Category: AI Economics

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

Capital Reallocation Dynamics: AI’s Influence on Venture Funding Allocation to Knowledge-Intensive Startups

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.22303555  73stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (88 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

This article examines how advancing AI capabilities reshape venture capital (VC) investment patterns toward knowledge-intensive startups. We develop a framework linking AI-driven predictive analytics, automation potential, and scalability assessments to VC decision-making criteria. Through a mixed-methods analysis of recent investment data and expert surveys, we identify three core mechanisms: ...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.22303555 73stabilfr·wdophcgmx
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[s]Reviewed Sources64%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI82%✓≥80% have a Digital Object Identifier
[b]CrossRef64%○≥80% indexed in CrossRef
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[l]Academic91%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
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[w]Words [REQ]1,249✗Minimum 2,000 words for a full research article. Current: 1,249
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.22303555
[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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (88 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI for Anti-Money Laundering: From Rule-Based to Neural Transaction Monitoring in Banking

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21908656  51stabilfr·wdophcgmx
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[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI75%○≥80% have a Digital Object Identifier
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[l]Academic83%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]1,138✗Minimum 2,000 words for a full research article. Current: 1,138
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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]55%✗≥60% of references from 2025–2026. Current: 55%
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[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 (61 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Anti-Money Laundering (AML) mechanisms are essential for maintaining financial integrity in the global banking sector. Traditional rule‑based transaction monitoring systems have long been employed to detect suspicious activity, yet they often generate high false‑positive rates, leading to operational inefficiencies and regulatory fatigue. Recent advances in neural network architectures and grap...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21908656 51stabilfr·wdophcgmx
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[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI75%○≥80% have a Digital Object Identifier
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[l]Academic83%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]1,138✗Minimum 2,000 words for a full research article. Current: 1,138
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21908656
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]55%✗≥60% of references from 2025–2026. Current: 55%
[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 (61 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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The Capability Adoption Stack: A Framework for Diagnosing Enterprise AI Readiness

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21715745  49stabilfr·wdophcgmx
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[t]Trusted94%✓≥80% from verified, high-quality sources
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[l]Academic91%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
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[w]Words [REQ]1,441✗Minimum 2,000 words for a full research article. Current: 1,441
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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]87%✓≥60% of references from 2025–2026. Current: 87%
[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 (48 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Enterprise AI adoption remains fragmented, with organizations struggling to diagnose their readiness across technical, human, and governance dimensions[1]. This article introduces the Capability Adoption Stack (CAS), a layered diagnostic model that maps data infrastructure, talent, governance, and integration maturity into a coherent framework[2]. By operationalizing these dimensions into measu...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21715745 49stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
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[l]Academic91%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References32 refs✓Minimum 10 references required
[w]Words [REQ]1,441✗Minimum 2,000 words for a full research article. Current: 1,441
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21715745
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]87%✓≥60% of references from 2025–2026. Current: 87%
[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 (48 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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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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[s]Reviewed Sources5%○≥80% from editorially reviewed sources
[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI85%✓≥80% have a Digital Object Identifier
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[l]Academic90%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]1,904✗Minimum 2,000 words for a full research article. Current: 1,904
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21550109
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]84%✓≥60% of references from 2025–2026. Current: 84%
[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 (68 × 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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[s]Reviewed Sources5%○≥80% from editorially reviewed sources
[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI85%✓≥80% have a Digital Object Identifier
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[l]Academic90%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]1,904✗Minimum 2,000 words for a full research article. Current: 1,904
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21550109
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]84%✓≥60% of references from 2025–2026. Current: 84%
[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 (68 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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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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[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI83%✓≥80% have a Digital Object Identifier
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[i]Indexed0%○≥80% have metadata indexed
[l]Academic92%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]1,525✗Minimum 2,000 words for a full research article. Current: 1,525
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21505176
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]58%✗≥60% of references from 2025–2026. Current: 58%
[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 (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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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI83%✓≥80% have a Digital Object Identifier
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[l]Academic92%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]1,525✗Minimum 2,000 words for a full research article. Current: 1,525
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21505176
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]58%✗≥60% of references from 2025–2026. Current: 58%
[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 (67 × 60%) + Required (2/5 × 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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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted60%○≥80% from verified, high-quality sources
[a]DOI20%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[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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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted60%○≥80% from verified, high-quality sources
[a]DOI20%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[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%)
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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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[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%)

