AI Sovereignty as Geopolitical Strategy: The EU–US Regulatory Divergence and Its Global Consequences

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Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18886429  42stabilfr·wdophcgmx
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The governance of artificial intelligence has become the defining axis of geopolitical competition in 2026. Where the United States has pivoted decisively toward federal deregulation — preempting state-level AI laws and dismantling Biden-era executive oversight — the European Union advances an increasingly assertive sovereignty architecture anchored by the AI Act, scheduled for full enforcement...

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Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18886429 42stabilfr·wdophcgmx
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Daily Review: AI Hallucinations in Wartime — When Chatbots Get Geopolitics Wrong

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18884216  39stabilfr·wdophcgmx
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Score = Ref Trust (30 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The deployment of large language models in high-stakes geopolitical contexts — from intelligence analysis to public information consumption during active conflicts — has e[REDACTED]sed a critical reliability gap that the AI industry has not adequately resolved. In March 2026, as US and Israeli forces conducted strikes on Iran, reports confirmed that Anthropic's Claude was embedded in US Central...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18884216 39stabilfr·wdophcgmx
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Israel-Iran Escalation: How Kinetic Conflict Tests AI Defense Infrastructure

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Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18883019  26stabilfr·wdophcgmx
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Score = Ref Trust (19 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

The March 2026 Israel-Iran kinetic conflict represents the most extensive real-world stress test of AI-integrated defense infrastructure in history. For the first time, artificial intelligence systems are not peripheral tools but embedded operational components — driving target identification, missile intercept prioritization, drone swarm coordination, and cyber-offensive operations at machine ...

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Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18883019 26stabilfr·wdophcgmx
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China AI Industrial Strategy: The 15th Five-Year Plan and the Weaponization of Technological Dominance

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Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18881597  40stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (33 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

On March 5, 2026, China's National People's Congress unveiled the 15th Five-Year Plan (2026–2030), setting an unambiguous course: embed artificial intelligence across the entire industrial and economic machine as a core pillar of national security. This analysis examines the plan's strategic architecture, its geopolitical signal value, and its implications for the global AI competition. Drawing...

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Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18881597 40stabilfr·wdophcgmx
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Score = Ref Trust (33 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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The Anthropic Alliance: Amazon, NVIDIA, and Big Tech’s Coalition Against Pentagon Supply-Chain Weaponization

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Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18879322  36stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (25 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

On February 27, 2026, U.S. Defense Secretary Pete Hegseth designated Anthropic a "supply-chain risk to national security," triggering an unprecedented industry response. Within days, Amazon, NVIDIA, OpenAI, and Apple had joined a formal Big Tech coalition challenging the designation — a coalition that signals a structural shift in the relationship between state power and commercial AI governanc...

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Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18879322 36stabilfr·wdophcgmx
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[w]Words [REQ]2,599✓Minimum 2,000 words for a full research article. Current: 2,599
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]29%✗≥60% of references from 2025–2026. Current: 29%
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Anthropic Pentagon Dispute: When AI Safety Clashes with National Security Contracts

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Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18875959  30stabilfr·wdophcgmx
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[t]Trusted37%○≥80% from verified, high-quality sources
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[w]Words [REQ]1,978✗Minimum 2,000 words for a full research article. Current: 1,978
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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]45%✗≥60% of references from 2025–2026. Current: 45%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (26 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

The escalating confrontation between Anthropic and the United States Department of Defense represents a watershed moment in the governance of frontier AI systems. Beginning with a $200 million classified-network contract signed in mid-2025, the dispute erupted in February 2026 when Secretary of Defense Pete Hegseth demanded unfettered access to Anthropic's Claude model—including the removal of ...

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Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18875959 30stabilfr·wdophcgmx
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[w]Words [REQ]1,978✗Minimum 2,000 words for a full research article. Current: 1,978
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18875959
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]45%✗≥60% of references from 2025–2026. Current: 45%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (26 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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The OpenAI-Pentagon-NATO Triangle: When AI Labs Become Defense Contractors

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Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18872864  38stabilfr·wdophcgmx
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[t]Trusted43%○≥80% from verified, high-quality sources
[a]DOI14%○≥80% have a Digital Object Identifier
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[l]Academic21%○≥80% from journals/conferences/preprints
[f]Free Access43%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]2,776✓Minimum 2,000 words for a full research article. Current: 2,776
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18872864
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]40%✗≥60% of references from 2025–2026. Current: 40%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (29 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The week of February 27–March 4, 2026 marked a structural inflection point in the geopolitics of artificial intelligence: OpenAI signed a classified-environment deployment agreement with the U.S. Department of Defense, then within days disclosed it was considering a contract with NATO's unclassified networks. Simultaneously, Anthropic was designated a national security "supply-chain risk" by De...

