# Audience: The Currency of Technology - Author: Kojin Glick - Published: 2025-01-21 - Canonical: https://www.kojinglick.com/blog/audience-the-currency-of-tech --- The “technology” company is an unfortunate misnomer for what a tech company really is: an audience company. This is particularly true of any company involved in social media, but this trend is true at a more abstract level for all “technology” companies. Producing novel technologies is highly costly. Therefore, in the currently dominant mode of economic production today, all technologies produced are compensated by the exchange of capital for audiences. In other words, all technology companies are only in the business of “technology” as it pertains to securing enough users or customers to justify their acquisition. When a technology company interfaces with a venture capitalist firm, or a larger firm who wants to acquire them, capital is offered in proportion to the value of that technology’s audience. From the perspective of tech companies, audiences - digital communities, populations, or market segments - are only valuable insofar as they accurately predict the consumption behavior of a group of individuals. Audience-makers seek to balance the trade-offs of two important features: audience size, and audience cohesion. Audience size determines the number of individuals who are given consumption signals. Audience cohesion determines the rate at which the audience will reliably respond to the consumption signals with a purchase. Audience cohesion also determines the extent to which a statement about a consumer applies to the entire audience. When audience sizes get too large, though, audience cohesion suffers, as scale limits the amount of commonality shared by individuals in this audience. When audience cohesion is too refined, audience size suffers, as the specificity of statements about audience members disqualify more and more people from the audience. While digital platforms certainly did allow the support for an unprecedented scale of audience-collection, this dynamic largely has had the same overall shape since the advent of market segmentation and cable television. Content programming - the sequencing of media texts into regular time slots - is a form of audience making. In the era of cable television, audiences were formed by individuals tuning in, every week, for certain content programming provided by cable networks. From the perspective of cable watchers, content programming helps individuals build a routine to consume their favorite shows. From the perspective of the cable network, content programming creates virtual waypoints for market segments to congregate around. When individuals tune in to the same set of content programs, advertisers can make statements describing these individuals. Deals between advertisers and cable networks are made when an advertiser’s target audience matches a market segment established by a cable network. Algorithmic feeds - the sequencing of media texts into an infinite-scroll feed - is a form of audience-making enabled by digital technology. In the era of social media companies and digital streaming, the relationship between individuals and the content they want to see is mediated by algorithms. For users, algorithms help users consume the content in which they have previously expressed interest. For tech companies, algorithms help steer individual users into behavioral groups according to how users express their interests. Algorithms help shape individual users on a platform into market segments. The digital nature of algorithmic audience-making allows companies to instantly combine and recombine user behaviors into countless permutations of market segments, creating highly specific audience lists at unprecedented scale. Moreover, unlike traditional content programming where audience segments were manually identified and tracked, algorithmic systems continuously and automatically update these audience lists in real-time as users interact with content, enabling dynamic and precise audience targeting. AI-generated content - the sequencing of associative references into coherent, human-like media texts - represents the most recent manifestation of audience-making. Where algorithmic lists organize individuals into audiences by their association to discrete textual corpora like posts, creators or even hashtags, Transformer-based AI systems atomize this process further. Large Language Models have the capacity to ingest massive swaths of content at an inhuman scale, identify latent or hidden relationships between corpora, and then use those associations to produce convincingly human text. For users, content is now immanent to the digital platform itself; no longer is there a need for a creative producer of content to be at the other end of the digital platform. For tech companies, the latent space of the AI systems which stores the learned relationships between corpora becomes a map of content to users. When users interact with AI-generated content, they are simultaneously consuming and training the system on their preferences, creating a recursive loop of audience refinement that collapses the distinction between content creation and consumption. The business relationship between platforms and advertisers transforms as well - rather than matching pre-existing audiences to content, AI systems can dynamically generate content optimized for any desired audience segment, making the audience-content relationship fully programmable. Technology companies may be simply audience companies, but how can one discount the degree to which the fundamental technology has changed, despite fulfilling the same strategic need? From negotiating market segments using Nielsen ratings to a fully-automated manager of conceptual preferences, the shape of audience-making has changed drastically, but the purpose of audience-making has not changed in the least.