Advances in artificial intelligence (AI) are rapidly transforming the world of work. As AI progresses, debates continue over whether this technology will complement human labour or displace it. In recent years, concerns have grown about AI’s impact on a range of professions. One striking example is the translation industry, where machine translation (MT) tools have seen rapid improvement. A 2024 survey found that over three-quarters of translators expect generative AI to adversely affect their future incomes, while others even question the enduring value of foreign language skills. Notably, The Economist recently remarked that ‘AI could make it less necessary to learn foreign languages’, a view echoed by OpenAI’s demonstration of Sky, which seamlessly translated speech between Italian and English in real time.

Yet, advancements in MT are not all that recent. The IBM701, the first MT system, was launched in 1954 in a collaboration between IBM and Georgetown University. Later milestones include free online translation services like Babel Fish in 1997 and Google Translate in 2006. However, it was the launch of Google Translate as an app on Android and iOS in 2010 – and its integration into browsers like Chrome – that triggered widespread adoption. As illustrated in our analysis, searches for ‘Google Translate’ spiked around this time, while queries for ‘Translator’ declined correspondingly.

Figure 1 Google searches for ‘translator’ and ‘Google Translate’, 2004-2024

Note: This graph plots monthly ‘interest’ in two Google search terms: ‘translator’ and ‘Google Translate’. The interest index is calculated by Google Trends.

In a recent paper, we examine the impact of this shift on translator employment and the demand for foreign language skills.

Machine translation at work

We begin our analysis by constructing a city-level dataset using individual records from the 2010–2023 American Community Survey (ACS), which provides annual 1-in-100 samples of the US population. We then integrate these data with measures of the geographic spread of Google Translate based on search engine data, job postings data from Lightcast, and local employment and wage statistics for translators and interpreters.

Figure 2 Changes in searches for ‘Google Translate’ across local labour markets, 2010-2023

Note: This map reports the 2010-2023 log change in internet searches for ‘Google Translate’ across local labour markets.

By comparing regions with high versus low adoption rates, we isolate the impact of MT on translator employment and wages. Recognising that changes in ‘Google Translate’ searches might also be driven by other unobserved factors affecting translator employment, we addressed this potential endogeneity by instrumenting interest in Google Translate with local changes in ‘Google Drive’ search activity. The rationale behind this instrument is that both digital products serve distinct functions yet are influenced by Google’s brand.

Our findings indicate that regions with greater use of Google Translate experienced a growth slowdown in translator and interpreter jobs. In fact, for each 1 percentage point increase in MT usage, translator employment growth dropped by approximately 0.7 percentage points, translating into an estimated loss of about 28,000 new translator positions that might otherwise have been created over the 2010–2023 period.

Impacts on foreign language demand

The ripple effects of machine translation extend well beyond the translation industry. Traditionally, foreign language proficiency has been highly valued across sectors – from customer service and international business to healthcare and education. However, as MT accuracy improves, this is changing. Our analysis of job postings shows that regions with high MT adoption experience slower growth in job advertisements requiring foreign language skills, notably affecting the demand for Spanish, Chinese, German, Japanese, and French proficiency.

Overall, our findings imply that as AI translation technology advances, the demand for bilingual skills is likely to continue its decline.

Broader implications: Translation and trade

Historically, linguistic differences have posed significant challenges to trade, with research indicating that sharing a common language can boost bilateral trade significantly. Improved machine translation could significantly boost global services trade, offering developing countries a new pathway for economic growth, especially in the wake of automation challenges in manufacturing-led growth.

With the establishment of AI technology improving communications, machine translation has the potential to enable billions of non-English speakers to participate in the global marketplace.

Outlook

As machine translation technology continues to advance, the effects on translator employment and language skills demand may intensify. Particularly, improvements in voice translation signal far-reaching implications for education and employment in language-related professions.

In conclusion, as AI reshapes the landscape of translation and language skills, industry players must adapt to thrive in this evolving environment.

References

Acemoglu, D et al. (2022), ‘Artificial intelligence and jobs’, Journal of Labor Economics.
Autor, D H (2015), ‘Why Are There Still So Many Jobs?’, Journal of Economic Perspectives.
Baldwin, R (2018), ‘Machine learning is tearing down language barriers’, VoxEU.org.
Frey, C B and P Llanos-Paredes (2025), ‘Lost in Translation: AI and the Demand for Foreign Language Skills’, Oxford Martin School Working Paper.