Greater Sudbury, ON · POP — · EMP 77,190 · DATA GRADE A

Greater Sudbury, ON

48.8 /100

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

#22 of 41

more exposed than 49% of Canadian CMAs

Secondary measures

21.5%

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

97.3%

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	2,290	—	61.7
Employment insurance and revenue officers	1,420	—	87
Retail and wholesale trade managers	1,780	—	68.2
Accounting and related clerks	1,130	—	86.6
Administrative assistants	1,025	—	94.9
General office support workers	1,245	—	74.2
Registered nurses and registered psychiatric nurses	1,930	—	43.3
Administrative officers	970	—	82.9
Elementary school and kindergarten teachers	1,590	—	46.9
Cashiers	1,635	—	41.9

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

Compare Greater Sudbury against any other metro — side by side.