AI Translation and Language Barriers

How machine translation and speech technology are lowering language barriers to trade, services and information, and who benefits.

Language differences have long been a barrier to trade, jobs and access to information. Machine translation and speech technologies powered by AI are rapidly lowering these barriers.

How AI translation improved

Early computer translation used rules and word-by-word substitutions, often producing awkward results. From the mid-2010s, neural machine translation, using deep learning, improved quality dramatically. Large language models have improved it further, handling context and informal language better.

Economic effects

Trade: research by Erik Brynjolfsson, Xiang Hui and Meng Liu found that when eBay introduced improved machine translation for listings in 2014, exports on the platform to Spanish-speaking Latin America rose significantly, by around 17 percent. Lower language barriers acted like lower trade costs.
Services: businesses can serve customers in many languages without hiring translators for every language.
Access to information: people can read content originally published in other languages.
Government services: translation and speech tools help deliver services to people in their own languages.

Speech technology

AI can convert speech to text and text to speech in many languages. This helps:

People with low literacy, who can speak and listen instead of reading and writing.
Blind and visually impaired people, who can have text read aloud in their own language.
Deaf people, through automatic captions.

Limits

Low-resource languages: languages with little digital text available still get poorer quality translation. Many Indian and African languages fall into this group, though efforts like Bhashini and open datasets aim to improve them.
Errors: machine translation can make mistakes, which matters in legal, medical and official settings.
Effects on translators: demand for routine translation work has fallen, while demand for editing and specialised translation continues.

A weaver sells abroad

A handloom weaver in Varanasi lists products on an online marketplace. Buyers in Spain and Japan see the listings automatically translated into their languages, and the weaver reads their questions translated into Hindi. Without translation, these sales might never happen. Language technology opens global markets to small producers.

Thinking machine translation works equally well for all languages

Quality depends heavily on how much digital text exists for a language. Widely used languages get much better results than languages with limited online content.

Key takeaways

Neural and AI-based translation greatly improved quality from the mid-2010s.
Improved translation on eBay raised exports to Spanish-speaking Latin America by around 17 percent.
Speech technology helps people with low literacy, blind people and deaf people.
Low-resource languages still get poorer results, and errors matter in important settings.