AI Onboarding Economics: The Hidden Cost of Getting Teams to Actually Use AI Tools

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Gap Research
Gap Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21634045  65stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (74 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The diffusion of artificial intelligence tools across enterprise environments promises productivity gains, yet many organizations encounter a stark discrepancy between technical capability and actual adoption. This article investigates the economic and sociotechnical dimensions of AI onboarding, framing the challenge as a multi‑dimensional cost problem that includes explicit monetary expenditur...

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Gap Research by Oleh Ivchenko DOI: 10.5281/zenodo.21634045 65stabilfr·wdophcgmx
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[w]Words [REQ]1,259✗Minimum 2,000 words for a full research article. Current: 1,259
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21634045
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]91%✓≥60% of references from 2025–2026. Current: 91%
[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)
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AI-Powered Economic Warfare: Sanctions Evasion Detection in Cross-Border Transactions

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Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21626136  58stabilfr·wdophcgmx
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[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI86%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed14%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]1,571✗Minimum 2,000 words for a full research article. Current: 1,571
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21626136
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]50%✗≥60% of references from 2025–2026. Current: 50%
[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 (73 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Economic sanctions have become a central instrument of geopolitical policy, yet enforcement agencies repeatedly encounter sophisticated evasion schemes that exploit layered corporate structures, blended finance, and digital asset ecosystems. Traditional rule‑based screening tools struggle with the combinatorial explosion of transaction pathways and the adaptive nature of illicit networks. Machi...

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Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.21626136 58stabilfr·wdophcgmx
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[a]DOI86%✓≥80% have a Digital Object Identifier
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[l]Academic100%✓≥80% from journals/conferences/preprints
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[r]References7 refs○Minimum 10 references required
[w]Words [REQ]1,571✗Minimum 2,000 words for a full research article. Current: 1,571
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21626136
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]50%✗≥60% of references from 2025–2026. Current: 50%
[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 (73 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Temporal Consistency in AI Research Articles: Measuring Citation Recency and Knowledge Cutoff Artifacts

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Quality Research
Quality Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21614026  58stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI93%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]1,518✗Minimum 2,000 words for a full research article. Current: 1,518
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21614026
[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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (73 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

The rapid deployment of large language models (LLMs) in scholarly workflows has blurred the boundary between human‑produced and machine‑generated research artifacts. This article investigates systematic temporal inconsistencies that arise when LLMs are used to draft or co‑author academic articles, focusing on three inter‑related phenomena: (1) citation recency drift, (2) knowledge‑cutoff artifa...

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Quality Research by Oleh Ivchenko DOI: 10.5281/zenodo.21614026 58stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
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[a]DOI93%✓≥80% have a Digital Object Identifier
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[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]1,518✗Minimum 2,000 words for a full research article. Current: 1,518
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21614026
[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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (73 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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From Black Box to Governance Dashboard: Integrating Explainability Metrics into Model Lifecycle Management

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Technical Research
Technical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21610501  64stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted96%✓≥80% from verified, high-quality sources
[a]DOI77%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed23%○≥80% have metadata indexed
[l]Academic96%✓≥80% from journals/conferences/preprints
[f]Free Access96%✓≥80% are freely accessible
[r]References26 refs✓Minimum 10 references required
[w]Words [REQ]689✗Minimum 2,000 words for a full research article. Current: 689
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21610501
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]72%✓≥60% of references from 2025–2026. Current: 72%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams1✓Mermaid architecture/flow diagrams. Current: 1
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Explainability has become a central concern for organizations deploying machine‑l[REDACTED]g systems at scale. While numerous techniques for post‑hoc interpretation have been proposed, the lack of a unified observability framework that combines fairness, transparency, and performance metrics across model versions limits actionable governance. This article introduces a Governance Dashboard that ...

