What Building AI Taught One Alum About Being Human–Nikhil Kaul

By Chad Laws (AES Communications Specialist)

When Nikhil Kaul talks about artificial intelligence, he doesn’t start with hype. He starts with honesty. “It’s a crazy time,” he says, laughing. “This is the worst AI will ever be.”

A Product Manager for Azure Cloud Native AI Infrastructure at Microsoft, Nikhil’s work places him squarely in the current technological advancement cyclone. Nikhil’s job is simple to describe and hard to do: take powerful, complex systems built by engineers—and make them intuitive and usable by humans. “Engineers can build a million features,” he explains. “My job is to understand, ‘the user only needs two of these. And they should look good. And work on a MacBook.’” It’s a role that sits at the intersection of technology and psychology —an intersection that has never been more important.

Nikhil’s relationship with technology started early. “My dad gave me my first computer when I was two or three,” he says. “I’ve always loved building things.” Born in Texas, raised in North Carolina, Bangalore, and Delhi, He grew up during the digital phone revolution—when the iPhone's release changed how the world interacted with technology. But he believes what’s happening now with AI is even bigger. He describes Today’s leading AI systems—like ChatGPT, Gemini, and Claude—as providing increasingly personal experiences.“They all have distinct personalities,” he states.

Nikhil uses different AI systems for different purposes. He finds Claude strong for structured work. He appreciates ChatGPT’s writing fluency and personality. He’s skeptical of Gemini’s tone—but praises its superiority in image and video generation due to Google’s data ecosystem. Behind the scenes, he explains, companies build “interest graphs”—systems that understand user behavior at scale. Platforms like Instagram refine recommendations based on user behavior. AI companies are now building something similar—learning from billions of interactions. The implication? AI tools are not neutral utilities. They reflect the data, culture, and philosophy of the organizations that build them.

The importance of Philosophy in the Age of AI

Nikhil didn’t set out to study computer science. He originally pursued economics before rediscovering a love for building technology—and ultimately earning a double major in computer science and philosophy at Duke University. His interest in AI ethics and humanity traces back to high school literature and philosophy courses at AES, including Theory of Knowledge. “Those classes planted the seed,” he reflects. “They made me ask what it means to be human.” Ironically, in an AI-saturated future, that question may matter more than ever.

He believes we’re heading toward a renewed investment in education, not less. As automation accelerates, uniquely human skills—judgment, empathy, creativity, synthesis—become economic differentiators. “The value of humans is going to rise,” he says. “Not fall.” He also sees a paradox emerging: as digital tools become commoditized, physical presence, craftsmanship, empathy, and emotional intelligence grow in value. “Humans want other humans,” he says. “That will not go away.” 

Despite his deep immersion in AI, Nikhil believes the future belongs to humans. In his role, he doesn’t struggle most with technology. He struggles with people. “Motivating people. Getting two people who disagree - to recognize that they’re saying the same thing - that’s the hard part.”

He credits his international school experience at AES for preparing him. Diverse classrooms, global perspectives, collaborative projects—those were not side benefits. They were foundational. “In this chaotic age, those human skills are going to matter more and more.”

Rethinking Education in the Age of AI

Nikhil questions one of education’s most basic assumptions: the practice of organizing students by age. “The only reason students are in the same grade is because they’re the same age,” he says. “Not because they’re at the same level.”

AI, he believes, could unlock radical personalization in learning. A growing body of research supports this claim. Intelligent systems can create adaptive learning paths that adjust content, pace, and difficulty in response to a student’s performance in real time. Rather than relying on periodic exams, AI enables continuous, skill-based assessment, pinpointing strengths and gaps within specific domains and guiding targeted practice. It also provides immediate feedback, a factor consistently linked in learning science to stronger retention and improved problem-solving. Emerging advances in affective computing go even further, offering deeper diagnostics of reasoning and emotional states—such as confusion or frustration—so support can be calibrated not only to what students know but also to how they are experiencing the learning process.

Instead of one-size-fits-all progression, he envisions systems that identify strengths and help students focus on them. “I’m a big believer in doubling down on your strengths,” he says. “Do what you love. Get great at it. Pay someone else to do what you’re weak at.” But personalization comes with a warning.

The Critical Thinking Crisis

When asked what worries him most about AI in education, Nikhil doesn’t hesitate. “The erosion of critical thinking. That’s the number one thing I’m worried about—for students, and for myself.” AI can be used to avoid thinking—or to deepen it.

Used passively, it becomes intellectual outsourcing. Used actively, it becomes the greatest personalized tutor ever created. “I’ve never learned as much as I’m learning now,” he says. “If I don’t understand something, I can ask. If I don’t know a word, I can dig deeper. It’s unbelievable.”

The difference lies in agency. Students must be taught how to question outputs and verify sources. They need to challenge conclusions and look for gaps in logic. At a higher level, we all need to use AI as a collaborator—not a substitute for our own thinking.

In short: coexistence, not dependence.

Key Takeaways for Students and Educators

Nikhil’s advice for navigating AI in education is clear:

1. Use AI to deepen understanding—not replace it.
2. Prioritize critical thinking above efficiency.
3. Double down on strengths.
4. Invest in human connection and collaboration.
5. Study philosophy alongside technology.
    

For Nikhil Kaul, the future isn’t about humans versus machines. It’s about humans who know how to work with machines—without losing what makes them human.

Find Nikhil online at: http://nikhilkaul.com