Seizing the moment for artificial intelligence: my take on the US-China rivalry

Today I am republishing a piece originally written for Gavekal Dragonomics clients a few months ago, on the US-China rivalry in artificial intelligence. Usually these pieces stay behind the paywall, but with our partner company Evergreen Gavekal we’re making it available for a general audience.

I wrote this piece as a way of sorting out of my own thinking on how to place recent technological trends in the broader story of China’s economic development. I make no claim to be an expert on artificial intelligence: these are just the thoughts of a China watcher trying to absorb what the technologists are saying.

The full text follows below, or you can download the PDF:


Seizing The Moment For Artificial Intelligence

by Andrew Batson (originally published May 3, 2018)

In the escalating trade dispute between the US and China, technology has increasingly become the key issue, overwhelming more traditional economic topics like tariffs, deficits and currency valuations. Both countries see their economic future as depending on their success in high technology, and each is worried they will lose out to the other. One of the most intense areas of focus is artificial intelligence, where recent rapid breakthroughs have captivated investors and the media—and where China has emerged as the main US rival.

In this piece, I will try to provide some nontechnical answers to the questions of the moment: What is artificial intelligence anyway, and why is it a hot topic? Why does China seem to be doing so well in artificial intelligence? And how should we think about the rivalry between the US and China to develop this technology? In brief, I think China will do well in artificial intelligence, in part because the technology is now in a phase that plays to its strengths. But it does not make sense to think of the US and China being engaged in an artificial-intelligence “race” along the lines of the US-Soviet space race.

Machines don’t think, but can do useful stuff

A common-sense definition of artificial intelligence, what a layman might understand the term to mean on the basis of reading the news and watching television, is probably something along the lines of: machines that can think like human beings. Artificial intelligence in this sense does not exist, and according to most researchers in the field there is no prospect of it coming into existence any time soon. There are enormous philosophical and technical challenges in understanding how human minds work and replicating those processes in software, and most of these challenges have not been solved.

A more precise definition of artificial intelligence, closer to that used in the industry, would be: the development of computer systems that can perform tasks associated with human intelligence, such as understanding speech, playing games, or driving cars. In a way the term is misleading, because what is being mimicked is not human intelligence itself, but the practical results of intelligence being applied in specific contexts. Artificial intelligence in this narrow sense does very much exist, and its progress is now attracting feverish interest from business, venture capitalists, governments and the media.

The turning point seems to have come around 2014-15. Since then, software programs have been able to match or exceed human performance in tasks that previously could not be reliably performed by machines: recognizing faces, transcribing spoken words, playing complex games. One landmark that had particular resonance in China was the 2016 victory of Google’s AlphaGo software over a master South Korean player of the board game Go (known as weiqi in Chinese); AlphaGo subsequently also defeated China’s top-ranked player. While chess programs have been beating human masters for years, Go is much more complex; the number of potential board positions is traditionally estimated at 10172, more than the number of atoms in the universe.

How are these feats possible? Most of what is referred to as “artificial intelligence” in the media is a subset of the field known as machine learning, and in particular a subset of machine learning called deep learning. All software works by following clearly specified instructions, known as algorithms, on how to perform a specific task. In machine learning, the algorithms are not fixed in advance, but evolve over time by building data-driven models.

Usually the type of software used in machine learning is called a neural network, because its structure is loosely inspired by the connections between neurons in a human brain. The network takes an input signal and repeatedly processes it into something more useful; what makes the learning “deep” is that there are a large number of “layers” that process the signal. The use of these techniques means that an algorithm can improve its performance of a task by repeated exposure to data. They are particularly useful where writing algorithms the traditional way—by specifying all possible details and eventualities in advance—is cumbersome or impossible. The concept of machine learning dates to the 1960s, and much of the original work underlying today’s approach of deep learning dates to the 1980s and 1990s.

More power, more data

The rapid improvement in the results of specific machine-learning applications in recent years is thus not a result of fresh theoretical breakthroughs. Rather, it has happened because advances in computing power have allowed machine-learning algorithms to run much faster, and the increased availability of very large amounts of structured data have given them much more to work with. The lesson has been that lots of processing of lots of data is required for the algorithms to be effective in finding patterns. This in itself is a sign that machine learning is not very much like human learning: humans can learn quickly from small numbers of examples, by building internal mental models. Machine learning by contrast is a massive and repetitive number-crunching exercise of building up statistical regularities.

