Does AI replace human translators?

1. The failure is fluent, which is what makes it expensive

Olga Beregovaya, VP of AI at Smartling, describes a case that will not show up in a readability check: an Italian researcher receiving text that was smooth Italian, while the phenomena being described were “100% American culture centric.” Her verdict is that “you do lose on factual accuracy and anthropological and phenomenological local phenomena.”

Coverage drives it. “the less covered and less represented the languages in the model, the higher the odds are that biases will be introduced.” For a company that, in her words, covers “every language under the sun,” that is the whole problem rather than an edge case.

She also pushes back on the assumption that the newest tool wins: “Quite often, neural machine translation would do much better than a large language model would do.”

2. Two counterintuitive findings from production

Write the prompt in English, even for a non-English target. The instinct is to have a native-speaking linguist write the prompt in their own language. Beregovaya reports the opposite result: “you quickly find out that a carefully crafted prompt in English will render a better result in the target language.”

The tokenizer is a hard floor. Before model quality is even in play, the text has to segment: “if you feed Swahili and all you have at your disposal is an English tokenizer, it’s not gonna get you far. It’s gonna give you the most disastrous results.” Her phrase for shipping into a language the model cannot reliably vectorize is “upload and pray.” See tokenization for the mechanism.

3. Retrieval is what makes the style guide affordable

Localization carries a large body of reference material: style guides that “can be a 100 pages,” translation memories, dictionaries, do-not-translate lists. Stuffing that into every prompt is, in her words, a “waste of fortune, make no money.”

So the pipeline uses retrieval to “only fetch what’s relevant from external sources,” which she credits as a game changer. It also narrows the failure surface: grounding output in a curated external source is how she describes “mitigating biases and hallucinations.” The same reasoning appears in what is RAG.

Worth noting what her team optimized, because it was not the model: “queuing, caching, multithreading, and everything else that helps reduce inference time.” A perfect prompt that takes five minutes to run, as she puts it, is “obviously not gonna fly.”

4. Where the human actually sits

Beregovaya’s division of labour is explicit: “AI does the heavy lifting, introduces predictability, does a lion chunk of the work, takes us much closer to human parity than ever before. And the human translator or fact checker or validator really compensates for the delta that AI is not able to cover.”

The economics are why it matters. Her industry baseline is that “On average, a translator can deliver 2,000 words a day.” On the gain from AI she is deliberately imprecise, saying only that it is a matter of “magnitude and order of magnitude” rather than quoting a multiple.

Fully unattended AI translation does work, but she scopes it to a specific shape of content: shorter shelf life, higher risk tolerance, instant turnaround. Opinion portals and forums are his examples. The interesting design decision is upstream of translating anything, which is routing: does this piece need a human translator, AI with human validation, or machine translation alone?

5. Multimodal collapsed steps that used to cost money

Two changes she flags as real. Localizing images used to mean desktop publishing: extracting text, translating, rebuilding the artwork. Now “all you really need to do is just say reproduce this image in a target language.”

And the speech pipeline shortened. Translating spoken audio “took four steps”: recognize, translate, render, synthesize. A multimodal model can now fit that in one. Her reason for caring is compounding error: “every time, every step you introduce, you introduce potential fine error.”

Why it matters

If you are judging AI translation by whether it reads well, you are checking the thing that stopped being the problem. The remaining failures are factual, cultural, and concentrated in exactly the languages your quality checks cover worst. Beregovaya’s condition for the human leaving the loop is specific: “until biases and hallucinations have been completely solved for and until we have even language coverage.”