From: redpointai
The development and adoption of AI-based voice systems, while promising, face significant hurdles related to latency and complex system integration [02:20:00]. These Challenges in AI product development hinder their full-scale deployment, particularly in established industries like telephony and customer support [02:00:00].
Latency in Voice AI
Initial voice AI systems suffered from high latency, with conversational turns taking around 4 seconds [02:48:00]. This made natural interaction difficult. However, advancements have drastically reduced this to an average of 320 milliseconds when using systems like LiveKit with GPT-4o’s real-time API [02:59:00]. This speed is now on par with human conversational turnovers, which average around 300 milliseconds [03:10:00]. In fact, some models, like those from Cerebras, can achieve inference in as little as 100 milliseconds, to the point where they respond too quickly, interrupting the user [02:42:00].
While advancements have largely overcome the technical latency challenges in model inference, the overall system latency remains a concern, especially in applications that require backend lookups or complex reasoning [02:15:00].
On-Device vs. Cloud Inference
A key aspect affecting latency and capability is the balance between on-device and cloud-based AI models [03:09:00].
- On-device models are crucial for immediate, “reflex” actions, particularly in robotics (e.g., a humanoid robot needing to react quickly to avoid an obstacle) [03:22:00].
- Cloud models are necessary for tasks requiring vast knowledge or complex reasoning, analogous to humans looking up information or calling support [03:28:00].
The ideal scenario for minimizing perceived latency might involve parallel processing, where both local and cloud models perform inference simultaneously, with the fastest relevant answer being used [03:41:00].
System Integration Challenges
Despite significant progress in latency, the broader adoption of voice AI, particularly in established sectors like customer support, faces substantial Challenges in AI Adoption and Deployment due to complex system integration [02:27:00].
- Existing Infrastructure: Many industries already have deeply embedded, large-scale systems for voice interaction (e.g., telephone-dominated customer support) [02:00:00] [02:27:00]. Swapping out these existing engines for new AI systems presents a significant risk to customer satisfaction [02:36:00].
- AI Model Imperfections: Current AI models are not perfect; they can hallucinate or make mistakes [02:52:00]. This necessitates a “human in the loop” to correct or take over from the AI agent, meaning organizations cannot fully eliminate their human contact centers [03:11:00].
- Backend System Updates: Many voice AI applications, such as customer support, require updating bespoke backend systems (e.g., Salesforce, custom ticket trackers) [02:34:00]. While AI models are developing the ability to “touch” or manipulate applications (like Anthropic’s computer use API), this capability is not yet fully mature [02:06:00] [02:46:00]. LiveKit is also building similar capabilities for agents to control headless browser instances and interact with web pages [02:48:00].
- Data Privacy: For privacy-sensitive use cases, there is a demand for purely local, on-device AI processing [03:45:00]. However, even new systems like Apple Intelligence often rely on secure cloud processing [03:59:00]. Sending data to the cloud is generally expected for legal reasons, generating updated training data, and handling erroneous examples [03:36:00].
The Role of LiveKit
LiveKit positions itself as the “nervous system” for AI, connecting human senses (eyes, ears, mouth via camera, microphone, speaker) to the AI “brain” (foundational models like OpenAI, Anthropic, Gemini) [05:39:00] [08:13:00].
The LiveKit SDK on a user’s device captures speech and transmits it over LiveKit’s Edge Network to a backend “agent” [02:55:00]. This agent processes the audio (e.g., converting speech to text for older models or sending raw audio directly to advanced multimodal models like GPT-4o) and sends the response back to the client device [04:10:00] [04:48:00]. LiveKit’s infrastructure aims to facilitate the flow of information between humans and AI, making advanced multimodal interactions possible [08:17:00]. This includes giving AI the “ability to touch” applications by controlling virtualized browser instances and streaming their video to users for interactive unblocking of agent tasks [02:48:00] [02:51:00].
Outlook
While speech recognition and AI models continue to advance, the next phase of AI-based voice systems will involve seamless integration of multiple modalities (voice, text, computer vision) and addressing the complexities of existing enterprise systems [11:51:00] [18:28:00]. The transition will likely be a hybrid approach, combining autonomous AI agents with human-in-the-loop oversight and diverse interfaces [10:18:00].