Researchers in the field sometimes describe machine-learning algorithms as being “narrow” and “brittle.” Narrow means that an algorithm trained to solve one problem in one dataset does not develop general competencies that allow it to solve another problem in another dataset; an algorithm has to be trained separately for each problem. The Go-playing algorithm is not also capable of analyzing MRI scans. Brittle means that the algorithm only knows its dataset, and can break down if confronted with real-world situations not well represented in the data it learned from. An often-used example is facial-recognition software that is trained on databases consisting largely of photos of white men, which then fails to accurately recognize faces of black women.

But while it is important to understand that machine learning is not a magic wand, it would also not do to underestimate its potential. Machine learning is essentially a way of building better algorithms. That means it could be applied to almost any process that already uses software—which, in today’s world, is quite a lot—as well as many new processes that could not be effectively automated before. The most obvious example is self-driving cars, which can already operate in restricted contexts and could be in general use within a decade. Machine learning is already being used to spot patterns that previously required trained human expertise, such as recognizing financial fraud or early-stage cancers. Because of this broad applicability, enthusiasts call machine learning a “general purpose technology” that, like electricity a century ago, can boost productivity across every part of the economy.

Throwing resources at the problem

The key point is that machine learning has now moved from a pure research phase into a practical development phase. According to Oren Etzioni of the Allen Institute for Artificial Intelligence, all of the major recent successes in machine learning have followed the same template: apply machine-learning algorithms to a large set of carefully categorized data to solve problems in which there is a clear definition of success and failure.

All parts of this procedure are quite resource-intensive. Huge amounts of computing power are required to run the algorithms. The algorithms need huge quantities of data to find patterns. That data must also be first carefully structured and labeled so that the algorithms can draw the right conclusions—for instance, labeling pictures of objects to train an image-recognition algorithm—a process that is extremely labor-intensive. The repeated training and refining of the algorithms also requires a lot of labor by highly skilled workers, whose numbers are necessarily limited. But the reason for the excitement over artificial intelligence is that there is a now a sense that the main remaining constraints on progress are these limitations of resources—and such limitations will be solved over time.

According to its many boosters, China has all of the necessary resources to make progress in machine learning. It has large and well-funded technology companies, including publicly traded giants like Tencent, Alibaba and Baidu, but also private companies with multi-billion-dollar valuations like ByteDance, which runs a popular news app with personalized recommendations, and SenseTime, which specializes in image and facial recognition.


China also has the world’s largest population of internet and mobile phone users, who are creating huge amounts of data on daily basis through their interactions with software. It has a huge population of relatively low-cost college graduates, for doing the more repetitive work of categorizing data. And it also has more top artificial-intelligence researchers than any country other than the US; indeed, many of the top Chinese in the field were educated in the US and have worked for US companies.

China also has a government that has decided that artificial intelligence is going to be the key technology of the future, and that will not accept being left behind. An ambitious national plan released in July 2017 calls for China to lead the world in artificial intelligence theory, technology and applications by 2030 (for detailed analysis of the plan, see these reports by the Paulson Institute and Oxford University’s Future of Humanity Institute). While it is difficult for government plans to create fundamental research breakthroughs on demand, such plans can be good at mobilizing lots of resources. So to the extent that advances in machine learning are now about mobilizing resources, it is reasonable to think China will indeed be able to make lots of progress.


The prospect of China being something close to a peer of the US in a major new technology is a shocking development for many Americans. Everyone knows that China’s economy has grown rapidly and that it has accomplished a lot. But most of its past successes in technology involve deploying things developed elsewhere, such as mobile phones, wind turbines or high-speed trains, on a large scale. China has a per-capita GDP of roughly US$8-9,000 at market exchange rates, lower than Mexico or Turkey—and no one is talking about their dominance in the technologies of the future. It is tempting to try to resolve this paradox by focusing on China’s state support for artificial intelligence, implying that its advantages are unfair. The rivalry is also not a purely economic one, since there are military uses for machine learning.

The paradox is more apparent than real. China is such a huge, diverse and unequal country that averages are not a good guide to the location of the cutting edge. The reality, as anyone who has visited Beijing, Shanghai, or Shenzhen in recent years can attest, is that the income, skills and education levels of its best people can be comparable to those in the US. That elite of course is not representative of all the hundreds of millions of their compatriots, but neither is the Silicon Valley elite representative of middle-class Americans.

