The rapid evolution of artificial intelligence is fundamentally reshaping the financial landscape, yet China's financial system is struggling to keep pace with the sector's explosive growth. A prominent former policy advisor has highlighted critical deficiencies in the current framework, urging a systemic shift towards an innovation-driven model. The transition from a debt-fueled cycle to one powered by technology, industry, and finance is seen as essential for sustainable economic progress.
At a recent forum focused on high-quality development, Wang Yiming, a former deputy director of the Development Research Center of the State Council, argued that the financial system must quickly adapt to the new technological paradigm. He pointed out that the current system is moving away from a real estate and land finance model. Four key weaknesses were identified, including the insufficient ability of financial institutions to assess AI companies, a lack of patient capital for early-stage ventures, high barriers for smaller firms seeking direct financing, and an incomplete synergy between industry and finance.
Data from the central bank underscores the changing dynamics, with direct financing surpassing indirect financing for the first time in 2025. This structural shift is closely linked to the needs of tech companies, which find traditional lending models ill-suited to their profiles. To address this, a full life-cycle funding approach is necessary, utilizing government guidance funds, venture capital, and eventual bank loans and market financing as companies mature.
To overcome these hurdles, five main recommendations were proposed. These include strengthening banks' capabilities to value and support tech firms, creating new financial products that rely less on physical collateral, and fostering a pool of long-term strategic capital. State-backed funds with longer cycles are being introduced, although their effectiveness is yet to be fully realized.
Further recommendations involve enhancing the multi-tiered capital market, with boards like the STAR Market playing a more significant role and exit mechanisms being improved. Finally, leveraging large-scale models and big data for intelligent valuation and risk control is suggested to boost efficiency and maintain rigorous oversight in the new landscape.
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