From: redpointai
Live Kit is a company that develops a real-time communication platform, which has become a crucial “nervous system” for modern voice AI applications [06:13:00]. Initially, Live Kit’s open-source project focused on connecting humans to other humans for applications like video conferencing and live streaming [05:41:40]. However, with the advent of AI, its focus shifted to connecting human beings with machines [05:58:30].
Live Kit’s Role in Voice AI Systems
Live Kit provides the infrastructure that enables seamless interaction between users and AI models, particularly in voice-driven applications like ChatGPT voice [00:44:00].
How it Works with ChatGPT Voice
Live Kit’s integration with AI voice models, such as OpenAI’s GPT-4o, involves several key components [04:51:00]:
- SDK on the Device The Live Kit SDK runs on the user’s device (e.g., phone, computer), accessing its camera and microphone [02:55:00]. It captures the user’s speech and sends it over Live Kit’s global Edge Network [03:08:00].
- Edge Network This network consists of servers worldwide that communicate to form a mesh fabric, ensuring efficient audio transmission from the user’s device to a backend “agent” [03:11:00].
- Agent Framework AI companies like OpenAI build agents using Live Kit’s framework, which act as application servers to receive audio data from users [03:53:00].
- Traditional Voice Mode Workflow (Pre-Advanced Voice)
- User audio is converted from speech to text [04:14:00].
- This text is sent to the LLM (Large Language Model) [04:25:00].
- As tokens stream out of the LLM, they are converted back into speech [04:30:00].
- The generated speech is sent back over Live Kit’s network to the client device and played out [04:35:00].
- Advanced Voice Mode Workflow (e.g., with GPT-4o)
- User speech is sent directly as audio from the client over Live Kit’s network to the agent [04:48:00].
- The agent then sends this audio directly into the GPU machine via a real-time API (websocket connection) to the AI model (e.g., GPT-4o) [04:51:00].
- Models like GPT-4o are trained with a joint embedding of text and speech tokens, allowing inference to be done directly on audio embeddings [05:10:00].
- Speech is then generated directly by the AI model and returned through Live Kit’s network to the device [05:22:00].
The “Nervous System” Analogy
Live Kit describes itself as the “nervous system” because if foundational model companies (like OpenAI, Anthropic, Gemini) are building the “brain” (AGI), Live Kit provides the means for that brain to interact with the world [06:05:00].
Human-like Interaction
Just as humans use eyes, ears, and mouths to communicate, AI systems need analogous input/output mechanisms [07:44:00].
- Eyes: Camera [07:59:00]
- Ears: Microphones [08:03:00]
- Mouth: Speaker [08:05:00]
Live Kit’s role is to transport information from these “senses” to the AI “brain” and then transmit the brain’s responses back out to the user [08:16:00].
Enabling AI to “Touch”
Beyond sight, sound, and speech, Live Kit is developing capabilities to give AI the ability to “touch” applications [02:46:00]. This is achieved through:
- Headless Chrome Instances: Running virtualized browser instances in the cloud [02:51:00].
- Playwright Interface: Allowing an AI agent to command the browser, loading web pages, clicking buttons, and filling out forms [02:57:00].
- Interactive Streaming: If an agent gets stuck, it can stream the browser as video to a human user, allowing the user to click on pixels (replaying events in the cloud) to unblock or guide the agent [02:27:00]. This parallels Anthropic’s computer use API [02:37:00].
Future of AI Interfaces
The integration of voice AI, facilitated by platforms like Live Kit, is expected to drastically change how humans interact with computers and the nature of work [08:29:00].
Evolution of Interaction
- Replacement of Keyboard and Mouse: Traditional interfaces will be replaced by microphones and cameras as primary interaction tools [00:29:00].
- Multimodal Creative Tools: Future creative tools will be more voice-based, interactive, and multimodal [09:18:00]. AI will act as the orchestrator, shaping assets without doing all the mechanical work [09:51:00].
- Hybrid of Co-pilots and Agents: The future workplace will likely see a blend of AI co-pilots (working alongside humans) and autonomous agents (taking full ownership of tasks), similar to how humans collaborate [10:12:00].
