Albert Gu aka the Mamba guy has the top ranked voice model now. How much of that is the architecture and how much is they just trained it better?

Introducing Multilingual Voices We partnered with Baklavastory, our neighbor in the Mission, to show what it sounds like when a business's character and warmth stay consistent, no matter who calls or what language they speak. It's easy to translate speech, but much harder to preserve a single identity across languages. Every language has different rhythms, tones, and emphasis. When you change the language, identity tends to get lost in that shift, and your voice ends up sounding like someone else. With Multilingual Voices, pick a voice and keep one brand identity in every market you serve: cartesia.ai/voices

We regret to inform our AI researcher Ronald that AI has replaced him. Sorry Ronald. Here's our team trying to tell his real voice from his cloned one. They were not good at it.

Simple beats complicated: We show that switching to a sliding-window attention mask with attention sinks (at no cost) beats linear attention post-training. Huge thanks to my collaborators @RheaSukthanker, @CameronPashmina, and @Emy_Aze. Paper: arxiv.org/abs/2608.28444

Sonic-3.6 is now generally available. In January we made a bet: stop tuning the existing paradigm, rebuild from the architecture up. How we topped our own best model in two months → cartesia.ai/blog/sonic-3.6/?…

Cartesia's Sonic 3.6 takes the #1 spot on both the Provider Voice and Controlled Voice Artificial Analysis Speech Arena leaderboards, surpassing Speechify AI's Simba 3.2 and Alibaba's Qwen-Audio-3.0-TTS-Plus, with Sonic 3.5 holding #2 on Controlled Voice Sonic 3.6 is the latest TTS model from @cartesia, supporting 40+ languages including English, Hindi, Spanish, French, German and Japanese. In the TTS Arena, the model has been preferred by voters and has demonstrated natural speech and adaptive pacing across conversations. Key takeaways: ➤ Controlled Voice: Sonic 3.6 takes #1 on the Controlled Voice Arena with an Elo of 1,144 (+18/-18) across 1,339 appearances, ahead of Cartesia's own Sonic 3.5 at 1,099 and ElevenLabs' Eleven v3 at 1,060 ➤ Provider Voice: Sonic 3.6 also takes #1 on the Provider Voice Arena with an Elo score of 1,286 (+19/-19) based on 1,300 arena appearances, placing it ahead of Simba 3.2 at 1,238 and Qwen-Audio-3.0-TTS-Plus at 1,237 ➤ Pricing: At $49 per 1M characters via the Cartesia platform, Sonic 3.6 is the most expensive of the top three models on quality, roughly 5x Speechify AI's Simba 3.2 at $10 and nearly double Alibaba's Qwen-Audio-3.0-TTS-Plus at $27.59, though still half the price of ElevenLabs' Eleven v3 at $100 ➤ Speed: Sonic 3.6 processes 136.1 characters per second of generation time, compared to 46.7 for ElevenLabs' Eleven v3 and 24.2 for Google's Gemini 3.1 Flash TTS

Good night DeepMind. Wow. FT: "Google is shifting control of its AI effort from London back to Silicon Valley" "executives and board members remained concerned by its weaker position in coding models and enterprise AI, where Anthropic and OpenAI have established an early lead." "Several current and former DeepMind employees said the reorganisation had sent shockwaves through the London-based lab, with some fearing it marked the end of the research culture that Hassabis has long protected. One former DeepMind executive at a rival company said they had already received calls from multiple staff who said they were ready to jump ship." "Over the past year, Hassabis has devoted more time to Isomorphic Labs, the AI drug discovery company he founded"

"Is this TTS model good?" gets harder to answer as models improve. "Good" is at least five axes: correctness, naturalness, contextual correctness, robustness, and most evals only capture the first. We wrote up the failure modes that make TTS eval hard: cartesia.ai/blog/is-this-tts…

Most genomic AI models use fixed rules to process DNA into chunks, imposing arbitrary boundaries on a sequence with its own biological structure. @arnavshah0, @victor_ljz, and team developed dnaHNet, a tokenizer-free foundation model that learns its own segmentation from scratch, supervised by @_albertgu, @genophoria, and @BoWang87.

Hybrid (transformer–RNN) models are fast becoming a serious alternative to the transformer, but a big question remains: how do they process tokens differently & how does this impact performance? We compared our transformer (Olmo 3) & hybrid (Olmo Hybrid) models to find out. 🧵

Excited to share last summer's work at Google Research! Most hybrid models today are static: each token sees the same interleaved pattern of your favorite linear model and attention. Oryx instead varies the model used across the sequence through shared representations. 1/

Most AI investing happens downstream of the frontier: a capability emerges, a category gets named, and capital rushes in. But by the time a category earns a clean box on a market map, the best builders have usually been living in the messy version for months. Agents. Reasoning. RL environments. World models. AI for Science. Recursive self-improvement. I call this frontier proximity: the ability to see what is becoming possible before it becomes consensus. My frontier proximity ladder: L0 Wrapper: uses today’s models. L1 Reactor: reacts fast to releases, but roadmap is downstream. L2 Anticipator: builds for where capabilities are going. L3 Native: depends on a non-obvious frontier bet. L4 Shaper: helps move the frontier itself. The point is not that every company needs to train models. Apps can have high frontier proximity if they understand what models will make possible next. Infra can have high frontier proximity if it knows what future agents, multimodal systems, robotics stacks, or scientific workflows will need. That is why we’re launching MoE Capital. MoE stands for Mixture of Experts. The idea is simple: build an AI fund around people closest to the frontier: frontier researchers, technical founders, AI-native builders, and seasoned operators. We don’t want to be another AI fund with a newsletter-level understanding of the frontier. We want to build the AI fund closest to the frontier. More in The Information: theinformation.com/newslette…

We released Sonic-3.5 and Ink-2, the #1 streaming models for text to speech and speech to text you can use in your voice agents today. New architectures enable new frontiers for speed and quality. We're now the only provider to have #1 models for both speaking and listening.