Multi-Cloud Strategy Economics: Arbitrage, Lock-In Costs, and AI Workload Optimization

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AI Economics
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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The Planning Illusion

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Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18824558  49stabilfr·wdophcgmx
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In my previous essay, "AI is not like us?", I argued that we systematically anthropomorphize AI systems — projecting human cognition onto what are, at their core, profoundly alien statistical machines. That argument was architectural and perceptual. This one is operational.

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18824558 49stabilfr·wdophcgmx
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AI is not like us?

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Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18824472  49stabilfr·wdophcgmx
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When Alan Turing proposed his famous imitation game in 1950, he embedded a premise so deep we rarely surface it: that intelligence, to be valid, must be indistinguishable from human intelligence. Turing, 1950 — Computing Machinery and Intelligence. The test was never about capability. It was about resemblance.

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18824472 49stabilfr·wdophcgmx
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Autonomous Systems Economics: Replacing Human Labor with Compute

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Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18822768  22stabilfr·wdophcgmx
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The fundamental economic question facing enterprises in 2026 is not whether autonomous systems can replace human labor, but when the compute-labor cost crossover makes replacement economically rational. This article examines the economics of autonomous system deployment across warehouse robotics, transportation, and knowledge work domains. Analysis of real-world implementations reveals that lab...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18822768 22stabilfr·wdophcgmx
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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  40stabilfr·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 40stabilfr·wdophcgmx
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The Spec-Driven AI Toolchain: From Specification to Deployment

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18820121  33stabilfr·wdophcgmx
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The transition from specification-centric development to deployed AI systems requires a comprehensive toolchain that bridges the gap between formal requirements and operational machine learning models. This article examines the current landscape of tools supporting spec-driven AI development, from specification authoring platforms through automated test generation to continuous validation pipel...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18820121 33stabilfr·wdophcgmx
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Daily Journal: The 95% Crisis — When AI Pilots Can’t Cross the Production Chasm

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Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18818387  21stabilfr·wdophcgmx
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Score = Ref Trust (11 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

February 28, 2026 — The AI industry faces a bifurcation point. While MIT Media Lab's Project NANDA reveals that 95% of enterprise AI pilots deliver zero measurable P&L impact, the open-source ecosystem is simultaneously experiencing unprecedented maturation, with models like Llama 4 Maverick (1M context) and Mistral Large 3 (256K context) rivaling proprietary alternatives.

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18818387 21stabilfr·wdophcgmx
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Formal Specification Economics: Measuring ROI of Spec Investment

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18816640  35stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]9%✗≥80% of references from 2025–2026. Current: 9%
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Score = Ref Trust (24 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Academic Citation: Ivchenko, O. (2026). Formal Specification Economics: Measuring ROI of Spec Investment. Spec-Driven AI Development Series. Odesa National Polytechnic University. DOI: 10.5281/zenodo.18818355 Abstract Formal specification practices in AI system development represent a significant upfront investment that enterprises must justify economically. This article presents a rigorous fra...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18816640 35stabilfr·wdophcgmx
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[w]Words [REQ]2,423✓Minimum 2,000 words for a full research article. Current: 2,423
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18816640
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]9%✗≥80% of references from 2025–2026. Current: 9%
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[x]Cited by0○Referenced by 0 other hub article(s)
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How Our War Prediction Model Anticipated the Iran Conflict

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DOI: 10.5281/zenodo.18816597  36stabilfr·wdophcgmx
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[t]Trusted60%○≥80% from verified, high-quality sources
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On February 28, 2026, the United States and Israel launched coordinated military strikes on Iran, marking the most significant Middle Eastern conflict escalation since the Iraq War. Our Stabilarity War Prediction Model had been tracking Iran's conflict probability for weeks, showing a 49.7% conflict probability with an increasing trend — a warning that materialized into reality within hours of ...

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DOI: 10.5281/zenodo.18816597 36stabilfr·wdophcgmx
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Enterprise AI: A Comprehensive Guide to Navigating Complexity and Avoiding the 80% Failure Rate

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18772218  30stabilfr·wdophcgmx
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Executive Summary: Despite unprecedented investment and executive enthusiasm, 80-85% of enterprise AI projects fail to deliver meaningful business value. This comprehensive analysis examines the technical, organizational, and economic factors driving this failure rate, drawing from academic research and industry studies. We present evidence-based frameworks for total cost of ownership (TCO) ana...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18772218 30stabilfr·wdophcgmx
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