How AI is changing computer & math work.
TaskExposed currently tracks 8 occupations in the computer & math family, representing approximately 3.0M workers. The group has an average AI exposure score of 60% and an average resilience score of 66.
The most exposed roles usually contain repeatable, text-heavy, data-heavy, or process-driven tasks. The most resilient roles usually depend on judgment, physical presence, trust, real-time decision-making, or cross-functional human coordination.
Use this page as a career map: compare risk levels, explore lower-exposure adjacent paths, and open individual profession reports for task-level detail.
Most AI-exposed computer & math careers
Roles with the highest task-level exposure scores.
Most resilient computer & math careers
Roles with the strongest human resilience scores.
High, moderate, and low exposure roles.
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Explore every computer & math profession.
Web developers face high task-level exposure — much of the implementation work that used to take days is now generated in minutes. Value is shifting toward architecture, UX judgment, and client communication.
Data analysts face high exposure in query writing, report generation, and data cleaning — but strong human advantage remains in hypothesis formation, business translation, and stakeholder storytelling.
QA engineers face high exposure in test generation and automation, where AI now writes test suites from specs. But exploratory testing, test strategy, and the judgment to prioritise what breaks badly still require humans.
Software engineers face heavy task-level exposure to language models, but maintain strong human-critical work in systems design, debugging ambiguous environments, and cross-team negotiation.
Data engineers face growing exposure in pipeline generation and schema work, but the architectural thinking, data contract ownership, and cross-system integration judgment remain strongly human.
DevOps engineers see strong AI assistance in configuration, scripting, and documentation, while real-time incident response, platform strategy, and on-call judgment under pressure stay firmly human.
ML engineers face a paradox: AI accelerates their tooling and code generation, but the research intuition, model evaluation, and production reliability work that defines the role requires deep human judgment.
Cybersecurity analysts benefit from AI in threat detection and log analysis, but adversarial reasoning, incident response under pressure, and attacker psychology remain distinctly human capabilities.