AI Is Driving Markets Higher But Debt, Leverage and Concentration Are Raising New Risks
Published: 24 September 2026
Category: AI • Institutional Finance • Macro & Global Markets
By: Akinyele Oluwale & Co. Investment Ltd.
Artificial intelligence is transforming technology, business investment and global financial markets.
The Nasdaq has returned to record territory as investors price in stronger AI adoption, expanding corporate investment and future productivity gains. Yet beneath the market optimism, another story is developing: the AI boom is becoming increasingly dependent on enormous capital expenditure, debt financing, concentrated equity exposure and expectations of exceptional future earnings.
This does not prove that AI is a bubble. It does, however, mean that investors must distinguish between the strength of the technology and the price being paid for exposure to it.
A revolutionary technology can transform the economy and still produce disappointing investment returns when valuations, leverage and expectations move ahead of realised profits.
What Is Driving the AI Investment Boom?
The development of advanced AI requires more than software.
It depends on a rapidly expanding physical infrastructure consisting of:
* Semiconductor manufacturing;
* High-performance computing chips;
* Hyperscale data centres;
* Cloud-computing capacity;
* Electricity generation and transmission;
* Cooling systems;
* Fibre and network infrastructure; and
* Specialised technical talent.
These requirements have produced one of the largest technology-investment cycles in modern history.
Companies are spending heavily because they believe AI will become a foundational layer of the global economy. The potential applications extend across healthcare, banking, manufacturing, education, defence, logistics and professional services.
There is a credible economic case for substantial investment.
The financial question is whether the eventual revenue, productivity improvements and cash flows will justify the amount of capital being committed today.
Debt Is Becoming Part of the AI Story
Many leading technology companies entered the AI era with strong cash positions and relatively manageable debt. However, the scale of the required infrastructure means that internal cash generation may not fund every planned investment.
Companies are therefore turning increasingly to debt markets and alternative financing structures.
A Federal Reserve governor has acknowledged that firms are tapping debt markets to finance AI-related capital investment. The Federal Reserve has also noted that much of the evidence points to an economy reorganising around AI, although the measurable effects remain concentrated in particular areas rather than broadly distributed throughout the economy.
Debt is not inherently dangerous. It can be an efficient way to finance productive long-term assets.
The danger arises when:
* Borrowing grows faster than dependable cash flow;
* Projects are based on excessively optimistic demand forecasts;
* Technology changes before infrastructure costs are recovered;
* Financing depends on continuously favourable capital markets; or
* Investors underestimate the cost of maintaining and upgrading AI systems.
The AI boom is therefore becoming partly a credit-market story, not merely an equity-market story.
Concentration Is Increasing
A relatively small group of technology companies, chipmakers, cloud providers and infrastructure businesses account for a significant part of market performance and AI-related capital expenditure.
This creates concentration risk.
When a limited number of companies drive a disproportionate share of index returns, investors may believe they are diversified because they own a broad market fund. In reality, their portfolios may remain heavily exposed to the same AI investment theme.
Concentration can be rewarding while the leading companies continue delivering earnings growth.
It becomes dangerous when investors, passive funds, hedge funds and lenders are all exposed to similar assumptions. A change in those assumptions can trigger correlated selling across equities, derivatives and credit markets.
Leverage Can Amplify the Adjustment
Leverage allows investors to control larger positions with borrowed money. It can increase returns when markets rise, but it also magnifies losses when prices move against the position.
The Federal Reserve reported in May 2026 that hedge-fund leverage remained close to historical highs and was concentrated among a relatively small number of large funds.
This does not mean an AI-related financial crisis is inevitable.
It means that if highly valued AI assets experience a sharp reassessment, leveraged investors may be forced to reduce positions quickly. Margin calls and risk-limit breaches can turn an orderly correction into accelerated selling.
The risk is therefore not only that an individual technology stock declines. The wider concern is how losses could travel through funds, banks, derivatives, private-credit arrangements and other interconnected institutions.
Why Regulators Are Paying Attention
The Bank for International Settlements says AI and digitalisation are changing the nature of financial-stability risk. The Financial Stability Board has also identified vulnerabilities involving third-party dependency, correlated market behaviour, cybersecurity, model governance and concentration among technology providers.
Financial institutions increasingly depend on a limited number of cloud, data and AI-service providers.
This can create operational efficiency, but it also creates common points of failure. If many institutions depend on the same models, datasets or technology providers, an error or disruption may affect several organisations simultaneously.
AI can also encourage correlated decision-making. When financial institutions use similar data and models, they may reach similar conclusions and execute similar trades at the same time.
Technology designed to improve decision-making could therefore increase systemic risk if it reduces diversity in market behaviour.
Innovation and Valuation Are Different Questions
Investors often make a critical mistake during periods of technological change: they assume that believing in the technology requires buying related assets at any price.
It does not.
Three separate questions must be considered:
1. Will AI transform the economy?
The evidence increasingly suggests that it will.
2. Which companies will capture the economic value?
This remains uncertain because technological leadership, competition and business models can change.
3. Are current asset prices justified by future cash flows?
That is a valuation question, not a technology question.
A company can participate in a major technological revolution and still become a poor investment if its shares are purchased at an excessive valuation.
What Investors Should Examine
Investors assessing AI-linked companies should look beyond revenue growth and headline announcements.
Important indicators include:
* Free cash flow after AI capital expenditure;
* Return on invested capital;
* Debt growth and interest coverage;
* Data-centre utilisation;
* Customer demand and contract duration;
* Dependence on a small number of suppliers;
* Energy and cooling costs;
* Competitive pricing pressure;
* Share-based compensation;
* Valuation relative to realistic earnings; and
* Exposure to regulatory or geopolitical restrictions.
The central question is not simply how much a company is investing in AI.
It is whether each additional unit of investment is producing an adequate economic return.
What to Watch Next
The next stage of the AI investment cycle will be determined by execution.
Investors should monitor:
* Whether AI revenue grows fast enough to justify capital expenditure;
* The amount and structure of new technology-sector debt;
* Credit spreads on AI-related corporate bonds;
* Profitability of data centres and cloud-computing services;
* Market concentration among the largest technology companies;
* Bank exposure to leveraged non-bank institutions;
* Energy availability and infrastructure constraints;
* AI regulation and cybersecurity requirements; and
* Whether productivity gains spread beyond the technology sector.
The Investor’s Perspective
AI may become one of the most important technologies of this century.
That conclusion does not remove the need for valuation discipline, diversification and risk management.
The greatest investment danger may not be failing to recognise the importance of AI. It may be recognising its importance but paying a price that assumes every optimistic forecast will be achieved.
Investors should participate with discipline rather than fear of missing out.
Technological transformation creates opportunities. Financial excess determines who keeps the returns.
Akinyele Oluwale & Co. Investment Ltd.
Where Global Finance Meets Tomorrow’s Technology.
Visit akinyeleoluwale.finance for institutional analysis of artificial intelligence, digital finance and global markets.
