Category: AI Economics

📚 Academic Citation: Ivchenko, O. (2026). AI Economics: MLOps Infrastructure Costs — The Hidden Price of Production AI. AI Economics Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18672439 Abstract Machine learning operations (MLOps) infrastructure has become the defining cost center for enterprise AI programs, yet it remains systematically underestimated in project planning and ROI calculations. This…

📚 Academic Citation: Oleh Ivchenko. (2026). Federated Learning Economics: Privacy vs Efficiency. AI Economics Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18662973 Abstract After seven years of implementing AI systems across healthcare, finance, and enterprise domains, I’ve observed a fundamental tension in modern machine learning: organizations need data to build effective models, but privacy regulations, competitive…

📚 Academic Citation: Ivchenko, O. (2026). AI Economics: Transfer Learning Economics — Leveraging Pre-trained Models. Economics of Enterprise AI Series, Article 18. Odesa National Polytechnic University. DOI: 10.5281/zenodo.18648770 The machine learning field has undergone a fundamental shift in how models are developed. Understanding this shift is essential for grasping transfer learning economics. timeline title Evolution…

📚 Academic Citation: Ivchenko, O. (2026). AutoML Economics — When Automated Machine Learning Pays Off. AI Economics Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18644645 Abstract Automated Machine Learning (AutoML) promises to democratize AI development by automating the traditionally labor-intensive processes of feature engineering, model selection, and hyperparameter optimization. This promise has driven explosive growth in…

AI Economics: Model Selection Economics — The Hidden Cost-Performance Tradeoffs That Make or Break AI ROI Author: Oleh Ivchenko Lead Engineer, Enterprise AI | PhD Researcher, ONPU Series: Economics of Enterprise AI — Article 16 of 65 Date: February 2026 DOI: 10.5281/zenodo.18629905 | Zenodo Archive Abstract Model selection represents one of the most consequential economic…

📚 Academic Citation: Ivchenko, O.. (2026). AI Economics: Bias Costs — Regulatory Fines, Legal Liability, and the Economics of Reputational Damage. AI Economics Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18627664 Abstract Algorithmic bias represents one of the most economically significant risks in enterprise AI deployment, yet its true costs remain chronically underestimated in project planning….

📚 Academic Citation: Ivchenko, O. (2026). AI Economics: Data Poisoning — Economic Impact and Prevention. AI Economics Research Series. Odessa Polytechnic National University. DOI: 10.5281/zenodo.18626697 Abstract Data poisoning represents one of the most insidious and economically devastating threats to enterprise AI systems. Unlike traditional cybersecurity attacks that target infrastructure, data poisoning corrupts the fundamental learning…

📚 Academic Citation: Ivchenko, O. (2026). Annotation Economics: Crowdsourcing vs Expert Labeling in Enterprise AI. AI Economics Series. Stabilarity Research Hub, ONPU. DOI: 10.5281/zenodo.18625150 Abstract Data annotation represents one of the most underestimated cost centers in enterprise AI development. While organizations meticulously budget for infrastructure, talent, and model training, annotation costs frequently emerge as budget-breaking…

AI Economics: Data Quality Economics — The True Cost of Bad Data in Enterprise AI Author: Oleh Ivchenko Lead Engineer, a leading technology consultancy | PhD Researcher, Odessa Polytechnic National University Series: Economics of Enterprise AI — Article 12 of 65 Date: February 2026 DOI: 10.5281/zenodo.18624306 | Zenodo Archive Data quality stands as the silent…