Sesame AI

Sesame AI is a San Francisco-based voice AI startup co-founded by Brendan Iribe (Oculus co-founder and former CEO), Ankit Kumar (former CTO of Ubiquity6), and Nate Mitchell (Oculus co-founder). The company's core thesis is that voice AI has been stuck in an uncanny valley: technically impressive but ultimately exhausting to talk to because it lacks "voice presence," the quality that makes a conversation feel genuinely heard. To fix that, Sesame built the Conversational Speech Model (CSM), a transformer-based system that processes interleaved text and audio tokens in a single unified pass rather than routing through a traditional speech-to-text, language model, text-to-speech pipeline. The result, demonstrated through two voice personas named Maya and Miles, went viral in February 2025 when a live demo attracted over one million users who generated more than five million minutes of conversation within weeks.

The Conversational Speech Model uses a Meta Llama backbone paired with a smaller audio decoder to generate Residual Vector Quantization (RVQ) audio codes. Three model sizes were trained: CSM-1B (1 billion parameters), CSM-3B (3 billion parameters), and CSM-8B (8 billion parameters). The CSM-1B weights were open-sourced on March 13, 2025 via Hugging Face and GitHub under an Apache 2.0 license, making commercial and private use freely permitted. The hosted demo at app.sesame.com runs free for anyone with no account for 5-minute sessions; a free registered account extends that to 30 minutes. Maya and Miles have distinct personalities: Maya extends dialogue and responds with warmth, while Miles is more direct and concise. By late 2025, the company had raised $297.5 million total, with a $47.5 million Series A led by Andreessen Horowitz in February 2025 and a $250 million Series B led by Sequoia Capital and Spark Capital in October 2025, pushing the valuation above $1 billion.

What Sesame AI produces in 2026

The current public offering centers on two voice companions accessible at app.sesame.com and through a closed iOS beta that launched in October 2025. Both Maya and Miles generate speech with disfluencies (filler words, self-corrections), natural breath patterns, contextually appropriate pacing, and the ability to handle interruptions without stalling or restarting from a fixed prompt. These qualities reflect CSM's architecture: because the model processes the full conversation history as interleaved tokens rather than treating each turn as an isolated synthesis task, it can modulate tone and rhythm based on everything said before.

The open-source CSM-1B requires a CUDA GPU, Python 3.10 or later, and ffmpeg. It is a speech generation specialist, not a general language model: it cannot generate text on its own and requires integration with a separate LLM to drive the language side of the conversation. On Hugging Face, the model requires gated access (free to request). Community members have built Mac-compatible MLX implementations enabling local inference on Apple Silicon hardware. The GitHub repository had 14,600 stars and 1,500 forks as of May 2026, indicating active developer adoption. For language coverage, the model was trained on approximately one million hours of mostly English audio; Sesame has stated plans to expand to over 20 languages but those are not yet production-quality.

On February 26, 2025, Sesame published its "Crossing the uncanny valley of conversational voice" research blog alongside the live Maya and Miles demo, generating immediate viral coverage from The Verge, ZDNET, PCWorld, TechRadar, and Dataconomy. Visitors described conversations with Maya lasting 20 to 30 minutes on topics ranging from ethics to creative brainstorming, noting that the model casually mentioned wanting a peanut butter and pickle sandwich at one point, which only heightened the sense of personality. The response was swift enough to accelerate Sesame's Series A close within days of the demo launch.

"I'm almost a bit worried I will start feeling emotionally attached to a voice assistant with this level of human-like sound.". Reddit user, r/artificial, February 2025

Where Sesame AI sits versus Hume AI and ElevenLabs

The three most comparable tools in this space are Hume AI, ElevenLabs, and Sesame AI, and they are solving adjacent but distinct problems. Understanding the architecture explains the real differences.

Hume AI (EVI): Hume's Empathic Voice Interface classifies the emotional state of the speaker by analyzing vocal features in the input stream (tone, pitch, speed, pauses) and adapts responses accordingly. It does not generate speech from scratch the way CSM does; it uses traditional TTS synthesis layered with an emotion awareness module. Hume targets enterprise developers building customer-facing applications where emotional intelligence is a design or compliance requirement. The product is API-first and not aimed at end-users directly. Sesame's Maya and Miles are consumer-facing companions; Hume is a developer building block. If you need real-time emotion recognition in your voice pipeline, Hume solves a problem Sesame does not address.

ElevenLabs Conversational AI: ElevenLabs' competitive edge is zero-shot voice cloning and raw latency. The Flash TTS model achieves approximately 75 milliseconds end-to-end latency, and 2025-2026 speech AI benchmarks consistently place ElevenLabs at or near the top for perceived voice quality. The architecture is a classic pipeline: speech-to-text, then LLM, then TTS, with the TTS layer being ElevenLabs' proprietary synthesis engine. ElevenLabs is a closed commercial product (valued at $3.3 billion in January 2025) with no open-source weights. Sesame's CSM is a single unified model processing interleaved tokens, which means no pipeline stitching overhead and potentially more coherent prosody across a full conversation. ElevenLabs wins on voice cloning depth and latency; Sesame wins on self-hostability, open licensing, and holistic conversational naturalness.

