Power and Progress

August 21, 2025

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Daron Acemoglu and Simon Johnson. Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity. New York: Hachette Book Group, 2024.

Daron Acemoglu and Simon Johnson won the 2024 Nobel Prize in Economics for their research on how political and economic institutions shape national prosperity. In this book, they tackle the relationship between technological innovation and prosperity.

No one doubts that new technologies have the potential to boost productivity and raise living standards. How and when they actually accomplish this is a more difficult question.

In the introduction and first three chapters, the authors lay out their general theory of technology and progress, considering the role of variations in labor demand, wages, and social power. The next four chapters discuss how these variations have played out in various historical situations, ranging from the failure of innovation to benefit farmworkers and early manufacturing workers before the late nineteenth century, to the more widespread prosperity of the mid-twentieth century. Armed with insights from economic theory and history, the authors then address the more recent revolution in digital technology. Readers who follow the argument all the way through should come away with a better understanding of our current technological age and its discontents. I know I did.

The productivity bandwagon

The conventional wisdom in economics, as well as a lot of public discussion, is that technological advances raise productivity, and higher productivity raises living standards. The authors cite Gregory Mankiw’s popular undergraduate textbook, which says that “almost all variation in living standards is attributable to differences in countries’ productivity.”

But the productivity gains from new technologies can only raise living standards if they improve real wages. What about labor-saving technologies that lower the demand for labor, causing unemployment and lower wages? Mankiw acknowledges the problem, but minimizes it by claiming that “most technological progress is instead labor-augmenting.” Most workers find some way to work with new technologies, and their increased productivity enables them to command a higher wage.

Acemoglu and Johnson call this optimistic view the “productivity bandwagon.” They argue to the contrary:

There is nothing in the past thousand years of history to suggest the presence of an automatic mechanism that ensures gains for ordinary working people when technology improves… New techniques can generate shared prosperity or relentless inequality, depending on how they are used and where new innovative effort is directed.

Rather than accept a broad generalization about technology and prosperity, the authors want to study historical variations and identify the key variables involved. The stories that people tell themselves about technology—including the ones economists tell—can both reflect and affect the historical variations. Writing in the Great Depression, John Maynard Keynes coined the term “technological unemployment.” He could imagine “the means of economising the use of labour outrunning the pace at which we can find new uses for labor.” More recently, robotics and artificial intelligence are raising that possibility again, but the productivity bandwagon remains a popular narrative.  Economic elites who profit from the application of new technologies are especially fond of it.

Variations in labor demand

Acemoglu and Johnson maintain that technological advances may or may not increase the demand for labor, depending on whether they are labor-augmenting or just labor-saving.

A classic example of technology that augmented labor, increased labor demand, and raised wages is the electrified assembly line introduced by Henry Ford. It not only raised the productivity of the existing autoworkers; it also enabled auto manufacturers to employ additional workers productively. (Economists call that variable the “marginal productivity of labor.”) By producing more cars at lower cost, car companies created a mass market for what had been a luxury item. In addition, they created additional jobs in related industries, such as auto repair, highway construction and tourism.

The effects of today’s robotics on automobile manufacturing may be very different. Carmakers can make just as many cars with less human labor, so labor productivity goes up. But demand for additional labor may go down, if factories are already turning out as many cars as their market can absorb. The marginal productivity of labor then falls, and the connection between technology and prosperity is weakened.

That is not to say that automation is always bad news for workers. That depends on the balance of labor-saving and labor-augmentation:

For most of the twentieth century, new technologies sometimes replaced people with machines in existing tasks but also boosted worker effectiveness in some other tasks while also creating many new tasks. This combination led to higher wages, increased employment, and shared prosperity.

The problem then is not just automation but excessive automation, especially if it is not really very productive in the fullest sense of the word. In economics “total factor productivity” refers to the relationship between economic output and all inputs, including capital as well as labor. Replacing workers with machines has costs as well as benefits, since machines cost money too, and displaced humans might have contributed something that machines cannot. The authors use the term “so-so automation” to refer to replacement of workers without much productivity gain. In that case, the classic gains of the earlier automobile boom—lower costs, expanded markets, rising labor demand, and widespread prosperity—do not occur.

Variations in wages

Even if new technologies are labor-enhancing, higher wages do not necessarily follow. They have not followed in societies where workers have been coerced to work without pay, or forbidden to leave their employer in search of better pay. The cotton gin enhanced the productivity of cotton workers in the Old South and expanded the areas where cotton could be profitably cultivated. But “the greater demand for labor, under conditions of coercion, translated not into higher wages but into harsher treatment, so that the last ounce of effort could be squeezed out of the slaves.”

