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In this paper, the historical effects and benefits of Moore's law for semiconductor technologies are reviewed, and it is offered that the rapid learning curve obtained to the benefit of society by feature size scaling might be continued in several different ways. The problem is that as features approach the range of a few nanometers, electron-based devices depart radically from the ideal switch and, in fact, become very leaky in the off state. It is argued that there are some short-term solutions involving more highly parallel manufacturing, increased design efficiency, and lower cost packaging technologies that could continue the steep learning curve for cost reductions that have historically been achieved via Moore's Law scaling. Another alternative might be to increase chip functionality by integrating devices that offer broadened chip functionality including, e.g., sensors, energy sources, oscillators, etc. A third alternative would be to invent an entirely new information processing state variable based on different physics, using electron spin, magnetic dipoles, photons, etc., to improve the performance and reduce switching energy for devices whose smallest features are on the order of a few nanometers. Each of these alternatives is being actively explored and an overview of each strategy and progress to date is given in the paper. A final alternative offered in the paper is to learn from information processing examples in nature, specifically in living systems. An E.coli cell of about one cubic micrometer volume is shown to be an incredibly powerful and energy-efficient information processor relative to the performance of an end-of-scaling silicon processor of the same volume. The paper concludes by pointing out some of the crucial differences between E.coli information processing and conventional approaches with the hope technologies can be invented using the hints offered by biosystems.