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
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[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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The EU AI Act Explanability Requirements: Technical Specification Analysis

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19993955  78stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources65%○≥80% from editorially reviewed sources
[t]Trusted88%✓≥80% from verified, high-quality sources
[a]DOI76%○≥80% have a Digital Object Identifier
[b]CrossRef65%○≥80% indexed in CrossRef
[i]Indexed65%○≥80% have metadata indexed
[l]Academic82%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,072✓Minimum 2,000 words for a full research article. Current: 2,072
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19993955
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]80%✓≥60% of references from 2025–2026. Current: 80%
[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 (82 × 60%) + Required (4/5 × 30%) + Optional (2/4 × 10%)

The rapid deployment of artificial intelligence systems across high‑risk domains has prompted regulators to demand greater transparency and accountability. The European Union’s Artificial Intelligence Act (EU AI Act) introduces a comprehensive framework for trustworthy AI, with particular emphasis on explicability obligations for high‑risk AI systems. This article dissects the technical specifi...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19993955 78stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources65%○≥80% from editorially reviewed sources
[t]Trusted88%✓≥80% from verified, high-quality sources
[a]DOI76%○≥80% have a Digital Object Identifier
[b]CrossRef65%○≥80% indexed in CrossRef
[i]Indexed65%○≥80% have metadata indexed
[l]Academic82%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,072✓Minimum 2,000 words for a full research article. Current: 2,072
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19993955
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]80%✓≥60% of references from 2025–2026. Current: 80%
[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 (82 × 60%) + Required (4/5 × 30%) + Optional (2/4 × 10%)
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AI Task Taxonomy by Complexity: A Cost Analysis Across Model Architectures (March 2026)

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19336575  64stabilfr·wdophcgmx
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Score = Ref Trust (54 × 60%) + Required (4/5 × 30%) + Optional (3/4 × 10%)

Effective enterprise AI deployment requires matching task complexity to model capability — not defaulting to the most capable model for every workload. This meta-analysis introduces a six-tier task complexity taxonomy calibrated to March 2026 API pricing across nineteen models from six major providers. We demonstrate that systematic model-task alignment reduces per-task costs by 60–95% compared...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19336575 64stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted56%○≥80% from verified, high-quality sources
[a]DOI52%○≥80% have a Digital Object Identifier
[b]CrossRef4%○≥80% indexed in CrossRef
[i]Indexed56%○≥80% have metadata indexed
[l]Academic52%○≥80% from journals/conferences/preprints
[f]Free Access81%✓≥80% are freely accessible
[r]References27 refs✓Minimum 10 references required
[w]Words [REQ]3,147✓Minimum 2,000 words for a full research article. Current: 3,147
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19336575
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]88%✓≥60% of references from 2025–2026. Current: 88%
[c]Data Charts5✓Original data charts from reproducible analysis (min 2). Current: 5
[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 (4/5 × 30%) + Optional (3/4 × 10%)
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Same Pill, 171x the Price: Interstate Drug Pricing Variance in U.S. Medicaid Data

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19170546  46stabilfr·wdophcgmx
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[a]DOI7%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed87%✓≥80% have metadata indexed
[l]Academic13%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]5,157✓Minimum 2,000 words for a full research article. Current: 5,157
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19170546
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Between 2018 and 2024, U.S. Medicaid prescription drug spending grew from $16.1 billion to $27.6 billion — a 71% increase in six years, driven by a handful of high-price biologics, a brand-generic cost gap of over 3,000x per unit, and interstate price variations so extreme they defy any market-rational explanation. This paper presents a data-driven analysis of 13 visualizations derived from pub...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19170546 46stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted40%○≥80% from verified, high-quality sources
[a]DOI7%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed87%✓≥80% have metadata indexed
[l]Academic13%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]5,157✓Minimum 2,000 words for a full research article. Current: 5,157
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19170546
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]29%✗≥60% of references from 2025–2026. Current: 29%
[c]Data Charts13✓Original data charts from reproducible analysis (min 2). Current: 13
[g]Code✓✓Source code available on GitHub
[m]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (38 × 60%) + Required (3/5 × 30%) + Optional (2/4 × 10%)
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