CLANKED

Bristol, UK · POP — · EMP 238,508 · DATA GRADE B

Bristol, UK

33.2 /100

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

#35 of 125

more exposed than 73% 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]
Information Technology Professionals 8,333
51.2
Sales Assistants and Retail Cashiers 10,123
37.5
Functional Managers and Directors 7,576
41.1
Other Administrative Occupations 5,262
58.9
Sales, Marketing and Related Associate Professionals 6,371
47.3
Teaching and other Educational Professionals 9,779
30.1
Caring Personal Services 11,065
20.6
Secretarial and Related Occupations 4,059
53.1
Administrative Occupations: Finance 3,906
53.6
Business, Research and Administrative Professionals 4,514
45.7

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