Category: AI Economics

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

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
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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
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[s]Reviewed Sources65%○≥80% from editorially reviewed sources
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[a]DOI76%○≥80% have a Digital Object Identifier
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19993955
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[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)
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XAI Tool Economics: The Cost Structure of Explanation Generation

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19872600  61stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (67 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Explainable Artificial Intelligence (XAI) tools are increasingly deployed to provide transparency in machine l[REDACTED]g models, yet their economic viability remains poorly understood. This article analyzes the compute and engineering costs associated with generating explanations at scale across three prominent XAI methodologies: feature attribution, counterfactual generation, and prototype-ba...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19872600 61stabilfr·wdophcgmx
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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]71%✓≥60% of references from 2025–2026. Current: 71%
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[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 (67 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Transparent AI Sourcing: Build vs Buy Economics When Explanations Matter

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19858760  31stabilfr·wdophcgmx
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[t]Trusted50%○≥80% from verified, high-quality sources
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[h]Freshness [REQ]25%✗≥60% of references from 2025–2026. Current: 25%
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[m]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (31 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 10%)

Enterprise AI procurement faces a critical dilemma: build custom solutions for tailored explainability or buy off-the-shelf platforms with faster deployment but limited transparency. This article analyzes the economic trade-offs in AI sourcing decisions when explainability requirements are paramount, drawing on the IEEE 3119-2025 standard for AI procurement and recent empirical studies. Our ana...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19858760 31stabilfr·wdophcgmx
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[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI25%○≥80% have a Digital Object Identifier
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[w]Words [REQ]766✗Minimum 2,000 words for a full research article. Current: 766
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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]Diagrams0○Mermaid architecture/flow diagrams. Current: 0
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (31 × 60%) + Required (2/5 × 30%) + Optional (0/4 × 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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[t]Trusted56%○≥80% from verified, high-quality sources
[a]DOI52%○≥80% have a Digital Object Identifier
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[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%)

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
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted56%○≥80% from verified, high-quality sources
[a]DOI52%○≥80% have a Digital Object Identifier
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[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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[t]Trusted40%○≥80% from verified, high-quality sources
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[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%)

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
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted40%○≥80% from verified, high-quality sources
[a]DOI7%○≥80% have a Digital Object Identifier
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[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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Knowledge Collapse Economics: The Hidden Cost of Outsourcing Cognition to AI

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19080440  58stabilfr·wdophcgmx
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[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI42%○≥80% have a Digital Object Identifier
[b]CrossRef33%○≥80% indexed in CrossRef
[i]Indexed75%○≥80% have metadata indexed
[l]Academic8%○≥80% from journals/conferences/preprints
[f]Free Access42%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]2,148✓Minimum 2,000 words for a full research article. Current: 2,148
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19080440
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]73%✓≥60% of references from 2025–2026. Current: 73%
[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 (53 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

The dominant narrative around artificial intelligence economics focuses on productivity gains, labor displacement, and cost optimization. A less examined but potentially more consequential dimension is emerging: the erosion of collective human knowledge when AI substitutes for cognitive effort rather than augmenting it. This article analyzes the economic implications of knowledge collapse — a p...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19080440 58stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI42%○≥80% have a Digital Object Identifier
[b]CrossRef33%○≥80% indexed in CrossRef
[i]Indexed75%○≥80% have metadata indexed
[l]Academic8%○≥80% from journals/conferences/preprints
[f]Free Access42%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]2,148✓Minimum 2,000 words for a full research article. Current: 2,148
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19080440
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]73%✓≥60% of references from 2025–2026. Current: 73%
[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 (53 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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AI Boom vs. Geopolitics: How Political Instability Reprices Artificial Intelligence

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19047758  55stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted61%○≥80% from verified, high-quality sources
[a]DOI30%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed58%○≥80% have metadata indexed
[l]Academic30%○≥80% from journals/conferences/preprints
[f]Free Access88%✓≥80% are freely accessible
[r]References33 refs✓Minimum 10 references required
[w]Words [REQ]2,025✓Minimum 2,000 words for a full research article. Current: 2,025
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19047758
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]60%✓≥60% of references from 2025–2026. Current: 60%
[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 (47 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

