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AI Economics: Bias Costs — Regulatory Fines, Legal Liability, and the Economics of Reputational Damage

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18627664  58stabilfr·wdophcgmx
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Score = Ref Trust (63 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Algorithmic bias represents one of the most economically significant risks in enterprise AI deployment, yet its true costs remain chronically underestimated in project planning. This article presents a comprehensive economic analysis of bias-related costs spanning regulatory penalties, legal liability, remediation expenses, and the often-catastrophic impact of reputational damage. Drawing from ...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18627664 58stabilfr·wdophcgmx
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[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI43%○≥80% have a Digital Object Identifier
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]11%✗≥60% of references from 2025–2026. Current: 11%
[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)
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Cost-Effective AI: Build vs Buy vs Hybrid — Strategic Decision Framework for AI Capabilities

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18626731  58stabilfr·wdophcgmx
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[t]Trusted88%✓≥80% from verified, high-quality sources
[a]DOI38%○≥80% have a Digital Object Identifier
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[i]Indexed69%○≥80% have metadata indexed
[l]Academic31%○≥80% from journals/conferences/preprints
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Score = Ref Trust (62 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The build-versus-buy decision for AI capabilities requires strategic sophistication beyond traditional IT procurement—a portfolio approach combining internal development, commercial solutions, and hybrid configurations.

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18626731 58stabilfr·wdophcgmx
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[t]Trusted88%✓≥80% from verified, high-quality sources
[a]DOI38%○≥80% have a Digital Object Identifier
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[h]Freshness [REQ]13%✗≥60% of references from 2025–2026. Current: 13%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (62 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Economics: Data Poisoning — Economic Impact and Prevention

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18626697  64stabilfr·wdophcgmx
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]3%✗≥60% of references from 2025–2026. Current: 3%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Data poisoning represents one of the most insidious and economically devastating threats to enterprise AI systems. Unlike traditional cybersecurity attacks that target infrastructure, data poisoning corrupts the fundamental l[REDACTED]g process of machine l[REDACTED]g models, leading to systematic failures that may remain undetected for months or years. In my experience at Enterprise AI Divisio...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18626697 64stabilfr·wdophcgmx
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[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI81%✓≥80% have a Digital Object Identifier
[b]CrossRef31%○≥80% indexed in CrossRef
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[l]Academic81%✓≥80% from journals/conferences/preprints
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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]3%✗≥60% of references from 2025–2026. Current: 3%
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The Enterprise AI Landscape — Understanding the Cost-Value Equation

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18625628  46stabilfr·wdophcgmx
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[t]Trusted53%○≥80% from verified, high-quality sources
[a]DOI34%○≥80% have a Digital Object Identifier
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]12%✗≥60% of references from 2025–2026. Current: 12%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (42 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Enterprise AI spending reached $154 billion globally in 2025, yet 73% of organizations report difficulty extracting measurable business value from their AI investments [1]. This disconnect between investment and return represents the central challenge of our generation's most transformative technology. In my fourteen years building enterprise systems and seven years researching AI economics at ...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18625628 46stabilfr·wdophcgmx
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[t]Trusted53%○≥80% from verified, high-quality sources
[a]DOI34%○≥80% have a Digital Object Identifier
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[l]Academic37%○≥80% from journals/conferences/preprints
[f]Free Access39%○≥80% are freely accessible
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18625628
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]12%✗≥60% of references from 2025–2026. Current: 12%
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Score = Ref Trust (42 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Economics: Annotation Economics — Crowdsourcing vs Expert Labeling

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18625150  63stabilfr·wdophcgmx
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[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI68%○≥80% have a Digital Object Identifier
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[l]Academic65%○≥80% from journals/conferences/preprints
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[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (70 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Data annotation represents one of the most underestimated cost centers in enterprise AI development. While organizations meticulously budget for infrastructure, talent, and model training, annotation costs frequently emerge as budget-breaking surprises that derail otherwise promising AI initiatives. In my fourteen years of software development and seven years of AI research, I have observed ann...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18625150 63stabilfr·wdophcgmx
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[s]Reviewed Sources42%○≥80% from editorially reviewed sources
[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI68%○≥80% have a Digital Object Identifier
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[l]Academic65%○≥80% from journals/conferences/preprints
[f]Free Access52%○≥80% are freely accessible
[r]References31 refs✓Minimum 10 references required
[w]Words [REQ]3,543✓Minimum 2,000 words for a full research article. Current: 3,543
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18625150
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]4%✗≥60% of references from 2025–2026. Current: 4%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (70 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Economics: Data Quality Economics — The True Cost of Bad Data in Enterprise AI

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18624306  58stabilfr·wdophcgmx
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[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI37%○≥80% have a Digital Object Identifier
[b]CrossRef26%○≥80% indexed in CrossRef
[i]Indexed74%○≥80% have metadata indexed
[l]Academic37%○≥80% from journals/conferences/preprints
[f]Free Access79%○≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]4,458✓Minimum 2,000 words for a full research article. Current: 4,458
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18624306
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]11%✗≥60% of references from 2025–2026. Current: 11%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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[m]Diagrams7✓Mermaid architecture/flow diagrams. Current: 7
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (63 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Lead Engineer, a leading technology consultancy | PhD Researcher, Odessa Polytechnic National University

