Where Did China's Q1 AI Funding Exceeding 100 Billion RMB Go?

marsbitОпубликовано 2026-05-26Обновлено 2026-05-26

Введение

In Q1 2026, China's AI sector raised over 110 billion yuan (approximately $152 billion) across nearly 600 financing deals, a 185.4% year-on-year increase. Major recipients included large model companies and embodied AI firms. Approximately 30-50% of funding was allocated to computing power (GPU procurement and cloud services), highlighting its critical role as a barrier to entry. Significant portions also went to R&D and global talent acquisition. In the large model sector, three key players emerged with distinct strategies: Moonshot AI (valued at $20 billion) pursued an open-source route, achieving rapid commercialization with its Kimi K2.5 model. StepFun (raising billions) focused on a trillion-parameter foundation model and terminal device integration, backed by smartphone supply chain capital. DeepSeek, launching its first funding round at a $45 billion valuation, maintained its open-source, cost-effective approach, now attracting state fund interest. The embodied AI sector saw over 50 deals totaling around 20 billion yuan, creating over 10 unicorns with valuations exceeding 10 billion yuan each. Leading companies like Galaxy General, Qianxun AI, Independent Variable Robotics, and Zhi Jian Power secured major funding, with some beginning initial product deliveries. However, a gap between high valuations and actual revenue poses bubble risks. Key trends identified include: a shift from VC-dominated funding to mixed industrial and state capital; rapidly rising valuation...

Author: Omnitools

In May 2026, Moonshot AI was revealed to have reorganized its corporate structure back from B2B to B2C, with its head hospitalized due to high-intensity work and cost pressure. Almost simultaneously, the company completed a new funding round of approximately $2 billion, pushing its valuation past $20 billion. On one side is 'burning money until people can't take it anymore,' and on the other is capital queuing up to get in.

This is not an isolated case. Data from CCTV Finance shows nearly 600 funding rounds in China's AI sector in Q1 2026, with total funding exceeding 110 billion RMB, a sharp increase of 185.4% year-on-year. In May alone, Moonshot AI and StepFun, two large model companies, secured over 30 billion RMB. The embodied AI track was also hot, with over 50 disclosed funding rounds in Q1, accumulating about 20 billion RMB.

When single-quarter funding surpasses 100 billion RMB and the funding amounts for leading companies are dozens of times their annual revenue, what direction is this money pushing China's AI competition? OmniTools reviewed the domestic AI projects that received the most funding in Q1 2026 based on public financing data, attempting to answer three questions: Who is getting the money, what are they doing, and what do the trends behind this mean?

Where Did the Money Go? Nearly 600 Funding Rounds, Computing Power Consumes 30-50%

First, look at the macro picture. Citing data from venture capital institutions, CCTV Finance reported that the total funding in China's AI sector in Q1 2026 exceeded 110 billion RMB across nearly 600 funding events. What does 110 billion RMB represent? It's nearly triple the amount from the same period in 2025. Even in the history of global tech financing, this is a number worth noting.

The money mainly went to two tracks. Large model companies took the lion's share; in May alone, Moonshot AI and StepFun raised over 30 billion RMB. Embodied AI followed closely, with over 50 disclosed funding rounds in Q1, more than 30 companies receiving investment, totaling about 20 billion RMB, a year-on-year increase of nearly 60%.

Where the money is going is more noteworthy. The CCTV Finance report clearly pointed out three major directions: R&D, computing power, and talent. Leading large model companies' R&D expenditures in 2025 generally reached tens of billions of RMB; GPU procurement and cloud service leasing accounted for 30% to 50% of the funding amounts; and attracting top global talent was the third major expenditure.

The fact that 30% to 50% is being poured into computing power is key to understanding this funding wave. It means that for every 1 billion RMB in funding, 300 to 500 million RMB directly becomes hardware and cloud service bills. This is not wastefulness, but rather the cost of admission at the current technological stage. Training a generation of large models requires clusters of tens of thousands of GPUs, and inference services require widely distributed computing nodes. These are not problems that can be solved with a light asset model. The essence of this funding is the rapid conversion of capital into hardware barriers.

At the same time, the figure of tens of billions in R&D investment needs to be viewed against a backdrop: most large model companies' revenues in 2025 were far below this level. The model of annual revenue in the billions and R&D investment in the tens of billions means that financing is the only lifeline sustaining competition.

