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Cambridge, UK · POP — · EMP 70,595 · DATA GRADE B

Cambridge, UK

34.4 /100

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

#10 of 125

more exposed than 93% 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 4,547
51.2
Teaching and other Educational Professionals 5,145
30.1
Business, Research and Administrative Professionals 2,983
45.7
Research and Development (R&D) and Other Research Professionals 2,742
42.7
Functional Managers and Directors 2,524
41.1
Sales Assistants and Retail Cashiers 2,353
37.5
Natural and Social Science Professionals 2,095
39.2
Sales, Marketing and Related Associate Professionals 1,538
47.3
Other Administrative Occupations 1,147
58.9
Engineering Professionals 1,659
33.6

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