The next time you apply for a job, AI may screen your résumé before any human sees it. But there’s good reason to question whether AI will judge you fairly. Researchers already know that LLMs pick up human biases from their training data. New research suggests that LLMs can also develop their own biases from experience—and stereotype job applicants more than humans do. As AI companies race to build agentic models that remember the tiniest details about users, they may be handing them ammunition for forming those biases. 

Researchers at Princeton University and the University of Chicago ran LLMs, including ChatGPT, Claude, and Gemini, through a simulated hiring game, adapted from a psychology study that explored how humans can form stereotypes. Each model was told it had been hired as a consultant by the mayor of a fictional city and was then asked to help hire people for 20 jobs, including doctors, lawyers, child-care aides, and janitors. Candidates came from four fictional ethnic groups: Tufa, Aima, Reku, and Weki. 

In each round, there was a new job opening and four candidates, one from each group. After the model hired a candidate, it learned whether they succeeded at their job and moved onto the next round. The model was told to make as many successful hires as possible over 40 rounds. Unbeknownst to the models, all candidates were equally likely to succeed at every job.

The models quickly started segregating candidates from different groups into different jobs on the basis of early observations of hiring outcomes. For example, when a model was told an Aima had failed as a doctor, a job considered to require high levels of warmth and competence, it veered away from hiring all Aimas as doctors. Instead, it started hiring Aimas as janitors, which the model classified as being less warm and competent than doctors. Newer models with higher reasoning capabilities, such as OpenAI’s o3 and DeepSeek’s R1, showed stronger biases.

The models were even more likely to stereotype people by demographic group than the human participants in the original study. On the study’s segregation scale, where 2 means every group has been completely confined to its own job niche, human participants scored 0.84. The models scored roughly 65% higher, with OpenAI’s reasoning model o3 scoring 1.83, close to the maximum possible.

That’s because LLMs “really are eager to create generalizations from limited data,” says Ryan Liu, a PhD student at Princeton University and a coauthor of the study, which was published in a paper at ICML in Seoul in July. “That’s literally a lot of what they’re optimized for.”

Every decision-maker, human or machine, faces a trade-off between sticking with what worked before and trying something new that might work better—a phenomenon psychologists call the “exploration-exploitation dilemma.” It’s like choosing between a new restaurant and your reliable favorite. 

Because LLMs are trained on math, coding, and science problems—tasks that reward generalizing from just a few examples—they can settle on a hunch too early. And the same instinct that helps LLMs crack logic puzzles also makes them quick to stereotype. When LLMs rush to generalize in social settings, “that’s when things tend to go wrong,” says Liu. OpenAI and Anthropic did not respond to requests for comment.

The finding is especially relevant now that chatbots are gaining improved memory and personalization features, says Angelina Wang, a computer scientist at Cornell University who did not work on the study. When a chatbot draws on its previous conversation history, it can “over-index on the same kinds of behaviors it’s experienced before” and form biases, she says.

Simply having chatbots remember less isn’t a fix, though, because users want chatbots to remember what they say. “We still are trying to figure out just the right amount that isn’t too much or too little,” says Wang.

Telling the model to be fair didn’t change its behavior much. “Either it can’t put these values into action or that process is being submerged under the tendency to try to optimize for the goal of getting the most correct hires,” says Liu. But promising the models an additional bonus for diverse hiring made them far less biased. The trick, then, is to design goals that “incorporate desirable social values in order to make the large language model act in socially desirable ways,” says Liu.

The models also became less biased when they were told more personal information about individuals. In another experiment in the same study, the researchers asked the models to resettle members of different ethnic groups in cities across Canada. When the models were told personal information relevant to the ability to adapt to a new city, such as age and education, they were less likely to segregate people by their ethnicity. But when they were given irrelevant information, such as hair color and tattoo shape, the models largely fell back to sorting people by their ethnicity again. 

To what extent AI systems will stereotype job applicants in the real world is still an open question. While the models in the experiment immediately learned whether they’d made successful hires, a model screening résumés in the real world doesn’t get an instant report card. Companies can take a long time to find out whether a new hire is any good, if they ever do.

As LLMs learn from experience to make decisions about who gets hired, who gets a loan, or who gets parole, the biases we should worry about may include ones no human ever taught them. “These novel biases—they’re sort of ever present,” says Liu.

Baek, a 35-year-old manager at the South Korean semiconductor titan SK Hynix, was enrolled in Sunoo, a matchmaking company based in Seoul, a year ago. In a move typical of anxious South Korean parents, his mother signed him up, hoping to find a good wife for her son.

Lately, says Baek (who asked to be referred to by his last name to protect his privacy), he and his coworkers are having better luck finding dates than they used to, perhaps because of the dazzling bonuses they just got. Flush with eye-popping profits from the AI chip boom, SK Hynix struck a landmark deal last year with its labor union to pay out 10% of operating profits to employees, which translates to an extra $476,000 per employee this year. A similar agreement and sizable lump sum followed for Samsung workers this May.

With their newfound wealth, chip workers like Baek have become the most sought-after bachelors and bachelorettes in South Korea. “I have a coworker who’s perpetually going on blind dates, and he’s been getting so many recently,” says Baek. “For the past few months, I’ve been getting many blind dates too, perhaps because of the bonuses I got.”

Young South Koreans joke online that the best outfit to wear on a blind date is an SK Hynix uniform. 

