Abstract
In Chapter 4, we analysed artificial intelligence (AI) as a technological innovation system (TIS) dominated by the tech giants. This chapter gives insights into the emergence and dynamics of this system. We explore the technological convergence between two tech giants with quite distinct origins using lexical analyses of these companies’ patents and scientific publications. We find that both Amazon and Microsoft have zoomed in their research and development (R&D) efforts on deep learning and neural networks as well as functional AI applications. We also find evidence of increasing centrality of harvesting, storing and processing data. R&D on cloud computing infrastructure is another area where both companies overlap. Given their dominant role in the AI TIS and the importance of economic factors in the selection of the cluster of technologies that constitute technological paradigms, we argue that these companies’ priorities are indicative of the prevailing directions within AI technological trajectories.
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Notes
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This advantage, as we explained in previous chapters, is explained by knowledge cumulativeness and, in particular for the digital sector, by network effects that results in more harvested data and therefore “more productive refineries”—more accurate AI models.
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Our access to this database included the following patent offices: USPTO, WIPO, European, Japan, Australian, British, Canadian, French, German, Russian and Korean patent offices.
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CorText is an open platform for performing bibliometric and semantic analysis that uses the spatial algorithms that draw on classic graph visualization methods for depicting the network maps (Fruchterman–Reingold). It can be accessed online at https://www.cortext.net/.
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“Words can have different meanings, the same meaning or concept can be expressed by different words, and words can have semantic associations in a hierarchical relation (e.g., animal versus mammal versus cat and dog). Last, not all words are of equal importance in deriving the meaning of phrases, but words with less significance appear with very high-frequency” (Van Looy & Magerman, 2019, p. 932).
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In network analysis, nodes occupying bridging positions are nodes that connect different clusters. They are of particular relevance for holding the clusters together and “in the dynamics of spreading processes across the network” (Fortunato & Hric, 2016).
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Appendix: Figures
Appendix: Figures
See Figs. 5.7, 5.8, 5.9, 5.10, 5.11 and 5.12
(Source Authors’ analysis based on Derwent Innovation)
Microsoft’s patents. Lexical analysis (1986–2008)
(Source Authors’ analysis based on Derwent Innovation)
Microsoft’s patents. Lexical analysis (2009–2013)
(Source Authors’ analysis based on Derwent Innovation)
Microsoft’s patents. Lexical analysis (2014–2017)
(Source Authors’ analysis based on Derwent Innovation)
Amazon’s patents. Lexical analysis (1996–2011)
(Source Authors’ analysis based on Derwent Innovation)
Amazon’s patents. Lexical analysis (2012–2013)
(Source Authors’ analysis based on Derwent Innovation)
Amazon’s patents. Lexical analysis (2014–2017)
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Rikap, C., Lundvall, BÅ. (2021). Amazon and Microsoft: Convergence and the Emerging AI Technology Trajectory. In: The Digital Innovation Race. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-89443-6_5
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DOI: https://doi.org/10.1007/978-3-030-89443-6_5
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