The fact that China now has the capability to contribute to cutting-edge research is also in large part a result of its integration with the US: it is the decades of sending top Chinese students to top US universities that have built up the necessary human capital. Rather than say there is a competition between the artificial intelligence sectors in the US and China, it might be more accurate to say that there is a single, global field of machine-learning research that has a significant presence in both North America (Canada also has some top people) and China.

There is no AI race

More fundamentally, it is wrong to think of China and the US as being in a “race” for supremacy in artificial intelligence. Evoking the “space race” with the Soviet Union in the 1960s is the wrong analogy. The space race was about achieving clear technical landmarks defined in advance: first satellite in orbit, first human in orbit, first human on the moon, etc. Today, it’s not clear what the technical landmarks for an artificial intelligence race might be. There is a vague goal of “general purpose artificial intelligence,” which means the kind of thinking, talking computers that are familiar from decades of portrayal in science fiction. But there is no race to make one, since no one knows how.

Rather, there are multiple related efforts going on to make progress on diverse sets of specific technical challenges and applications. If China is the first to achieve some technical breakthrough, that does not prevent the US from also doing so, nor does it guarantee that a Chinese company will control the market for applying that breakthrough. Recall that machine-learning applications can be “narrow” and “brittle”: software that is excellent at predicting, say, the video-watching habits of Chinese will not necessarily also dominate the American market. What we can say is that there are economies of scale and scope in machine-learning research: teams of experts who have successfully developed one machine-learning application themselves learn things that will make them better at developing other machine-learning applications (see this recent paper by Avi Goldfarb and Daniel Trefler for more).

Artificial intelligence is not a prize to be won, or even a single technology. Machine learning is a technique for solving problems and making software. At the moment, it is far from clear what the most commercially important use of machine learning will be. In a way, it is a solution in search of problems. China is making a big push in this area not because it knows what artificial intelligence will be able to do and wants to get there first, but because it does not know, and wants to make sure it does not lose out on the potential benefits. China’s development plan for artificial intelligence is mostly a laundry list of buzzwords and hoped-for technical breakthroughs.

The fact that machine learning is now in a resource-intensive phase does play to China’s strengths. There is an enormous amount of venture-capital money and government largesse flowing toward anything labeled “artificial intelligence” in China, and Chinese companies have had some notable successes in attracting high-profile figures in the field to join them. But fears that China will somehow monopolize the resources needed to make progress in machine learning are fanciful. After all, most of the key resources are human beings, who have minds of their own. And many of the key tools and concepts for creating machine-learning applications are in the public domain.

Will today’s advantages endure?

It is also not certain that the current resource-intensive phase of machine learning will last forever. As is usually the case when limited resources constrain development, people are trying to find ways to use fewer resources: in this case, refining machine learning so that it does not require so much human effort in categorizing data and fine-tuning algorithms. Some of the current buzzwords in the field are “unsupervised learning,” where the machine-learning algorithm is trained on raw data that is not classified or labeled, and “transfer learning,” where an algorithm that has already been trained on one dataset is repurposed onto another dataset, which requires much less data the second time around. Progress in these areas could lessen the advantages of China’s “big push” approach, though of course Chinese researchers would also benefit from them.

China’s current strength in machine learning is the result of a convergence between its own capabilities and the needs of the technology; since both are evolving, this convergence may not be a permanent one. But China’s government is correct to see the current moment as a great opportunity. China was already becoming one of the global clusters of machine-learning research even before the government decided to throw lots of subsidies at the technology. The self-reinforcing dynamics of clusters mean that today’s successes will make it easier for China to attract more machine-learning experts and companies in the future.

The biggest loser from this trend, however, is not the US, which already has well-established clusters of machine-learning research, but smaller nations who would also like to become home to such clusters. European countries, for instance, seem to be struggling to hold their own. The perception that there is a rivalry or race between the US and China ultimately derives from the fact that the two countries are rivals rather than friends. Artificial intelligence may indeed be the first example of a major cutting-edge technology whose development is led by geopolitical competitors—the US and China—rather than a group of friendly nations. The rising tensions between the US and China pose the question of whether a global artificial-intelligence field structured in this way is sustainable, or will be forced to split into national communities. The loss of those exchanges would slow progress in both countries.

Mudde & Kaltwasser on populism

I found Populism: A Very Short Introduction by Cas Mudde and Cristobal Rovira Kaltwasser to be very useful and conceptually clear, a worthy addition to Oxford’s charming Very Short Introductions series.

