Can the rapid development of AI lead to a widening gap between developing and
developed countries?
AI national strategies in developed countries :
Many developed countries have recognized the potential of AI to drive economic growth, improve public
services, and advance scientific research. As a result, they have developed national strategies to support
the development and deployment of AI technologies in a way that is responsible, ethical, and beneficial
to society.
The United States, released a national AI strategy called the “American AI Initiative” in 2019, which
focused on promoting public–private partnerships, investing in AI research and development, and
increasing access to data and computing resources for AI researchers. The initiative is based on the
following five key pillars:
1. Investing in research and development: The United States is investing in AI-focused research
institutions and incubators, and is providing support for businesses that are developing AI-
related products and services.
2. Fostering public–private partnerships: The United States is promoting collaboration between
government agencies, academia, and the private sector to advance AI research and
development.
3. Promoting the responsible and ethical use of AI: The United States is implementing policies and
initiatives to promote the responsible and ethical use of AI by engaging with stakeholders and
addressing potential negative impacts of AI.
4. Supporting the growth of the AI industry: The United States is providing support for businesses
that are developing AI-related products and services, and is implementing policies to support
the growth of the AI industry.
5. Building the technological infrastructure and capabilities needed to enable the use of AI: The
United States is investing in the development of the technological infrastructure and capabilities
needed to enable the use of AI, by implementing policies to support the growth of the AI
industry.
Canada has implemented the Pan-Canadian Artificial Intelligence Strategy, which is focused on
supporting the growth of the AI industry, and on using AI to address challenges in areas such as
healthcare and transportation. The strategy is based on the following four key pillars:
1. Investing in research and development: Canada is investing in AI-focused research institutions
and incubators, and is providing support for businesses that are developing AI-related products
and services.
2. Supporting the growth of the AI industry: Canada is providing support for businesses that are
developing AI-related products and services, and is implementing policies to support the growth
of the AI industry.
3. Using AI to address challenges: Canada is using AI to address challenges in areas such as
healthcare and transportation, by implementing AI-powered solutions and initiatives.
4. Building the technological infrastructure and capabilities needed to enable the use of AI: Canada
is investing in the development of the technological infrastructure and capabilities needed to
enable the use of AI, by implementing policies to support the growth of the AI industry.
The United Kingdom also launched its strategy “AI Sector Deal” in 2018. This strategy includes a
number of initiatives to support the growth of the country’s AI industry, including investments in AI
research and development, the establishment of an AI skills institute, and the creation of an AI advisory
council to help develop ethical guidelines for the use of AI. The strategy is based on key pillars similar to
the USA.
In Europe, the European Union has also been working on a comprehensive AI strategy “EU AI
Strategy”, which includes initiatives to support the development and deployment of AI technologies, as
well as measures to ensure the responsible and ethical use of AI. The EU AI Strategy is based on three
key pillars:
1. Investing in research and development: The European Union is investing in AI-focused research
institutions and incubators, and is providing support for businesses that are developing AI-
related products and services.
2. Supporting the growth of the AI industry: The European Union is providing support for
businesses that are developing AI-related products and services, and is implementing policies to
support the growth of the AI industry.
3. Addressing ethical and societal concerns related to AI: The European Union is implementing
policies and initiatives to address ethical and societal concerns related to AI, by engaging with
stakeholders and promoting the responsible and ethical use of AI.
Other developed countries, such as Japan and South Korea, are also taking steps to develop national
AI strategies. Japan has developed the Society initiative, which aims to use AI and other emerging
technologies to drive economic growth and social development.
The Society initiative is based on four key pillars similar to Canada.
South Korea has adopted the AI National Development Plan, which is focused on investing in AI
research and development, supporting the growth of the AI industry, and promoting the use of AI in
various sectors. The AI National Development Plan is based on three key pillars:
1. Investing in research and development: South Korea is investing in AI-focused research
institutions and incubators, and is providing support for businesses that are developing AI-
related products and services.
