Fluent Essays

Please use the easy grammar to write 250 words reflection of the attached reading, you only need to write chapter 4s reflection which is from page 34 till the end. Thank you wk_1_2_zuboff__ch_3___4_the_age_of_surveillance_capitalism__the_fight_for_a_human_future_at_the_new_frontier_of_power_publicaffairs_books__2019__redacted.pdf Unformatted Attachment Preview CHAPTER THREE THE DISCOVERY OF BEHAVIORAL SURPLUS He watched the stars and noted birds in flight; A river flooded or a fortress fell: He made predictions that were sometimes right; His lucky guesses were rewarded well. —W. H. AUDEN SONNETS FROM CHINA, VI I. Google: The Pioneer of Surveillance Capitalism Google is to surveillance capitalism what the Ford Motor Company and General Motors were to mass-production–based managerial capitalism. New economic logics and their commercial models are discovered by people in a time and place and then perfected through trial and error. In our time Google became the pioneer, discoverer, elaborator, experimenter, lead practitioner, role model, and diffusion hub of surveillance capitalism. GM and Ford’s iconic status as pioneers of twentieth-century capitalism made them enduring objects of scholarly research and public fascination because the lessons they had to teach resonated far beyond the individual companies. Google’s practices deserve the same kind of examination, not merely as a critique of a single company but rather as the starting point for the codification of a powerful new form of capitalism. With the triumph of mass production at Ford and for decades thereafter, hundreds of researchers, businesspeople, engineers, journalists, and scholars would excavate the circumstances of its invention, origins, and consequences.1 Decades later, scholars continued to write extensively about Ford, the man and the company.2 GM has also been an object of intense scrutiny. It was the site of Peter Drucker’s field studies for his seminal Concept of the Corporation, the 1946 book that codified the practices of the twentieth-century business organization and established Drucker’s reputation as a management sage. In addition to the many works of scholarship and analysis on these two firms, their own leaders enthusiastically articulated their discoveries and practices. Henry Ford and his general manager, James Couzens, and Alfred Sloan and his marketing man, Henry “Buck” Weaver, reflected on, conceptualized, and proselytized their achievements, specifically locating them in the evolutionary drama of American capitalism.3 Google is a notoriously secretive company, and one is hard-pressed to imagine a Drucker equivalent freely roaming the scene and scribbling in the hallways. Its executives carefully craft their messages of digital evangelism in books and blog posts, but its operations are not easily accessible to outside researchers or journalists.4 In 2016 a lawsuit brought against the company by a product manager alleged an internal spying program in which employees are expected to identify coworkers who violate the firm’s confidentiality agreement: a broad prohibition against divulging anything about the company to anyone.5 The closest thing we have to a Buck Weaver or James Couzens codifying Google’s practices and objectives is the company’s longtime chief economist, Hal Varian, who aids the cause of understanding with scholarly articles that explore important themes. Varian has been described as “the Adam Smith of the discipline of Googlenomics” and the “godfather” of its advertising model.6 It is in Varian’s work that we find hidden-in-plain-sight important clues to the logic of surveillance capitalism and its claims to power. In two extraordinary articles in scholarly journals, Varian explored the theme of “computer-mediated transactions” and their transformational effects on the modern economy.7 Both pieces are written in amiable, down-to-earth prose, but Varian’s casual understatement stands in counterpoint to his often-startling declarations: “Nowadays there is a computer in the middle of virtually every transaction… now that they are available these computers have several other uses.”8 He then identifies four such new uses: “data extraction and analysis,” “new contractual forms due to better monitoring,” “personalization and customization,” and “continuous experiments.” Varian’s discussions of these new “uses” are an unexpected guide to the strange logic of surveillance capitalism, the division of learning that it shapes, and the character of the information civilization toward which it leads. We will return to Varian’s observations from time to time in the course of our examination of the foundations of surveillance capitalism, aided by a kind of “reverse engineering” of his assertions, so that we might grasp the worldview and methods of surveillance capitalism through this lens. “Data extraction and analysis,” Varian writes, “is what everyone is talking about when they talk about big data.” “Data” are the raw material necessary for surveillance capitalism’s novel manufacturing processes. “Extraction” describes the social relations and material infrastructure with which the firm asserts authority over those raw materials to achieve economies of scale in its raw-material supply operations. “Analysis” refers to the complex of highly specialized computational systems that I will generally refer to in these chapters as “machine intelligence.” I like this umbrella phrase because it trains us on the forest rather than the trees, helping us decenter from technology to its objectives. But in choosing this phrase I also follow Google’s lead. The company describes itself “at the forefront of innovation in machine intelligence,” a term in which it includes machine learning as well as “classical” algorithmic production, along with many computational operations that are often referred to with other terms such as “predictive analytics” or “artificial intelligence.” Among these operations Google cites its work on language translation, speech recognition, visual processing, ranking, statistical modeling, and prediction: “In all of those tasks and many others, we gather large volumes of direct or indirect evidence of relationships of interest, applying learning algorithms to understand and generalize.”9 These machine intelligence operations convert raw material into the firm’s highly profitable algorithmic products designed to predict the behavior of its users. The inscrutability and exclusivity of these techniques and operations are the moat that surrounds the castle and secures the action within. Google’s invention of targeted advertising paved the way to financial success, but it also