A few weeks ago I wrote about the economic value of AI based on a recently published report by PwC. The report’s overriding finding was that AI technologies and applications will increase global GDP by up to 14% between now and 2030, the equivalent of an additional $15.7 trillion contribution to the world’s economy. According to PwC, AI is the biggest economic opportunity over the next 10-15 years.
I now want to discuss a September, 2018 report by the McKinsey Global Institute on the impact of AI on the world economy.
The McKinsey report is based on simulation models of the impact of AI at the country, sector, company and worker levels. It looked at their adoption of five broad categories of AI technologies: computer vision, natural language, virtual assistants, robotic process automation, and advanced machine learning. Its data sources included survey data from approximately 3,000 firms in 14 different sectors; around 400 potential AI use cases across a variety of industries and functions; AI’s potential to automate and transform 800 existing occupations in 46 countries; and economic data from a number of organizations including the United Nations, the World Bank, the Organization for Economic Co-operation and Development (OECD), and the World Economic Forum.
Below is a summary of the report’s key findings.
AI has a large potential to contribute to global economic activity
Overall, the McKinsey models showed that AI has the potential to incrementally add 16 percent or around $13 trillion by 2030 to current global economic output, – an annual average contribution to productivity growth of about 1.2 percent between now and 2030. These overall growth estimates are fairly similar to PwC’s, which is noteworthy given that they’re each based on different data sets and on different modeling and analysis methods.
“If delivered, this impact would compare well with that of other general-purpose technologies through history,” notes McKinsey. “Consider, for instance, that the introduction of steam engines during the 1800s boosted labor productivity by an estimated 0.3 percent a year, the impact from robots during the 1990s around 0.4 percent, and the spread of IT during the 2000s 0.6 percent.”
Several factors will significantly impact such AI-driven economic changes
AI could lead to a gross GDP growth of around 26 percent or $22 trillion by 2030. The major contributors to this figure are the automation of labor, – which could add up to 11 percent or around $9 trillion to global GDP by 2030; and innovations in products and services, -which could increase GDP by about 7 percent or around $6 trillion by 2030.
However, in addition to its economic benefits, AI will also lead to significant disruptions for workers, companies and economies. There will likely be considerable costs associated with managing labor-market transitions, especially for workers being left behind by AI technologies, which could reduce the gross impact of AI by around 10 percentage points, leading to the aforementioned net GDP increase of 16 percent or $13 trillion by 2030.
A December, 2017 Mckinsey report analyzed the evolution of work over the next several years and concluded that between now and 2030, a growing technology-based economy should be able to create a significant number of new occupations, which will more than offset declines in occupations displaced by automation. But, the report added that “while there may be enough work to maintain full employment to 2030 under most scenarios, the transitions will be very challenging – matching or even exceeding the scale of shifts out of agriculture and manufacturing we have seen in the past.”
The economic impact may emerge gradually and be visible only over time
McKinsey’s models showed that AI marketplace adoption will likely follow a typical S curve pattern, – that is, a slow start in the early stages, followed by a steep acceleration as the technology matures and firms learn how to best deploy it, then tapering off in the technology’s late stages. In the case of AI, the contributions to growth are likely to be 3 to 5 times higher by 2030 and beyond than between now and 2023.
At a November, 2017 conference on AI and the Future of Work, MIT professor Erik Brynjolfsson explained that such S-curve deployment patterns are fairly typical of transformative, general purpose technologies like the steam engine, electricity and computing. Their deployment time-lags are longer because attaining their full benefits requires a number of complementary co-inventions and investments, including additional technologies, applications, processes, business models, and regulatory policies.
Over time, AI will likely become such a historical transformative technology. But, – other than a relatively small number of leading-edge firms, – we’re still in the early stages of AI’s deployment. It’s only been in the last few years that complementary innovations like machine learning have taken AI from the lab to early adopters in the marketplace. Considerable innovations and investments are required for its wider deployment in robotics, self-driving cars, truly intelligent personal assistants, and advanced applications like smart healthcare.
AI adoption could widen gaps between countries, companies, and workers
In terms of AI readiness, countries fall into four main groups:
- Global leaders. China and the US are currently responsible for the vast majority of AI-related activities, including investments, talent, patents and publications.
- Economies with robust foundations. Developed economies are well positioned to capture the benefits of AI, as well as highly motivated to do so due to their slow productivity growth. This group includes large economies like Germany, Japan, the UK, France, South Korea and Canada, as well as smaller, globally connected economies like Sweden, Singapore and Finland.
- Economies with moderate foundations. India, Italy, and Malaysia are in this category. While their foundations lag behind the more developed economies, they have strengths in specific areas around which they may be able to build their AI capabilities, e.g., India produces 1.7 million graduates a year with STEM degrees.
- Developing economies. These economies have relatively underdeveloped foundations, including digital infrastructure, investment capacity and talent. They risk falling further behind as more developed countries embrace and capture the economic benefits of AI.
Adoption rates among firms generally fall into three main categories:
- Front-runners. Early adopters, comprising about 10 percent of companies, will benefit disproportionately by embracing a broad set of AI technologies and applications over the next 5 to 7 years. As a result, a set of winner-take-all firms may well capture the bulk of the profit pool in their respective industries.
- Followers. This group, comprising 20 to 30 percent of firms, are slowly, cautiously embracing AI, having seen the benefits enjoyed by front-runners as well as the competitive threats of falling behind.
- Laggards. This final group, comprising 60 to 70 percent of firms, are not seriously investing in AI, – if at all. Capability issues may prevent such companies from embracing AI, forcing them to respond by reducing costs and cutting investments.
In addition, AI will lead to large shifts in the demand for skills, potentially widening the gaps between workers. The report estimates that “up to 375 million workers, or 14 percent of the global workforce, may need to change occupations – and virtually all workers may need to adapt to work alongside machines in new ways.” While some workers are at risk of being replaced by machines, there could be a shortage of workers whose value is greatly amplified by working alongside machines. “Overall, the picture that emerges is one of rising wage and employment opportunity inequality… groups with superior skill sets may capture a disproportionate share of gains.”
“The economic impact of AI is likely to be large, comparing well with other general-purpose technologies in history,” notes the report in conclusion. “At the same time, there is a risk that a widening AI divide could open up between those who move quickly to embrace these technologies and those who do not adopt them, and between workers who have the skills that match demand in the AI era and those who don’t. The benefits of AI are likely to be distributed unequally, and if the development and deployment of these technologies are not handled effectively, inequality could deepen, fueling conflict within societies.”
