Artificial Intelligence (AI) refers to machines that perform cognitive tasks like thinking, perceiving, learning, problem-solving and decision making. From an international affairs point of view, the most critical risk stems from the fundamentally centralizing and monopolizing characteristic of AI given its requirements for scale both for companies and countries. This, in turn, is likely to create winner-takes-all economics favouring the data giants like the USA and China. Middle-sized emerging markets like Turkey are in danger of losing recently-gained economic power and international standing.
To understand the reason for the risk posed by AI, it is important to understand technology. While there are many branches of AI, most of the popular AI applications today involve “machine learning” – so much so that the terms are used interchangeably – which refers to machines’ ability to learn from data without being explicitly programmed. The computers as we know today simple apply ordinary rules to transform an input into an output: “If X and Y happen, then Z.” With the application of AI, computers do not need these rules. AI enables computers to analyze data and discover the patterns, learn from data, and then set the rules – data now tells us if X and Y happens, then Z will happen. This learning process is generally repetitive and incremental. The more data you feed into the AI, the better the rules become.
AI is a general-purpose technology feeding into a wide range of applications – available and to be innovated in the upcoming years, including speech and image recognition, natural language processing, targeted advertising, predictive maintenance for machines, driverless cars and drones. In this sense, it is similar to electricity. One hundred and thirty years ago, Thomas Edison commercialized electricity. With his innovations connecting electricity to the grid, applications in manufacturing, lighting and home appliances became feasible.
When discussing the implications of AI as a general-purpose technology, it is important to understand that we are already at a post-Edison stage. The first AI algorithms were already developed by Alan Turing in 1957. The application was constrained until recently, not because of the unavailability of fundamental technology, but because of the unavailability of the computing power to collect and process data. At the moment, both computing power and AI algorithms are widely available through cloud computing. What will really make the difference in the AI application race is the availability of data? More data leads to better products, which in turn attracts more users, who generate more data to improve the product further.
The scale of data required to develop advanced AI applications is the basis of the centralization and monopolization impact of AI. It is not hard to understand why the USA and China are pioneers in AI today. Both have access to abundant data. The USA is the largest economy in the world, the third most populated nation, and has traditionally been the most connected given it is where the Internet has been “invented.” American big-tech companies lead the world in AI applications. It is no coincidence that Google, Amazon, Facebook, Microsoft and Apple overtook traditional energy and finance companies as the largest five corporations in the USA by market capitalization in the last 10 years when we keep in mind the AI revolution. Many other American startups became some of the world’s most valued companies in the last decade mainly by developing AI-based algorithms in the USA and then scaling them globally. Reid Hoffman, the founder of Linkedin, calls this “blitzscaling” – scaling to many markets around the world to dominate those markets, collect more data, improve your AI applications with abundant data, and strengthen your market power. Examples include Uber, which turns private cars into taxis, Airbnb, which turns private apartments into hotels, and Netflix, which turns your computer screen into a private theatre room. None of these companies is profitable yet, but they enjoy extreme valuations based on the global reach of their user basis, i.e., the data they collect from around the world.
China is an up and coming AI giant. It is the most populated country in the world and the second largest economy. However, given the fast adoption of digital technologies in China, the level of data produced by the Chinese population is disproportionately high compared to its size. Compared to the USA, China has 3 times more mobile devices, 10 times more online food delivery, and 50 times more mobile payments. As argued by Kaifu Lee in his recent book “AI Superpowers,” China’s Sputnik moment regarding AI came in May 2017, when AlphaGo, a computer owned by Google, defeated Ke Jie, the leading grandmaster of Go, a traditional Chinese board game that has an order of magnitude larger number of possible positions than chess. With this, the Chinese Communist Party announced its focus on AI, catalyzing local governments, incubators and universities to support AI-based businesses. Chinese big-tech companies Baidu (the Google of China), Tencent (the Facebook of China) and Alibaba (the Amazon of China) are still mostly local. When Chinese AI companies start to globalize at scale, guided and supported by government policies, their impact and market power are likely to increase exponentially.
Only a few small countries can position themselves as global AI innovation hubs vis-à-vis the duopoly of the USA and China. Examples include Israel, Singapore and Estonia (and possibly the UAE as a regional hub). These countries are so small that their startups never target the local market and are born global from day zero. They are also relatively stable rich economies that can grow or attract entrepreneurial talent. As it is extremely difficult for AI-based startups from these countries to survive once they scale to the global market, they are generally sold to big-tech companies from the USA or China. This is a great way of value creation not only for these startups but also for these countries with small populations. For instance, Mobileye, an Israeli startup with key technologies for self-driving cars, was sold to Intel for USD 15 billion in 2017. From the Mobileye deal, the Israeli government got USD 1 billion of tax revenue – USD 125 per every citizen.
Mid-sized emerging countries like Turkey, Brazil, Mexico, Indonesia, or South Africa are stuck in the middle. These countries are not a natural home for global business models, because the local markets are not small. Yet, the local markets do not produce enough data for AI companies to reach to scale at home and then blitzscale globally. The fact that the local markets are large (but not that large) becomes a curse. These countries are able to grow unicorn companies, but they are largely focused on local markets with little potential for global expansion. For instance, Indonesia has 4 unicorns: two local marketplaces for goods and services, one for motorcycle hailing, and one for local travel booking. Turkey has –according to some sources—one unicorn so far: Trendyol, an e-commerce website for the domestic market. In comparison, Israel has 18 unicorns, all with global business models.
Will Turkey be able to create a break-through in AI and use this new general-purpose technology to become a global hub for entrepreneurial talent or venture capital? Or will the AI just reinforce the middle-income trap for Turkey? Its tricky size requires Turkey to follow a fine-tuned national AI strategy in the upcoming years. Turkey is not yet one of the 22 countries that have already adopted a national AI strategy. This is why at EDAM we are starting a new research agenda for 2019 that will discuss elements of a national AI strategy from different angles, including competition and regulation, industrial and labour policy, democracy, national security, and international economic relations including integration into the EU’s Digital Single Market. Follow EDAM’s AI research for further discussions.
CEO at EDAM