Bitcoin

Transformers are the backbone of modern LLMs, and their inefficiency is a growing bottleneck. If alternatives like sparse attention or power retention succeed, they could drastically lower the cost of running AI models, accelerate adoption, and shift the competitive landscape in both tech and crypto. For crypto, this ties into the broader narrative of AI x crypto convergence, where efficient AI could enable more on-chain inference and autonomous agents.

Startups are developing alternatives to transformer architecture for large language models, including sparse attention and power retention mechanisms.