Part II: After the Stall

In Part I of this book I showed how generativity—both at the PC and network layers—was critical to the explosion of the Net, and how it will soon be critical to the explosion of the Net in a very different sense. In Part II I drill down a bit more into this concept of generativity. What is it? What does it mean? Where do we see it? Why is it good?

This part of the book offers an analytic definition of generativity and describes its benefits and drawbacks. It then explores the implications of a technological ecosystem in which non-generative devices and services—sterile “tethered appliances”—come to dominate. This trend threatens to curtail future innovation and to facilitate invasive forms of surveillance and control. A non-generative information ecosystem advances the regulability of the Internet to a stage that goes beyond addressing discrete regulatory problems, instead allowing regulators to alter basic freedoms that previously needed no theoretical or practical defense. I then turn to ways in which some systems—such as Wikipedia—have managed to retain their essential generative character while confronting the internal limits and external scrutiny that have arisen because of their initial successes.

Some principles jump out:

Our information technology ecosystem functions best with generative technology at its core. A mainstream dominated by non-generative systems will harm innovation as well as some important individual freedoms and opportunities for self-expression. However, generative and non-generative models are not mutually exclusive. They can compete and intertwine within a single system. For example, a free operating system such as GNU/Linux can be locked within an information appliance like the TiVo, and classical, profit-maximizing firms like Red Hat and IBM can find it worthwhile to contribute to generative technologies like GNU/Linux.1 Neither model is necessarily superior to the other for all purposes. Moreover, even if they occupy a more minor role in the mainstream, non-generative technologies still have valuable roles to serve. But they develop best when they can draw on the advances of generative systems.

Generativity instigates a pattern both within and beyond the technological layers of the information technology ecosystem. This book has so far described a trajectory for the generative Internet and PC, which begins in a backwater, accepts contribution from many quarters, experiences extraordinary success and unexpected mainstream adoption, and then encounters new and serious problems precisely because of that success. These problems can pose a lethal threat to generative systems by causing people to transform them into, or abandon them for, sterile alternatives. The forces that can stall the progress of the open Internet and return us to the days of proprietary networks can affect opportunities for generative enterprises like Wikipedia; such ventures are much more difficult to start without an open PC on a neutral Net. Moreover, the generative pattern of boom, bust, and possible renewal is not unique to technologies. It can also be found in generative expressive and social systems built with the help of those technologies. Recognizing the generative pattern can help us to understand phenomena across all the Internet’s layers, and solutions at one layer—such as those offered by Wikipedians in the face of new pressures at the content layer—can offer insight into solutions at others, such as the problems of viruses and spam at the technical layer.

Proponents of generative systems ignore the drawbacks attendant to generativity’s success at their peril. Generative systems are threatened by their mainstream success because new participants misunderstand or flout the ethos that makes the systems function well, and those not involved with the system find their legally protected interests challenged by it. Generative systems are not inherently self-sustaining when confronted with these challenges. We should draw lessons from instances in which such systems have survived and apply these lessons to problems arising within generative systems in other layers.