官方首次对稳定币定性,稳定币的幻想可以结束了

深潮Published on 2025-11-29Last updated on 2025-11-30

也意味着行业从此不需要再围绕“灰色可能性”反复试探。

来源:曼昆区块链法律服务

这是28日的一场会议,重要程度远超新闻标题本身。

公安部、网信办、中央金融办、两高、外管局、证监会、金融监管总局等一整套“国家级监管班底”全部到位,本身就说明监管层认为虚拟货币问题已经到了必须再次统一口径、统一行动的阶段。

但真正值得讨论的,是会议里出现的一句关键表述——“稳定币是虚拟货币的一种形式。” 这是第一次,中国官方在正式文件中明确给稳定币下定义,并且直接把它纳入“虚拟货币非法金融活动”这一监管框架。

过去几年所有围绕稳定币的模糊、揣测、侥幸空间,从今天开始,全部消失。

过去行业一直认为:虽然中国对虚拟货币的监管态度明确,但稳定币是否属于其中,始终存在“表述上的空隙”。不少创业者把这个空隙理解为“可能存在讨论空间”,并因此在“跨境支付”“供应链金融结算”“外贸代付”“链上人民币”“区块链试点”这些方向里反复试探。

但今天这句话的出现,等同于监管站到了台前,把那条模糊边界划成了实线。稳定币既然被纳入虚拟货币范畴,那它就自动适用此前关于虚拟货币的各项监管政策中,不存在例外,也不存在试点。

行业里最常见的误区,是用技术视角推测监管逻辑。

认为只要技术先进、安全性提升、底层资产透明,就可能获得政策空间。但监管这次的逻辑非常直接:稳定币的现实风险远大于其技术价值。

会议通稿里反复强调三件事情——洗钱、诈骗、跨境资金流动。这三者正是过去三年所有涉虚拟货币案件的完整链路。无论是跑分、网络赌博、诈骗资金链条,还是地下钱庄、非法换汇,稳定币都已经成为最核心的结算层。它解决了“快、跨境、难追踪”这些灰色业务最需要的要素,自然就成为监管眼中风险的起点。

只要这条风险链路没有被解决,讨论稳定币的商业价值就没有意义。监管的优先顺序从来都是“风险优先、创新靠后”,稳定币在当前现实条件下无法满足 KYC、AML、资本项下监管等要求,这就决定了它不会有政策窗口。

行业许多人把内地与中国香港、新加坡、美国的监管逻辑放在同一框架理解,认为海外在做的事情,中国迟早也会讨论;但这次会议已经给出了唯一正确的判断方式:中国不会用“同样路径”讨论稳定币,中国的监管目标从来不是“让市场更高效”,而是“让风险更可控”。

这一点被明确定性之后,所有所谓的“缝隙式创新”“小范围试点”“监管沙盒”“链上人民币”都失去了现实基础。监管的态度不是“严格”,而是“直接终止可能性”。

许多创业团队过去几年一直在问相同的问题:是否能只做链上技术?是否能不触达用户、只做系统研发?是否能由海外主体负责发行、国内团队负责技术?是否能在自贸区探索跨境金融试点?这些问题,从今天开始都不需要解释了。

因为只要稳定币被定义为虚拟货币,就直接落入“虚拟货币相关活动属于非法金融活动”的总框架。只要你的业务链路里某个环节与中国内地产生联系——用户、资金、服务器、推广、结算、技术服务、撮合匹配、代理发行——风险等级都是一样的,并不存在“技术公司就没事”或者“只服务 B 端就合法”。稳定币的法律属性已经不允许这种区分。

今天的信号非常明确,监管已经从“保持模糊”进入“明确态度”。模糊曾经是某种程度的管理手段,但稳定币已经不适合继续模糊下去,它已经是许多跨境犯罪链路的“关键要素”。只要这件事的社会风险远大于其经济价值,监管就不会给出任何试验空间。

