A recent New York Times article reports that ‘Typical A.I. specialists, including both Ph.Ds fresh out of school and people with less education and just a few years of experience, can be paid from \$300,000 to \$500,000 a year or more in salary and company stock, according to nine people who work for major tech companies or have entertained job offers from them’ (Metz 2017b). When Google acquired DeepMind in 2014, it paid \$650 million for a company of 50 employees; in 2016, the lab’s ‘staff costs’ alone, as it expanded to 400 employees, totalled \$138 million, an average of \$345,000 an employee. In the light of such figures, it has been suggested that ML experts are ‘the new investment bankers’ (Shead 2017). The rewards are even higher, of course, for executives with experience managing AI projects. In a court case against Uber over ownership of autonomous car technologies, Google revealed that one of the leaders of its self-driving car division took home over \$120 million in incentives before jumping ship to join their competitor. However, even fresh graduates with skills in ML may make ‘in excess of £100,000 and sometimes up to £1 million’ while still in their mid-twenties (Shead 2017).
The AI industry is international in scope. Between 2016 and 2018 it became widely recognized as a critical axis of technological competition between the United States and China, particularly given its potential for military application in an era of growing tensions. Important Chinese AI developers include its largest search engine corporation, Baidu, and ecommerce giant Alibaba (K-F. Lee 2018). Other important national sites for the AI industry include Canada, Israel and the United Kingdom. However, nearly all assessments suggest that the United States is the leading location (Jang 2017; Rapp and O’Keefe 2018; Fabian 2018). By one estimate, which surveyed over 3,000 companies around the world involved in aspects of AI development, 40 per cent are in the
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