深度 · 5337

If it's just tokenized assets and doesn't connect to DeFi, what's left of RWA?

Author: Jesus Rodriguez, co-founder of Sentora Compiled by: Luffy, Foresight News Original title: Does RWA still make sense without DeFi? Discussions in the RWA industry often begin with a simple vision: take a treasury bill, fund share, stock, invoice, megawatt hour, or GPU for one hour, then mint a token representing it. Is it useful? It's really useful. But can it be called transformative? It's far from there. This is like putting a bar code on a container and claiming that a global trade problem has been solved. Barcodes make containers recognizable and machine-readable, but they don't create ports, cranes, customs, insurance, financing, shipping routes out of thin air, or bring in buyers from afar. A token is simply an addressable token of interest, and DeFi is a marketplace operating system. The question really worth discussing is not how many types of assets can go on the chain, but how many assets can complete valuation, financing, hedging, transaction monetization, and loss disposal in a stressful environment, and there is no need for offline meetings and coordination every time a transaction occurs. Tokenization completes the representation of equity; what DeFi brings is actual utility. Tokenization is just a bar code, and a similar scene has happened in the history of the supply chain finance market. The reason why mortgages can be scaled up is not as simple as turning a paper document into an electronic record. To actually achieve large-scale expansion, a complete set of operating mechanisms was created around this type of asset: credit review, post-loan services, securitization, credit rating, warehousing and financing, repurchases, hedging, clearing and settlement, and loss allocation rules. RWA also needed to go through the exact same evolutionary process. An asset that can be adapted to DeFi requires six levels: legally enforceable rights, reliable data sources, clear transfer and redemption rules, enforceable secondary market liquidity, collateral parameters that match actual behavior, and a credible settlement and loss disposal path. Most tokenization projects, on the other hand, tend to stop at the top five levels. There is a simple test that can be used to test the maturity of an asset. It only requires answering three questions: How much is this asset currently worth? Can the agreement complete withdrawal and monetization at this point? If the first two judgments are all wrong, who bears the loss? When smart contracts can definitively answer the above three questions, RWA can truly become a basic component of finance. Before that, it was mostly just a digital packaging shell. The deepest technical contradiction of RWA's quadruple time clock is that RWA runs under multiple sets of different time clocks at the same time. The blockchain can complete settlement in seconds and operate uninterrupted for 7 x 24 hours; oracles may update prices every hour or every day; underlying traditional exchanges are closed at night and on weekends; custodians follow bank working days; and the asset redemption process may take 1 day, 5 days, or even 30 days. If you use such a slow-paced RWA asset to support fast-maturing DeFi liabilities, such as stablecoin loans. This is the term shift, and it is also the core model that banks have relied on for hundreds of years: using short-term debt to fund long-term slow assets. This model has practical value, but the risk must be reasonably priced. Imagine a scenario: At 2 a.m. on Sunday, assets hit the liquidation threshold. Smart contracts can seize tokens immediately, but the underlying real-world market won't open until Monday, and the issuer's redemption business will not be processed until Tuesday. On-chain liquidation has been completed, and real-world asset disposal has only just begun. This creates a clearing gap. DeFi requires immediate withdrawal for monetization, but the real world does not allow it. The time difference between the two. This gap has counterintuitive consequences. Even treasury bonds with very low volatility are riskier than native crypto assets that are more volatile when used as collateral. The price of ETH fluctuates drastically, but it can be traded around the clock; the price of RWA assets appears to be stable, and it may only be up to a dozen hours without a new price tag. A flat price sometimes represents safety, and sometimes it's just a disguise of stale data. Liquidity is an exit channel, not TVL. The digital public also has common misunderstandings about liquidity. Liquidity is not equal to TVL, does not equal the existence of a trading pair, nor does it mean that the issuer promises to eventually redeem it according to net worth. Liquidity refers to the ability to convert a position into the settlement asset you need at an acceptable discount within the time window allowed by your debt. Take a crowded theater for example: the size of the hall cannot determine whether it is safe in the event of a fire; what really matters is the width of the exit channel. One copy of RWA to...

