Special Report by The Economist: “The data economy is a work in progress. Its economics still have to be worked out; its infrastructure and its businesses need to be fully built; geopolitical arrangements must be found. But there is one final major tension: between the wealth the data economy will create and how it will be distributed. The data economy—or the “second economy”, as Brian Arthur of the Santa Fe Institute terms it—will make the world a more productive place no matter what, he predicts. But who gets what and how is less clear. “We will move from an economy where the main challenge is to produce more and more efficiently,” says Mr Arthur, “to one where distribution of the wealth produced becomes the biggest issue.”
The data economy as it exists today is already very unequal. It is dominated by a few big platforms. In the most recent quarter, Amazon, Apple, Alphabet, Microsoft and Facebook made a combined profit of $55bn, more than the next five most valuable American tech firms over the past 12 months. This corporate inequality is largely the result of network effects—economic forces that mean size begets size. A firm that can collect a lot of data, for instance, can make better use of artificial intelligence and attract more users, who in turn supply more data. Such firms can also recruit the best data scientists and have the cash to buy the best ai startups.
It is also becoming clear that, as the data economy expands, these sorts of dynamics will increasingly apply to non-tech companies and even countries. In many sectors, the race to become a dominant data platform is on. This is the mission of Compass, a startup, in residential property. It is one goal of Tesla in self-driving cars. And Apple and Google hope to repeat the trick in health care. As for countries, America and China account for 90% of the market capitalisation of the world’s 70 largest platforms (see chart), Africa and Latin America for just 1%. Economies on both continents risk “becoming mere providers of raw data…while having to pay for the digital intelligence produced,” the United Nations Conference on Trade and Development recently warned.
Yet it is the skewed distribution of income between capital and labour that may turn out to be the most pressing problem of the data economy. As it grows, more labour will migrate into the mirror worlds, just as other economic activity will. It is not only that people will do more digitally, but they will perform actual “data work”: generating the digital information needed to train and improve ai services. This can mean simply moving about online and providing feedback, as most people already do. But it will increasingly include more active tasks, such as labelling pictures, driving data-gathering vehicles and perhaps, one day, putting one’s digital twin through its paces. This is the reason why some say ai should actually be called “collective intelligence”: it takes in a lot of human input—something big tech firms hate to admit….(More)”.