Scaling AI to the masses
Networks are the distribution layer of AI. They connect infrastructure to users, enabling AI capabilities to flow into enterprises and consumers. As adoption scales, these companies gain bargaining power and become essential gatekeepers of AI diffusion.
Cloud providers |
Telecoms |
Hyperscalers |
|---|---|---|
| Offer scalable computing infrastructure that enables enterprises and developers to train, deploy, and run AI models. They are the backbone of AI accessibility, allowing businesses to tap into powerful compute resources without owning physical hardware. | Provide the connectivity infrastructure - fiber, mobile networks, and data transmission - that allows AI-powered services to reach end users. As AI applications become more bandwidth-intensive, telecoms play a growing role in enabling real-time data flow and edge computing. | Are massive-scale cloud and data center operators that build and manage the infrastructure needed to support AI workloads at global scale. They invest heavily in compute, storage, and networking to support AI training and inference across industries. |
| Examples include Amazon Web Services (AWS), Microsoft Azure, Google Cloud | Examples include AT&T, Verizon, Telstra | Examples include Microsoft, Meta, Google, Amazon |
Why they matter
Networks are positioned to capture mid-cycle value as AI moves from development to deployment across industries.
