Swansea, UK · POP — · EMP 100,613 · DATA GRADE B

Swansea, UK

31.8 /100

ILO GenAI exposure index via empirical SOC2020↔ISCO crosswalk · 3-digit data — own scale, not comparable with US/Canada

#69 of 125

more exposed than 46% of UK areas (England & Wales)

Secondary measures

Scenario: if replacement-level AI arrives in 2030

2027 2035

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

OccupationJobsMedian wageExposure [range]
Sales Assistants and Retail Cashiers 5,909 —
37.5
Other Administrative Occupations 2,805 —
58.9
Administrative Occupations: Government and Related Organisations 2,727 —
52.1
Caring Personal Services 6,492 —
20.6
Teaching and other Educational Professionals 3,980 —
30.1
Customer Service Occupations 2,256 —
50.3
Secretarial and Related Occupations 1,752 —
53.1
Functional Managers and Directors 2,113 —
41.1
Information Technology Professionals 1,691 —
51.2
Administrative Occupations: Finance 1,586 —
53.6

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