The evolutionary dynamics of the artificial intelligence ecosystem

We analyze the sectoral and national systems of firms and institutions that collectively engage in artificial intelligence (AI). Moving beyond the analysis of AI as a general-purpose technology or its particular areas of application, we draw on the evolutionary analysis of sectoral systems and ask, “Who does what?” in AI. We provide a granular view of the complex interdependency patterns that connect developers, manufacturers, and users of AI. We distinguish between AI enablement, AI production, and AI consumption and analyze the emerging patterns of cospecialization between firms and communities. We find that AI provision is characterized by the dominance of a small number of Big Tech firms, whose downstream use of AI (e.g., search, payments, social media) has underpinned much of the recent progress in AI and who also provide the necessary upstream computing power provision (Cloud and Edge). These firms dominate top academic institutions in AI research, further strengthening their position. We find that AI is adopted by and benefits the small percentage of firms that can both digitize and access high-quality data. We consider how the AI sector has evolved differently in the three key geographies—China, the United States, and the European Union—and note that a handful of firms are building global AI ecosystems. Our contribution is to showcase the evolution of evolutionary thinking with AI as a case study: we show the shift from national/sectoral systems to triple-helix/innovation ecosystems and digital platforms. We conclude with the implications of such a broad evolutionary account for theory and practice

The power of modularity today: 20 years of “Design Rules”

In 2000, Carliss Baldwin and Kim Clark published “Design Rules: The Power of Modularity,” a book that introduced new ways of understanding and explaining the architecture of complex systems This Special Issue of Industrial and Corporate Change celebrates this seminal work, the research it has inspired, and the insights that these collective efforts have generated. In this introductory essay, we review the impact of “Design Rules” across numerous fields, including organization theory, competitive strategy, industry structure, and innovation management. We offer perspectives on key themes that emerge from contributions in this issue, including the alignment between organizational and technical designs (“mirroring”), the dynamics of industry evolution, and the role that individuals play in shaping and responding to system designs. We close by highlighting opportunities to apply the theory in Design Rules to new phenomena and puzzles that have emerged in the past 20 years

Can we assess teaching quality on the basis of student outcomes? A stochastic frontier application

This paper proposes a new application of Stochastic Frontier Analysis (SFA) for estimating the student performance gap and how this can be used to assess changes of teaching quality at the individual unit-of-study level (module-level). Although there have been other examples in the literature that assess ‘efficiency’ in student outcomes, this is the first study that proposes the use of SFA specifically at the module level and with the goal of creating an aggregate measure of ‘quality’, thus avoiding the known issue of the statistical inconsistency of unit-specific SFA estimates. A case study is presented on how the approach can be applied in practice, with discussion on potential implementation issues. This paper is targeted to academics and policy makers that are interested in the quantitative assessment of student outcomes and specifically to those who want to assess how changes in module structure and/or delivery have affected said student outcomes

An exploratory study into everyday problem solving in the design process of medical devices

We investigated accounts of how individuals in public and private organisations operating in the medical device industry use different forms of capital (social e.g. networks and cultural e.g. knowledge) to solve design based problems. We define capital as resources embedded in social networks, knowledge or economic wealth (Bourdieu, 1986). Data were collected from interviews and written diaries from individuals involved in the design process of medical devices using interpretative analysis. Inferences made from our analyses suggested that individuals working in organisations who successfully solve problems may do so by using both social and cultural capital and so may be more likely to engage in innovative activity than others. These exploratory findings suggest workers in large organisations may have the capability to use a greater level of in-house social and cultural capital, whereas those in smaller organisations may be more reliant on high levels of social capital in order to ‘tap into’ cultural capital beyond organisational boundaries

Explicitly searching for useful inventions: dynamic relatedness and the costs of connecting versus synthesizing

Inventions combine technological features. When features are barely related, burdensomely broad knowledge is required to identify the situations that they share. When features are overly related, burdensomely broad knowledge is required to identify the situations that distinguish them. Thus, according to my first hypothesis, when features are moderately related, the costs of connecting and costs of synthesizing are cumulatively minimized, and the most useful inventions emerge. I also hypothesize that continued experimentation with a specific set of features is likely to lead to the discovery of decreasingly useful inventions; the earlier-identified connections reflect the more common consumer situations. Covering data from all industries, the empirical analysis provides broad support for the first hypothesis. Regressions to test the second hypothesis are inconclusive when examining industry types individually. Yet, this study represents an exploratory investigation, and future research should test refined hypotheses with more sophisticated data, such as that found in literature-based discovery research