2. Supporting the growth of the AI industry: South Korea is providing support for businesses that
are developing AI-related products and services, and is implementing policies to support the
growth of the AI industry.
3. Promoting the use of AI in various sectors: South Korea is promoting the use of AI in various
sectors, by implementing AI-powered solutions and initiatives in areas such as healthcare and
transportation.
AI is also increasingly adopted by a number of developing countries in MENA region, including the
United Arab Emirates (UAE), Saudi Arabia, and Qatar.
These countries have made significant investments in the development and use of AI technologies, and
have implemented a number of initiatives and policies to support the growth of the AI industry.
For example, the UAE has established partnerships with leading tech companies to develop AI-powered
healthcare solutions, and has launched initiatives to support the use of AI in education.
Saudi Arabia has also invested heavily in research and development in AI, and has implemented policies
to support the growth of the AI industry.
AI national strategies in developing countries :
Many developing countries are still in the early stages of developing and implementing AI national
strategies, as the technology is relatively new and can be expensive to implement. In addition,
developing countries face challenges such as limited access to technology and funding, as well as a
shortage of skilled workers with expertise in AI.
As a result, it is likely that the adoption of AI in developing countries will be slower compared to more
developed countries.
The use of AI benefits both developed and developing countries. However, the ways in which these
countries approach AI can be quite different. In general, developed countries have the resources and
infrastructure necessary to support the development and implementation of advanced AI technologies.
As a result, AI national strategies in these countries focus on using technology to improve efficiencies
and productivity in various industries, such as healthcare, finance, and transportation.
In contrast, developing countries have more limited resources and infrastructure, so their AI national
strategies tend to focus on using technology to address specific needs in their communities. For
example, a developing country prioritizes using AI to improve access to education or healthcare or to
promote economic growth. Additionally, developing countries are focused on using AI to help bridge the
gap between themselves and developed countries, in terms of technological advancement and
economic growth. Overall, the AI national strategies of developed and developing countries tend to
differ in terms of their focus and priorities.
The impact of application AI :
New technologies like artificial intelligence, machine learning, robotics, big data, and networks are
expected to revolutionize production processes, but they could also have a major impact on developing
economies.
Robots substitute for workers: The “artificial intelligence revolution” in our framework is an increase in
the productivity of robots.
The landscape is likely going to be much more challenging for developing countries which have hoped
for high dividends from a much-anticipated demographic transition.
Share-in-production: Advanced economies have higher wages because total factor productivity
is higher. These higher wages induce firms in advanced economies to use robots more
intensively to begin with, especially when robots easily substitute for workers. Then, when robot
productivity rises, the advanced economy will benefit more in the long run. This divergence
grows larger, the more robots substitute for workers.
Investment flows: The increase in productivity of robots fuels strong demand to invest in robots
and traditional capital (which is assumed to be complementary to robots and labor). This
demand is larger in advanced economies due to robots being used more intensively there (the
“share-in-production” channel discussed above). As a result, investment gets diverted from
developing countries to finance this capital and robot accumulation in advanced economies,
thus resulting in a transitional decline in GDP in the developing country.
Terms-of-trade: A developing economy will likely specialize in sectors that rely more on
unskilled labor, which it has more of compared to an advanced economy. Assuming robots
replace unskilled labor but complement skilled workers, a permanent decline in the terms of
trade in the developing region may emerge after the robot revolution. This is because robots will
disproportionately displace unskilled workers, reducing their relative wages and lowering the
price of the good that uses unskilled labor more intensively. The drop in relative price of its main
output, in turn, acts as a further negative shock, reducing the incentive to invest and potentially
leading to a fall not just in relative but in absolute GDP.
