AI innovation continues to accelerate, driving fundamental changes in how data is processed, moved, and accessed across global networks.

Google introduced AI Mode, an experimental version of search powered by its Gemini 2.0 model, a preview of what Google search may evolve into over time. AI Mode can handle complex, multi-step queries, provide follow-up options, and pull live local and product information – all in a conversational, chatbot-like experience. Unlike traditional search, AI Mode synthesizes data from multiple related sources at once, aiming to reduce the need for users to visit individual websites. 

This shift reflects a larger trend: AI systems are increasingly dependent on seamless, high-speed connectivity between data sources, processing engines, and applications. AI-powered search, personalized recommendations, content generation, and even real-time fraud detection rely on continuous data exchange across distributed environments .

Meanwhile, AI is also making its way deeper into industrial processes. Companies are actively applying AI to automate product design, simulate manufacturing processes, and detect quality issues in real time.

The trend is clear: as AI capabilities expand, so do the underlying data movement requirements. AI isn’t just consuming more compute power, it’s placing new demands on the infrastructure that moves data to and from training clusters, inference engines, and end-user applications.