THE AI INDUSTRY AND THE OLIGOPOLISTS OF MACHINE INTELLIGENCE
While corporate interest in the actual and potential uses of the new AI are manifold, ranging from retail sales to entertainment and industrial production, the actual production of AI systems is a central concern for a more limited circle of high-tech companies. We refer to this complex as ‘the AI industry’, distinct from the broader field of commercial AI applications. While business-oriented publications continually remind us that AI will ‘revolutionize’ capitalist production (Columbus 2016), our analysis suggests that such a transformation, if it occurs, is still in its earliest phases. Instead, we see AI as one emerging industry whose influence is tied up with that of other emerging technologies and is as yet difficult to ascertain with certainty. Although business interest in AI is high, outside the AI industry this does not entail high levels of actual investment in the technology. A 2017 survey of attendees at an applied artificial intelligence conference concluded that ‘AI adoption … remains low with the majority of major success stories coming only from the largest tech players in the industry’ (Rayo 2018).
AI development first appeared as a distinct industrial sector in the 1980s. This first era of the AI industry was based around GOFAI expert systems. During this era, the AI industry consisted of a few small companies which produced systems as means of production for, and typically in cooperation with, their corporate customers. In some cases, large firms established internal AI departments to develop proprietary expert systems. Such systems required a considerable degree of specialization, had extremely narrow fields of application and required a lot of labour to produce and update. While attempts were made to develop ‘generic’ expert systems which could be applied to any field, they ultimately failed (Roland and Shiman 2002: 205). The commercial craze for these systems subsided in the 1990s, but around the same time the ML approach gained traction in academia and, during the 2010s, returned AI to the commercial realm, propelled by advances in computing power and improved learning algorithms. By 2017, The Economist (2017a) was proposing a shortlist of domains in which ML’s power to ‘sift through data to recognize patterns and make predictions without being explicitly programmed to do so’ was becoming commercially important. It is worth surveying a few.
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inhuman power