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.”