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The world is obsessed with building bigger AI models in a world where the internet drops without warning for billions in rural parts of the world. In rural and low-connectivity regions, cloud-based AI struggles because of internet connectivity. The global AI race has a blind spot: we’re designing intelligence for environments that most of the world doesn’t have access to. But research like the <strong>TinyStories</strong> paper has already proven that small, well-trained models can reason, generate language, and teach without massive compute. This talk makes the case for an offline-first AI future powered by Small Language Models (SLMs). We’ll explore how SLMs run locally on low-end hardware, why they're a realistic path to global AI access, and how developers can build lightweight, practical systems that thrive where Big AI fails.
