In late 2025, tech hardware leaders signaled a shift in how AI will be processed — moving away from solely cloud-based models toward more on-device AI. HP announced plans to ship AI-enabled devices that run local models, reducing reliance on remote servers and enhancing data privacy. This appears part of a broader trend favoring decentralization and user control over personal data. 

The trend is being driven by growing concerns over data sovereignty, latency, and security vulnerabilities inherent in cloud-based AI. Running AI locally on devices — whether mobile, personal computers, or IoT endpoints — helps address these risks while unlocking use-cases that require real-time or offline processing. As on-device compute power and efficient AI chips improve, we may see an acceleration in privacy-first AI adoption across consumer and enterprise segments.

This shift also has broader implications for how AI ecosystems evolve: less dependence on centralized data centers may decentralize innovation, lower barriers for entry, and reduce exposure to single-point failures or geopolitical restrictions. For hardware manufacturers and developers, it represents an opportunity to build a new class of AI-enabled devices tailored for privacy, responsiveness, and user control.