Task Exposed

Professionals facing AI-driven workplace change often encounter a credibility gap: broad predictions about job displacement lack the specificity needed for personal career planning. TaskExposed fills this gap by shifting the lens from jobs to tasks, enabling workers across industries to identify which parts of their current role are already addressable by frontier AI models.

The platform serves professionals who want concrete insights rather than speculation. Whether you're a software engineer, copywriter, paralegal, or nurse, TaskExposed breaks down your profession into discrete tasks and assigns each an AI exposure rating. The methodology draws from established labor frameworks including O*NET occupational data and Bureau of Labor Statistics employment research, alongside recent work on task-level AI capabilities. This grounding distinguishes the platform from speculative takes on AI's economic impact.

The interface presents information at multiple levels of granularity. A task stream shows real-world examples: writing unit tests scores 88 percent exposure while mentoring junior teammates rates just 11 percent. Browsing by profession reveals aggregated patterns. The software engineer role shows 65 percent median exposure with median annual earnings of 132,000 dollars. Paralegals rank at 82 percent exposure, while registered nurses sit at 32 percent, reflecting genuine variation in how AI affects different work domains. Career family groupings allow for cross-cutting analysis, comparing entire fields rather than individual roles.

Each role includes not just exposure percentages but also a resilience metric—a signal of which professions retain durable human-critical tasks despite automation pressure. The Computer and Math family shows 67 percent resilience, while Business and Finance drops to 53 percent. These metrics help separate temporary disruption from structural change.

An iOS application provides mobile access to these insights, and the platform includes a personal AI risk calculator for individual assessment. The specific percentages and role-level breakdowns suggest investment in occupation-by-occupation analysis rather than blanket pronouncements about AI's impact.

The core value proposition remains straightforward: let people understand precisely which parts of their work AI can already address, which remain human-critical, and where the market dynamics are shifting fastest. For professionals considering career moves, employers planning upskilling initiatives, or policymakers weighing workforce interventions, this task-level lens offers more actionable data than aggregate displacement estimates.