How the rapid spread of AI is leaving developing countries behind: AI is advancing at such a rate that many nations are unable to keep up. New AI technologies are transforming education, health care, commerce, research, agriculture, and public services. However, while some nations are pouring resources into digital infrastructure, research, and AI ecosystems, others are finding it difficult to lay the foundations.
Now that widening chasm has set the UN to work.
UN Secretary General Antonio Guterres has been pushing for the establishment of a $3 billion Global Fund on AI in an effort to support developing nations to develop the skills, data, low-cost computing capacity, and broader tech ecosystem necessary for them to compete in the AI era. In February 2026, Guterres initially made the publicly announced $3 billion request in a call to establish this fund at the India AI Impact Summit. A UN report in September 2026 later called for establishing this fund.
On top of that, the idea raises an obvious but crucial question: Why neglect to support nations that lack the ability to develop their own AI systems?
The AI divide is becoming more than a technology problem.
Most of us think about the digital divide as access to the internet, mobile phones, or computers. The AI divide is more complex.
You may have the AI chatbot, but you don’t have the data or the scale of things to be able to build your own AI, train models, work with large datasets, or run the infrastructure needed for such. Would you?
There are many more ingredients to a healthy AI ecosystem than just software. Countries require electricity, internet access, computing infrastructure, high-quality data, talent, research, and organizations that know how to harness technology for good.
The report from the UN in September points to stark variations between these grounds. The World Bank in June 2025 reported that high-income countries hosted 77% of all the world’s shared-co-location data center capacity and low-income countries less than 0.1%. On AI, it found that only 20% of the world’s least developed countries had a national AI plan in 2025.
This is why the debate about access to AI is moving.
It is no longer just the question of “Can someone use AI?.
The defining questions now become who has the infrastructure, expertise, and resources to decide how AI is built and applied.
Why is the UN proposing a $3 billion fund?
Guterres has said that AI should not be “a technology owned or even dictated by just a few countries and firms.”
Speaking at India’s AI Impact Summit in New Delhi in February, he warned that if countries don’t make investments, many could be “logged out” of the AI era. He proposed a Global Fund on AI to develop the most basic AI capabilities in developing nations—and in particular the skills, data, cheap computing power, and inclusive tech ecosystems they need.
The dollar figure itself is just $3 billion, but that’s not what matters.
The report on the UN in September says the amount mentioned is an indicative initial amount, indicative initial contribution levels of around $3 billion over two to four years. It is not any kind of declaration that a few billion have been raised or disbursed. It suggested that the General Assembly begin establishing the fund and defining its purpose, governance, and administration.
In addition, the fund will work in conjunction with the current funding arrangements rather than take their place.
Put another way, we want to generate an additional coordinated investment stream to address those fillable gaps that the current offer doesn’t satisfy in every case.
What would developing countries actually need?
The most common false belief about closing the AI gap is that nations require access to superior AI algorithms.
In reality, the foundations are much broader.
Digital public infrastructure, including data, computing, and connectivity, and fresh talent to deploy and refine AI.
But technical infrastructure alone is not enough.
Governments and institutions require the capability to formulate rules, put protections in place, assess AI systems, purchase technology, and know how those systems impact people.
So the UN report frames AI capacity-building as the investment in the underlying infrastructure and institutional mechanisms to deploy AI effectively.
This could be especially crucial for countries that have quality talent and high-quality local ideas but do not have the deep pockets to mass deploy AI solutions.
What does this mean for NGOs?
The fund might be especially useful for NGOs.
Development nonprofits are already exploring how AI might assist them in raising funds, researching, communicating, analyzing data, translating, planning programs, and delivering services. However, the potential to test AI is not the same as having the capacity to deploy it effectively.
A community organization with small staff and limited technology. Such an organization may know its community from top to bottom but not have the financial or technical capacity to build complex digital systems.
Meanwhile, a larger international organization might have a number of dedicated tech teams, consultants, cloud infrastructure, and data tech to play with.
Both groups could be excited by AI, but they’re not exactly on equal footing.
That’s part of the reason capacity-building of AI is so important to the nonprofit sector.
AI could help NGOs tackle local challenges.
Consider an NGO that already has some farmers as participants.
AI, given the right equipment and data from the country, can support the organization with the analysis of agricultural information, from the identification of risks to the dissemination of relevant information for farmers.
A nonprofit in the healthcare industry might leverage data tools to analyze service demand and help map out outreach strategies.
An education organization could investigate local language AI-supported learning resources.
A humanitarian organization might use data analysis to predict evolving needs in crisis situations.
These possibilities are contingent upon more than simply acquiring an AI subscription.
Companies require trained personnel. They require dependable data. They require secure systems. They require people who understand how to determine whether the result of an AI output is real and useful.
They require safeguards for access to sensitive data.
That’s where building capacity may be a more valuable resource than providing organizations with individual AI applications.
The danger of creating a new digital divide
Another worry created by a growing AI market is that of the economic gains from AI being concentrated in countries that already have robust digital infrastructure.
This story is from: More stories on: “Even after US$580 billion of investment in AI infrastructure (on a rolling average basis) and a lot more capacity gaps in developing countries, a needs-based fund is needed to mobilize and target resources,” the UN report said.
Developing countries might end up as just consumers of technology designed by other people if they don’t get access to AI infrastructure and skills.
