Naval attended Dartmouth College, where he studied algorithms and theory and how markets work. His path wasn’t linear—it was driven by understanding incentives.
“I was actually originally going to do English and history and then I realized I didn’t want to drive a taxi. I was very ambitious so I switched to computer science and economics.” This shift reveals early judgment: recognizing that passion without economics leads to financial slavery. He chose disciplines that compound over decades rather than those that signal cultural sophistication.
At Dartmouth, Naval demonstrated the accountability that would define his career. “At one point in college I was washing dishes in the school cafeteria and said, ‘F this. I hate this. I can’t do this anymore.’ I sweet-talked my way into a teaching assistant job for a computer science professor, even though I was completely unqualified.” This wasn’t just a job change—it was using knowledge as leverage. He climbed the status hierarchy by acquiring what others couldn’t: specific knowledge of computer algorithms.
The transition from dishwasher to TA illustrates Naval’s approach to iteration. He didn’t accept his position; he took ownership of upgrading it. This pattern—identifying asymmetric opportunities and acting on them—would later define his investing philosophy.
Despite his formal education, Naval considers Dartmouth less formative than self-directed learning. “My real education, frankly, even before Dartmouth came from a magnet high school in New York called Stuyvesant, which is a magnet math and science school that was absolutely brutal and eye opening and educational.” But the real foundation came earlier: “Even before that, I would say the real education begins in the library. It begins with books.”
This hierarchy—books, then experience, then institutions—reflects Naval’s sovereign mindset. He learned to distinguish signal from noise early, prioritizing first-principles thinking over institutional credentials.
Naval’s retrospective analysis reveals his preference for fundamentals: timeless principles over fashionable techniques. Microeconomics teaches how humans respond to incentives. Algorithms reveal the mathematical structure underlying computation. These foundations don’t become obsolete—they become more valuable as complexity increases.
This philosophy—seeking the unchanging within the changing—would later guide his approach to building lasting wealth and understanding human nature through game theory and evolutionary psychology.