Meeting AI Demand Starts With The Right Infrastructure

​Juan Font is President and CEO of CoreSite.

In the words of Benjamin Franklin: “Great haste makes great waste.” It’s an apt perspective for viewing the rush to AI deployment in the enterprise today, where organizational leaders race into AI deployment decisions without always having the infrastructure in place to support them.

For many enterprises, the default setting is to contract directly with major frontier labs such as Anthropic or OpenAI and big cloud providers such as Google or Microsoft. Effectively, they’re racing to allow employees to use tokens for almost anything—often without much oversight.

This rush is strikingly reminiscent of when the cloud was introduced in the 2010s, and employees opened public cloud accounts, running up very large tabs—again with very little oversight over the projects being undertaken.

Today, companies are rushing to consume tokens with relatively little thought given to how that consumption is supported. In many cases, the problem isn’t the AI deployment itself. It’s the infrastructure supporting it. And solving that problem is critical to developing and realizing a sound AI strategy.

A Foundation for Enterprise AI

At the innermost layer, AI requires compute. But compute in the form of GPU chips is extremely scarce. To deal with that scarcity, a whole new segment of companies called neoclouds that offer GPU as a service has emerged.

Enterprises that want to develop or train their own medium-sized models are renting or leasing compute infrastructure from these neoclouds. As that market matures, some companies are beginning to buy the hardware themselves, particularly in financial services, where firms require massive amounts of compute as well as real-time access to financial data and exchanges.

Connectivity is the AI Multiplier

Building Infrastructure for Scale

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