This is also a commissioned book review, but because I took an explicitly critical stance, the piece apparently was not used (if it does get used, I will revise and repost it here). I am criticizing Alibaba, but not only Alibaba. In fact, many so-called internet companies that have achieved success do not necessarily really represent new thinking or new paradigms; they are nothing more than old capital and industrial logic dressed up in internet clothes. Some other so-called “successes” are nothing more than using the power of capital to carve up territory and seize the initiative, using rapid expansion driven by capital investment to squeeze out competitors prematurely and thus achieve monopolistic success, without necessarily proving that the thinking involved is new. I am not opposed to the role of capital, nor am I opposed to old wine in new bottles, but if we summarize the victories of these old models as features of a new way of thinking and a new era, that can easily mislead people.
Cloud China: A Turbulent Digital Future, CITIC Publishing Group, 2021.
This book is a hymn of self-congratulation for Alibaba. It lists countless success stories to show how various enterprises and institutions achieved remarkable results after reaching strategic cooperation agreements with Alibaba. Its central idea is nothing more than that so-called “digital-intelligent transformation” (essentially a kind of dataism) is the general trend, and Alibaba is the pioneer of the new era.
But I was not stunned by these dazzling success stories. For a behemoth like Alibaba, it is very easy to produce some success stories, but at the same time there are two key points that may not be written down. First, these success stories may not have succeeded thanks to new ideas or new methods; many of them probably owed a great deal to the resource tilt and channel platforms Alibaba provided. On platforms already holding considerable monopolistic positions—1688, Taobao, Tmall, Alipay, and so on—controlled by Alibaba, if you give partners even a slight tilt, they will naturally be able to reap the benefits; nothing unusual about that. Second, in a work of this self-congratulatory sort, of course only the good news is reported and never the bad; the success stories are written up in glowing detail, but the situation of the failures is not known.
In fact, Alibaba’s investments and partnerships in recent years have clearly produced a large number of failures, and these failures are probably even more worth mentioning. It is only natural for enterprises backed by Alibaba to achieve success, but what is going on with those enterprises that, despite being backed with all Alibaba’s might, nevertheless grow worse and worse? Only by figuring this out can we truly understand what effect Alibaba’s so-called methods and ideas actually had.
The success stories listed in this book are mostly concentrated in agriculture, industry, government administration, and other traditional sectors, but in emerging industries and the tertiary sector, Alibaba’s operations in recent years have amounted to outright failure across the board. After acquiring Yahoo China, Yahoo China was gone; after acquiring Koubei, Koubei collapsed; after acquiring TTPod, TTPod disappeared; after acquiring Youku Tudou, Youku Tudou withered; after acquiring Wandoujia, Wandoujia also went silent; its investment in Hello TransTech still cannot beat Meituan and Didi; it grandly proposed the concept of “Big Entertainment,” only to accomplish nothing; and its attempts to enter the social networking field met repeated setbacks, while Tencent, by leveraging WeChat Pay, launched a counteroffensive against Alibaba’s core business and has already achieved considerable results. In retail, it is now also being carved up by Pinduoduo.
These enterprises had originally been the best in their industries, but after Alibaba’s management team and “advanced ideas” were introduced, and even with the behemoth platform’s vigorous support, they instead went from bad to worse. Why is that? I think we can actually look for the reason in this book. Let us take a look at what Alibaba so eagerly praises and so confidently takes to be its core idea.
In brief, I call Alibaba’s idea “preconfigured precision.” The book states explicitly: “The difference between old and new business lies in precision. Precision means exactness and accuracy. They correspond respectively to network collaboration and data intelligence. In the future, enterprises that cannot provide users with precise services will soon be eliminated.”
There is an example that rather vividly illustrates Alibaba’s “precision.” A few years ago, a netizen published a piece of research arguing that “the annual Double 11 sales data from Taobao from 2009 to 2018 were fabricated,” because the growth curve formed by the annual sales figures was “too perfect,” as if it had been calculated directly from an equation, and its fit to the ideal curve exceeded 99.94 percent. How could such a perfect curve possibly be data that had actually occurred? On this basis, the netizen concluded that Alibaba had falsified the data. But later, other netizens analyzed the matter and argued that such a “perfect fit” result was not necessarily the result of human manipulation of the data; it was more likely the result of precise control over actual sales activity. In other words, Alibaba and the major sellers within it all had an expected target for sales, and then during the actual sales process they could adjust discounts in real time according to how sales were going, so as to get as close as possible to the preset target.
This example shows that precise prediction of the sales process and sales outcomes can be a “self-fulfilling” goal—in other words, the more I pursue precision, the more likely I am to bring precision about.
The critics failed to realize this “outcome-first” method of precise control, and thus misunderstood Alibaba’s operations. Indeed, a curve too perfect could not have formed naturally; it was the result of human manipulation, but this manipulation was not “post hoc manipulation,” rather it was “preemptive manipulation.” In the past, only after sales did you know the performance; now, before sales even begin, you already know the result.
