Convenience or Exploitation? The Cloud Empire Has Made Everyone as Business Data

Krystal Long

Modern life is inseparable from the Internet. The social media we use to communicate and the service software that aids our daily life have brought us unprecedented convenience. In addition to convenience, a big reason for the public to choose these software is that most software is free. However, while enjoying the convenience and free of charge, users also pay for their use with their personal information. Such invisible exchange reveals that colonialism in the behavior of Internet companies has hidden under the high technology, which has a lot common with the economic logic of historical colonialism. Nick Couldry and Ulises A. Mejias (2019) have present the concept of data colonialism with the definition of “the extractivist processes through which life gets newly appropriated by capitalism”. These organizations that provide software and platforms are often huge multinational Internet companies, like Facebook, Amazon, etc. Nick and Ulises (2019) defined them as the Cloud Empire to describe “the overall organization of resources and imagination that emerges from the practices of data colonialism”. While historical imperialists achieved colonialism by seizing raw materials and slaves as labor from colonies, today, data colonialism turns raw materials into extracted information of social life, and labor extends to users, gigs that depend on the platform, company employees, etc. It is still essentially the exploitation of colonized objects. The transformation of these “raw materials” into wealth or power is done through the social quantification sector that Nick and Ulises (2019) defined as “the consortium of private and public players who engage in data colonialism to achieve their financial and political goals”. Through technology, the Cloud Empire has expanded the objects it colonizes. The Cloud Empire has unconsciously exploited even the users. It is reasonable to argue that the colonial nature of Cloud Empire is squeezing the business value of everyone through technology. To demonstrate the argument, I will use Meituan Waimai as a case to discuss how the Cloud Empire exploits in daily life.

Meituan Waimai is a food delivery app that has monopolized the food delivery industry in China with its biggest competitor, Eleme. In addition to Meituan Waimai, Meituan has other e-services platforms such as Meituan and Dianping. Their services cover more than 200 categories: catering, takeaway, fresh food retail, taxi, bike-sharing, hotel and travel, movie, leisure, and entertainment. Today, Meituan’s business revolves around the “Food+ Platform” strategy and is centered on “eating” as its core. As a sound performance in business, Meituan was officially listed on the Main Board of the Stock Exchange of Hong Kong Limited in 2018. From Meituan’s annual report in 2020, Meituan (2021) claimed that due to COVID-19, “in 2020, gross transaction value of our food delivery business increased by 24.5% year over year to RMB488.9 billion” and calculated that number of food delivery transactions in 2020 had exceeded over 10 billion. (see Figure.1) Behind the vast number of transactions, the data colonialism shown among platform, restaurants, users and riders in Meituan Waimai is inseparable.

(Figure.1 Meituan annual report in 2020)

Meituan Waimai has reshaped the relationship between restaurants and consumers in the restaurant industry and profited from both restaurants and consumers. To explain how the Cloud Empire practices data colonialism, Nick and Ulises (2019) concluded that “one reason the Cloud Empire is so successful at appropriating all of human life is because its biggest corporations can dominate our data relations both as sellers and buyers”. Looking back at the development of China’s food delivery industry, it can be seen that the rise of Meituan Waimai and Eleme has transformed China’s catering industry structurally. The traditional restaurant industry consists of restaurants and consumers. In contrast, with the development of the food delivery industry, the food delivery platform has become a significant part of the restaurant industry. Because of the monopoly, Meituan Waimai and Eleme, in terms of platform, infrastructure, and price, control the rules of the entire food delivery industry. For restaurants, the convenience of food delivery has changed most people’s dining habits. Not joining the food delivery platform means that restaurants will lose promotional opportunities and a large number of orders. These restaurants that join Meituan Waimai always need to pay commissions to the platform. Commissions even went up during the COVID-19. It’s not hard to imagine that Meituan Waimai takes advantage of the slump in the offline restaurants under the blockade to improve its profitability. Good reviews and sales volume are critical evaluation criteria for restaurants on Meituan Waimai. Generally, restaurants with high positive reviews and sales volume are shown in the restaurant list first.

However, most small and medium-sized restaurants are not as well known as chain restaurants, especially new restaurants. Comparing these small and medium-sized restaurants with chain restaurants in the same restaurant list means that small and medium-sized restaurants have less chance of showing up at the top. To gain an advantage in the rankings, small and medium-sized restaurants need to pay Meituan Waimai more for advertising. For users, their information of personal address, phone number, bank card, dining habits and other data will be collected. Nick and Ulises (2019) have argued that “there is a lot of unpaid work performed by humans on social quantification platforms” and “social quantification sector use unpaid labor to exploit users”. Although it is just a meal for users, various information about the users is collected, aiming to enhance the personalized recommendation algorithm to make users more stick to the platform. Besides, the vast number of users is also a strong tool to attract restaurants joining Meituan Waimai. Restaurants need to follow Meituan Waimai’s evaluation system to get more orders and exposure. Users need to reveal their personal information to enjoy convenient food delivery services. Meituan Waimai breaks the previous offline-based service of the restaurant industry and builds the rules of online ordering service, which both restaurants and users need to pay for it.

