Gaming Managers

Management

AI exposure

  • Data source: BLSPublished: 2026-08

    High· relative

    LowFour relative bandsVery high

    Group-level value

    Scale, basis and source

    Four relative bands (Low / Moderate / High / Very high)

    831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band

    Source dataset (XLSX download)

  • Data source: AnthropicPublished: 2026-03

    0.000

    0.000Range of values carried here0.745
    Scale, basis and source

    Observed exposure index, 0–1 as published

    Mapped onto O*NET tasks

    Source dataset

  • Data source: ILOPublished: 2025

    0.32

    0.09Range of values carried here0.70

    Group-level value

    Scale, basis and source

    Generative AI exposure index, 0–1 as published

    ISCO-08 unit group — every occupation sharing the code gets this value

    Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 64% score at or above this value.

    Source dataset

What kind of figure this source publishes

The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.

Task-level exposure

Exposed tasks only

Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.

TaskβE1 + 0.5 × E2
Maintain familiarity with all games used at a facility, as well as strategies or tricks employed in those games.

O*NET Task ID 7187

1.0
Explain and interpret house rules, such as game rules or betting limits.

O*NET Task ID 7190

1.0
Prepare work schedules and station arrangements and keep attendance records.

O*NET Task ID 7193

1.0
Track supplies of money to tables and perform any required paperwork.

O*NET Task ID 7196

1.0
Set and maintain a bank and table limit for each game.

O*NET Task ID 7197

1.0
Direct the compilation of summary sheets that show wager amounts and payoffs for races or events.

O*NET Task ID 7201

1.0
Resolve customer complaints regarding problems, such as payout errors.

O*NET Task ID 7185

0.5
Train new workers or evaluate their performance.

O*NET Task ID 7188

0.5
Monitor staffing levels to ensure that games and tables are adequately staffed for each shift, arranging for staff rotations and breaks and locating substitute employees as necessary.

O*NET Task ID 7191

0.5
Interview and hire workers.

O*NET Task ID 7192

0.5
Remove suspected cheaters, such as card counters or other players who may have systems that shift the odds of winning to their favor.

O*NET Task ID 7186

0.0
Circulate among gaming tables to ensure that operations are conducted properly, that dealers follow house rules, or that players are not cheating.

O*NET Task ID 7189

0.0
Record, collect, or pay off bets, issuing receipts as necessary.

O*NET Task ID 7200

0.0
Notify board attendants of table vacancies so that waiting patrons can play.

O*NET Task ID 7202

0.0

β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.

Data sources & licenses — O*NET®, Eloundou et al. (2023): see full notices on the Credits page

Occupation information