1d agoForesight News#DeFi #RWA

DingTalk launches AI office app QwenNote, hardware QwenNote A2 revealed

Comparative news, according to the “Reading Best” report, DingTalk is promoting a new AI office application QwenNote (Listen to a Thousand Questions). The application is positioned as an AI portable assistant. It integrates real-time voice transcription, summary, and translation through a combination of software and hardware, and is deeply integrated with AI Agent to embed Agent capabilities into voice input and promote a shift from simple recording to automated execution. The application supports real-time transcription and Chinese-English bilingual recognition and language switching, can generate structured meeting minutes, outlines, and to-do tasks, and has built-in AI Q&A and shortcuts based on memorized materials. QwenNote provides a voice memo function. You need to scan the code to connect to the recording device. Press and hold the button on the back of the device to record inspiration. After the recording is completed, it automatically files, generates a title and brief summary, and marks the time. The product also has an incognito protection mode. After opening, the original audio will be physically deleted, and only the transcribed text will be kept to suit confidential scenarios. The supporting hardware QwenNote A2 has been introduced in the app. It also belongs to the Thousand Question Listening hardware ecosystem. The ecosystem also includes DingTalk A1, DingTalk A1 Pro, Cleer H1, etc., and users can scan the code to complete the binding. According to the report, DingTalk hopes to use the integration of software and hardware to complete offline voice collection portals to form a closed loop of live recording, real-time bilingual transcription, AI minutes Q&A, and DingTalk organization collaboration. The listening material can be synchronized to DingTalk AI to listen to and support personal private isolation. The software side continues to embed large models in scenarios such as documents, meetings, and IM, while the hardware side expands the Thousand Question Listening product line. More official release information has not been widely publicly retrieved for related hardware.

2d ago

RootData establishes strategic partnership with Tiger Research

Comparatively, Web3 asset data platform RootData has established a strategic partnership with Tiger Research, Korea's leading crypto research institution. According to the agreement, Tiger Research will fully adopt the API data interface provided by RootData to provide underlying data support for various industry research reports, market insights and project analysis. As a comprehensive data platform, RootData has a large number of indicators such as project fundamentals, financing history, team background, and popularity index. This collaboration will enable Tiger Research's research team to efficiently obtain structured, highly reliable data sets, significantly improve research efficiency and data breadth, and produce more timely and in-depth analytical results. At the same time, RootData's data partner network has also been further expanded, marking the verification of the application value of its data services in professional research scenarios. At present, RootData has accumulated more than 220 data partners, making it one of the key structured data providers in the cryptographic industry.

2d ago

Australian ASIC: Removed more than 3,100 crypto scams in a single fiscal year, and deep AI falsified it as the main modus operandi

In comparison, according to Bitcoin.com, the Australian Securities and Investments Commission (ASIC) revealed that more than 19,400 online scams were removed in the 2026 fiscal year (surging 182% year over year), including 3,106 cryptocurrency investment fraud platforms, an increase of about 30% over the previous fiscal year. ASIC pointed out that fraud gangs are making extensive use of generative AI technology to build a systemic fraud ecosystem: by falsifying deep falsification videos of famous people such as Australian Prime Minister Albanese and financial commentators, to use fake cryptographic trading robots and simultaneously build counterfeit media websites, AI-generated reviews, and false income dashboards to induce victims to continue to invest more, and the funds eventually flow to overseas criminal gangs. Only one high-profile person was fraudulently used, and the related losses have already exceeded 7.4 million Australian dollars.

2d ago

Yao Shunyu reorganizes Tencent's multi-modal route: closer to Liang Wenfeng and away from Li Feifei

