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Discover Your TRIZAN PlanThere is a category of business risk that boards consistently underweight: the risk that arrives not gradually, giving time to adapt, but suddenly, having crossed a capability threshold that changes the competitive landscape in the span of months. The history of technology disruption is dotted with examples — the iPhone's launch in 2007 was preceded by years of gradual mobile development but its impact on entire industries materialized within three years. The commercial deployment of GPS disrupted navigation, logistics, and location-based services simultaneously and within a window that most incumbents found too short to respond effectively. Artificial General Intelligence — AGI — presents a risk of this character, and the weight of recent evidence suggests it is arriving on a timeline that most business leaders have not incorporated into their strategic planning. The conventional treatment of AGI in boardroom conversations positions it as a 20-to-30-year theoretical construct: fascinating to speculate about, irrelevant to the current planning cycle. The researchers at the frontier of AI development — the people who have watched GPT-4, Claude, Gemini, and their successors demonstrate capabilities that previous generation models couldn't approach — are increasingly talking about timelines measured in single digits, not decades. The Narrow AI to AGI Capability Gradient Understanding why AGI timelines have compressed requires understanding the specific nature of the capability gains that frontier AI systems have demonstrated over the past four years. Narrow AI systems — the AI that most businesses currently deploy — are designed and trained to perform specific tasks within defined domains. A fraud detection model classifies transactions. A demand forecasting model predicts inventory requirements. A language model generates text. These systems perform their specific tasks with remarkable precision but fail when asked to generalize: the fraud model cannot predict demand; the demand model cannot generate text; and neither can do what a reasonably intelligent human employee can do — reason across domains, apply judgment developed in one context to a novel situation in another, and coordinate multiple types of knowledge to solve problems that weren't anticipated at training time. The systems at the current frontier have demonstrated meaningful generalization across these boundaries. Large language models now solve university-level mathematics, pass professional examinations across law, medicine, and accounting, write and debug complex software in dozens of languages, reason through multi-step logical and ethical dilemmas, and do so in a conversational interface that allows users to iteratively guide the reasoning. This cross-domain generalization — the ability to operate competently across many different types of tasks without being specifically trained on each — is the defining characteristic that separates narrow AI from AGI. We have not yet reached AGI by most technical definitions. Current frontier models still fail in important ways: they confabulate confidently, they struggle with precise spatial reasoning, they lose coherence over very long contexts, and they do not exhibit the sustained goal-directed behavior that is part of what we mean by general intelligence. But the direction of travel is clear, the pace of improvement has been faster than