Category: AI Economics

AI Economics: Risk, Cost, and ROI Research by Oleh Ivchenko

Foundation Model Commoditization: How API Parity Is Reshaping the AI Stack in 2025

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21550109  61stabilfr·wdophcgmx
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Score = Ref Trust (67 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

In this article we analyze the commoditization of foundation model APIs, showing how parity in capability metrics is reshaping competitive dynamics across the AI stack in 2025. Using a mixed-methods approach combining benchmark analysis, cost modeling, and market trend evaluation, we identify three dominant research questions: (RQ1) How have benchmark scores converged across leading frontier mo...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21550109 61stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
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Shadow Banking Detection with Graph Neural Networks: Mapping Unofficial Lending Networks

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21535837  61stabilfr·wdophcgmx
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Score = Ref Trust (68 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Shadow banking has emerged as a critical stress point in the global financial system, enabling credit expansion outside regulated intermediation. This article develops a graph‑based anomaly detection framework that maps unofficial lending networks using transaction‑level data from Eastern European regulatory pilots. By treating financial entities as nodes and payment flows as weighted edges, we...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21535837 61stabilfr·wdophcgmx
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Opportunity Cost of AI Waiting: Economic Modeling of Delayed Enterprise AI Adoption

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21505176  55stabilfr·wdophcgmx
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[h]Freshness [REQ]58%✗≥60% of references from 2025–2026. Current: 58%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (67 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Enterprises that delay AI adoption face a measurable competitive disadvantage that can be quantified in economic terms. This article answers three research questions: (RQ1) What is the average competitive disadvantage cost for enterprises that delay AI adoption by 12–24 months relative to early adopters? (RQ2) Which industry sectors exhibit the highest cost differentials? (RQ3) What are the mac...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21505176 55stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
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AI-Driven Market Concentration: Measuring Oligopoly Risk in Foundation Model Economics

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21434928  64stabilfr·wdophcgmx
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[h]Freshness [REQ]62%✓≥60% of references from 2025–2026. Current: 62%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Foundation models are increasingly central to digital economies, yet their market structure remains sparsely quantified. This article asks how economic dynamics of winner‑take‑all markets manifest in the foundation‑model sector, what barriers to entry shape competitive equilibrium, and how switching costs influence firm behavior. We address these questions through a three‑pronged empirical stra...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21434928 64stabilfr·wdophcgmx
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Score = Ref Trust (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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The Inference Cost Collapse: Economic Implications of 10x Annual Price Reductions for LLM APIs

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21363672  36stabilfr·wdophcgmx
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Score = Ref Trust (40 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)

Academic Citation: Ivchenko, Oleh, Ivchenko, Iryna (2026). The Inference Cost Collapse: Economic Implications of 10x Annual Price Reductions for LLM APIs. Research article: The Inference Cost Collapse: Economic Implications of 10x Annual Price Reductions for LLM APIs. Odessa National Polytechnic University, Department of Economic Cybernetics. DOI: 10.5281/zenodo.21363672  ·  View on Z...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21363672 36stabilfr·wdophcgmx
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]50%✗≥60% of references from 2025–2026. Current: 50%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (40 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)
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OSS AI License Compliance: Legal Risks and Enterprise Obligations Under Custom AI Licenses

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21311225  66stabilfr·wdophcgmx
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[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References46 refs✓Minimum 10 references required
[w]Words [REQ]1,296✗Minimum 2,000 words for a full research article. Current: 1,296
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21311225
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]96%✓≥60% of references from 2025–2026. Current: 96%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (75 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

This article investigates the legal ramifications of non‑OSI‑approved AI licenses promulgated by major technology firms—including Meta’s Llama, Falcon, and Anthropic’s Claude—on enterprise adoption of open‑weight models. We frame the problem through three research questions: (RQ1) What contractual and regulatory obligations arise when deploying models distributed under custom licenses? (RQ2) Ho...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21311225 66stabilfr·wdophcgmx
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[a]DOI98%✓≥80% have a Digital Object Identifier
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[w]Words [REQ]1,296✗Minimum 2,000 words for a full research article. Current: 1,296
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]96%✓≥60% of references from 2025–2026. Current: 96%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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AI Wage Premium Evidence: Measuring Productivity Uplift and Salary Effects in Knowledge Work