The real contribution of the book is that it provides a definition of populism that is both conceptually clear and empirically useful–no mean feat. Here it is:

We define populism as a thin-centered ideology that considers society to be ultimately separated into two homogeneous and antagonistic camps, “the pure people” versus “the corrupt elite,” and which argues that politics should be an expression of the general will of the people. …

Populism must be understood as a kind of mental map through which individuals analyze and comprehend political reality. It is not so much a coherent ideological tradition as a set of ideas that, in the real world, appears in combination with quite different, and sometimes contradictory, ideologies.

The points that populism is a set of ideas but not exactly an ideology, and that those ideas can mesh with both left-wing and right-wing political programs, seem to me clearly true. A lot of writing about populism or populist phenomena considers it to have some essential nature, but what I think this book is good at is showing how “thin” that essential nature is, and therefore how flexible and various populism is in practice.

The book is also good at explaining the relationship between democracy and populism, another fraught topic of late:

Populism is essentially democratic, but at odds with liberal democracy, the dominant model in the contemporary world. Populism holds that nothing should constrain “the will of the (pure) people” and fundamentally rejects the notions of pluralism and, therefore, minority rights as well as the “institutional guarantees” that should protect them. In practice, populists often invoke the principle of popular sovereignty to criticize those independent institutions seeking to protect fundamental rights that are inherent to the liberal democratic model. Among the most targeted institutions are the judiciary and the media.

In sum, I found the book to be a helpful aid in getting closer to an objective understanding of our present moment.


So far, my favorite book about the 2016 election is one that came out in 2014

Like many good books, Martin Gurri’s The Revolt of the Public and the Crisis of Authority in the New Millennium is about One Big Idea, though the implications take a while to work through. It’s basically a story of how technology and media are changing politics.

Though Gurri does not, I found it helpful to put the idea in economists’ terms: a dramatic increase in the supply of information and media has pushed down its value. For many established institutions, this has meant a decline in influence, as their once-authoritative statements must now compete for attention and truth-value with a growing horde of statements from the margins. The struggles of old-line newspapers, political parties, and governments in the new environment are thus all of a piece:

The grand hierarchies of the industrial age feel themselves to be in decline, and I’m disposed to agree. They evolved to operate on a more docile social structure – one in which far less information circulated far more slowly among far fewer persons. Today a networked public runs wild among the old institutions, and bleeds them of the power to command attention and define the intellectual and political agenda. Every expert is surrounded by a horde of amateurs eager to pounce on every mistake and mock every unsuccessful prediction or policy. Every CRU has its hacker, every Mubarak his Wael Ghonim, every Barack Obama his Tea Party. Nothing is secret and nothing is sacred, so the hierarchies some time ago lost their heroic ambitions and now they have lost their nerve. They doubt their own authority, and they have good reason to do so.

Gurri really won my heart when he brought this insight together with the work of the great anthropologist Mary Douglas, who had an uncanny knack for creating powerful and useful analytical frameworks:

Another way to characterize the collision of the two worlds is as an episode in the primordial contest between the Center and the Border. The terms were employed by Mary Douglas and Aaron Wildavsky in another context, long before the advent of the information tsunami, but they are singularly apt for our present condition. “Center” and “Border” can be applied to organizations embracing specific structures, ideals, and beliefs about the future. The two archetypes are relative to each other, and perform a kind of dance which determines the direction of social action.

The Center, Douglas and Wildavsky write, is dominated by large, hierarchical organizations. It frankly believes in sacrificing the few for the good of the whole. It is smug about its rigid procedures. It is too slow, too blind to new information. It will not believe in new dangers and will often be taken by surprise. The Center envisions the future to be a continuation of the status quo, and churns out program after program to protect this vision. The Border, in contrast, is composed of “sects” – we would say “networks” – which are voluntary associations of equals. Sects exist to oppose the Center: they stand firmly against. They have, however, “no intention of governing” and develop “no capacity for exercising power.” Rank means inequality, hierarchy means conspiracy to the Border. Rather than articulate programs as alternatives to those of the Center, sects aim to model the behaviors demanded from the “godly or good society.” Making a program is a center strategy; attacking center programs on behalf of nature, God, or the world is border strategy.

