Category: AI Economics

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

Feedback Loop Economics: The Cost Architecture of Self-Improving AI Systems

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18910135  41stabilfr·wdophcgmx
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Score = Ref Trust (34 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Feedback loops are the metabolic engine of enterprise AI — the mechanism by which deployed models ingest operational signals, update their representations, and compound value over time. Yet the economics of this metabolic process remain poorly understood in enterprise planning. This article presents a systematic economic analysis of AI feedback loop architectures, decomposing their cost structu...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18910135 41stabilfr·wdophcgmx
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[h]Freshness [REQ]8%✗≥60% of references from 2025–2026. Current: 8%
[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 (34 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Governance Economics: The Cost of Compliance in the Regulatory Era

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18892313  37stabilfr·wdophcgmx
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[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI14%○≥80% have a Digital Object Identifier
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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 (28 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The emergence of mandatory AI governance frameworks—principally the European Union's AI Act (August 2026 enforcement), NIST AI Risk Management Framework, and ISO/IEC 42001—is transforming enterprise AI compliance from a voluntary discipline into a mandatory cost centre. Gartner projects AI governance platform spending to reach $492 million in 2026 and surpass $1 billion by 2030, as regulatory f...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18892313 37stabilfr·wdophcgmx
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[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI14%○≥80% have a Digital Object Identifier
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[r]References21 refs✓Minimum 10 references required
[w]Words [REQ]2,628✓Minimum 2,000 words for a full research article. Current: 2,628
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[h]Freshness [REQ]32%✗≥60% of references from 2025–2026. Current: 32%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (28 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Productivity Paradox: When Economy-Wide Gains Remain Elusive Despite Task-Level Breakthroughs

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

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18870948 37stabilfr·wdophcgmx
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[a]DOI15%○≥80% have a Digital Object Identifier
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Score = Ref Trust (28 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Inference Economics: The Hidden Cost Crisis Behind Falling Token Prices

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18869615  34stabilfr·wdophcgmx
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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]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...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18869615 34stabilfr·wdophcgmx
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[t]Trusted17%○≥80% from verified, high-quality sources
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[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
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (22 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Apple Siri Reimagined: Economics of On-Device AI at Scale

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18862953  29stabilfr·wdophcgmx
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[h]Freshness [REQ]33%✗≥60% of references from 2025–2026. Current: 33%
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (14 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

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

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18862953 29stabilfr·wdophcgmx
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[t]Trusted12%○≥80% from verified, high-quality sources
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[w]Words [REQ]2,148✓Minimum 2,000 words for a full research article. Current: 2,148
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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]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
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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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Agentic AI Infrastructure: Platform Economics of Multi-Agent Systems

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18842928  30stabilfr·wdophcgmx
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[t]Trusted20%○≥80% from verified, high-quality sources
[a]DOI7%○≥80% have a Digital Object Identifier
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[i]Indexed13%○≥80% have metadata indexed
[l]Academic7%○≥80% from journals/conferences/preprints
[f]Free Access20%○≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]2,756✓Minimum 2,000 words for a full research article. Current: 2,756
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18842928
[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 (16 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The emergence of multi-agent AI systems represents a fundamental architectural transition — from monolithic large language model (LLM) deployments to distributed, coordinated agent ecosystems that share infrastructure, tools, and context. This article examines the platform economics governing this transition: how network effects, switching costs, and infrastructure commoditization interact to c...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18842928 30stabilfr·wdophcgmx
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[t]Trusted20%○≥80% from verified, high-quality sources
[a]DOI7%○≥80% have a Digital Object Identifier
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[l]Academic7%○≥80% from journals/conferences/preprints
[f]Free Access20%○≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]2,756✓Minimum 2,000 words for a full research article. Current: 2,756
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18842928
[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 (16 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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The $110B OpenAI Round: What Mega-Funding Means for AI Economics

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18835583  34stabilfr·wdophcgmx
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[t]Trusted35%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
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[l]Academic12%○≥80% from journals/conferences/preprints
[f]Free Access41%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,367✓Minimum 2,000 words for a full research article. Current: 2,367
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18835583
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]53%✗≥60% of references from 2025–2026. Current: 53%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (22 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

