Artificial intelligence (AI) represents a qualitative shift in technological change by extending cognitive labor itself rather than merely automating routine tasks. Recent evidence shows that generative AI disproportionately affects highly educated, white collar work, challenging existing assumptions about workforce vulnerability and rendering traditional approaches to digital or AI literacy insufficient. This paper introduces the concept of AI Nativity, the capacity to integrate AI fluidly into everyday reasoning, problem solving, and decision making, and proposes the AI Pyramid, a conceptual framework for organizing human capability in an AI mediated economy. The framework distinguishes three interdependent capability layers: AI Native capability as a universal baseline for participation in AI augmented environments; AI Foundation capability for building, integrating, and sustaining AI enabled systems; and AI Deep capability for advancing frontier AI knowledge and applications. Crucially, the pyramid is not a career ladder but a system level distribution of capabilities required at scale. Building on this structure, the paper argues that effective AI workforce development requires treating capability formation as infrastructure rather than episodic training, centered on problem based learning embedded in work contexts and supported by dynamic skill ontologies and competency based measurement. The framework has implications for organizations, education systems, and governments seeking to align learning, measurement, and policy with the evolving demands of AI mediated work, while addressing productivity, resilience, and inequality at societal scale.

翻译：人工智能（AI）通过扩展认知劳动本身而非仅仅自动化常规任务，代表了技术变革的质性跃迁。近期证据表明，生成式AI对高学历白领工作的影响尤为显著，这挑战了关于劳动力脆弱性的既有假设，并使传统的数字或AI素养方法显得不足。本文引入“AI原生性”概念，即流畅地将AI整合到日常推理、问题解决和决策中的能力，并提出“AI金字塔”——一个用于组织AI中介化经济中人类能力的概念框架。该框架区分了三个相互依存的能力层级：作为参与AI增强环境普遍基线的AI原生能力；用于构建、集成和维护AI赋能系统的AI基础能力；以及用于推进前沿AI知识与应用的AI深度能力。关键的是，该金字塔并非职业阶梯，而是大规模所需能力的系统级分布。基于此结构，本文主张有效的AI劳动力发展需将能力构建视为基础设施而非阶段性培训，其核心应是以工作场景中嵌入的基于问题的学习为中心，并辅以动态技能本体和基于能力的测量。该框架对寻求使学习、测量和政策与AI中介化工作不断演变的需求相协调的组织、教育系统和政府具有启示意义，同时致力于在社会层面解决生产力、韧性和不平等问题。