The outlook figures on this site are estimates made by AI models. They are not observed statistics or probabilities, and they are not predictions about any individual.
Below, for each service: what the number means, the criteria, the data used and the limits.
The date shown for each job is when, counting from 2026, the average employer is estimated to start visibly reducing the hiring of people for this job because of AI. It is not when half of the work changes or when the job disappears; it is when replacing people actually begins.
For each job, an AI model scores the items below, each with a one-line reason, and then sets the years by overall judgment rather than a formula. The judgment draws on public information the model knew as of mid-2026 (the state of AI, industry trends, rules and institutions).
Assumption: the criteria assume fast AI progress and fast adoption by industry, while taking licences, rules and physical work into account as they are. The dates can therefore be earlier than those of a more conservative outlook.
Consistency: within the same field, assistant roles are not judged later than professional roles, physical work is not judged earlier than cognitive work, and licensed jobs are not judged earlier than unlicensed ones. Reference ranges for a few jobs keep the scale aligned (for example call-center agents 0.5–2 years; plumbers, electricians and welders 20–30 years), and the results were kept from bunching at 3–5 years.
The date shown for each major is when AI is estimated to start putting visible downward pressure on new hiring in the main career paths of that major’s typical graduates in that country. It does not mean the program will close or that all graduates will lose their jobs.
Shows two numbers for each of 321 Korean universities. Lifespan is the number of years left until falling freshman numbers make it hard to fill 80% of the university’s places (capped at 20 years; “already under quota” if this year’s enrolment rate is already below 80%). Reaching that line does not mean the university closes, so the figure is shown as “at least N years”. It starts from this year’s in-quota enrolment rate and lowers it as the national number of births (Statistics Korea) falls, with how fully a school feels that fall set by its student-demand grade. 10-year outlook is the average of three measures (student pull, financial strength, and location and licensed programmes) on a 0–100 scale, in four grades: Growing (75+), Steady (55–74), Decreasing (40–54) and Shrinking (below 40). The three measures group six detailed items (recruitment, student retention, protected departments, financial strength, policy signals and location) that an AI model rated from the last three years of public data. Both are AI estimates, not an official government grade or a probability of closure. Full criteria (in Korean): 우리 대학 수명 · 10년 전망 기준.
The Korean page “우리 지역 수명” (Area Countdown) shows two numbers for each of Korea’s 256 cities, counties and districts: the area lifespan described below, and an outlook score for 5, 10 and 20 years ahead. The outlook score combines seven items scored from 0 to 100 (in steps of 5); the national rank uses this score. For the first five items an AI model researches public data and scores each period; public safety and the natural environment are calculated from national statistics (added 30 September 2026).
| Item | Weight |
|---|---|
| Jobs and industry | 25% |
| Commuting and outside links | 10% |
| Schools and education | 15% |
| Healthcare and everyday infrastructure | 15% |
| Local government finances and capacity | 10% |
| Public safety (calculated from statistics) | 15% |
| Natural environment and air (calculated from statistics) | 10% |
Public safety combines the five major crimes per 1,000 residents (Korean National Police Agency, 2024) at 50% with the Ministry of the Interior and Safety’s regional safety index grades for traffic, fire and everyday safety (2025) at 50%. Natural environment and air combines annual average fine dust PM2.5 (AirKorea, 2023–2025) at 50%, forest and water (share of forest and river land, end of 2025) at 25%, and the number of burden types present among coal power plants, nuclear power plants, national industrial complexes and airport noise zones at 25% (coal plants count only from 100 MW of capacity). Each input is turned into a percentile among all 256 areas (equal values share a position), the percentiles are blended, and the resulting order is mapped onto the same raw-score scale as the AI items (the matching position among all 10-year raw scores of the five AI items, in steps of 5). These indicators change slowly, so the same score is used for 5, 10 and 20 years; missing inputs are left out of the blend (if an area has no safety index grade at all, that part alone is set to the national middle). Crime is counted where it happens, so busy downtown and tourist areas with many visitors show higher rates per resident. The index’s crime grade and its suicide and infectious-disease fields are not used. The safety index grades are relative within each type of area (cities, counties, districts), not an absolute national level; Sejong, Jeju City and Seogwipo have no such grade and use their province-level grades instead. National industrial complexes that are not yet built, or that only touch an area’s edge (under 0.5 km² and under 20% of the complex), and research, business, digital, publishing and bio-research complexes (Daedeok Innopolis, Seoul Digital, Paju Publishing, Gwangju Cheomdan, Osong Bio, Jeju Science Park and similar) are not counted, and airport noise covers only the designated zones around civilian airports (military airfields are not included). Some inputs for non-autonomous districts use the whole city’s value, and districts created in 2026 use their predecessor’s value; the item card says so.
