The article discusses Cartesia, a startup that has developed a new architecture for artificial intelligence (AI) models called state space models (SSM). The company’s technology is designed to be more efficient and scalable than traditional transformer-based architectures, which are widely used in AI development. Cartesia’s SSMs can process large amounts of data quickly and efficiently, making them suitable for real-time applications.

The article highlights the following key points:

1. Cartesia’s technology: The company has developed a new architecture for AI models that uses state space models (SSMs) to process large amounts of data quickly and efficiently.
2. Efficiency and scalability: Cartesia’s SSMs are designed to be more efficient and scalable than traditional transformer-based architectures, which can handle large amounts of data but can be slow and computationally expensive.
3. Real-time applications: The company’s technology is suitable for real-time applications such as customer service, sales and marketing, robotics, security, and more.
4. Funding: Cartesia has closed a $22 million funding round led by Index Ventures, bringing the company’s total raised to $27 million.
5. Competitors: Other startups such as Zephyra, Mistral, and AI21 Labs have also developed hybrid Mamba-based models that challenge traditional transformer-based architectures.

The article concludes that Cartesia is well-positioned for success due to its innovative technology and recent funding round. The company’s vision is to create real-time intelligence that can reason over massive contexts, making it a leader in the field of AI development.

Key quotes:

• "By challenging the traditional reliance on transformer-based architectures, Cartesia has unlocked new ways to build real-time, cost-effective, and scalable AI applications," said Shardul Shah, partner at Index Ventures.
• "We have a big, long-term vision of becoming the go-to multimodal foundation model for every device," said Kartik Kulkarni, co-founder and CEO of Cartesia.

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