Category: AI Economics

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

The Context-Window Premium: Why Long-Context Models Command Higher Prices Than Token Count Alone Explains

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.23487250  79stabilfr·wuedophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (70 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)

Providers now advertise context windows of one million tokens as a headline feature, but their price lists treat that capability in very different ways. We ask whether long context is priced as a distinct feature or bundled into ordinary per-token rates. We built a dataset of 83 offerings from 14 model developers, sold first-party or through three hosts, read directly from public pricing pages ...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.23487250 79stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources16%○≥80% from editorially reviewed sources
[t]Trusted78%○≥80% from verified, high-quality sources
[a]DOI68%○≥80% have a Digital Object Identifier
[b]CrossRef16%○≥80% indexed in CrossRef
[i]Indexed62%○≥80% have metadata indexed
[l]Academic70%○≥80% from journals/conferences/preprints
[f]Free Access78%○≥80% are freely accessible
[r]References37 refs✓Minimum 10 references required
[w]Words [REQ]4,015✓Minimum 1,000 words of article body. Current: 4,015
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]32✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 32
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23487250
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness79%✓≥60% of references from 2025–2026. Current: 79%
[c]Data Charts4✓Original data charts from reproducible analysis (min 2). Current: 4
[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 (70 × 60%) + Required (4/4 × 30%) + Optional (5/7 × 10%)
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AI for Anti-Money Laundering: From Rule-Based to Neural Transaction Monitoring in Banking

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21908656  69stabilfr·wuedophcgmx
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[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI75%○≥80% have a Digital Object Identifier
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[l]Academic83%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
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[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness55%○≥60% of references from 2025–2026. Current: 55%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (61 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

Anti-Money Laundering (AML) mechanisms are essential for maintaining financial integrity in the global banking sector. Traditional rule‑based transaction monitoring systems have long been employed to detect suspicious activity, yet they often generate high false‑positive rates, leading to operational inefficiencies and regulatory fatigue. Recent advances in neural network architectures and grap...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21908656 69stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI75%○≥80% have a Digital Object Identifier
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[i]Indexed0%○≥80% have metadata indexed
[l]Academic83%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]1,968✓Minimum 1,000 words of article body. Current: 1,968
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]7✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 7
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21908656
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness55%○≥60% of references from 2025–2026. Current: 55%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (61 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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The Capability Adoption Stack: A Framework for Diagnosing Enterprise AI Readiness

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21715745  62stabilfr·wuedophcgmx
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[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI3%○≥80% have a Digital Object Identifier
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[l]Academic90%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References31 refs✓Minimum 10 references required
[w]Words [REQ]1,541✓Minimum 1,000 words of article body. Current: 1,541
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21715745
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness90%✓≥60% of references from 2025–2026. Current: 90%
[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 (47 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

Enterprise AI adoption remains fragmented, with organizations struggling to diagnose their readiness across technical, human, and governance dimensions[1]. This article introduces the Capability Adoption Stack (CAS), a layered diagnostic model that maps data infrastructure, talent, governance, and integration maturity into a coherent framework[2]. By operationalizing these dimensions into measu...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21715745 62stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI3%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
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[l]Academic90%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References31 refs✓Minimum 10 references required
[w]Words [REQ]1,541✓Minimum 1,000 words of article body. Current: 1,541
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]26✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 26
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21715745
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness90%✓≥60% of references from 2025–2026. Current: 90%
[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 (4/4 × 30%) + Optional (3/7 × 10%)
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Foundation Model Commoditization: How API Parity Is Reshaping the AI Stack in 2025

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21550109  74stabilfr·wuedophcgmx
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[s]Reviewed Sources5%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI84%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic89%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]1,949✓Minimum 1,000 words of article body. Current: 1,949
[u]Original [REQ]97%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 97% unique
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21550109
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness89%✓≥60% of references from 2025–2026. Current: 89%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (67 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

In this article we analyze the commoditization of foundation model APIs, showing how parity in capability metrics is reshaping competitive dynamics across the AI stack in 2025. Using a mixed-methods approach combining benchmark analysis, cost modeling, and market trend evaluation, we identify three dominant research questions: (RQ1) How have benchmark scores converged across leading frontier mo...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21550109 74stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources5%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI84%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic89%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]1,949✓Minimum 1,000 words of article body. Current: 1,949
[u]Original [REQ]97%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 97% unique
[e]Evidence [REQ]15✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 15
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21550109
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness89%✓≥60% of references from 2025–2026. Current: 89%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (67 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)
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Opportunity Cost of AI Waiting: Economic Modeling of Delayed Enterprise AI Adoption

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21505176  74stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted91%✓≥80% from verified, high-quality sources
[a]DOI82%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic91%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]1,605✓Minimum 1,000 words of article body. Current: 1,605
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21505176
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness64%✓≥60% of references from 2025–2026. Current: 64%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (66 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

Enterprises that delay AI adoption face a measurable competitive disadvantage that can be quantified in economic terms. This article answers three research questions: (RQ1) What is the average competitive disadvantage cost for enterprises that delay AI adoption by 12–24 months relative to early adopters? (RQ2) Which industry sectors exhibit the highest cost differentials? (RQ3) What are the mac...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21505176 74stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted91%✓≥80% from verified, high-quality sources
[a]DOI82%✓≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic91%✓≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References11 refs✓Minimum 10 references required
[w]Words [REQ]1,605✓Minimum 1,000 words of article body. Current: 1,605
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]8✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 8
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21505176
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness64%✓≥60% of references from 2025–2026. Current: 64%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (66 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)
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Cash Flow Anomaly Detection: AI Models for Identifying Undeclared Income in SME Tax Returns

