Artificial Intelligence and Big Data: Good for Innovation?

11 September, 2017 / Articles

Artificial intelligence is firmly embedded throughout the economy. 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 potential of AI to boost economic growth has been discussed in numerous forums, including by Accenture, the Council on Foreign Relations, the McKinsey Global Institute, the World Economic Forum, and President Obama’s Council of Economic Advisers, among others.

The most dramatic advances in AI are coming from a data-intensive technique known as machine learning. Machine learning requires lots of data to create, test and “train” the AI. Thus, as AI is becoming more important to the economy, so too is data. The Economist highlighted the important role of data in a recent cover 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, have become important tools businesses use to compete against each other, and against potential entrants.

A 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 not enter, established firms will not feel competitive pressures, and innovation may suffer. Thus, in March 2017 CFPB Director Richard Cordray noted: “We recognize that data access makes it possible to realize the many benefits of competition and innovation.”

The hurdles faced by entrants may have implications beyond competition and innovation. A common technique that entrants currently use to overcome the lack of customer data is to train their AI on publicly available datasets. But, if these datasets are biased in some way, then the resulting AI will reflect the bias. The worry is that if many entrants use similarly biased datasets, then bias quickly propagates, as argued by Amanda Levendowski.

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). An established firm’s access to data allows it to refine its AI over time, allowing it to get better at offering its AI-enabled product over time. Due to this learning-curve effect, there are increasing returns to scale in data control. As Daron Acemoglu and Simon Johnson put it, “the real power—for good and ill—is in software and increasing returns to data. If one self-driving car company does well initially, it will be able to collect more data—and then further improve its algorithm. Other companies will not be able to catch up.”

The learning-curve effect is not just about learning how to use your data better, it is about how to better organize your business around the new technology. Take the case of Netflix and Blockbuster in the early 2000s. At the time, Blockbuster was the dominant firm in the video rental business and Netflix was the technology-oriented niche player. The two firms had very different business models: Blockbuster targeted impulse renters; Netflix targeted technologically sophisticated movie buffs via DVDs by mail. Netflix relied on sophisticated logistics software to track its rentals and manage its DVD inventory. Blockbuster eventually realized the threat posed by Netflix and attempted to replicate Netflix’s DVD by mail business, but was unable to do so successfully. By that time, Netflix had benefited from shipping and tracking millions of DVDs and had moved so far down the learning curve, and refined its logistics so well, that Blockbuster was unable to replicate it effectively.

The science man and innovator, Fernando Fischmann, founder of Crystal Lagoons, recommends this article.



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