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Oxford, UK · POP — · EMP 73,335 · DATA GRADE B

Oxford, UK

32.7 /100

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

#46 of 125

more exposed than 64% 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]
Teaching and other Educational Professionals 5,947
30.1
Research and Development (R&D) and Other Research Professionals 2,834
42.7
Information Technology Professionals 2,165
51.2
Sales Assistants and Retail Cashiers 2,929
37.5
Business, Research and Administrative Professionals 2,125
45.7
Functional Managers and Directors 2,123
41.1
Other Administrative Occupations 1,355
58.9
Sales, Marketing and Related Associate Professionals 1,456
47.3
Caring Personal Services 3,126
20.6
Other Elementary Services Occupations 2,743
23.2

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