Artificial intelligence is usually discussed as a technology story.
The bigger story may increasingly be a debt story.
Building AI infrastructure requires enormous amounts of capital: data centers, GPUs, electricity generation, transmission networks, fiber, cooling systems and land. As companies race to build that infrastructure, borrowing is becoming an increasingly important source of financing.
The Bank of England says AI companies' use of debt accelerated sharply in the first half of 2026. Morgan Stanley estimates that more than half of external financing for global data-center investment between 2026 and 2028 could come from debt.
That matters far beyond Silicon Valley.
Because the AI infrastructure boom is increasingly reaching emerging markets—and it could create both a major development opportunity and a new source of financial vulnerability.
AI Is Becoming a Capital-Intensive Industry
The first phase of the AI boom was largely about software and models.
The next phase is physical.
AI requires extraordinary computing power, and computing power requires physical infrastructure.
A modern AI data center needs:
- GPUs and other accelerators
- massive electricity supplies
- high-capacity fiber networks
- advanced cooling systems
- backup generation
- land
- buildings
- transformers and grid connections
- cybersecurity infrastructure
- skilled technical workers.
The scale of investment is already enormous.
The IMF estimates that AI-related capital expenditure could reach $3.4 trillion through 2029. Major hyperscalers have already raised more than $100 billion through bond financing since January 2025, supplemented by loans and other financing structures.
This changes the financial character of AI.
AI is no longer simply a startup industry looking for venture capital.
It is becoming an infrastructure industry capable of absorbing hundreds of billions of dollars in debt and equity capital.
Why Emerging Markets Matter
The AI infrastructure race is running into a physical constraint.
The world's largest technology companies need more electricity, land and data-center capacity.
But established technology hubs face limitations involving grid capacity, land availability and permitting.
That is pushing investors toward new locations.
The Atlantic Council says investment is increasingly moving toward emerging markets across Southeast Asia, the Middle East, Latin America and Africa, where investors can find land, renewable-energy potential, stranded energy and growing digital demand.
This could be transformative.
A country that previously exported commodities could potentially become a destination for digital infrastructure.
Instead of exporting only oil, gas, minerals or agricultural products, emerging economies could increasingly export something else:
computing capacity.
But AI Infrastructure Comes With a Debt Problem
There is a major difference between borrowing to build an infrastructure project that produces predictable cash flows and borrowing billions to build infrastructure whose future demand is still uncertain.
AI infrastructure sits somewhere in between.
Data centers can have long-term contracts with technology companies.
But technology itself evolves extremely quickly.
A GPU purchased today can lose economic value faster than conventional infrastructure because newer chips can deliver substantially better performance per unit of energy and cost.
The Bank of England has warned that growing AI debt financing creates risks around companies' ability to service debt if expectations for future earnings deteriorate.
The BIS has also highlighted a phenomenon it calls “shadow borrowing.”
Companies can finance data centers through special-purpose vehicles and joint ventures, with debt sitting outside the main company's balance sheet while the technology company commits to long-term leases or capacity agreements.
That structure can make financing more flexible.
It can also make the underlying financial risks harder to see.
The Emerging-Market Version Could Be Even More Complicated
For wealthy technology companies, borrowing billions for AI infrastructure can be supported by strong balance sheets and investment-grade credit ratings.
Emerging markets often do not have that luxury.
They may face:
- higher interest rates;
- weaker currencies;
- limited domestic capital markets;
- electricity shortages;
- political and regulatory risk;
- expensive imported equipment;
- smaller pools of institutional capital;
- and higher country-risk premiums.
The IMF's research on AI in Sub-Saharan Africa highlights the problem.
AI deployment requires substantial upfront capital, while unreliable electricity, limited broadband, fragmented markets and higher perceived investment risks can constrain financing. The IMF also notes that private infrastructure investment in the region frequently requires public support such as co-financing or guarantees.
That creates an important question:
Who ultimately carries the debt when an emerging market wants to build AI infrastructure?
A technology company?
A private infrastructure fund?
A development bank?
A local government?
A sovereign government?
Or taxpayers?
