Anticipatory Intelligence Gap Research by Dmytro Grybeniuk
By Dmytro Grybeniuk, AI Architect | Anticipatory Intelligence Specialist | Stabilarity Hub | February 12, 2026 Target’s anticipatory system created billion in value that reactive competitors couldn’t capture — the difference between systems worth billions and those worth nothing lies in architectural design. The $12 Billion Question: Why Did Target Know Before the Father Did?…
By Dmytro Grybeniuk, AI Architect | Anticipatory Intelligence Specialist | Stabilarity Hub | February 2026 1. Problem Statement: The Prediction Paradox The machine learning industry has invested over $340 billion globally in predictive systems since 2018, yet enterprise prediction accuracy for market behavior, content performance, and demand forecasting remains stubbornly capped at 65-72% for horizons…
Defining Anticipatory Intelligence: Taxonomy and Scope By Dmytro Grybeniuk, AI Architect | Anticipatory Intelligence Specialist | Stabilarity Hub | 2026-02-11 Abstract The term “Anticipatory Intelligence” has proliferated across academic literature, national security discourse, and commercial AI marketing materials—yet rigorous definitional consensus remains absent. This article establishes a formal taxonomy of anticipatory systems, distinguishes them from…
The Black Swan Problem: Why Traditional AI Fails at Prediction By Dmytro Grybeniuk, AI Architect | Anticipatory Intelligence Specialist | Stabilarity Hub | 2026-02-11 Abstract Traditional recurrent neural network architectures—including LSTMs and GRUs—exhibit systematic failure modes when confronted with Black Swan events: rare, high-impact occurrences that fall outside the training distribution. This technical analysis quantifies…