AI is becoming less a single product than a layer of infrastructure spread across search, software, factories, laboratories, and public services. The important question is no longer whether machines can generate useful outputs, but how institutions will organize around them.
The shift changes where power sits. Companies that control computing, data centers, chips, cloud platforms, and distribution channels can influence what kinds of AI become cheap and widely available. At the same time, open models and smaller systems are lowering the cost of experimentation for universities, startups, and individuals.
That creates a practical policy challenge. Societies need rules for privacy, accountability, competition, and safety without freezing useful innovation. The most durable approach is likely to treat AI as infrastructure: something that should be reliable, auditable, interoperable, and subject to clear responsibilities.