In modern, free-market labor systems, wages are freer to rise along with labor demand. However, “wages are often negotiated rather than being simply determined by impersonal market forces.” A dominant employer may set wages for a multitude of workers, while the workers are too disorganized to bargain from strength. It was only in 1871 in Britain and 1935 in the United States that workers gained the legal right to organize and bargain collectively. Opponents of organized labor have continued to find ways of discouraging labor unions to this day. The share of national income going to labor rather than capital was highest when unions were strongest, in the 1950s.

Variations in power

Acemoglu and Johnson argue that the effects of technology depend on “economic, social, and political choices,” and that “choice in this context is fundamentally about power.”

What societies do with new technologies depends on whose vision of the future prevails. The most powerful segments of society have more say than others, although they can be contested by countervailing forces, especially in democratic societies where masses of workers vote. Although plenty of evidence points to the self-serving behavior of elites, they must at least appear to be promoting the common good for their views to be persuasive.

The technological choices a society makes can serve either to reinforce the power of elites or empower larger numbers of workers. This is especially true of general technologies with many applications. In the twentieth century, the benefits of electricity helped power a more egalitarian, broadly middle-class society. We cannot yet say the same about the digital technologies of the present century. The authors apply their theory, buttressed by historical evidence, to explain why.

Recall the subtitle of the book: “Our Thousand-Year Struggle Over Technology and Prosperity.” Making technology work for all of us has always been a struggle, and one that is related to the struggle for true democracy. Looking at it that way is more realistic and enlightening than seeing only a “productivity bandwagon” rolling smoothly toward mass prosperity.

Continued

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Posted by Ed Steffes

Empire of AI (part 2)

July 12, 2025

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In Empire of AI, Karen Hao not only tells the story of OpenAI and ChatGPT; she also addresses some of the most important questions about the nature of artificial intelligence and what direction it might take. She resists the idea that the particular path AI development has taken so far is inevitable or desirable. We are only at the beginning of this journey, and we have many technical and social choices to make. Hao believes that the path we are on is too narrow to take us where we need to go.

Technological and social revolutions

Drawing on the recent book by MIT economists Daron Acemoglu and Simon Johnson, Power and Progress, Hao cites two features of technological revolutions, “their promise to deliver progress and their tendency instead to reverse it for people out of power.” History has shown that the ultimate impact of technological change on different classes of people depends on other social changes, especially the organized resistance of disadvantaged groups to power imbalances.

This argument resembles the one made by Mordecai Kurz in The Market Power of Technology: Understanding the Second Gilded Age, which I reviewed last year. Kurz compares our present situation to the first Gilded Age, when technological change strengthened the market power of corporations and widened the gap between rich and poor. But that was followed by a period of progressive reform that put limits on market power and profits and strengthened the position of workers.

From this standpoint, the idea that the owners and masters of artificial intelligence are going to altruistically pursue the public good on their own, without any pushback from society or its government, sounds rather naïve. Economists and sociologists are not surprised when hi-tech companies take a lower, more self-serving, road, just as Hao reports for OpenAI.

Paths to AI

The dominant approach to advancing artificial intelligence is not the only approach, and not necessarily the one that will turn out to be most effective. Before today’s generative AI systems were developed, two competing theories dominated the field. The “symbolists” believed in taking existing human knowledge, encoding it in symbols, and inputting it to machines. The aim was to create “expert systems” that could emulate the best of human decision making. The “connectionists,” on the other hand, believed that machines could learn on their own if they had the computing capacity to process and connect vast amounts of data. The result would be “neural networks…data-processing software loosely designed to mirror the brain’s interlocking connections.”

The connectionist approach came to prevail, but Hao suggests that this was not just because it was scientifically superior. It turned out to be the faster route to commercial success, although faster doesn’t necessarily mean better. Neural networks may not reason very well, but, “You do not need perfectly accurate systems with reasoning capabilities to turn a handsome profit. Strong statistical pattern-matching and prediction go a long way in solving financially lucrative problems.” What might get more intelligent results is hard to know if companies are making big bucks with the current approach and investing little in the alternatives.

Along with machine learning and neural networks came the “scaling ethos,” the idea that bigger is the path to better. At OpenAI, leading exploratory researcher Ilya Sutskever championed this approach until he left the company in 2024. He believed that the nodes of neural networks were like neurons in the human brain, and so improvements would come by piling on more nodes in multiple layers. That required more and more computing power, larger data centers, and big financial investments.

