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Peterborough, UK · POP — · EMP 100,962 · DATA GRADE B

Peterborough, UK

30.7 /100

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

#100 of 125

more exposed than 21% 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]
Sales Assistants and Retail Cashiers 5,252
37.5
Other Administrative Occupations 2,526
58.9
Information Technology Professionals 2,521
51.2
Road Transport Drivers 5,188
24.3
Caring Personal Services 5,225
20.6
Functional Managers and Directors 2,542
41.1
Customer Service Occupations 2,050
50.3
Sales, Marketing and Related Associate Professionals 2,167
47.3
Administrative Occupations: Finance 1,583
53.6
Secretarial and Related Occupations 1,557
53.1

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