Have you ever had this experience? The more AI tools you install on your phone, the thicker your monthly bills become. Take a look at the "digital colleagues" you've loaded — Advanced versions, High-tier versions, Flagship versions, Enterprise versions, Pro versions, Plus versions... The dazzling array of options comes with clearly priced skills, each one more expensive than the last. They accompany us around the clock, but they also bring a real-world question: Should I renew my membership next month? Should I opt for the "enterprise-grade" annual package? When generating videos or rewriting text and images, how exactly are the background Token consumption calculated? If AI answers incorrectly and you have to redo it, how should that be billed?

All these questions point to the same issue: as artificial intelligence becomes as ubiquitous in daily life as water and electricity, how should computing power — this "new utility" — be fairly billed and transparently circulated? Recently, the Sichuan Provincial Development and Reform Commission and other departments jointly issued the "Several Policy Measures for Promoting the Construction of Computing Power Networks in Sichuan Province (Trial)", introducing 19 measures to advance computing power network construction. Sichuan's first "Token computing power scenario" loan was also successfully disbursed recently — a high-tech enterprise in Chengdu High-tech Zone received a special credit loan. Unlike traditional loans that rely on collateral such as property or equipment, the bank's core basis for evaluating the enterprise's credit this time was a "computing power bill."

Previously, the China Academy of Information and Communications Technology, together with enterprises, released the "Artificial Intelligence Token Operation Management Capability Requirements," establishing clear rules for complex AI consumption, covering areas such as usage statistics and billing rules. This signals that discussions on computing power inclusiveness are no longer just about "whether it's available," but also about "how to use it," "how to calculate it," and "how to pay for it." The government has laid the pipes to the doorstep, but for computing power to truly flow into daily life, a matching consumption yardstick is needed.

Making it affordable is what will allow AI inclusiveness to go further. AI charging is an inevitable trend. Large language models are not a free lunch — training requires computing power, inference costs resources, and tool-based products need commercial returns to survive. The problem is that as AI tools multiply, the pricing models become increasingly complex. A membership fee of a few dozen yuan, followed by paying for Tokens once you top up — building Agents, making posters, composing music, producing short videos — then stacking points packages, Token packages, advanced model acceleration packages on top... the nesting dolls within nesting dolls add up to no small sum.

For large companies, this might just be a line item in the office budget; but for small shops, individual entrepreneurs, and ordinary workers, the math looks very different. AI is supposed to reduce costs and increase efficiency. If "efficiency" goes up but "cost" also rises significantly, the enthusiasm of ordinary people will inevitably be drained by the price tag. "Making it affordable" doesn't mean demanding that all AI be free, nor does it require all AI application companies to cut prices. A more realistic direction is to make pricing models more user-friendly. Only use it three times a month? How about pay-per-use? Only need one feature? Can you avoid buying the "family bucket"? Professional users are willing to pay for better models and faster speeds, but light users should also have a lightweight entry point. Adapting different payment methods to different needs is how inclusiveness can go further. After all, to drink a glass of water, not everyone needs to buy the entire well first.

Clear calculation is what will make AI consumption more reassuring. Sometimes, more unsettling than high prices is not knowing why something is expensive. Tokens, points, Credits, credits, computing power values... entering the AI world feels like dealing with multiple "exchange rates." 100 yuan for 10,000 points seems clear, but how much is one image actually worth? How much more expensive is switching models? How much extra does adding 5 more seconds to a video cost? Too often, the screen just shows a cold message: "This consumption: 500 points." What is 500 points equal to in yuan? Why 500? How many more uses can the remaining points cover? None of this is easy to understand at a glance.

We don't need to learn how to generate electricity to pay our electric bill, and we don't need to research water treatment plants to pay for water. The complex part should be the model, not the bill. Particularly worth discussing is the "cost of trial and error." The generated portrait has an extra finger — you pay to fix it; the last few seconds of a video output are abnormal — you pay to regenerate; the system fails midway through generation — you pay to start over. When users actively change their needs or repeatedly adjust, generating new computing costs, paying is entirely reasonable. But if the model itself makes obvious errors, the system reports a bug, or generation fails, how should that be billed? This is an area in AI consumption rules that deserves clarification. In the future, can we clearly distinguish between "user-initiated redo" and "system-failure redo"? Since computing power is like water, the bill should also be like a mirror — every drop of Token the user spends should be reflected as clearly as possible.

Collecting transparently also means refunding transparently. In today's consumer environment, we're used to "returns." If clothes don't fit, you can return them. If you no longer need a digital service you've purchased, what do you do? Of course, computing power isn't clothing. Model invocations that have already happened can't be pretended away, but AI consumption does face new problems: How are unused points handled? Can auto-renewal give more prominent advance reminders? What happens to remaining credits when a plan changes? If a product or service undergoes major changes, do users have more convenient exit options? These are questions the industry will gradually need to answer.

A mature commercial system isn't just about making purchases easier — it should also make after-sales processes like cancellation, downgrades, renewals, and refunds increasingly clear. Ultimately, the reason we discuss AI pricing today isn't to turn everything into bargain-basement goods. Good technology deserves market returns. From an industry development perspective, only with a healthy, sustainable business model can AI companies continue investing in R&D and launching popular models. As AI becomes more deeply integrated into ordinary people's work and lives, pricing itself should evolve along with it.

As the ancients said: "Weigh it, and then you know the heaviness; measure it, and then you know the length." Thousands of years later, technology has been transformed beyond recognition, but the most basic principle of commerce remains unchanged — how much you use should have a standard; how much you spend should have a clear account. As computing power surges forward, what should also mature is that increasingly simple and increasingly clear "AI bill."