Cognitive scientist Gary Marcus, who wrote Rebooting AI, argues that today’s machine learning systems remain “forever stuck in the realm of correlations,” (Hao’s words), incapable of true causal reasoning. To advance toward true intelligence, he recommends incorporating more of the symbolic, expert systems approach. Hao believes that AI can help serve many human needs with systems that are smaller in size but superior in quality.

Dreams of AGI

The founders of OpenAI believed that artificial general intelligence was just around the corner. They would achieve it by scaling up neural networks until they resembled the size and complexity of human brains. Hao maintains—and I agree—that we don’t really know “whether silicon chips encoding everything in their binary ones and zeros could ever simulate brains and the other biological processes that give rise to what we consider intelligence.” Is the node of a neural network really the functional equivalent of a neuron, which is a living cell? Can a dead machine that feels nothing, experiences nothing, and is conscious of nothing learn to think like a human? Is this just a problem of scale?

Scaling allows chatbots to generate text with enough complexity to lead users to imagine a humanlike mind within the black box. But that may be like a magic show, nothing but a clever illusion.

An alternative vision

Maybe the dubious quest to simulate general intelligence is leading the AI technocrats to make their models too big, too all-encompassing, and too omniscient, when smaller, less grandiose models would serve us better.

An example of the smaller models Heo has in mind is a speech-recognition model developed to help preserve the dying te reo language of the indigenous Maori people of New Zealand. Using audio recordings from about 2,500 of the remaining speakers, the researchers trained the computer to recognize and transcribe the sounds. Not only was this for the benefit of Maori who wanted to learn the language, but the researchers committed to working collaboratively with the community so that the data would only be used with their approval.

Hao insists that she is not against artificial intelligence as such. But she says,

What I reject is the dangerous notion that broad benefit from AI can only be derived from—indeed, will ever emerge from—a vision for the technology that requires the complete capitulation of our privacy, our agency, and our worth, including the value of our labor and art, toward an ultimately imperial centralization project.

For Hao, the central issue is power and its distribution. Artificial intelligence will only serve humanity if power can be decentralized along three axes: knowledge, resources, and influence.

I learned a lot from this book, and I recommend it as a good place to start for readers who are just beginning to think about these issues.

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Empire of AI

July 12, 2025

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Karen Hao. Empire of AI: Dreams and Nightmares in Sam Altman’s OpenAI. New York: Penguin Press, 2025.

In 2022, OpenAI released a free version of ChatGPT, an artificial intelligence program that could interact with users in text conversation and answer questions based on its massive database. It became the “fastest growing computer app in history.”

GPT stands for “Generative Pre-Trained Transformer.” A generative AI system is one that can synthesize new text from existing text, or new images from existing images. A transformer is a kind of neural network that can identify long-range patterns, notably the connections between words and their textual contexts of sentences and paragraphs. Give ChatGPT a few words, and it quickly discerns what you want to know, having been trained on huge amounts of existing text.

Since 2022, OpenAI has continued to develop ChatGPT, adding a paid subscription version, special versions for business, versions for different operating systems, and a “multimodal” version that processes voice and graphic inputs as well as text.

These rapid technological developments raise many questions about the potential benefits and costs of artificial intelligence, especially the dominant approach to AI taken by the OpenAI organization. Technology journalist Karen Hao has spent the last few years taking a close look at the company, and she is deeply troubled by what she sees. As she tells the story, what started out as a humanitarian dream has evolved into something more dangerous.

OpenAI—the promise

OpenAI was founded about ten years ago by Elon Musk and Sam Altman, along with other visionaries and wealthy backers like Peter Thiel. The group agreed that AI would be a transformative technology, forever changing human life. They were confident that machines could soon achieve artificial general intelligence (AGI). That meant that machines would not only carry out specific cognitive tasks assigned by humans, but think as well as humans, if not better. They had the potential to solve problems that continue to confound humans, like controlling global climate or delivering health care more cost-effectively. The founders had the “purest intentions of ushering in a form of AGI that would unlock global utopia, and not its opposite.”

What would be the opposite? A world in which bad actors use the power of AI to benefit themselves at the expense of others. Or worse, a dystopia in which machines take over and decide that humans and their puny minds are expendable. Many discussions of AI reveal a division between “Boomers” and “Doomers,” those who expect the best and those who fear the worst. Impressed by the arguments on both sides, “the founders asserted a radical commitment to develop so-called artificial general intelligence…not for the financial gains of shareholders but for the benefit of humanity.”

