The negative effects that industrial revolutions unleash on human society always stem from an overestimation and abuse of the power of new technologies. It has never been more important to heed this point than today. Big data and artificial intelligence (AI) are bringing forth a new industrial revolution, and the blind worship of these innovations is already on full display in some quarters.
In early September, Russian President Vladimir Putin said that the development of artificial intelligence technology has created "huge and unpredictable new opportunities and threats." “Whoever leads this field will rule the world,” Putin declared. In the Russian leader’s eyes, AI is not only a major strategic opportunity, but the key to his country’s survival.
China is also keen to lead the way in AI. On July 8, the State Council issued the "New Generation Artificial Intelligence Development Plan." Promoting the development of the AI sector is of course a very good idea, but the crucial question is, how best to do this. Is it, as some have suggested, through state planning? To that question, we can answer with great certainty: the planned approach will not work. Those who use the planning method can only follow in the footsteps of others, which implies lagging behind.
In the history of mankind, all technological revolutions, all moments of revolutionary innovation, have emerged from the market and from entrepreneurs acting on private initiative. They are the final result of survival-of-the-fittest competition.
In the hot debate about AI, some prominent figures have asserted that artificial intelligence could be capable of producing a new form of planned economy. One example they cite is the financial sector, an important application field for AI. Since investment decisions can be made by machines, isn’t this a form of planned economy?
The boundaries of the planned economy have therefore re-emerged as an important and fundamental question for the future.
The problems are institutional
Before answering this question, the first thing I would emphasize is that China’s biggest problems are systemic, and should not be thought of as an assortment of technical issues that can be fixed individually. Ultimately, it is not important whether China is a global leader in this or that industry. The important thing is whether the national economy as a whole is doing well.
Last year, a survey by McKinsey found that China’s labor productivity is only 15-30% of the average among the OECD countries. This means that China is still several times less productive than the developed nations. There is a general backwardness in China’s economy that dwarfs the progress in a few cutting-edge fields.
Labor productivity is low, but at the same time labor costs are very high. In 2016, unit labor costs in China were higher than in the United States and Western Europe, according to analysis by the Economist Intelligence Unit. An Oxford Economics survey conducted the same year found that China’s labor costs were only 4% lower than in the US. In other words, if we are looking purely at labor costs, it is now more expensive to make a product in China than the US. This is strange.
If labor costs are so high, does this mean that Chinese workers are earning too much money? No. China’s household income accounts for only slightly more than one-third of China’s GDP, the lowest level in the world. In most other countries, household income takes up more than half of GDP, so China’s workers are not getting much.
Labor costs are high; workers don’t get much money; enterprises bear a lot of the costs; and the enterprises themselves are not getting much either. How can all this be true? The answer is that the government is taking all that money for itself. The government’s fiscal revenue growth rate has been growing faster than GDP for more than 20 years, and it still is today. The Chinese government’s income is the highest in the world, and this causes workers and enterprises to bear huge institutional costs.
Systemic issues like these are the ones we need to solve. We can focus on fixing technical issues in the Chinese economy, such as upgrading traditional industries by integrating new, cutting-edge technologies, and these efforts are sure to produce positive results. But they cannot solve the wider economic problems we face.
What’s more, industrial upgrading has the potential to create negative as well as positive effects.
Lessons from revolutions
When people mistakenly believe that the latest scientific and technical breakthroughs surpass all the knowledge accumulated by humanity over thousands of years, they sometimes rush to replace the socio-economic systems produced in recent centuries, which often leads to disastrous results. A consensus is that the emergence of big data and artificial intelligence are leading to another industrial revolution. But when discussing where these changes will lead us, we need to look calmly at the many negative lessons from past industrial revolutions, relating to the overestimation and abuse of the power of new technologies. Industrial revolutions are not always positive in all respects and we must be wary of the possible dangers involved.
One of the worst lessons from previous industrial revolutions is the creation of state-ownership dominated central planning systems. This idea was first produced during the first two industrial revolutions. At that time, some ultra-left intellectuals mistakenly believed that human beings had acquired the ability to understand everything, and that they therefore had the ability to control all aspects of society.
The mistaken belief that the social planner is able to know what is good for everyone, can take care of everyone’s welfare, and can plan as well as implement the plan for every technological change, all production, and everything: this is the underlying idea leading to central planning. Because there had never been an industrial revolution before in human history, some intellectuals overestimated their own power, and this led them to abuse it. At its height, one-third of the world’s population lived in state-owned and centrally-planned economies.
Another common problem arising from the hubris of intellectuals is environmental destruction. There are two clear examples of this. The first is huge water conservancy projects. People think that they have the ability to plan the system of rivers, lakes and land. They build dams to make huge artificial lakes. Then, when this produces catastrophic consequences, they realize that there are a lot of things they don’t know. Even if a government with huge resources and power has good intentions, it can still create disasters.
A second example is climate change, a problem that did not exist before the industrial revolution, and the cause is the same — people mistakenly believe that they are smarter than they are. When Hurricane Irma swept through the Caribbean, with wind speeds reaching 300 kilometers (186 miles) per hour, 95% of the buildings on some islands were completely flattened. This natural disaster was manmade. The cause was people who believe that we could concentrate all resources on solving society’s problems. But in the process, so much carbon was emitted from fossil fuels that the climate has been destabilized. These old lessons are still far from being fully absorbed, but the big data and AI revolution is coming. Some scientists have issued warnings, but most focus on ethical issues like the possibility of using robots in warfare. I want to emphasize another problem, that of the system and the relationship between the market and the central plan.
Some governments and large, monopolistic companies may try to use their mastery of big data to control society, to replace the market — to use technological progress against society. It would bring disaster. We must rather make sure that this industrial revolution truly benefits mankind.
（Xu Chenggang is professor of economics at Cheung Kong Graduate School of Business. He has previously taught at the London School of Economics, the University of Hong Kong, and Harvard. He was one of the first recipients of the China Economics Prize for his contributions to advancing the understanding of government and enterprise incentive mechanisms in China’s transitioning economy.）