Enhanced 2025-2030 Model Incorporating METR Task-Doubling Data
of work hours potentially automated by 2030
AI task capability doubling time (METR data)
exposure risk for routine cognitive jobs
timeline for major workforce transitions
AI automation risk is concentrated in the middle skill tier. High-skill strategic roles and low-skill embodied work show greater resilience, creating a "barbell" distribution of job security.
Incorporating METR's finding that AI task complexity doubles every 6-10 months, far exceeding previous 12-18 month estimates. This acceleration fundamentally changes displacement timelines.
Emergence of integrated agent toolchains (AutoGen, LangGraph) reduces deployment friction, enabling workflow automation rather than just task automation.
Regulatory environments create patchwork adoption patterns. EU AI Act compliance costs may slow deployment versus more permissive jurisdictions.
Our enhanced model addresses key limitations in prior work by incorporating dynamic capability curves, economic feedback loops, and implementation constraints.
| Factor | MIT-IBM Baseline | Enhanced Model | Impact |
|---|---|---|---|
| Capability Benchmark | GPT-3.5 level | GPT-4.5/5 + agents | +15-25% exposure |
| Task Doubling Time | 12-18 months | 6-10 months (METR) | Accelerated timeline |
| Implementation Costs | Uniform assumption | Size/sector specific | Uneven adoption |
| Regulatory Friction | Moderate uniform | Geographic variation | Patchwork deployment |
| Economic Feedback | Not modeled | Demand elasticity | Job creation offsets |
Analysis at sub-occupation level using O*NET task descriptions, weighted by time allocation and automation feasibility scores derived from current AI capabilities.
Incorporates wage elasticity, substitution costs, and productivity spillovers. Higher-wage roles face faster automation incentives despite higher implementation costs.
Large enterprises (>1000 employees) show 3-5x faster adoption rates due to economies of scale in AI implementation and training.
Routine Cognitive
Analytical Professional
Strategic/Creative
Manual/Embodied
Customer/Social
Code Generation
Examples: Legal clerks, insurance claims processors, compliance officers, schedulers
Why vulnerable: High task repetition, clear success metrics, extensive training data available
Timeline: Significant impact by 2026-2027
Examples: Junior accountants, market researchers, data analysts, technical writers
Why vulnerable: Structured inputs/outputs, but requires domain knowledge and judgment
Timeline: Gradual displacement 2027-2030, with augmentation preceding replacement
Examples: Product managers, UX designers, lawyers, engineers
Why resilient: High-stakes decisions, novel problem solving, human stakeholder management
Outlook: Significant augmentation, selective task automation, productivity gains
Software development, traditionally considered high-skill and safe, now shows 40-60% task automation potential due to advances in code generation models. However, system design and architecture roles remain more secure.
GPT-4.5 + basic agents
8-12% hours automated
Agent orchestration mature
15-22% hours automated
Enterprise integration
25-35% hours automated
Workflow-native AI
35-45% hours automated
Stable equilibrium
45-55% hours automated
The organizations and individuals who thrive will be those who master human-AI collaboration rather than viewing AI as pure replacement technology. This requires new mental models, workflows, and skill sets.
| Sector | Risk Level | Primary Actions | Timeline |
|---|---|---|---|
| Financial Services | High | Accelerate process automation, retrain analysts | 2025-2027 |
| Legal | Medium-High | AI-assisted research, focus on strategy/negotiation | 2026-2029 |
| Healthcare | Medium | Diagnostic augmentation, administrative automation | 2027-2030 |
| Education | Medium | Personalized learning, teacher role evolution | 2026-2030 |
| Manufacturing | Low | Quality inspection, predictive maintenance | 2028-2032 |