“Free-riding members” are ubiquitous—does this mean “Chinese users are stingy” or “lack a habit of paying”?
TechFlow Selected TechFlow Selected
“Free-riding members” are ubiquitous—does this mean “Chinese users are stingy” or “lack a habit of paying”?
The pricing of these tools is simply not designed for the Chinese market.
Navigating Web3 tides with focused insightsAuthor: FX Trader
Scroll through the Simplified Chinese community on X (formerly Twitter), and you’ll notice that the most-liked and most-shared posts fall into two categories: in-depth industry analysis—and guides to accessing AI services for free.
Posts titled “How to Use Claude for Free,” “Gemini Student Verification,” or “How U.S. Military Personnel Get GPT Plus at No Cost” consistently dominate engagement metrics.
Head over to Xianyu (Alibaba’s secondhand marketplace), and the picture becomes even more vivid: listings like “One-Year Pro Membership” are openly priced at RMB 10–30, with stores routinely racking up thousands of orders. Every AI tool you want—there’s a “budget alternative” for it here.
Many online commentators attribute this phenomenon to “Simplified Chinese users being overly frugal.”
But that explanation is lazy.
The real issue is that these tools’ pricing was never designed for the Chinese market.
ChatGPT Plus costs $20 per month—roughly RMB 2,000 annually. For knowledge workers in Silicon Valley, that’s the cost of a few lunches; for average white-collar workers in Beijing, Shanghai, or Guangzhou, it’s equivalent to a full month’s food budget. The price anchors simply exist in entirely different coordinate systems.
This creates a peculiar market vacuum: demand is real, yet almost no one purchases through official channels. And vacuums always get filled.
The Xianyu stores are precisely those fillers. Their supply sources fall into several categories: credit card cashback deals, low-cost subscriptions purchased via Turkish or Argentine regional accounts and resold, bulk sign-ups exploiting student discounts, and even shared accounts split and resold piecemeal. It’s a gray area—but it works.
You could call this “piracy thinking.” But consider this: when a product’s official pricing deters 90% of its potential users, the pricing itself is flawed.
Some might ask: “Why should a U.S. company offer lower prices for China?”
That brings us to an old question: Should software products adopt regional pricing?
Netflix does—it charges Indian users just one-quarter the U.S. monthly fee. Spotify does too—its student plans across Southeast Asia are significantly cheaper. Steam is perhaps the clearest example: game prices vary wildly across Russian, Arab, and Turkish regions.
Why do they do it? Because they’ve run the numbers.
Digital products have near-zero marginal cost—every additional user equals pure incremental revenue. Rather than losing those users entirely to gray markets, it makes financial sense to win them back with locally appropriate pricing. Even if unit revenue is low, multiplied by massive user volume, total revenue ends up higher.
In this AI boom, however, most companies haven’t taken this step yet.
There are several possible reasons. First, they’re too busy—focused on fundraising, rapid iteration, and capturing market share, leaving little bandwidth for nuanced operational decisions. Second, they fear arbitrage—if regional price gaps are too wide, low-priced subscriptions from cheaper regions will flood high-price markets, undermining core revenue. Third, many simply don’t take the Chinese market seriously—either viewing it as too complex or too small.
Yet the truth is: demand for AI tools in China may be far greater than anyone imagines.
Check the comment sections under those “free access” tutorials—you’ll see mostly salaried workers, students, and entrepreneurs. They aren’t unwilling to pay; they simply can’t afford the current price.
This is a textbook case of “failed price discrimination”: money that could’ve been earned instead flows straight into scalpers’ pockets.
Ironically, this gray market is doing AI companies’ user education for them. Many users first experience overseas AI services through these unofficial channels—and once they grow accustomed to them, dependency sets in. When their income rises—or when gray-market access gets shut down—a portion of them will convert into paying customers.
In other words, those Xianyu stores are, to some extent, doing free market penetration work for Silicon Valley.
Of course, this logic has flaws. If gray-market access persists indefinitely, users will never feel motivated to go legit. So sooner or later, these companies must choose: continue turning a blind eye and cede this massive market to scalpers—or step in proactively with fair regional pricing to reclaim users.
Some companies have already begun moving. OpenAI has piloted more affordable subscription tiers in select regions.
