The Anthropic Economic Index (Massenkoff & McCrory, 2026) identifies computer and mathematical occupations as theoretically the most AI-exposed occupation group in the U.S. economy, with 94% of tasks rated as feasible for LLM acceleration. Yet observed automation covers only 33% of those tasks — producing a 61-percentage-point capability-adoption gap that is the largest absolute gap of any occu...
Frontier AI Consolidation Economics: Why the Big Get Bigger
The frontier AI industry is consolidating at a pace that mirrors — and in some dimensions exceeds — the platform monopolization patterns of previous technology waves. As of early 2026, three providers control approximately 88% of enterprise AI API spending, with Anthropic commanding 40%, OpenAI 27%, and Google 21% of enterprise market share. Training costs for frontier models now exceed $100 mi...
Silicon War Economics: The Cost Structure of Chip Nationalism
The global semiconductor industry, projected to reach $1 trillion in revenue by late 2026, has become the primary arena for a new form of economic warfare: chip nationalism. Nations are pouring hundreds of billions of dollars into domestic fabrication capacity, driven not by comparative advantage but by strategic anxiety. This paper examines the economic cost structure of semiconductor reshorin...
Enterprise AI Agents as the New Insider Threat: A Cost-Effectiveness Analysis of Autonomous Risk
The rapid deployment of autonomous AI agents across enterprise environments has introduced a novel category of insider threat that traditional cybersecurity frameworks are ill-equipped to address. According to the Thales 2026 Data Threat Report, 61% of organizations now cite AI as their top data security concern, while only 34% maintain visibility into where all their data resides. This article...
Policy Implications and a Decision Framework for Shadow Economy Reduction in Ukraine
Paper 3 of 3 in the series "Shadow Economy Dynamics." Builds on Paper 1: Problem Landscape and Paper 2: Scenario Analysis.
Scenario Analysis: Modeling Three Futures for Ukraine’s Shadow Economy (2025–2030)
In Paper 1 of this series (Ivchenko, Ivchenko & Grybeniuk, 2026a), we established that Ukraine's shadow economy has remained persistently high — between 30% and 45% of official GDP over the decade 2015–2025. We identified two competing feedback loops: a reinforcing cycle where high tax burdens push economic actors into informality, and a balancing mechanism where digitalization increases tr...
The Legal 15%: Liability Is Not a Technical Problem
The Anthropic Economic Index (Massenkoff & McCrory, 2026) reveals a persistent and structurally significant anomaly: legal occupations exhibit only 15% observed AI exposure despite theoretical automation potential that rivals software engineering. This article examines the economic architecture of that gap. Unlike healthcare, where clinical decision liability and FDA approval pathways create te...
Tax Burden, Digitalization, and Shadow Economy in Ukraine: A Problem Landscape (2015–2025)
Ukraine's shadow economy remains one of the largest in Europe, consistently estimated at 30–45% of official GDP over the past decade. This phenomenon directly undermines fiscal stability, distorts market competition, and complicates the country's path toward EU accession. The interaction between tax policy, digital governance, and informal economic activity forms a complex adaptive system where...
The Measurement Crisis: Saturation, Goodhart’s Law, and the End of AI Leaderboards
The AI evaluation ecosystem is in crisis. Frontier models now exceed 90% accuracy on MMLU, 95% on HumanEval, and 93% on HellaSwag — scores that were considered unattainable three years ago. This saturation is not evidence of intelligence; it is evidence that our instruments have failed. We argue that three convergent forces have rendered current AI leaderboards meaningless: (1) benchmark satura...
Buy vs Build in 2026: Why CIOs Are Choosing Integrated Agentic Ecosystems
The classic "build vs buy" dilemma in enterprise software has been resolved for most AI deployments in 2026 — not by a clear winner, but by a third option that renders the original question obsolete. As Gartner projects worldwide AI spending at $2.5 trillion in 2026, enterprises are abandoning bespoke AI moonshots in favour of orchestrated integration across incumbent vendor ecosystems. This ar...