This article conceptualizes the platform not merely as a digital tool that mediates consumption, but as a technical object that actively shapes practices, temporalities, expectations, and affective orientations. Drawing on Object-Oriented Ontology and selectively engaging with Actor-Network Theory, I argue that Instacart cannot be reduced to user experience, corporate strategy, or symbolic representation. The platform and the objects staged in its advertising narrative participate in relations while simultaneously producing an excess that exceeds these relations. Methodologically, this study does not rely on ethnographic interviews or semiotic coding methods. Instead, it proposes an object-oriented reading of a branded platform and its advertising narrative in order to trace how objects, interfaces, algorithms, commodities, and technical infrastructures acquire relative autonomy within hybrid socio-technical environments composed of human and non-human entities. The analysis proceeds across four interconnected layers: the mental construction of objects, the physical design of objects, the use of objects as signs, and object-oriented capitalism. The article argues that the Instacart case makes visible a more general transformation of everyday life under platform capitalism: consumption is reorganized not only through meanings and representations, but also through the operational agency of technical objects. It concludes that a critical analysis of digital platforms requires not only political economy and cultural critique, but also an ontology that takes objects seriously as participants in social reality.
This paper introduces a thought experiment in reverse alignment, in which, instead of aligning artificial intelligence (AI) with human values, we examine how humans inadvertently align with AI systems. We posit a theoretically perfect AI assistant and demonstrate that even this idealized system can produce concerning dynamics. In particular, we build on principles from pattern reinforcement and behavioral inertia to intuitively show that as the quality of AI assistance increases, human decision making tends to converge toward a utility-tilted sharpening of the assistant’s predictive distribution. Critically, this convergence exhibits strong path dependence: withdrawing assistance does not, by itself, restore cognitive independence, because the update contains no force that returns an agent to its pre-assistance policy. Recovery requires an active novelty incentive, and even then, it proceeds through history-dependent transients whose duration can exceed institutional planning horizons. We formalize these dynamics through two lenses: the Alice-Bob divergence, which tracks how adoption timing alone creates persistent cognitive divergence between identical agents, and the Three Inevitabilities, which captures the multi-scale forces (i.e. individual, social, and species-level) that drive convergence. Our theoretical and mathematical analysis reveals a fundamental trilemma: systems cannot simultaneously maintain optimal task outcomes, persistent human cognitive independence, and the absence of artificial novelty incentives. We conclude by identifying escape mechanisms and interventions that preserve human agency while retaining beneficial AI assistance to navigate the tension between augmentation and autonomy.
The public sector faces major challenges, including rising expectations, demographic shifts, and persistent efficiency problems. AI is increasingly seen as a possible remedy, but its application remains limited. This paper explores both the potential and the limitations of AI in the public sector. Drawing on an interdisciplinary conceptual framework, we analyse key barriers—organisational, regulatory, technical, and ethical—to adoption and use. We also simulate the long-term impact of AI on labour demand and productivity in Sweden, using detailed administrative data and occupational-level AI exposure measures. The findings suggest that AI may help reduce labour shortages and raise productivity, but the effects vary significantly across subsectors and types of organisations. The paper contributes a synthesis of barriers and institutional conditions for effective AI use, and provides an empirically grounded, scenario-based analysis with implications for digitalisation policy and practice.
With AI reshaping the production, distribution, and consumption of media content, media organizations are seeking theoretically grounded, practical guidance for using AI responsibly. This article presents an AI ethics instrument tailored to the media sector, co-created in collaboration with a large media conglomerate, that offers practical support for value elicitation, structured deliberative dialogue, and critical reflection in early phases of data and AI projects. Building on earlier digital ethics work in the public sector, “Data Ethics Decision Aid for Media” (D4M) helps media organizations build relevant documentation and foster critical AI literacy. D4M adopts a socio-technical perspective on AI, prompting reflection beyond technical performance and foregrounding social and organizational aspects of AI. The article details its triangulated design and development approach and traces successive prototypes of the instrument and the iterative processes with evaluative focus groups through which content and design were refined. It also explores efforts to ‘land and expand’ the tool within the organization: being in the room, winning over crowds and brokers. It concludes with reflections on the collaborative action research trajectory as a whole and considers the future of the instrument.