This method appears in many of the “success stories.” For example, the vice president of Red Dragonfly, one of the book’s successful cases, said: “In the past, whatever was produced was what was sold; now, what the customer needs is what is produced.” In short, traditional sales always meant that production was completed first and only then was the product pushed into the market, but how the market would respond was unclear, and how well it would sell could only be known after actual sales. Alibaba’s strategy now is to use data analysis technology to estimate market demand and sales conditions, and then determine production volume according to the expected sales situation.
I will not for the moment doubt the authenticity of these success stories; such a strategy is certainly capable of success. But facts have shown that this strategy does not always succeed. For example, Alibaba’s failure in the “Big Entertainment” domain proves that this strategy is not always effective. Alibaba seems to have tried to learn from the myth of House of Cards’ success, believing that artistic creation can also be pre-analyzed in terms of audience demand: “In the past, whatever was created was what was broadcast; now, what the audience needs is what is created.” But Alibaba’s group plainly has not graduated yet; its results in the entertainment field probably did not precisely match its own expectations, did they?
Alibaba’s success stories are mostly in industry and agriculture, while its failures in social networking and entertainment are the most obvious. Why is this? I think it is probably because an excessive obsession with data supremacism and an excessive faith in preemptive precise control caused Alibaba to ignore the fact that human nature also has some complex dimensions that are difficult to reduce to data. A work of art or literature is captivating not necessarily because it satisfies people’s preexisting needs; many times, elements such as “surprise,” “suspense,” “novelty,” and “weirdness” are even more attractive, and these are all very difficult to predict precisely through big data. Of course, big-data analysis still has a guiding role, but if you rely too heavily on data, the best result artistic creation can produce is nothing more than mediocrity. The same is true in social networking: WeChat Pay opened a breach in Alibaba’s moat through “red envelope snatching,” and Pinduoduo seized share from Alibaba by “cutting one yuan off,” but did they succeed through “precision”? On the contrary, they attacked from originally ambiguous overlapping fields. The domains in which “red envelope snatching” and “cutting one yuan off” took place were precisely those centered on “people,” not centered on “data.”
In industrial and agricultural production, those traditional production fields, things had already long since been “dehumanized.” “Automation” was not Alibaba’s invention, but a trend that had been developing continuously since the nineteenth century. When the philosopher Heidegger analyzed modern technology, he regarded the “preconfigured system of total ordering” as the essence of modern technology; everything, including human beings and nature, is treated as “stock,” as “resources” that can be measured mathematically. Heidegger believed that modern industrial technology and “exact science” share the same root, and that “precision” is simultaneously the underlying logic of modern science and modern technology. Earlier still, Marx’s insight into human “alienation” and his critique of “commodity fetishism” and “money fetishism” had already foreshadowed this trend.
In other words, Alibaba’s much-vaunted “preconfigured precision” is nothing new at all; it is merely the basic logic of the industrial age, which philosophers have already been criticizing for more than a hundred years.
Of course, entrepreneurs need not heed philosophers’ criticisms and can still continue along the trend of precision, because this really is the success of the industrial age. But the question is: does this logic still apply in the new digital age? I think perhaps not entirely. Alibaba’s greater success in agriculture and industry, and its repeated failures in emerging internet fields, also corroborate this point—Alibaba’s ideas are less advanced than they are behind the times.
What matters is that this idea has a power of “self-fulfillment.” If Alibaba’s logic were allowed to prevail, it might indeed become more and more successful, but the price would be further alienation of human nature. For when precision encounters the complex and ambiguous realities of human nature and does not fit so well, not everyone will think the problem lies with precision itself; they can also try to transform human nature, to make people more “precise” through education and discipline. For example, by continuously steeping audiences in shallow and vulgar works of art and literature, getting them used to simple stimuli and unable to understand profound or complex content, the precision of artistic creation will become easier and easier; for example, by making social interaction increasingly monotonous and making people’s circles increasingly closed and polarized, precise prediction in the social domain will also become easier and easier. But the question is, what price are we paying for precision?
I want to restate a philosophical proposition, perhaps one of the most important maxims in the history of modern philosophy: “Human beings are ends, not means.” The technology of “precision” may be effective, but no matter how effective it is, it can only be treated as a means serving “human beings.” For instance, statistical data on sales should only be a means of evaluating the sales process; the ultimate purpose is to ensure that merchants and consumers both gain real benefits. In artistic creation, the use of statistical techniques such as user-demand analysis should also only be a means; the ultimate purpose is to create works rich in humanity that bring people enjoyment, shock, entertainment, or inspiration. If we invert what is primary and secondary, and instead make “precision” itself the goal, then so-called success becomes a danger. Despite many failures, Alibaba is undoubtedly a successful company overall, but from what angle its success should be praised is something we ought to consider carefully.
Translated from the Chinese original with AI assistance. The original text is authoritative.
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