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Tokens as Currency: A Novel Framework to Sustain AI Adoption and Profitability

by Siddharth Nandagopal
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 46
Year of Publication: 2025
Authors: Siddharth Nandagopal
10.5120/ijais2024452009

Siddharth Nandagopal . Tokens as Currency: A Novel Framework to Sustain AI Adoption and Profitability. International Journal of Applied Information Systems. 12, 46 ( Mar 2025), 44-51. DOI=10.5120/ijais2024452009

@article{ 10.5120/ijais2024452009,
author = { Siddharth Nandagopal },
title = { Tokens as Currency: A Novel Framework to Sustain AI Adoption and Profitability },
journal = { International Journal of Applied Information Systems },
issue_date = { Mar 2025 },
volume = { 12 },
number = { 46 },
month = { Mar },
year = { 2025 },
issn = { 2249-0868 },
pages = { 44-51 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number46/tokens-as-currency-a-novel-framework-to-sustain-ai-adoption-and-profitability/ },
doi = { 10.5120/ijais2024452009 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-03-01T23:35:10.981637+05:30
%A Siddharth Nandagopal
%T Tokens as Currency: A Novel Framework to Sustain AI Adoption and Profitability
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 46
%P 44-51
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Large Language Models (LLMs) are widely adopted across sectors for tasks like text generation, analytics, and automated support. However, many organizations struggle with token-based cost escalation, hampering long-term sustainability. The objective of this study is to address the urgent demand for an integrated framework that optimizes token usage and preserves financial stability. The research employs a conceptual, theoretical methodology by examining existing literature, synthesizing best practices, and proposing hypothetical use cases that illustrate potential benefits. Results reveal that the proposed Token-Efficient AI Utilization Framework (TEA-UF) can significantly improve token efficiency, enhance operational scalability, energy efficiency, and reduce overall expenditures linked to LLM deployments. Organizations adopting TEA-UF can mitigate vendor lock-in risks, balance on-premises and cloud infrastructures, and forecast costs more accurately. This approach underscores the viability of sustainable AI adoption without sacrificing innovation. In conclusion, the framework holds promise for revolutionizing LLM integration across diverse industries while maintaining economic responsibility. By implementing TEA-UF, businesses can achieve deeper market penetration, faster product evolution, and streamlined budgeting processes. The solution fosters global collaboration and fuels robust AI-driven growth, confirming that token efficiency and resource optimization can serve as cornerstones for successful LLM deployment strategies. Hence, it fosters synergy for enduring, future-proof AI adoption.

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Index Terms

Computer Science
Information Sciences
Algorithms
Econometrics
System Architecture
Scalability
Privacy
Resource Management

Keywords

Token Efficiency Sustainable AI LLM Deployment Cloud-Local Integration Cost Optimization Hybrid Strategies

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