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.