Inception raises $50 million to build diffusion models for code and text | TechCrunch

"The leader of the project is Stanford professor Stefano Ermon, whose research focuses on diffusion models - which generate outputs through iterative refinement rather than word-by-word. These models power image-based AI systems like Stable Diffusion, Midjourney and Sora. Having worked on those systems since before the AI boom made them exciting, Ermon is using Inception to apply the same models to a broader range of tasks."

"Together with the funding, the company released a new version of its Mercury model, designed for software development. Mercury has already been integrated into a number of development tools, including ProxyAI, Buildglare, and Kilo Code. Most importantly, Ermon says the diffusion approach will help Inception's models conserve on two of the most important metrics: latency (response time) and compute cost."

Inception raised $50 million in seed funding led by Menlo Ventures, with additional angel investments from Andrew Ng and Andrej Karpathy. Stanford professor Stefano Ermon leads the project and applies diffusion models, which generate outputs via iterative refinement rather than token-by-token prediction. Diffusion approaches power image systems such as Stable Diffusion, Midjourney, and Sora and are being adapted to broader tasks including software development. The company released Mercury, a model optimized for coding workflows, and reported integrations with ProxyAI, Buildglare, and Kilo Code. Diffusion-based LLMs aim to reduce latency and compute cost compared with autoregressive models like GPT-5 and Gemini.

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