Multimodal AI · May 16, 2024 · 2 min read

GPT-4o and the Shift to Real-Time Multimodal AI

OpenAI's GPT-4o ("omni") lands with a clear theme: one model handling text, vision, and audio with much lower latency. Around the same time, Google's I/O underscored the same direction with its long-context multimodal models. The industry is converging on responsive, multimodal assistants.

Latency is a feature

Fast responses change what's possible. Sub-second, conversational interaction makes voice interfaces and live assistance feel natural rather than clunky. When you're designing AI UX, latency budgets deserve as much attention as accuracy.

Designing for multiple modalities

Native multimodality means you can build experiences that mix a photo, a question, and a spoken reply seamlessly. The design challenge shifts to grounding, error handling, and giving users control when the model is uncertain.

Our take: multimodal doesn't mean "throw everything at the model." The best products are still opinionated about the few interactions that matter, then make those feel instant and reliable.