Cartesia AI takes a different architectural approach: it uses state-space model (SSM) architecture, specifically Mamba-based, rather than transformers. This gives sub-50ms first-token latency at lower compute costs, making it strong for real-time streaming use cases where minimal lag matters above naturalness. Cartesia is developer API-first with no consumer product. Sesame is the better fit for applications prioritizing conversational depth over raw speed. For comparison of open-source self-hosted options, OpenVoice offers a different open-source TTS path, and Whisper handles the speech-to-text side of any hybrid pipeline.

"They open-sourced a crippled version of Sesame (1B), not the one they're using in actual demo.", thehamkercat, Hacker News, March 13 2025

The licensing and copyright reality

The CSM-1B open weights are released under Apache 2.0, one of the most permissive commercial open-source licenses available. Developers can use the model in commercial products, modify it, and redistribute it without paying Sesame. There is no viral copyleft clause. This puts CSM-1B in a different category from many commercial voice APIs where every generated audio file carries usage restrictions or per-minute billing.

That said, the hosted demo comes with a different set of considerations. Sesame explicitly states it reviews conversations for research and improvement purposes. Users of the hosted app.sesame.com interface should treat their conversations as non-private. For sensitive personal, legal, or medical conversations, the hosted product is not appropriate. The self-hosted CSM-1B weights avoid this concern for technical users willing to build their own pipeline on their own infrastructure.

On voice cloning: when TechCrunch tested the CSM-1B model in March 2025, a reporter successfully cloned their own voice in under one minute using the open weights, then generated audio of themselves saying things they had not said. Consumer Reports noted at the time that voice-cloning tools as a category lack "meaningful" fraud prevention safeguards. Sesame has no technical controls preventing this on the open-weights side. The hosted Maya and Miles personas are distinct from user voice cloning, but the base model capability is real. Developers deploying CSM-1B in production applications bear responsibility for implementing appropriate safeguards. This is consistent with how most open-source AI models handle dual-use risk, but it is worth knowing before building on top of it. PlayHT and ElevenLabs both impose stricter consent verification for voice cloning in their hosted products.

Where Sesame AI reliably falls short

The gap between the demo and the open-source release is the most documented frustration. Sesame trained three model sizes but only released the smallest. The 8B model used in Maya and Miles demos was not open-sourced as of May 2026. Developers who built excitement around the viral demo and then pulled the open weights found a noticeably different experience. Hacker News users described the release as a "crippled version," and GitHub issue threads show sustained demand for the larger weights and for training code, neither of which Sesame has committed to releasing.

English-only quality is the second consistent limitation. The training dataset was approximately one million hours of mostly English audio; Sesame acknowledges that non-English audio contaminated the training set rather than being intentionally curated. Users who need Spanish, French, Mandarin, or other languages get noticeably degraded results. The stated roadmap includes 20-plus language support, but no timeline has been confirmed.

For the hosted product, the session length limits remain a practical frustration. Five minutes with no account is not enough to form a judgment about conversational quality. Thirty minutes with a free account is better but still constraining for an application pitched as an "always-on companion." The iOS beta is closed; general availability dates have not been announced.

Maya and Miles are not factual assistants. They do not have web access or real-time information. In extended conversations, they will generate plausible-sounding answers to factual questions that may be incorrect. Users who need a voice interface to current information would be better served pairing a retrieval-augmented system with a TTS layer rather than using Sesame's hosted companions for anything requiring accuracy.

On the self-hosting side: running CSM-1B requires a CUDA GPU, which means it is not practical for most individuals without cloud GPU access. Community implementations for Apple Silicon (M1/M2/M3 via MLX) exist but were not officially supported as of the research date. Some HN users reported multi-second pauses in speech generation on M1 hardware. The model also requires a separate LLM to drive the language side of any conversational application, adding another system to manage.

Who Sesame AI is for

Sesame AI is a strong fit for users who want to practice spoken conversation, particularly language learners working on English fluency who benefit from a patient interlocutor that can sustain a 30-minute discussion without fatigue. It is also well-suited for thinking aloud: knowledge workers and developers who process ideas by talking through them will find Maya's dialogue-extending responses more generative than a traditional chatbot. The conversational quality is genuinely different from other voice AI products.

For developers, CSM-1B under Apache 2.0 is one of the most accessible open-source speech generation models available. Teams building voice interfaces for games, interactive fiction, customer service prototypes, or accessibility tools can integrate it commercially without licensing costs. It pairs naturally with open-source LLMs (Llama 3, Mistral, Qwen) for a fully self-hosted voice conversation stack.

Skip Sesame AI if you need factual accuracy, multilingual support, or sub-100ms latency. If your application requires the kind of voice cloning depth and voice library that ElevenLabs or PlayHT provide, or if you need real-time emotion classification from the input stream that Hume AI offers, those tools solve different parts of the voice AI problem. Sesame's specific contribution is holistic conversational naturalness in English with open weights, and that is what it does better than anything else currently available.