When “Computing Power” Becomes the “New Oil”: What’s Really Happening with the Marriage Between AI and Finance?
Hello everyone, I’m your financial observer. Recently, there were many new terms at the 2026 China International Fair for Trade in Services (CIFTIS), such as “computing power loans” and “computing power guarantees.” It sounds fancy, but in simple terms, it means that banks and insurance companies are starting to provide funding and support to companies working in artificial intelligence (AI).
Behind this is a significant shift in logic: Previously, we thought of finance as a “money-printing machine,” but now we realize that AI is the “new engine” that can change the rules of the game and even reshape the financial industry.
To help you understand this better, I’ve broken down the key points of this news into five sections, explained in plain language:
1. Core Summary: A Mutually Beneficial Transformation
In one sentence: AI technology is forcing the financial industry to reinvent itself, while financial capital is desperately seeking ways to support the AI sector.
The strongest signal from CIFTIS is that AI is no longer just a matter for the tech world; it has become a critical issue for the financial community.
- For AI companies: It used to be difficult to get loans because they lacked tangible assets like real estate or land. Now, banks offer “computing power loans” based on the amount of computing power and technology a company has.
- For banks: They used to earn profits from lending, but now they need to understand code and algorithms and use AI to manage their risks.
- The main challenge: AI evolves too quickly, and the financial system struggles to keep up. Additionally, financial data is highly sensitive, making it difficult for AI to integrate into core banking systems.
2. Why Are Banks Suddenly Favoring AI Companies?
Key terms: Difficulty in valuation, long development cycles, and light assets
Banks used to prefer loans to the real estate and manufacturing sectors because those industries provided tangible collateral. However, AI companies often have “light assets”—just a few servers in an office, formulas on the walls, and little cash on the balance sheet, sometimes even operating at a loss.
This creates problems for banks:
- Valuation challenges: Financial institutions struggle to accurately assess the value of AI companies. Should they base the value on the number of lines of code or the number of users? Without a clear metric, banks are hesitant to lend.
- Long development times: AI research and development is costly and can take 5-10 years to yield profits. Banks, which typically invest in short-term projects, are not willing to wait.
- Lack of collateral: AI requires significant investment in foundational technology and computing power, which is hard to use as collateral.
The current bottleneck is the mismatch between the need for “patient capital” (long-term investment) from AI companies and the “impatient capital” in the market.
3. How Are Banks Adapting?
Key terms: Computing power loans, full lifecycle support, and integrated investment and lending
To address these challenges, banks are adopting new strategies:
- Treating computing power as an asset: Instead of traditional collateral, banks are lending based on a company’s computing power, such as the China Bank’s “Science and Technology Computing Power Loan” for purchasing servers and building data centers.
- Full lifecycle support: Banks are offering a range of financial products for different stages of a company’s development, from inception to maturity.
- Integrated investment and lending: Banks are providing long-term loans (3-5 years) even before companies start making a profit, sharing in their future growth through equity or options. This allows banks to earn long-term returns without diluting the founders’ shares.
4. How Is AI Helping Banks Improve Efficiency and Security?
Key terms: Intelligent risk management, data security, and private deployment
AI is also transforming how banks operate:
- Advantages: AI can analyze large amounts of data quickly and identify potential fraud in tech startups, reducing information asymmetry.
- Challenges: Data security is a major concern. Banks must ensure that AI systems used for risk management are compliant and secure, as they handle sensitive information.
- Solutions: Banks often use private deployment, keeping data on local servers to maintain security. However, this reduces efficiency.
5. The Future Path: Government and Market Collaboration
Key terms: Patient capital, ecosystem collaboration, and regulatory compliance
This transformation requires the cooperation of the entire ecosystem:
- Government support: The government should foster long-term, patient capital and provide infrastructure, such as computing power centers and guidance funds.
- Market participation: Private investors (PE/VC) should help identify high-quality AI companies and reduce financial risks.
- Regulatory oversight: Regulations must balance innovation with security. Data security and compliance must be fundamental for AI in finance.
In summary, the future financial ecosystem will be a combination of AI technology, financial capital, and government guidance.
- For individuals: This could lead to more financial products related to personal digital assets.
- For entrepreneurs: Financing options will expand, but the bar will be higher; they need to prove the practicality of their technology.
- For investors: Be cautious of AI projects that rely on hype without real computing power and focus on companies that address data security issues.
One last thing to remember: Technological revolutions are never easy; they are competitive eliminations. The synergy between AI and finance is more than the sum of their parts—it’s a chemical reaction that can create significant value. Those who successfully balance security and efficiency will be the winners.