers dominate public debate on the “rise of the robots
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
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Fire in the Valley: The Birth and Death of the Personal Computer
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Doing Good Better: How Effective Altruism Can Help You Make a Difference
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The Meritocracy Myth
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Still Broke: Walmart's Remarkable Transformation and the Limits of Socially Conscious Capitalism
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Selfie: How We Became So Self-Obsessed and What It's Doing to Us
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Plutocrats: The Rise of the New Global Super-Rich and the Fall of Everyone Else
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Final Jeopardy: Man vs. Machine and the Quest to Know Everything
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Rethinking Money: How New Currencies Turn Scarcity Into Prosperity
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More From Less: The Surprising Story of How We Learned to Prosper Using Fewer Resources – and What Happens Next
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Rationality: What It Is, Why It Seems Scarce, Why It Matters
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Kindle Formatting: The Complete Guide to Formatting Books for the Amazon Kindle
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The New Class Conflict
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Upgrade
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Overcomplicated: Technology at the Limits of Comprehension
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The Clock Mirage: Our Myth of Measured Time
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The New Snobbery
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Aftershocks: Pandemic Politics and the End of the Old International Order
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Why We Can't Afford the Rich
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The Great Shark Hunt: Strange Tales From a Strange Time
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What's Wrong With Economics: A Primer for the Perplexed
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Emotional Labor: The Invisible Work Shaping Our Lives and How to Claim Our Power
by Rose Hackman · 27 Mar 2023
Radicals Chasing Utopia: Inside the Rogue Movements Trying to Change the World
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Can It Happen Here?: Authoritarianism in America
by Cass R. Sunstein · 6 Mar 2018 · 434pp · 117,327 words
Beyond Outrage: Expanded Edition: What Has Gone Wrong With Our Economy and Our Democracy, and How to Fix It
by Robert B. Reich · 3 Sep 2012 · 124pp · 39,011 words
Picnic Comma Lightning: In Search of a New Reality
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Team of Teams: New Rules of Engagement for a Complex World
by General Stanley McChrystal, Tantum Collins, David Silverman and Chris Fussell · 11 May 2015 · 409pp · 105,551 words
The Deepest Map
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Uncanny Valley: A Memoir
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Laziness Does Not Exist
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Into the Raging Sea
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The Seventh Sense: Power, Fortune, and Survival in the Age of Networks
by Joshua Cooper Ramo · 16 May 2016 · 326pp · 103,170 words
Where Does It Hurt?: An Entrepreneur's Guide to Fixing Health Care
by Jonathan Bush and Stephen Baker · 14 May 2014 · 238pp · 68,914 words
The Rich and the Rest of Us
by Tavis Smiley · 15 Feb 2012 · 181pp · 50,196 words
PostgreSQL: Up and Running, 3rd Edition
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The Start-Up of You: Adapt to the Future, Invest in Yourself, and Transform Your Career
by Reid Hoffman and Ben Casnocha · 14 Feb 2012 · 176pp · 55,819 words
The Ages of Globalization
by Jeffrey D. Sachs · 2 Jun 2020
Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech
by Sara Wachter-Boettcher · 9 Oct 2017 · 223pp · 60,909 words
Generations: The Real Differences Between Gen Z, Millennials, Gen X, Boomers, and Silents—and What They Mean for America's Future
by Jean M. Twenge · 25 Apr 2023 · 541pp · 173,676 words
The System: Who Rigged It, How We Fix It
by Robert B. Reich · 24 Mar 2020 · 154pp · 47,880 words
Paper Girl: A Memoir of Home and Family in a Fractured America
by Beth Macy · 6 Oct 2025 · 373pp · 97,653 words
Who Needs the Fed?: What Taylor Swift, Uber, and Robots Tell Us About Money, Credit, and Why We Should Abolish America's Central Bank
by John Tamny · 30 Apr 2016 · 268pp · 74,724 words
Masters of Deception: The Gang That Ruled Cyberspace
by Michelle Slatalla and Joshua Quittner · 15 Jan 1995
Financing Basic Income: Addressing the Cost Objection
by Richard Pereira · 5 Jul 2017 · 177pp · 38,221 words
Magical Urbanism: Latinos Reinvent the US City
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The Power of Glamour: Longing and the Art of Visual Persuasion
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January Fifteenth
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The Politics Industry: How Political Innovation Can Break Partisan Gridlock and Save Our Democracy
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The Cost of Inequality: Why Economic Equality Is Essential for Recovery
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Portfolios of the poor: how the world's poor live on $2 a day
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Career and Family: Women’s Century-Long Journey Toward Equity
by Claudia Goldin · 11 Oct 2021 · 445pp · 122,877 words
American Made: Why Making Things Will Return Us to Greatness
by Dan Dimicco · 3 Mar 2015 · 219pp · 61,720 words
Wanderers: A Novel
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The Participation Revolution: How to Ride the Waves of Change in a Terrifyingly Turbulent World
by Neil Gibb · 15 Feb 2018 · 217pp · 63,287 words
Nomadland: Surviving America in the Twenty-First Century
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The Great Divide: Unequal Societies and What We Can Do About Them
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The Jobs to Be Done Playbook: Align Your Markets, Organization, and Strategy Around Customer Needs
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Unit X: How the Pentagon and Silicon Valley Are Transforming the Future of War
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Dark Money: The Hidden History of the Billionaires Behind the Rise of the Radical Right
by Jane Mayer · 19 Jan 2016 · 558pp · 168,179 words
EuroTragedy: A Drama in Nine Acts
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Explaining Humans: What Science Can Teach Us About Life, Love and Relationships
by Camilla Pang · 12 Mar 2020 · 256pp · 67,563 words
This Is Not a Drill: An Extinction Rebellion Handbook
by Extinction Rebellion · 12 Jun 2019 · 138pp · 40,525 words
Hostile Environment: How Immigrants Became Scapegoats
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Sickening: How Big Pharma Broke American Health Care and How We Can Repair It
by John Abramson · 15 Dec 2022 · 362pp · 97,473 words
Beyond Diversification: What Every Investor Needs to Know About Asset Allocation
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Irresistible: How Cuteness Wired our Brains and Conquered the World
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The Crying of Lot 49
by Thomas Pynchon · 1 Jan 1966 · 165pp · 47,320 words