London, UK · POP — · EMP 4,360,056 · DATA GRADE B

London, UK

34.3 /100

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

#11 of 125

more exposed than 92% 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]
Functional Managers and Directors 235,316 —
41.1
Information Technology Professionals 155,090 —
51.2
Sales, Marketing and Related Associate Professionals 147,184 —
47.3
Sales Assistants and Retail Cashiers 176,896 —
37.5
Finance Professionals 107,501 —
53.4
Other Administrative Occupations 89,693 —
58.9
Business, Research and Administrative Professionals 110,694 —
45.7
Secretarial and Related Occupations 93,590 —
53.1
Teaching and other Educational Professionals 156,523 —
30.1
Artistic, Literary and Media Occupations 118,000 —
35.2

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