Poor and under-developed countries can tap on emerging global AI and data connectivity trends to stay afloat, argues this Huawei economist.
While it is still too early to tell how severely the current pandemic will damage the world’s rich economies, some worry that the devastation will be far worse in poor ones, where healthcare and infrastructure lag.
Even before the pandemic, the traditional path to middle-class prosperity was growing rocky in the poor countries. Historically, people have left small farms to take factory jobs in the cities, fueling export-led growth that raised incomes and living standards.
Until recently, escaping poverty through this type of de-industrialization meant competing mainly against the developed Western economies, relying on low wages as a competitive advantage. But global manufacturing chains have become hyper-competitive:
- China dominates through sheer scale; cheap labor is no longer enough.
- In Latin America, automation has eliminated manufacturing jobs by making existing workers more productive.
- In Africa, workers are abandoning factories to work as shop keepers, day laborers, or kitchen staff—often because bad roads make commuting impossible.
- In Asia, garment factories have laid off workers as COVID-19 has forced the shut-down of clothing businesses in North America and Europe.
Whatever the reasons behind the de-industrialization, some worry that if the trend continues, developing countries may soon have little of value they can sell to the world. Huawei’s research suggests a more hopeful outcome. We believe Artificial Intelligence and other technologies will create trade international flows that provide some of the world’s poorest citizens with higher-paying jobs and better lives.
Intelligent connectivity ecosystems
Increasingly, the global economy depends on cross-border flows of digital data. Digital products such as software and e-books, and digital services such as streaming video, are creating value that can exceed what is reflected in traditional growth measures such as GDP.
Soon, 5G and the Internet of Things will lead to products such as autonomous cars that rely on complex digital systems whose components can be sourced from a variety of countries. This new class of class of product will create cross-border value chains built on machine learning, data analytics, and connectivity.
An example of what could fast become a typical intelligent connectivity ecosystem
Suppose a German automaker wants to make self-driving cars (see graphic). It may design them at its headquarters, then work with data scientists in Israel to develop algorithmic models and analytics for the new vehicles. It can then team up with a computer-vision company in India, whose people perform the data-tagging that trains automotive software to recognize objects such as pedestrians and street signs.
Finally, a car-rental company in Australia could buy the vehicles for its fleet. The Australian car fleet collects data on emissions and other performance criteria, which are sent for analysis to the team in India, helping the German car-maker produce better vehicles in the future.
One might assume the big winner in that scenario would be Germany and Australia. But the greatest opportunities for ‘relative’ economic advancement may take place in the developing world. Before data can be used to create software, someone first has to ‘clean’ it, ensuring that datasets are accurate and complete. Data must also be labeled before computers can learn from it. Older AI systems, for example, could not distinguish a bicycle from a cat, but given one million images labeled ‘bicycle,’ the AI will eventually learn to recognize one.
Smart AI derives value from data
Data cleaning, labeling, and similar tasks will lay the groundwork for the kinds of sophisticated AI applications that will be used to guide driverless cars. Countries with abundant and (relatively) low-cost labor will possess a competitive advantage in performing this type of work.
Another example: China has produced more than a dozen ‘AI unicorns’—companies worth more than US$1 billion each. The Economist notes that much of China’s AI success is built on inexpensive, well-organized workers who clean and label the country’s immense data sets.
For example, a company called MBH runs some of China’s largest ‘data factories’, employing about 300,000 data labelers in some of the country’s poorer provinces. Each labeler works a six-hour shift, tagging faces, medical images, and photos of cities. Without this data-labeling infrastructure, China’s AI unicorns would get nowhere.
Opportunities for digital work
Immense opportunities exist for developing countries to compete in data-labeling and other parts of the digital economy, such as localizing apps for different languages and cultures. These opportunities will grow as AI is adopted around the world.
Because digital ecosystems rely heavily on cross-industry collaboration, protectionist policies and de-industrialization trends could deprive push a vital development ladder away from the world’s poor.
Even as opportunities in traditional de-industrialization and manufacturing are growing scarce, alternative value chains are being created. Governments that take act quickly can use them as an alternative path to higher productivity, prosperity, and growth.