In fact, as the infrastructure that supports the Meituan Waimai delivery system, riders are the most exploited labor, which reveals Meituan Waimai’s data colonialism. While market capitalization and revenue have increased, Meituan Waimai’s exploitation of riders has also suffered a public opinion crisis with the increasing number of traffic accidents involving delivery riders. Although Meituan Waimai claims that it has created millions of jobs for society, Meituan Waimai distinctly does not adequately protect these riders. To reduce labor costs, the Cloud Empire tends to increase the underpaid labor and develop a gig economy. In the definition of the gig economy, Nick and Ulises (2019) have concluded that “known as the sharing, peer, or on-demand economy, the gig economy continues the trend of replacing salaried full-time work with contingent part-time ‘gigs'”. Riders in Meituan Waimai are the embodiment of the gig economy. Although riders are registered and certified on Meituan Waimai’s official website and paid by Meituan Waimai, Meituan Waimai claims that Meituan Waimai does not sign direct labor contracts with the riders but instead signs service contracts with the third-party labor companies, which manage the riders. Therefore, Meituan Waimai is not directly responsible for the riders. However, this third-party labor company has never appeared in public. There is more and more news about riders not being able to get compensation for work-related injuries after a traffic accident on their way to deliver food.

Behind the increasing number of traffic accidents, Meituan Waimai’s algorithm cannot be ignored as a tool to surveil riders. In the context of the Cloud Empire, surveillance has become more datafication. Nick and Ulises (2019) consider that datafication has transformed “how workers are managed, which entails an increasing degree of monitoring”. Algorithms are a new trend in surveillance. Meituan Waimai’s algorithm is mainly used to assign orders to riders, plan delivery routes, evaluate riders with information such as timeout rates and user ratings, etc. But as the amount of data that can be analyzed grows, the algorithm becomes more and more demanding in its calculation of delivery time. In the news of revealing traffic accidents in riders, Ryan McMorrow and Nian Liu (2020) has reported that “the fast and erratic driving of the companies’ couriers has long been a source of criticism” and give the practical example that “in Shanghai alone, there were 325 delivery-related accidents in the first half of 2019, with two-thirds involving Meituan or Eleme riders”. Meituan Waimai always highlights speed as their competitive weapon in the competition with other food delivery platforms. Based on an exposé of Meituan riders, Ryan and Nian (2020) have reported that “the company’s time limit for completing a 3km delivery had ticked down from one hour in 2016 to 45 minutes in 2017, and 38 minutes in 2018”. To pursue the fastest delivery speed, the algorithm sometimes even instructs riders to violate traffic rules, such as going against traffic and crossing dangerous roads. The overtime rate is an evaluation system that many riders care about. Once the time is exceeded, the rider will be fined by Meituan Waimai from the average $1 per order salary. In order to deliver food within the time limit, riders always ride over the speed limit. Such results illustrate the rigidity of the algorithms. Speed is what the programmers care about while training the algorithms. However, as the riders run faster to meet the time limit, the riders’ data faster trains the algorithms to tighten time limits. Just as Lai Youoxuan (2020) argued that “the forces pushing riders ever faster include their own data”. As a result, the exploited labor joins in the process that is designed to exploit themselves.

However, human is not just data. Nick Seaver (2017) has presented the concept of “algorithms as culture” and explained that “algorithms are not singular technical objects that enter into many different cultural interactions, but are rather unstable objects, culturally enacted by the practices people use to engage with them”. Algorithms are not only formed through rational processes but also a consideration of the psychological states of riders. When Meituan Waimai trains the algorithms, they should consider more elements beyond the speed.

The reduction in labor costs has benefited more to the company. However, with the increase of negative news, the company is thus caught in public opinion and government pressure. State Administration for Market Regulation in China has filed an investigation into the alleged monopolistic behavior of Meituan in 2021. Meituan Waimai’s behaviors toward the restaurants and riders have destroyed the rules of the restaurant industry, which relate to the market and employment, and caused the discontent of the masses. Even though many authors worry about the close collaboration between the Chinese government and internet companies may cause deeper colonization within the domestic crowd, the Chinese government will not turn a blind eye to any factor influencing its dominant position. Human is not just data or symbol of commercial value. Through high technology and legally structural exploitation, the Cloud Empire trades data for profit. More and more people are pursuing such business models but forget that high technology is supposed to be for a better life, not more exploitation.


Couldry, N., & Mejias, U. A. (2019). The costs of connection : how data is colonizing human life and appropriating it for capitalism . Stanford, California: Stanford University Press.

Seaver, N. (2017). Algorithms as culture: Some tactics for the ethnography of algorithmic systems. Big Data & Society, 4(2), 205395171773810–.

•Lai, Y. (2020). Delivery Workers, Trapped in the System. Renwu. Retrieved 17 May 2021, from https:// . Translated by chuang (2020), retrieved from

•Ryan McMorrow, & Nian Liu. (2020). China’s Meituan and tackle backlash against demands on couriers.

•Meituan. (2021). ANNUAL REPORT 2020 (pp. 8-11). Retrieved from http://media-

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