- Persistence of Text: Text interfaces, like chat, will remain essential for human-to-human communication and will continue to be used in AI interactions alongside voice and computer vision [11:34:00].
- Hybrid Voice/Text/UI: For complex tasks, such as ordering from a new restaurant, a hybrid interface combining voice commands with on-the-fly generated UI (e.g., a menu to tap) will be more effective than purely voice-based interaction [12:40:00].
- Hands-Free Contexts: Voice is a natural modality for hands-free scenarios like driving or cooking [13:12:00].
The “Thin Client Dream”
The widespread adoption of chat interfaces (texting, Telegram, WhatsApp, Twitter, Slack) demonstrates a human preference for a consistent UI [15:15:00]. The “chat interface” could become the “Thin Client dream” for AI interactions, offering one universal UI for every application, seamlessly blending voice, dynamically generated UI, and text [15:43:00]. Future UI explorations will treat modalities more fluidly, mimicking mixed-modality human interactions, like pair programming where typing, voice, and computer vision are all in play [17:20:00].
Use Cases and Market Penetration
Live Kit observes two main categories of voice AI use cases [19:18:00]:
- Emergent Use Cases: These are new applications pushing the boundaries of AI, like AI tutors, therapists, and information lookup systems (e.g., OpenAI’s voice models, Gemini Live, Character AI, Perplexity) [19:03:00].
- “Low-Hanging Fruit” (Telephony): This area involves integrating AI into existing massive-scale voice systems, particularly in the telecommunications space [19:56:00]. This includes customer service IVR systems and even proactive AI calls to humans (e.g., for insurance eligibility lookups) [20:20:20]. Companies like Sierra and Parloa are disrupting this space by reducing costs [20:41:00]. The telephone, being a voice-native system for over 50 years, sees billions of calls monthly, making it an immediate, high-penetration use case for AI-based voice [20:56:00].
Challenges and Evolution of AI Voice
The full adoption of voice AI, particularly in areas like customer support, faces challenges beyond just latency [22:18:00].
- System Integration: Swapping out existing, large-scale human-driven systems with AI presents a significant risk to customer satisfaction (NPS) [22:27:00]. Integrating with bespoke backend systems (e.g., Salesforce, custom ticket tracking) for tasks like updating records is also complex [25:06:00].
- AI Imperfections: Current AI models are not perfect; they can hallucinate and require human-in-the-loop oversight to correct agents or take over tasks they cannot perform [22:52:00].
- Latency Improvements:
- In early 2023, conversational latency for AI voice was around 4 seconds [23:43:00].
- With GPT-4o and Live Kit’s real-time API, latency averages around 320 milliseconds, nearing human-level conversational speed (around 300 milliseconds) [23:53:00].
- Faster responses (e.g., Cerebras at 100 milliseconds) can sometimes be too fast, causing the AI to interrupt the user [24:24:00]. This indicates that current latency is already viable for many use cases [24:55:00].
On-Device vs. Cloud Models
The future will likely involve a hybrid approach for AI models:
- On-Device Models: Crucial for real-time, reflex actions, especially in robotics (e.g., a humanoid robot’s movement kinematics) where delays (like waiting for a cloud response to avoid a car) are unacceptable [32:38:00].
- Cloud Models: Necessary for accessing the “world’s information” or handling complex reasoning that goes beyond a device’s local knowledge (e.g., looking up information on a phone, troubleshooting a router) [34:23:00].
- Parallel Processing: Ideally, both local and cloud models would perform inference simultaneously, with the fastest, most accurate response being chosen [35:36:00].
- Data to Cloud: Data will generally be sent to the cloud for legal reasons, generating updated training data, data labeling, and correcting erroneous examples, though privacy-sensitive use cases might prefer purely local processing [36:36:00].
Future of AI in Entertainment
One area of particular excitement for Live Kit is the application of voice AI in video games, creating:
- Dynamic NPCs: Characters that are lifelike and responsive [43:49:00].
- Evolving Storylines: “Choose Your Own Adventure” games with infinite possibilities and permutations [43:56:00].
- Natural Interaction: Users interacting with game characters using natural human inputs, including voice [44:29:00]. This application of voice AI is seen as underhyped [44:21:00].