Reading, UK · POP — · EMP 88,859 · DATA GRADE B
Reading, UK
ILO GenAI exposure index via empirical SOC2020↔ISCO crosswalk · 3-digit data — own scale, not comparable with US/Canada
more exposed than 77% of UK areas (England & Wales)
Secondary measures
Scenario: if replacement-level AI arrives in 2030
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 | Exposure [range] | |
|---|---|---|---|---|
| Information Technology Professionals | 5,594 | — | 51.2 | |
| Sales Assistants and Retail Cashiers | 4,339 | — | 37.5 | |
| Functional Managers and Directors | 3,318 | — | 41.1 | |
| Sales, Marketing and Related Associate Professionals | 2,859 | — | 47.3 | |
| Other Administrative Occupations | 1,741 | — | 58.9 | |
| Caring Personal Services | 4,393 | — | 20.6 | |
| Finance Professionals | 1,683 | — | 53.4 | |
| Teaching and other Educational Professionals | 2,935 | — | 30.1 | |
| Administrative Occupations: Finance | 1,633 | — | 53.6 | |
| Secretarial and Related Occupations | 1,427 | — | 53.1 |
Table 2. Ranked by exposure × local employment. Bands on the 0–100 occupation scale.