Artificial intelligence is transforming the technology industry, but the race to build more powerful AI systems is creating a major financial challenge: how to pay for it all.
Technology companies are investing heavily in data centres, advanced chips, cloud infrastructure and the energy systems needed to support AI. While some of these investments are funded through existing cash reserves, companies are increasingly turning to debt markets to finance their expansion.
Former UK Prime Minister Rishi Sunak has warned that technology companies could become larger net issuers of long-term debt than the US government by 2027. His forecast highlights how rapidly AI investment is changing the global financial landscape. The Bank of England has also raised concerns about the growing scale of technology companies’ borrowing.
For businesses, investors and technology enthusiasts, the question is no longer just how advanced AI will become. It is also whether the financial returns from this technology will justify the enormous cost of building it.
Why AI Is Pushing Technology Companies to Borrow More
Artificial intelligence requires more than software development. Behind every large-scale AI service is a complex network of computing infrastructure, data storage, electricity supply and high-performance hardware.
Building this infrastructure requires substantial capital before companies can generate revenue from it.
1. Data centres require enormous investment
AI models need powerful processors to train, operate and respond to user requests. As demand for AI services grows, technology companies are expanding their data centre capacity.
These facilities require significant spending on:
- Advanced AI chips and servers.
- Buildings, cooling systems and networking equipment.
- Reliable electricity supplies and backup power.
- Cloud computing infrastructure and data storage.
- Security systems and ongoing maintenance.
Unlike some digital products, which can reach new customers with relatively low additional infrastructure costs, AI infrastructure requires continued physical investment.
Consequently, companies may need to raise external financing even when their existing businesses remain profitable.
2. The AI race is increasing competition
Major technology companies are competing to build AI models, cloud platforms and enterprise services.
Companies that delay investment risk losing customers and market share to competitors with greater computing capacity.
This creates pressure to spend now, even when the full commercial benefits may take years to materialise.
According to a Reuters report on AI-related debt issuance, Morgan Stanley projected in June 2026 that global AI-related debt issuance could approach $570 billion during the year.
The forecast illustrates the scale of financing demand associated with the AI infrastructure boom. However, actual borrowing can change as market conditions, investment plans and investor appetite evolve.
Could Technology Companies Issue More Long-Term Debt Than the US Government?
Sunak’s warning focuses on a potentially significant change in capital markets.
Governments regularly issue bonds to finance public spending, refinance existing obligations and manage their fiscal requirements. Technology companies, meanwhile, issue corporate bonds to fund business expansion and other investment needs.
If AI investment continues to increase, technology companies could become a dominant source of demand for long-term financing.
However, understanding this prediction requires an important distinction: net debt issuance is not the same as total outstanding debt.
Net issuance generally measures new borrowing after accounting for debt repayments or other relevant reductions. Therefore, a sector can issue substantial amounts of debt without necessarily becoming the largest holder of outstanding debt.
Sunak’s forecast also depends on the period being measured, the definition of technology companies and the types of government and corporate debt included.
The prediction should therefore be understood as a warning about a possible shift in borrowing patterns, rather than a confirmed outcome for 2027.
How Big Tech Is Financing the AI Boom
Technology companies have several options for funding AI infrastructure. The right approach depends on their cash flow, credit rating, investment plans and willingness to take financial risks.
Corporate bonds
Corporate bonds allow companies to borrow money from investors and repay it according to agreed terms.
For established technology companies, bond issuance can provide access to large pools of capital without requiring them to sell additional shares.
However, companies must pay interest and repay principal when due. If borrowing costs rise, refinancing existing debt or issuing new bonds can become more expensive.
Private credit and alternative financing
Some AI infrastructure projects use private credit, investment partnerships and specialised financing arrangements.
These structures can help companies fund expensive equipment and data centres. Nevertheless, they may also make it more difficult for investors to understand where risks ultimately sit, particularly when several companies are connected through financing agreements.
A UBS analysis of AI-related debt structures explains how borrowing, chip ownership and infrastructure leasing can create interconnected financial relationships.
Internal cash flow
Companies can also use money generated by their existing businesses.
This reduces the need to borrow, but heavy capital expenditure can leave less cash available for acquisitions, research, dividends and other priorities.
For this reason, even companies with strong revenues must balance their AI ambitions against their wider financial obligations.
What Does Rising Technology Debt Mean for Financial Markets?
An increase in technology-sector borrowing could affect investors, businesses and the wider economy in several ways.
1. Greater competition for investor capital
Technology companies and governments often seek financing from overlapping groups of investors.
If corporate bond issuance rises sharply, investors may demand higher yields to compensate for the additional supply or the risks associated with particular borrowers.
This does not mean government borrowing costs must automatically rise. Interest rates also depend on inflation, central bank policy, economic growth, government finances and investor demand.
Nevertheless, a large increase in corporate borrowing can change the balance of supply and demand in credit markets.
