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GCI 2019: Rewiring Economic Development with Global AI Value Chains

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ByAndrew Williamson

January 20, 2020

Andrew Williamson

A few years ago, before certain politically driven retrenchments from globalisation overwhelmed discourse and led to a shift away from global thinking in many quarters, an alternative concern was at the forefront of economic discussion: that of ‘premature de-industrialisation’ across emerging markets and the developing world.

Post-industrial Value

Dani Rodrick (Ford Foundation Professor of International Political Economy at Harvard’s John F. Kennedy School of Government) best described the issue as the following. “Countries are running out of industrialization opportunities sooner and at much lower levels of income compared to the experience of early industrializers.” Essentially, the previous well-trodden economic development path of rural to urban migration, export-led industrialisation and increasing wealth appeared to be floundering. Many less developed countries were losing their manufacturing jobs without getting rich first.

Globalisation over the last three decades has undoubtedly been a boon for worldwide economic development. It has helped to raise hundreds of millions of people out of abject poverty. If the twin trends of increasing protectionism and premature de-industrialisation continue, this could pull away a vital development ladder from the world’s remaining poor. Fortunately, a restructuring of the world’s economy driven by new technologies suggests an emerging alternative opportunity. According to McKinsey, flows of services and data now play a much bigger role in linking the global economy together than even a few years ago. Not only is trade in services growing faster than trade in goods, but digital services are creating value far beyond what national accounts measure.

In December I was lucky enough to participate at Huawei’s inaugural ‘Trust-in-Tech’ symposium in London. Here I outlined the findings of our 2019 Global Connectivity Index (GCI) report. A key component of our research in 2019 was to identify and explain these nascent global value chains, driven by opportunities around machine learning, data and connectivity.

The Stakeholder Ecosystem

We take as our explainer the prospect of autonomous vehicles. Let’s suppose a European multinational automaker planned an Intelligent Connectivity ecosystem to design and manufacture self-driving cars. The global ecosystem for this could look something like the graphic above.

In step one, the company or Decision Maker invests in designing and developing autonomous vehicles at its European headquarters. Once the initial planning is agreed upon, step two sees the Decision Maker identify and bring on-board a team of Data Scientists and machine learning (ML) engineers based in say Israel. This team is charged with developing algorithmic models and analytics for the proposed new vehicles.

The Data Scientists’ program is then tasked with designing computer vision models that interpret changing road views. In step three, this group identifies and invests in a US/Indian computer vision platform that can in step four outsource visual image data tagging or Data Collector work efficiently and cost effectively in India. Next is step five, where the new application’s End Users could then be the buyer of a fleet of autonomous vehicles to serve as rental cars in Australia. But even the End User can also play a role of data provider and data collector in the ecosystem (and possibly earn income streams), as the vehicle generates massive amounts of data that can later be analysed by the Data Scientist and data tagging teams in India.

The common assumption might be that the “Decision Makers” and “End Users” would derive the greatest share of the economic value from this type of Intelligent Connectivity ecosystem. However, return-on-investment  may in fact be more evenly apportioned to nations and industries across the full development spectrum, than initially supposed. The idea that Intelligent Connectivity’s benefits accrue only to the most technologically advanced countries is likely to be inaccurate. In fact, the greatest opportunity for relative economic advancement and development may in fact occur in developing countries through their contributions to this type of digital value chain.

This is because data alone are not much use for building AI software. They must first be cleaned and labelled. Data for machine learning needs to have the contextual information that computers need in order to make the statistical associations between factors in data sets and their meaning to human beings (and repeatedly test those associations). The competitive advantage to do this type of work will come from those countries with educated but relatively low-cost and abundant labour.

China’s Data Factories

As The Economist noted recently, much of the success of China’s AI industry has in fact been built on well-organised cheap labour, who clean and label the immense data sets that are being generated right now.  They assert that without China’s extensive data-labelling infrastructure, China’s “AI unicorns” would be nowhere. An example provided is for a company called MBH, which provides some of China’s largest ‘data factories’. The company currently employs 300,000 data labellers across China’s poorest provinces. Each labeller works a six-hour shift each day, tagging a stream of faces, medical imagery and cityscapes. Growth for the sector continues to be robust and is likely to accelerate rapidly.

The opportunities for other emerging markets and developing countries to compete with China’s data-labelling infrastructure are therefore immense. In fact, policymakers and industry leaders in nations at every stage of economic development are discovering new ways to participate in Intelligent Connectivity ecosystems. However, those with isolationist and protectionist inclinations will likely lag behind, as ecosystems at the local, regional, and global scale will increasingly rely on cross-industry and international collaboration to create value. The window of opportunity is now and closing fast.

Visit the Huawei GCI 2019 minisite for more on global business paradigm, national ICT performance rankings, and to download the full report: GCI 2019 Powering Intelligent Connectivity with Global Collaboration.


Disclaimer: Any views and/or opinions expressed in this post by individual authors or contributors are their personal views and/or opinions and do not necessarily reflect the views and/or opinions of Huawei Technologies.

3 thoughts on “GCI 2019: Rewiring Economic Development with Global AI Value Chains

    1. It’s not unreasonable to be concerned with the responsible use of technology. We believe that the purpose of all ICT including AI is to serve humanity, to make life better for everyone, and to empower the unempowered. Please have a look at our digital inclusion initiative TECH4ALL which aims to do this by focusing on the domains education, environment, health and development.

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Andrew Williamson

Vice President, Government Affairs and Economic Adviser, Huawei In this role, Andrew is a key aide on global macroeconomic, political and industry trends. His research also involves the contribution ICT makes to economic growth, and society.

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