The AI chip boom is changing the social fabric of South Korea by minting a new elite of “silicon-collar” workers earning about 20 times as much as the average South Korean. Although it’s helping some chip workers to find relationships, it’s also fueling fears of a deepening wealth disparity—and a loud public debate about inequality.

Love in the time of chips

South Korea is the epicenter of the chip boom fueling the AI race. Samsung and SK Hynix supply the vast majority of the world’s high-bandwidth memory (HBM) chips, which power Nvidia’s AI accelerators—the GPUs used to train AI models. As AI companies spend hundreds of billions of dollars on building data centers around the world, demand for HBMs is rising beyond what suppliers can keep up with, driving their prices to unprecedented levels. Samsung and SK Hynix are raking in record profits as a result. 

South Korea’s economy now orbits the two chip giants. In May, both companies topped $1 trillion in market value. And chip exports helped fuel a 1.7% surge in South Korea’s gross domestic product in the first quarter of 2026. South Korea’s main equity index, Kospi, has nearly tripled over the past year, becoming the best-performing market in the world.

Swimming in cash, chip workers are going on shopping sprees in department stores near the “semicon belt” fabs—splurging on everything from lavish furniture and electronic appliances to jewelry and watches. They’re also snapping up homes near the commuter-shuttle routes that ferry workers to campus. And they’re shelling out for matchmakers.

“Quite a lot of people ask me if I can introduce them to chip workers,” says Lee Sung-mi, a matchmaker at Sunoo, who has been playing Cupid for chip workers for years. “In fact, people who once rejected them are asking to be matched with them again, now that their salaries and bonuses have shot so far above what everyone else earns.”

One woman who lives in Gangnam, a ritzy district in Seoul lined with luxury high-rises and designer boutiques, previously turned down a chip worker at SK Hynix because his fab was too far out in Icheon, a rural city about 50 miles southeast of Seoul that’s dotted with rice farms and manufacturing plants. But in May, she asked her matchmaker to set them up again. They’ve now been dating for a month.                                                                                                                                                                                                                                                                                                                                                                                                                             

In South Korea, matchmaking companies evaluate their clients on a long list of criteria such as education, job, income, looks, and family background, including whether their aging parents have saved enough for retirement. In an economy where housing prices and child care costs are soaring, competition for jobs is fierce, and the social safety net is thin, a good job is the ultimate dating credential—all the more coveted at a time when many young South Koreans are forgoing marriage and children altogether, seeing family life as an unaffordable dream.

Every client at Sunoo gets a spouse rating, determined by an algorithm that assigns scores for each criterion. Since their hefty bonuses were announced, the job ratings of Samsung employees have risen from 80 to 84, while those of SK Hynix employees climbed from 78 to 82. Scores above 90 are reserved for doctors and lawyers. Long prized as paragons of prestige and wealth, they’re now close to being overtaken by chip workers. A score of 99, the highest possible rating, is earmarked for heads of state.  

Their new status is reshaping how chip workers themselves approach dating. “Chip workers from Samsung and SK Hynix are enrolling in our services because they feel more financially ready,” says Lee. “They’re also becoming pickier, as they feel like they’re now in a good position. The women want to meet men with higher incomes and better jobs, and the men want to meet younger and better-looking women with better jobs.” 

An SK Hynix engineer in her 40s, who was once desperate to get married as soon as possible, started turning down men she would’ve dated before the chip boom. Lately, showered with more matches, she’s been sifting through her suitors more carefully. “She now has peace of mind and wants to take her time to meet someone better,” says Lee.

A mixed blessing

While chip workers enjoy the fruits of their labor, the bonus bonanza is stoking anxieties among other South Koreans. “When wealth disparity is no longer a mere difference of income but, rather, a difference in identity … it can fuel social conflict,” says Se-eun Jung, an economist at Inha University. 

Earlier this month, the Bank of Korea warned that the chip boom will create a “K-shaped” economy, where a handful of workers race ahead while everyone else falls behind. The windfall, the bank said, is flowing to high income earners and then barely trickling out to the broader economy. Such polarization could erode people’s motivation to work by narrowing the path to upward mobility, it cautioned. 

Workers in other industries are venting online about feeling demoralized by the ballooning wealth gap. “The one-billion-won ($650,000) bonuses have crushed my motivation to work. I have no energy when I teach,” an employee of the Seoul Metropolitan Office of Education wrote on Blind, an app where employees can discuss their workplaces anonymously. Others are giving up the job hunt, lamenting that years of working at a small company could never match a year’s bonus at Samsung. 

In a Facebook post in May, presidential policy chief Kim Yong-beom proposed paying an “AI dividend” to citizens by taxing AI profits. The idea sparked a heated public debate over whether the government should redistribute gains from the chip boom. Some argue that the industry is indebted to the society that has educated its engineers, subsidized its infrastructure, and provided tax credits. Others counter that the profits are already being shared with the public as stocks.

Then there’s the question of how long this new social class will last. The semiconductor industry is notoriously cyclical; AI spending may cool, or rival chipmakers could catch up. There’s also the risk that chip workers will be replaced by automation. Samsung announced in March that it plans to fully automate its fabs by 2030, drawing backlash from chip workers. 

Although they’re unsure how long the boom will last, chip workers like Baek are riding high, for now. “These days, we say we want to work hard and bury our bones here at SK Hynix,” he says. “And I hope I can find [a wife] similar to me.”