Artificial intelligence is not primarily a threat to human labour — it is a repricing of human cognition. Drawing on Jürgen Schmidhuber's formal theory of intelligence as compression, Robert Sheckley's satirical science fiction, and Isaac Asimov's prescient design specifications for autonomous systems, this essay argues that AI is catalysing the most significant cognitive economy shift since th...
The single most damaging piece of misinformation in enterprise AI is the promise of rapid return. Vendor decks routinely project ROI within 6-12 months; the empirical reality is 18-36 months for most use cases, with a mandatory investment trough in between. Drawing on 52 enterprise AI deployments analyzed or directly managed between 2021 and 2025, alongside published data from McKinsey, Gartner...
(!)️ Citation Freshness Notice: This article contains citations primarily from 2019–2023. While the foundational research remains valid, readers are encouraged to verify current developments, as the field may have evolved significantly since publication.
Academic Citation: Dmytro Grybeniuk & Oleh Ivchenko. (2026). Gap Analysis: Explainability-Accuracy Tradeoff in High-Stakes Domains. Anticipatory Intelligence Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18662985 Abstract The explainability-accuracy tradeoff represents one of the most economically consequential yet technically intractable gaps in anticipatory AI syste...
The artificial intelligence renaissance has created a gravitational pull toward machine learning solutions for problems that may not require them. In my analysis of 156 enterprise AI implementations across financial services, logistics, and manufacturing sectors, I found that 34% of deployed ML systems would have achieved equal or superior outcomes using deterministic algorithms at 85-95% lower...
Large Language Model deployments present enterprises with a deceptively complex cost structure that extends far beyond simple API pricing. After analyzing 47 enterprise LLM implementations across my consulting work, I have identified that organizations consistently underestimate their true Total Cost of Ownership by 340-580%, primarily due to overlooked indirect costs including prompt engineeri...
Model selection represents one of the most consequential economic decisions in enterprise AI deployment, yet organizations consistently underestimate its financial implications. This paper examines the economics of choosing between model architectures—from simple linear regression to complex transformer networks—through the lens of total cost of ownership, inference economics, and organizationa...
Algorithmic bias represents one of the most economically significant risks in enterprise AI deployment, yet its true costs remain chronically underestimated in project planning. This article presents a comprehensive economic analysis of bias-related costs spanning regulatory penalties, legal liability, remediation expenses, and the often-catastrophic impact of reputational damage. Drawing from ...
The build-versus-buy decision for AI capabilities requires strategic sophistication beyond traditional IT procurement—a portfolio approach combining internal development, commercial solutions, and hybrid configurations.
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 process of machine learning models, leading to systematic failures that may remain undetected for months or years. In my experience at Enterprise AI Division, I hav...