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If you have used voice assistants before, you have probably experienced unexpected interruptions. You talk with the system, and the moment you pause briefly to think of the right term, it starts talking. Then you interrupt it so you can continue. This is a common frustration, because most voice models can either listen or speak, but not both at the same time.

Newer voice models, like OpenAI’s GPT-Live-1, change that by listening and speaking at the same time. The model constantly decides whether it should stay quiet, interrupt, or start talking. This makes the conversation feel more natural with fewer unintentional interruptions. Under the hood, these systems combine a new generation of voice model architecture with a serving system optimized for low latency. To understand how it all works end to end, we met with engineers on the GPT Voice team, Zahan Malkani and Justin Uberti (who created WebRTC). We thank both of them for sharing the details with us.

In this article, you’ll learn: the three generations of voice systems, including cascaded pipelines, turn-based end-to-end models, and full-duplex models; delegating thinking from talking, the core idea behind GPT-Live; the engineering behind the serving system, including the live and async paths; how evaluation is different in full-duplex voice systems; and engineering lessons for building realtime systems and what is next for voice.