Have you ever wondered how AI models understand different languages? It's more complex than you might think. Large language models (LLMs) don't simply read text like humans do. They break down words into smaller units called tokens, a process known as tokenization. But this process isn't always fair across languages. New research explores how tokenization methods used by popular LLMs, like GPT-4 and others, can inadvertently create inequalities between languages. The study examined various tokenizers, including those used by GPT-3, GPT-4, and BERT, across several languages using datasets like the Universal Declaration of Human Rights, excerpts from the Book of Genesis, and Meta's FLORES-200. They found that languages like Bengali can require significantly more tokens than English for the same text, leading to higher processing costs, increased latency, and limitations in handling longer texts. Imagine an AI-powered medical intake bot that's significantly slower for a Bengali speaker than an English speaker – that's the kind of disparity this research highlights. These disparities not only affect cost but also impact the accessibility and performance of AI services for speakers of different languages, especially those considered 'low-resource' languages with less digital data available. This raises crucial questions about inclusivity and fairness in AI. How can we ensure AI serves everyone equally, regardless of language? This research pushes for a more equitable approach to tokenization and urges developers to consider these inequalities, particularly when designing and deploying AI for real-world applications in healthcare and beyond. Addressing these hidden language barriers in AI is crucial for a future where technology benefits everyone, not just a select few.