The AI’s raw scores cluster in the middle, so the page shows a nationally comparable score produced by one formula applied to every area, period and item: item score = 69 + 1.2 × (raw score − 55), with results below 0 set to 0 and above 100 set to 100. The overall score is the weighted sum of these item scores, and grades and ranks use it. There are four grades: 75 or more Growing (성장), 63 or more Steady (유지), 57 or more Decreasing (감소) and below 57 Shrinking (위축), with the same bands for 5, 10 and 20 years (Growing at 75 or more, the same as the university outlook). If any single item cannot be judged, the overall result for that period is withheld.
Area lifespan is an AI estimate of the years left until young adults (18–39) fall below half the number of people aged 65 and over, or the population falls by 20%, based on the last three years of resident-registration data (at most 20 years; beyond that it shows “20 years or more”). Reaching that line does not mean the area disappears, so the figure is shown as “at least N years”. Where young adults are already below half, it shows “already at high risk of depopulation”. The lifespan is not part of the score or the rank.
Housing prices and living costs are not scored. The scores are interpretations under shared rules, not probabilities, official ratings or measurements. Base date: 24 September 2026.
Three questions about your situation and 24 questions about activities you enjoy (answered on a 5-point scale) produce scores on six interest types (Hands-on, Analytical, Creative, Helping, Persuading, Organizing), which are compared with each job’s O*NET interest profile to calculate a fit score. Candidates are only jobs judged slow to be replaced in that country (7 years or more with an AI-assisted or Shielded impact, or 12 years or more). If you choose a preparation time of 6 months or less, jobs that need a licence or exam are left out. The questions were written for this site, following the format of the O*NET Interest Profiler.
From your current savings, monthly spending, income and the date your paycheck stops, it calculates a simple cash flow: what you save until then and when spending afterwards uses the money up. If you pick a job, you can use that job’s estimated replacement date as the date your pay stops, and you can compare changes such as spending cuts, extra income, re-employment, investment returns and inflation. It does not predict job loss or bankruptcy, and the amounts you enter are not sent to a server or stored.
Questions in 8 stages (23 to 46 depending on the person) cover 12 areas: money (income, spending, assets, debt, housing), work, public safety nets, people you can rely on, health, care, recent shocks and the future, and deprivation. The answers are combined around the worst conditions into a level from Lv.1 to Lv.10. The calculation runs on your device with fixed rules; answers are not sent to AI or a server (only one anonymous count, such as the level, is sent; see the Privacy Policy). The level boundaries have not yet been checked against the real distribution of users.
27–32 questions (split into five paths by the first answer: student, graduating soon, looking for a first job, changing jobs while employed, returning to work) produce scores on three axes: school (a tier, not the school’s name), experience and skills. The scores are compared with a synthetic group modeled on all job seekers in that country (students, new graduates and experienced workers) to assign Tier 1 to 10. The weight of the three axes differs by country (school, experience, skills: Korea 25/40/35%, US 15/45/40%, Japan 25/30/45%, Taiwan 20/40/40%). Goal conditions, field conditions and age are not part of this tier; they feed a separate “how hard is my goal” result. It is not a probability, and reference values such as the mix of job seekers contain many estimates. Answers are calculated only in your browser and are not sent to a server.
This page includes information from the O*NET 29.1 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license. O*NET® is a trademark of USDOL/ETA. YOU’RE NEXT (Opening Step) has modified all or some of this information. USDOL/ETA has not approved, endorsed, or tested these modifications.
AI models research and organize public information and assess the outlooks. The assessments and explanations go through additional AI review and automated checks of format, value ranges, ordering within a field and links to evidence. A person conducts the final review before publication. Job and major explanations are based on the collected information and supporting assessment material.
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