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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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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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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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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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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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
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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
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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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Data Mining Chapter 5: Supervised Learning Taxonomy — Classification and Regression

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Data Science
Data Science by Iryna Ivchenko & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18626630  40stabilfr·wdophcgmx
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This chapter presents a hierarchical taxonomy of supervised l[REDACTED]g methods, organized along three primary dimensions: algorithmic architecture, l[REDACTED]g mechanism, and model interpretability. We trace the evolutionary development from early statistical classifiers through decision tree families, neural architectures, kernel methods, and ensemble strategies. Special attention is given ...

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Data Science by Iryna Ivchenko & Oleh Ivchenko DOI: 10.5281/zenodo.18626630 40stabilfr·wdophcgmx
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Anticipatory Intelligence: Anticipatory vs Reactive Systems — A Comparative Framework

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18626628  46stabilfr·wdophcgmx
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Score = Ref Trust (42 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

By Dmytro Grybeniuk, AI Architect | Anticipatory Intelligence Specialist | Stabilarity Hub | February 12, 2026

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18626628 46stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]14%✗≥60% of references from 2025–2026. Current: 14%
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Score = Ref Trust (42 × 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
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[f]Free Access93%✓≥80% are freely accessible
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[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 (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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[r]References15 refs✓Minimum 10 references required
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]17%✗≥60% of references from 2025–2026. Current: 17%
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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%)
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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
[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%
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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 (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
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[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI29%○≥80% have a Digital Object Identifier
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[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
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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]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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AI Economics: Hidden Costs of AI Implementation — The Expenses Organizations Discover Too Late

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18617979  64stabilfr·wdophcgmx
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[s]Reviewed Sources50%○≥80% from editorially reviewed sources
[t]Trusted91%✓≥80% from verified, high-quality sources
[a]DOI59%○≥80% have a Digital Object Identifier
[b]CrossRef50%○≥80% indexed in CrossRef
[i]Indexed59%○≥80% have metadata indexed
[l]Academic59%○≥80% from journals/conferences/preprints
[f]Free Access55%○≥80% are freely accessible
[r]References22 refs✓Minimum 10 references required
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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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AI Economics: ROI Calculation Methodologies for Enterprise AI — From Traditional Metrics to AI-Specific Frameworks

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18617078  57stabilfr·wdophcgmx
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Return on Investment (ROI) calculation for artificial intelligence projects presents unique methodological challenges that traditional IT investment frameworks fail to adequately address [2]. Drawing from fourteen years in enterprise software development and seven years of AI research, this article presents a comprehensive analysis of ROI calculation methodologies specifically designed for ente...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18617078 57stabilfr·wdophcgmx
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[s]Reviewed Sources18%○≥80% from editorially reviewed sources
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[a]DOI53%○≥80% have a Digital Object Identifier
[b]CrossRef24%○≥80% indexed in CrossRef
[i]Indexed41%○≥80% have metadata indexed
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18617078
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[h]Freshness [REQ]8%✗≥60% of references from 2025–2026. Current: 8%
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[g]Code—○Source code available on GitHub
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