Copilot+ PCs Can Speed Up AI Development With NPU, Microsoft MVP Says

Microsoft is pushing developers toward AI-powered apps built with Electron, while also making it easier to tap into on-device AI through NPUs on Copilot+ PCs.

According to Windows Central, Microsoft MVP Lance McCarthy recently demonstrated how developers can use built-in Windows AI APIs to quickly add AI features without relying on cloud infrastructure.

Local AI development powered by NPU

The key enabler is the Windows AI APIs included in the Windows App SDK, designed to run directly on devices equipped with an NPU.

This approach removes many traditional barriers. Developers don’t need cloud APIs, REST calls, usage-based billing, or even custom ONNX models to get started.

However, there is one requirement. Apps must run on Copilot+ PCs that include NPUs capable of handling these AI workloads locally.

Built-in AI features are ready to use

Microsoft already provides several ready-to-use AI capabilities that developers can plug into their apps.

These include Phi Silica for local language processing, AI-powered OCR for text recognition, imaging tools, and Windows Studio Effects.

In one example, a developer added AI-generated image descriptions to improve accessibility for visually impaired users reading comics.

The implementation reportedly took around 10 minutes, highlighting how low the barrier to entry has become.

Why this shift matters

This push signals a broader move toward on-device AI instead of cloud-dependent workflows.

Running AI locally offers faster response times, improved privacy, and eliminates ongoing API costs for developers.

It also aligns with Microsoft’s broader strategy around Copilot+ PCs, where NPUs play a central role in enabling next-generation AI experiences.

Interestingly, this push toward native, NPU-powered AI comes as Microsoft also experiments in the opposite direction.

The company is actively building a team focused on fully native Windows apps, yet its own Copilot app on Windows 11 recently shifted to a web-based architecture.

This creates a contrast between encouraging high-performance local AI development while simultaneously adopting web technologies for first-party apps.