Category: AI Economics

📚 Academic Citation: Ivchenko, O. (2026). Edge AI Economics: When Edge Beats Cloud. Research article: Edge AI Economics: When Edge Beats Cloud. ONPU. DOI: 10.5281/zenodo.18830495 Abstract 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…

📚 Academic Citation: Ivchenko, O. (2026). Multi-Cloud Strategy Economics. AI Economics. ONPU. DOI: 10.5281/zenodo.18825821 Abstract 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…

📚 Academic Citation: Ivchenko, O. (2026). AI Infrastructure Investment ROI — The Capex War Winners and Losers. AI Economics Research Series. Odessa Polytechnic National University. DOI: https://doi.org/10.5281/zenodo.18821329 Abstract 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…

📚 Academic Citation: Ivchenko, O. (2026). Testing and Validation Costs in Enterprise AI: Economic Analysis of Quality Assurance Investment. AI Economics Series. Odessa Polytechnic National University. DOI: 10.5281/zenodo.18755863 Abstract Testing and validation represent 10-15% of total AI development costs, yet inadequate investment in this phase contributes significantly to the 80-95% failure rate of AI projects….

📚 Academic Citation: Ivchenko, O. (2026). Integration Economics: Legacy System Adaptation for AI Deployment. AI Economics: Risk Landscape, Cost-Effective Mitigations, and Economic Impact Analysis. Odessa Polytechnic National University. DOI: 10.5281/zenodo.18740871 Abstract Integrating artificial intelligence into existing enterprise infrastructure represents one of the most significant economic challenges in AI deployment. While substantial research examines AI development…

📚 Academic Citation: Ivchenko, O. (2026). Compliance Costs: GDPR, AI Act, and Industry-Specific Regulations. AI Economics Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18730888 Author: Oleh Ivchenko, PhD Candidate | Series: AI Economics | Focus: Regulatory compliance cost frameworks Abstract Regulatory compliance represents a critical economic dimension of enterprise AI deployment, with costs ranging from $20,000…

📚 Academic Citation: Ivchenko, O. (2026). Security Investment — Adversarial Attack Prevention. AI Economics. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18730508 Author: Oleh Ivchenko Series: AI Economics (Article 24 of 65) Date: February 22, 2026 Abstract Adversarial attacks represent a critical security threat to machine learning systems, with global estimated losses reaching approximately $6 trillion in…

📚 Academic Citation: Ivchenko, O. (2026). Scalability Costs in Enterprise AI Systems: Linear vs Exponential Growth Patterns. AI Economics Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18709900 Abstract Enterprise AI systems often encounter catastrophic cost overruns during scaling, with many organizations experiencing 300-800% budget increases when transitioning from pilot to production. This article analyzes the fundamental…

📚 Academic Citation: Ivchenko, O. (2026). GPU Economics — Buy, Rent, or Serverless: A Decision Framework for AI Compute Procurement. AI Economics Series. Odessa National Polytechnic University. DOI: https://doi.org/10.5281/zenodo.18693701 Abstract The economics of GPU compute have become central to every serious AI investment discussion. As large language models, diffusion architectures, and deep learning pipelines consume…

📚 Academic Citation: Ivchenko, O. (2026). Cloud vs On-Premise Economics for AI: A Structured Cost Framework for Enterprise Decision-Making. AI Economics Series. Odessa National Polytechnic University. DOI: https://doi.org/10.5281/zenodo.18678386 Abstract The deployment of artificial intelligence workloads involves one of the most consequential infrastructure decisions in modern enterprise technology strategy: whether to run AI systems in the…