Languages don't disappear because people stop speaking them. They disappear because systems stop recognising them. The shift from print to digital to AI has accelerated this phenomenon, creating invisible hierarchies of linguistic value.
The world's largest AI models treat most of the Global South as a statistical rounding error. This isn't the result of intentional discrimination but of mathematical realities: public datasets overrepresent English to a degree that marginalizes other languages substantially.
When AI models lack exposure to certain languages during training, those languages become economically and commercially invisible. Market access, business opportunities, and technological participation all flow toward languages that AI systems recognize and process effectively.
Current model architecture decisions embed linguistic hierarchies with real economic consequences for non-English speaking populations. The scale of the problem is staggering—billions of people speak languages that are barely represented in the training data of frontier AI models.
Addressing this requires deliberate changes to how training datasets are constructed and how models themselves are designed. This is not merely a matter of fairness. It is a matter of market access and economic participation. Companies that build models reflecting the full spectrum of global languages will unlock markets that monolingual AI cannot reach.
The opportunity is clear: the Global South represents the fastest-growing digital populations on earth. AI companies that invest in linguistic diversity today will be positioned to serve these markets tomorrow. Those that don't will find themselves locked out of the next wave of growth.