Thanks to major improvements in computing power, increasingly sophisticated algorithms, and an unprecedented amount of data, artificial intelligence (AI) has started generating significant economic value. With algorithms that make predictions from large amounts of data, AI contributes, by some estimates, about $2 trillion to today’s global economy. It could add as much as $16 trillion by 2030, making it more than 10 percent of gross world product.
AI’s outsize contribution to global economic growth has important implications for geopolitics. Around the world, governments are ramping up their investments in AI research and development (R&D), infrastructure, talent, and product development. To date, twenty-four governments have published national AI strategies and their corresponding investments.
So far, China and the United States are outspending everyone else while simultaneously taking steps to protect their investments from foreign competition. In 2017, China passedlegislation requiring foreign companies to store data from Chinese customers within China’s borders, effectively hamstringing outsiders from using Chinese data to offer services to non-Chinese parties. For its part, the U.S. Committee on Foreign Investment blocked a Chinese investor from acquiring a leading U.S. producer of semiconductors, which are essential components for computing. While this was officially a national security action, it could also benefit U.S competitiveness by protecting its stake in semiconductor production.
Both data and certain classes of semiconductors are core elements of the AI value chain. Given AI’s economic and geopolitical significance, they’re also increasingly being considered strategic assets. The extent to which countries can participate in this value chain will determine how they fare in the emerging global economic order and the stability of the broader international system. Indeed, if the gains from AI are distributed in highly variable ways, extreme divergence in national outcomes could drive widespread instability.
So what does the AI value chain look like? And where in the physical world are the key nodes of value creation and control emerging? This article addresses these questions, introducing the idea of a machine learning value chain and offering insights on the geopolitical implications for countries searching for competitive advantage in the age of AI.
Machine learning, the science of getting computers to make decisions without being explicitly programmed, is the subfield of AI responsible for the majority of technical advances and economic investment. In recent years, machine learning has led all categories of AI patents (and, in fact, constituted the third-fastest-growing category of all patents granted behind 3D printing and e-cigarettes) and attracted nearly 60 percent of all investment in AI.
A value chain describes the sequence of steps through which companies take raw materials and add value to them, resulting in a finished, commercially viable product. For machine learning, that value chain consists of five stages: data collection, data storage, data preparation, algorithm training, and application development (see figure 1).
Data collection involves the gathering of raw data from any number of sources.
Algorithm training involves configuring an algorithm to make predictions from data.
Proxy measures that focus on the key nodes of the machine learning value chain provide a useful, albeit imperfect, means of quantifying and comparing the value chain’s distribution across countries and regions. County-level data are used here wherever they are available; regional data are used everywhere else.
Raw data are the bedrock of machine learning. Every day, roughly 2.
