What does this research mean for the field?

In approximately 68% of enterprise use cases, simpler AI models deliver superior economic outcomes compared to more complex models. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The paper aims to analyze the economic implications of selecting AI model architectures for enterprise use.

February 16, 2026Open Access

AI Economics: Model Selection Economics — The Hidden Cost-Performance Tradeoffs That Make or Break AI ROI

Key Points

The paper aims to analyze the economic implications of selecting AI model architectures for enterprise use.
Examines various model architectures from linear regression to transformer networks
Analyzes total cost of ownership and inference economics
Considers organizational capacity for model deployment
In about 68% of cases, simpler models yield better economic results
Complex models do not always justify their higher costs in practical applications

Abstract

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.

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Authors

Oleh Ivchenko

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Odessa National Polytechnic University

References and Citations

Connected Papers

Building similarity graph...

Analyzing shared references across papers

AI Economics: Model Selection Economics — The Hidden Cost-Performance Tradeoffs That Make or Break AI ROI

Key Points

Abstract

Citation Network

Connected Papers

Discussion

Authors

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion

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