AI that transforms global speed, yet payments are stuck in a bygone era

In 2026, a new AI company is born globally on average every hour.

The competition in the AI industry has reached such a white - hot stage: the parameters of large models are in a race, the inference speed is in a race, the speed of application implementation is in a race, and even the story density of financing PPTs is in a race. For a while, everyone's attention was focused on the "front - end" - whoever has a stronger model and whose product comes out first can gain a foothold in this arms race.

However, outside this hustle and bustle, a problem ignored by most people is quietly eroding the profits of every AI enterprise: can the money flow smoothly?

As the procurement lists of AI enterprises become more and more globalized, every break in the payment link means real cost losses and in - house efficiency losses. When products start to generate revenue from global users, exchange losses in cross - currency settlements, the fragmentation of local payment methods, and the complexity of billing models have erected another barrier on the collection side. With both the inflow and outflow ends blocked, it is impossible to establish a closed - loop of funds.

This is the real dilemma that tens of thousands of Chinese AI enterprises experience every day.

Globalized procurement lists, outdated payment tools

To understand this problem, we must first understand the cost structure of AI enterprises.

If we compare an AI company to a manufacturing enterprise, its "raw material" list is roughly as follows: overseas GPU cluster leasing, overseas cloud services such as AWS, Azure, and Google Cloud, API calls to overseas large models such as OpenAI, Anthropic, and Claude, overseas vector databases and MLOps platforms, overseas data annotation services, etc.

This procurement list has a common feature: almost all need to be purchased overseas and settled in US dollars or euros.

Meanwhile, computing power procurement is an inevitable path for AI companies. According to Gartner's prediction, the total global AI expenditure will reach $2.52 trillion in 2026, of which more than 54% will be invested in infrastructure, with the core being computing power and storage. The market consensus is that the capital expenditure of AI - enabled enterprises will reach $450 billion, $520 billion, and $540 billion in 2025, 2026, and 2027 respectively.

For Chinese AI startups, it has become an industry norm that the expenditure on computing power and cloud services accounts for 60% - 80% of the total corporate expenditure. For a growing AI enterprise, the monthly bill for computing power and cloud services alone may reach hundreds of thousands or even millions of RMB. And most of these bills need to be paid in foreign currencies.

Many AI enterprises have reported a fact: the procurement lists of AI enterprises are becoming more and more globalized, but the payment tools remain in the previous era. The most typical example is that domestic credit cards are not supported, and overseas credit cards have high handling fees and chaotic reconciliation of payments across multiple platforms. It can be said that every link is leaking money and efficiency.

The reasons behind this are not only the mismatch of traditional financial tools but also the phased dilemmas of AI enterprises themselves. Most companies are in a period of rapid cash - burning. Algorithm iteration, product refinement, and financing roadshows always take top priority, and the optimization of payment efficiency is difficult to make it onto the core agenda. It is not until a payment failure causes a disruption in computing power or a large amount of unexplained cost losses are found during the end - of - month reconciliation that the problem gets attention.

On the other hand, large multinational enterprises have a complete financial team to build a cross - border payment system, while most AI startups have neither the resources nor the energy. In the early stage, the team size is small, and payment problems are often dealt with manually. However, as the business expands and the expenditure level jumps, the traditional solutions will start to get out of control, making the monthly expenditure a mess.

The hardware cost in the current AI industry is still rising. The price of high - end GPU chips has increased by more than 50% and is in short supply. The price of an AI server is 5 - 10 times that of a traditional server. In the competition for computing power, efficiency is life. Every cent of money eaten up by handling fees and exchange losses is a high opportunity cost.

It can even be said that whoever can solve the closed - loop problem of funds for AI enterprises will master a key variable in this global competition.

The black hole on the payment side: is every link leaking money?

After talking to several founders, we realized how common the pitfalls that AI enterprises encounter in cross - border procurement are.

The founder of a company providing AI inference services mentioned that in the early stage, when the company rented an overseas GPU cluster and tried to pay the AWS bill with a domestic credit card, the payment was directly rejected. For companies that already have overseas corporate cards, cross - border handling fees are also an unavoidable cost. Banks usually charge a 1% - 3% handling fee for foreign currency transactions. Over a quarter, hundreds of thousands of RMB flow out just in handling fees.

Many financial managers of AI enterprises interviewed reported that the more headache - inducing part is reconciliation. The algorithm team, data team, and operation and maintenance team each use different cards to make payments on different platforms. At the end of the month, the finance department has to manually pull bills from seven or eight platforms. "Sometimes, the man - days spent on this step are more expensive than the handling fees."

In addition, traditional prepaid cards require prepayment to lock up funds. For AI startups with tight cash flow, this occupied dormant capital is also a considerable pressure.

This systematic mismatch has given rise to a type of card - issuing and expense - control products specifically for the cross - border payment scenarios of enterprises. Among them, Airwallex's Issuing product is one of the optional solutions for AI enterprises. As a Visa principal member institution, Airwallex's core idea is to integrate card - issuing, control, multi - currency wallets, and fund management into the same system on the payment side to help enterprises reduce the complexity caused by the splicing of multiple suppliers.

