1. Pull each occupation's core tasks from O*NET

O*NET defines 8–15 tasks per occupation, ranked by importance. We select the tasks that cover 80% of the role's importance weight.

2. Score each task against shipped AI products

Perplexity searches the web for commercially deployed AI systems — not research papers or demos — and scores each task on a 7-point scale. Every non-zero score must cite a named product, company, and source URL. This includes robotics and industrial automation alongside software AI — both are commercially deployed systems transforming how tasks are performed.

3. Classify into a tier based on depth of change

We count how many tasks are deeply changed by AI — meaning AI handles major parts, leads the work, or fundamentally transforms the workflow. The count determines the tier:

Inspired by Andrej Karpathy's framework.

Task selection

For each occupation, we query the O*NET Web Services API for task statements, importance ratings (IM scale), and frequency ratings (FT scale). We select tasks covering 80% of cumulative importance weight, capped at 8–15 tasks. If importance/frequency data is unavailable, we fall back to all “Core” tasks.

AI research via Perplexity

Each occupation's task list is sent to Perplexity's sonar-pro model with pro search enabled and high search context. The prompt includes the 7-point rubric (−3 to +3), disambiguation hints (alternate job titles, workflow-specific search terms), and domain exclusions (O*NET, BLS, job boards). Perplexity returns a score, evidence quality rating, deployment scope, and core-output match for each task, plus a list of products and workforce signals.

Quality-adjusted scoring

Raw task scores are not used directly. Each task's contribution is adjusted by three factors: core output match (does the AI product perform the task's core output, or just adjacent admin?), deployment scope (pilot = 0.45× weight, industry standard = 1.0×), and evidence quality (weak = 0.55×, strong = 1.0×). This prevents pilot-stage or loosely-matched tools from inflating scores.

Tier classification

We count deep-change tasks: tasks scoring ±2 or higher, or labeled “AI handles major parts.” Four or more deep-change tasks triggers “Reshaping.” For “Replacement,” we also require evidence of actual workforce displacement (headcount reduction, role elimination) or a strongly negative overall score.

Validation

Research results that fail validation — hollow retrieval (no meaningful citations beyond O*NET/BLS), schema failures, or missing task coverage — are quarantined and excluded from the treemap. Occupations without valid research appear as “Score pending.”