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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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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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  39stabilfr·wdophcgmx
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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 39stabilfr·wdophcgmx
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AI Productivity Paradox: When Economy-Wide Gains Remain Elusive Despite Task-Level Breakthroughs

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18870948  39stabilfr·wdophcgmx
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Goldman Sachs' analysis of Q4 2025 corporate earnings 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 productivity p...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18870948 39stabilfr·wdophcgmx
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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
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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  35stabilfr·wdophcgmx
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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 35stabilfr·wdophcgmx
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AI Agents in the Trough: The Reality Check on Agentic AI

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18865601  35stabilfr·wdophcgmx
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[h]Freshness [REQ]50%✗≥80% of references from 2025–2026. Current: 50%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (24 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The enterprise AI landscape in early 2026 is undergoing a critical inflection point. After two years of proclamations about the "Year of the Agent," empirical evidence now paints a sobering picture: only 5 percent of enterprise-grade generative AI systems reach production, agentic AI pilots exhibit failure rates approaching 70 percent on complex multi-step tasks, and Goldman Sachs finds "no mea...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18865601 35stabilfr·wdophcgmx
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Observability for AI Systems: Why OpenTelemetry Is Not Enough and What the Community Needs

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Technical Research
Technical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18864333  33stabilfr·wdophcgmx
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[a]DOI6%○≥80% have a Digital Object Identifier
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[l]Academic17%○≥80% from journals/conferences/preprints
[f]Free Access33%○≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,801✓Minimum 2,000 words for a full research article. Current: 2,801
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[h]Freshness [REQ]6%✗≥80% of references from 2025–2026. Current: 6%
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Score = Ref Trust (20 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Modern AI systems deployed in production remain fundamentally opaque to the engineers who operate them. While OpenTelemetry has emerged as the de facto standard for distributed systems observability, its extension to AI and large language model (LLM) workloads exposes critical gaps: latency traces do not capture hallucination rates, infrastructure metrics do not surface semantic drift, and no v...

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Technical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18864333 33stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]6%✗≥80% of references from 2025–2026. Current: 6%
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Apple Siri Reimagined: Economics of On-Device AI at Scale

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18862953  29stabilfr·wdophcgmx
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[l]Academic7%○≥80% from journals/conferences/preprints
[f]Free Access13%○≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]2,146✓Minimum 2,000 words for a full research article. Current: 2,146
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18862953
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]40%✗≥80% of references from 2025–2026. Current: 40%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (14 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The 2026 reimagining of Apple's Siri represents one of the most economically significant deployments of artificial intelligence in history — not because of its technical novelty alone, but because of the unprecedented scale at which on-device inference economics operate. With over 2.5 billion active Apple devices and 1.5 billion iPhones serving as a distributed inference platform, Apple's archi...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18862953 29stabilfr·wdophcgmx
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[t]Trusted13%○≥80% from verified, high-quality sources
[a]DOI7%○≥80% have a Digital Object Identifier
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[l]Academic7%○≥80% from journals/conferences/preprints
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[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]2,146✓Minimum 2,000 words for a full research article. Current: 2,146
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18862953
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]40%✗≥80% of references from 2025–2026. Current: 40%
[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
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Score = Ref Trust (14 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Tech Cold War 2026 — Microsoft, AWS, and the Geopolitics of AI Infrastructure

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Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18860354  31stabilfr·wdophcgmx
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The year 2026 marks a decisive inflection point in the global contest over artificial intelligence infrastructure. With the "Big Five" hyperscalers — Amazon, Microsoft, Google, Meta, and Oracle — collectively forecast to exceed $600 billion in capital expenditure, representing a 36% increase over 2025, the construction of data centers, GPU clusters, and regional cloud regions has become a prima...

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Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18860354 31stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted30%○≥80% from verified, high-quality sources
[a]DOI4%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed17%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access4%○≥80% are freely accessible
[r]References23 refs✓Minimum 10 references required
[w]Words [REQ]2,554✓Minimum 2,000 words for a full research article. Current: 2,554
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18860354
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]35%✗≥80% 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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (18 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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HPF Experimental Validation: Multi-Strategy Portfolio Optimization for Ukrainian Pharmaceutical Markets

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DOI: 10.5281/zenodo.18855073  58stabilfr·wdophcgmx
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[s]Reviewed Sources22%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef89%✓≥80% indexed in CrossRef
[i]Indexed11%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access11%○≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]1,446✗Minimum 2,000 words for a full research article. Current: 1,446
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18855073
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]10%✗≥80% of references from 2025–2026. Current: 10%
[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
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Score = Ref Trust (73 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

This chapter presents the full experimental validation of the Holistic Portfolio Framework (HPF-P) on a synthetic but econometrically realistic pharmaceutical portfolio dataset representing the Ukrainian market. The experimental design employs five distinct company scenarios spanning the breadth of market conditions encountered by domestic manufacturers — from the stable generics environment of...

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DOI: 10.5281/zenodo.18855073 58stabilfr·wdophcgmx
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[s]Reviewed Sources22%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI100%✓≥80% have a Digital Object Identifier
[b]CrossRef89%✓≥80% indexed in CrossRef
[i]Indexed11%○≥80% have metadata indexed
[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access11%○≥80% are freely accessible
[r]References9 refs○Minimum 10 references required
[w]Words [REQ]1,446✗Minimum 2,000 words for a full research article. Current: 1,446
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18855073
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]10%✗≥80% of references from 2025–2026. Current: 10%
[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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