Large Model Track: Three $10 Billion+ Players, Three Different Playbooks

Among the three large model companies that attracted the most attention in this funding wave are Moonshot AI, StepFun, and DeepSeek. Their commonality is that their valuations have already exceeded or are approaching $10 billion, but their approaches are distinctly different.

Moonshot AI: Open-Source Route Accelerates Commercialization

Moonshot AI has had the densest funding rhythm in the past six months. According to reports from Securities Times and Investment Community, from December 2025 to May 2026, Moonshot AI completed multiple funding rounds within half a year, accumulating over $3.9 billion (approximately 37.6 billion RMB). Its valuation soared from $4.3 billion to $20 billion. In half a year, the valuation multiplied nearly fivefold.

Supporting this valuation is the commercial performance following the release of Kimi K2.5. Investment Community cited data showing that revenue in the 20 days after K2.5's release exceeded that of the entire year 2025, and its global payment ranking rose from outside the top 100 to 9th place. This means Moonshot AI's commercialization is not just 'showing signs of life' but has experienced a turning point-level leap.

The open-source strategy of K2.5 is the key variable driving this turning point. The model became the only Chinese open-source model integrated into Cursor and the official main model for OpenClaw, directly penetrating the overseas developer ecosystem. Silicon Valley VC a16z co-founder Marc Andreessen evaluated it as 'basically replicating GPT-5-level reasoning capabilities.' Investor Chamath Palihapitiya called it the 'Kimi K2.5 moment'—the first time an open-source model truly shook the closed-source system.

StepFun: Trillion-Parameter Foundation Model, Betting on Edge Deployment

StepFun is taking a different path. In January 2026, the company completed a B+ round of over 5 billion RMB, setting a then-record for the largest single round in the large model track in 12 months. In May, another new round of nearly $2.5 billion in funding was nearing completion.

Unlike Moonshot AI's open-source route, StepFun emphasizes the parameter scale of its foundation model and edge deployment. Its trillion-parameter Step-2 model is positioned as an enterprise-grade foundation, with multimodal capabilities covering scenarios like visual understanding and video generation. On the product side, Step 3.5 Flash topped the OpenRouter fastest model chart on its release day.

More noteworthy is who is investing in StepFun. In the May funding round, smartphone industry chain companies like Huaqin, Longcheer, Omnivision, and ZTE collectively entered. This is no coincidence. Yin Qi, former co-founder of Megvii, has taken the role of Chairman at StepFun. The company has dismantled its VIE structure and is sprinting towards a Hong Kong IPO. The entry of smartphone industry chain capital means StepFun's strategy is not to be a pure software company, but to embed AI capabilities into edge devices, pursuing a 'model + hardware' integrated route.

DeepSeek: From No Funding to a $45 Billion Valuation

DeepSeek's first funding round was one of the most symbolic events of 2026. According to reports from Securities Times and Caijing, DeepSeek has initiated its first funding round, with its valuation soaring to $45 billion. It plans to raise over 50 billion RMB, with the Big Fund in talks to lead the investment.

Prior to this, DeepSeek had never conducted external financing. Founder Liang Wenfeng personally invested 20 billion RMB to support the company's operations. DeepSeek is known for its open-source models and extreme cost-effectiveness. Its technical approach has exerted significant downward pressure on API prices across the industry, pushing mainstream commercial models into the 'cent era'.

'Even companies that don't need money are starting to take it'—this signal is more important than any funding figure. It indicates that the intensity of the current AI competition has reached a critical point: even with technological and cost advantages, facing the industry-wide computing power arms race and talent war, not raising funds could lead to falling behind. The Big Fund's negotiations to lead the investment mean state capital is formally entering the core layer of large models. The $45 billion valuation corresponds to a company that has not yet scaled its commercialization, and the pressure to justify this valuation is self-evident.

Embodied AI: Over 10 Companies Valued at 10B+ RMB: Boom or Bubble?

Embodied AI was another main line of funding in Q1. Data from IT Juzi and Securities Times show over 50 disclosed funding rounds in this track in Q1, accumulating about 20 billion RMB, a year-on-year increase of nearly 60%. Statistics from Guangzhou Daily show over 10 companies have already broken the 10 billion RMB valuation mark.

