What the language industry actually disagrees about in 2026. Hint: it's a lot.

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

There’s one thing we don’t often talk about in the language industry: the beauty of languages. And nothing reveals their beauty better than when they’re spoken. Written text can hide, pretend, mislead. And it often does. Spoken words, less so.

What does AI actually read? Mostly the open web. And Common Crawl, the page-by-page archive that sits at the foundation of GPT, LLaMA, Gemini and other major language models, looks nothing like the world that speaks. Hindi: 610 million speakers, 0.22% of pages; three percent of the rate it is spoken. Bengali sits at the same ratio. English is 41% of pages, falling slowly. The training data that shapes how machines understand language is wildly uneven. Eight years of snapshots, 29 languages,...

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.