Category: AI Economics

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

AI Economics: Model Selection Economics — The Hidden Cost-Performance Tradeoffs That Make or Break AI ROI

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18629905  58stabilfr·wdophcgmx
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[t]Trusted82%✓≥80% from verified, high-quality sources
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[f]Free Access82%✓≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
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[h]Freshness [REQ]17%✗≥60% of references from 2025–2026. Current: 17%
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[m]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (62 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Model selection represents one of the most consequential economic decisions in enterprise AI deployment, yet organizations consistently underestimate its financial implications. This paper examines the economics of choosing between model architectures—from simple linear regression to complex transformer networks—through the lens of total cost of ownership, inference economics, and organizationa...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18629905 58stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources18%○≥80% from editorially reviewed sources
[t]Trusted82%✓≥80% from verified, high-quality sources
[a]DOI36%○≥80% have a Digital Object Identifier
[b]CrossRef18%○≥80% indexed in CrossRef
[i]Indexed82%✓≥80% have metadata indexed
[l]Academic36%○≥80% from journals/conferences/preprints
[f]Free Access82%✓≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]2,384✓Minimum 2,000 words for a full research article. Current: 2,384
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18629905
[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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[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: 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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[s]Reviewed Sources14%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI43%○≥80% have a Digital Object Identifier
[b]CrossRef21%○≥80% indexed in CrossRef
[i]Indexed64%○≥80% have metadata indexed
[l]Academic43%○≥80% from journals/conferences/preprints
[f]Free Access86%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]5,280✓Minimum 2,000 words for a full research article. Current: 5,280
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18627664
[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]Diagrams8✓Mermaid architecture/flow diagrams. Current: 8
[x]Cited by0○Referenced by 0 other hub article(s)
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
BadgeMetricValueStatusDescription
[s]Reviewed Sources14%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI43%○≥80% have a Digital Object Identifier
[b]CrossRef21%○≥80% indexed in CrossRef
[i]Indexed64%○≥80% have metadata indexed
[l]Academic43%○≥80% from journals/conferences/preprints
[f]Free Access86%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]5,280✓Minimum 2,000 words for a full research article. Current: 5,280
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18627664
[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]Diagrams8✓Mermaid architecture/flow diagrams. Current: 8
[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 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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[s]Reviewed Sources31%○≥80% from editorially reviewed sources
[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI81%✓≥80% have a Digital Object Identifier
[b]CrossRef31%○≥80% indexed in CrossRef
[i]Indexed16%○≥80% have metadata indexed
[l]Academic81%✓≥80% from journals/conferences/preprints
[f]Free Access59%○≥80% are freely accessible
[r]References32 refs✓Minimum 10 references required
[w]Words [REQ]4,571✓Minimum 2,000 words for a full research article. Current: 4,571
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18626697
[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%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[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
BadgeMetricValueStatusDescription
[s]Reviewed Sources31%○≥80% from editorially reviewed sources
[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI81%✓≥80% have a Digital Object Identifier
[b]CrossRef31%○≥80% indexed in CrossRef
[i]Indexed16%○≥80% have metadata indexed
[l]Academic81%✓≥80% from journals/conferences/preprints
[f]Free Access59%○≥80% are freely accessible
[r]References32 refs✓Minimum 10 references required
[w]Words [REQ]4,571✓Minimum 2,000 words for a full research article. Current: 4,571
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18626697
[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%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[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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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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[s]Reviewed Sources42%○≥80% from editorially reviewed sources
[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI68%○≥80% have a Digital Object Identifier
[b]CrossRef61%○≥80% indexed in CrossRef
[i]Indexed32%○≥80% have metadata indexed
[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
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]4%✗≥60% of references from 2025–2026. Current: 4%
[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 (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
[b]CrossRef61%○≥80% indexed in CrossRef
[i]Indexed32%○≥80% have metadata indexed
[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
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]4%✗≥60% of references from 2025–2026. Current: 4%
[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 (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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[s]Reviewed Sources16%○≥80% from editorially reviewed sources
[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
[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%)

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
BadgeMetricValueStatusDescription
[s]Reviewed Sources16%○≥80% from editorially reviewed sources
[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
[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
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
[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
[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%)
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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
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%)
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AI Economics: Vendor Lock-in Economics — The Hidden Cost of AI Platform Dependency

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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
[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%)

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
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
[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
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
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*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
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AI Economics: Hidden Costs of AI Implementation — The Expenses Organizations Discover Too Late

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18617979  64stabilfr·wdophcgmx
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Enterprise AI implementations routinely exceed initial budgets by 40-75%, a pattern I have observed repeatedly across my 14 years in software engineering and 7 years specializing in AI systems at a leading technology consultancy. While organizations meticulously plan for obvious expenses such as infrastructure, licensing, and talent acquisition, they consistently underestimate or completely ove...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18617979 64stabilfr·wdophcgmx
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