China’s financial regulator has warned that the global rush to finance artificial intelligence is concentrating capital in a relatively small group of technology companies, driving up leverage and borrowing costs while making it harder for traditional industries to secure funding.
Xiao Yuanqi, vice head of China’s National Financial Regulatory Administration, raised the concerns on Tuesday at the Hong Kong Monetary Authority’s Global Financial Leaders’ Summit, arguing that the scale and concentration of AI investment could create risks beyond the technology sector.
“A massive influx of capital into the technology sector and leading firms has driven up leverage levels within these areas, potentially fueling excessively rapid expansion,” Xiao said.
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He warned that the concentration of investment was creating a crowding-out effect on traditional industries by raising their borrowing costs and increasing their financial burden. Higher financing expenses, he said, could weaken companies’ ability to reinvest, expand production and sustain business growth, while exposing lenders to greater credit risks.
The remarks highlight a growing concern among financial policymakers that the AI investment boom could produce imbalances in capital markets. Technology companies developing advanced AI models, data centers, chips, and cloud infrastructure are attracting enormous funding commitments. However, the concentration of financing in these areas could leave businesses in other sectors facing more expensive credit and reduced access to capital.
The issue has drawn significant attention because traditional industries remain central to employment, industrial production and economic activity. Manufacturers, transport operators, construction companies and other businesses require regular financing to maintain operations, upgrade equipment and expand capacity. If lenders and investors increasingly favor technology companies, established businesses could face higher financing costs even when their projects remain commercially viable.
Xiao’s warning points to two related risks: excessive borrowing among technology companies and the potential displacement of investment in other parts of the economy.
The first concerns the scale of capital being directed towards AI infrastructure. Building data centers, securing electricity supplies, purchasing advanced processors, and developing computing capacity require substantial upfront investment. Companies are increasingly relying on debt and other financing arrangements to fund these commitments, creating exposure to higher interest costs and the possibility that expected returns may not materialize quickly enough.
When large amounts of capital flow into a narrow group of companies and projects, valuations and financing commitments can rise faster than the underlying cash flows. If demand for AI services or the revenue generated from them falls short of expectations, heavily financed businesses could struggle to service their obligations. That would create risks for lenders and investors with exposure to the sector.
The second concern is the effect on businesses outside technology. Banks and other financial institutions allocate capital according to expected returns, credit quality, and perceived growth opportunities. If AI companies offer more attractive investment prospects, traditional businesses may face competition for available financing.
Higher borrowing costs can have consequences beyond the immediate expense of servicing debt. Companies facing more expensive credit may postpone equipment purchases, delay expansion plans, or reduce investment in productivity improvements. Over time, weaker capital formation could affect industrial output and economic growth.
The concern is not that investment in AI necessarily comes at the expense of every traditional industry. AI applications can improve manufacturing efficiency, logistics, energy management, and financial services. The risk identified by Xiao is that an excessive concentration of funding could distort capital allocation, leaving productive businesses with less affordable financing even as investment in technology accelerates.
Xiao called on policymakers to prepare frameworks specifically addressing the economic transformation driven by AI, including measures for macroeconomic management and the prevention of financial risks.
His comments suggest that regulators are beginning to examine AI not only as a technology and productivity issue but also as a source of potential financial instability. The scale of investment means that developments in the sector could influence credit conditions, corporate leverage and the distribution of capital across the wider economy.
This has created the challenge of monitoring financing risks without restricting investment that could generate long-term productivity gains for regulators. This includes examining debt exposure, lending standards and the financial links between technology companies, infrastructure providers and their investors.
The warning also raises questions about how banks assess the creditworthiness of AI-related projects. Data centers and computing infrastructure require substantial initial spending, while the commercial returns may take years to emerge. Lenders must therefore assess not only the value of the infrastructure being financed but also the reliability of customer demand, the financial strength of operators, and their ability to generate sufficient cash flow.
A sharp deterioration in AI investment returns could affect several parts of the financing chain, particularly where projects rely on substantial borrowing or long-term commitments from a limited number of customers.
Xiao’s intervention adds a financial stability dimension to the debate over the AI boom. Governments and companies have largely focused on the technology’s potential to improve productivity and create new markets. Financial regulators are now also examining how the funding required to build that capacity could affect credit availability and investment elsewhere.