Same AI exposure.
Opposite futures.
At AI exposure 7 or higher, one group of roles grows because cheaper AI output creates more demand (software, engineering, creative tech). Another group shrinks because the output volume is fixed (admin, customer support, medical secretaries). Same exposure score, different demand dynamic, opposite 2035 curves.
The fork opens past exposure 6.
Each bubble is a SOC occupation. Area ∝ UK employment. Colour by zone. The fork appears past AI exposure 6: the upper-right is where Jevons pays off, the lower-right is where Anti-Jevons bites.
Jevons gives. Then agentic systems take it back.
A single scenario illustrating how demand elasticity can reverse. Under this model, UK developers boom from 741.6K to a peak of 867.7K by 2027 as cheap AI-assisted code makes new projects viable, then compress to 111.2K by 2035 as agentic systems remove the human-in-the-loop. That's a 87% displacement from peak: the Jevons paradox playing out on a time delay.
Latent demand opens up. Cheap AI assistance expands the addressable surface.
Zero-knowledge entry: non-coders spin up production code. Junior layer cut first.
Direct business-to-machine. The IDE-as-interface dissolves; the model is the build pipeline.
Small senior-verifier remnant. In this scenario, around £41B/yr in UK coding wages no longer goes to humans.
Same twelve-year window. Different shapes by demand elasticity.
Jevons roles arc up and plateau · Anti-Jevons roles bend down from the start · The middle group peaks then cliffs. Adoption scenario ×1.00 applied. Click a card to open its role page.
Top 10 of each branch by wages at stake.
| # | Role | Workers | AI | Outlook | Wages |
|---|---|---|---|---|---|
| 01 | Admin assistant | 473.3K | 9 | -3% | £11.6B |
| 02 | Customer service assistant | 337.1K | 7 | -6% | £8.4B |
| 03 | Office manager | 125.1K | 7 | -3% | £4.1B |
| 04 | Customer services manager | 90.4K | 7 | -6% | £3.5B |
| 05 | Executive assistant | 69.2K | 7 | -3% | £3.0B |
| 06 | Purchasing manager | 62.7K | 7 | -3% | £2.8B |
| 07 | Bilingual secretary | 69.2K | 9 | -3% | £2.4B |
| 08 | Sales administrator | 80.3K | 8 | -6% | £2.0B |
| 09 | Retail buyer | 52.4K | 7 | -6% | £2.0B |
| 10 | E-commerce manager | 42.7K | 8 | -6% | £1.8B |
| # | Role | Workers | AI | Outlook | Wages |
|---|---|---|---|---|---|
| 01 | Network manager | 231.1K | 7 | +20% | £11.6B |
| 02 | Artificial intelligence (AI) engineer | 94.4K | 9 | +20% | £5.2B |
| 03 | Software developer | 94.4K | 9 | +20% | £5.0B |
| 04 | Computer games developer | 94.4K | 9 | +20% | £4.6B |
| 05 | App developer | 94.4K | 9 | +20% | £4.5B |
| 06 | Web developer | 94.4K | 9 | +20% | £4.1B |
| 07 | Data scientist | 68.8K | 9 | +20% | £3.9B |
| 08 | IT support technician | 110.1K | 7 | +20% | £3.3B |
| 09 | Business analyst | 75.3K | 8 | +20% | £2.9B |
| 10 | Technical architect | 43.0K | 8 | +20% | £2.8B |
AI displacement is not gender-neutral.
Twentieth-century occupational sorting routed women into clerical, administrative, and customer-service work, the exact mid-skill roles most amenable to LLM automation. Men clustered into trades (Baumol-protected) and engineering (Jevons-expanded). So the same AI exposure score predicts very different labour outcomes by sex.
| Role | Women | % F | AI | Outlook |
|---|---|---|---|---|
| Admin assistant | 365.0K | 77% | 9 | -3% |
| Office manager | 95.0K | 76% | 7 | -3% |
| Bilingual secretary | 67.0K | 97% | 7 | -3% |
| Medical secretary | 43.0K | 95% | 8 | -3% |
| Health records clerk | 51.0K | 77% | 8 | -3% |
| Payroll administrator | 118.0K | 70% | 9 | +3% |
| Bookkeeper | 118.0K | 70% | 9 | +3% |
Pull the levers. Watch the Gini move.
Four dials, four AI-exposure zones, one national wage pool. Every number recomputes live across all 738 occupations. Base pay, UK employment and per-occupation zone are held constant.
Drag to model the split.
Four levers, applied by AI-exposure zone. Everything else stays fixed: occupations, employment, base pay.
Gini is computed per-occupation weighted by UK employment (738 roles, 24.7M workers). The baseline Gini (0.226) reflects pay inequality across occupations, not individuals. A positive shift means inequality rises.
The entry-level layer is being cut first.
Graduate programmes are the Anti-Jevons canary. AI has replaced the analytical scaffolding (spreadsheets, first drafts, decks) that used to justify large cohorts. Cuts range from −11% to −29% across the Big Four between 2023 and 2024.
| Firm | 2023 | 2024 | Δ | Stated reason |
|---|---|---|---|---|
| KPMG | 1,399 | 942 | -29% | AI + low attrition |
| Deloitte | 1,700 | 1,400 | -18% | Restructuring + offshore |
| EY | 1,800 | 1,600 | -11% | Partner cull + consulting cuts |
| PwC | 1,600 | 1,500 | -6% | AI + offshoring to acceleration centres |
High-exposure postings fell almost twice as fast.
IMF / academic meta-analysis, pooled effect across 2022–2025 studies.