2. Higher financing costs for businesses
When market interest rates rise, businesses may face more expensive loans and bond financing.
Smaller companies can be particularly vulnerable because they often have fewer funding options than large technology corporations.
For example, a startup planning to expand its cloud infrastructure might postpone hiring or product development if the cost of borrowing increases.
Likewise, an established business considering an AI upgrade may need to reassess whether the expected productivity gains justify the financing costs.
3. Greater scrutiny of AI profitability
Investors are increasingly interested in whether AI spending can generate sustainable revenue and profits.
A company may invest billions in computing infrastructure, but that spending alone does not guarantee commercial success.
Businesses must still attract paying customers, manage operating costs and develop products that solve real problems.
The central financial question is whether future cash flows will be sufficient to cover infrastructure costs, interest payments and other obligations.
What Happens If AI Returns Fall Short of Expectations?
AI could create significant economic value. However, the timing and distribution of those benefits remain uncertain.
If demand for AI services grows more slowly than expected, companies that have borrowed heavily to build infrastructure could face pressure.
Potential consequences include:
- Lower profitability: Revenue from AI products may not cover the cost of infrastructure and financing.
- Delayed projects: Companies could postpone data centre construction or reduce planned investment.
- Investor losses: Corporate bond prices and technology stocks could come under pressure if financial expectations deteriorate.
- Refinancing challenges: Borrowers may face higher interest rates or stricter lending conditions when existing debt matures.
- Reduced spending elsewhere: Companies may cut costs in other business areas to protect cash flow.
These outcomes are risks, not certainties. Strong AI adoption, disciplined spending and improved operating efficiency could support companies’ ability to repay their obligations.
The key is whether investment decisions are supported by realistic revenue expectations rather than competition alone.
What Businesses and Entrepreneurs Should Learn From the AI Debt Boom
The growing cost of AI infrastructure offers practical lessons for businesses of every size.
Invest in AI with a clear business objective
Businesses should identify the problem an AI tool is expected to solve before committing significant resources.
Possible objectives include reducing repetitive work, improving customer support, accelerating software development or making internal information easier to access.
A defined objective makes it easier to measure results and decide whether further investment is justified.
Calculate the total cost of adoption
The price of an AI subscription may be only one part of the overall expense.
Businesses should also consider integration, employee training, data security, maintenance and ongoing usage costs.
For example, a small company might begin with an affordable AI customer-support tool before investing in a customised system.
This approach allows the business to test its value before committing more money.
Protect cash flow
AI investment should not come at the expense of essential business operations.
Before borrowing to fund new technology, business owners should assess repayment obligations, potential revenue gains and the consequences of slower-than-expected adoption.
A phased investment strategy can reduce exposure while allowing the business to learn from early results.
Measure outcomes, not hype
Useful measures include time saved, operating costs reduced, customer satisfaction and additional revenue generated.
If an AI project cannot demonstrate meaningful benefits, the business should reconsider its scope or approach.
The broader lesson is simple: technology investment works best when innovation and financial discipline develop together.
Frequently Asked Questions
Why are technology companies borrowing money for AI?
Technology companies need substantial funding to build data centres, purchase specialised chips, expand cloud services and develop AI products. Borrowing allows them to finance these investments while preserving some of their existing cash.
Could technology companies become larger net issuers of long-term debt than the US government?
Former UK Prime Minister Rishi Sunak has forecast that this could happen by 2027. It remains a prediction, and the outcome depends on corporate borrowing, government debt issuance, repayments and the definitions used in the comparison.
Does rising AI debt mean the technology industry is in a bubble?
Not necessarily. Borrowing can support productive investment, but excessive debt can create vulnerabilities if revenue fails to meet expectations. Assessing whether valuations are sustainable requires examining company earnings, cash flow, financing costs and future demand.
How could AI-related borrowing affect ordinary businesses?
Higher financing costs could make loans, equipment purchases and technology upgrades more expensive. However, AI tools could also help businesses improve productivity and reduce certain operating costs.
Should businesses stop investing in AI because of rising debt?
No. Businesses should evaluate AI investments based on their objectives, budgets and expected returns. Starting with manageable projects and measuring results can help companies capture benefits without taking unnecessary financial risks.
Conclusion: AI’s Next Challenge Is Financial
The AI boom is creating a new challenge for the technology industry. Companies must not only develop increasingly powerful systems but also finance the infrastructure required to operate them.
Rishi Sunak’s warning about technology companies potentially outpacing the US government in net long-term debt issuance by 2027 highlights the scale of this transformation.
Yet borrowing is not inherently a problem. The more important questions are how efficiently companies use the capital, whether AI generates sustainable returns and whether debt remains manageable if market conditions change.
For entrepreneurs, professionals and business leaders, the lesson is to approach AI with both ambition and financial discipline.
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