From the sand that becomes silicon to the people paying for the result, every layer of the AI ecosystem explained in plain English, with the companies that matter at each one.
AI can run on conventional processors, but specialised accelerators make large modern AI workloads dramatically more efficient. This layer designs, manufactures and packages them.
AI's infrastructure problem is increasingly not just whether GPUs can be bought, but whether they can be powered and cooled. That runs from generation through the grid to the rack.
The buildings, campuses and cloud platforms where AI actually runs. Hyperscale cloud, colocation, specialised AI and HPC facilities, and sovereign infrastructure are four different businesses.
Training a large model means tens of thousands of processors behaving as one machine. Networking can become the bottleneck even when there is plenty of compute available.
The city's libraries, records and archives. Models are only one part of the system: enterprises also need data that is accessible, trusted, governed, structured, searchable, permissioned and connected to business context.
The organisations training foundation models and serving them through APIs and products. Several of the most important are private companies, which is inconvenient but does not make them less important.
Companies that use AI to deliver products, workflows and services people actually buy. This is where model capability meets a budget holder with a problem.
Security is not a step in the sequence. Every layer adds attack surface, from firmware in the data centre to an agent holding credentials on someone's behalf.
Rules shape what can be built, where data may sit, and what a deployed model must be able to explain about itself. They cut across every layer rather than sitting on top of one.
Technology creates economic value only when people and organisations use it. Demand runs across every layer rather than sitting on top of one, which is why it is treated here as a cross-cutting question.