Tech Cold War 2026 — Microsoft, AWS, and the Geopolitics of AI Infrastructure

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Geopolitical Research
Geopolitical Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18860354  31stabilfr·wdophcgmx
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The year 2026 marks a decisive inflection point in the global contest over artificial intelligence infrastructure. With the "Big Five" hyperscalers — Amazon, Microsoft, Google, Meta, and Oracle — collectively forecast to exceed $600 billion in capital expenditure, representing a 36% increase over 2025, the construction of data centers, GPU clusters, and regional cloud regions has become a prima...

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Geopolitical Research by Oleh Ivchenko DOI: 10.5281/zenodo.18860354 31stabilfr·wdophcgmx
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HPF Experimental Validation: Multi-Strategy Portfolio Optimization for Ukrainian Pharmaceutical Markets

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DOI: 10.5281/zenodo.18855073  55stabilfr·wdophcgmx
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This chapter presents the full experimental validation of the Holistic Portfolio Framework (HPF-P) on a synthetic but econometrically realistic pharmaceutical portfolio dataset representing the Ukrainian market. The experimental design employs five distinct company scenarios spanning the breadth of market conditions encountered by domestic manufacturers — from the stable generics environment of...

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DOI: 10.5281/zenodo.18855073 55stabilfr·wdophcgmx
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HPF-P Platform Architecture: From Theoretical Framework to Production System

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Framework Research
Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18855053  40stabilfr·wdophcgmx
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HPF-P transforms the abstract DRI/DRL framework into a concrete computational system that ingests real-world pharmaceutical data, computes decision readiness diagnostics, applies conditionally permitted optimisation strategies, and produces auditable portfolio recommendations. The platform targets commercial pharmaceutical portfolios — the inventory allocation and revenue optimisation decisions...

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Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.18855053 40stabilfr·wdophcgmx
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HPF-P Platform Technical Overview: From Specification to Deployment

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Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18845469  25stabilfr·wdophcgmx
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HPF-P is the reference implementation of the Holistic Portfolio Framework (HPF), providing a web-based platform for pharmaceutical portfolio decision support through DRI computation, DRL assignment, and strategy-appropriate optimization. This paper provides a technical overview of HPF-P: its architecture, API design, core algorithms, and deployment configuration. We describe the spec-driven dev...

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Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.18845469 25stabilfr·wdophcgmx
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Environmental Entropy and Pharma Portfolio Stability: Ukraine Market Analysis

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Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18845461  31stabilfr·wdophcgmx
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Portfolio decision quality degrades when environmental entropy — the degree of unpredictability in the market system — exceeds the capacity of available information to characterize it. This paper formalizes the concept of environmental entropy in the context of pharmaceutical portfolio management and demonstrates its impact on Decision Readiness Index (DRI) dimension R5 (temporal stability). We...

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Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.18845461 31stabilfr·wdophcgmx
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Five-Level Portfolio Optimization: From Abstention to Multi-Objective AI

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Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18845442  34stabilfr·wdophcgmx
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The Decision Readiness Levels (DRL) framework prescribes one of five optimization strategies for each pharmaceutical portfolio segment, conditioned on that segment's Decision Readiness Index (DRI) score. This paper provides a complete specification of DRL-1 through DRL-5: the conditions under which each level is appropriate, the optimization methods employed at each level, the mathematical form...

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Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.18845442 34stabilfr·wdophcgmx
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Decision Readiness Index (DRI): Measuring Information Sufficiency for Portfolio Decisions

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Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18845429  34stabilfr·wdophcgmx
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Effective pharmaceutical portfolio optimization requires not only capable algorithms but also information of sufficient quality to support those algorithms. This paper provides a formal specification of the Decision Readiness Index (DRI), the core diagnostic component of the Holistic Portfolio Framework (HPF). DRI quantifies information sufficiency across five dimensions — data completeness (R1...

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Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.18845429 34stabilfr·wdophcgmx
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HPF: A Holistic Framework for Decision-Readiness in Pharmaceutical Portfolio Management

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Framework Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18845421  31stabilfr·wdophcgmx
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Pharmaceutical portfolio management operates at the intersection of scientific uncertainty, regulatory complexity, and market volatility. Traditional optimization approaches assume a stable, well-characterized information environment — an assumption that routinely fails in practice, particularly in emerging markets subject to geopolitical disruption. This paper introduces the Holistic Portfolio...

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Framework Research by Oleh Ivchenko DOI: 10.5281/zenodo.18845421 31stabilfr·wdophcgmx
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[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%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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Super-Agent Front Door: Who Controls the Interface Controls the Market

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18844227  33stabilfr·wdophcgmx
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The most consequential battle in technology today is not about model performance or compute efficiency — it is about interface control. As AI agents evolve from reactive chatbots into proactive orchestrators of digital tasks, a new structural question emerges: who sits at the "front door" through which users and enterprises engage the agent layer? Historical precedent — from browsers to search ...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18844227 33stabilfr·wdophcgmx
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[a]DOI5%○≥80% have a Digital Object Identifier
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[l]Academic5%○≥80% from journals/conferences/preprints
[f]Free Access16%○≥80% are freely accessible
[r]References19 refs✓Minimum 10 references required
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18844227
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[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 (10 × 60%) + Required (4/5 × 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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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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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18842928
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]38%✗≥60% of references from 2025–2026. Current: 38%
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[g]Code—○Source code available on GitHub
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