Abstract
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.
Highlights
We focus on the emerging division of labor between different types of firms that engage in artificial intelligence (AI), moving beyond secondary data reports
Teece 2007), we look at the evolving dynamics of AI
AI giants (e.g., Google, Amazon, Alibaba, Tencent) have the capability to produce the AI they need for internal and external use. These companies operate every brick of the AI enablement and production sectors and consume what they produce in different parts of their business (e.g., Google search engine, which has a constant need for AI improvement as its competitive advantage critically relies on its predictive power; ditto Amazon with its ability to target customers and optimize logistics)
Summary
Industry architecture and autonomous driving—and more will soon emerge. the conversation has shifted from a highbrow debate about the nature of intelligence and humanity to a practical discussion of business models, regulation, ethics, data property rights, reskilling, and the impact on employment structures. AI consumption and production are intricately connected in some key segments, ML: AI consumption (i.e., using an algorithm) can provide the data to calibrate it, and this leads to a positive feedback loop This makes AI unique among GPTs: no steam-engine or electric-motor output would endogenously improve by being put into use, setting aside learning-curve and application improvements. AI communities, such as those on GitHub (acquired by Microsoft in 2018) and Kaggle, provide an online space in which developers can access and contribute to myriad data sets, algorithms, and models and advance their AI knowledge through online courses.4 They serve as a flexible, dynamic platform to spur AI innovation and commercialization
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