If this is starting to sound a bit like what happened in the primaries last year, that is no accident. Gurri I think provides a useful way of thinking about the realignment of politics made clear by the 2016 election in the US, and the UK’s Brexit referendum:

I was trained, as even the youngest of us were, to think in terms of the old categories: to think, for example, that the direction of American politics depended on the balance between Democrats and Republicans. Yet both parties are, in form and spirit, organizations of the Center. Both are heavily invested in the established order, offering the public minor differences in perspective on the same small set of questions. Surprises in America’s political trajectory are unlikely to come from the alternation of Democrat and Republican. The analyst searching for discontinuities – for the possibility of radical change – must wrench his mind free of the old categories and turn to the subterranean strife of hierarchy and network: in the political parties, between “netroots” activists and a variety of Tea Party networks on one side, and the Democratic and Republican organizations on the other. There, different languages are spoken, potent contradictions can be found. …

The book is consistently interesting, and although he refrains from making explicit predictions it nonetheless often feels quite prescient. Gurri’s blog extends the analysis into 2016, though that piece will make more sense after having read the book.


Are Xi and Trump really so different?

The contrast last week between Xi Jinping giving a pro-globalization speech at Davos and Donald Trump giving his “America First” inauguration speech has captured the imagination of the chattering classes. It has led to the unusual spectacle of the arch-nationalist Steve Bannon and the arch-globalist Martin Wolf actually agreeing on something. Here’s what Bannon told the Washington Post:

“I think it’d be good if people compare Xi’s speech at Davos and President Trump’s speech in his inaugural,” Bannon said. “You’ll see two different world views.”

And here’s Wolf’s latest column:

Xi Jinping, president of China, made a speech last week on globalisation at the World Economic Forum that one would have expected to come from a US president. At his inauguration, Donald Trump made remarks on trade that one would never have expected to come from a US president. The contrast is astounding.

I think there is less to this contrast than meets the eye. A lot of the intellectual class is predisposed to see an epic battle of ideas between globalization and nationalism, and so that is what they saw in the headlines from the speeches. But it has been clear for years that Xi Jinping is one of the premier nationalists of our day: his “China Dream” rhetoric is a not very distant cousin to Trump’s “Make America Great Again.”

In reality, Xi’s speech at Davos had plenty of nationalist self-interest; it was just packaged in a different wrapper. Xi praised globalization not because it is good in the abstract but because globalization has made China rich and powerful.

In the Chinese view, globalization has worked for them not because they blindly embraced Western economic theories, but because they managed the process of globalization appropriately, with a keen eye to China’s national interests:

China has become the world’s second largest economy thanks to 38 years of reform and opening-up. A right path leads to a bright future. China has come this far because the Chinese people have, under the leadership of the Communist Party of China, blazed a development path that suits China’s actual conditions.

Other countries can and should be doing the same:

We should act pro-actively and manage economic globalization as appropriate so as to release its positive impact and rebalance the process of economic globalization. We should follow the general trend, proceed from our respective national conditions and embark on the right pathway of integrating into economic globalization with the right pace.

China has therefore succeeded not because globalization gave them something, but because China has been clever and hard-working in taking advantage of its opportunities:

Such achievements in development over the past decades owe themselves to the hard work and perseverance of the Chinese people, a quality that has defined the Chinese nation for several thousand years. We Chinese know only too well that there is no such thing as a free lunch in the world. For a big country with over 1.3 billion people, development can be achieved only with the dedication and tireless efforts of its own people. We cannot expect others to deliver development to China, and no one is in a position to do so.

Implicit in this line of thinking is that countries for whom globalization has not been a success–a group that Trump and Bannon seem to think includes the US–have only themselves to blame. They didn’t do it right; China did. Trump seems to agree, as he wants to renegotiate US trade deals.

Because China has successfully managed globalization, it thinks globalization is a good thing and wants it to continue. And this is why Xi’s speech is ultimately one of nationalist self-interest. This is not an altruistic conception. In particular, China sees the next stage of its development as involving the spread of its corporations out of its home market and around the globe. So Xi wants to make sure that new barriers are not thrown up in their way:

In the coming five years, China is expected to import $8 trillion of goods, attract $600 billion of foreign investment and make $750 billion of outbound investment. Chinese tourists will make 700 million overseas visits. … we hope that other countries will also keep their door open to Chinese investors and keep the playing field level for us.

If Xi is now trying to present China, however implausibly, as a defender of a liberal global economic order, it’s because he wants something from the rest of the world.

The American left has a surprising new enthusiasm for states’ rights

Many traditional US political alignments are being scrambled by Donald Trump’s election. We may soon be able to add to that list another one: the usual partisan attitudes towards the federal and local governments.

