Some enterprises are going further, adopting older models or open source alternatives for appropriate use cases. Qualcomm, for instance, has invested in running models on its own hardware rather than relying exclusively on cloud-based model providers. This approach requires more technical sophistication but can dramatically reduce per-token costs for high-volume applications.
The real challenge
Here’s what concerns me most about the current situation. Many enterprises deployed AI without putting adequate cost management infrastructure in place up front. They got caught up in the excitement of the technology, the competitive pressure to move fast, and the belief that the benefits would justify whatever the costs turned out to be. That approach worked when AI projects were small-scale experiments. Now that AI is becoming core to business operations, the lack of financial controls is becoming a serious problem. We need to bring the same rigor to AI procurement and deployment that we’ve brought to every other significant technology investment.
The organizations that succeed will treat AI token costs as a managed operational expense rather than an unpredictable variable. That means deploying the same tools and disciplines that have worked for cloud cost management: visibility, accountability, optimization, and continuous improvement.
