How many people go to work when you pour in a billion won?
Whenever the government announces it will invest trillions of won in the AI industry, one phrase never fails to appear: "job creation." But is that really true? To answer the question, we first have to dust off an old metric.
The employment inducement coefficient
It measures the number of wage workers created, directly and indirectly, across an industry and its related industries when one billion won of final demand arises in that industry. Calculated by the Bank of Korea on the basis of its input-output tables, it is the most intuitive yardstick for showing how many people's livelihoods a given industry actually supports.
As of 2023, the employment inducement coefficient for Korean industry as a whole stood at 6.2. In other words, for every billion won of demand generated, about 6.2 jobs follow.
The trouble is that this figure varies wildly from one industry to the next.
Two jobs per billion in chips, thirteen in education
Between services and manufacturing lies an unbridgeable gap. In labor-intensive services such as education, health and social welfare, and food and lodging, the coefficient comfortably tops ten. Construction is much the same. Industries that need human hands turn money into jobs almost immediately.
Advanced manufacturing such as semiconductors and electronic components is a different story. Capital-intensive and heavily automated, these sectors generate only two or three jobs even when a billion won is poured in. Software and IT services, too, absorb far less labor than traditional services, because they are built to deliver high productivity with few people.
The AI industry pushes this tendency to an extreme. A handful of GPU clusters and a small team of engineers can generate hundreds of billions of won in revenue. The ratio of revenue to headcount at an AI company is barely comparable to that of a traditional industry. According to data the Bank of Korea released in December 2025, Korea's pool of specialized AI talent was estimated at roughly 57,000 as of 2024—a strikingly small number relative to the scale of an industry attracting trillions of won in investment.
It is already happening
Set theory aside and look at reality. A report titled "The Spread of AI and the Contraction of Youth Employment," released by the Bank of Korea's employment research team in October 2025, proves the case in numbers.
In the three years after ChatGPT launched (July 2022 to July 2025), 211,000 jobs held by young people (ages 15 to 29) disappeared. Of those, 208,000 were lost in industries highly exposed to AI—98.6 percent of the total decline.
The picture sharpens when you look closer. Youth employment in computer programming and systems integration, including software development, fell by 11.2 percent. Publishing dropped 20.4 percent and information services 23.8 percent. Even in professional services such as law and accounting, employment slipped by 8.8 percent.
What is striking is the movement among workers in their fifties. Over the same period, jobs held by people in their fifties grew by 209,000, and 146,000 of those were in AI-exposed industries. The Bank of Korea called this "seniority-biased technological change." As AI replaces the routine tasks of junior workers, the analysis goes, the organizational and judgment skills of senior workers have become, if anything, more scarce.
In sum: the AI industry generates few jobs on its own. At the same time, in the other industries where AI spreads, it eliminates existing jobs—pushing out, above all, the young people trying to enter the labor market.
We are measuring a new era with an old yardstick
And yet government industrial policy still leans on the formula "investment leads to growth leads to employment." Trillions of won for AI chips, trillions for AI data centers, hundreds of billions to nurture AI startups. These investments will surely help lift GDP. But the employment inducement coefficient is far too low to justify hanging out a "job creation" sign.
The coefficient itself has limits as a metric. Because it is based on input-output tables, it fails to capture the ripple effects of AI as they cross the boundaries of industrial classification. The process by which AI raises manufacturing productivity and cuts employment while simultaneously spawning entirely new occupations in unrelated fields simply cannot be measured by the existing coefficient.
The point is this: to gauge the employment results of national industrial investment, the old employment inducement coefficient alone is not enough.
We need a new yardstick
Employment policy in the age of AI needs at least three supplementary metrics.
First, an "employment displacement coefficient." We need to measure how much investment in one industry reduces or transforms employment in others. If a billion won invested in the AI sector creates three jobs there while eliminating five in traditional services, the net employment effect is negative. We have to abandon the practice of calculating an investment's employment effect only within the industry receiving it.
Second, an "employment quality index." We have to look at the wage levels, job security, and skill requirements of the jobs being created. If AI generates few jobs but pays well and offers stability, the assessment becomes something different from a simple head count. Conversely, if jobs displaced by AI are converted into platform work or the gig economy, that is not job "creation" but job "degradation."
Third, a "generational employment impact assessment." As the Bank of Korea's research showed, the employment shock of AI plays out in opposite directions across generations. For seniors in their fifties it becomes an opportunity; for young people in their twenties it becomes a barrier. If investment policy ends up blocking a particular generation's entry into the labor market, that is not merely a failure of industrial policy but a social hazard.
Change the criteria for investment, not the direction
This is not an argument against investing in AI. Fall behind in the race for AI technology and the future of entire industries is at stake. That much is clear.
But the habit of measuring the returns on AI investment by "how many jobs did it create" is now an anachronism. To attach the label "job creation" to an industry whose employment inducement coefficient is a mere two or three is dishonest—to the public and to the policy itself.
It is time, instead, to redesign the performance metrics for AI investment policy honestly. We should put indicators such as contribution to productivity gains, an industrial-competitiveness index, and technological self-sufficiency front and center, and separate the employment question into a distinct policy track. A more realistic approach would be to design a structure that redistributes part of the added value AI creates into fields with high employment inducement coefficients—education, care, and health.
How many people go to work when you pour in a billion won? If that old question still holds in the age of AI, then we have to change the way we answer it first. You cannot measure a new era with an old yardstick.
