The AI Industry Is Shifting from Intelligence to Intelligence Per Dollar

The dominant conversation in AI still revolves around which company has the smartest frontier model. However, the real economic transition underway is from maximizing raw intelligence to maximizing intelligence per dollar.

Enterprises are rapidly discovering that most workloads do not require the most expensive frontier models. As inference costs continue collapsing at an extraordinary rate, organizations are shifting from asking "Does AI work?" to "Does it work economically at scale?" This change favors cheaper, "good enough" models (including open-weight ones) for the vast majority of tasks, while reserving frontier intelligence only for high-value problems where additional capability creates outsized returns.

As intelligence becomes abundant and commoditized for routine work, economic value is migrating in two important ways:

•⁠ ⁠Toward the owners of installed compute (hyperscalers), who benefit whether demand grows faster than efficiency or efficiency gains make existing infrastructure dramatically more productive.

•⁠ ⁠Even more powerfully, toward the orchestration layer — the platforms that intelligently route different tasks to the optimal model based on cost, latency, accuracy, governance, security, and compliance (e.g., AWS Bedrock, Azure AI Foundry, Google Vertex AI).

Models gradually become interchangeable components. The orchestration platform becomes the sticky, high-switching-cost layer that governs how intelligence is deployed across the enterprise.

This Ledger Entry expands how readers think about the economics of AI by showing that as inference costs collapse and open models improve, value is migrating away from frontier model builders toward the owners of installed compute and especially the orchestration layers that intelligently route workloads across many models based on cost, capability, governance, and compliance.