Creating virtual cards in seconds is just the entry point. What really improves efficiency is the management method of "dedicated cards for specific purposes". The expenditures of AI enterprises are highly dispersed. Every payment for different cloud platforms, different teams, and different projects needs to be attributed. In Airwallex's system, each platform and each team can have its own independent card, and the bills are classified from the source, so end - of - month reconciliation is no longer a nightmare of manual comparison.

For AI enterprises that need to settle accounts among US dollars, euros, and Singapore dollars every month, the issue of currency is also significant. In the traditional model, cross - currency payments often require currency exchange before payment, and both the exchange rate and the timing are uncontrollable. Airwallex supports enterprises to complete payments in more than 170 transaction currencies covered by Visa; in currencies and regions that support Direct Billing, fees can be directly deducted from the corresponding multi - currency wallet balance, without the need to lock up single - card funds in advance like traditional prepaid cards, which also helps to reduce unnecessary currency exchange costs.

In terms of financial control, another problem that AI enterprises have long faced is the "black box" of expenditures: multiple teams using cards, lack of approval processes, and abnormal situations are only discovered at the end of the month. To address this pain point, Airwallex provides refined control capabilities at the card level, which can set rules according to dimensions such as consumption amount, merchant category, and validity period, and support real - time transaction control and status management. Abnormal cards can be frozen at any time.

A Singaporean AI cloud computing company operating computing power clusters in Southeast Asia. This company has deployed its business in Malaysia, Indonesia, and Japan simultaneously, with monthly infrastructure expenditures between $150,000 and $200,000. For a long time, the founder's personal card and the company card were used interchangeably, facing the pressure of compliance audits. After connecting to Airwallex, they created exclusive virtual cards for different platforms respectively. The USD bills are deducted from the multi - currency wallet, bypassing unnecessary currency exchange links, and the financial accounts have also become traceable.

It's hard to make money overseas, and it's even harder to bring the money back

After an AI company completes its internal capacity building, the next step is to sell its products and services globally.

From model APIs, SaaS tools to AI hardware, the overseas expansion path of Chinese AI enterprises is maturing rapidly, but overseas collection is also fraught with difficulties.

It is not uncommon for an AI enterprise to be paying US dollars to AWS on one hand while being unable to receive payments from Southeast Asian customers' local e - wallets on the other. This fragmentation is the result of the long - term mismatch between the special business model of the AI industry and the traditional payment infrastructure.

The billing model of AI services is naturally complex: billing by tokens, by API call times, by computing power consumption, superimposed with various combinations such as fixed subscriptions, tiered pricing, and mixed billing. Traditional payment tools cannot support this refined logic. Once there is an error in measurement, for large customers, the monthly loss can range from hundreds of thousands to millions of dollars.

Meanwhile, payment habits vary greatly in different markets. Credit cards are used in Europe and the United States, local e - wallets are used in Southeast Asia, and bank transfers are used in Japan. If only two or three mainstream payment methods are supported, a low payment success rate and a prolonged payment collection cycle are almost inevitable. In addition, the VAT in the EU, state - level sales taxes in the United States, and local bill formats in Southeast Asia are all different. Traditional systems simply cannot be uniformly adapted, and the superposition of compliance risks and exchange losses also erodes profits.

The dilemma behind this is that the globalization of AI enterprises is technology - native, but the financial infrastructure supporting it is lagging. The procurement end and the collection end operate independently, and there is no system that can connect the entire link.

It is this market gap that has given rise to a billing management solution specifically for the AI - native business model. The core idea of Airwallex's Global Billing Management (Billing) product is to integrate bill management, subscription management, and usage - based billing into the same system, so that the complexity of the billing logic is borne by the system rather than being pieced together by the enterprise itself.

A leading domestic AI large - model service provider shared that there was an approximately 5% error in the usage - based billing of customer token consumption and API calls. Facing the massive calls of large customers, the direct loss due to missed and incorrect collections exceeded one million yuan per month. After connecting to Airwallex's billing API, the system can measure usage in real - time and process token consumption and API call data with low latency, thereby improving billing accuracy.

More importantly, with the tiered pricing mechanism, the larger the usage of customers, the lower the unit price. This mechanism directly stimulates the growth of large customers' usage. According to their feedback, the overall gross profit margin has increased by 12%.

A similar dilemma exists in the subscription scenario. A domestic AI creative generation platform has millions of global monthly active users. Previously, the automatic renewal failure rate was as high as 30%. Problems such as expired credit cards and insufficient balances led to user loss. After connecting to Airwallex's intelligent retry mechanism and overdue reminder for subscription management, according to their feedback, the renewal success rate has increased from 70% to over 90%, and the subscription revenue has increased by over 30%.

A new consensus is emerging in the industry: products can be globally native, but whether the revenue can truly be realized depends on how mature the financial infrastructure behind it is.

In a sense, the arms race in the AI industry is not only about model capabilities but also about capital efficiency. The financial infrastructure of AI enterprises remains a neglected shortcoming to this day.

The logic behind this is that AI - native enterprises need a financial infrastructure redesigned from the bottom up, rather than a simple superposition of traditional tools. Currently, the industry's focus is on the "front - end" explicit competition, but the "back - end" capital turnover rate, global payment cost, and billing accuracy may become increasingly crucial decisive factors in the future competition of AI companies.

The globalization level of an AI company is not only reflected in its model performance and user scale but also in its ability to truly connect the global flow of funds. This may determine how far it can go in this global competition.