Economics is not that simple
What gets lost in the excitement is that convenience has a complex cost structure. The same characteristics that make the public cloud attractive to AI also make it more expensive to operate at scale. You pay not only for the raw infrastructure, but also for abstraction, acceleration, service layering, managed operations, premium tools, and provider margin. As the success of AI grows, so do operating costs.
This is important because AI is not a one-application story. Businesses rarely stop at a single model, pilot or use case. They want dozens of solutions spanning customer service, software development, supply chain planning, security operations, analytics and internal productivity. Every dollar spent on one expensive AI cloud job is one dollar not available for another. This is a strategic question that too many companies overlook.
The question is not whether the cloud can run AI. Of course he can. In many cases, this is the fastest route to appreciation. The more important question is whether long-term operating expenses will leave enough room in the budget to build a portfolio of AI solutions, rather than a few isolated wins. If the answer is no, premium comfort starts to look less like an acceleration and more like a limitation.