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AI Profiler

NLx data disclaimer. The National Labor Exchange (NLx) Data Trust bears no responsibility for the analyses or interpretations of the data presented here.

AIMatch aggregates from the NLx corpus summarize occupation and industry exposure to AI-coded task and skill language.

How to read this page. This is an exploratory profile of AI intensity in national NLx postings. Strict AI codes are narrower; lenient and composite measures are broader. Rankings are most useful for larger occupation or industry cells and for score differences above the validation floor. The occupation and industry ranking tables require at least 30,000 active jobs from January 2021 through September 2025.
Validation note. Posting-level LLM validation supports ordering for score gaps of about 0.05 or larger, while aggregate rankings are most defensible for large cells. The external benchmark below compares broad NAICS-2 sector averages from NLx with Census BTOS reported firm AI use; it does not validate every detailed industry ranking.

External benchmark: NLx AI intensity vs. Census BTOS firm AI use

Figure NAI-1. BTOS Firm AI Use vs. NLx Strict AI Intensity

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Data source: inputs/crosswalks/ai_intensity_aggregation_btos_benchmark.csv, exposed in the dashboard as jaat.AGGR_AiIntensityAggregationBtosBenchmark. This is a cross-sectional convergent-validity check: latest Census BTOS sector AI-use rates are compared with NLx NAICS-2 AIMatch averages from the public NLx aggregate ending in September 2025. A time-series validation is not possible in this public package because nonmissing BTOS responses to the current AI-use question begin in November 2025, after the NLx public aggregate ends.

Table NAI-1. BTOS Correlation Summary
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The strict-code measure has the strongest rank agreement with BTOS firm AI use. Mean AI score also moves in the expected direction, but less strongly.

Show benchmark source rows

Table NAI-2. BTOS Sector Benchmark Rows
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Strict AI Skill Posting Share

Figure NAI-2. Strict AI Skill Code Matches as a Share of Active Postings

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This series uses strict-score AIMatch AI skill-code matches divided by active postings in the NLx corpus. It is not a de-duplicated count of postings with any strict code, because a posting can contribute more than one strict AI code.

Occupation AI Rankings

Table 1. Occupation AIMatch rankings (NLx corpus; minimum 30,000 active jobs)

Terms. Rank follows the dropdown measure. Mean AI score is the average statement-level AI intensity score. Strict and lenient columns are AI-code matches per monthly active job (MAJ). Composite log score is the precomputed broad AIMatch index in the NLx aggregate files: it combines logged versions of mean AI score, strict-code matches, and lenient-code matches. The dashboard averages monthly cell scores with active-job weights before ranking.
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Figure NAI-3. Selected Occupation AI Score Trend

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The chart shows month-to-month changes in the selected occupation’s AI score.

Show exploratory NAICS-6 industry rankings

These detailed industry rows are retained as a lead list only. The BTOS benchmark supports broad sector ordering, while NAICS-6 cells can look odd because of industry coding, posting mix, and the way AI language is distributed across employers.

Table 2. Six-digit industry AIMatch rankings (NLx corpus; minimum 30,000 active jobs)

Terms. Industry rankings use the same selected measure and column definitions as the occupation table, with NAICS-6 industries replacing O*NET-SOC occupations.

Use the NAICS-6 table as an exploratory lead list. The external BTOS check above validates broad sector ordering, while detailed industry coding and compositional differences can make individual six-digit industries noisy.
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Evidence Path

The AI-score validation is summarized in Finding Emerging Skills. Reproducibility checks and public artifacts are listed in Agreement And Audit Trail.