A working dictionary of the terms, frameworks, and vendor concepts for how AI translates, generates, evaluates, and governs multilingual content.

Companies have attributed some 80,000 layoffs in tech alone to AI year-to-date. Sure, just a fraction of the 8.8 million jobs lost in the broader financial crisis that followed the collapse of Lehman Brothers in 2008. But still a significant number. And one that's growing.

AI search and localization are starting to overlap. AEO (Answer Engine Optimization) is described as optimizing content so AI systems cite it or feature it in direct answers across tools such as ChatGPT, Google AI Overviews, Perplexity, and Copilot. But for global companies, that definition is incomplete unless it includes language.

Last week I read Elena Verna’s newsletter on high-impact IC work. I kept nodding. Elena’s thesis is about tech and growth, but essentially the same is happening in localization too. We may be entering an era where a senior, high-leverage individual contributor (IC) can own a much larger slice of localization end-to-end than was practical over the last two decades. Not everywhere, and it’s not for everyone, of course. But in the right contexts, it’s becoming a real operating model, not just a...

AI amplifies what’s already there. And for most teams, that’s the problem. Last week I co-hosted Session 1 of GALA Academy’s three-part course on AI for the language industry. L’Meese Greaney is up next (Sales, May 12). Erik Vogt closes it out (Growth, May 19). They’ll both be excellent.

What is Multilingual AI? Multilingual AI is a capability stack that lets software handle language across regions. It goes well beyond just translating and includes reasoning about meaning, tone, and correctness across locales. It typically shows up as: Text: search, chat, summarization, writing, support Speech: transcription, speech-to-text and speech-to-speech translation, dubbing Multimodal: voice + text + UI, with market-specific expectations baked in Translation is part of it, but...