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Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18872864 38stabilfr·wdophcgmx
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[t]Trusted43%○≥80% from verified, high-quality sources
[a]DOI14%○≥80% have a Digital Object Identifier
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[l]Academic21%○≥80% from journals/conferences/preprints
[f]Free Access43%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]2,776✓Minimum 2,000 words for a full research article. Current: 2,776
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18872864
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]40%✗≥60% of references from 2025–2026. Current: 40%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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[m]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (29 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Productivity Paradox: When Economy-Wide Gains Remain Elusive Despite Task-Level Breakthroughs

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18870948  37stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted45%○≥80% from verified, high-quality sources
[a]DOI15%○≥80% have a Digital Object Identifier
[b]CrossRef10%○≥80% indexed in CrossRef
[i]Indexed15%○≥80% have metadata indexed
[l]Academic15%○≥80% from journals/conferences/preprints
[f]Free Access25%○≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]2,842✓Minimum 2,000 words for a full research article. Current: 2,842
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18870948
[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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (28 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Goldman Sachs' analysis of Q4 2025 corporate [REDACTED]gs reveals a striking empirical paradox: while management teams reporting task-specific AI adoption documented median productivity gains of approximately 30%, no meaningful relationship exists between AI adoption and productivity at the economy-wide level. This paper examines this bifurcation through the lens of Solow's classical productivi...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18870948 37stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted45%○≥80% from verified, high-quality sources
[a]DOI15%○≥80% have a Digital Object Identifier
[b]CrossRef10%○≥80% indexed in CrossRef
[i]Indexed15%○≥80% have metadata indexed
[l]Academic15%○≥80% from journals/conferences/preprints
[f]Free Access25%○≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]2,842✓Minimum 2,000 words for a full research article. Current: 2,842
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18870948
[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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (28 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Inference Economics: The Hidden Cost Crisis Behind Falling Token Prices

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18869615  34stabilfr·wdophcgmx
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Token prices have fallen by up to 80% year-over-year, yet enterprise AI budgets are in crisis. This paradox — cheaper per-unit AI, costlier total AI — defines the emerging discipline of inference economics. As organizations transition from experimental generative AI deployments to always-on agentic workflows, inference now constitutes 85% of enterprise AI budgets, up from roughly one-third in 2...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18869615 34stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted17%○≥80% from verified, high-quality sources
[a]DOI11%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
[i]Indexed28%○≥80% have metadata indexed
[l]Academic17%○≥80% from journals/conferences/preprints
[f]Free Access22%○≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,396✓Minimum 2,000 words for a full research article. Current: 2,396
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18869615
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]37%✗≥60% of references from 2025–2026. Current: 37%
[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 (22 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Bridging the Gap: Startup Workflows for AI Productivity Integration

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18868149  34stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
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[a]DOI12%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
[i]Indexed29%○≥80% have metadata indexed
[l]Academic12%○≥80% from journals/conferences/preprints
[f]Free Access29%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,814✓Minimum 2,000 words for a full research article. Current: 2,814
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18868149
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[h]Freshness [REQ]35%✗≥60% of references from 2025–2026. Current: 35%
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Startups occupy a paradoxical position in the 2026 AI landscape: unburdened by legacy infrastructure, yet resource-constrained in ways that make AI adoption both essential and precarious. Gartner projects that 40% of enterprise applications will incorporate task-specific AI agents by end of 2026, up from less than 5% in 2025 — a near order-of-magnitude leap that compresses traditional adoption ...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18868149 34stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted24%○≥80% from verified, high-quality sources
[a]DOI12%○≥80% have a Digital Object Identifier
[b]CrossRef6%○≥80% indexed in CrossRef
[i]Indexed29%○≥80% have metadata indexed
[l]Academic12%○≥80% from journals/conferences/preprints
[f]Free Access29%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,814✓Minimum 2,000 words for a full research article. Current: 2,814
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18868149
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]35%✗≥60% of references from 2025–2026. Current: 35%
[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 (23 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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