No land, no factory buildings, no large equipment, and no traditional collateral — yet a start-up AI company can still obtain a bank loan by "burning computing power." Since the beginning of this year, several domestic banks have rolled out "Token loan" products, bringing Tokens from a mere unit of measurement into the视野 of bank credit approval.

As soon as news of "Token loans" landing broke, it sparked widespread discussion in the market, and many readers raised common questions: How are "Token loans" underwritten and disbursed? What is the bank's core approval logic? Is a Token a collateral or a business evaluation standard? These questions strike at the product essence, risk control core, and underlying industrial logic of "Token loans," and they are also the key to the public understanding technology finance innovation in the AI era.

To understand "Token loans," one must first clarify a core foundational concept: What is a Token? A Token is the "unit of measurement" in the AI world — just like "kilowatt-hours" on an electricity meter or "tons" on a water meter. Every time an AI large model completes a user Q&A or content generation, it consumes a corresponding amount of Tokens. For AI companies, the more business orders and the busier the services, the higher the frequency of model calls, and the greater the Token consumption. Therefore, Tokens are like a "business water meter" for AI companies — every unit consumed corresponds to a real AI business computation, directly reflecting the company's operational activity.

Behind the continuous rise in Token consumption is the sustained investment and consumption of computing power, which is the second key logic for understanding "Token loans." Computing power is like water conservancy in the agricultural age and electricity in the industrial age — it has become the core productive force of the digital economy. Like raw materials for traditional manufacturing, computing power services are consumables for AI companies. In this regard, Wu Qi, a senior researcher at the Pangu Institute, told the Financial Times that supporting computing power requires GPU clusters to work in coordination, and the supporting costs include hardware, software, and operations and maintenance. These include not only electricity (power bills) but also software, networking, storage, and O&M manpower and materials, as well as the most important GPU hardware. Among these, compared with electricity costs, the cost or consumption of hardware is greater. In short, every Token consumed by an AI company is backed by a whole set of costs. The scale of Token consumption also indirectly records the company's real production activities — this is precisely the industrial foundation for the birth of "Token loans."

After clarifying the underlying logic of Tokens and computing power, the product connotation and innovative value of "Token loans" become clear. Traditional bank credit looks at factory buildings, land, and fixed assets, whereas the credit basis for "Token loans" is digital operational data such as the company's Token output and consumption, the value of computing power service contracts, business receivables, and Token commission settlement volume. In response to readers' most critical confusion: Tokens are not collateral. Banks will not take Tokens as collateral, dispose of them, or cash them out. "Tokens are not qualified collateral such as factory buildings or equipment; they are a reference indicator for measuring a company's operational activity," Wu Qi said. Most AI technology companies are asset-light companies lacking qualified collateral. Their business requires calling large models, which consumes Tokens. This also means that the logic of bank lending is no longer about "how much wealth you have accumulated in the past," but about "how many Tokens you actually run through every day." "Data flow" is being regarded as a quantifiable operating capability.

For asset-light AI companies, the dilemma of "having technology but no collateral" now has a way to break through. It should be noted that in the actual approval process for "Token loans," Token consumption is merely one reference indicator for bank credit — it is not the sole basis for credit approval. Banks will also cross-verify materials such as the company's computing power procurement contracts, business settlement flows, corporate credit reports, and overall operating conditions to prevent companies from inflating volume or fabricating Token consumption to create false prosperity, thereby strictly guarding the risk control bottom line and ensuring the precision and security of credit approval.

Some readers also asked about the repayment source for "Token loans." It should also be made clear here that the company's repayment source is not Tokens. Tokens are a measurement trace of a company's business activities and cannot be used directly for repayment. The repayment source for this loan, like other corporate loans, is the company's operating income. "This income requires the company to call large models and consume Tokens to make products and services. After these products and services are sold, order payments are generated, and those payments are the repayment source," Wu Qi said. "For example, an AI image generation application company in Beijing developed an AI portrait mini-program. Users spend 9.9 yuan to generate a set of portraits. After payment, the system calls the model to generate images, and the money enters the company's account. That is the order and the payment." Therefore, the underlying repayment logic of "Token loans" has not changed; it merely establishes a suitable credit observation window for the AI industry.

As an industrial financial product that has just landed, the exploratory significance of "Token loans" deserves recognition. It has built a new science and technology innovation financial system in which "data is credit and computing power is qualification," opening a financing channel for asset-light sci-tech innovation companies. At the same time, however, it should be noted that at the current stage, Token statistical standards are not yet fully unified, and Token costs and values differ across platforms. How to identify invalid Token consumption remains a risk control issue that banks need to continuously refine. The product's actual implementation results and commercial sustainability still need to be tested by the market and by time.

In summary, "Token loans" are a pragmatic attempt by financial institutions to keep pace with the iteration of the digital industry. As the AI industry continues to develop at scale, new industrial data such as computing power and Tokens will gradually move toward standardization and normalization. In the future, the financial industry will continue to rely on new data credit systems to iteratively launch more financial products suited to sci-tech innovation companies.