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I've been going on about this book to people for almost a year, a must-read! Open access.

How the innate physical properties of different technologies influence the strategy and structure of the organizations implementing the technologies, the sequel to Design Rules: The Power of Modularity.

In Design Rules, volume 2, Carliss Baldwin offers a comprehensive view of the digital economy by putting forth an original theory that explains how technology shapes organizations in a market economy. The theory claims that complementarities arising from the physical nature of technologies can be arrayed on a spectrum ranging from strong to very weak. Two basic types of technologies in turn exhibit different degrees of complementarity between their internal components. Flow production technologies, which are found in steel mills and auto factories, specify a series of steps, each of which is essential to the final product. In contrast, platform technologies, which are characteristic of computer hardware, software, and networks, are modular systems designed to provide options.

Baldwin then investigates the dynamics of strategy for firms in platform ecosystems. Such firms create value by solving technical bottlenecks—technical barriers to performance that arise in different parts of the system as it evolves. They capture value by controlling and defending strategic bottlenecks—components that are (1) essential to the functioning of some part of the system; (2) unique; and (3) controlled by a profit-seeking enterprise. Strategic bottlenecks can be acquired by solving technical bottlenecks. They can be destroyed via tactics such as substitution, reverse engineering, bypassing the bottleneck, and enveloping a smaller bottleneck within a larger one. Strategy in platform ecosystems can thus be viewed as the effective management of technical and strategic bottlenecks within a modular technical system.

Data is a contextual phenomenon. It reflects the social and material context from which it is derived and in which it is generated. It embeds the purposes, assumptions and rationales of those who produce, collect, use, share and monetize it. In the AI and digital platform economy, data's role is primarily infrastructural. Its core uses are internal to companies. Data only rarely serves as a medium of exchange or commodity, and more frequently serves to profile users, train models, produce predictions, bundle and extend product capabilities which in turn are sold to advertisers and other customers. Insofar as they focus on the former, many technical, economic and legal attempts at defining data have inspired reductive policy efforts that include data protection, data ownership and limited data sharing remedies. This paper argues that understanding data as part of infrastructural pipelines can have significant conceptual and policy implications, and can redirect the way privacy, property and antitrust experts understand and govern data. This argument becomes more salient as market actors and regulators grapple with the catalyzing effects of neural networks and generative AI models on digital markets. In antitrust and competition law especially, regulators are consciously adopting a view of data as an infrastructural input into AI and other digital markets. Treating data as an input over which certain firms have competitive advantages can have significant implications for nascent AI markets, and yet the views in antitrust remain too narrow. Understanding data infrastructurally means viewing it not only as a critical input but also as inseparable from other material digital resources such as protocols, algorithms, semiconductors, and platform interfaces; as having important collective functions; and as calling for public interest regulation. Understanding data as infrastructure can move us past limited legal efforts and remedial solutions such as data separations, data sharing, and individual controls, and help reorient how data is produced, stored and managed toward public uses.

The White Paper sets out an initial high-level framework for states, policymakers, civil society, workers and others to dismantle Big Tech's concentrated power over digital ecosystems, and to encourage the emergence of a fair digital economy that is open, decentralised, democratic and serves the common good.

Woodrow Hartzog - Industry will take everything it can in developing artificial intelligence (AI) systems. We will get used to it. This will be done for our benefit. Two of these things are true and one of them is a lie. It is critical that lawmakers identify them correctly. In this Essay, I argue that no matter how AI systems develop, if lawmakers do not address the dynamics of dangerous extraction, harmful normalization, and adversarial self-dealing, then AI systems will likely be used to do more harm than good.

Eric Goldman

I delivered this talk as the 2024 Nies Lecture at Marquette University School of Law, Milwaukee, WI. The talk compares the recent proliferation of Generative AI with the Internet’s proliferation in the mid-1990s. In each case, it was clear that the technology would have revolutionary but uncertain impacts on society. However, the public sentiments toward the two innovations have differed radically. The Internet arrived during a period of widespread techno-optimism, creating a regulatory environment that fostered the Internet’s growth. Generative AI, in contrast, has arrived during widespread techno-pessimism and following decades of conditioning about the dangers of “AI.” The difference is consequential: The prevailing regulatory and legal responses to Generative AI will limit or even negate its benefits. If society hopes to achieve the full potential of Generative AI, we’ll need to adopt a new regulatory approach quickly.

Carliss Baldwin, Eric von Hippel

In this paper, we assess the economic viability of innovation by producers relative to two increasingly important alternative models: innovations by single-user individuals or firms and open collaborative innovation. We analyze the design costs and architectures and communication costs associated with each model. We conclude that both innovation by individual users and open collaborative innovation increasingly compete with and may displace producer innovation in many parts of the economy. We explain why this represents a paradigm shift with respect to innovation research, policy making, and practice. We discuss important implications and offer suggestions for further research.

This article takes as its subject the growth of "governance beyond the state." It highlights the problems resulting from the large number of organizations, networks and practices which are making authorita- tive rules and policies outside the state, and which lie beyond the control of nationaldemocratic and consti- tutional structures. Having set out the double dilemma posed by the rapid growth of transnational governance and its problematic relationship to democracy, the article criticizes existing approaches to the dilemma. The dominant current perspective, which I label the "compensatory approach," takes the view that democracy cannot be transposedfrom the national to the transnationalarena, and that other compensatory mechanisms must be found to regulate transnationalgovernance. I take issue with the general consensus that democratization of transnational governance is not plausible ,and I argue that any convincing attempt to reform transnational governance must contend with the democracy problem. Although our contemporary understanding of the concept of democracy is closely tied to the state context, I argue that we should not jettison democratic ideals when attempting to design more legitimate governance structures beyond the state. Rather, we should acknowedge the powerful normative and social appeal of democracy as a governing ideal, try to identify its co ceptual "building blocks," and think about the possible design of legitimate democracy-oriented governance processes beyond and between states.

In this spirit, the article proposes an approach to transnational governance which I call the democratic-striving approach. To ensure the public-oriented nature of norms and policies, this approach is built on one particular building-block of democracy: the fullest possible participation and representation of those affected. To illustrate the general argument in more concrete terms, the article draws on the example of the InternationalFinancialInstitutions and the recent reform of their development-assistance policies, known as the Poverty Strategy Reduction Program. The example demonstrates the practical potential of the democratic-striving approach for the reform of transnational governance, and suggests that it could be applied to many other instances of governance beyond the state.

Decentralization is a term widely used in a variety of contexts, particularly in political science and discourses surrounding the Internet. It is popular today among advocates of blockchain technology. While frequently employed as if it were a technical term, decentralization more reliably appears to operate as a rhetorical strategy that directs attention toward some aspects of a proposed social order and away from others. It is called for far more than it is theorized or consistently defined. This non- specificity has served to draw diverse participants into common political and technological projects. Yet even the most apparently decentralized systems have shown the capacity to produce economically and structurally centralized outcomes. The rhetoric of decentralization thus obscures other aspects of the re-ordering it claims to describe. It steers attention from where concentrations of power are operating, deferring worthwhile debate about how such power should operate. For decentralization to be a reliable concept in formulating future social arrangements and related technologies, it should come with high standards of specificity. It also cannot substitute for anticipating centralization with appropriate mechanisms of accountability.