Galaxy General is the highest-valued project in this track. In March 2026, the company completed a 2.5 billion RMB funding round, with a valuation exceeding 20 billion RMB. Investors included Big Fund III, Sinopec, and CITIC. This was Big Fund III's first investment in the embodied AI field. Galaxy General's core products are general-purpose humanoid robots and the 'Galaxy Star Brain' embodied large model system. Its robots cooperate with CATL, achieving 7×24-hour fully autonomous operation in battery factories.

Qianxun Intelligence completed two consecutive rounds totaling nearly 2 billion RMB, reaching a valuation of over 10 billion RMB in 26 months since its founding, with participation from Yunfeng Capital, Chaos Investment, Sequoia Capital, etc. Qianxun's humanoid embodied intelligent production line has been deployed at CATL's Zhongzhou base.

Independent Variable Robotics completed a 1 billion RMB A++ round and a several hundred million RMB strategic round, with a valuation exceeding 10 billion RMB. It is the only embodied AI company simultaneously invested in by all three of China's internet giants: Alibaba, Meituan, and ByteDance. Founder Wang Qian is one of the earliest scholars globally to introduce the attention mechanism into neural networks. Independent Variable pursues a full-stack in-house R&D route for both software and hardware and has launched the 'Quantum One' and 'Quantum Two' humanoid robot bodies.

Zhi Jian Power is the most special project in this funding wave. The company was founded only in July 2025 and raised five rounds in 8 months, accumulating 2 billion RMB, with a valuation exceeding $1 billion, becoming the youngest embodied AI unicorn. Sequoia, Tencent, and Alibaba all participated in the investment. Achieving a valuation over $1 billion in less than a year is extremely rare in the hardware track.

Lingxin Qiaoshou focuses on a single category, achieving perfection. The company completed a nearly 1.5 billion RMB Series B round, with a valuation over 10 billion RMB. It is the world's only manufacturer producing a thousand high-degree-of-freedom dexterous hands per month, holding over 80% of the global market share. By becoming a global leader in a niche hardware category, Lingxin Qiaoshou's path differs from humanoid robot companies making complete machines; it's more like a 'hidden champion' in the embodied AI industry chain.

Vita Power and Luming Robot represent two directions: consumer-grade and industrial capital. Vita Power's Pre-A round raised nearly 500 million RMB, a record for the largest single round in the consumer-grade embodied AI track. The Vbot super-powered robotic dog has started initial deliveries of 500 units. Luming Robot accumulated nearly 1 billion RMB in funding, led by Mitsubishi Electric. The founder is a former senior executive from Dreame. Revenue in 2026 is expected to reach the hundred-million RMB level.

The embodied AI track is mass-producing 10-billion-RMB unicorns; that is a fact. However, very few companies have disclosed revenue. Unitree Technology's 2025 revenue was 1.708 billion RMB; Leju Robotics' revenue was 258 million RMB and applied for an IPO—leading companies indeed have real income. But most 10-billion-RMB valuation companies are still in the early validation stage. The chasm between valuation and revenue is the biggest uncertainty. When over ten companies simultaneously break the 10-billion-RMB valuation mark, the pressure of an industry valuation bubble cannot be ignored. The high expectations of capital ultimately need to be realized on client sites and production lines.

Behind the Ledger: Five Emerging Trends

Based on the above funding data and project review, OmniTools summarizes five emerging trends.

Trend 1: Capital structure is shifting from VC-led to a mix of industrial capital and state-backed funds. Big Fund III investing in Galaxy General, Big Fund negotiating to lead DeepSeek's first round, Mitsubishi Electric leading Luming Robot's round, smartphone industry chain capital investing in StepFun. Industrial capital brings orders and application scenarios; state-backed funds bring strategic intent. The proportion of purely financial investors is decreasing. For companies, this means that taking money is not just about delivering returns but also about being responsible for industrial implementation and compliance.

Trend 2: Valuation ceilings are rapidly rising, and the Matthew Effect is intensifying. Moonshot AI at $20 billion, DeepSeek at $45 billion, Galaxy General at 21 billion RMB—top companies are absorbing the majority of funds. Meanwhile, in the embodied AI track, over 30 companies received funding, but only about 10 raised over 1 billion RMB. As funds concentrate at the top, the risk of consolidation for mid-to-lower tier projects is rising.

Trend 3: IPO channels are accelerating. Leju Robotics' ChiNext IPO has been accepted, planning to raise 2.6 billion RMB, becoming the first AI company to apply the ChiNext's fourth set of listing standards. StepFun dismantled its VIE structure and is sprinting for a Hong Kong listing. Zhipu and MiniMax have also initiated listing processes. The high valuations in the primary market need the secondary market to take over; 2026 to 2027 may become a concentrated listing window for Chinese AI companies.

