Kingston, ON · POP — · EMP 76,230 · DATA GRADE A

Kingston, ON

51 /100

exposure score (OpenAI task-exposure index via NOC crosswalk — single-index tier)

#16 of 41

more exposed than 63% of Canadian CMAs

Secondary measures

22.7%

of workers are in occupations where ≥50% of tasks are LLM-exposed (Eloundou β; threshold-sensitive — note)

94.3%

of area employment matched to scored occupations (grade A)

Scenario: if replacement-level AI arrives in 2030

2027 2035 show involuntary layoffs only

Figure 1. Modeled displacement under the median preset (diffusion k=0.8, ceiling 0.75, automation share 0.45, friction lag 1.5y, attrition 3%/y). Solid: positions eliminated. The gap between gross and layoffs is natural attrition — speed of diffusion, not depth of exposure, determines layoffs. This is a scenario, not a forecast: adjust every assumption.

Where the losses land — and your assumptions

Positions eliminated by 2035 per occupation group, under the arrival year selected above. Drag any multiplier if you think we're wrong about a group — your model, your numbers. Multipliers scale that group's task exposure (×0 = immune, ×2 = double).

Table 3. Group exposure = employment-weighted mean task exposure (Eloundou β over the group's local occupations). Bars use the same scenario engine as Figure 1 (median preset).

Most exposed local occupations

Occupation	Jobs	Median wage
Retail salespersons and visual merchandisers	1,945	—	61.7
Administrative officers	1,355	—	82.9
Administrative assistants	1,160	—	94.9
Retail and wholesale trade managers	1,600	—	68.2
Post-secondary teaching and research assistants	1,275	—	81.4
University professors and lecturers	1,445	—	71.4
Registered nurses and registered psychiatric nurses	2,200	—	43.3
Cashiers	1,530	—	41.9
Elementary school and kindergarten teachers	1,285	—	46.9
Social and community service workers	1,220	—	49.2

Table 2. Ranked by exposure × local employment. Bands on the 0–100 occupation scale.

Compare Kingston against any other metro — side by side.