Category: AI Economics

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

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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[h]Freshness [REQ]29%✗≥60% of references from 2025–2026. Current: 29%
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[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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[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%)
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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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[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%)

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
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[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
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI11%○≥80% have a Digital Object Identifier
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[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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[t]Trusted19%○≥80% from verified, high-quality sources
[a]DOI4%○≥80% have a Digital Object Identifier
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[i]Indexed7%○≥80% have metadata indexed
[l]Academic4%○≥80% from journals/conferences/preprints
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[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%
[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 (14 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

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
[t]Trusted19%○≥80% from verified, high-quality sources
[a]DOI4%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
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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%
[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 (14 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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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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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted31%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic6%○≥80% from journals/conferences/preprints
[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%
[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%)

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
[t]Trusted31%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed8%○≥80% have metadata indexed
[l]Academic6%○≥80% from journals/conferences/preprints
[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%
[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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The Legal 15%: Liability Is Not a Technical Problem

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19015448  40stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted29%○≥80% from verified, high-quality sources
[a]DOI12%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed24%○≥80% have metadata indexed
[l]Academic12%○≥80% from journals/conferences/preprints
[f]Free Access35%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,125✓Minimum 2,000 words for a full research article. Current: 2,125
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19015448
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]67%✓≥60% of references from 2025–2026. Current: 67%
[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 (23 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)

The Anthropic Economic Index (Massenkoff & McCrory, 2026) reveals a persistent and structurally significant anomaly: legal occupations exhibit only 15% observed AI e[REDACTED]sure despite theoretical automation potential that rivals software engineering. This article examines the economic architecture of that gap. Unlike healthcare, where clinical decision liability and FDA approval pathways cr...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19015448 40stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted29%○≥80% from verified, high-quality sources
[a]DOI12%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed24%○≥80% have metadata indexed
[l]Academic12%○≥80% from journals/conferences/preprints
[f]Free Access35%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,125✓Minimum 2,000 words for a full research article. Current: 2,125
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19015448
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]67%✓≥60% of references from 2025–2026. Current: 67%
[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 (23 × 60%) + Required (4/5 × 30%) + Optional (1/4 × 10%)
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Review: EcoAI-Resilience — When R² = 0.99 Should Make You Nervous, Not Confident

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18998542  46stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted71%○≥80% from verified, high-quality sources
[a]DOI29%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed47%○≥80% have metadata indexed
[l]Academic59%○≥80% from journals/conferences/preprints
[f]Free Access76%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]1,563✗Minimum 2,000 words for a full research article. Current: 1,563
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18998542
[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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (52 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

ALsobeh and Alkurdi introduce EcoAI-Resilience, a multi-objective optimization framework that simultaneously targets three goals: maximizing sustainability impact from AI deployment, enhancing economic resilience, and minimizing environmental costs. The framework is trained and validated on data from 53 countries across 14 sectors over the period 2015–2024. The authors report extraordinarily hi...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18998542 46stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources6%○≥80% from editorially reviewed sources
[t]Trusted71%○≥80% from verified, high-quality sources
[a]DOI29%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed47%○≥80% have metadata indexed
[l]Academic59%○≥80% from journals/conferences/preprints
[f]Free Access76%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]1,563✗Minimum 2,000 words for a full research article. Current: 1,563
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18998542
[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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (52 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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The Agentic Infrastructure Bet: What the VC Surge Into AI Agents Tells Us About the Next Platform Shift

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18964582  43stabilfr·wdophcgmx
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There is a moment in every technology transition when the smart money moves from the application layer to the plumbing. It happened in cloud computing around 2010, when AWS, Rackspace, and their successors attracted investment not because they built apps but because they built the infrastructure apps would run on. It happened in mobile in 2012, when the money moved from apps themselves to the S...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18964582 43stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted50%○≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
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[l]Academic50%○≥80% from journals/conferences/preprints
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[w]Words [REQ]1,744✗Minimum 2,000 words for a full research article. Current: 1,744
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18964582
[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%
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[g]Code—○Source code available on GitHub
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (47 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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The 8× Gap: Why Healthcare AI Will Never Reach Its Theoretical Ceiling (And What That Means for Every Other High-Stakes Industry)

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18964576  51stabilfr·wdophcgmx
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There is a number buried in Anthropic's January 2026 Economic Index that should alarm every chief information officer, hospital administrator, and healthcare AI vendor currently claiming that artificial intelligence will transform clinical medicine. The number is 8. That is the gap multiplier between what AI systems can do in healthcare — 40% theoretical task coverage — and what hospitals are a...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18964576 51stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources17%○≥80% from editorially reviewed sources
[t]Trusted67%○≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
[b]CrossRef17%○≥80% indexed in CrossRef
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[w]Words [REQ]2,116✓Minimum 2,000 words for a full research article. Current: 2,116
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18964576
[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]Diagrams3✓Mermaid architecture/flow diagrams. Current: 3
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (51 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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