Meshes of Surveillance, Prediction, and Infrastructure: On the Cultural and Commercial Consequences of Digital Platforms

Abstract

Digital platforms like Spotify, Netflix, and YouTube rely on mass data collection, algorithmic forms of prediction, and the development of closed digital systems. Seemingly technical and trivial, such operational and infrastructural features have both commercial and cultural consequences in need of attention. As with any other kinds of infrastructure, the surveillance practices and digital ecosystems that are now installed and solidified will have long-term effects and will be difficult to challenge. We suggest that the cultural and commercial ramifications of such datafied infrastructural developments can be unpacked by analyzing digital platforms—in this case Netflix—as surveillance-based, predictive infrastructures. Digital platforms fortify their market positions by transitioning surveillance-based assets of audience metrics into infrastructural and informational assets that set conditions for other actors and approaches at work in the domain of cultural production. We identify the central forces at play in these developments: digital platforms critically depend on proprietary surveillance data from large user bases and engage in data-structuring practices (Flyverbom and Murray 2018) that allow for predictive analytics to be a core component of their operations. Also, digital platforms engage in infrastructural development, such as Netflix’s decentralized system of video storage and content delivery, Open Connect. These meshes of user surveillance, predictive analytics, and infrastructural developments have ramifications beyond individual platforms and shape cultural production in extensive and increasingly problematic ways.