In my limited experience, LLMs can have issues with translation tone — but these issues are pretty easily fixed with good prompting.

I want to believe there will be even more translators in the future. I really want to believe it.

Fascinating thought piece. While I agree with the thrust of the piece: 'that llms can't really replace engineers', unfortunately the way the industry works is that the excuse of AI, however grounded in reality has been repurposed as a cudgel against actual software industry workers. Sure eventually everyone might figure out that AI can't really write code by itself - and software quality will degrade.. But unfortunately we've long been on the path of enshitification and I fear the trend will only continue. If google's war against its own engineers has resulted in shittier software - and things start break twice a year instead of once - would anyone really blink twice?

This seems like a terrible comparison since Google Translate is completely beat by DeepL, let alone LLMs. (Google Translate almost surely doesn't use an LLM, or at least not a _large_ one given its speed)

> ... a translators’ and interpreters’ work is mostly about ensuring context, navigating ambiguity, and handling cultural sensitivity. This is what Google Translate cannot currently do.

Google Translate can't, but LLMs given enough context can. I've been testing and experimenting with LLMs extensively for translation between Japanese and English for more than two years, and, when properly prompted, they are really good. I say this as someone who worked for twenty years as a freelance translator of Japanese and who still does translation part-time.

Just yesterday, as it happens, I spent the day with Claude Code vibe-coding a multi-LLM system for translating between Japanese and English. You give it a text to be translated, and it asks you questions that it generates on the fly about the purpose of the translation and how you want it translated--literal or free, adapted to the target-language culture or not, with or without footnotes, etc. It then writes a prompt based on your answers, sends the text to models from OpenAI, Anthropic, and Google, creates a combined draft from the three translations, and then sends that draft back to the three models for several rounds of revision, checking, and polishing. I had time to run only a few tests on real texts before going to bed, but the results were really good--better than any model alone when I've tested them, much better than Google Translate, and as good as top-level professional human translation.

The situation is different with interpreting, especially in person. If that were how I made my living, I wouldn't be too worried yet. But for straight translation work where the translator's personality and individual identity aren't emphasized, it's becoming increasingly hard for humans to compete.

The distinction between what people typically imagine a translator's job is and the reality reminds me of pixar movies being "localized" instead of just translated (green beans on a plate in the Japan release instead of broccoli because that's the food that Japanese kids don't like).

Lacking cultural context while reading translated texts is what made studying history finally interesting to me.

This article is spot on about a lot of things. One thing I think it fails to address is this:

> I feel confident in asserting that people who say this would not have hired a translator or learned Japanese in a world without Google Translate; they’d have either not gone to Japan at all, or gone anyway and been clueless foreigners as tourists are wont to do.

The correlation here would be something like: the people using AI to build apps previously would simply never have created an app, so it’s not affecting software development as a career as much as you first expect.

It would be like saying AI art won’t affect artists, because the people who would put in such little effort probably would never have commissioned anyone. Which may be a little true (at least in that it reduces the impact).

However, I don’t necessarily know if that’s true for software development. The ability to build software enabled huge business opportunities at very low costs. I think the key difference is this: the people who are now putting in such low effort into commissioning software maybe did hire software engineers before this, and that might throw off a lot of the numbers.

As long as the person you are talking or writing to is aware that you're not a native speaker, they will understand that you won't be able to follow conventions around polite languages or understand subtle nuances on their part. It's really a non issue. The finer clues of language are intended for people who are from the same culture.

>All this is not to say Google Translate is doing a bad job

Google Translate is doing a bad job.

The Chrome translate function regularly detects Traditional Chinese as Japanese. While many characters are shared, detecting the latter is trivial by comparing unicode code points - Chinese has no kana. The function used to detect this correctly, but it has regressed.

Most irritatingly of all, it doesn't even let you correct its mistakes: as is the rule for all kinds of modern software, the machine thinks it knows best.

While it's just anecdotal evidence, I have translator friends and work has indeed been drying up over the past decade, and that has only accelerated with the introduction of LLMs. Just check any forum or facebook group for translators, it's all doom and gloom about AI. See this reddit thread, for example: https://www.reddit.com/r/TranslationStudies/comments/173okwg...

While professionals still produce much better quality translations, the demand for everything but the most sensitive work is nearly gone. Would you recommend your offspring get into the industry?

Anyone can make their own website, most won't. But AI can make technical people super-powered. If you loose your job to it, helping businesses integrate AI into their processes would be a good alternate to try.