That redesign reaches deep into the networking and data movement. Nvidia recently announced plans to invest $2 billion each in photonics companies Lumentum and Coherent, which underscores where the pressure points are emerging. The issue is no longer only raw compute. It is also how quickly data can move between processors, racks, and clusters without creating unacceptable bottlenecks or power inefficiencies. As AI systems scale, latency, throughput, and energy usage become first-order economic concerns.
All of this suggests that AI will absolutely drive more demand for public cloud computing, but it will do so unevenly. Public cloud providers remain the fastest way to access advanced infrastructure, global scale, and managed AI services. At the same time, the cost profile of large, persistent AI workloads is prompting many enterprises to reconsider whether the traditional hyperscaler model should remain the default destination for every stage of the AI life cycle.
Most AI starts in the public cloud
When companies are experimenting, speed matters more than optimization. Public clouds give teams immediate access to GPUs, foundation model APIs, vector databases, orchestration tools, security controls, and integration services. They also allow businesses to quickly start pilots without waiting for procurement cycles, data center expansions, or specialized infrastructure teams.
Given the high level of uncertainty, the public cloud is often the right choice for first-generation AI. Enterprises do not yet know which use cases will deliver value, how much inference traffic they will see, or which architecture model will ultimately survive. At this stage, the ability to quickly try many things is more important than squeezing every dollar from the underlying infrastructure. Managed services reduce friction, and friction is the enemy of early adoption.
