如何预测未来科技发展？

The bread and butter of investing for Silicon¹ Valley tech companies is stale（陈腐的）. Instead, a new method of predicting the evolution of technology could save tech giants millions in research and development or developments of new products -- and help analysts² and venture capitalists determine which companies are on the right track. The high-tech³ industry has long used Moore's Law as a method to predict the growth of PC memory. Moore's Law states that the number of chips on a transistor⁴ doubles every 18 months (initially every year). A paper by Gareth James and Gerard Tellis, professors at the USC Marshall School of Business and their co-authors Ashish Sood, at Emory and Ji Zhu at the University of Michigan, concludes that Moore's Law does not apply for most industries, including the PC industry.   High-tech companies traditionally use Moore's Law and other similar heuristics（启发法） to predict the path of evolution of competing technologies and to decide where to funnel⁵ millions into research and development or new product development. The paper's researchers claim that these models are outdated⁶ and inaccurate⁷.   The paper offers a new model, Step and Wait (SAW), which more accurately⁸ tracks the path of technological⁹ evolution in six markets that the authors tested. According to the researchers, Moore's Law and other models such as Kryder's Law and Gompertz Law predict a smooth increasing exponential curve for the improvement in performance of various technologies. In contrast, the authors found that the performance of most technologies proceeds in steps (or jumps) of big improvements interspersed¹⁰ with waits (or periods of no growth in performance).   The sweet spot is in knowing which technology to back based on predicting when a new technology is going to have a jump in performance.   "We looked at the forest rather than the trees and see 'steps' and 'waits' across a variety of technologies," Tellis said. While no one law applies to every market, Tellis and his co-authors looked at 26 technologies in six markets from lighting¹¹ to automobile¹² batteries, and found that the SAW model worked in all six, in contrast to several other competing models.   What Tellis and his colleagues did come up with, are average performance improvements for the industry in terms of "steps" and wait times (see table to the right). The challenge for strategists is to invest in various technologies to beat these averages.   Tellis said that tablet and mobile phone manufacturers can leverage#p#分页标题#e#¹³（手段，影响力） this data. "Any manager has first to break down his or her products into components¹⁴, find components for each technology, and then predict the future path of those technologies. For example, the mobile phone consists of three important technological components: memory, display, or CPU, the first two of which the authors analyzed¹⁵. Similarly, tablets, manufacturers could rely on the figures for display and memory technologies."