Trend 4: Open-source routes are gaining differentiated competitive advantages. Kimi K2.5 penetrated the overseas developer ecosystem through open source. DeepSeek's open-source strategy lowered prices across the industry. Open source is no longer a 'free strategy' but a means to gain global developer entry and build ecosystem barriers. For downstream clients, this means more cost-effective model options and continuously declining API costs.

Trend 5: Embodied AI is moving from proof-of-concept to small-batch delivery. Vita Power's Vbot delivering its first 500 units, Galaxy General's robots operating at CATL, Unitree shipping 5,500 humanoid robots in 2025. It's not all PowerPoint anymore; some products are already working at client sites. However, the journey from 'small-batch' to 'large-scale' involves challenges in supply chain, quality control, after-sales service, and cost control—each stage is difficult.

The 110 billion RMB in Q1 2026 funding is pushing China's AI competition into a new stage. The characteristic of this stage is no longer 'whose model has more parameters,' but 'whose money can last until the day commercialization is realized.' R&D investments in the tens of billions, computing power consuming 30-50%, the batch emergence of 10-billion-RMB valuations—these figures are proof of the industry's vitality but also signals of a high-intensity war of attrition. When the pace of fundraising becomes a core competitive advantage, and GPU reserves determine model iteration speed, the final outcome of this wave of AI entrepreneurship may not be decided by a single technological breakthrough, but by cash flow and management capabilities—who can stay at the table.

Связанные с этим вопросы

QAccording to the article, what percentage of the financing raised by top AI companies in China is typically spent on computing power, and what does this indicate?

AAccording to the article, 30% to 50% of the financing raised by top large model companies is allocated to computing power, specifically for GPU procurement and cloud services. This indicates that in the current technological stage, massive computing power has become a fundamental entry barrier and a key area of intense competition, transforming capital directly into hardware infrastructure.

QWhat are the different strategic approaches taken by the three major large model companies mentioned: Moonshot AI, StepFun, and DeepSeek?

AMoonshot AI follows an open-source strategy, which accelerated its global developer adoption and commercialization. StepFun focuses on building a large-scale trillion-parameter foundational model and embedding AI capabilities into terminal devices through partnerships with hardware manufacturers. DeepSeek, known for its cost-effective open-source models, maintained independence but has now initiated its first major funding round, indicating the intense pressure to keep up with the industry's computing power and talent race.

QWhat are the key trends identified in the financing landscape of China's AI sector for Q1 2026?

AThe article identifies five key trends: 1) A shift from VC-led financing to a mix of industrial capital and state-backed funds. 2) Rapidly rising valuation ceilings and intensifying Matthew Effect (concentration of capital in top companies). 3) An acceleration in the opening of IPO exit channels. 4) Open-source strategies becoming a source of differentiated competitiveness. 5) Embodied AI transitioning from proof-of-concept to small-batch delivery in real-world scenarios.

QDespite high valuations, what is the primary concern regarding many companies in the embodied AI (robotics) sector?

AThe primary concern is the significant gap between their high valuations (with over 10 companies valued over 10 billion RMB) and their actual disclosed revenue. Most are still in early validation stages, creating potential valuation bubbles. The pressure lies in converting capital expectations into real-world, large-scale commercial deployment with proven revenue.

QWhat is the fundamental nature of the competition in China's AI sector as suggested by the conclusion of the article?

AThe article concludes that the competition has entered a stage defined by a high-intensity war of attrition. The outcome may not be determined by a single technological breakthrough, but rather by which companies have sufficient financial resources (cash flow) and operational management capabilities to sustain themselves until their business models achieve large-scale commercial success. Financing pace and GPU reserves have become core competitive factors.

