Target International Journal of Translation Studies· 2026Q1
Visibility as value
- 0citations
- Q1SCImago
- 2026year
Short summary
Generative AI and neural machine translation commodify language services, shifting economic value from linguistic content to user data and making labor invisible.
AI-generated from the title and abstract; the full text is not read.
Key points
- AI technologies have commodified translation services, making them a free feature on digital platforms.
- Economic value in the language industry has shifted from linguistic content to data generated by user interactions.
- The labor involved in training and maintaining AI systems is largely invisible, low-skilled, and poorly paid.
- Opacity in data collection and exploitation enables appropriation of human content and raises copyright issues.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract AI-powered linguistic technologies, particularly neural machine translation and generative AI, have profoundly disrupted the language services market since 2016. While demand for translation grows, it is increasingly embedded as a free, commodified feature on platforms, operated by a handful of very large tech companies. In parallel, financial value increasingly stems from data generated by user interactions and not linguistic content per se. This article examines the role of invisibility and opacity in the reconfiguration of economic value in the language industry, focusing on three interconnected dimensions: the digital economy, the shift from content to data, and labour conditions. The opacity of data collection and exploitation processes has allowed for large-scale appropriation of human content and raises issues of copyright. Meanwhile, the labour required to train and maintain these systems — for the most part low-skilled, crowdsourced, and poorly remunerated — remains largely invisible, which exacerbates inequalities and deskilling. The article argues for greater scrutiny of generative AI’s business models and calls on stakeholders to document these practices and to challenge the narrative of automated efficiency.
The authors' abstract, as published at the source. Target International Journal of Translation Studies, 2026 · DOI ↗
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Field: Computer Science Applications
Computer Science ApplicationsComputer Science