Machine Translation Post-Editing (MTPE) is said to be “firmly established (…) as the dominant mode of production” across global language service workflows. (Varga, 2025). Likewise, the European Language Industry Survey (ELIS, 2025) reported that, in 2025, “For the first time, actual MT usage exceeds the 50% mark among independent professionals and language companies” surveyed. Translation education shows a similar tendency with an “increased compulsory inclusion of machine translation, post-editing, and quality evaluation” in academic curricula (Rothwell et al., 2025). MTPE is now an integral part of translation teaching at universities, which is a further indicator of the widespread use of MTPE in the industry (Rothwell et al., 2025). But even if MTPE has become the new normal, professional translators remain wary of large-scale, uncritical automation and report negative attitudes toward generative AI as a translation tool (ELIS, 2025; Jiménez-Crespo, 2025). Scholars and industry surveys indicate that translators view AI/MT applications as a cause of a decrease in pay for human translation work due to the depreciation of the perceived value of translation services (ELIS, 2024; Jiménez-Crespo, 2025). Some negative sentiment can also be put down to a lack of transparency on the side of companies who send freelancers MTPE jobs disguised as revision jobs (ATLAS and ATLF, 2023), or whose expectations regarding the use of MTPE remain implicit. Transparency becomes a key issue in what Moorkens (2022) calls “a disparity of power” between agencies and freelancers, noting that freelancers have “little say in processes and conditions” when MTPE regimes are imposed. Ethical frameworks in translation emphasise the need for fair working conditions (Moorkens, 2020) as well as enhancements to translators’ autonomy and control (Jiménez-Crespo, 2025).
Despite researchers and translation professionals explicitly calling for transparency in the industry and empowerment of translators, there are still under-researched avenues that impact translators’ agency; for example, little scholarly attention has been dedicated to the very first stage of professional engagement, namely job advertisements. Whether institutions employing translation services ensure ethical translation standards through transparent communication about MT use in their job postings remains an open question. This paper aims at addressing this gap by presenting findings from a corpus-assisted analysis of 36 translation job advertisements of centralised cryptocurrency exchanges (CEXs) published online between January 2025 and January 2026 on various job portals. The corpus has been built using Sketch Engine and comprises publicly posted job advertisements directed at translators. The initial dataset consisted of 100 job advertisements within the cryptocurrency space; however, the initial manual screening removed duplicated, inauthentic and near-identical ads, nearly halving the dataset and revealing that the majority of the postings come from CEXs, and leading to the final selection of CEX-focused translation job ads. CEXs, as part of the blockchain-based space, are a highly technologised, fast-growing economic segment, where translation demand and AI adoption are both particularly intense. The final dataset, consisting of 11,719 words, has been studied using concordance to retrieve all occurrences of ‘MT’, ‘MTPE’, ‘AI’ and other related phrases to inspect their context within the job postings. This study is particularly interested in how MT and AI are framed in the job descriptions and how they shape the translation job roles.
The findings show that approximately 30% of job advertisements from the collected sample mention MT, MTPE and/or AI. Although not stated explicitly, some job postings specify extremely high volumes of 100,000+ words per month, which may indicate AI/MT incorporation into their workflows. Such volumes could be shared across a team, but the job advertisements do not specify that. The current analysis of the dataset, paired with available research on the technology used within the translation industry, suggests that MTPE use is widespread but underdisclosed in the job ads. The cryptocurrency sector, as an emerging segment of the specialised translation market, mentions MT/AI use in a noticeable number of job advertisements. Whether this type of disclosure stems from the common alleged transparency ethos of organisations with roots in blockchain technology (Cheeseman, 2022) remains unclear. The motivations can vary, with some institutions prioritising optimisation and cost efficiency and therefore informing of their practices to ensure that candidates are aware of the working arrangements. Conversely, job posters who omit the technology-related information could assume that MTPE is already the default method of work, may be unaware of the technologies, or may deliberately obfuscate their practices; although, due to no direct evidence, these possibilities remain speculative.
Lack of clear disclosure of using machine translation by organisations in their job offers poses a risk to informed consent. In line with calls for “sustainable work systems” benefiting all stakeholders (Moorkens, 2025), this paper argues that translators should have access to fair and transparent working conditions at all stages of interacting with jobs, including their initial engagement with job advertisements. Organisations utilising translation services should inform about their AI/MT requirements as early as at the job posting stage to provide translators with a fair chance to acknowledge the terms of the professional engagement and prepare to negotiate their position and compensation. This paper’s focal point is to advocate for transparency and emphasise the importance of disclosing technology requirements in the fast-evolving translation environments, where workflows are constantly shifting in the post-AI era and processes are becoming particularly complex (Rothwell et al., 2025; Moorkens, 2025).
It is crucial to acknowledge the limitations of this study related to its small scale: the collected data is restricted in time and location, as only a specific moment of advancements in the translation industry expanding into the cryptocurrency sector is captured. To address this issue, future research should focus on comparing this sector with other fields. The data presented in this paper concentrates on publicly available job advertisements, which may ineluctably show only the job posters’ perspective and an idealised prescriptive view of how organisations advertise their translation processes. As such portrayals may differ from true work practices, subsequent phases of this project will bring attention to translators’ actual work environments and routines. This complex approach will enable creating a comparison between the job advertisement discourse and real-life business standards. To further explore this emerging market for translators, future research related to cryptocurrency translators will study the nature and extent of AI/MT tools in the workflows, asking about translation agency and strategies for translation style management in longer creative texts. The combined findings will offer insight into the perceived value of human expertise in MT-/AI-driven translation environments
References
References
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