Decentralized crypto compute powers AI as LLM adoption hits 46%

Large Language Models, or LLMs, are rapidly entering U.S. workplaces. Recent data in the article reports 45.9% of U.S. workers now use LLMs, up from 30.1% in December 2024 and rising to 43.2% by April 2025. That heavier and broader usage is increasing demand for faster, larger-scale compute to support model inference and more complex workloads. The piece notes a chart by AlphaTarget illustrating the adoption trend and includes a quick take that the summary is Artificial Intelligence generated and newsroom reviewed.

Spheron Network is presented as one decentralized solution positioned to meet that demand. The platform offers community-driven infrastructure that distributes inference workloads across a global network, aiming to scale efficiently while lowering costs compared with traditional cloud services. Spheron emphasizes accessibility for developers and businesses, arguing that its model leverages unused resources to make Artificial Intelligence computation more affordable and broadly available.

The article also describes a broader industry pivot from crypto mining to Artificial Intelligence compute. Bitfarms is highlighted as an example of a crypto-focused firm repurposing capacity for high-performance computing and AI tasks, with CEO Ben Gagnon saying the market for AI compute is massive. Decentralized networks can create token-based incentives and tap existing power and hardware to supply compute at scale. Looking ahead, the article concludes that as LLM adoption climbs, decentralized compute could play a critical role in filling the gap between blockchain infrastructure and the future of Artificial Intelligence by offering flexible, cost-efficient alternatives to centralized cloud providers.