This is a research tool that visualizes 342 occupations from the Bureau of Labor Statistics Occupational Outlook Handbook, covering 143M jobs across the US economy. Each rectangle's area is proportional to total employment. Color shows the selected metric — toggle between BLS projected growth outlook, median pay, education requirements, and AI exposure. Click any tile to view its full BLS page. This is not a report, a paper, or a serious economic publication — it is a development tool for exploring BLS data visually.
LLM-powered coloring: The source code includes scrapers, parsers, and a pipeline for writing custom LLM prompts to score and color occupations by any criteria. You write a prompt, the LLM scores each occupation, and the treemap colors accordingly. The "Digital AI Exposure" option is one example — it estimates how much current AI (which is primarily digital) will reshape each occupation. But you could write a different prompt for any question — e.g. exposure to humanoid robotics, offshoring risk, climate impact — and re-run the pipeline to get a different coloring.
Caveat on Digital AI Exposure scores: These are rough LLM estimates, not rigorous predictions. A high score does not predict the job will disappear. Software developers score 9/10 because AI is transforming their work — but demand for software could easily grow as each developer becomes more productive. The score does not account for demand elasticity, latent demand, regulatory barriers, or social preferences for human workers. Many high-exposure jobs will be reshaped, not replaced.
本页将国家统计局分行业就业与平均工资(占位示例,可替换为年鉴或 data.stats.gov.cn 发布数据)与《职业分类大典》代表性职业做加权拆解,矩形面积表示估算岗位规模,颜色表示所选维度。职业名称、别名搜索与 AI 影响指数为工程示意,非人社部或统计局官方结论。详细目录见 人社部职业分类信息查询。
说明: 中国版暂无美国 BLS 式的「行业增速」分层,故隐藏「BLS Outlook」;薪资默认为人民币年平均工资(元/年)。数据更新请编辑 china/raw_industry_stats.json 后运行 python build_site_data_cn.py。