What about domestic AI vendors? This is a golden opportunity.
Overseas products are expensive, hard to pay for, and often blocked behind the Great Firewall. In theory, homegrown AI apps should effortlessly capture this overflow demand.
Yet reality tells a different story: most domestic AI tools are mimicking Silicon Valley’s pricing playbook.
Kimi, Tongyi Qwen, Zhipu AI, Minimax—they’re somewhat cheaper than overseas alternatives, but not cheap enough to eliminate psychological friction.
More crucially, they haven’t established differentiated price positioning in users’ minds.
What do users perceive? “Domestic tools are slightly cheaper—but not much cheaper—and their capabilities lag noticeably.” Once that perception takes root, reversing it becomes extremely difficult.
Domestic vendors could take a completely different path: “so cheap it feels embarrassing to seek free alternatives.”
Recall how Pinduoduo challenged Taobao—not by offering 10% or 20% discounts, but by slashing prices so deeply that comparing prices felt like a waste of time. Once price drops below a certain threshold, users’ mental accounting shifts fundamentally—from “Which option gives me better value?” to “At this price, why would I hesitate?”
AI subscriptions work similarly. What happens if a domestic tool dares to price its Pro tier at RMB 9.9 per month—or even lower—effectively eliminating users’ decision-making friction?
First, gray-market Xianyu shops instantly lose relevance. Why spend hours hunting for scalpers—and risk account bans—just to save a few yuan?
Second, user loyalty locks in. Once users grow accustomed to a particular tool, switching costs become extremely high. Unlike video streaming platforms where swapping services is trivial, AI assistants accumulate valuable assets: your chat history, usage habits, and even the model’s personalized “understanding” of you. Acquire users with ultra-low pricing first, build ecosystem stickiness, then gradually adjust pricing—that’s internet strategy 101.
Third, it flips market education on its head. When domestic tools drive prices to rock-bottom levels, overseas products’ high pricing starts looking absurd. Users begin asking: “Why does ChatGPT cost over RMB 100 a month?” Once that question takes hold, the competitive landscape shifts.
Of course, low pricing isn’t a panacea. Without solid product capability, even free offerings won’t attract users. Yet today’s leading domestic AI models already meet most everyday needs—writing copy, researching topics, translating, brainstorming—more than adequately. The gap isn’t technical—it’s strategic.
Another overlooked opportunity lies in the enterprise market.
Individual users are highly price-sensitive, but businesses operate differently. Corporate purchasing decisions hinge on ROI. Prove that your AI tool saves employees one hour daily, and a few hundred yuan monthly becomes trivial.
Domestic AI vendors should pursue a dual-track strategy: aggressively undercutting consumer prices to acquire users and cultivate habits on the C-side, while delivering standardized, high-margin solutions to B-side clients. Leverage C-side buzz to boost B-side sales—and use B-side revenue to subsidize C-side growth. This model has already been validated by Meituan, Didi, and Pinduoduo.
So what do we actually see? Domestic vendors trying to have it both ways—imitating Silicon Valley’s premium pricing while chasing China’s mass-market scale. The result? They get neither.
A deeper issue lies beneath: many domestic AI firms still operate with a “pitch-to-VC” mindset.
In funding narratives, high ARPU signals high ceilings—and thus stronger valuation support. Set your membership fee at RMB 9.9, and investors will ask: “Can this ever be profitable? How does the financial model work?”
This traps them in a paradox: to keep financials looking attractive, they dare not lower prices; but without lowering prices, users flee to gray markets; and as users flee, growth metrics suffer—further undermining the next funding round.
The end result? A vicious cycle.
Breaking it requires courage—the willingness for someone to stand up and say: “I’m opting out of this game. I’ll crush all competition on price first, maximize user scale, and worry about monetization later.”
Whoever grasps this first will reap the biggest rewards in China’s AI application market.
After all, users frantically scouring Xianyu and the web for free access guides aren’t refusing to pay—they’re just waiting for a fair price.
Join TechFlow official community to stay tuned
Telegram:https://t.me/TechFlowDaily
X (Twitter):https://x.com/TechFlowPost
X (Twitter) EN:https://x.com/BlockFlow_News
@fxtrader
