B

AI-Era Job Survival Years Prediction Report

3.05

Derivation Chain

Step 1 AI adoption leads to half of employees being laid off
→
Step 2 Self-diagnosis of the probability that my job will be replaced by AI
→
Step 3 Providing expected job survival years + alternative job transition roadmap

Competitor references

No usable source links are available for this idea. Its signal notes and scores need independent verification.

Problem

Although AI layoff news is reported daily, office workers aged 45-55 have no objective basis to determine 'exactly how many years until my job becomes at risk.' McKinsey and Goldman Sachs reports are in English and don't align with Korean job classifications, and the Korea Employment Information Service's occupational outlook is updated every two years, failing to reflect the pace of AI change. Many either rush into retirement out of vague anxiety or, conversely, fail to recognize the crisis and are laid off without preparation.

Solution

(1) Enter job title, industry, and main tasks to score AI replacement potential based on the O*NET job analysis framework, (2) provide an 'automation risk index' along with three alternative jobs that can be transitioned to from similar roles, (3) present a roadmap of certifications, training programs, and estimated costs needed for each alternative job. It reflects the Korean context by linking to the Ministry of Employment and Labor's employment trends by occupation.

Target: Office and managerial workers aged 45-57 in finance, manufacturing, and retail, who feel anxious about AI adoption news and need to work at least 5-10 more years due to children's college tuition and other financial obligations.
Revenue Model: Free quick assessment (3 questions); detailed report (job analysis + transition roadmap + education recommendations) for 14,900 KRW per transaction (approx. $11.20); B2B group assessment for corporate HR at 390,000 KRW per month (approx. $292.50).
Ecosystem Role: Regulation
MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty
2.0/5
U Urgency
4.0/5
M Market
4.0/5
R Realizability
3.0/5
V Validation
3.0/5
NUMR-V Scoring System
N Novelty1-5How uncommon the service is in market context.
U Urgency1-5How urgently users need this problem solved now.
M Market1-5Market size and growth potential from proxy indicators.
R Realizability1-5Buildability for a small team with realistic constraints.
V Validation1-5Validation signal quality from competition and demand data.
Each dimension is scored 1-5 and weighted into grade S/A/B/C/D.
N=.15 U=.20 M=.15 R=.30 V=.20

Feasibility (70%)

Tech Complexity
29.3/40
Data Availability
20.8/25
MVP Timeline
20.0/20
API Bonus
0.0/15
Feasibility Breakdown
Tech Complexity/ 40Difficulty of core implementation stack.
Data Availability/ 25Practical availability and cost of required data.
MVP Timeline/ 20Expected time to ship a usable MVP.
API Bonus/ 15Bonus for viable public API leverage.
Total 100 points; below 50% is filtered out.

Market Validation (55/100)

Competition
8.0/20
Market Demand
6.2/20
Timing
18.0/20
Revenue Signals
7.5/15
Pick-Axe Fit
7.5/15
Solo Buildability
8.0/10
Validation Breakdown
Competition/ 20Signal quality from competitor landscape.
Market Demand/ 20Demand proxies from search and mention patterns.
Timing/ 20Fit with current shifts in tech, behavior, and regulation.
Revenue Signals/ 15Reference evidence for monetization viability.
Pick-Axe Fit/ 15How well the concept serves participants in a trend.
Solo Buildability/ 10Practicality for lean-team implementation.
Total 100 points; below 50 is filtered out.

Technical Requirements

Backend [medium] Frontend [low] Data Pipeline [medium]
Dashboard