Trump’s victory in the Electoral College, despite his receiving about 2 million fewer votes than Hillary Clinton, has highlighted once again the way the US political system is designed to give power to places where relatively few people live. A recent NY Times piece has a good rundown of the history of this system:

The Electoral College is just one example of how an increasingly urban country has inherited the political structures of a rural past. Today, states containing just 17 percent of the American population, a historic low, can theoretically elect a Senate majority, Dr. Lee said. …

When the framers of the Constitution were still debating the shape of institutions we have today, 95 percent of America was rural, as the 1790 census classified the population. The Connecticut Compromise at the time created the Senate: one chamber granting equal voice to every state to counterbalance the House, where more populous states spoke louder.

And they made sure the compromise stuck. Today, equal state representation in the Senate is the only provision in the Constitution that remains singled out for protection from the amendment process; no state can lose its full complement of senators without its permission.

But even as a deliberately undemocratic body, the Senate has slipped further out of alignment with the American population over time. The Senate hasn’t simply favored sparsely populated states; politicians in Washington created sparsely populated states to leverage the Senate’s skewed power. … Republicans in Congress passed the 1862 Homestead Act, offering free land to settlers who would move to territories that would eventually become states — creating more Senate seats and Electoral College votes for a Republican Party eager to keep government control away from Southern Democrats. They even managed to divide the Dakota Territory into two states, worth twice the political power.

Population density explains a lot about recent voting patterns: low-density places tend to vote Republican, and high-density places tend to vote Democratic. So this quirk of the US system has large electoral consequences. The US population is also becoming steadily more concentrated in large urban areas. According to Census Bureau data, the share of the US population that lives in the 20 largest metropolitan areas (of which four are in California) has risen from 37.7% in 2010 to 38.2% in 2015, a steady pace of one-tenth of a percentage point per year. If this trend continues, and I don’t see why it will not, the structural disadvantage of Democrats in the Senate and Electoral College will only increase. So it will probably become harder and harder for Democrats to achieve unified control of the federal government.

This is a challenge to both parties’ recent self-image: Republicans have been the party of states rights’ and local autonomy, campaigning against federal overreach, while Democrats have been the party of federally guaranteed civil and political rights, campaigning against entrenched local inequality and discrimination. But with the federal government firmly in Republican hands for the next few years, and possibly longer, Democrats seem to be having a change of heart about the role of local governments. Democrats are now praising local governments’ sovereignty and autonomy as a way to preserve important rights and values–in much the same way that Republicans traditionally have.

Some of the public statements by Democrats since the election seem to me quite extraordinary in the historical context of their party. Brad DeLong’s recent declaration that “The sovereign equal dignity of the states is our friend, whatever happens at the national level” is one good example. There have also been many statements from Democratic local officials about their lack of interest in cooperating with any Trump administration crackdown on illegal immigrants. Probably the most striking speech was from New York City Mayor Bill de Blasio:

And part of what’s important to remember is our own power in this moment here in New York City and in cities around the country, because in the confusion something important has gotten lost. There is not a national police force. You don’t go to federal schools to get your children an education. No. We in the City of New York, we protect our people with the NYPD. We provide education to our children with our New York City public schools. We provide healthcare with our public hospitals; and all over the country the same. Our constitution says it – that so much of what is decided in the governance of our people is decided at the local level, according to the values of the people who are governed. In the Declaration of Independence there is one of the most simple and powerful passages – it says, governments are instituted deriving their just powers from the consent of the governed. We don’t consent to hatred.

And we will fight anything we see as undermining our values. And here is my promise to you as your mayor – we will use all the tools at our disposal to stand up for our people.

If all Muslims are required to register, we will take legal action to block it.

If the federal government wants our police officers to tear immigrant families apart, we will refuse to do it.

If the federal government tries to deport law-abiding New Yorkers who have no representation, we will step in. We will work and build on the work of the City Council to provide these New Yorkers with the lawyers they need to protect them and their families.

If the Justice Department orders local police to resume stop and frisk, we will not comply.

In the 1960s the racist right was defying the federal government in order to preserve segregation; in the 2010s, the multicultural left is threatening to defy the federal government in order to preserve immigrant- and minority-friendly policies. That is a pretty amazing turn of the historical wheel–even more so when you recall that the Democratic Party has been the home of both the 1960s segregationists and the contemporary integrationists.