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Technical Research by Oleh Ivchenko DOI: 10.5281/zenodo.21610501 64stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted96%✓≥80% from verified, high-quality sources
[a]DOI77%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed23%○≥80% have metadata indexed
[l]Academic96%✓≥80% from journals/conferences/preprints
[f]Free Access96%✓≥80% are freely accessible
[r]References26 refs✓Minimum 10 references required
[w]Words [REQ]689✗Minimum 2,000 words for a full research article. Current: 689
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21610501
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]72%✓≥60% of references from 2025–2026. Current: 72%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams1✓Mermaid architecture/flow diagrams. Current: 1
[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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Energy Transparency in Open-Source AI: Training Carbon Footprints and Power Consumption Reporting Standards

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Open Source Research
Open Source Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21577288  56stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References2 refs○Minimum 10 references required
[w]Words [REQ]1,707✗Minimum 2,000 words for a full research article. Current: 1,707
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21577288
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[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 (59 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Open-source AI model development has transformed machine l[REDACTED]g deployment, yet energy transparency remains inconsistent. This article surveys 250 publicly released models from leading repositories, quantifies reporting gaps relative to the EU AI Act’s upcoming sustainability mandates, and proposes a standardized Energy Disclosure Framework (EDF) for future releases. We find that only 12%...

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Open Source Research by Oleh Ivchenko DOI: 10.5281/zenodo.21577288 56stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic100%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References2 refs○Minimum 10 references required
[w]Words [REQ]1,707✗Minimum 2,000 words for a full research article. Current: 1,707
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21577288
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]100%✓≥60% of references from 2025–2026. Current: 100%
[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 (59 × 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
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21550109  61stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI85%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[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 (67 × 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 Sources0%○≥80% from editorially reviewed sources
[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI85%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[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 (67 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Shadow Banking Detection with Graph Neural Networks: Mapping Unofficial Lending Networks

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21535837  61stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI85%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic92%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References26 refs✓Minimum 10 references required
[w]Words [REQ]1,948✗Minimum 2,000 words for a full research article. Current: 1,948
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21535837
[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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (68 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Shadow banking has emerged as a critical stress point in the global financial system, enabling credit expansion outside regulated intermediation. This article develops a graph‑based anomaly detection framework that maps unofficial lending networks using transaction‑level data from Eastern European regulatory pilots. By treating financial entities as nodes and payment flows as weighted edges, we...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21535837 61stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI85%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic92%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References26 refs✓Minimum 10 references required
[w]Words [REQ]1,948✗Minimum 2,000 words for a full research article. Current: 1,948
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21535837
[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]Diagrams4✓Mermaid architecture/flow diagrams. Current: 4
[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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Structured Prompting as Executable Specification: DSLs for Reliable LLM Behavior

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Academic Research
Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21537106  60stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI80%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic90%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]1,345✗Minimum 2,000 words for a full research article. Current: 1,345
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21537106
[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 (66 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Structured prompting has emerged as a pivotal technique for constraining large language model (LLM) outputs to well-defined contracts, thereby enhancing reliability across diverse applications. This article surveys the landscape of domain-specific languages (DSLs) and structured prompt formats — including JSON Schema, Instructor, LMQL, and Guidance — that translate high-level specifications int...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.21537106 60stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI80%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic90%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]1,345✗Minimum 2,000 words for a full research article. Current: 1,345
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21537106
[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 (66 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Model Distillation ROI: When Smaller Models Outperform Larger Ones on Domain-Specific Tasks

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21526800  63stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
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Model distillation comprises techniques for transferring knowledge from large, high-capacity neural networks—often referred to as teacher models—into compact, resource-efficient student models. While classic distillation paradigms have demonstrated modest accuracy gains, recent empirical investigations reveal that under carefully scoped enterprise conditions, distilled models can not only match...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.21526800 63stabilfr·wdophcgmx
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[t]Trusted96%✓≥80% from verified, high-quality sources
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Score = Ref Trust (71 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Rare Earth Geopolitics and the AI Supply Chain: Modeling Mineral Concentration Risk

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Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21510558  52stabilfr·wdophcgmx
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The global AI hardware boom has intensified dependence on rare earth elements (REEs) for high‑performance GPUs, TPUs, and specialized accelerators. While the United States and Europe have increased mining exploration budgets by 42% in 2025, China continues to control over 60% of rare earth processing capacity, creating a critical bottleneck for AI infrastructure scaling. This article quantifies...

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Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.21510558 52stabilfr·wdophcgmx
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[t]Trusted85%✓≥80% from verified, high-quality sources
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Score = Ref Trust (63 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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