Notes from the
frontier: Modeling the impact of AI on the world economy PART II
2.
A key challenge is that
adoption of AI could widen gaps among countries, companies, and workers
Although Al can deliver
a boost to economic activity, the benefits are likely to be uneven.
How AI could affect countries
Potentially, AI might
widen gaps between countries, reinforcing the current digital divide. Countries
might need different strategies and responses as AI-adoption rates vary.
Leaders of AI adoption
(mostly in developed countries) could increase their lead over developing
countries. Leading AI countries could capture an additional 20 to 25 percent in
net economic benefits, compared with today, while developing countries might capture
only about 5 to 15 percent. Many developed countries might have no choice but
to push AI to capture higher productivity growth as their GDP-growth momentum
slows—in many cases, partly reflecting the challenge due to aging populations.
Moreover, in these economies, wage rates are high, which means that there is
more incentive to substitute labor with machines than there is in low-wage,
developing countries.
In contrast, developing
countries tend to have other ways, including catching up with best practices
and restructuring their industries, to improve their productivity. Therefore,
they might have less incentive to push for AI (which, in any case, might offer
them a relatively smaller economic benefit than it does advanced economies).
Some developing countries might prove to be exceptions to this rule. For
instance, China has a national strategy in place to become a global
leader in the AI supply chain and is investing heavily.
How AI could affect companies
It is possible that AI
technologies could lead to a performance gap between front-runners (companies
that fully absorb AI tools across their enterprises over the next five to seven
years) and nonadopters (companies that do not adopt AI technologies at all or
have not fully absorbed them in their enterprises by 2030).
At one end of the
spectrum, front-runners are likely to benefit disproportionately. By 2030, they
could potentially double their cash flow (economic benefit captured minus
associated investment and transition costs). This implies additional annual net
cash-flow growth of about 6 percent for longer than the next decade.
Front-runners tend to have a strong starting IT base, a higher propensity to
invest in AI, and positive views of the business case for AI.
At the other end of the
spectrum, nonadopters might experience around a 20 percent decline in their
cash flow from today’s levels, assuming the same cost and revenue model as
today. One important driver of this profit pressure is the existence of strong
competitive dynamics among companies that could shift market share from
laggards to front-runners and might prompt debate about the unequal
distribution of the benefits of AI.
How AI could affect workers
A widening gap might
unfold at the level of individual workers (see video, “A minute with the
McKinsey Global Institute: What AI can and can’t [yet] do”). Demand for jobs
could shift away from repetitive tasks toward those that are socially and
cognitively driven and require more digital skills. Job profiles characterized by
repetitive activities or that require a low level of digital skills could
experience the largest decline as a share of total employment to around 30
percent by 2030, from some 40 percent. The largest gain in share could be in
nonrepetitive activities and those that require high digital skills, rising
from roughly 40 percent to more than 50 percent.
These shifts would have
an impact on wages. We simulate that around 13 percent of the total wage bill
could shift to categories requiring nonrepetitive and high digital skills,
where incomes could rise, while workers in the repetitive and
low-digital-skills categories could experience a stagnation or even a cut in their
wages. The share of the total wage bill of the latter group could decline to 20
percent, from 33 percent.
There have been many exciting
breakthroughs in AI recently—but significant challenges remain. Partner Michael
Chui explains five limitations to AI that must be overcome.
A direct consequence of
this widening gap in employment and wages would be an intensifying war
for people, particularly those skilled in developing and using AI tools. On the
other hand is the potential for structural excess supply for a still relatively
high portion of people lacking the digital and cognitive skills necessary to
work with machines.
Overall, the adoption
and absorption of AI might not have a significant impact on net employment.
There will likely be substantial pressure on full-time-employment demand, but
the total net impact in aggregate might be more limited than many fear. Our
average global scenario suggests that total full-time-equivalent-employment
demand might remain flat, or even that there could be a slightly negative net
impact on jobs by 2030.
The opportunity of AI is
significant, but there is no doubt that its penetration might cause disruption.
The productivity dividend of AI probably will not materialize immediately. Its
impact is likely to build up at an accelerated pace over time; therefore, the
benefits of initial investment might not be visible in the short term. Patience
and long-term strategic thinking will be required.
Policy makers will need
to show bold leadership to overcome understandable discomfort among citizens
about the perceived threat to their jobs as automation takes hold. Companies
will also be important actors in searching for solutions on the mammoth task
of skilling and reskilling people to work with AI. Individuals
will need to adjust to a new world in which job turnover could be more
frequent, they might have to transition to new types of employment, and they
likely must continually refresh and update their skills to match the needs of a
dynamically changing job market.
Using historical trends
of new jobs created to old jobs, and adjusting for a lower labor-output ratio
that considers the likely labor-saving nature of AI technologies via smart
automation, new jobs driven by investment in AI could augment employment by
about 5 percent by 2030. The total productivity effect could have a positive
contribution to employment of about 10 percent.
By Jacques Bughin, Jeongmin Seong, James Manyika, Michael Chui, and Raoul Joshi
https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-frontier-modeling-the-impact-of-ai-on-the-world-economy?cid=other-eml-alt-mgi-mck-oth-1809&hlkid=5ebe957bb3594f96bedda5695e4664fd&hctky=1627601&hdpid=677435cb-04b0-445e-afba-4588aa47d2fe
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