This shift also suggests that local sovereignty will be an enduring part of the American political system, even given the massive increase in the federal government’s power since the 1920s, as both left and right can find reasons to support it when they are out of power at the federal level.

Zhao Lingmin on the roots of Chinese elite support for Trump

A definitive overview of this question is over at Ma Tianjie’s Chublic Opinion, but one of the sources in that piece I thought was worth digging into a bit more. It’s a column by Zhao Lingmin, originally published on the FT Chinese site back in October, that focuses on what the enthusiasm for Trump says about Chinese society. My translation follows:

Compared to his American supporters, Trump’s Chinese supporters have two notable differences. One, they have “true love” for Trump. Even though some Americans do not like Trump personally, or even despise him, they have still decided to vote for Trump because of their anger at the status quo. Trump’s supporters in China are not deciding who to vote for, and there are no real interests at stake; many of them simply like Trump himself. Second, it is widely recognized that some of Trump’s supporters in the US are not of high social status and belong to the lower middle class, so Hillary Clinton could say that half of them are “deplorables,” or “people who feel that the government has let them down, the economy has let them down.”

But among Trump supporters in China, there are some successful people and members of the elite: they are well-educated, rational, with high social status. On this point, you only have to look at WeChat or Zhihu; in those places public criticism of Trump’s remarks is rare, and there are a lot of people who excuse them or give them a positive spin.

Why is the Chinese elite not like the American elite in opposing Trump? There are different national conditions, there are differences of opinion, but in my opinion the most important difference between the two countries’ elites is the different environments they have grown up in. This has led some Chinese elites to endorse Trump’s views on political correctness, terrorism, Islam, and other issues.

Trump has been most criticized for his undisguised degradation and humiliation of immigrants, Muslims, and women, which for many American elites, whose awareness of equal rights comes from their baptism in the civil rights movement, is completely unacceptable. The recent revelation of the recording in which Trump insults women touched the bottom line of American society, and made some of the rest of the elite draw the line. By contrast, some of China’s elite, having risen up in an atmosphere of social Darwinism, do not find Trump’s statements so offensive as to cause anger and condemnation—although they do not quite endorse them either.

The past 30 years of China’s economic growth and social development began after a period of chaos [i.e., the Cultural Revolution], and there was no Enlightenment-like intellectual movement. Government officials, in order to mobilize reform, exaggerated the evils of the old benefit system as “everyone eating from one big pot,” which, with the assistance of some scholars, led to an almost complete social consensus that a market economy means completely free competition. With no restraint from ethics or rules, the “law of the jungle” that the weak are prey to the strong became nearly universal in society. Amid all the worship of the strong and disdain for the weak, an atmosphere of care and equal treatment of disadvantaged groups has not formed. Therefore “political correctness,” which is for the protection of vulnerable groups, basically does not exist in Chinese society, and the language of discrimination, objectification of women, and mockery of disabled people is everywhere.

This way of thinking is further reinforced among some Chinese elites: they succeed because they are better able to adapt to and dominate this kind of environment. In this process, they are hurt by others, they hurt others, and gradually they develop a heart of stone and a feeling of superiority—that their success is due to their own efforts and natural abilities, and the losers in competition must be those who don’t work hard because they are lazy or have some other problems. Therefore, they believe in free competition and personal striving even more than ordinary people, and also feel more strongly that poor people deserve their low position, are more wary of the abuse of welfare by lazy people, and are more supportive of Trump’s attacks on political correctness.

Many Chinese elites feel that the Democratic Party and the left represented by Hillary Clinton has turned a blind eye to the many problems of the black community, such as single mothers and the high crime rate, and put the blame on society rather than black people’s own issues. In order to protect the rights of transgender people, they have gone so far as to ignore public safety and allow them to freely choose whether to use male or female toilers. In the face of this obsession with political correctness, Trump has the courage to face reality and is willing to risk offending people in order to tell the truth—this is honest and admirable.

As for Trump’s insulting remarks about women, the Chinese elite also thinks that this is not such a big deal. You could say that many male members of the Chinese elite are the biggest beneficiaries of the current imbalance between men and women in China. The deformed marriage market has made them insufferably arrogant, and in terms of objectifying and demeaning women they are much worse than ordinary people. In the case of a male journalist who raped a female intern, most of the male colleagues supported him, and maintained that the woman was taking revenge on him for refusing her. In the case of a male professor who was suspended for molesting female students, many colleagues and students argued that the punishment was excessive, and some even doubted the female students’ mental state. In fact, a not inconsiderable number of men do not think there was anything fundamentally wrong with the actions of the journalist and the professor. People who have grown up in this kind of social atmosphere naturally cannot understand why Trump has been universally condemned for some dirty talk.

