In the era of AI, just getting people into an app is no longer enough.

Seeing the domestic AI companies spend money during this Lunar New Year, my first reaction wasn’t excitement, but familiarity. Tencent Yuanbao gave out a 1 billion cash red envelope on February 1st; Baidu Wenxin distributed red envelopes totaling 500 million from January 26th until mid-March; Alibaba’s Qwen launched a “treat plan” of 3 billion on February 6th; and Doubao leveraged the Spring Festival Gala for AI interactions to push its presence. My judgment is straightforward: this is still an inertial action left over from the previous era of the internet—first, pull people into the App, and second, build up usage frequency; everything else can wait. But the business of AI isn’t quite like a traffic-driven business.

This approach is too much like the old internet

The logic of growth in the old internet was simple. Users arrive, they stay for a few more minutes, the app ranks higher, and traffic follows. With traffic, ads can be sold, and fundraising stories are easier to tell. During high-frequency social periods like Chinese New Year, it’s naturally a user acquisition window, so it’s normal for big tech companies to think of red envelopes, subsidies, and freebies at this time. This wave of AI competition this year follows the same underlying logic. The Paper mentioned that several large tech companies’ AI marketing budgets during the Chinese New Year period have easily exceeded 5 billion yuan. QuestMobile’s data is also very clear: on the first day of the “3 Billion Freebie” campaign by Qianwen on February 6th, the DAU directly increased by 7.3 times, reaching 58.48 million; and on the first day of the red envelope activity by Yuanbao on February 1st, the DAU also increased by 2.1 times. So, do you think throwing money around is useful? Of course it is. At least it can get a large number of people who wouldn’t normally open AI to click in and try it out, breaking down the psychological barrier of “I didn’t know I could use this thing.” That’s not the problem. The problem is: after bringing people in, how will AI make money?

The AI Ledger Isn’t Just a Traffic Count

This is what I feel many people haven’t fully grasped yet. Short videos, news feeds, and general utility software have very low marginal costs. If one more person spends a few minutes scrolling, the server load will increase, of course, but overall it still operates on the traditional software cost model. AI is different. Every extra question a user asks, every additional search run, and every piece of code, document, or image generated comes with real inference costs behind it. Electricity, GPUs, cooling, data centers, model research, distillation, training, and inference clusters—when these things stack up, both the fixed and ongoing costs are much higher than software services from previous eras. On March 23rd, Liu Liehong, Director of the National Data Bureau, disclosed that China’s average daily Token consumption has exceeded 140 trillion. This number is important not just because it’s large, but because it lays bare the commercial reality of AI: every time a user “casually asks something,” compute power is being consumed. What’s even more interesting is that official sources have framed Tokens as the “value anchor” and “settlement unit” of the intelligent era. I think this point is crucial. It directly points out that AI is not a business model purely reliant on monetizing attention; it is inherently closer to a business settled by results, by calls, or by deliverables. This is also why people are simultaneously spending money while desperately trying to control costs, implement tiers, set limits, and offer free quotas. Alibaba Cloud is still offering free trials for AI products, with pages explicitly stating “Million Free Call Quotas” and “Free Experience of Large Model Tokens exceeding 70 Billion.” On the surface, it looks like they are building habits, but in reality, they are using subsidies to exchange for future paid conversions. The problem is that if users come in just to claim red envelopes, chat a bit, or create a few stickers, the commercial value of this traffic cannot sustain the underlying compute bill.

AI is More Like a Value Economy

I’m increasingly feeling that the AI era resembles a value economy rather than an attention economy. The attention economy sells dwell time, while the value economy sells results. The difference between the two is significant. Why are users willing to pay for AI? Not because it can chat, not because its app is aesthetically pleasing, and not because it sent me some red envelopes during the Spring Festival. What truly makes people open their wallets is when it does things for them, and the cost savings compared to hiring someone else are evident. For example, writing a piece of code. Before, you had to pore over API documentation, manually assemble parameters, and stumble through several pitfalls yourself. Now, AI can take care of a large portion of that manual labor—even taking away the initial draft, error debugging, and documentation cleanup all at once. As long as the result is acceptable, many people are willing to pay for it. Because this isn’t “playing with me”; this is “doing work for me.” Take Qianwen’s strategy during this Spring Festival. Although it superficially appears to be a subsidy, there is something correct about it: it is not content with just having you chat more; it aims to push AI toward being “capable of performing tasks.” Actions like placing orders, ordering takeout, shopping, or navigation—once these are genuinely connected, the business logic is much more solid than mere chatting. Because it starts approaching delivery. Simply put, where AI truly holds value is not in trapping people within a chat box, but in transforming needs into results.

Having you write less code for an afternoon is valuable.
Having you perform one round of spreadsheet cleaning is valuable.
Having you save me from hiring a freelancer, or saving me a day of troubleshooting time, is also valuable. These types of value are tangible, quantifiable, and are the areas where users are most receptive to paying.

Red envelopes can only bring people to the door.

Therefore, my view on this round of money-throwing by domestic AI companies during the Spring Festival is neither pessimistic nor overly excited. It is useful, but it is just an appetizer. Red envelopes, free meals, and CCTV Gala exposure—these things solve the problem of “getting people to try it first.” However, in the business of AI, what ultimately matters is not who can trick users into coming in once, but who can consistently see the process through, and do it faster, cheaper, and more reliably than human labor. If we continue following the old internet playbook—only focusing on download volume, time spent, or festive DAU—we will likely find that AI’s cost structure fundamentally does not support such a brute-force approach. Conversely, if a model can genuinely help people write code, organize documents, conduct searches, run workflows, and make consumption decisions reliably, users won’t just stay; they will be willing to pay. Because the money spent is no longer about “how long I stayed in the App,” but rather “how much time you saved me, how much labor you saved, and how many things you accomplished for me.” This, to my understanding, is AI commercialization. It’s not about pulling people into an App. It’s about making users feel that the money spent was worth it.

References

Writing Notes

Original Prompt

Prompt: During the Spring Festival, domestic vendors are spending money to cultivate user habits, encouraging users to use AI for anything, just to get them using it. In my view, this is an inertial mindset. The internet of the previous era ran on the attention economy. When a user came, and they stayed within the app, I gained traffic, which allowed me to raise funds, and I could even sell the users’ attention through advertising. In the AI era, two points are different: cost—electricity + GPU + cooling + model development. These fixed costs far exceed those of previous software services. The AI era is more about a value economy. A direct manifestation of this is that if you need to write a piece of code, AI can do it for me, which is more cost-effective than hiring someone. Users are willing to pay for AI.

Summary of Writing Ideas

Retained the core judgment that “giving out money during Chinese New Year is an old internet habit,” rather than writing a news summary about the “Spring Festival AI War.”
Used the subsidy actions of Tencent, Baidu, and Alibaba during the 2026 Spring Festival to provide concrete factual anchors for the concept of “cultivating spending habits through handouts.”
Shifted the focus of the argument from traffic and downloads to Tokens, inference costs, computing power bills, and settlement units.
Used the example of “writing code is more cost-effective than hiring people” to ground the abstract commercialization problem back to whether users are willing to pay.
Structurally, first explained why this approach feels familiar, then argued why AI’s cost structure does not support a business model based solely on traffic, and finally concluded with the necessity of “paying for results.”