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18624306 58stabilfr·wdophcgmx
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[s]Reviewed Sources16%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI37%○≥80% have a Digital Object Identifier
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[i]Indexed74%○≥80% have metadata indexed
[l]Academic37%○≥80% from journals/conferences/preprints
[f]Free Access79%○≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]4,458✓Minimum 2,000 words for a full research article. Current: 4,458
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18624306
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]11%✗≥60% of references from 2025–2026. Current: 11%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams7✓Mermaid architecture/flow diagrams. Current: 7
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (63 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Economics: Data Acquisition Costs and Strategies — The First Economic Gatekeeper of Enterprise AI

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18623221  53stabilfr·wdophcgmx
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[s]Reviewed Sources7%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI14%○≥80% have a Digital Object Identifier
[b]CrossRef7%○≥80% indexed in CrossRef
[i]Indexed86%✓≥80% have metadata indexed
[l]Academic14%○≥80% from journals/conferences/preprints
[f]Free Access93%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]2,823✓Minimum 2,000 words for a full research article. Current: 2,823
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18623221
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]20%✗≥60% of references from 2025–2026. Current: 20%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams7✓Mermaid architecture/flow diagrams. Current: 7
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (54 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Academic Citation: Ivchenko, O. (2026). AI Economics: Data Acquisition Costs and Strategies — The First Economic Gatekeeper of Enterprise AI. AI Economics Series. Odesa National Polytechnic University. DOI: 10.5281/zenodo.18623221 Abstract Data acquisition represents the foundational economic challenge of enterprise AI implementation, often consuming 40-80% of total project budgets before a sin...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18623221 53stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources7%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI14%○≥80% have a Digital Object Identifier
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[i]Indexed86%✓≥80% have metadata indexed
[l]Academic14%○≥80% from journals/conferences/preprints
[f]Free Access93%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]2,823✓Minimum 2,000 words for a full research article. Current: 2,823
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18623221
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]20%✗≥60% of references from 2025–2026. Current: 20%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams7✓Mermaid architecture/flow diagrams. Current: 7
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (54 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Economics: Open Source vs Commercial AI — The Strategic Economics of Build Freedom

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18622040  58stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources11%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI44%○≥80% have a Digital Object Identifier
[b]CrossRef11%○≥80% indexed in CrossRef
[i]Indexed67%○≥80% have metadata indexed
[l]Academic44%○≥80% from journals/conferences/preprints
[f]Free Access89%✓≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]3,815✓Minimum 2,000 words for a full research article. Current: 3,815
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18622040
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]8%✗≥60% of references from 2025–2026. Current: 8%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams9✓Mermaid architecture/flow diagrams. Current: 9
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (62 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Academic Citation: Ivchenko, O. (2026). AI Economics: Open Source vs Commercial AI — The Strategic Economics of Build Freedom. AI Economics Series. Odesa National Polytechnic University. DOI: 10.5281/zenodo.18622040 Abstract The choice between open source and commercial AI solutions represents one of the most consequential economic decisions enterprise leaders face today [1]. This paper provide...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18622040 58stabilfr·wdophcgmx
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[s]Reviewed Sources11%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI44%○≥80% have a Digital Object Identifier
[b]CrossRef11%○≥80% indexed in CrossRef
[i]Indexed67%○≥80% have metadata indexed
[l]Academic44%○≥80% from journals/conferences/preprints
[f]Free Access89%✓≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]3,815✓Minimum 2,000 words for a full research article. Current: 3,815
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18622040
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]8%✗≥60% of references from 2025–2026. Current: 8%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams9✓Mermaid architecture/flow diagrams. Current: 9
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (62 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Economics: Vendor Lock-in Economics — The Hidden Cost of AI Platform Dependency

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18620726  56stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources7%○≥80% from editorially reviewed sources
[t]Trusted87%✓≥80% from verified, high-quality sources
[a]DOI27%○≥80% have a Digital Object Identifier
[b]CrossRef7%○≥80% indexed in CrossRef
[i]Indexed80%✓≥80% have metadata indexed
[l]Academic27%○≥80% from journals/conferences/preprints
[f]Free Access93%✓≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]3,997✓Minimum 2,000 words for a full research article. Current: 3,997
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[g]Code—○Source code available on GitHub
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (59 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Vendor lock-in represents one of the most underestimated economic risks in enterprise AI adoption, with switching costs typically ranging from 2.3x to 5.7x the original implementation investment.

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18620726 56stabilfr·wdophcgmx
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[s]Reviewed Sources7%○≥80% from editorially reviewed sources
[t]Trusted87%✓≥80% from verified, high-quality sources
[a]DOI27%○≥80% have a Digital Object Identifier
[b]CrossRef7%○≥80% indexed in CrossRef
[i]Indexed80%✓≥80% have metadata indexed
[l]Academic27%○≥80% from journals/conferences/preprints
[f]Free Access93%✓≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]3,997✓Minimum 2,000 words for a full research article. Current: 3,997
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18620726
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]17%✗≥60% of references from 2025–2026. Current: 17%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (59 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Economics: AI Talent Economics — Build vs Buy vs Partner

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18619213  53stabilfr·wdophcgmx
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[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI29%○≥80% have a Digital Object Identifier
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18619213
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (54 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

*Scarcity Index: Composite score (1-10) based on demand/supply ratio, salary growth, and time-to-fill

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18619213 53stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI29%○≥80% have a Digital Object Identifier
[b]CrossRef21%○≥80% indexed in CrossRef
[i]Indexed57%○≥80% have metadata indexed
[l]Academic21%○≥80% from journals/conferences/preprints
[f]Free Access71%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]3,350✓Minimum 2,000 words for a full research article. Current: 3,350
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18619213
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams7✓Mermaid architecture/flow diagrams. Current: 7
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (54 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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