In addition, the vigilance against Islamic extremism displayed in Trump’s speeches is quite similar to the worldview of many of China’s elite. Since 9/11, “Islamophobia” has become a worldwide phenomenon, and China is no exception. Chinese Islamophobia has domestic causes, but it also cannot be separated from the impact of international events, particularly the refugee crisis and frequent terrorist attacks in Europe over the last couple of years. This has made many people shake their head at the European left, and think that Muslims are just using their high birth rate to occupy Europe and destroy the foundations of European civilization. European intellectuals and elites are so burdened by multicultural policies and political correctness that they cannot reject any plea from the refugees, do not dare to point out any of the issues with refugees, and even downplay crimes committed by refugees. Such naivety and wishful thinking in the end is nothing but nourishing a snake in one’s own bosom. Because of these views, Trump’s talk about banning Muslims and attacking terrorists is more welcomed by Chinese people than Hillary Clinton’s rhetoric about inclusiveness and cooperation.

Looking at the personal style of the two candidates, American elites do not like the fact that Trump’s speech is often illogical, vulgar and extreme. But in China’s imperfect market system, many elites come from rough backgrounds. Furthermore, decades of revolutionary ideology have made the whole society valorize coarseness, slovenliness, and lack of hygiene. This makes many people see Trump’s vulgarity and inconsistency as amusing, straightforward and honest. Hillary Clinton’s image as an orthodox politician, by contrast, leaves many people cold.

A Singaporean perspective on American and Chinese nationalism

I enjoyed this talk from long-time Singaporean diplomat Bilahari Kausikan for its relatively objective view of the peculiarities of both the US and China, and how it roots the difficulties the two countries have in their respective sense of identity. I’ve pulled out some of the key passages below:

The essential source of American and Chinese nationalism is a sense of exceptionalism; the US and China both consider themselves exceptional countries. But the conclusions they draw are different.

America is an inclusive culture that wants everyone to become like it and believes that the world would be a better place if this were so. … China’s rise has been psychologically unsettling to many in the West because in China, capitalism flourishes without democracy. This is regarded as unnatural and illegitimate because it punctures the western myth of the universality of its political values and of the inevitability of the development of political forms similar to its own. Unlike the former Soviet Union, China cannot be dismissed as an economic failure and thus challenges in a very fundamental way the western sense of self which assumes its political and moral superiority as a key element. …

I think the US knows that preservation of CCP rule is the most vital of Chinese core interests and is reluctant to endorse this explicitly. The US deals with the CCP pragmatically; it has no choice. But to invest CCP rule with legitimacy requires a redefinition of American values, including a de facto abandonment of the idea of universality that is apparently too painful to bear. …

China has an exclusive culture that rejects the notion that anyone could become like China as impossibly pretentious. To China, the best others can do is humbly acknowledge China’s superiority and the sooner we do so the better for everyone.

This is a very ancient and deeply ingrained feature of China’s approach to international relations. Throughout its history, China took great pains to preserve the forms of its centrality, at least in its own mind, even when the facts were otherwise. It never lost its sense of superiority even when powerless before the West and Japan. Now that China has re-emerged as a major power, this sense of superiority has become the underlying cause of the difficulties in China’s relations with many countries. The attitude that China is entitled to have its superiority acknowledged and that failure to do so can only be due to recalcitrance or ill-intention, is why I think China will always suffer a deficit in ‘soft power’ and evoke resentment. …

One of the basic functions of diplomacy is to see the world through your competitors’ eyes in order to understand the frame of reference he is operating within, and thereafter one of the basic purposes of statecraft to use what means are available and appropriate to manoeuvre him into your preferred frame of reference or if this is not possible, to operate within the same frame in order to achieve your purposes. A stable modus vivendi can only be reached if all parties are operating within the same frame of reference. Are the US and China operating within the same frame of reference? I think they do substantially but not entirely and therefrom arises the complexity and risks of the relationship. Can they be brought within a common framework? That is not yet clear. …

If a new modus vivendi requires the US to acknowledge that different political systems can have their own legitimacy, it requires China to resist the temptations of triumphalist nationalism.

There’s a lot more, but I had to condense more than usual to keep this post from getting too long; a transcript and video of the full talk is at the link.