That could create long-term dependencies.
A nation with a thriving ecosystem could create AI, control the underlying infrastructure, and attract investment, while a nation with a poor ecosystem might lack influence over the design or application of those systems.
This is of concern to development organizations, because technology increasingly has an impact in the same areas of work as NGOs: education, health, agriculture, employment, humanitarian aid, and public services.
Local knowledge needs to remain at the center.
Building AI capacity should not mean simply bringing technology into developing countries and expecting it to solve local problems.
Local knowledge matters.
An AI system developed using data from one population may not automatically work well for another. Languages, cultural practices, economic conditions, and community priorities can be very different.
For NGOs, this is especially important.
An organization working directly with communities often understands details that may not appear in a dataset. Staff members may know why a program works in one village but not another. They may understand cultural sensitivities that an automated system cannot recognize.
That is why AI capacity should include the ability to evaluate, adapt, and govern technology, not simply use it.
The goal should be to give countries and organizations greater control over how AI is applied to their own challenges.
Why skills may be just as important as computing power
A powerful computer cannot solve a problem if nobody knows how to use it effectively.
This is why skills are such an important part of the proposed fund.
Developing countries need researchers, engineers, data specialists, policymakers, educators, and professionals who understand both the opportunities and risks associated with AI.
NGOs need another layer of skills.
Their teams may not need to become AI engineers, but they increasingly need people who understand how to use AI responsibly in fundraising, communications, program management, and research.
They also need to know when not to use AI.
That judgment can be just as important as technical knowledge.
For example, an NGO may be comfortable using AI to help organize public information but decide that highly sensitive beneficiary information should remain under stronger human control.
Responsible AI requires that kind of decision-making.
What could the fund mean for smaller NGOs?
Large international organizations are often better positioned to experiment with new technology because they have larger budgets and specialized teams.
Smaller NGOs face a different reality.
They may be working on extremely important local problems while operating with limited staff and funding.
For these organizations, even relatively small investments in technology training, data systems, or digital infrastructure could make a meaningful difference.
Support could help organizations train staff, improve their internal systems, develop locally relevant applications, or collaborate with universities and technology organizations.
It could also give smaller organizations an opportunity to participate in conversations about how AI should be used in their communities rather than simply receiving technologies designed elsewhere.
That could make AI development more inclusive.
This is also an opportunity for funders.
The proposal could eventually create new opportunities for foundations, governments, technology companies, and development organizations to support AI capacity-building.
Instead of focusing only on donating software or providing temporary access to AI platforms, funders could invest in the foundations that allow organizations to use technology over the long term.
That might include supporting digital infrastructure, technical training, local research, responsible data systems, AI governance, and community-focused technology projects.
For NGOs, this could also open new possibilities for partnerships between civil society, universities, governments, and technology companies.
The UN’s Pact for the Future already calls for increased investment, including from the private sector and philanthropy, to scale AI capacity-building for sustainable development.
AI should not become another source of inequality.
The promise of AI is enormous.
It could help improve healthcare, expand educational opportunities, support farmers, strengthen disaster preparedness, and make public services more accessible.
Guterres highlighted many of these potential benefits during his February 2026 address, while also warning that AI could deepen inequality, amplify bias, and create new forms of harm if its development is not handled responsibly.
That is the central challenge.
AI itself is not automatically inclusive or unequal.
The way societies invest in it, govern it, and distribute access will influence who benefits.
If only wealthy countries and large companies had the infrastructure and expertise needed to build advanced AI systems, existing inequalities could become harder to overcome.
But if more countries gain the capacity to develop and adapt AI according to their own needs, the technology could become a much broader development tool.
What happens next?
The proposed $3 billion fund is still a proposal, not an established pool of money that is already operating.
The UN’s September report recommends that the General Assembly advance work on the fund’s strategic objectives, governance, and administration. It also recommends regular reporting on AI capacity across countries and expanding the UN-supported Global Network for Exchange and Cooperation on AI Capacity-Building.
So there is still a lot to determine.
Who contributes?
Which countries receive support?
How will funding decisions be made?
How will projects be measured?
How will local communities and civil society participate?
And perhaps most importantly, how can the funding ensure that countries develop their own long-term capacity instead of becoming dependent on outside technology?
These questions will matter as much as the size of the fund itself.
The bigger picture
The debate over the proposed Global AI Fund is ultimately about much more than artificial intelligence.
It is about who gets the opportunity to participate in the next major technological transformation.
AI is increasingly becoming part of the infrastructure of modern economies. It is influencing how businesses operate, how governments provide services, and how organizations approach problems.
If developing countries do not have access to the skills, computing power, data, and institutions needed to participate, the gap between AI leaders and AI users could become another dimension of global inequality.
For NGOs, this conversation is particularly important because they work directly with the communities that are often most affected by unequal access to technology.
The goal should not be to make every NGO an AI company.
It should be to make sure that NGOs, governments, communities, and local innovators have enough capacity to decide where AI can genuinely improve people’s lives, how it should be used, and where human judgment must remain at the center.
The UN’s proposed $3 billion fund is one possible step in that direction.
The bigger question is whether the world can build an AI ecosystem in which developing countries are not simply users of technology created elsewhere but active participants in shaping what comes next.
Because the future of AI will not only be about how powerful the technology becomes.
It will also be about who has the opportunity to use it, shape it, and benefit from it.
