China AI Industrial Strategy: The 15th Five-Year Plan and the Weaponization of Technological Dominance

Posted on by
Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18881597  40stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted46%○≥80% from verified, high-quality sources
[a]DOI15%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed31%○≥80% have metadata indexed
[l]Academic31%○≥80% from journals/conferences/preprints
[f]Free Access46%○≥80% are freely accessible
[r]References13 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.18881597
[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 (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...

Show moreHide
Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18881597 40stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted46%○≥80% from verified, high-quality sources
[a]DOI15%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed31%○≥80% have metadata indexed
[l]Academic31%○≥80% from journals/conferences/preprints
[f]Free Access46%○≥80% are freely accessible
[r]References13 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.18881597
[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 (33 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Geopolitical RiskRead More
Read more

The Anthropic Alliance: Amazon, NVIDIA, and Big Tech’s Coalition Against Pentagon Supply-Chain Weaponization

Posted on by
Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18879322  36stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted45%○≥80% from verified, high-quality sources
[a]DOI5%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed15%○≥80% have metadata indexed
[l]Academic15%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]2,599✓Minimum 2,000 words for a full research article. Current: 2,599
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18879322
[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 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 (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...

Show moreHide
Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18879322 36stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted45%○≥80% from verified, high-quality sources
[a]DOI5%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed15%○≥80% have metadata indexed
[l]Academic15%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]2,599✓Minimum 2,000 words for a full research article. Current: 2,599
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18879322
[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 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 (25 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Geopolitical RiskRead More
Read more

Anthropic Pentagon Dispute: When AI Safety Clashes with National Security Contracts

Posted on by
Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18875959  30stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted37%○≥80% from verified, high-quality sources
[a]DOI11%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed21%○≥80% have metadata indexed
[l]Academic21%○≥80% from journals/conferences/preprints
[f]Free Access37%○≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[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
[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 (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 ...

Show moreHide
Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18875959 30stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted37%○≥80% from verified, high-quality sources
[a]DOI11%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed21%○≥80% have metadata indexed
[l]Academic21%○≥80% from journals/conferences/preprints
[f]Free Access37%○≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[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
[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 (26 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
Geopolitical RiskRead More
Read more

The OpenAI-Pentagon-NATO Triangle: When AI Labs Become Defense Contractors

Posted on by
Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18872864  38stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted43%○≥80% from verified, high-quality sources
[a]DOI14%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed21%○≥80% have metadata indexed
[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
[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 (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...

Show moreHide
Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18872864 38stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted43%○≥80% from verified, high-quality sources
[a]DOI14%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed21%○≥80% have metadata indexed
[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
[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 (29 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Geopolitical RiskRead More
Read more

AI Productivity Paradox: When Economy-Wide Gains Remain Elusive Despite Task-Level Breakthroughs

Posted on by
AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18870948  37stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[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...

Show moreHide
AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18870948 37stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[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%)
AI EconomicsRead More
Read more

Inference Economics: The Hidden Cost Crisis Behind Falling Token Prices

Posted on by
AI Economics
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%)

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...

Show moreHide
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%)
AI EconomicsRead More
Read more

Bridging the Gap: Startup Workflows for AI Productivity Integration

Posted on by
Applied Research
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%)

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 ...

Show moreHide
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%)
Cost-Effective Ent…Read More
Read more

AI Agents in the Trough: The Reality Check on Agentic AI

Posted on by
Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18865601  33stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI8%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic8%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]2,174✓Minimum 2,000 words for a full research article. Current: 2,174
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18865601
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]38%✗≥60% of references from 2025–2026. Current: 38%
[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 (20 × 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...

Show moreHide
Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18865601 33stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI8%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic8%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]2,174✓Minimum 2,000 words for a full research article. Current: 2,174
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18865601
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]38%✗≥60% of references from 2025–2026. Current: 38%
[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 (20 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
Future of AIRead More
Read more

Observability for AI Systems: Why OpenTelemetry Is Not Enough and What the Community Needs

Posted on by
Technical Research
Technical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18864333  32stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted20%○≥80% from verified, high-quality sources
[a]DOI5%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed20%○≥80% have metadata indexed
[l]Academic20%○≥80% from journals/conferences/preprints
[f]Free Access40%○≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]2,815✓Minimum 2,000 words for a full research article. Current: 2,815
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18864333
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]5%✗≥60% of references from 2025–2026. Current: 5%
[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 (19 × 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 e[REDACTED]ses critical gaps: latency traces do not capture hallucination rates, infrastructure metrics do not surface semantic drift, a...

Show moreHide
Technical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18864333 32stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted20%○≥80% from verified, high-quality sources
[a]DOI5%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed20%○≥80% have metadata indexed
[l]Academic20%○≥80% from journals/conferences/preprints
[f]Free Access40%○≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]2,815✓Minimum 2,000 words for a full research article. Current: 2,815
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18864333
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]5%✗≥60% of references from 2025–2026. Current: 5%
[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 (19 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
AI ObservabilityRead More
Read more

Apple Siri Reimagined: Economics of On-Device AI at Scale

Posted on by
AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18862953  29stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted12%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed12%○≥80% have metadata indexed
[l]Academic12%○≥80% from journals/conferences/preprints
[f]Free Access24%○≥80% are freely accessible
[r]References17 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.18862953
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[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 (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...

Show moreHide
AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18862953 29stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted12%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed12%○≥80% have metadata indexed
[l]Academic12%○≥80% from journals/conferences/preprints
[f]Free Access24%○≥80% are freely accessible
[r]References17 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.18862953
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
[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 (14 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
AI EconomicsRead More
Read more