📝 SummarXiv

Abstract

Today, data and information have become overabundant resources within a global network of machines that exchange signals at speeds approaching that of light. In this highly saturated environment, communication has emerged as the most central form of interaction, supported by a rapidly evolving technical infrastructure. These new communication tools have created an overwhelming surplus of information so much so that no human could process it all. In response, platforms like Facebook, YouTube, and Google use algorithms to filter and suggest content. These algorithms act as sorting mechanisms, reducing the informational noise and presenting users with content tailored to their habits and preferences. However, by placing themselves between users and data, these platforms gain control over what users see and, in doing so, shape their preferences and behaviors. In physical terms, we might say they function like selective filters or control gates in an information system directing flows and creating feedback loops. Over time, this can lead to a kind of informational inertia, where users become increasingly shaped by algorithmic influence and lose the ability to form independent judgments. This process reflects a broader trend that Bernard Stiegler describes as a new kind of proletarianization where individuals lose the knowledge and skills that are absorbed and automated by digital systems. Borrowing from physics, we study this as a shift toward higher entropy. However, platforms like Wikipedia and arXiv demonstrate how digital tools can support collective knowledge without leading to cognitive degradation. Inspired by Elinor Ostrom work on commons, we propose a model for a digital commons economy where information is shared and governed collaboratively, helping to restore a balance between entropy and organization in our digital environments.