Category: AI Economics

AI Economics: Risk, Cost, and ROI Research by Oleh Ivchenko

The 8× Gap: Why Healthcare AI Will Never Reach Its Theoretical Ceiling (And What That Means for Every Other High-Stakes Industry)

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18964576  72stabilfr·wdophcgmx
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There is a number buried in Anthropic's January 2026 Economic Index that should alarm every chief information officer, hospital administrator, and healthcare AI vendor currently claiming that artificial intelligence will transform clinical medicine. The number is 8. That is the gap multiplier between what AI systems can do in healthcare — 40% theoretical task coverage — and what hospitals are a...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18964576 72stabilfr·wdophcgmx
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Why Healthcare AI Is Stuck at 5%: The Quality Threshold Problem

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18954212  39stabilfr·wdophcgmx
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The Anthropic Economic Index (2026) reveals one of the most striking asymmetries in technology adoption history: Healthcare Support occupies 40% theoretical AI coverage yet achieves only 5% observed deployment — an 8× gap between what AI systems can do and what healthcare providers actually use them for. This article analyses the structural drivers of this gap, arguing that the problem is not m...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18954212 39stabilfr·wdophcgmx
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Agent Economy Investment Surge: VC Bets on Agentic Infrastructure

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18943141  30stabilfr·wdophcgmx
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February 2026 produced the largest monthly venture capital figure ever recorded: $189 billion, of which AI startups captured $171 billion — 90% of the total. Three companies (OpenAI, Anthropic, Waymo) accounted for 83% of that sum alone. But beneath the headline megadeals, a quieter structural shift is underway: seed and Series A funding is flowing specifically into agentic infrastructure — the...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18943141 30stabilfr·wdophcgmx
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The Coverage Gap: What AI Can Do vs. What We Actually Use It For

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18911661  50stabilfr·wdophcgmx
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Score = Ref Trust (59 × 60%) + Required (2/5 × 30%) + Optional (1/4 × 10%)

Anthropic published something rare this week: a paper that uses actual usage data instead of speculation. Most labor displacement research asks "what tasks could AI theoretically do?" and then declares a crisis. Massenkoff and McCrory asked a different question: "what tasks are people actually using it for?" The gap between those two answers is the most important number in AI economics right no...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18911661 50stabilfr·wdophcgmx
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Agentic OS Economics: Why the Platform That Wins Won’t Be the Smartest One

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18911437  68stabilfr·wdophcgmx
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Agentic platforms are racing on capability. The decisive variable will be economics — and none of the flagship papers (Anthropic guide, Wang et al., Magentic-One) model it. Token cost curves, context handoff overhead, Jevons effects at scale: all missing.

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18911437 68stabilfr·wdophcgmx
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Agentic OS Economics: Why the Platform That Wins Won’t Be the Smartest One

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18910811  55stabilfr·wdophcgmx
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This article reflects my thinking from early 2025, based on papers available at that time (Anthropic engineering guide, Wang et al. 2024, Magentic-One). I am keeping it here because the reasoning was honest and the core economic argument was right — but the field moved, new January 2026 surveys added important context, and my framing evolved.

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18910811 55stabilfr·wdophcgmx
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Feedback Loop Economics: The Cost Architecture of Self-Improving AI Systems

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18910135  44stabilfr·wdophcgmx
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Feedback loops are the metabolic engine of enterprise AI — the mechanism by which deployed models ingest operational signals, update their representations, and compound value over time. Yet the economics of this metabolic process remain poorly understood in enterprise planning. This article presents a systematic economic analysis of AI feedback loop architectures, decomposing their cost structu...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18910135 44stabilfr·wdophcgmx
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AI Governance Economics: The Cost of Compliance in the Regulatory Era

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18892313  39stabilfr·wdophcgmx
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Score = Ref Trust (30 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

The emergence of mandatory AI governance frameworks—principally the European Union's AI Act (August 2026 enforcement), NIST AI Risk Management Framework, and ISO/IEC 42001—is transforming enterprise AI compliance from a voluntary discipline into a mandatory cost centre. Gartner projects AI governance platform spending to reach $492 million in 2026 and surpass $1 billion by 2030, as regulatory f...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18892313 39stabilfr·wdophcgmx
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (30 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Productivity Paradox: When Economy-Wide Gains Remain Elusive Despite Task-Level Breakthroughs

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18870948  39stabilfr·wdophcgmx
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Goldman Sachs' analysis of Q4 2025 corporate earnings reveals a striking empirical paradox: while management teams reporting task-specific AI adoption documented median productivity gains of approximately 30%, no meaningful relationship exists between AI adoption and productivity at the economy-wide level. This paper examines this bifurcation through the lens of Solow's classical productivity p...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18870948 39stabilfr·wdophcgmx
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[w]Words [REQ]2,830✓Minimum 2,000 words for a full research article. Current: 2,830
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18870948
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Inference Economics: The Hidden Cost Crisis Behind Falling Token Prices

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18869615  35stabilfr·wdophcgmx
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Token prices have fallen by up to 80% year-over-year, yet enterprise AI budgets are in crisis. This paradox — cheaper per-unit AI, costlier total AI — defines the emerging discipline of inference economics. As organizations transition from experimental generative AI deployments to always-on agentic workflows, inference now constitutes 85% of enterprise AI budgets, up from roughly one-third in 2...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18869615 35stabilfr·wdophcgmx
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[s]Reviewed Sources6%○≥80% from editorially reviewed sources
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[a]DOI13%○≥80% have a Digital Object Identifier
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[w]Words [REQ]2,392✓Minimum 2,000 words for a full research article. Current: 2,392
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18869615
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