Transitions in the balance of powers in the international system are often motivated by technology. Technology is at the heart of current high power competition between China and the United States. But how does technology affect the balance of powers? This question is not as simple as it seems. Jeffrey Ding and The Rise of Great Powers (Princeton University Press) is upset a lot of conventional wisdom technology.
Conventional wisdom goes something like that. Countries benefit more by taking front in what are called the main sectors. They innovate, acquire the period, then, for a brief period, accumulate the advantage of first furniture or the monopoly rents of their domination in the main technologies. Thus, during the industrial revolution, Great Britain acquired a cutting-edge advantage in critical innovations in industries such as textiles. During the second industrial revolution, when Germany challenged Great Britain, it did so by acquiring an advantage in what became the main sectors, such as the chemical industry.
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At the end of the 1970s, many people thought Japan would give the United States a race for its money depending on the fact that it dominated industries such as consumer electronics and cars. And currently, followers of technological competition also look at domination in the leading sectors. Will China earn an irrevocable lead in electric cars, for example?
Ding maintains that this concentration on the leading sectors as an explanation of changes in world power sales is wrong. A great status of power is not reached by the domination of a leading sector. Rather, it is achieved by the dissemination of what he calls technologies for general purposes (GPT). It is a cliché to say that not all technologies are created. But the specific distinction that stimulates the ding argument is between technologies for general purposes and the main technologies in the sector. The first technologies are, like its name, as its name, innovations that modify economic activity by stimulating productivity gains in a large number of sectors.
Thus, in the first industrial revolution, the advantage of Great Britain did not come from textiles; It came from its ability to produce and diffuse iron -based machines in a large number of sectors. A contemporary example could be that AI is a technology that can potentially be adopted in a large number of sectors, leading to productivity gains through the economy. An advantage in electric cars, for example, could have overflow effects, but does not transform a large number of sectors.
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Using this distinction as a basis, Ding increases conventional economic history and geopolitics. Great Britain succeeded in the first industrial revolution not due to a leading sector, but due to the general dissemination of engineering and machining skills. In the second revolution, Germany was ahead of most of the leading sectors. But the United States has stolen thunder by being able to disseminate engineering skills and institute standards that allowed general technologies such as electricity to adopt much faster. In the third industrial revolution, Japan apparently had the advantage in certain leading sectors such as consumer electronics. But its ability to disseminate GPTs as computerization was limited. The United States, on the other hand, was not based on domination in a few sectors, but a greater diffusion of technology that could transform a certain number of sectors simultaneously.
Ding’s discussion on the distinction between these technologies is sophisticated and leads to testable predictions. But the central message is clear: if you want to be a leader in technology, you must focus less on domination in specific sectors and more on wide policies that allow the dissemination of GPTs. The institutions oriented towards the creation of innovations or turns in the leading sectors are often very different in kind of institutions which allow the dissemination of GPTs on a whole range of applications. The short version of Ding’s message is: broadcast is fate.
But the most important point of Ding is that the error that economic historians make by focusing on the main technologies of the sector are often reflected by the leaders and designers of industrial policy. These often seek to create a comparative advantage in a few sectors. And often, the gains in these sectors only last as long as you can create monopoly benefits of these sectors, in part by denying the rise of others in these sectors. The leading sectors can bring earnings to the economy in terms of exports or employment. But they are not a reliable base to build national power.
This argument has deep implications, for development and geopolitics. For development and for countries like India, the lesson is as follows: even if we think of industrial policies for particular sectors, the basis of national power will have to be the dissemination of GPTs which improve productivity through the economy and produce a range of improvements accumulated between the complementary sectors. One of the reasons why politicians hate to think about the dissemination of GPTs is partly because it does not make the headlines. But, in part, it is due to the fact that the diffusion of GPTs requires a more systemic change. This does not require the skills of human capital for specific primary sectors, but a general improvement in human capital, institutional adaptability and facilitation of the interoperability of technologies. It requires consistent, widespread and fundamental investments rather than the creation of technologies in mission mode.
For geopolitics, the implications of the ding argument are revealing. He concedes that in terms of analysis of the leading sectors, China has done well impressively, in the production of electric cars, for example. But he thinks that the United States, unless Trump spies the whole system, can still have the advantage in the adoption of GPT. The problem will not be that “invents” technologies like Deepseek. It will be – which is able to broadcast these technologies widely.
This argument should be evaluated at two levels. The first is at the framework: is the ability to disseminate technology a better basis for power than the main sectoral innovations? The second is at the level of the application of the argument. Is the United States’s ability to disseminate GPTs even better than that of China, even if China could have the advantage in certain leading technologies?
Ding thinks that the United States always has the advantage, and it tries to provide evidence for this, including in the dissemination of relevant engineering skills. But the lesson for India is clear. As Ding says, the spotlights should not be on the innovation clusters. He must focus on “small businesses, small towns, engineers who govern and the channels that connect the technological border to the rest of the economy”. We need a widespread dissemination of human capital and a deep institutional reform.
The writer contributes the publisher, The Indian Express