Meta's No Language Left Behind model—covering 200 languages including 55 African languages—triggered immediate investor exits from African natural language processing startups. "Facebook has solved it, so your little puny startup is not going to be able to do anything," investors told founders, according to AI ethics researcher Timnit Gebru.
OpenAI representatives use similar tactics with language organizations globally. "OpenAI is going to put you out of business soon because we're going to make our models better in your language. You're better off collaborating with us and supplying us data for which we're going to pay you peanuts," they tell small teams.
Abeba Birhane argues 'AI for Good' narratives function as deflection against criticism. "It allows companies to say 'Look, we're doing something good! Everything about AI is not bad. And you can't criticize us,'" she stated.
The researchers challenge claims that giant models serve global interests. Gebru characterizes the approach as "stealing data, killing the environment, exploiting labor" without empirical evidence of societal benefits. They advocate for resource-efficient, task-specific models instead of resource-intensive large language models.
The pattern reveals systematic market capture across developing economies: Big Tech announces comprehensive language coverage, investors withdraw funding from regional specialists, then major labs acquire training data at minimal cost from financially weakened organizations.
This movement shifts ethics discussions from bias mitigation within existing systems to fundamental questions about AI business models and technical paradigms. Researchers demand proof of societal benefits and resource efficiency comparisons against alternative approaches.