对于中国创业者,只要想做稳定币,路径就只有一种:项目必须是彻底的海外项目。

海外法律主体、海外银行账户、海外审计、海外用户、海外监管牌照,最关键的一条是:不能向中国用户提供任何形式的服务,也不能在业务链路上触达中国资金。只要某个环节落回到中国境内,这个项目就自动落入“非法金融活动”的定性。这是一条非常清晰的红线。

你会看到,中国香港、新加坡、中东、欧洲正在不断推出稳定币监管框架,这些地区的监管目标完全不同:他们希望用稳定币提升本地金融的国际竞争力;而中国内地的目标是确保资本项目管理能力和金融安全。

目标不同,路径自然不同。

对内地创业者而言,这次定性并不是“全面封杀”,而是彻底告诉你:不要再浪费时间在不可能落地的方向,该把精力投向海外市场。

它意味着内地的稳定币幻想结束了,也意味着行业从此不需要再围绕“灰色可能性”反复试探。对于创业者来说,这是坏消息,因为方向被关掉了;但这也是好消息,因为判断变得清晰,不需要在错误的方向上继续消耗时间。

监管把话说清楚了,接下来该做判断的,是行业自己。

Related Reads

After Aave's Exit and TVL's Sharp Fluctuation, Where Does MegaETH's Valuation Anchor Lie?

Following the withdrawal of Aave and a sharp drop in its Total Value Locked (TVL), the valuation of the high-performance DeFi blockchain MegaETH faces scrutiny. Once a highly anticipated project with a fully diluted valuation (FDV) reaching around $2 billion, MegaETH saw its TVL plummet from a May peak of $245 million to just over $30 million in July, a roughly 70% decline. Its native token, MEGA, currently trades around $0.048 with a market cap of approximately $54 million and an FDV of about $480 million. The report identifies a core vulnerability: MegaETH's TVL was heavily dependent on a single protocol, Aave V3, which at its peak contributed around 90% of the chain's TVL. A significant portion of this capital is attributed to leveraged yield-farming strategies involving stablecoins like USDe. When the profitability of these strategies diminished, capital rapidly exited, exposing the lack of diversified, sustainable activity. Three key mismatches between MegaETH's valuation and its fundamentals are highlighted: 1. **Valuation vs. Real Usage:** With an FDV of ~$4.8B but only ~$1M in annualized protocol revenue and ~2,600 daily active addresses, the valuation appears disconnected from current economic activity. 2. **Token Narrative vs. Ecosystem Reality:** Despite its DeFi narrative, nearly 80% of the chain's recent protocol revenue comes from a trading card game, Monster, not from core DeFi applications like Aave. The chain's native stablecoin, USDM, also shows low trading volume and a declining market cap. 3. **Short-Term Hype vs. Long-Term Delivery:** Initial hype from token generation, blue-chip integrations, and influencer support has faded. Major protocols like Uniswap now hold minimal TVL on the chain, indicating that early capital was largely transient and driven by incentives rather than organic demand. The situation reflects a broader market trend where investors are becoming less tolerant of valuations based on inflated TVL and narrative, demanding clearer evidence of sustainable transactions, revenue, and ecosystem development. While MEGA's price may experience short-term rebounds from market sentiment, a fundamental re-rating likely depends on the team's ability to convert its remaining resources into tangible, user-retaining applications and genuine ecosystem growth.

链捕手2h ago

After Aave's Exit and TVL's Sharp Fluctuation, Where Does MegaETH's Valuation Anchor Lie?

链捕手2h ago

Goldman Sachs In-Depth Report: Who Will Be the Long-Term Winners in China's AI Large Model Industry?

