Public cloud providers are missing the mark with AI



We’re already seeing the consequences. More enterprises are exploring alternative approaches, including private AI infrastructure and hybrid solutions. They’re finding that the promise of simple, scalable AI deployment in the public cloud often comes with hidden complexities and costs that make it difficult to achieve growth.

This isn’t just about technical limitations, it’s about business model adaptation. Public cloud providers need to recognize that AI requires a different approach to infrastructure, pricing, and service delivery. The current model of charging for general compute resources and adding premium fees for AI-specific services isn’t sustainable for most enterprises, and they are moving on to non-cloud alternatives.

The stakes are high. As enterprises continue to invest heavily in AI initiatives, they’ll gravitate toward platforms that can provide predictable performance, reasonable costs, and specialized infrastructure. Currently, that is AI private clouds, traditional on-prem hardware, managed service providers, and the new AI-focused microclouds, such as CoreWeave. Public cloud providers risk losing their position as the default choice for enterprise computing if they can’t adapt quickly enough. Since they are still lashing out at me, I suspect they have yet to get a clue.



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