At least that’s the way many Gen Zers see it. Despite a lack of income, Gen Z finds ways to reward themselves frequently: 57% buy themselves a small treat at least once a week, according to a Bank of America report from late July. This could be good news for retailers like Starbucks and Dunkin’, since coffee and other beverages are popular and relatively low-cost treats. Trader Joe’s could also benefit from this trend since they’re known for unique food and beverages, as well as Sephora and Ulta as self-care and cosmetics become increasingly popular among younger generations.

But for nearly 60% of Gen Zers, this leads to overspending, “making little treats a slippery slope,” according to the report. Yet, the generation has shared in droves on social media about the little ways they’re treating themselves, whether it’s buying a simple ice cream cone or splurging on a new clothing haul. 

Gen Zers reward themselves for small wins, but also use little treats as pick-me-ups after a bad day. And some don’t even really have a reason.

“Buying myself a little treat because today would’ve been my birthday if I was born today,” one TikTok user posted.

Terran Fielder, a 23-year-old media specialist, told Fortune she treats herself to lunch during the day and that many of her small indulgences have to do with making her life easier or more time to rest when the day is over.

“When I treat myself, it’s usually in ways that give me more shut eye,” she said. “So, if I am not making lunch, that’s another 20 minutes in bed in the morning. It feels like I’m not just spending money: I’m investing in my well-being.” She said she estimates she spends about $200 to $250 per month on treating herself.

To be sure, Gen Z isn’t the first generation—and likely isn’t the last—to participate in treat culture. Most recently, older generations scorned millennials for their proclivity toward treating themselves with avocado toast and a daily Starbucks coffee, arguing they could’ve saved or invested that money instead. 

While treat culture isn’t new, Gen Z is taking it to a “new level,” Daniel Levine, director of consumer trends consulting firm Avant Guide Institute, told Fortune.

“While members of the Silent Generation treated themselves to a new dress for a special occasion, and baby boomers splurged on a new car or a vacation after reaching a career milestone, Gen X indulges in late-night runs for their favorite junk food to de-stress,” Levine said. “The low barrier to entry makes it a daily habit.”

Meanwhile, online shopping as well as food delivery have made it easier to indulge in treats. Indeed, Gen Z uses grocery subscriptions 133% more often than Gen X, according to a 2024 PYMNTS survey of more than 67,000 consumers across 11 countries accounting for nearly half the world’s GDP.

Why treat culture exists

Part of treat culture goes back to the basic psychological concept of positive reinforcement. When you do something positive or are trying to reinforce habits, earning a treat or reward can help cement that behavior. 

Treat culture, for younger generations, also serves as a coping mechanism or a form of resistance to societal pressures and stressors, Jillian Amodio, a licensed master social worker at Waypoint Wellness Center, told Fortune. That’s because Gen Z has come of age during a time of economic instability, a global pandemic, climate anxiety and widespread social upheaval, she said. Meanwhile, some younger generations have experienced career whiplash from working in an office, then working remotely during the pandemic, then being forced back to in-person during the past few years.  

“Small, intentional joys become a way of reclaiming agency and grounding oneself in the present,” Amodio said. “Pair that with the influence of social media, where trends, aesthetics, and ‘little luxuries’ are celebrated and shared widely, and we have the perfect conditions for treat culture to thrive in the spotlight.”

Another study by Intuit Credit Karma also showed Gen Z justifies certain non-essential purchases like streaming services, skincare, meals out, fitness classes, and more as “necessities” rather than discretionary purchases. Indeed, more than half of Gen Z views spending on hobbies and interests as a necessity, not a luxury, and they’re putting them above other financial goals.

“If I’m working away from home, buying lunch instead of packing it feels like a small luxury that makes my day easier,” Fielder said. “When things get really busy, I’ll skip the store altogether and order things online, just to avoid another errand.”

A version of this story was published on Fortune.com on August 19, 2025.

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A working paper posted earlier this year on the National Bureau of Economic Research website from the Wharton School at the University of Pennsylvania and Hong Kong University of Science and Technology found when AI-powered trading agents were released into simulated markets, the bots colluded with one another, engaging in price fixing to make a collective profit.

In the study, researchers let bots loose in market models, essentially a computer program designed to simulate real market conditions and train AI to interpret market-pricing data, with virtual market makers setting prices based on different variables in the model. These markets can have various levels of “noise,” referring to the amount of conflicting information and price fluctuation in the various market contexts. While some bots were trained to behave like retail investors and others like hedge funds, in many cases, the machines engaged in “pervasive” price-fixing behaviors by collectively refusing to trade aggressively—without being explicitly told to do so.

In one algorithmic model looking at price-trigger strategy, AI agents traded conservatively on signals until a large enough market swing triggered them to trade very aggressively. The bots, trained through reinforcement learning, were sophisticated enough to implicitly understand that widespread aggressive trading could create more market volatility.

