CAPITAL-CONSTRAINED AI INVESTMENT: A REAL OPTIONS AND EFFICIENCY FRONTIER APPROACH TO LANGUAGE MODEL FINE-TUNING
DOI:
https://doi.org/10.64734/bjss.2-1-02Keywords:
real options, capital budgeting, efficiency frontier, AI investment, language model fine-tuningAbstract
Fine-tuning investments in large language models (LLMs) have become central to corporate AI strategies in recent years. This study models fine-tuning configuration selection not merely as a technical choice but as an irreversible investment decision under capital constraints, proposing a decision architecture that integrates real options theory with portfolio efficiency frontier framework. The propositions are empirically illustrated through four complete fine-tuning runs on the Qwen3.5 model family. Findings indicate that the Qwen3.5-9B configuration constitutes the optimal stopping point on the efficiency frontier.
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Copyright (c) 2026 Ertunç ERERDİ- Onur ALTUNTAŞ- Tuba ÇELİK- Mücahit AKIN (Author)
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