The Opportunity: Turning Energy Into Compute
For some emerging economies, the AI boom could create an entirely new economic model.
Consider a country with:
- abundant renewable energy;
- natural gas;
- large amounts of available land;
- underutilized power generation;
- subsea-cable access;
- and a growing technology workforce.
Historically, those resources might have supported traditional industries.
AI creates another possibility:
use the energy to produce computing services.
Instead of simply exporting electricity or natural resources, countries could use them to support data centers and digital infrastructure.
This is particularly significant for Africa.
The continent has enormous renewable-energy potential but remains severely under-electrified in many regions.
If AI investment is structured correctly, new data-center infrastructure could potentially accelerate investment in power generation, fiber networks and grid infrastructure.
But there is a catch.
AI Data Centers Can Compete With Ordinary Consumers for Electricity
A country cannot simply celebrate a billion-dollar data center investment.
It has to ask:
Where does the electricity come from?
If a massive AI data center receives priority access to limited electricity, what happens to households and local businesses?
If governments provide subsidized power to attract technology companies, who absorbs the subsidy?
If taxpayers finance new transmission infrastructure, who owns it?
And if the AI facility creates relatively few jobs compared with the amount of capital invested, is the economic return sufficient?
These questions are particularly important in countries where electricity access remains inadequate.
AI infrastructure should therefore not be evaluated only by the size of the investment announcement.
The more important metric is how much productive economic capacity the investment creates for the wider economy.
The Debt Can Create a Development Trap
There is a dangerous scenario.
An emerging-market government borrows heavily to build AI infrastructure.
The government assumes AI demand will continue rising.
It builds power plants, transmission lines, data centers and fiber networks.
The infrastructure attracts foreign technology companies.
Then AI economics change.
Perhaps computing prices fall dramatically.
Perhaps more efficient chips reduce demand for older facilities.
Perhaps hyperscalers build fewer facilities than expected.
Perhaps a recession reduces AI investment.
Perhaps the expected customer never signs the long-term contract.
The infrastructure remains.
But the debt remains too.
This is the classic infrastructure-financing problem:
The asset has a long life. The technology driving its economic value may not.
There Is Also a Currency Risk
This issue is particularly important for emerging markets.
AI hardware is generally purchased in major reserve currencies, especially U.S. dollars.
Debt financing may also be denominated in dollars.
But revenues from domestic users may be generated in local currency.
That creates a mismatch.
Imagine an African company borrows $500 million to build an AI data center.
If its revenue is primarily denominated in local currency and that currency depreciates sharply, the company's dollar debt becomes more expensive in real terms.
The same problem can affect governments that provide guarantees.
AI infrastructure therefore introduces not only technology risk but also foreign-exchange risk.
The China Question
Emerging markets also have another option: Chinese capital and technology.
China has become a major supplier of telecommunications infrastructure, renewable energy equipment, batteries, servers and other digital infrastructure.
For developing countries, Chinese financing can provide an alternative to Western capital markets.
But that can create strategic questions around:
- data sovereignty;
- ownership;
- cybersecurity;
- technology standards;
- equipment dependence;
- and long-term control of critical infrastructure.
The AI infrastructure race is therefore becoming part of a larger geopolitical competition over digital infrastructure.
For emerging economies, the challenge is not simply finding money.
It is finding capital without surrendering excessive control over strategic infrastructure.
The “AI Debt” Problem Could Spread Through Financial Markets
The implications do not stop at technology companies.
The BIS warns that AI infrastructure financing increasingly connects hyperscalers with banks, private-credit funds, insurers and other institutional investors.
That means an AI investment slowdown could eventually affect parts of the broader financial system.
Imagine a chain:
Investor → private-credit fund → data-center project → AI company → cloud customer.
If AI demand weakens, problems can move backward through that chain.
The IMF has similarly warned that the enormous projected capital requirements of AI could create balance-sheet pressures if future earnings and cash buffers fail to keep pace with investment.
For now, major hyperscalers remain much stronger borrowers than most emerging-market companies.
But the infrastructure financing ecosystem is expanding.
That means regulators need to watch not just how much AI debt exists, but where the debt sits.
