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Artificial Intelligence And Big Data: Good For Innovation?

Financial services firms use it to provide investment advice to customers, automakers are using it in vehicle autopilot systems, technology companies are using it to create virtual assistants like Alexa and Siri, and retailers are using artificial intelligence (AI) together with customers’ prior sales histories, to predict potential purchases in the future, to name but a few examples.

The Economist highlighted the important role of data in a recentcover story in which it stated “the world’s most valuable resource is no longer oil, but data.” In this sense, both the ability to obtain data about customers, together with the ability to program AI to analyze the data, havebecome important tools businesses use to compete against each other, and against potential entrants.

potential entrant that lacks access to good data faces substantial hurdles, and this has led some regulators to question the extent to which control over data creates barriers to entry.

For example, in December 2015 FTC Commissioner Terrell McSweeney asked: “Can one company controlling vast amounts of data possess a kind of market power that creates a barrier to entry?” This is a worry, because if barriers to entry are too high, entrants will notenter, established firms will not feel competitive pressures, and innovation may suffer.

Access To Data Helps Firms Move Down A Learning Curve An established firm’s access to data may allow it to take advantage of a learning curve, which may exacerbate barriers to entry for other firms (Michael Spence’s 1981 article in Journal of Political Economy is a classic on this topic).

A recent report by McKinsey Global Institute estimates that established firms spent $2 to $3 billion on AI-related acquisitions in 2016, and between $18 and $27 billion on internal corporate investment in AI-related projects in 2016.

seed-stage financing is apparently down 40% from a peak in mid-2015, and some of the decline may be due to fear of established firms, particularly large, platform-oriented technology firms.

Recent empirical work by Wen Wen and Feng Zhu document that when a platform starts to appropriate features of its developers’ applications, the application developers cut back on innovations to thoseapplications.

Under such a model, a customer would maintain possession of some core data that he or she could then take from one company to a rival, much in the way that a phone customer can take his or her phone number from one provider to another (which was not always the case).

In principle, this should help reduce barriers to entry, because any potential customer of a new entrant could easily shift her data from the established firm to the entrant.

Even if a customer could “port” her own data to a rival social media platform, she would not be able to port her friends' data, and so customer data portability would have little effect on competition and innovation in this sector.

In contrast, customer data portability would have a larger effect on competition and innovation in other sectors such as online shopping or financial services, where a customer could port her prior purchase or transaction history.

Ultimately, there may be technological solutions that help maintain customer privacy while allowing for easy customer data portability, as outlined by Ryan Calo in a recent AI policy paper.

In the meantime, policy makers will need to weigh potential risks to consumers from privacy breaches against the potential benefits to consumers from increased competition, and ultimately increased innovation, that may result from customer data portability.

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