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Abstract
Abstract This article develops the concept of joint journeys as a metaphor to analyze how smart speakers become embedded in everyday domestic life and to trace the reciprocal, linguistically-mediated processes of domestication. While the domestication framework is well established in media studies, AI-based, networked technologies like smart speakers challenge its underlying assumptions by connecting private households to global infrastructures, thereby blurring boundaries between the public and the private. Drawing on video and audio recordings from German households, the article explores how conversational linguistic practices contribute to the domestication of smart speakers. Using methods from ethnomethodological conversation analysis and interactional linguistics, the study traces how smart speakers become integrated into everyday life, not just materially and functionally but also discursively, through practices relating to placement decisions, adaptation to sequential structures, personalization features, and reactions to malfunction. The article shows that mutual accommodation takes place: while users adapt their language to interface constraints, devices also get ‘personalized’ towards their users. The metaphor of joint journeys emphasizes that the co-evolution of users and devices is an ongoing, non-linear expedition shaped by language, socio-material environments, and infrastructural logics. These observations make it clear that it is through practices and language that AI technologies become integrated into everyday culture, which also raises questions about the broader datafied ecosystems to which interactions with them contribute.
Highlights
Media technologies have become ever more portable in the last decade—leading to significant changes not just in media usage and in society at a broader level (Haddon 2003; Hartmann 2013b; Ling 2008)—and yet, the concept of the private home and personalized living environments continues to be cherished in societies that remain primarily sedentary (Argandoña Rámiz et al 2021)
Germany (AI)—not the generative AI used by large language models (LLMs) or text-to-image models, but processes of machine learning (ML) and natural language processing (NLP), which fall under the umbrella term of AI
With this theoretical and methodological setup, I seek to answer the following research questions: How do conversational linguistic practices contribute to the domestication of smart speakers? How do linguistic practices—along with the domestication of conversational interfaces—bring AI-based technologies into being through cultural practice in domestic environments? To address these questions, I highlight and analyze certain domestic and interactional situations that occur along these journeys (Sect. 4) and foreground moments in the process of “everydayification” (Ayaß 2012, 3)
Summary
Media technologies have become ever more portable in the last decade—leading to significant changes not just in media usage and in society at a broader level (Haddon 2003; Hartmann 2013b; Ling 2008)—and yet, the concept of the private home and personalized living environments continues to be cherished in societies that remain primarily sedentary (Argandoña Rámiz et al 2021). The emergence of smart home technologies as an important industry sector testify to this, as does the rise of stationary, voice-controlled devices called smart speakers, better known by their product names, e.g., Alexa, Siri, and Google Home. The latter devices deploy, to some degree, ‘artificial intelligence’. I conceptualize AI as a form of cultural practice, i.e., AI is brought into being by humans and nonhumans in specific situations of interaction and “interfacing” (Lipp and Dickel 2022) that stretch across space and time (see the introduction to this special issue). The practices involved in AI can be seen as a cycle: Starting with human interaction and practices, the cycle follows the steps of capturing these practices, transforming them into data, and feeding them into processes of machine learning, leading to statistically-calculated outputs
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