All regions
Region · #1 of 12 for Jevons exposure
London.
3.7M workers, 15% of the UK total. Weighted AI exposure 5.41/10, demand elasticity 5.7/10, projected 2035 outlook +4%. 48.8% female. SOC-joined from soc_regional × soc_gender × elasticity.json.
Jevons cohort
27.6%
1.0M workers · UK avg 15.3%
Anti-Jevons cohort
21.5%
#2 of 12 · UK avg 19.7%
Women at risk
312.5K
17.2% of regional female workforce in exp≥7 + elast≤4
Avg pay
£36,027
Workforce-weighted across 20+ roles
Exposure / Elasticity
5.41 / 5.7
Higher exposure × lower elasticity ⟹ at-risk
Zone composition
Where the London workforce lives in the AI split.
Total 3.7M workers
Grows with AI
28.7% · 1.1M
Hands-on, growing
10.2% · 380.7K
Could go either way
7.4% · 274.2K
Mixed signals
29.3% · 1.1M
Shrinks with AI
21.5% · 797.1K
Barely exposed
2.9% · 106.5K
Top Jevons roles
Where AI is expanding total demand in this region.
Network manager
53.2K workers · exp 7 · elast 7.5
+10%
Business project manager
48.0K workers · exp 7 · elast 7.5
+10%
Financial adviser
26.4K workers · exp 7 · elast 7.5
+10%
Investment analyst
26.4K workers · exp 8 · elast 7.5
+11%
Charity fundraiser
26.3K workers · exp 6 · elast 8.5
+12%
Business adviser
25.2K workers · exp 7 · elast 7.5
+10%
Business analyst
25.2K workers · exp 8 · elast 7.5
+11%
Management consultant
25.2K workers · exp 7 · elast 7
+8%
Public relations officer
24.6K workers · exp 7 · elast 8.5
+13%
App developer
22.8K workers · exp 9 · elast 8.5
+17%
Top Anti-Jevons roles
Where AI replaces labour at fixed total demand.
Bank manager
113.2K workers · exp 7 · elast 3.5
-6%
Admin assistant
73.2K workers · exp 9 · elast 2
-15%
Civil Service manager
53.7K workers · exp 7 · elast 3.5
-6%
Customer service assistant
40.6K workers · exp 7 · elast 2.5
-10%
Bookkeeper
32.4K workers · exp 9 · elast 3.5
-7%
Payroll administrator
32.4K workers · exp 9 · elast 2
-15%
Civil Service executive officer
20.1K workers · exp 7 · elast 2.5
-10%
Data protection officer
18.9K workers · exp 8 · elast 3.5
-7%
Bilingual secretary
17.9K workers · exp 9 · elast 2.5
-12%
Executive assistant
17.9K workers · exp 7 · elast 2.5
-10%
Baumol winners
Low exposure, positive outlook: trades and physical services benefiting from AI-driven wage inflation.
Cleaner
53.7K workers · exp 1 · elast 7.5
+10%
Taxi driver
45.6K workers · exp 3 · elast 7.5
+7%
Waiter
44.1K workers · exp 2 · elast 4.5
+3%
Chef
41.0K workers · exp 2 · elast 7.5
+9%
Delivery van driver
34.8K workers · exp 3 · elast 6.5
+5%
Care worker
28.0K workers · exp 2 · elast 6.5
+7%
Palliative care assistant
28.0K workers · exp 2 · elast 6.5
+7%
Residential support worker
28.0K workers · exp 2 · elast 6.5
+7%
Nursing associate
22.8K workers · exp 3 · elast 6.5
+5%
Hairdresser
15.9K workers · exp 1 · elast 3.5
+3%
Largest occupations
The 20 roles that anchor London's workforce.
| # | Role | Outlook |
|---|---|---|
| 1 | Bank manager Business & Finance | -6% |
| 2 | Admin assistant Administration | -15% |
| 3 | Civil Service manager Government Services | -6% |
| 4 | Cleaner Home Services | +10% |
| 5 | Network manager Computing & Digital | +10% |
| 6 | Business project manager Business & Finance | +10% |
| 7 | Primary school teacher Teaching & Education | -2% |
| 8 | Taxi driver Transport | +7% |
| 9 | Waiter Hospitality & Food | +3% |
| 10 | Chef Hospitality & Food | +9% |
| 11 | Customer service assistant Retail & Sales | -10% |
| 12 | Sales assistant Retail & Sales | -1% |
| 13 | Higher education lecturer Teaching & Education | -2% |
| 14 | Retail manager Retail & Sales | -4% |
| 15 | GP Healthcare | -1% |
| 16 | PE teacher Sports & Leisure | -3% |
| 17 | Secondary school teacher Teaching & Education | -3% |
| 18 | Delivery van driver Delivery & Storage | +5% |
| 19 | Bookkeeper Business & Finance | -7% |
| 20 | Payroll administrator Business & Finance | -15% |
Gender × zone cross-section
312.5K women in London work in AI-exposed, demand-capped roles.
That is 17.2% of the region's female workforce in roles where exposure ≥ 7 and elasticity ≤ 4, the Anti-Jevons threshold. The region is 48.8% female overall, but the admin / clerical / customer-service pipelines that dominate this zone are historically female-led, so the zone itself skews that way.