The negative impact of AI :
1. Loss of Certain Jobs : While many jobs will be created by artificial intelligence and many
people predict a net increase in jobs or at least anticipate the same amount will be created to
replace the ones that are lost thanks to AI technology, there will be jobs people do today that
machines will take over. This will require changes to training and education programmes to
prepare our future workforce as well as helping current workers transition to new positions
that will utilize their unique human capabilities.
2. A shift in Human Experience: If AI takes over menial tasks and allows humans to significantly
reduce the amount of time they need to spend at a job, the extra freedom might seem like a
utopia at first glance. However, in order to feel their life has a purpose, humans will need to
channel their newfound freedom into new activities that give them the same social and
mental benefits that their job used to provide. This might be easier for some people and
communities than others. There will likely be economic considerations as well when
machines take over responsibilities that humans used to get paid to do. The economic
benefits of increased efficiencies are pretty clear on the profit-loss statements of businesses,
but the overall benefits to society and the human condition are a bit more opaque.
3. Accelerated Hacking: Artificial intelligence increases the speed of what can be accomplished
and in many cases, it exceeds our ability as humans to follow along. With automation,
nefarious acts such as phishing, delivery of viruses to software and taking advantage of AI
systems because of the way they see the world, might be difficult for humans to uncover until
there is a real quagmire to deal with.
4. AI Terrorism: Similarly, there may be new AI-enabled form of terrorism to deal with: From
the expansion of autonomous drones and the introduction of robotic swarms to remote
attacks or the delivery of disease through nano robots. Our law enforcement and defense
organizations will need to adjust to the potential threat these present.
It will take time and extensive human reasoning to determine the best way to prepare for a
future with even more artificial intelligence applications to ensure that even though there is
potential for adverse impacts with its further adoption, it is minimized as much as possible. As
is the case with any disruptive event, these aren’t easy situations to solve, but as long as we
still have humans involved in determining solutions, we will be able to take advantage of the
many benefits of artificial intelligence while reducing and mitigating the negative impacts.
Robots and wages: Our results critically depend on whether robots indeed substitute
for workers. While it may be too early to predict the extent of this substitution in the
future, we find suggestive evidence that this is the case. In particular, we find that
higher wages coincide with significantly higher use of robots, consistent with the idea
that firms substitute away from workers and towards robots in response to higher labor
costs.
Improvements in the productivity of robots drive divergence between advanced and
developing countries if robots substitute easily for workers. In addition, those
improvements will tend to increase incomes but also increase income inequality, at least
during the transition and possibly in the long run for some groups of workers, in both
advanced and developing economies.
Challenges addressed referring to Davos 2025 Forum :
AI and energy consumption:
The energy demand of data centers, is projected to grow from 1% in 2022 to over 3% by 2030.
AI is already helping companies reduce energy use by up to 60% in some instances. Key use cases
include optimizing energy storage, battery efficiency, and smart grid management.
Coordinated efforts are needed to enable sustainable AI adoption across industries. Key focus areas for
action include regulation, financial incentives, technological innovation and market development.
While there have been numerous forecasts around the energy demands of artificial intelligence (AI) and
the efficiency gains it will unlock, it is hard to predict these with certainty, given the rapidly evolving
landscape.
A recently published white paper from the World Economic Forum titled Industries in the Intelligent Age
- Artificial Intelligence’s Energy Paradox: Balancing Challenges and Opportunities, suggests four key
interlinked areas for navigating this uncertainty, managing challenges and unlocking opportunities for
sustainable AI deployment.
These include:
1. Leveraging AI deployment for decarbonization.
2. Transparent and efficient AI energy use.
3. Innovation in technology and design.
4. Effective ecosystem collaboration.
5. AI’s energy consumption
AI presents opportunities and challenges in the energy landscape. With around 72% of surveyed
companies leveraging AI for at least one business function, its transformative potential is clear.
According to aggregated estimates from Accenture based on data from Goldman Sachs, the
International Energy Agency and the Organization for Economic Co-operation and Development,
accompanying this rise in adoption, AI-related electricity consumption can be expected to grow by as
much as 50% annually from 2023 to 2030, posing a challenge to power systems.