Text | Miao Zheng Editor | Wang Jing Source | Letter AI Tencent's mixed element multi-modal team has undergone another personnel change. According to media reports, Lin Xudong, who was responsible for xAI's multi-modal understanding, has left xAI and joined Tencent's mixed element as the head of the multi-modal content generation algorithm. The reason this personnel news is worth paying attention to is that it takes place in the context of continuous adjustments of mixed and multi-modal teams. Over the past period of time, news of the departure of the person in charge, the transfer of researchers, and the addition of new members came out one after another within the mixed yuan. Hu Han, the former head of multimodal understanding, left his career to start a business, and Tian Yonglong and others joined Tencent. The reporting relationship between the original multi-modal team also changed with the integration of the big language model department and the multimodal model department. However, does this mean that Tencent's multi-modal team is “changing the dynasty” is currently unable to draw a direct conclusion. What can be confirmed by public information is that mixed forces have indeed experienced personnel movements and organizational restructuring. The rumor of Lin Xudong's addition is more like a new signal in this adjustment: Tencent is recombining the two routes of multimodal understanding and content generation. So the question is, what exactly did Lin Xudong come from, and what abilities can he add to Tencent? And is Tencent's multi-modal approach shifting from “generating content” to Yao Shunyu's more biased “understanding context and acting in the world”? What is Lin Xudong's origin and what can he do after joining Tencent? According to public information, Lin Xudong graduated from Tsinghua University in 2018 and then went to Columbia University to study for his doctorate. While studying at the blog, his research interests included embedded learning, video analysis, and generative models. He also participated in the Vx2Text project in collaboration with Columbia University and Facebook AI. V indicates video, x indicates unknown, can be sound, voice, or even ambient sound. 2 represents TO, and Text represents subtitles. Its logic is to first convert different modes such as video and sound into vectors similar to “language tokens”, then uniformly feed the language model for fusion, and finally generate open text by an autoregressive decoder. Transformer can only understand tokens, so AI essentially doesn't understand video and audio file formats, making it even less likely to convert them into text. For example, if a dog jumps into the water next to a swimming pool, Vx2Text's video recognizer (V) will output keywords: dog, jump, pool; sound reader (x) will output: sound of water, fluttering. Although the product function of Vx2Text is “generation,” the core difficulty of the product is “understanding.” Of course, Vx2Text doesn't simply “translate” a screen into a few sentences. Models need to recognize people, objects, movements, and events from videos, understand how these things change over time, and finally organize visual information into language. After graduating from his PhD, Lin Xudong joined DeepMind and participated in Gemini-related multi-modal pre-training and post-training work. In 2025, he also joined xAI. According to public information, it is responsible for the direction of multimodal understanding and participating in the training of multimodal content understanding and generation models. Now that he has joined Tencent Hybrid, he will be responsible for the hybrid multi-modal content generation algorithm. Lin Xudong was added not so much to improve the performance of mixed-element multi-modal generation, but rather to solve a problem that plagues all multimodals — understanding. The previous generation model was more like a picture maker. Give it a hint, and it can generate an image or a video. But as long as users make more complex requests, the model just can't keep up. For example, the characters change in the long video, the shape of the object is not consistent before and after, the camera movement does not match the spatial relationship, etc. It's not because the model doesn't generate, but because it doesn't remember and understand the world steadily. Therefore, putting Lin Xudong in the position of multi-modal content generation is probably because he “translated” multi-modality into something AI can understand. Lin Xudong's addition can only be clearly seen in a larger context. That is, now Tencent's mixed element is reorganizing its multi-modal route. In January 2025, Tencent Outstanding Scientist (Tencent Distinguished Scientist) Hu Han succeeded Liu Wei, who had previously left his job, and was fully responsible for the research and development of mixed-element multi-modal models, and also served as Tencent's mixed-element big model Tech Lead. Tencent's internal organization was adjusted in the second half of 2025. He transferred from the Multimodal Model Department to the “Frontier” Frontier Technology Research Group under the Big Language Model Department. The title was changed to Head of the Multimodal Understanding Direction, and the reporting line was also changed to report to Yao Shunyu. The actual position changed from “the head of an independent department” to a “big language model...

2d ago字母AI#AI #Li Feifei #Liang Wenfeng #Tencent

The migrant workers who got on the bus with 1 yuan/share finally waited until the day Yuju went public

