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After Douyin Doubao introduced paid subscriptions, its monthly active users decreased by 6.1 million. This number is like a splash of cold water on the dream of "commercialization implementation" that large model players are eager to discuss. The situation is simple: Doubao, an AI assistant owned by ByteDance, was previously free, and everyone enjoyed using it. Now, with the introduction of paid plans, over six million users have voted with their feet and quietly left. This is far from being a m
Analysis
After Douyin Doubao introduced paid subscriptions, its monthly active users decreased by 6.1 million. This number is like a splash of cold water on the dream of "commercialization implementation" that large model players are eager to discuss. The situation is simple: Doubao, an AI assistant owned by ByteDance, was previously free, and everyone enjoyed using it. Now, with the introduction of paid plans, over six million users have voted with their feet and quietly left. This is far from being a milestone in commercialization—it is clearly an embarrassing failure of a "trust test."
All manufacturers claim that large models represent the future, productivity, and disruption. But the premise of "future" and "disruption" is that users truly believe you are worth the money—or, even more crucially, that they cannot do without you. The loss of 6.1 million users brutally reveals a reality: for a significant portion of early adopters, current AI assistants are essentially "nice to have but not essential"—an entertainment or light-use tool. The core value they provide is nowhere near enough to convince users to continuously pay out of their own pockets. When it was free, people were "just playing"; once payment was required, they immediately "played out."
Behind this is the collective growing pains of the entire industry shifting from a frantic "land grab" phase to a "cost-accounting" phase. Previously, all companies were burning money and subsidizing users—even at a loss—to acquire users, gather data, and train models. Reports painted a prosperous picture of "tens of millions in monthly active users" and "astonishing growth rates." But how much of that traffic came from "bargain hunters," and how much of it genuinely formed usage habits and reliance? Once payment was introduced, it was like the tide going out to reveal who was swimming naked. Doubao’s attempt effectively served as a costly stress test for the entire industry.
Manufacturers may argue that this is about exploring reasonable business models and a necessary step in market education. However, my point of contention is: isn’t this "education" a bit too hasty? Are the model’s capabilities truly stable enough to seamlessly support various paid scenarios? In professional fields like document processing, code generation, and complex reasoning, the "hallucinations" and unreliability of large models remain a Damocles sword hanging over everyone’s heads. Users are willing to pay for tools that are stable, precise, and solve real pain points—but they will be reluctant to continuously pay for an "intelligent companion" that is unpredictable and requires repeated manual verification and correction.
Look at the competition: OpenAI’s ChatGPT Plus and Claude’s Pro plans also face growth bottlenecks. Globally, the paid conversion rate for AI applications is a universal challenge. This is not just a pricing issue—it’s a matter of product strength and user mindset. When free alternatives (including open-source models and free quotas from other providers) are still abundant, where exactly is the "moat" for paid services? Is it just early access and a slight speed advantage?
Doubao’s setback might offer all AI manufacturers a chance to step back and reflect: stop celebrating model parameter races and high funding rounds. Return to the users themselves and solve those niche—but real and payment-worthy—"must-have" scenarios. Is it more precise industry report generation? More reliable automated office workflows? Or irreplaceable personalized creative production? Excelling at one thing to the point where users feel they "cannot do without it" is far more promising than building an "all-purpose but mediocre" chatbot.
The departure of 6.1 million users is not a rejection of AI—it is a rejection of a version they deemed "not worth paying for." After the technological revelry, the logic of business has never changed: value determines price, not the other way around. It seems the coming-of-age for large models will require a few more cycles of "charging—user loss—further improvement" to truly mature.
Disclaimer: The above content is generated by AI and is for reference only.