On February 27, 2026, OpenAI announced the largest private funding round in technology history: $110 billion led by Amazon ($50B), Nvidia ($30B), and SoftBank ($30B), at a pre-money valuation of $730 billion. This paper examines the structural economic implications of this capital event — not merely as a venture milestone, but as a market-shaping force that will redefine enterprise AI economics...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18835583 34stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted35%○≥80% from verified, high-quality sources
[a]DOI6%○≥80% have a Digital Object Identifier
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[l]Academic12%○≥80% from journals/conferences/preprints
[f]Free Access41%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]2,367✓Minimum 2,000 words for a full research article. Current: 2,367
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18835583
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]53%✗≥60% of references from 2025–2026. Current: 53%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (22 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Edge AI Economics: When Edge Beats Cloud

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18830495  37stabilfr·wdophcgmx
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[s]Reviewed Sources14%○≥80% from editorially reviewed sources
[t]Trusted29%○≥80% from verified, high-quality sources
[a]DOI21%○≥80% have a Digital Object Identifier
[b]CrossRef14%○≥80% indexed in CrossRef
[i]Indexed14%○≥80% have metadata indexed
[l]Academic29%○≥80% from journals/conferences/preprints
[f]Free Access29%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]2,731✓Minimum 2,000 words for a full research article. Current: 2,731
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18830495
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]20%✗≥60% of references from 2025–2026. Current: 20%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (28 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Edge AI — the deployment of artificial intelligence inference workloads on devices and infrastructure proximate to data sources rather than in centralised cloud environments — is transitioning from an engineering curiosity to a mainstream economic necessity. With the global edge AI market valued at approximately $35.81 billion in 2025 and projected to reach $385.89 billion by 2034, the financia...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18830495 37stabilfr·wdophcgmx
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[s]Reviewed Sources14%○≥80% from editorially reviewed sources
[t]Trusted29%○≥80% from verified, high-quality sources
[a]DOI21%○≥80% have a Digital Object Identifier
[b]CrossRef14%○≥80% indexed in CrossRef
[i]Indexed14%○≥80% have metadata indexed
[l]Academic29%○≥80% from journals/conferences/preprints
[f]Free Access29%○≥80% are freely accessible
[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]2,731✓Minimum 2,000 words for a full research article. Current: 2,731
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18830495
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]20%✗≥60% of references from 2025–2026. Current: 20%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (28 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Multi-Cloud Strategy Economics: Arbitrage, Lock-In Costs, and AI Workload Optimization

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18825821  27stabilfr·wdophcgmx
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Multi-cloud strategy has evolved from a risk-mitigation posture into a primary economic lever for enterprise AI operations. As generative AI workloads consume an increasing share of cloud budgets — projected at 10–15% of total cloud spend by 2030 according to Goldman Sachs research — the economic calculus of distributing workloads across AWS, Azure, and GCP has become significantly more complex...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18825821 27stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
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[a]DOI7%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed7%○≥80% have metadata indexed
[l]Academic7%○≥80% from journals/conferences/preprints
[f]Free Access20%○≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]2,372✓Minimum 2,000 words for a full research article. Current: 2,372
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18825821
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[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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AI Infrastructure Investment ROI — The Capex War Winners and Losers

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18821329  39stabilfr·wdophcgmx
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The AI infrastructure investment cycle has reached unprecedented scale, with hyperscalers projected to spend over $600 billion in 2026—a 36% increase over 2025. This paper analyzes the economic fundamentals underlying this capital expenditure war, revealing a stark ROI crisis: AI data centers commissioned in 2025 face $40 billion in annual depreciation costs while generating only $15-20 billion...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18821329 39stabilfr·wdophcgmx
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[s]Reviewed Sources3%○≥80% from editorially reviewed sources
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[a]DOI3%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed83%✓≥80% have metadata indexed
[l]Academic14%○≥80% from journals/conferences/preprints
[f]Free Access24%○≥80% are freely accessible
[r]References29 refs✓Minimum 10 references required
[w]Words [REQ]2,454✓Minimum 2,000 words for a full research article. Current: 2,454
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18821329
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]55%✗≥60% of references from 2025–2026. Current: 55%
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[g]Code—○Source code available on GitHub
[m]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
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Score = Ref Trust (31 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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