Model selection represents one of the most consequential economic decisions in enterprise AI deployment. This paper examines the economics of choosing between model architectures—from simple linear regression to complex transformer networks—through the lens of total cost of ownership, inference economics, and organizational capacity. Analysis reveals that in approximately 68% of enterprise use cases, simpler models deliver superior economic outcomes.
Oleh Ivchenko (2026) studied this question.