Похожее

Fei-Fei Li's Team Clarifies the Concept of 'World Models', Sora Merely a Renderer

"World Models" has become a widely used yet confusing term in AI. To address this, a team led by Fei-Fei Li and World Labs proposed a functional taxonomy based on the Partially Observable Markov Decision Process framework. This taxonomy categorizes systems called "world models" into three distinct projections: Renderers, Simulators, and Planners. Renderers, like OpenAI's Sora and other video generation models, focus on producing photorealistic visual outputs for human perception. They prioritize visual fidelity over physical accuracy. Simulators, such as NVIDIA Omniverse, aim to compute precise future environmental states for computational tasks like engineering analysis or digital twins. Planners, like Vision-Language-Action models, take in observations and goals to output executable actions for robots or agents. The article clarifies that most current "world models," including Sora, are primarily Renderers. They generate convincing visuals but lack the core ability to simulate state transitions based on actions, a key requirement for a true world model in classic reinforcement learning definitions. This conceptual confusion has practical implications, leading to potential misalignment in technology selection, investment, and public understanding of AI capabilities. Clear categorization is crucial. It helps enterprises avoid costly mistakes (e.g., using a renderer for robot training), allows investors to accurately assess markets, and enables researchers to build comparable benchmarks. While future systems may integrate these functions, recognizing current boundaries is essential for honest assessment and progress.

marsbit6 мин. назад

Fei-Fei Li's Team Clarifies the Concept of 'World Models', Sora Merely a Renderer

marsbit6 мин. назад

Bloomberg Uncovered: How Do China's Wealthy Circumvent the Annual $50,000 Limit to Transfer Assets?

**Summary: How Wealthy Chinese Circumvent $50,000 Annual Foreign Exchange Limits** Despite China's strict capital controls, including an annual $50,000 per person foreign exchange quota, an estimated $150 billion in funds still leaves the country annually via various gray and underground channels. This report outlines the evolution of China's "capital wall" and the methods used to bypass it. **The Evolving Capital Controls:** * **Foundation (1994):** The system of "current account convertibility with strict capital account controls" was established. * **Quota Set (2007):** The $50,000 individual annual forex purchase limit was formalized. * **Crackdown Begins (2015-2017):** Following market volatility, enforcement tightened. Banks were required to scrutinize transactions, and channels like using UnionPay cards for Hong Kong insurance premiums or buying overseas property were blocked. * **Digital & Legal Upgrades (2024-2026):** Enhanced algorithms now flag suspicious patterns (e.g., "smurfing"). The Common Reporting Standard (CRS) provides Chinese tax authorities with data on citizens' offshore accounts. Unlicensed cross-border brokers have been targeted. **Five Primary Methods for Moving Capital:** 1. **Underground Banking / "Hawala" (Duiqiao):** The largest-scale method. No money crosses borders. Clients pay RMB to a domestic account; an overseas associate deposits equivalent foreign currency into the client's offshore account. Risks include high fees, account freezes, and legal penalties. 2. **"Smurfing" or "Ant Moving":** Using multiple individuals' $50,000 quotas to pool funds for one offshore recipient. Increasingly detected by anti-money laundering algorithms. 3. **Trade Invoice Manipulation:** Businesses over-invoice imports or under-invoice exports via offshore shell companies, creating a pretext to transfer excess funds abroad under the guise of trade. 4. **Channel Migration:** After a crackdown on internet brokers, funds flow toward more compliant but costly channels like major banks' cross-border wealth management services or Qualified Domestic Institutional Investor (QDII) quotas. 5. **Structural Arrangements:** High-net-worth individuals use complex, high-cost legal structures involving offshore trusts, insurance, and investment migration programs to transfer asset ownership. **Regulatory Response: Focusing on People, Not Just Money** The current strategy extends oversight from enterprises to **individual residents**. Tools like CRS allow retroactive visibility into offshore assets. Cryptocurrencies, once seen as a potential loophole, are now actively monitored and prosecuted as an illegal channel. The underlying driver remains: with significant wealth concentrated among millions of affluent households seeking diversification amid domestic economic shifts, the incentive to move assets offshore persists despite regulatory barriers.

marsbit26 мин. назад

Bloomberg Uncovered: How Do China's Wealthy Circumvent the Annual $50,000 Limit to Transfer Assets?

marsbit26 мин. назад

Ethereum's Ballmer Moment: As Everyone Is Bearish, the Circulating Supply Is Disappearing