The electricity demand of data centres, is projected to grow from 1% of global energy demand in 2022
to over 3% by 2030.
However, such projections can vary. Uncertainty remains around how profound AI’s overall energy
impact will be and which strategies could mitigate challenges that arise or enable new solution
opportunities.
Despite AI’s rapid expansion, AI data centre electricity consumption will still likely account for only a
small fraction of global electricity demand.
However, when combined with other major demand drivers (such as the electrification of transport and
buildings), it can still contribute to an increased strain on power grids and energy providers.
To address this, strategies such as energy-efficient hardware, AI-optimized cooling, and smarter data
centre design and operations are being explored to limit AI’s energy consumption.
Surfacing, scaling and replicating successful use-case implementation strategies that deliver measurable
energy efficiency and optimization benefits can drive sustainable AI approaches and enable cross-
industry collaboration.
Regulatory, policy and financial enablers can incentivize responsible AI development through
compliance frameworks and funding mechanisms.
Industry players have highlighted the need for alignment towards harmonized metrics. For example,
decarbonization assessment tools and alignment between emerging voluntary industry standards with
government regulations (like the European Union’s AI Act).
Ongoing assessment will be critical to understanding AI’s net energy impact as its adoption accelerates
navigating this uncertainty and ensuring we manage the challenges and unlock the opportunities for
sustainable AI deployment. These four areas include (adaptable over time):
1. Leveraging AI deployment for decarbonization: Expand AI’s role in clean energy solutions, a
decarbonized energy grid and energy optimization.
2. Transparent and efficient AI energy use: Promote open data and optimize energy use in AI
development and operations.
3. Innovation in technology and design: Advance energy-efficient hardware and AI systems through
technological advancements and sustainable design principles.
4. Effective ecosystem collaboration: Foster sustainable AI through a collaborative ecosystem
involving regulators, industry players and academia.
Although AI's energy impact remains uncertain, proactively monitoring its evolving intersection can help
clarify challenges, uncover opportunities and guide transformative solutions.
AI and transportation field :
Freight logistics accounts for nearly half of all transport-related greenhouse gas emissions, contributing
8% to global emissions. This white paper explores how artificial intelligence (AI) can transform the
sector, reducing emissions by up to 15% through optimized operations, improved capacity use and shifts
to lower-carbon transport modes.
As global transport faces mounting pressure, AI emerges as a powerful tool to bridge the 5.5 gigatonne
emissions gap projected by 2050.
Ai and healthcare field :
We outlines the transformative potential of artificial intelligence (AI) in healthcare, identifying
opportunities to enhance care delivery, optimize operations and improve outcomes globally. As AI and
digital technologies radically change the way in which industries worldwide do business, healthcare
leaders have a choice to make: fundamentally transform how healthcare is accessed and delivered or
continue down the road of incremental improvements.
Despite advances, healthcare is below average in AI adoption at scale. Challenges such as complexity of
AI in health deterring policy-makers and business leaders , misalignment of technical choices with
strategic visions and low confidence in AI within a fragmented regulatory and governance framework ,
To overcome these challenges, scale AI and achieve transformative change, six transitional shifts are
needed for healthcare leaders to drive value creation as from dreaming of breakthroughs to delivering
near-term benefits that accelerates a long-term vision ,from the private sector progressing technology
independently to public private ecosystems driving shared objectives and benefits ,from fighting on
infrastructures to winning on services , from leaders with good intentions to leaders who make
responsible technical decisions , from waiting for guidelines to proactively building trust ,from dispersed
data to deliberate integration .
The Digital Healthcare Transformation Initiative, launched by the World Economic Forum’s Centre for
Health and Healthcare and the Boston Consulting Group, aims to support leaders to adopt these six
shifts to accelerate AI integration in healthcare and enhance health outcomes globally.