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21270839  64stabilfr·wdophcgmx
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[l]Academic100%✓≥80% from journals/conferences/preprints
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[w]Words [REQ]926✗Minimum 2,000 words for a full research article. Current: 926
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21270839
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The rapid adoption of artificial intelligence (AI) technologies in knowledge-intensive industries has sparked debate about its impact on compensation structures and wage inequality. This article investigates whether AI tools increase or decrease salary differentials across skill levels and firm sizes, focusing on empirical evidence from firms with high AI penetration. We pose three research que...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21270839 64stabilfr·wdophcgmx
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[l]Academic100%✓≥80% from journals/conferences/preprints
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[w]Words [REQ]926✗Minimum 2,000 words for a full research article. Current: 926
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21270839
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Cash Flow Anomaly Detection: AI Models for Identifying Undeclared Income in SME Tax Returns

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21209524  34stabilfr·wdophcgmx
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[t]Trusted60%○≥80% from verified, high-quality sources
[a]DOI20%○≥80% have a Digital Object Identifier
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[l]Academic40%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References5 refs○Minimum 10 references required
[w]Words [REQ]1,304✗Minimum 2,000 words for a full research article. Current: 1,304
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21209524
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Unreported income among small and medium enterprises (SMEs) remains a critical challenge for tax authorities globally, with recent studies indicating that undeclared income accounts for 15-25% of the tax gap in European OECD nations. This article addresses the gap in current tax compliance tools by developing and evaluating machine l[REDACTED]g models specifically designed to detect anomalous c...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21209524 34stabilfr·wdophcgmx
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[t]Trusted60%○≥80% from verified, high-quality sources
[a]DOI20%○≥80% have a Digital Object Identifier
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[f]Free Access100%✓≥80% are freely accessible
[r]References5 refs○Minimum 10 references required
[w]Words [REQ]1,304✗Minimum 2,000 words for a full research article. Current: 1,304
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21209524
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (32 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Total Cost of Ownership for Enterprise LLMs: A 2025 Framework Beyond GPU Cost

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21186552  47stabilfr·wdophcgmx
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Score = Ref Trust (54 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Enterprise adoption of large language models (LLMs) has progressed from experimental pilots to core production workloads, yet most organizations continue to compute return on investment (ROI) using GPU‐hour pricing as the sole cost driver. This narrow view systematically underestimates the true economic burden of LLMs, omitting fine‑tuning expenses, retrieval‑augmented generation (RAG) infrastr...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21186552 47stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted75%○≥80% from verified, high-quality sources
[a]DOI63%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic75%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References8 refs○Minimum 10 references required
[w]Words [REQ]1,889✗Minimum 2,000 words for a full research article. Current: 1,889
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21186552
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]57%✗≥60% of references from 2025–2026. Current: 57%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (54 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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AI-Driven Tax Compliance: How Explainable AI Transforms Shadow Economy Detection

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.20267924  60stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI84%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic89%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]1,638✗Minimum 2,000 words for a full research article. Current: 1,638
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20267924
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]89%✓≥60% of references from 2025–2026. Current: 89%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (66 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Shadow economies impose massive revenue losses on governments worldwide, yet detecting illicit financial activity remains a persistent challenge. Traditional statistical and rule‑based methods often lack the interpretability needed for regulators to trust automated alerts. Recent advances in Explainable Artificial Intelligence (XAI) offer a pathway to illuminate decision‑making processes, enabl...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.20267924 60stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI84%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic89%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]1,638✗Minimum 2,000 words for a full research article. Current: 1,638
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.20267924
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]89%✓≥60% of references from 2025–2026. Current: 89%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (66 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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