"Ethereum's Ballmer Moment: Circulation Shrinks Amid Bearish Sentiment" Amid widespread bearish sentiment, with prominent figures like Bankless founder David Hoffman selling ETH and young developers flocking to Solana, some argue Ethereum is entering its "Ballmer era"—akin to Microsoft's perceived stagnation under Steve Ballmer. While surface-level criticisms about slow protocol development, cautious leadership, and competitive pressure are valid, underlying fundamentals tell a different story. Approximately 30% of ETH is staked, major holders like BitMine are accumulating, and spot ETFs continue to absorb supply. Regulatory clarity, including the SEC/CFTC's March ruling on staking rewards and the potential passage of the CLARITY Act, is transforming crypto from a regulatory threat into a legitimized framework. This institutionalization, alongside a shrinking circulating supply (with net issuance around 0.23% annually), creates significant buy-side pressure independent of fee-based value capture. The broader crypto total addressable market is expanding through regulated stablecoins, tokenized assets, and institutional adoption. While public chains face competition from permissioned alternatives, the winning model appears to be permissioned assets settling on public chains like Ethereum and Solana. The author advocates a non-maximalist, barbell strategy: holding ETH for its institutional role and supply squeeze, SOL for consumer/throughput trends, BTC as a macro hedge, and a basket of next-gen L1s. Key bullish drivers for ETH include rapid circulation shrinkage, potential Q2 staked ETF approvals, regulatory tailwinds solidifying its role as a default settlement layer, and the optionality of an eventual "Satya moment" leadership shift. Despite bearish consensus, the current setup—where crypto is "not hot" and regulatory groundwork is being laid—presents a compelling investment opportunity. The crypto cycle's focus may have shifted to AI, but blockchain infrastructure is gaining a legal and institutional foothold precisely while attention is elsewhere.

marsbit26 мин. назад

Ethereum's Ballmer Moment: As Everyone Is Bearish, the Circulating Supply Is Disappearing

marsbit26 мин. назад

Торговля

Спот
Фьючерсы

Популярные статьи

Как купить S

Добро пожаловать на HTX.com! Мы сделали приобретение Sonic (S) простым и удобным. Следуйте нашему пошаговому руководству и отправляйтесь в свое крипто-путешествие.Шаг 1: Создайте аккаунт на HTXИспользуйте свой адрес электронной почты или номер телефона, чтобы зарегистрироваться и бесплатно создать аккаунт на HTX. Пройдите удобную регистрацию и откройте для себя весь функционал.Создать аккаунтШаг 2: Перейдите в Купить криптовалюту и выберите свой способ оплатыКредитная/Дебетовая Карта: Используйте свою карту Visa или Mastercard для мгновенной покупки Sonic (S).Баланс: Используйте средства с баланса вашего аккаунта HTX для простой торговли.Третьи Лица: Мы добавили популярные способы оплаты, такие как Google Pay и Apple Pay, для повышения удобства.P2P: Торгуйте напрямую с другими пользователями на HTX.Внебиржевая Торговля (OTC): Мы предлагаем индивидуальные услуги и конкурентоспособные обменные курсы для трейдеров.Шаг 3: Хранение Sonic (S)После приобретения вами Sonic (S) храните их в своем аккаунте на HTX. В качестве альтернативы вы можете отправить их куда-либо с помощью перевода в блокчейне или использовать для торговли с другими криптовалютами.Шаг 4: Торговля Sonic (S)С легкостью торгуйте Sonic (S) на спотовом рынке HTX. Просто зайдите в свой аккаунт, выберите торговую пару, совершайте сделки и следите за ними в режиме реального времени. Мы предлагаем удобный интерфейс как для начинающих, так и для опытных трейдеров.

1.4k просмотров всегоОпубликовано 2025.01.15Обновлено 2026.06.02

Как купить S

Sonic: Обновления под руководством Андре Кронье – новая звезда Layer-1 на фоне спада рынка

Он решает проблемы масштабируемости, совместимости между блокчейнами и стимулов для разработчиков с помощью технологических инноваций.

2.3k просмотров всегоОпубликовано 2025.04.09Обновлено 2025.04.09

Sonic: Обновления под руководством Андре Кронье – новая звезда Layer-1 на фоне спада рынка

HTX Learn: Пройдите обучение по "Sonic" и разделите 1000 USDT

HTX Learn — ваш проводник в мир перспективных проектов, и мы запускаем специальное мероприятие "Учитесь и Зарабатывайте", посвящённое этим проектам. Наше новое направление .

1.8k просмотров всегоОпубликовано 2025.04.10Обновлено 2025.04.10

HTX Learn: Пройдите обучение по "Sonic" и разделите 1000 USDT

Обсуждения

Добро пожаловать в Сообщество HTX. Здесь вы сможете быть в курсе последних новостей о развитии платформы и получить доступ к профессиональной аналитической информации о рынке. Мнения пользователей о цене на S (S) представлены ниже.

活动图片