The Inference Crisis, First Edition

In 2023, something fundamental shifted. Not just in what AI could do, but in how enterprises began to think about AI as operational infrastructure rather than a research capability. The question stopped being “Can we build a model?” and started being “Can we run it profitably, reliably, and at scale?”

We spent three decades building and optimizing the software stack for the internet era. Web servers, databases, message queues, orchestration layers, service meshes, each generation of infrastructure unlocked a new generation of applications. What we built in those decades let companies like Google, Netflix, and Amazon operate at a scale that was unimaginable before.

We are at exactly that same inflection point now. Except this time, the workload isn’t serving HTML or processing transactions. It’s running intelligence. And the infrastructure requirements are unlike anything we’ve built before. This outlook paper is about what that infrastructure looks like. It’s about why the systems enterprises are building today are breaking under the weight of real AI operations. And it’s about the new architectural layer that needs to exist, one that treats AI workloads the way they actually behave, not the way we wish they did.

“Training created the AI era. Inference infrastructure will determine which enterprises can operationalize AI profitably at scale.” The runtime era is here. The question is whether your infrastructure is ready for it.

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