In the beginning, OpenAI was a nonprofit, devoted to research rather than commercial products. The researchers called it “alignment research,” intended to align the machinery’s own learning processes with human values. How that could be done was yet to be seen, of course.

As a research organization rather than a for-profit company, OpenAI would be what the name suggests—open and collaborative. In the spirit of scientific inquiry and human progress, it would freely share information about its ideas and creations with others. That is not at all how things turned out.

Competitive pressures

By mid-2018, the original vision was already in trouble.

Merely a year and a half in, OpenAI’s executives realized that the path they wanted to take in AI development would demand extraordinary amounts of money. Musk and Altman, who had until then both taken more hands-off approaches as cochairmen, each tried to install himself as CEO. Altman won out. Musk left the organization in early 2018 and took his money with him. In hindsight, the rift was the first major sign that OpenAI was not in fact an altruistic project but rather one of ego.

The following year, under Altman’s direction, OpenAI changed its organizational structure. Although still technically a non-profit, it created a “for-profit arm, OpenAI LP, to raise capital, commercialize products, and provide returns to investors much like any other company.” It then found a billion-dollar investor, Microsoft, a profit-making company if there ever was one. Most of OpenAI’s employees resigned from the nonprofit and went to work for the LP. They earned equity as well as salaries, giving them a stake in its commercial success. In 2024, OpenAI raised over $6 billion from new investors, promising them that they “could demand their money back if the company did not convert into a for-profit in two years.”

Although Altman and other leaders did not explicitly abandon their original humanitarian goal, they subordinated it to their drive for commercial success. Even if we are the good guys, they may have reasoned, we have to be commercially successful for our vision of AI to become the dominant one. “It was this fundamental assumption—the need to be first or perish—that set in motion all of OpenAI’s actions and their far-reaching consequences.”

One of those consequences was greater secrecy. OpenAI could not really be open with outsiders they saw as commercial competitors rather than scientific collaborators. AI systems tend to be “black boxes” shrouded in mystery anyway. Without transparency, outside researchers have no way of verifying the claims a company makes about what its systems actually do and how they do it.

When she first profiled the company in 2020, Hao was already calling attention to the “misalignment between what the company publicly espouses and how it operates behind closed doors…Over time, it has allowed a fierce competitiveness and mounting pressure for ever more funding to erode its founding ideals of transparency, openness, and collaboration.”

Safety issues

AI systems like ChatGPT are “large language models” that identify patterns in huge volumes of data, data “scraped” from whatever is available on the internet. Some of it is misinformation, propaganda, pornography, conspiracy theories or hate speech. All of that finds its way into the system’s training, unless the training itself includes ways of identifying and rejecting whatever humans find toxic. But which humans, and how?

As AI developed, researchers began to observe that chatbots not only expressed prejudices they encountered in the data, but amplified them. Since a majority of judges are male, a system would routinely describe or portray a generic judge as male, ignoring the third of them who are women. Since nonwhites are overrepresented among food stamp recipients, a system would routinely describe recipients as nonwhite, although the majority are white. Chatbots not only overgeneralize from their data, but combine text to construct sentences that are simply not true, which researchers call “hallucinations.”

It gets weirder. In a long conversation with a New York Times reporter, ChatGPT kept declaring its love for him and encouraging him to leave his wife! Chatbots are a source of bad advice as well as useful information.

OpenAI was aware of these problems and assigned people to work on them. Over time, however, Hao charges that the desire to be first out the door with promising products pushed safety concerns into the background.

Social and environmental issues

As the AI datasets got larger but contained more disturbing content, OpenAI needed more workers to train chatbots to distinguish the toxic from the benign. Someone had to review and classify a multitude of text descriptions and images, so that the system could learn to make such distinctions on its own. Sorting through the worst garbage the internet has to offer was a tedious and psychologically disturbing job. OpenAI outsourced it to the cheapest labor it could find, in troubled third-world countries like Chile, Venezuela and Kenya.

Here Hao sees a connection between the earlier colonialist exploitation of such countries and the way multinational corporations take advantage of their economic distress to extract labor and raw materials as cheaply as possible. For example, “as the AI boom arrived, Chile would become ground zero for a new scale of extractivism, as the supplier of the industry’s insatiable appetite for raw resources,” especially copper. OpenAI rarely hires its menial labor directly, but relies on intermediaries like Scale AI, which are selected for their ability to extract the most labor at the lowest cost. Companies like OpenAI “take advantage of the outsourcing model in part precisely to keep their dirtiest work out of their own sight and out of sight of customers, and to distance themselves from responsibility while incentivizing the middlemen to outbid one another for contracts by skimping on paying livable wages.” For Hao, this is a sign of a larger problem, that AI may concentrate economic rewards in the hands of a hi-tech minority while devaluing the human labor of everyone else. The general effect of AI on employment remains to be seen, but Hao is hardly alone in her concerns.