The artificial intelligence investment boom of 2024–2026 has collided with an era of escalating geopolitical fragmentation. While global AI spending surpassed $300 billion in cumulative commitments by early 2026, the simultaneous intensification of chip e[REDACTED]rt controls, sovereign AI mandates, and regional conflicts has introduced a new class of repricing risk into AI capital allocation. ...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19047758 55stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted61%○≥80% from verified, high-quality sources
[a]DOI30%○≥80% have a Digital Object Identifier
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[i]Indexed58%○≥80% have metadata indexed
[l]Academic30%○≥80% from journals/conferences/preprints
[f]Free Access88%✓≥80% are freely accessible
[r]References33 refs✓Minimum 10 references required
[w]Words [REQ]2,025✓Minimum 2,000 words for a full research article. Current: 2,025
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19047758
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]60%✓≥60% of references from 2025–2026. Current: 60%
[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 (47 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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The Computer & Math 33%: Why the Most AI-Capable Occupation Group Still Automates Only a Third of Its Tasks

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19040207  36stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI11%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed22%○≥80% have metadata indexed
[l]Academic17%○≥80% from journals/conferences/preprints
[f]Free Access67%○≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,545✓Minimum 2,000 words for a full research article. Current: 2,545
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19040207
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]12%✗≥60% of references from 2025–2026. Current: 12%
[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 (25 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The Anthropic Economic Index (Massenkoff & McCrory, 2026) identifies computer and mathematical occupations as theoretically the most AI-e[REDACTED]sed occupation group in the U.S. economy, with 94% of tasks rated as feasible for LLM acceleration. Yet observed automation covers only 33% of those tasks — producing a 61-percentage-point capability-adoption gap that is the largest absolute gap of a...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19040207 36stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI11%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed22%○≥80% have metadata indexed
[l]Academic17%○≥80% from journals/conferences/preprints
[f]Free Access67%○≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,545✓Minimum 2,000 words for a full research article. Current: 2,545
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19040207
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]12%✗≥60% of references from 2025–2026. Current: 12%
[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 (25 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Frontier AI Consolidation Economics: Why the Big Get Bigger

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19028157  29stabilfr·wdophcgmx
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The frontier AI industry is consolidating at a pace that mirrors — and in some dimensions exceeds — the platform monopolization patterns of previous technology waves. As of early 2026, three providers control approximately 88% of enterprise AI API spending, with Anthropic commanding 40%, OpenAI 27%, and Google 21% of enterprise market share. Training costs for frontier models now exceed $100 mi...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19028157 29stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
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[l]Academic4%○≥80% from journals/conferences/preprints
[f]Free Access19%○≥80% are freely accessible
[r]References27 refs✓Minimum 10 references required
[w]Words [REQ]2,169✓Minimum 2,000 words for a full research article. Current: 2,169
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19028157
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]52%✗≥60% of references from 2025–2026. Current: 52%
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[g]Code—○Source code available on GitHub
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Silicon War Economics: The Cost Structure of Chip Nationalism

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19021816  31stabilfr·wdophcgmx
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The global semiconductor industry, projected to reach $1 trillion in revenue by late 2026, has become the primary arena for a new form of economic warfare: chip nationalism. Nations are pouring hundreds of billions of dollars into domestic fabrication capacity, driven not by comparative advantage but by strategic anxiety. This paper examines the economic cost structure of semiconductor reshorin...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19021816 31stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
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[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
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[f]Free Access17%○≥80% are freely accessible
[r]References36 refs✓Minimum 10 references required
[w]Words [REQ]2,530✓Minimum 2,000 words for a full research article. Current: 2,530
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19021816
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]43%✗≥60% of references from 2025–2026. Current: 43%
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[g]Code—○Source code available on GitHub
[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
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