According to God of Prompt, current large language models (LLMs) communicate by generating text sequentially, which is slow, costly, and can lose nuance during translation between models (source: @godofprompt, Twitter, Jan 17, 2026). The new concept, C2C (model-to-model communication), aims to enable direct, meaning-rich information transfer between AI models, bypassing traditional text outputs. This development could significantly reduce latency, lower operational costs, and enable more efficient AI-to-AI collaboration, opening up business opportunities in enterprise automation, scalable agent systems, and advanced AI integrations.

According to God of Prompt, C2C introduces a neural 'Fuser' component that connects the KV-Cache memory storage of individual AI models, enabling efficient information sharing and collaborative processing between models. This advancement addresses a critical challenge in multi-model systems, where isolated memory often limits joint performance. The Fuser’s capability to bridge KV-Cache architectures opens new business opportunities for scalable AI solutions, such as multi-agent workflows, advanced conversational AI, and collaborative robotics, by facilitating seamless cross-model knowledge transfer (source: @godofprompt, Jan 17, 2026).

According to @godofprompt on Twitter, researchers have introduced Cache-to-Cache (C2C) technology, enabling large language models (LLMs) to communicate directly through their key-value caches (KV-Caches) without generating intermediate text. This method results in an 8.5-10.5% accuracy increase, operates twice as fast, and eliminates token waste, marking a significant leap in AI efficiency and scalability. The C2C approach has major business implications, such as reducing computational costs and accelerating multi-agent AI workflows, paving the way for more practical and cost-effective enterprise AI solutions (source: @godofprompt, Jan 17, 2026).