Author: David, Shenchao TechFlow Original title: Yu Shu with a market value of 400 billion yuan, and a “multi-millionaire” migrant worker with a market value of 1 yuan/share. On August 19, Yushu Technology landed on the Science and Technology Innovation Board. The opening market rose 629% to 1,100 yuan, and at one point the market capitalization reached 444.9 billion yuan. The retail investors that won the lottery made a profit of 470,000. The entire network is keen to watch the rich-making effects after the stock listing. For example, founder Wang Xingxing's net worth was 133.5 billion, and the richest man in the post-90s changed hands. Lei Jun's Shunwei capital also surged 15.2 billion dollars due to previous investments. The largest external shareholder, Meituan, surpassed 333 billion dollars in profit. Even Liang Wenfeng's Deep Search and Magic Square, and Daxin have also collected 1.1 billion dollars. The bosses counted the money, the retail investors were overwhelmed, and it was a lively meal of wealth. It's just that these numbers have nothing to do with the vast majority of migrant workers. When the spotlight falls on Wang Xingxing, who is ringing the bell, and VC investors, the story that is actually closer to the ceiling of migrant workers getting rich is actually hidden in a company called “Shanghai Yuyi” in Yushu Technology's prospectus. This company does not have “Yuki” in its name, but it is Yushu's employee equity incentive platform and holds 10.94% of Yushu Technology's shares. The batch of 1 yuan/share options that Yu Shu signed to employees in 2017 when they couldn't pay their wages were packed inside. Today, among the owners of this batch of options, the highest net worth at current prices has reached 1.58 billion. Yu Yi and Yu Yi followed the prospectus to check the above. Shanghai Yu Yi is a limited partnership. Employees do not directly hold Yuki shares, but rather hold shares in this partnership company and use it to hold shares indirectly. The partner list contains several layers of shareholding platforms and dozens of natural employees. At the top of the list were the three post-90s. Yang Zhiyu, head of mechanical structure, born in 1991, majoring in machinery and automation at Zhejiang University. He joined the company as soon as it was founded in 2016 and has indirect shareholding of approximately 1.7837 million shares. Based on Yu Shu's high stock price on the first day, the paper net worth was 1.58 billion yuan. Chen Li, head of sales and service system, born in 1990, holds approximately 946,400 shares and has a net worth of 840 million yuan. Zhang Yangguang, head of algorithms and software, born in 1993, majoring in automation at Nankai University. At the 2025 CCTV Spring Festival Gala, “Yang BOT”, a robot twisting songs, went viral all over the country, and he led the development of the function of generating action programs directly from videos. It holds approximately 546,000 shares and has a net worth of 480 million yuan. But in addition to these 3 people, where are the equity incentives for the more migrant workers mentioned earlier? Shanghai Yuyi has only 6 direct partners in total. Wang Xingxing, Chen Li, and Yang Zhiyu are executive partners, plus two partnership companies, Hangzhou Yixin and Hangzhou Yiyi. However, the vast majority of employees' names don't fit into this list. Since limited partnerships only allow 50 partners at most, Yushu Company also added a “share container” with two shares, the next heart and the next intention, on top of it, forming a three-tier structure of “Shanghai Yuyi, Next Heart, Second Mind, Employees”. Therefore, more of this company's incentives for ordinary migrant workers are included in the above two second-level platforms. And when the list goes up to this level, it's where ordinary migrant workers pile up. More than 60 front-line R&D technical supervisors and core technical employees received shares ranging from 0.01% to 0.05% through the platform; based on the closing market value of 358 billion dollars on the first day, their net worth was between 35 million and 170 million. The equity plan that was signed when wages could not be paid went back to 2017. Yushu was founded in the second year. After the financing was spent, the wages could not be paid. In November of that year, Tian Jiangchuan, the original capital, met Wang Xingxing. After talking for a long time but not investing, he wrote four words in his internal investment notes: background grass roots. Three years later, the original capital re-entered at a valuation of 4 times, and Taegawa later attributed his initial misjudgment to his “elitist arrogance.” However, in the early days when there was no capital injection, Wang Xingxing's decision was to stop his own wages and pay employees out of his own pocket. In September of that year, the company signed the first batch of option agreements with 17 first-generation core employees including Yang Zhiyu, at an exercise price of 1 yuan/registered capital. Over the next few years, the company carried out multiple rounds of equity incentives one after another, and eventually all of them were managed uniformly by the Shanghai Yuyi platform. The three 90s at the top of the list, and more than 100 people closely behind, came in one by one. now...

3d ago深潮TechFlow#public #Yushu Technology

Tang Jie, founder of Zhi Spectrum: Big model scaling is not just heap parameters; the core of future competition will shift to post-training and reasoning capabilities

