A browser-based tool that helps workers understand how prepared they are for AI-driven labor market transitions, based on peer-reviewed economics research.
This is a research translation project. Economists at Brookings measured "adaptive capacity" across 752 occupations — a composite of skill transferability, geographic labor market density, financial reserves, and age — and published the framework in Manning & Aguirre (2026). This tool makes that framework explorable for individual workers.
You select your occupation, enter a few personal inputs, and get back a composite score with a breakdown of what's working for you and what isn't. All computation runs in your browser. Nothing is stored, sent, or tracked.
No build step. No dependencies. Just a static site:
python3 -m http.server 8000
Then open http://localhost:8000.
The composite score combines four equally weighted components, converted to Z-scores and mapped to a percentile via normal CDF approximation:
| Component | Source | What it measures |
|---|---|---|
| Skill transferability | O*NET skill profiles + BLS employment projections | How easily your occupation's skills map to other growing occupations |
| Geographic density | BLS OEWS + Census CBSA data | How many alternative employers exist in your labor market |
| Financial runway | User input (months of expenses covered) | How long you could search for a good-fit role rather than the first available |
| Age factor | User input | Time horizon for recouping retraining investments |
Transferability and density are occupation-level benchmarks from the study. Financial runway and age are personal inputs mapped to population distributions using simplified assumptions.
The data/ directory contains pre-built JSON files, so running the pipeline is optional. If you want to rebuild from source:
Requirements: Python 3, numpy, openpyxl (pip install numpy openpyxl)
O*NET 30.1 database files must be downloaded separately from onetcenter.org and extracted to /tmp/onet_30_1/db_30_1_text/.
Run scripts in order:
| Script | What it does |
|---|---|
01_parse_onet_skills.py |
Parses O*NET 30.1 skill + work activity importance ratings into 76-dimension vectors per occupation |
02_compute_skill_similarity.py |
Percentile-rank normalizes profiles, computes pairwise cosine similarity across all occupations |
03_parse_bls_projections.py |
Downloads BLS 2024–2034 employment projections (employment, growth, wages, education) |
04_compute_transferability.py |
Implements the Manning & Aguirre transferability formula: employment-weighted similarity to growing occupations |
05_compute_density.py |
Implements the Manning & Aguirre density formula: employment-weighted average log(employment/area) across metros |
06_bundle_ai_exposure.py |
Downloads Eloundou et al. AI exposure scores from the GPTs-are-GPTs repository |
07_build_benchmarks.py |
Merges all sources into a single per-occupation benchmark table with composite scores |
08_build_filter_tree.py |
Builds a guided quiz decision tree for occupation selection |
| Source | Version |
|---|---|
| O*NET | 30.1 (December 2025) |
| BLS Employment Projections | 2024–2034 |
| BLS OEWS | May 2024 |
| Census CBSA Gazetteer | 2024 |
| AI exposure scores | Eloundou et al. (2024) |
- Skill transferability is measured by O*NET skill profile similarity, which may not capture all relevant factors in career transitions.
- The composite score combines occupation-level benchmarks with self-reported personal inputs using simplified distributional assumptions for wealth and age.
- Occupation-level averages mask within-occupation variation. Two software developers may have very different adaptive capacity depending on their specific skills, employer, and network.
- Geographic density uses the occupation's national employment distribution, not the user's specific metro. The metro selection provides context but does not change the density score.
Manning, A. & Aguirre, J. (2026). "Who Bears the Costs of AI Displacement?" NBER Working Paper 34705. nber.org/papers/w34705
Eloundou, T., Manning, S., Mishkin, P. & Rock, D. (2024). "GPTs are GPTs: Labor Market Impact Potential of LLMs." Science 384(6702). doi.org/10.1126/science.adj0998