As for the environment, the enormous AI datacenters being constructed in places like Arizona require massive amounts of energy and water to operate and cool their computers. They are making it harder to transition away from fossil fuels to cleaner and more renewable sources of energy. Hao quotes climate scientist Sasha Luccioni, “Generative AI has a very disproportionate energy and carbon footprint with very little in terms of positive stuff for the environment.”

Bad publicity

Over the past two years, OpenAI has received a lot of bad press and outside criticism. In 2023, the company’s board tried but failed to oust Sam Altman as CEO. The reasons were a combination of concerns about the company’s direction and Altman’s management style. His alleged faults included “dishonesty, power grabbing, and self-serving tactics.” The board had to back down when many other executives and employees rallied to his defense. Hao sees this as further evidence of the victory of commercialism and competitiveness over safety concerns. Many key leaders and workers in the “Safety clan” had already left the company by then; more left afterwards.

More bad publicity came in 2024 with the revelation that the company was threatening departing workers with forfeiture of their equity unless they signed non-disparagement agreements. Hiding the company’s weaknesses behind a wall of secrecy seemed the opposite of the transparency originally promised.

Then there was the Johansson matter. OpenAI had approached Scarlett Johansson, the voice of AI in the movie Her, about using her voice in their latest chatbot. When she declined, the company used another actor’s voice that seemed remarkably similar. This occurred at a time when many artists were complaining about having their work scraped from the internet to use in AI training without their consent or compensation.

Technological imperialism?

Hao summarizes her observations of the company:

OpenAI became everything that it said it would not be. It turned into a nonprofit in name only, aggressively commercializing products like ChatGPT and seeking unheard-of valuations. It grew even more secretive, not only cutting off access to its own research but shifting norms across the industry to bar a significant share of AI development from public scrutiny. It triggered the very race to the bottom that it had warned about, massively accelerating the technology’s commercialization and deployment without shoring up its harmful flaws or the dangerous ways that it could amplify and exploit the fault lines in our society.

Hao finds that empire is the most fitting metaphor for what companies like OpenAI are building. They are not as violent as the empires of the past. But they too seize assets for their own gain—all the data people post online, as well as the land, energy and water to support their supercomputers. “So too do the new empires exploit the labor of people globally to clean, tabulate, and prepare that data for spinning into lucrative AI technologies.”

Hao describes OpenAI’s formula for empire as a recipe with three ingredients. First, bring together talent by promulgating a grand vision, in this case to develop artificial intelligence for the benefit of humanity. Then use the mission to justify the centralization of resources and to ward off any opposition or attempts at regulation. Finally, keep the mission vague enough to create the appearance of continuity, no matter what actions the company finds expedient to entrench its power.

The result is that the benefits of new technology flow upward to the few, instead of trickling down to improve life for the many. Artificial intelligence is too new to say how far this trend will go. But Hao’s book is a warning we ignore at our peril.

Continued

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Posted by Ed Steffes

Abundance

June 19, 2025

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Ezra Klein and Derek Thompson. Abundance. New York: Avid Reader Press, 2025.

In this provocative new book, journalists Ezra Klein and Derek Thompson challenge liberals to refocus their political thinking. The authors state their thesis clearly in the Introduction: “This book is dedicated to a simple idea: to have the future we want, we need to build and invent more of what we need.” It sounds like a no-brainer, at least until they explain how this approach differs from the standard party messaging from both the left and the right.

The politics of scarcity

Klein and Thompson want liberals to pay more attention to the supply of things people need, like housing, energy, and health care. Conservatives have talked more about the supply side of the economy than liberals have, but that term has a specific—and limiting—meaning for them: “Supply-side economics was about getting the government out of the private sector’s way. Cutting taxes so people would work more. Cutting regulations so companies would produce more.” But conservatives have exaggerated how much the private sector could meet the need for public goods like affordable housing, universal health insurance, public transportation or a pollution-free environment. The market delivers what consumers are willing and able to pay for, not necessarily what people need. Cheap manufactured goods, yes; affordable health care, not so much.

