Category: AI Economics

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

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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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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[g]Code—○Source code available on GitHub
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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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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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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
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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
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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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Score = Ref Trust (47 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

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
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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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Score = Ref Trust (51 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

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
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Why Healthcare AI Is Stuck at 5%: The Quality Threshold Problem

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18954212  37stabilfr·wdophcgmx
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[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
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Score = Ref Trust (27 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The Anthropic Economic Index (2026) reveals one of the most striking asymmetries in technology adoption history: Healthcare Support occupies 40% theoretical AI coverage yet achieves only 5% observed deployment — an 8× gap between what AI systems can do and what healthcare providers actually use them for. This article analyses the structural drivers of this gap, arguing that the problem is not m...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18954212 37stabilfr·wdophcgmx
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Agent Economy Investment Surge: VC Bets on Agentic Infrastructure

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18943141  29stabilfr·wdophcgmx
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[t]Trusted10%○≥80% from verified, high-quality sources
[a]DOI10%○≥80% have a Digital Object Identifier
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]64%✓≥60% of references from 2025–2026. Current: 64%
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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%)

February 2026 produced the largest monthly venture capital figure ever recorded: $189 billion, of which AI startups captured $171 billion — 90% of the total. Three companies (OpenAI, Anthropic, Waymo) accounted for 83% of that sum alone. But beneath the headline megadeals, a quieter structural shift is underway: seed and Series A funding is flowing specifically into agentic infrastructure — the...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18943141 29stabilfr·wdophcgmx
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[w]Words [REQ]1,878✗Minimum 2,000 words for a full research article. Current: 1,878
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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]64%✓≥60% of references from 2025–2026. Current: 64%
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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%)
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The Coverage Gap: What AI Can Do vs. What We Actually Use It For

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18911661  44stabilfr·wdophcgmx
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[t]Trusted64%○≥80% from verified, high-quality sources
[a]DOI45%○≥80% have a Digital Object Identifier
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[l]Academic45%○≥80% from journals/conferences/preprints
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[w]Words [REQ]1,645✗Minimum 2,000 words for a full research article. Current: 1,645
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18911661
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]10%✗≥60% 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 (49 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Anthropic published something rare this week: a paper that uses actual usage data instead of speculation. Most labor displacement research asks "what tasks could AI theoretically do?" and then declares a crisis. Massenkoff and McCrory asked a different question: "what tasks are people actually using it for?" The gap between those two answers is the most important number in AI economics right no...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18911661 44stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted64%○≥80% from verified, high-quality sources
[a]DOI45%○≥80% have a Digital Object Identifier
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[l]Academic45%○≥80% from journals/conferences/preprints
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[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]1,645✗Minimum 2,000 words for a full research article. Current: 1,645
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18911661
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]10%✗≥60% 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 (49 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)
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Agentic OS Economics: Why the Platform That Wins Won’t Be the Smartest One

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18911437  54stabilfr·wdophcgmx
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Agentic platforms are racing on capability. The decisive variable will be economics — and none of the flagship papers (Anthropic guide, Wang et al., Magentic-One) model it. Token cost curves, context handoff overhead, Jevons effects at scale: all missing.

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18911437 54stabilfr·wdophcgmx
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[h]Freshness [REQ]44%✗≥60% of references from 2025–2026. Current: 44%
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Agentic OS Economics: Why the Platform That Wins Won’t Be the Smartest One

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18910811  48stabilfr·wdophcgmx
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This article reflects my thinking from early 2025, based on papers available at that time (Anthropic engineering guide, Wang et al. 2024, Magentic-One). I am keeping it here because the reasoning was honest and the core economic argument was right — but the field moved, new January 2026 surveys added important context, and my framing evolved.

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18910811 48stabilfr·wdophcgmx
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