Comparing news, Tang Jie, founder of Smart Spectrum, published a thought article on the big model Scaling Law on the X platform, saying that currently, the artificial intelligence industry's understanding of improving model capabilities is shifting from “expanding the scale of parameters” to multi-dimensional expansion. The number of parameters is not the only indicator for measuring the model's ability; it also requires a comprehensive evaluation based on data scale, computational resource allocation methods, and actual model operation scenarios. Tang Jie pointed out that early research had driven the industry to rapidly expand the scale of model parameters. Kaplan et al.'s research in 2020 suggests that the growth rate of model parameters should be higher than the data growth rate, driving the development of large-scale models such as GPT-3, Gopher, and MT-NLG. However, by analyzing hundreds of models in 2022, Hoffmann and others discovered that the optimal calculation solution is closer to “about 20 training tokens for each parameter,” and that model parameters and data scale should continue to grow at the same time. In the past, the path of pursuing a trillion parameter model was actually a “yaw” experienced by the industry. As model application scenarios change, inference costs have gradually become an important part of life cycle costs, and optimization goals have also shifted from simply reducing training costs to improving long-term operation efficiency. Therefore, the “small model+fuller training” route has begun to receive attention. Tang Jie said that the sparse hybrid expert (MoE) architecture has further changed the scaling logic. In MoE models, the total number of parameters determines how much knowledge the model can store, and activation parameters and effective depth affect the model's ability to complete complex inference tasks. For tasks that require a long chain of reasoning, such as finding bugs, the ability does not come from simply memorizing more information, but rather requires models to maintain the continuity of the multi-step inference process. Recent research shows that there is no uniform answer for the optimal “token/parameter ratio”: memory-oriented tasks require more parameters, while biased reasoning tasks rely more on data and computational depth. At a fixed scale of training data, blindly increasing total parameters may even weaken reasoning ability, while increasing the number of active experts is more helpful in improving model performance. Regarding the latest development of the smart spectrum model, Tang Jie revealed that GLM-5.3 is an experiment in the direction of scaling. This model uses the same basic model, architecture, and total parameters and activation parameter scales as GLM-5.2, but post-training optimization is performed through a month of large-scale long-term environmental training and reinforcement learning (RL). The performance improvement does not come from an increase in parameters, but from an expansion in the post-training phase. He concluded that competition for large models has moved from simply competing for parameter sizes to the stage of exploring “multi-dimensional scaling”. Future model capability improvements will rely more on continuous optimization of training strategies, inference depth, and post-training capabilities.

3d ago

$21 billion AI chip upstart Etched questioned: performance has not been verified by a third party so far

Comparing the news, AI News, Etched has caught the attention of the chip community after having just completed $700 million at a valuation of 21 billion US dollars. The Tiny Corp, an AI computing team founded by famous hacker George Hotz, the team behind tinygrad, an open source deep learning framework, publicly questioned Etched's technical propaganda: there are many photos of investors, orders, and hardware, but too little data to actually verify performance. One of Etched's core selling points is LVI, which allows the chip to run AI inference at lower voltages. Etched claims that this allows the trillion-parameter sparse MoE to reach over 80% of its theoretical peak computing power. Chip design practitioner Wesley Yue questioned that a high ratio does not mean absolute performance is strong. MFU (model computing power utilization) measures the ratio of actual computational power to the theoretical peak. If the chip itself has lower peak computing power, even if the utilization rate reaches 80%, it may not be able to outperform its rivals. Etched has yet to disclose full FLOPs, power consumption, and third-party benchmarks. The official website still only writes that early customer tests have reached the leading level, and that detailed performance data will be published later. However, there is currently no evidence that Etched was a fraud. The Wall Street Journal and Reuters have both confirmed that their chips have been shipped. Jane Street got its first complete rack last month, and deployment has already begun. The biggest question now is not whether there is a chip or not, but whether this chip has been advertised that well.

3d ago

Liang Wenfeng made a profit of 1.1 billion when Xinyu Technology made a profit of 1.1 billion

Comparing news, Yushu Technology officially entered the Science and Technology Innovation Board. The opening market surged 629.44% to 1100 yuan/share, with a total market value of 444.9 billion yuan. Liang Wenfeng's in-depth exploration, Magic Square Quantification, and Jiuzhang Asset were allocated a total of 1.1916 million shares through strategic placement and offline sales. Based on the issue price, Liang Wenfeng's profit of Xinyu Shu Technology surpassed 1.1 billion yuan. (Sino-Singapore Economic Link)

3d ago

Xu Jiayin destroyed the second generation of Northeast China's wealth of 4.2 billion

