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Warwick, UK · POP — · EMP 72,753 · DATA GRADE B

Warwick, UK

34.5 /100

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

#9 of 125

more exposed than 94% 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 3,367
51.2
Functional Managers and Directors 3,897
41.1
Sales, Marketing and Related Associate Professionals 2,646
47.3
Teaching and other Educational Professionals 3,755
30.1
Engineering Professionals 3,048
33.6
Sales Assistants and Retail Cashiers 2,626
37.5
Other Administrative Occupations 1,554
58.9
Secretarial and Related Occupations 1,362
53.1
Production Managers and Directors 1,971
36
Business, Research and Administrative Professionals 1,406
45.7

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