JobAIRisk

Gambling Change Persons and Booth Cashiers

Gambling Change Persons and Booth Cashiers — AI exposure, safer roles, and a pivot plan.

Also known as: Cashier · Cage Cashier · Bingo Cashier · Booth Cashier · Booth Monitor · Casino Banker

AI Task Exposure Score

Very High exposure

More exposed than 79% of 968 occupations · Rank #182 (1 = most exposed)

This score estimates how exposed the tasks in a role are to current and near-term AI capabilities. It does not predict whether a specific person will lose a job.

Most exposed tasks

Highest structured exposure values in this role’s task mix — the work AI systems can already do most of.

  • Keep accurate records of monetary exchanges, authorization forms, and transaction reconciliations.72
  • Reconcile daily summaries of transactions to balance books.72
  • Count money and audit money drawers.71

Augmentable tasks

Work where AI assists rather than replaces — the productivity frontier of this role.

  • Maintain cage security according to rules.59
  • Check identifications to verify age of players.58
  • Clean casino areas.58

Most durable tasks

Lowest exposure — typically judgment, relationships, physical presence, or accountability. This is the human moat.

The current data release does not distinguish durable tasks for this role.

Task exposure values and classifications come from the versioned data release — they are structured data, not model output. Bars show exposure contribution relative to this role’s task mix.

What this means

A score of 63 puts Gambling Change Persons and Booth Cashiers in the most-exposed quarter of analyzed occupations. In practice, exposure this high is about the mix: 8 of 13 analyzed tasks lean automatable, 5 augmentable, and 0 durable. The useful question isn’t “will AI take this job” — it’s which tasks go first, which get faster, and where to reposition time. That’s what the personalized report maps against your actual week.

One next move: audit how much of your week sits in the exposed tasks above — then shift time toward the durable set or investigate the adjacent roles below.

Lower-exposure adjacent roles

No adjacent role in the current data release is at least 10 points lower with ≥50% skill overlap — we don’t label anything “safer” unless the data supports it.

Labor-market context

  • $36,220median wage
  • 21,530employed
  • 4,000annual openings
  • -6.6%projected growth

Context only — labor statistics are not inputs to the exposure score. See methodology.

Your week probably doesn’t match the average

This page scores the occupation. The $9 Personalized Risk & Action Report scores your task mix — paste what you actually do and get your own score, confidence level, task matrix, human moat, and a 7/30/90-day plan.

Personalize my result — $9

Related roles

Adjacent by skills or family — no exposure claim implied.

FAQ — Gambling Change Persons and Booth Cashiers

What does a score of 63 mean for a Gambling Change Persons and Booth Cashiers?
It means that, weighted across the 13 tasks we analyzed for this role, the task mix sits at 63 on a 0–100 exposure scale — in the most-exposed quarter of analyzed occupations. It measures task exposure to current and near-term AI capabilities, not the probability of losing a job.
Which tasks in this role are most exposed to AI?
The highest-exposure tasks are: Keep accurate records of monetary exchanges, authorization forms, and transaction reconciliations; Reconcile daily summaries of transactions to balance books; Count money and audit money drawers. Exposure is scored per task from structured data, not generated by a language model.
Which parts of this job are most durable?
The current data release does not distinguish durable tasks for this role.
Is this score personalized to me?
No — this page shows the occupation-level baseline. Two people with the same title often do different work. The $9 personalized report recalculates the score from the tasks you actually do and builds a concrete 7/30/90-day plan around them.

Score version jr-v1 · data release 2026.07.11-r1 · updated 2026-07-11 · baseline mapping: 13 of 13 tasks carry source-level provenance · methodology