For Kiara Nirghin, the 24-year-old co-founder and chief technology officer of applied AI lab Chima, the idea that her generation uses artificial intelligence as a cheat code isn’t just wrong—it overlooks a fundamental shift in human cognition.
The Stanford computer science alum and fellow argued that while older generations view AI as a tool to be adopted, . Yet this fluency carries a unique burden: the “AI anxiety” of keeping pace with technology that’s currently the “worst” it will ever be.
Speaking at in San Francisco, Nirghin addressed the tension between how Gen Z is perceived and their reality as builders. “The truth is, the younger generation isn’t adopting AI,” she said. “We’re growing up fluent in it.” This distinction is critical in the workplace. Where a manager might see an employee using an AI agent as cutting corners, Nirghin sees a shift in the very structure of work itself.
“We don’t think about coding from scratch,” she explained. “We think about coding alongside a coding agent.” Far from being a generation of shortcut-takers, Gen Z are trailblazers, she argued.
“That fundamentally changes how you write, take tests, apply for jobs or other applications—because it’s not starting from the ground up,” Nirghin said of working side by side with an agent. “What that really means is that this broad range of use cases and applications we’re seeing is truly being pioneered by the younger generation.”
The ‘lazy’ myth vs. deep thinking
One of the most persistent criticisms of the digital native generation is that their reliance on large language models (LLMs) . Nirghin rejects this outright. “I think the biggest misconception is that young people use AI to avoid thinking things through,” she said— that they’re using it “as a shortcut.”
Instead, Nirghin said intelligent users leverage these tools to offload cognitive labor, freeing themselves to explore complex subjects with greater depth. It’s not just handing off “cognitive load” to an AI model, she noted—it’s about thinking “differently… even deeper” on a specific topic, because the agent takes hours of menial work off your plate.
As an example, she pointed to creating in-depth financial market research reports that might take hours to generate manually. By automating that work, the user is free to analyze implications rather than just gather data. “What does that unlock for you?” she asked the audience, urging them to consider how much more they can do with these tools at their “fingertips.”
The anxiety of infinite improvement
Nirghin said her generation faces a daunting reality others don’t appreciate: the relentless speed of obsolescence, and their own awareness of it. She drew parallels between AI fears and “climate anxiety”—a term she knows from her early research on climate change, which she defined as the feeling that “climate change is coming, we don’t know exactly what to do about it, but we know it’s inevitable—and nobody is moving fast enough to solve it.”
This anxiety ties to the realization that today’s impressive technology is primitive compared to what’s next. “The models right now are as ‘dumb’ as they’ll ever be,” Nirghin warned. “From here on out, every new model will be faster, more advanced and more intelligent.”
For Gen Z workers, this creates a high-pressure environment where staying ahead is a daily requirement. Nirghin noted that recent model releases have “outpaced benchmarks in such an enormous way” that previous capabilities can be “10xed” overnight—imagine showing up to work tomorrow able to produce 10 times as much as the day before. If a worker doesn’t stay on top of these updates, “you get left behind.” The fear isn’t about taking too many shortcuts—it’s about not figuring out every pathway and update to hit that 10x mark.
Taste as the new IQ
If intelligence is being commoditized by exponentially improving AI models, what becomes the new metric for human value? According to Nirghin, it’s “taste.”
Nirghin, whose background includes work at , argued that accuracy benchmarks 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’s ‘vibe coded’ if you’ve ever worked with a coding agent,” she joked. The differentiator for the future workforce won’t be the ability to generate code or text—it will be human-centered judgment to decide what users actually want to see. “As models, use cases and efficiencies evolve,” Nirghin said, “the key differentiator is taste.”
Nirghin’s advice extends beyond her peers to the older generations managing them. She stressed that “AI fluency is just as important for people already in the workforce,” urging them to use tools like ChatGPT or Gemini as daily “co-pilots.”
Ultimately, Nirghin views AI’s rapid evolution not as a threat to jobs, but as a challenge to adapt. Whether automating back-office processes or launching “deep research agents,” the economic “unlock” from these models is already incredible—even if they never improve again. But the anxiety of keeping up is the new price of admission for the future of work.