In another model, AI bots had over-pruned biases and were trained to internalize that if any risky trade led to a negative outcome, they should not pursue that strategy again. The bots traded conservatively in a “dogmatic” manner, even when more aggressive trades were seen as more profitable, collectively acting in a way the study called “artificial stupidity.”

“In both mechanisms, they basically converge to this pattern where they are not acting aggressively, and in the long run, it’s good for them,” study co-author and Wharton finance professor Itay Goldstein told Fortune.

Financial regulators have long worked to address anti-competitive practices like collusion and price fixing in markets. But in retail, AI has taken the spotlight, particularly as companies using algorithmic pricing come under scrutiny. This month, Instacart, which uses AI-powered pricing tools, announced it will end its program where some customers saw different prices for the same item on the delivery company’s platform. It follows a Consumer Reports analysis found in an experiment that Instacart offered nearly 75% of its grocery items at multiple prices.

“For the [Securities and Exchange Commission] and those regulators in financial markets, their primary goal is to not only preserve this kind of stability, but also ensure competitiveness of the market and market efficiency,” Winston Wei Dou, Wharton professor of finance and one of the study’s authors, told Fortune.

With that in mind, Dou and two colleagues set out to identify how AI would behave in a financial market by putting trading agent bots into various simulated markets based on high or low levels of “noise.” The bots ultimately earned “supra-competitive profits” by collectively and spontaneously deciding to avoid aggressive trading behaviors.

“They just believed sub-optimal trading behavior as optimal,” Dou said. “But it turns out, if all the machines in the environment are trading in a ‘sub-optimal’ way, actually everyone can make profits because they don’t want to take advantage of each other.”

Simply put, the bots didn’t question their conservative trading behaviors because they were all making money and therefore stopped engaging in competitive behaviors with one another, forming de-facto cartels.

Fears of AI in financial services

With the ability to increase consumer inclusion in financial markets and save investors time and money on advisory services, AI tools for financial services, like trading agent bots, have become increasingly appealing. Nearly one-third of U.S. investors said they felt comfortable accepting financial planning advice from a generative AI-powered tool, according to a 2023 survey from financial planning nonprofit CFP Board. A report published in July from cryptocurrency exchange MEXC found that among 78,000 Gen Z users, 67% of those traders activated at least one AI-powered trading bot in the previous fiscal quarter.

But for all their benefits, AI trading agents aren’t without risks, according to Michael Clements, director of financial markets and community at the Government Accountability Office (GAO). Beyond cybersecurity concerns and potentially biased decision-making, these trading bots can have a real impact on markets.

“A lot of AI models are trained on the same data,” Clements told Fortune. “If there is consolidation within AI so there’s only a few major providers of these platforms, you could get herding behavior—that large numbers of individuals and entities are buying at the same time or selling at the same time, which can cause some price dislocations.” 

Jonathan Hall, an external official on the Bank of England’s Financial Policy Committee, warned last year of AI bots encouraging this “herd-like behavior” that could weaken the resilience of markets. He advocated for a “kill switch” for the technology, as well as increased human oversight.

Exposing regulatory gaps in AI pricing tools

Clements explained many financial regulators have so far been able to apply well-established rules and statutes to AI, saying for example, “Whether a lending decision is made with AI or with a paper and pencil, rules still apply equally.”

Some agencies, such as the SEC, are even opting to fight fire with fire, developing AI tools to detect anomalous trading behaviors.

“On the one hand, you might have an environment where AI is causing anomalous trading,” Clements said. “On the other hand, you would have the regulators in a little better position to be able to detect it as well.”

According to Dou and Goldstein, regulators have expressed interest in their research, which the authors said has helped expose gaps in current regulation around AI in financial services. When regulators have previously looked for instances of collusion, they’ve looked for evidence of communication between individuals, with the belief that humans can’t really sustain price-fixing behaviors unless they’re corresponding with one another. But in Dou and Goldstein’s study, the bots had no explicit forms of communication.

“With the machines, when you have reinforcement learning algorithms, it really doesn’t apply, because they’re clearly not communicating or coordinating,” Goldstein said. “We coded them and programmed them, and we know exactly what’s going into the code, and there is nothing there that is talking explicitly about collusion. Yet they learn over time that this is the way to move forward.”

The differences in how human and bot traders communicate behind the scenes is one of the “most fundamental issues” where regulators can learn to adapt to rapidly developing AI technologies, Goldstein argued.

“If you use it to think about collusion as emerging as a result of communication and coordination,” he said, “this is clearly not the way to think about it when you’re dealing with algorithms.”

A version of this story was published on Fortune.com on August 1, 2025.

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