Toronto, ON · POP — · EMP 2,903,480 · DATA GRADE A

Toronto, ON

57 /100

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

#2 of 41

more exposed than 98% of Canadian CMAs

Secondary measures

32.2%

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

96.6%

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	76,080	—	61.7
Retail and wholesale trade managers	62,220	—	68.2
Information systems specialists	43,550	—	93.4
Financial auditors and accountants	47,530	—	81.1
Administrative officers	43,535	—	82.9
Administrative assistants	32,730	—	94.9
Software engineers and designers	30,415	—	96.5
Professional occupations in advertising, marketing and public relations	39,055	—	74.3
Software developers and programmers	26,430	—	99.9
Accounting and related clerks	30,235	—	86.6

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

Compare Toronto against any other metro — side by side.