laid the cornerstone of a more far-reaching development: the discovery and elaboration of surveillance capitalism. Its business is characterized as an advertising model, and much has been written about Google’s automated auction methods and other aspects of its inventions in the field of online advertising. With so much verbiage, these developments are both over-described and under-theorized. Our aim in this chapter and those that follow in Part I is to reveal the “laws of motion” that drive surveillance competition, and in order to do this we begin by looking freshly at the point of origin, when the foundational mechanisms of surveillance capitalism were first discovered. Before we begin, I want to say a word about vocabulary. Any confrontation with the unprecedented requires new language, and I introduce new terms when existing language fails to capture a new phenomenon. Sometimes, however, I intentionally repurpose familiar language because I want to stress certain continuities in the function of an element or process. This is the case with “laws of motion,” borrowed from Newton’s laws of inertia, force, and equal and opposite reactions. Over the years historians have adopted this term to describe the “laws” of industrial capitalism. For example, economic historian Ellen Meiksins Wood documents the origins of capitalism in the changing relations between English property owners and tenant farmers, as the owners began to favor productivity over coercion: “The new historical dynamic allows us to speak of ‘agrarian capitalism’ in early modern England, a social form with distinctive ‘laws of motion’ that would eventually give rise to capitalism in its mature, industrial form.”10 Wood describes how the new “laws of motion” eventually manifested themselves in industrial production: The critical factor in the divergence of capitalism from all other forms of “commercial society” was the development of certain social property relations that generated market imperatives and capitalist “laws of motion”… competitive production and profit-maximization, the compulsion to reinvest surpluses, and the relentless need to improve labour-productivity associated with capitalism.… Those laws of motion required vast social transformations and upheavals to set them in train. They required a transformation in the human metabolism with nature, in the provision of life’s basic necessities.11 My argument here is that although surveillance capitalism does not abandon established capitalist “laws” such as competitive production, profit maximization, productivity, and growth, these earlier dynamics now operate in the context of a new logic of accumulation that also introduces its own distinctive laws of motion. Here and in following chapters, we will examine these foundational dynamics, including surveillance capitalism’s idiosyncratic economic imperatives defined by extraction and prediction, its unique approach to economies of scale and scope in raw-material supply, its necessary construction and elaboration of means of behavioral modification that incorporate its machine-intelligence–based “means of production” in a more complex system of action, and the ways in which the requirements of behavioral modification orient all operations toward totalities of information and control, creating the framework for an unprecedented instrumentarian power and its societal implications. For now, my aim is to reconstruct our appreciation of familiar ground through new lenses: Google’s early days of optimism, crisis, and invention. II. A Balance of Power Google was incorporated in 1998, founded by Stanford graduate students Larry Page and Sergey Brin just two years after the Mosaic browser threw open the doors of the world wide web to the computer-using public. From the start, the company embodied the promise of information capitalism as a liberating and democratic social force that galvanized and delighted second-modernity populations around the world. Thanks to this wide embrace, Google successfully imposed computer mediation on broad new domains of human behavior as people searched online and engaged with the web through a growing roster of Google services. As these new activities were informated for the first time, they produced wholly new data resources. For example, in addition to key words, each Google search query produces a wake of collateral data such as the number and pattern of search terms, how a query is phrased, spelling, punctuation, dwell times, click patterns, and location. Early on, these behavioral by-products were haphazardly stored and operationally ignored. Amit Patel, a young Stanford graduate student with a special interest in “data mining,” is frequently credited with the groundbreaking insight into the significance of Google’s accidental data caches. His work with these data logs persuaded him that detailed stories about each user—thoughts, feelings, interests—could be constructed from the wake of unstructured signals that trailed every online action. These data, he concluded, actually provided a “broad sensor of human behavior” and could be put to immediate use in realizing cofounder Larry Page’s dream of Search as a comprehensive artificial intelligence.12 Google’s engineers soon grasped that the continuous flows of collateral behavioral data could turn the search engine into a recursive learning system that constantly improved search results and spurred product innovations such as spell check, translation, and voice recognition. As Kenneth Cukier observed at that time, Other search engines in the 1990s had the chance to do the same, but did not pursue it. Around 2000 Yahoo! saw the potential, but nothing came of the idea. It was Google that recognized the gold dust in the detritus of its interactions with its users and took the trouble to collect it up.