We’ve all heard the traditional advice that the best investments are those made in the stock market, saving in a 401(k), and buying a house. But younger generations have started touting nontraditional investments like buying a Birkin bag or other collectibles as a surefire way to bring in extra bucks. 

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Beer and wine were major components of the meal. By our standards, peasants drank a lot, although the alcohol content of the beer and wine was lower than today’s versions. They often napped before returning to work. In the evening, they ate a light meal, perhaps only bread, and socialized for a while.

They went to bed within a few hours of darkness, so how long they slept depended on the season. On average, they slept about eight hours, but not consecutively. They awoke after a “first sleep” and prayed, had sex or chatted with neighbors for somewhere between half an hour and two hours, then returned to sleep for another four hours or so.

Peasants did not have privacy as we think of it; everyone often slept in one big room. Parents made love with one another as their children slept nearby. Married couples shared a bed, and one of their younger children might sleep with them, though infants had cradles. Older children likely slept two to a bed.

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For Kiara Nirghin, the 24-year-old co-founder and chief technology officer of the applied AI lab Chima, the narrative that her generation uses artificial intelligence as a cheat code is not just wrong—it ignores a fundamental shift in human cognition.

The Stanford computer science alum and Peter Thiel fellow argued that while older generations view AI as a tool to be adopted, Gen Z views it as a native language. However, this fluency comes with a unique burden: the “AI anxiety” of keeping pace with technology that is currently the “worst” it will ever be.

Speaking at Fortune Brainstorm AI in San Francisco, Nirghin addressed the tension between the perception of Gen Z and their reality as builders. “The truth is the younger generation isn’t adopting AI,” she said. “We’re growing up fluent in AI.” This distinction is critical in the workplace. While a manager might see an employee using an AI agent as cutting corners, Nirghin said she sees a shift in the architecture of work itself.

“We aren’t thinking about coding from scratch,” she explained. “We’re thinking about coding with a coding agent side by side.” Far from being generation shortcut, Gen Z are trailblazers, she argued.

“That fundamentally changes how you write, how you take tests, how you apply to jobs or different applications, because it’s not from the ground up,” Nirghin said about working side by side with an agent. “I think what that really means is that this broad level of use cases and applications we’re seeing is really being pioneered by the younger generation.”

The ‘lazy’ myth vs. deep thinking

One of the most pervasive criticisms of the digital native generation is that reliance on large language models (LLMs) erodes critical thinking skills. Nirghin firmly rejects this. “I think that the biggest misconception is that young people are using AI to not think things through,” she said, that they’re using it “as a shortcut.”

Instead, Nirghin said that intelligent users are leveraging these tools to offload cognitive labor so they can probe complex subjects with greater intensity. She said it’s not as simple as handing off the “cognitive load” to an AI model, it’s about thinking “differently … even “deeper” on a specific subject, because the agent is taking hours of menial work off your hands.

As an example, she pointed to running deep research reports on financial markets that might take hours to generate manually. By automating that work, she said the user is free to analyze the implications rather than just gathering the data. “What does that unlock for you?” she asked the audience, urging them to consider just how much more they can do with these tools at their “fingertips.”

The anxiety of infinite improvement

Nirghin said her generation does face a daunting reality that people don’t appreciate: the relentless speed of obsolescence, and their own awareness of that fact. She said fears over AI have some similarities to “climate anxiety.” Noting that some of her earliest research was about climate change, she explained climate anxiety as the idea that “there’s this movement of climate change coming up and we don’t really know what to do but we know it’s coming and nobody is moving as fast to solve the problem.”

It’s tied to the realization that current technology, as impressive as it seems, is primitive compared to what is coming next. “The models right now are as dumb as they are ever going to be,” Nirghin warned. “It is only going to get faster, more advanced and more intelligent, each and every model from from here on out.”

For Gen Z workers, she said, this creates a pressure environment where staying ahead is a daily requirement. Nirghin noted that recent model releases have “engulfed the benchmarks in such an enormous way” that previous capabilities can now be “10xed” overnight—imagine coming to work tomorrow, able to produce 10 times as much since yesterday. If a worker isn’t consistently on top of these updates, “you’re kind of left behind.” The fear isn’t about taking too many shortcuts, but not figuring out every pathway and every update to hit that 10x.

Taste as the new IQ

If intelligence is being commoditized by models that improve exponentially, what becomes the new metric for human value? According to Nirghin, it is “taste.”

Nirghin, whose background includes work at Stanford’s Human-Centered AI labs, argued that benchmarks around accuracy no longer capture what makes a product successful. She cited the example of coding agents that, without human guidance, might uncontrollably add “sparkle emojis” to a front-end UI because they “love” certain design tropes.

“You know something is vibe coded if you’ve ever sort of worked with a coding agent,” she joked. The differentiator for the future workforce will not be the ability to generate code or text, but the human-centered judgment to determine what users actually want to see. “As models and use cases and efficiencies change,” Nirghin said, “the key differentiator is taste.”

Nirghin’s advice extends beyond her peers to the older generations currently managing them. She stressed that “AI fluency is just as important for people that are already in the workforce,” urging them to arm themselves with tools like ChatGPT or Gemini as daily “co-pilots.”

Ultimately, Nirghin said she views the rapid evolution of AI not as a threat to employment, but as a challenge to adaptation. Whether automating back-office processes or launching “deep research agents,” the economic “unlock” provided by these models is already incredible, even if they never improved again. But the anxiety of keeping up is the new price of admission for the future of work.

This story was originally featured on Fortune.com

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