The deployment of Large Language Models (LLMs) has ignited concerns about technological unemployment. Existing task-based evaluations predominantly measure theoretical "exposure" to AI capabilities, ignoring critical frictions of real-world commercial adoption: liability, compliance, and physical safety. We argue occupations are not eradicated instantaneously, but gradually encroached upon via atomic actions. We introduce a Tech-Risk Dual-Factor Model to re-evaluate this. By deconstructing 923 occupations into 2,087 Detailed Work Activities (DWAs), we utilize a multi-agent LLM ensemble to score both technical feasibility and business risk. Through variance-based Human-in-the-Loop (HITL) validation with an expert panel, we demonstrate a profound cognitive gap: isolated algorithmic probabilities fail to encapsulate the "institutional premium" imposed by experts bounded by professional liability. Applying a strictly algorithmic baseline via mathematical bottleneck aggregation, we calculate Relative Occupational Automation Indices ($OAI$) for the U.S. labor market. Our findings challenge the traditional Routine-Biased Technological Change (RBTC) hypothesis. Non-routine cognitive roles highly dependent on symbolic manipulation (e.g., Data Scientists) face unprecedented exposure ($OAI \approx 0.70$). Conversely, unstructured physical trades and high-stakes caretaking roles exhibit absolute resilience, quantifying a profound "Cognitive Risk Asymmetry." We hypothesize the emergent necessity of a "Compliance Premium," indicating wage resilience increasingly tied to risk-absorption capacity. We frame these findings as a cross-sectional diagnostic of systemic vulnerability, establishing a foundation for subsequent Computable General Equilibrium (CGE) econometric modeling involving dynamic wage elasticity and structural labor reallocation.

翻译：大型语言模型（LLM）的部署已引发对技术性失业的担忧。现有基于任务的评估主要衡量人工智能能力的理论“暴露度”，而忽视了现实世界商业采用中的关键摩擦：责任、合规性与物理安全性。我们认为，职业并非瞬间消亡，而是通过原子化行动逐步被侵蚀。为此，我们引入了一个技术-风险双因素模型进行重新评估。通过将923个职业分解为2,087项详细工作活动（DWA），我们利用多智能体LLM集成对技术可行性与商业风险进行评分。通过基于方差的人机协同验证（HITL），与专家小组协作，我们揭示了深刻的认知差距：孤立的算法概率无法涵盖受职业责任约束的专家所施加的“制度溢价”。采用严格算法基线并通过数学瓶颈聚合方法，我们计算了美国劳动力市场的相对职业自动化指数（$OAI$）。我们的发现挑战了传统常规偏向型技术变革（RBTC）假说。高度依赖符号操作的非日常认知型角色（如数据科学家）面临前所未有的暴露度（$OAI \approx 0.70$）。相反，非结构化的体力劳动与高风险的照料角色展现出绝对韧性，量化了深刻的“认知风险不对称性”。我们假设“合规溢价”将应运而生，这表明工资韧性越来越依赖于风险吸收能力。我们将这些发现定位为系统性脆弱性的横截面诊断，为后续涉及动态工资弹性与结构性劳动力再配置的可计算一般均衡（CGE）计量经济建模奠定基础。