ILO Working Paper 140 (2025): Generative AI and Jobs: A Refined Global Index of Occupational Exposure
Task-level occupational exposure framework for generative AI, built from expert input and model predictions.
OPEN SOURCE ↗Remote online notarization is making the traditional notary appointment obsolete for standard documents. Specialist international and complex notarial work survives.
Notaries authenticate documents, witness signatures, administer oaths, and certify copies — providing legal authentication services that underpin property transactions, international documents, and formal legal acts. The traditional in-person notary appointment for standard documents is being replaced by remote online notarization (RON).
RON platforms (Notarize, DocVerify, NotaryPro) use video verification and electronic seals to notarize documents remotely. 42 US states have enacted RON legislation. The UK is expanding electronic witnessing. For standard documents — powers of attorney, statutory declarations, certified copies — RON is already dominant in progressive jurisdictions.
What survives: the specialist notary (in civil law countries, the civil law notaire) who drafts and authenticates complex legal documents — property transactions, corporate acts, inheritance agreements — and bears professional responsibility for their legal validity. UK notaries specialising in international documents (apostille, legalisation) retain specialist expertise that AI cannot replicate.
The profession is contracting fastest at the commodity end (standard witnessing) and most protected at the specialist end.
These are the strongest arguments for why this job might survive. We take them seriously. Below each is the counterargument that explains why they are insufficient.
Put the case that Notary Public / Notary will survive AI displacement. The system responds with counterarguments from the research base. Strong arguments shift the score — up to a maximum of ±15 points. The system is not an AI. It is a structured argument engine.
This question layer is generated from the job verdict, the resistance case, the regional rollout logic, and the evidence status of this page. Use the filters to focus the discussion, or trigger a random question and work through the role from multiple angles.
Replace broad inference with occupation-specific literature, regulators, labour statistics, or professional-body evidence before publication-grade use.
TIER 1 review queue with 6 core sources and 3 framework signals.
This page is grounded in task exposure research and labour-market trend reports, then translated into a reasoned occupation-level argument.
This site now treats exact timelines, total job-loss counts, and regional speed as interpretive estimates unless a cited source states them directly. The argument on this page should be read as a structured forecast, not a guaranteed future.
These impact figures are site estimates for comparison and should not be read as official labour-market counts.
Task-level occupational exposure framework for generative AI, built from expert input and model predictions.
OPEN SOURCE ↗Finds clerical work is the most highly exposed occupational group and that augmentation is often more likely than full occupation automation.
OPEN SOURCE ↗Shows AI exposure is highest in many white-collar cognitive occupations, while manual occupations tend to have lower exposure.
OPEN SOURCE ↗Advanced economies are more exposed to AI because they have more cognitive-intensive jobs; infrastructure and skills limit adoption elsewhere.
OPEN SOURCE ↗Large-employer survey showing clerical roles among the fastest-declining and care, education, software and green-transition jobs among growth areas.
OPEN SOURCE ↗Argues advanced economies are better positioned to benefit from AI due to infrastructure, skills, and institutions.
OPEN SOURCE ↗