PERSON
Gordon Moore
The chemist who extrapolated six data points into a half-century prophecy—not about transistor density, as he always insisted, but about the economics of cost collapse—and whose framework for how exponential curves hit walls and rotate to new dimensions is the clearest lens on the AI scaling moment.
In the spring of 1965, Gordon Moore drew a straight line through six points on a sheet of semi-logarithmic paper and published a prediction that the number of transistors on an integrated circuit would keep doubling roughly every year. He assumed it would hold for a decade. It held for fifty years and organized a civilization.
Moore’s Law—the observation that became a self-fulfilling prophecy when an entire industry synchronized its roadmaps to the trend line he had merely noticed—is the deepest structural lens available for reading the
AI scaling moment. Moore himself was always clear about what his law actually measured: not capability, but
cost. The doubling of transistors per chip was significant not because more transistors meant faster computers but because more transistors per chip meant lower cost per transistor, and cheaper computation meant new users, new markets, and new kinds of possible work. Applied