This paper examines how industry changes through the nonlinear view of chaos theory. Chaos theory implies that a dynamic system is nonlinear. Accordingly, we developed a triangulated analysis constituted by five mathematical approaches, including the BDS test, Hurst exponent, correlation dimension, Lyapunov exponent, and local Lyapunov exponent, to explore industry change as a nonlinear dynamic system. Empirically, we choose to compare the semiconductor industry and the biotechnology industry, using data collected from the Philadelphia Semiconductor Index and the NASDAQ Biotechnology Index. We found and confirmed that both industries evolved in a nonlinear manner, with the semiconductor industry being less unpredictable, more path-dependent, more sensitive to initial conditions, and has more time-paced incremental innovations than the biotechnology industry. We then complement this analysis with historical case studies that in turn leads us to argue that such differences have much to do with the inherent nature of the industries themselves. While the presence of Moore’s Law as a roadmap projects the semiconductor industry into a more predictable path, the distributed nature of biotechnology coupled with the lack of a dominant technology directs the industry towards a system with greater volatilities and nonlinearities.