Africa Faces a Particularly Interesting Choice
Africa could either become a major beneficiary of the AI infrastructure cycle or remain primarily a consumer of foreign AI services.
The difference will depend heavily on infrastructure ownership.
If African countries simply provide land and electricity while foreign companies own the data centers, GPUs, cloud platforms and intellectual property, much of the value may leave the continent.
But if local companies, governments and regional institutions participate in ownership, financing and service delivery, the economic benefits could be broader.
That means the conversation should move beyond:
“How can Africa attract AI companies?”
toward:
“How can Africa own part of the AI infrastructure it attracts?”
That is a fundamentally different development strategy.
The AfCFTA Opportunity
For Africa, this is where regional integration becomes important.
A single African market of more than a billion people provides a much stronger economic proposition than dozens of fragmented national markets.
AI companies need customers.
Cloud providers need scale.
Data centers need predictable demand.
Infrastructure investors need long-term contracts.
A genuinely integrated African digital market could make the continent more attractive for AI infrastructure investment while allowing African companies to capture more of the value chain.
But fragmentation remains a major obstacle.
Different regulations.
Different currencies.
Different data rules.
Different electricity markets.
Different tax regimes.
Different digital standards.
The AI economy rewards scale.
Africa needs to create that scale internally.
Borrowing Isn't the Enemy
There is an important distinction here.
Debt itself is not necessarily bad.
Infrastructure has historically been built with debt.
Airports, telecommunications networks, power plants, highways and data centers can all require borrowing.
The question is whether the expected economic return justifies the financing structure.
Good AI borrowing could finance infrastructure that produces long-term revenue and strengthens the wider economy.
Bad AI borrowing could finance expensive infrastructure based on unrealistic assumptions about future demand.
The difference comes down to:
who borrows, what they build, how they repay it and who ultimately owns the asset.
What Emerging Markets Should Watch
Countries considering AI infrastructure investment should pay particular attention to several issues.
1. Debt currency
Borrowing in dollars while earning revenue in local currency can create substantial foreign-exchange risk.
2. Long-term contracts
Infrastructure should ideally have credible customers and contractual revenue before enormous amounts of debt are accumulated.
3. Power economics
Cheap electricity is an advantage only if it remains available after accounting for households, industry and future demand.
4. Technology obsolescence
Data centers may last decades, but computing hardware can become obsolete much faster.
5. Ownership
Governments should understand who owns the land, power infrastructure, data centers and digital networks after construction.
6. Sovereign guarantees
Government guarantees can transfer private-sector risks onto taxpayers.
7. Local value creation
The objective should not be simply attracting foreign capital. It should be building local capabilities, companies, workers and intellectual property.
The New Emerging-Market Resource May Be Compute
For decades, the development model for many emerging economies was built around commodities.
Oil.
Gas.
Copper.
Iron ore.
Lithium.
Agricultural products.
The digital economy creates another possibility.
Compute.
A country with cheap energy, strong connectivity and reliable infrastructure can potentially turn electricity into a globally traded digital service.
That could become one of the most important economic opportunities created by AI.
But there is a difference between hosting AI infrastructure and owning AI infrastructure.
The first creates activity.
The second can create strategic economic power.
The AI Boom Is Also a Global Borrowing Experiment
The most important financial question surrounding AI may no longer be whether companies can raise enough money.
They clearly can.
The question is whether the future revenues generated by AI will be large enough to justify the enormous capital being committed today.
The Bank of England has already identified rapidly growing AI debt financing as an emerging financial-stability issue, while the BIS is tracking increasingly complex financing structures around data centers.
For emerging markets, the stakes are even higher.
They have the opportunity to attract billions of dollars into electricity, connectivity and digital infrastructure.
But they also face greater borrowing costs, currency risks and weaker financial buffers.
The wrong financing model could leave countries with expensive infrastructure and long-term debt.
The right model could turn abundant energy, land and connectivity into a new export industry.
That is why the next AI race will not simply be about who builds the smartest model.
It will increasingly be about who owns the infrastructure, who finances it, who carries the debt—and who captures the economic value.
For emerging markets, that may be the most important AI question of all.
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