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.23486189  56stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥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]Indexed0%○≥80% have metadata indexed
[l]Academic43%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]1,649✓Minimum 1,000 words of article body. Current: 1,649
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]1✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 1
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23486189
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness25%○≥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]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (39 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

Unreported income among small and medium enterprises (SMEs) remains a critical challenge for tax authorities globally, with recent studies indicating that undeclared income accounts for 15-25% of the tax gap in European OECD nations. This article addresses the gap in current tax compliance tools by developing and evaluating machine learning models specifically designed to detect anomalous cash ...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.23486189 56stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥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]Indexed0%○≥80% have metadata indexed
[l]Academic43%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]1,649✓Minimum 1,000 words of article body. Current: 1,649
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]1✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 1
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.23486189
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness25%○≥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]Diagrams2✓Mermaid architecture/flow diagrams. Current: 2
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (39 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Total Cost of Ownership for Enterprise LLMs: A 2025 Framework Beyond GPU Cost

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.21186552  64stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted71%○≥80% from verified, high-quality sources
[a]DOI57%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic71%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]1,788✓Minimum 1,000 words of article body. Current: 1,788
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]15✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 15
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21186552
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness67%✓≥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 (50 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)

Enterprise adoption of large language models (LLMs) has progressed from experimental pilots to core production workloads, yet most organizations continue to compute return on investment (ROI) using GPU‐hour pricing as the sole cost driver. This narrow view systematically underestimates the true economic burden of LLMs, omitting fine‑tuning expenses, retrieval‑augmented generation (RAG) infrastr...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.21186552 64stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted71%○≥80% from verified, high-quality sources
[a]DOI57%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed0%○≥80% have metadata indexed
[l]Academic71%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References7 refs○Minimum 10 references required
[w]Words [REQ]1,788✓Minimum 1,000 words of article body. Current: 1,788
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]15✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 15
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.21186552
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness67%✓≥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 (50 × 60%) + Required (4/4 × 30%) + Optional (3/7 × 10%)
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The EU AI Act Explanability Requirements: Technical Specification Analysis

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19993955  83stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources61%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI72%○≥80% have a Digital Object Identifier
[b]CrossRef61%○≥80% indexed in CrossRef
[i]Indexed61%○≥80% have metadata indexed
[l]Academic78%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,081✓Minimum 1,000 words of article body. Current: 2,081
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]12✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 12
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19993955
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness80%✓≥60% of references from 2025–2026. Current: 80%
[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 (78 × 60%) + Required (4/4 × 30%) + Optional (4/7 × 10%)

The rapid deployment of artificial intelligence systems across high‑risk domains has prompted regulators to demand greater transparency and accountability. The European Union’s Artificial Intelligence Act (EU AI Act) introduces a comprehensive framework for trustworthy AI, with particular emphasis on explicability obligations for high‑risk AI systems. This article dissects the technical specifi...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19993955 83stabilfr·wuedophcgmx
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[s]Reviewed Sources61%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI72%○≥80% have a Digital Object Identifier
[b]CrossRef61%○≥80% indexed in CrossRef
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[l]Academic78%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]2,081✓Minimum 1,000 words of article body. Current: 2,081
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]12✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 12
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19993955
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness80%✓≥60% of references from 2025–2026. Current: 80%
[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 (78 × 60%) + Required (4/4 × 30%) + Optional (4/7 × 10%)
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AI Task Taxonomy by Complexity: A Cost Analysis Across Model Architectures (March 2026)

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.19336575  69stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted57%○≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
[b]CrossRef4%○≥80% indexed in CrossRef
[i]Indexed54%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access82%✓≥80% are freely accessible
[r]References28 refs✓Minimum 10 references required
[w]Words [REQ]3,409✓Minimum 1,000 words of article body. Current: 3,409
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]20✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 20
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19336575
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness88%✓≥60% of references from 2025–2026. Current: 88%
[c]Data Charts5✓Original data charts from reproducible analysis (min 2). Current: 5
[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 (4/4 × 30%) + Optional (5/7 × 10%)

Effective enterprise AI deployment requires matching task complexity to model capability — not defaulting to the most capable model for every workload. This meta-analysis introduces a six-tier task complexity taxonomy calibrated to March 2026 API pricing across nineteen models from six major providers. We demonstrate that systematic model-task alignment reduces per-task costs by 60–95% compared...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.19336575 69stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted57%○≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
[b]CrossRef4%○≥80% indexed in CrossRef
[i]Indexed54%○≥80% have metadata indexed
[l]Academic50%○≥80% from journals/conferences/preprints
[f]Free Access82%✓≥80% are freely accessible
[r]References28 refs✓Minimum 10 references required
[w]Words [REQ]3,409✓Minimum 1,000 words of article body. Current: 3,409
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]20✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 20
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19336575
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness88%✓≥60% of references from 2025–2026. Current: 88%
[c]Data Charts5✓Original data charts from reproducible analysis (min 2). Current: 5
[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 (4/4 × 30%) + Optional (5/7 × 10%)
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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  57stabilfr·wuedophcgmx
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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
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed87%✓≥80% have metadata indexed
[l]Academic13%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]5,468✓Minimum 1,000 words of article body. Current: 5,468
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]10✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 10
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19170546
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness29%○≥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 (4/4 × 30%) + Optional (3/7 × 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 57stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted40%○≥80% from verified, high-quality sources
[a]DOI7%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed87%✓≥80% have metadata indexed
[l]Academic13%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]5,468✓Minimum 1,000 words of article body. Current: 5,468
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]10✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 10
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.19170546
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness29%○≥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 (4/4 × 30%) + Optional (3/7 × 10%)
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