Open-Weight Frontier Models vs Closed APIs: The Sovereignty Trade-Off
Choosing between open-weight models you can host and closed frontier APIs is less a beauty contest than a sovereignty, cost,...
AI Accelerators & Silicon
Inside the AI Accelerator Market: GPUs, Custom ASICs, and Who Wins Inference
AI accelerators used to mean one thing: an NVIDIA GPU. Today the field is crowded. Hyperscalers build their own chips,...
Multi-Trillion-Parameter Training: What the Next Silicon Generation Must Deliver
The compute ceiling for frontier models is no longer set by any one chip. It is set by the whole...
Vertical Integration or Cosplay? Why AI Labs Want Dirt-to-Model Stacks
A few years ago, an AI lab's supply chain fit on one invoice: rent GPUs from a cloud provider, train...
InfiniBand vs Ethernet for AI Clusters: The Fabric War Inside the AI Factory
Figure 1. Conceptual view of a GPU fabric inside an AI factory: east–west paths that keep accelerators busy. (AI-generated illustration...
On-Device AI in 2026: NPUs, Local Models, and What Still Needs the Cloud
Hero: phones and PCs now carry dedicated neural silicon for private, low-latency inference. For most of the generative AI boom,...
Physical AI Data Factories: How Synthetic Data Is Accelerating Industrial Robots
Industrial robots have never been short of compute. They have been short of examples. A vision-guided bin picker needs to...