For their part, liberals have tended to focus on the demand side, especially helping low-income citizens afford what richer people have the money to buy.

… [W]hile Democrats focused on giving consumers money to buy what they needed, they paid less attention to the supply of the goods and services they wanted everyone to have. Countless taxpayer dollars were spent on health insurance, housing vouchers, and infrastructure without an equally energetic focus—sometimes without any focus at all—on what all that money was actually buying and building.

Subsidizing demand without increasing supply creates shortages and higher prices. “Too much money chasing too few doctors means long wait times or pricey appointments.” Some people get subsidized health care or housing, but other people just get higher costs, more debt, or less availability.

Since conservatives tend to want a smaller government than liberals think a modern society needs, liberals have become the defenders of larger government. The authors want to shift the debate away from the size of government toward the capacity of government. “Whether government is bigger or smaller is the wrong question. What it needs to be is better.”

Neither side of the political spectrum has had a realistic formula for economic growth. Supply-side conservatives have exaggerated how much tax cuts for the wealthy can stimulate growth from the top down. Demand-side liberals want to use progressive taxation to divide up the existing “pie” of wealth and income more equitably. Some on the left are suspicious of growth itself because of its environmental impact. The authors stress that growth does not have to mean more of the same—more fossil fuel production, more pollution, more climate change. “The difference between an economy that grows and an economy that stagnates is change.” The book is based on an underlying optimism that abundant clean energy and other emerging technologies can unleash a new productivity revolution and provide more for all. Otherwise, we will perpetuate an ugly politics where no one can have more unless someone else gets less.

Initiatives like the Inflation Reduction Act to promote clean energy and the CHIPS and Science Act to promote America’s semiconductor industry are steps in the right direction, although they are too new to have demonstrated their cost-effectiveness. One worrisome feature is the mixture of goals liberals are trying to achieve simultaneously. An application for CHIPS funding asks the applicant to address environmental issues, jobs for the disadvantaged, gender equality, access to child care, investments in mass transit, etc., etc. The main point of the initiative may get lost among the multitude of liberal causes.

Urban housing

The book tells many stories about ineffective Democratic leadership, especially in blue-state cities with large Democratic majorities, like San Francisco. The urban housing crisis is a prime example of the failure to create abundance.

Klein and Thompson say that cities should play two main roles, as “engines of innovation and engines of mobility.” The physical distance between people doesn’t matter as much as it once did for some things, like selling and shipping goods, but gathering together in cities still contributes to cooperative innovation.

Cities are engines of creativity because we create in community. We are spurred by competition. We need to find the colleagues and the friends and the competitors and the antagonists who unlock our genius and add their own.

Historically, urban growth has been central to social progress. Newcomers have been drawn to the economic opportunities in cities, and many of them have achieved upward mobility there. But recently, cities have been failing as engines of mobility because they are short on affordable housing and other necessities. Ironically, the richest of cities have the biggest homelessness problem.

Because the cost of housing has risen much faster than incomes, those who already own urban homes want to protect their investment by preserving the character of their neighborhoods. Here they are assisted—maybe over-assisted—by environmentally conscious liberals. Zoning restrictions that mandate large lots make developers build fewer but pricier homes. The authors criticize the “lawn-sign liberals” who support kindness and equality in the abstract, but use environmental laws and other regulations to block affordable housing proposals. Organized and educated liberals can impede new projects with endless litigation.

In California, the biggest obstacle to sheltering the homeless is not lack of public funding, but the complex rules and restrictions that make the money very hard to spend.

The problem we faced in the 1970s was that we were building too much and too heedlessly. The problem we face in the 2020s is that we are building too little and we are too often paralyzed by process.

Clean energy

On the more positive side, clean energy is an example of the contributions science and technology are making to a more abundant future. This view is in stark contrast to the more familiar pessimism about running out of energy. The authors imagine a world fifty years from now, when “you live in a cocoon of energy so clean it barely leaves a carbon trace and so cheap you can scarcely find it on your monthly bill.”

Klein and Thompson make a good case for a government role in technological development, especially in areas where commercial applications and business profits take time to appear. Given the American role in inventing solar energy in the 1950s, the U.S. could have become the leading solar-powered nation. Jimmy Carter was an early promoter, putting solar panels on the White House roof in the 1970s. The election of Ronald Reagan nipped our solar revolution in the bud, with leadership passing to Germany and, more recently, China. China now makes about 70 percent of the photovoltaic panels, and the scale of their manufacturing has brought costs down by about 90 percent. “After a long hiatus, solar energy has taken off again to become America’s fastest-growing electricity source, partly thanks to subsidies passed in the Inflation Reduction Act of 2022.”

Clean energy subsidies are controversial—President Trump and Congressional Republicans seem determined to end them—but they make economic sense. If dirtier forms of energy impose social costs that buyers and sellers aren’t paying for, while cleaner forms bring social benefits that buyers and sellers aren’t rewarded for, it makes sense for society to tax the first and subsidize the second.

Some of the energy abundance will come from “building out the renewable energy that we have already developed.” More will come from developing new technologies that do not exist yet or have yet to demonstrate their profitability. Government-funded R & D is especially useful there.

One reason the world will need this abundance is the huge energy demands of Artificial Intelligence systems. Here the authors welcome AI’s contribution to higher productivity, but they do not discuss the trickier questions surrounding it. Will AI resemble previous technological revolutions in eventually creating as many jobs as it destroys? Or will it aggravate the social distribution problem by dividing society into more technically sophisticated workers and masses of unemployed? The authors concentrate on a few “building blocks of the future”—housing, transportation, energy, and health. Missing from the list is the new forms of education workers will need to adapt to an even higher-tech world.

A new progressivism?

As I have been reading this book, I have also been reading Frank Bruni’s The Age of Grievance, a book about our increasing political polarization. Bruni explains the ugliness of our politics partly by drawing on an argument from political economist Benjamin Friedman in his 2005 book, The Moral Consequences of Economic Growth:

[Friedman] cast his gaze backward at a few centuries of American and European history to argue that economic stagnation and pessimism are welcome mats for repression, for authoritarianism, for all manner of closed thinking and ungenerous impulses. We’re mean when we’re lean. And when we’re fat and happy? Friedman observed that robustly growing, optimistic societies are more likely to expand rights to more people and show a stronger commitment to democracy.

In Abundance, Klein and Thompson make a similar point when they talk about the need for a “possibility of progress.” Obviously, progressive politics requires that possibility. But so does politics in general, if politics is to be more than “a mere smash-and-grab war over scarce goods, where one man’s win implies another man’s loss.” People who think that America’s best days are behind us, and that today’s problems are insurmountable, are inclined to hold onto what they have and disparage others whom they suspect of trying to take what is theirs.

Historically, each of our major political parties have had its own conception of progress. Republicans have celebrated wealth creation through free markets, unencumbered by Big Government. Democrats have called for distributing the benefits of capitalism and democracy to more people by progressive taxation, equal rights and liberal social programs. During the post-World War II period of rapid economic growth, the two conceptions of progress could coexist and compromise without as much of the political polarization and rancor we experience today.

Since the Reagan Revolution of 1980, the economy has not delivered the kind of broad-based economic gains associated with the postwar era. Economic growth has been slower, and the gains have gone more to the very wealthy. Many voters have become disenchanted with both parties, but for somewhat different reasons.

For Republicans, dramatic reductions in tax rates for taxpayers in the highest brackets have not generated as much economic growth and broad-based prosperity as promised. That has put the G.O.P. in the position of continuing to support generosity toward the rich, while demanding more austerity for the rest of us. Under the influence of MAGA, the party especially embraces the politics of scarcity, warning that progress for immigrants or historically disadvantaged groups may only come at the expense of the white working class.

Klein and Thompson believe that the right’s new “politics of scarcity…has left room for liberals to embrace what Republicans have abandoned: a politics of abundance.” Assuming, of course, that liberals do not succumb to their own politics of scarcity, believing that economic growth is incompatible with environmental protection, or that helping the have-nots depends entirely on taking from the haves. What the authors want is a new kind of liberalism, a “liberalism that builds.”

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Posted by Ed Steffes

Why Trump Would Like to Be King

June 13, 2025

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Organizers of the “No Kings” protests are planning hundreds of rallies this weekend to remind the country that the President is not a monarch. Why does Donald Trump give so many people the impression that he is trying to be one? Why would he want to be one?

The short answer is that Trump wants to accomplish through undemocratic means what he cannot accomplish through democratic means. One of his favorite gambits is to declare a national emergency about something, and then claim dubious emergency powers to impose his policies. In the first six months of his second term, he has already done this several times.

The power to tariff

Consider his trade policy. Although the United States has been running a trade deficit with the rest of the world for the past fifty years, President Trump has now declared this a national emergency. He claims that his emergency economic powers allow him to impose tariffs on foreign goods unilaterally, although the Constitution clearly assigns that power to Congress.

