Learn why the best silicon is useless without the right AI developer tools.
Nov. 27, 2025 –
For years, edge AI has been about hardware. Buyers compared architectures, accelerators, and feature sets. But that’s shifting fast. The new priority is ease of deployment.
Building models in the cloud or on a PC has become second nature for AI teams. But getting those models running efficiently on an edge device? That’s still a major pain point. Specialized skills, fragmented tools, and steep learning curves have slowed time to market. Product builders are now demanding something different: streamlined, developer-friendly tools that integrate with familiar workflows.
Hardware providers saw this change coming and invested in SDKs tied to frameworks like TensorFlow, PyTorch, and ONNX. Still, many of those efforts haven’t measured up. The proof is in the recent wave of acquisitions—Qualcomm, Infineon, Nordic Semiconductor—all aimed at improving deployment tools.
Meanwhile, embedded leaders like Arm, NXP, and ADI have embraced Microsoft Visual Studio (VS) Code as a front end for edge AI development. The message is clear: ease of use is now the battleground. For builders, spec sheets no longer win deals; reducing deployment friction does.
Because even the most powerful system is useless if developers can’t harness it. Performance isn’t just about silicon; it’s about how easily developers can optimize and deploy their models onto a given SoC. Good tools can make or break the process. That means: