Accelerator Landscape: GPUs, TPUs, NPUs, ASICs The AI “compute market” is not one market. It is a set of hardware families with different assumptions about how models run, where they run, and what matters most: flexibility, throughput, latency, cost, power, supply, and integration risk. Teams that treat accelerators as interchangeable often end up with surprises […]

Cost per Token Economics and Margin Pressure Token economics is where AI becomes infrastructure. A system can be technically impressive and still be commercially fragile if the unit economics do not hold under real usage. “Cost per token” is not only a billing metric. It is a compact way to see whether a serving stack […]

On-Prem vs Cloud vs Hybrid Compute Planning Compute planning for AI systems is a strategy problem disguised as a hardware problem. The decision is not only where inference or training runs today, but how quickly the system can scale, how resilient it is to failures, how predictable the cost curve becomes, and how much operational […]

Power, Cooling, and Datacenter Constraints AI infrastructure often looks like a software story from a distance: models, prompts, tools, orchestration. Up close, the pace and price of deployment are frequently set by physical constraints. Power delivery, cooling capacity, rack density, and facility readiness decide how many accelerators you can actually run, how reliably they operate, […]

Supply Chain Considerations and Procurement Cycles AI infrastructure is not only a technical problem. It is also a supply problem. When a workload becomes GPU-bound, the constraint is rarely a clever piece of code. The constraint is often whether you can acquire, deploy, and keep enough reliable compute online at the right cost. Supply chain […]