Google's Gemini multimodal AI model has been designed from scratch to handle multiple data types simultaneously, making machine intelligence more practical for real-world problems. This native multimodal design represents a significant shift in AI development. Multimodal AI functions like human intelligence by integrating multiple senses simultaneously, combining different data forms to create a complete picture rather than fragmented insights. These systems excel where single-mode systems fail by shifting seamlessly between different data formats, reducing ambiguity in AI responses and providing contextual richness. However, teams must be cautious of phenomena like hallucinations in multimodal models, which can impact the reliability of AI outputs. Multimodal AI is already transforming industries, including agentic AI systems that act on users' behalf, medical diagnostics, customer service, e-commerce platforms, education, and healthcare. The model's unified architecture enables cross-modal attention at every layer, allowing more sophisticated reasoning across modalities. Gemini's training methodology breaks new ground by simultaneously training on aligned multimodal data at unprecedented scale, creating rich conceptual connections between what things look like, how they're described, and how they behave in videos.