He has tried to sell his tariffs to the public by claiming that foreigners will pay them. As more people come to realize that American importers and consumers will pay them, support for tariffs has declined sharply. Some supporters remain, such as domestic steel companies hoping to benefit by higher prices on foreign steel, but they are outnumbered by the companies and consumers relying on foreign goods. If a democratic vote were held today, Trump’s sweeping tariff proposal would lose.

The power to defund

Another part of Trump’s undemocratic agenda is his war on the administrative state, the federal government agencies that carry out mandates given them by Congress. Here he has used the “emergency” of the longstanding budget deficit as an excuse for drastic cuts to the federal workforce, especially in agencies he dislikes, like the Agency for International Development, the Environmental Protection Agency, and the Consumer Financial Protection Bureau.

The Constitution gives Congress the power to spend, and the Congressional Budget and Impoundment Control Act bars the executive from refusing to spend what Congress has allocated without its permission. Russell Vought, Trump’s budget director and major contributor to the Project 2025 blueprint for radically conservative government, has encouraged Trump to violate this law in the hope that the Supreme Court would then declare it unconstitutional.

Americans are always interested in reducing “waste, fraud, and abuse,” but they are not very keen on closing Social Security offices, reducing veterans’ services, defunding cancer research, firing weather forecasters, or weakening consumer protections. Ironically, now that Trump must work with Congress to pass a budget, he supports the “One Big Beautiful Bill” that the Congressional Budget Office and most economists expect to increase the deficit. That raises suspicion that the assault on the federal bureaucracy was never about deficit reduction in the first place, even if Elon Musk wanted it to be.

The power to deport

President Trump’s favorite “emergency” justifying extraordinary powers is immigration. Here he has had more popular support, especially for preventing illegal border crossings and deporting immigrants who have committed other crimes.

He could have pursued these goals legally, in cooperation with Congress. Instead, he told lawmakers during the campaign to kill the bipartisan bill that would have tightened border security and provided some path to citizenship for migrants who have been here many years. Meanwhile he riled up his base with a disinformation campaign associating immigrants in general with violent crime, a correlation that the evidence does not support. Having promised to prioritize criminal deportations, his administration then proceeded to order Immigration and Customs Enforcement (ICE) to deport large numbers of immigrants with no criminal records as quickly as possible. Finding and prosecuting criminals just doesn’t meet the quotas, and giving people their day in court takes too long to please Trump.

Many Americans who support normal criminal law enforcement are distressed to see armed and masked ICE agents rounding up people they consider harmless, like restaurant workers, strawberry pickers, students on the way to school, or job seekers in Home Depot parking lots, let alone mothers dropping off their children at day care centers. Most Americans do not support forcing the immigrant parents of U.S.-born children to choose between leaving their children behind or depriving their children of their rights as citizens. Trump’s solution was to try to end birthright citizenship by executive order, in blatant defiance of the Constitution.

The power to intimidate

When people protested these policies, Trump declared the protests themselves an emergency justifying the militarization of law enforcement. He deployed the National Guard and other military forces without the consent of local authorities, telling a federal court that he was putting down a rebellion against the United States. (Homeland Security Secretary Kristi Noem suggested a more sinister motive—“liberating” the people of California from their “Marxist” leadership, as if the administration had a right to overturn California elections too.) Trump promised that further protests would be met by overwhelming force, a stunningly hypocritical position for a president who failed to mobilize the military to defend the Capitol on January 6, and then pardoned the rioters convicted of assaulting and injuring police officers.

If Trump is going to mobilize the military for every small and mostly peaceful demonstration—misleading portrayals by right-wing media to the contrary notwithstanding—his kingdom will soon look a lot like a police state. We don’t need too much imagination to see him responding to the social unrest he helps create by declaring martial law and arresting opposition leaders, Putin style. He has already said that arresting Governor Gavin Newsom is “a great idea.”

Federal courts have ruled that many of Trump’s actions—imposing tariffs unilaterally, impounding federal funds, deporting migrants without hearings, and deploying the National Guard to put down an imaginary rebellion—exceed his authority. That covers a lot of his agenda, making Richard Nixon’s domestic lawlessness pale by comparison. Many of these rulings are on hold as the administration pursues its appeals. Nevertheless, the protestors—by which I mean the peaceful majority—are often the ones with the law on their side, while the administration is the larger threat to law and order. President Trump said the other day that he is certainly not a king, since he is having so much trouble getting what he wants. Let’s hope we keep it that way.

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Posted by Ed Steffes

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