… Google exploits information that is a by-product of user interactions, or data exhaust, which is automatically recycled to improve the service or create an entirely new product.13 What had been regarded as waste material—“data exhaust” spewed into Google’s servers during the combustive action of Search—was quickly reimagined as a critical element in the transformation of Google’s search engine into a reflexive process of continuous learning and improvement. At that early stage of Google’s development, the feedback loops involved in improving its Search functions produced a balance of power: Search needed people to learn from, and people needed Search to learn from. This symbiosis enabled Google’s algorithms to learn and produce ever-more relevant and comprehensive search results. More queries meant more learning; more learning produced more relevance. More relevance meant more searches and more users.14 By the time the young company held its first press conference in 1999, to announce a $25 million equity investment from two of the most revered Silicon Valley venture capital firms, Sequoia Capital and Kleiner Perkins, Google Search was already fielding seven million requests each day.15 A few years later, Hal Varian, who joined Google as its chief economist in 2002, would note, “Every action a user performs is considered a signal to be analyzed and fed back into the system.”16 The Page Rank algorithm, named after its founder, had already given Google a significant advantage in identifying the most popular results for queries. Over the course of the next few years it would be the capture, storage, analysis, and learning from the by-products of those search queries that would turn Google into the gold standard of web search. The key point for us rests on a critical distinction. During this early period, behavioral data were put to work entirely on the user’s behalf. User data provided value at no cost, and that value was reinvested in the user experience in the form of improved services: enhancements that were also offered at no cost to users. Users provided the raw material in the form of behavioral data, and those data were harvested to improve speed, accuracy, and relevance and to help build ancillary products such as translation. I call this the behavioral value reinvestment cycle, in which all behavioral data are reinvested in the improvement of the product or service (see Figure 1). The cycle emulates the logic of the iPod; it worked beautifully at Google but with one critical difference: the absence of a sustainable market transaction. In the case of the iPod, the cycle was triggered by the purchase of a high-margin physical product. Subsequent reciprocities improved the iPod product and led to increased sales. Customers were the subjects of the commercial process, which promised alignment with their “what I want, when I want, where I want” demands. At Google, the cycle was similarly oriented toward the individual as its subject, but without a physical product to sell, it floated outside the marketplace, an interaction with “users” rather than a market transaction with customers. This helps to explain why it is inaccurate to think of Google’s users as its customers: there is no economic exchange, no price, and no profit. Nor do users function in the role of workers. When a capitalist hires workers and provides them with wages and means of production, the products that they produce belong to the capitalist to sell at a profit. Not so here. Users are not paid for their labor, nor do they operate the means of production, as we’ll discuss in more depth later in this chapter. Finally, people often say that the user is the “product.” This is also misleading, and it is a point that we will revisit more than once. For now let’s say that users are not products, but rather we are the sources of rawmaterial supply. As we shall see, surveillance capitalism’s unusual products manage to be derived from our behavior while remaining indifferent to our behavior. Its products are about predicting us, without actually caring what we do or what is done to us. To summarize, at this early stage of Google’s development, whatever Search users inadvertently gave up that was of value to the company they also used up in the form of improved services. In this reinvestment cycle, serving users with amazing Search results “consumed” all the value that users created when they provided extra behavioral data. The fact that users needed Search about as much as Search needed users created a balance of power between Google and its populations. People were treated as ends in themselves, the subjects of a nonmarket, self-contained cycle that was perfectly aligned with Google’s stated mission “to organize the world’s information, making it universally accessible and useful.” Figure 1: The Behavioral Value Reinvestment Cycle III. Search for Capitalism: Impatient Money and the State of Exception By 1999, despite the splendor of Google’s new world of searchable web pages, its growing computer science capabilities, and its glamorous venture backers, there was no reliable way to turn investors’ money into revenue. The behavioral value reinvestment cycle produced a very cool search function, but it was not yet capitalism. The balance of power made it financially risky and possibly counterproductive to charge users a fee for search services. Selling search results would also have set a dangerous precedent for the firm, assigning a price to indexed information that Google’s web crawler had already taken from others without payment. Without a device like Apple’s iPod or its digital songs, there were no margins, no surplus, nothing left over to sell and turn into revenue. Google had relegated advertising to steerage class: its AdWords team consisted of seven people, most of whom shared the founders’ general antipathy toward ads. The tone had been set in Sergey Brin and Larry Page’s milestone paper that unveiled their search engine conception, “The Anatomy of a LargeScale Hypertextual Web Search Engine,” presented at the 1998 World Wide Web Conference: “We expect that advertising funded search engines will be inherently biased towards the advertisers and away from the needs of the consumers. This type of bias is very difficult to detect but could still have a significant effect on the market… we believe the issue of advertising causes enough mixed incentives that it is crucial to have a competitive search engine that is transparent and in the academic realm.”17 Google’s first revenues depended instead on exclusive licensing deals to provide web services to portals such as Yahoo! and Japan’s BIGLOBE.18 It also generated modest revenue from sponsored ads linked to search query keywords.19 There were other models for consideration. Rival search engines such as Overture, used exclusively by the then-giant portal AOL, or Inktomi, the search engine adopted by Microsoft, collected revenues from the sites whose pages they indexed. Overture was also successful in attracting online ads with its policy of allowing advertisers to pay for high-ranking search listings, the very format that Brin and Page scorned.20 Prominent analysts publicly doubted whether Google could compete with its more-established rivals. As the New York Times asked, “Can Google create a business model even remotely as good as its technology?”21 A well-known Forrester Research analyst proclaimed that there were only a few ways for Google to make money wi ... Purchase answer to see full attachment