Goldman Sachs Report: China's AI Models at an Inflection Point China's open-source/open-weight large language models (LLMs) have reached performance parity with top global proprietary models, according to a Goldman Sachs report. This is driven by architectural innovations and higher parameter efficiency, allowing Chinese models to achieve comparable capabilities at 2%-10% the parameter size and significantly lower cost. The market is evolving into a two-tiered structure: a high-end segment (e.g., GLM5.2, Qwen3.7 Max) with premium pricing and a low-end, price-sensitive segment for global SMEs and individual users. Key points: * **Cost & Performance:** Innovations like Mixture of Experts (MoE) enable high performance with smaller models. Projects like Meituan's LongCat 2.0, trained on domestic hardware, highlight progress in tech self-sufficiency. * **Open-Source Strategy:** Most Chinese players use open-source/open-weight models for flexibility and ecosystem growth. However, Goldman notes this may underreport actual deployment and revenue. A shift toward "open-weight + community license" models with revenue sharing (e.g., MiniMax) could improve monetization. * **Market Shift & Global Expansion:** Enterprise AI adoption is shifting from "token maximization" to "ROI-first." International expansion, especially in non-US markets, is a major growth driver. Chinese models are increasingly available on global platforms like AWS Bedrock and Microsoft Copilot. * **Competitive Landscape:** Using a framework based on pricing power, cost advantage, and financial strength, Goldman identifies **Zhipu AI and DeepSeek** as the strongest in foundational text models, and **ByteDance** as the leader in multimodal/video generation. The report maintains Buy ratings on MiniMax and Kuaishou. * **Market Growth:** China's AI model API and subscription revenue is projected to grow from an estimated ¥35 billion in 2026 to ¥879 billion by 2030.

marsbit2h ago

Goldman Sachs In-Depth Report: Who Will Be the Long-Term Winners in China's AI Large Model Industry?

marsbit2h ago

Goldman Sachs Deep Dive Report: Who Will Become the Long-Term Winners in China's AI Large Model Industry?

Goldman Sachs Report: Who Will Be the Long-Term Winners in China's AI Large Model Industry? China's AI large model sector is at a historic inflection point. Goldman Sachs argues that the intelligence of Chinese open-source/open-weight models is approaching top global proprietary models. Rapid adoption by domestic enterprises and global SMEs is creating a data flywheel effect that will further drive model iteration. The evolution is summarized as moving from "DeepSeek's cost-efficiency moment last year to GLM's model-intelligence moment this year." Chinese models achieve near-state-of-the-art performance at significantly lower cost, primarily due to architectural innovations like Mixture of Experts (MoE) and higher parameter efficiency. Models like DeepSeek V4 Pro (1.6T params), GLM5.2 (0.7T), and MiniMax M3 (0.4T) are much smaller than global leaders. Recent advancements in coding capability are attributed to better data curation and RLHF. Landmarks like Meituan's LongCat 2.0, trained fully on domestic AI chips, demonstrate progress in hardware stack independence. The market is forming a "two-tiered structure." The high-end tier (e.g., GLM5.2, Alibaba's Qwen3.7 Max) prices around $1 per million tokens, about 10-25% of US top models, with estimated inference gross margins of 10-20%. The low-end tier (priced as low as $0.06-$0.2 per million tokens) targets price-sensitive global SMEs and individuals. MiniMax derives 60-70% of revenue overseas. Goldman forecasts China's AI model API/subscription revenue to grow from an estimated RMB 35bn in 2026 to RMB 879bn by 2030. Most Chinese players adopt open-source/open-weight strategies for deployment flexibility and community feedback, though this limits monetization as deployments on third-party platforms (e.g., Alibaba Cloud) may not generate direct revenue. A shift towards "open-weight + community license" models with revenue-sharing agreements (like MiniMax's approach) could improve unit economics. International expansion, particularly in non-US markets, is the key growth driver. The global enterprise AI paradigm is shifting from "token maximization" to "ROI prioritization." Chinese models are already hosted on major global platforms like AWS Bedrock and are under consideration for integration into Microsoft Copilot. Using a competitive framework based on pricing power, cost advantage, and financial strength, Goldman identifies the strongest players: In foundational text models, Zhipu AI (initiated coverage) and DeepSeek lead. In multimodal/video generation, ByteDance's Seed is the frontrunner, with Kuaishou's Kling and MiniMax's Hailuo also well-positioned. Goldman maintains a Buy rating on MiniMax, citing its attractive valuation.

链捕手2h ago

Goldman Sachs Deep Dive Report: Who Will Become the Long-Term Winners in China's AI Large Model Industry?

链捕手2h ago

Trading

Spot
活动图片