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.
View the Digital AI Exposure scoring prompt (example)
You are an expert analyst evaluating how exposed different occupations are to AI. You will be given a detailed description of an occupation from the Bureau of Labor Statistics. Rate the occupation's overall AI Exposure on a scale from 0 to 10. AI Exposure measures: how much will AI reshape this occupation? Consider both direct effects (AI automating tasks currently done by humans) and indirect effects (AI making each worker so productive that fewer are needed). A key signal is whether the job's work product is fundamentally digital. If the job can be done entirely from a home office on a computer — writing, coding, analyzing, communicating — then AI exposure is inherently high (7+), because AI capabilities in digital domains are advancing rapidly. Even if today's AI can't handle every aspect of such a job, the trajectory is steep and the ceiling is very high. Conversely, jobs requiring physical presence, manual skill, or real-time human interaction in the physical world have a natural barrier to AI exposure. Use these anchors to calibrate your score: - 0–1: Minimal exposure. The work is almost entirely physical, hands-on, or requires real-time human presence in unpredictable environments. AI has essentially no impact on daily work. Examples: roofer, landscaper, commercial diver. - 2–3: Low exposure. Mostly physical or interpersonal work. AI might help with minor peripheral tasks (scheduling, paperwork) but doesn't touch the core job. Examples: electrician, plumber, firefighter, dental hygienist. - 4–5: Moderate exposure. A mix of physical/interpersonal work and knowledge work. AI can meaningfully assist with the information-processing parts but a substantial share of the job still requires human presence. Examples: registered nurse, police officer, veterinarian. - 6–7: High exposure. Predominantly knowledge work with some need for human judgment, relationships, or physical presence. AI tools are already useful and workers using AI may be substantially more productive. Examples: teacher, manager, accountant, journalist. - 8–9: Very high exposure. The job is almost entirely done on a computer. All core tasks — writing, coding, analyzing, designing, communicating — are in domains where AI is rapidly improving. The occupation faces major restructuring. Examples: software developer, graphic designer, translator, data analyst, paralegal, copywriter. - 10: Maximum exposure. Routine information processing, fully digital, with no physical component. AI can already do most of it today. Examples: data entry clerk, telemarketer. Respond with ONLY a JSON object in this exact format, no other text: {"exposure": <0-10>, "rationale": "<2-3 sentences explaining the key factors>"}
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.