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Milton Keynes, UK · POP — · EMP 143,378 · DATA GRADE B

Milton Keynes, UK

33.5 /100

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

#29 of 125

more exposed than 78% 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 7,255
51.2
Sales Assistants and Retail Cashiers 6,902
37.5
Functional Managers and Directors 6,139
41.1
Other Administrative Occupations 3,750
58.9
Sales, Marketing and Related Associate Professionals 4,499
47.3
Administrative Occupations: Finance 2,996
53.6
Customer Service Occupations 2,618
50.3
Secretarial and Related Occupations 2,328
53.1
Road Transport Drivers 4,989
24.3
Finance Professionals 2,226
53.4

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