Source: Phoenix News Finance “Company Research Institute” Recently, a ruling by the Hong Kong High Court brought an old account that had been sunk for five years back to the table. Yingjia International Real Estate applied to the court for an injunction to stop Evergrande's liquidators from collecting the debt, but it was rejected. The liquidators wanted HK$5.97 billion, with principal and interest. And behind this huge dispute is a fixed growth game that took place during the peak of Evergrande Auto. In 2021, a second-generation wealthy person from Northeast China paid out 4.2 billion yuan, and Evergrande shares in exchange were nearly zero. What was thought to be just a bridge loan was turned into a huge debt of nearly HK$6 billion hanging over an offshore shell company. Cross-border crossing of HK$01 billion, a seemingly seamless closed loop. On January 24, 2021, Evergrande Motor issued an announcement to complete the IPO with six subscribers. A total allocation of 952 million shares, or HK$27.3 per share, raised a total of HK$26 billion. At that time, Evergrande Auto's market capitalization once surpassed 600 billion Hong Kong dollars, putting pressure on BYD and topping the domestic car companies' market capitalization list. Heyirong International Trading Co., Ltd., controlled by Wang Kaiguo, born in 1989, is also one of the subscribers. It promised to invest HK$5 billion to win about 183 million new shares, with a 12-month sales ban. The paper agreement has been settled, yet the financial problem is looming. It is necessary to mobilize funds in the amount of HK$5 billion to participate in Hong Kong stock subscriptions. The formal foreign exchange approval cycle is long, and Xu Jiayin cannot wait. Add up the two sides and come up with a quick way to pay. The whole process was implemented in three steps. The first step is domestic loans. In March 2021, Heyirong signed a RMB loan agreement with Evergrande, and Heyirong lent funds equivalent to HK$5 billion to Evergrande. From April 7 to 9, Heyirong remitted a total of RMB 4.2 billion to the Guangzhou Kailong Real Estate Co., Ltd. account designated by Evergrande in three transactions. Based on the exchange rate on the day of the transfer, it was just HK$5 billion. The second step is overseas loans. Also in March 2021, Guoxiong Holdings, a subsidiary of Evergrande, signed a loan agreement with Yingjia International Real Estate, wholly-owned by Wang Lihua. Guoxiong loaned HK$5 billion to Yingjia for a period of two years, repaid on a regular schedule without interest, and accrued interest on a 4% annual interest rate. From April 7 to 9, the HKD was also credited to the Yingjia account in three installments. The third step is to complete the IPO. After receiving HK$5 billion, Yingjia immediately transferred the full amount to Hongchang International Trade, another Hong Kong entity controlled by Wang Kaiguo. On April 9, Hongchang International successfully obtained Evergrande Motor's share certificate for 183 million new shares. According to Yingjia International Real Estate's claim in the lawsuit, there was an internal agreement between Evergrande's former management and Yingjia International Real Estate that no actual repayment was required for the above loans. However, on January 29, 2024, the Hong Kong High Court issued a winding-up order for China Evergrande. The liquidator took over the assets and contract files, and this loan agreement with complete procedures and complete settlement of funds was overturned. The old management's verbal tacit agreement was not binding on the liquidators. The contract is written in black and white with a principal amount of HK$5 billion and 4% overdue interest. This is a real claim with legal effect. In May 2025, Guoxiong Holdings officially issued a letter requesting Yingjia International Real Estate to repay nearly HK$6 billion in principal and interest. Yingjia refused to comply with the contract and in turn applied to the Hong Kong High Court for an injunction in an attempt to prevent Guoxiong Holdings from filing a winding-up petition. During the trial, Yingjia International Real Estate changed its arguments several times. First, they claimed that the loan was a false transaction, then changed their rhetoric to saying that there was a special funding arrangement, and finally put forward the core statement: the two parties had an oral subsidiary agreement exempt from enforcement. In response, presiding judge Chen Jingfen found that the oral subsidiary agreement claimed by Yingjia was “recently fabricated,” and rejected all of its defenses one by one. Chen Jingfen said that the loan contract signed in writing in the case and the funds were paid in full constituted a real claim. It was impossible to deny the legal effect of the formal contract based only on an oral agreement claimed by one party afterwards. The execution of the judgment on August 7 means that Evergrande's liquidators can officially commence the winding-up procedure against Yingjia and recover nearly HK$6 billion in claims. However, Yingjia itself is only an offshore shell company; it is still unknown how many actual assets it has that can be executed under its name. These offshore shell companies often only assume the functions of holding shares and transferring capital, making it difficult to get a glimpse of the real trading context of Fujia. To understand the private capital giant's layout in the A-share market, we also need to start with Wang Kaiguo, the core agent who was pushed to the front of the stage. 02 He took 5 directors' seats at age 32 and quietly left the market on April 21, 2021. Financial Street Holdings issued a director candidate announcement. The name “Wang Kaiguo” first appeared in the official disclosure documents of A-share listed companies. Five days later, on April 26, Goldwind Technology announced the “Proposed Election of Non-Executive Directors” on the Hong Kong Stock Exchange...

3d agoWendy#Evergrande #BYD #Wang Kaiguo #Xu Jiayin