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吴说获悉，日本上市公司 eole（东证 Growth：2334）于 7 月 28 日以约 1,008 万日元（约 6.16 万美元）购入 1,078.2547 枚 HYPE，平均成本约 9,352.77 日元（约 57.10 美元），成为日本首家购买 HYPE 的上市公司。公司计划在 8 月底前分批追加购买，使 HYPE 总投入达到 1 亿日元（约 61.05 万美元），并将其纳入“Neo Crypto Bank”构想下的数字资产储备，用于支持链上金融及 Web3 业务。

#为什么是中国# #WhyChina# The Global Logic of China's Economic Growth in the First Half of 2026: A 4.7% GDP Increase 2026年上半年GDP增长4.7%：中国经济增长的全球逻辑 According to the latest semi-annual report, China's gross domestic product (GDP) reached 69.6 trillion yuan, representing a year-on-year increase of 4.7% at constant prices, in line with the annual growth target. Against a backdrop of intertwined international complexities and volatilities, China's economic performance is commendable. Yet, a closer look at the data reveals that the logic underpinning China's economic growth is being reshaped. (I) At the Industry Level, Notable Highlights Emerge: First, new quality productive forces are being cultivated and strengthened at an accelerated pace.** In the first half of the year, industrial production grew robustly. The value-added output of the equipment manufacturing sector increased by 9.3% year-on-year, and that of high-tech manufacturing grew by 13.3%, both outpacing the overall growth rate of industrial output above a designated scale. This underscores a clear trend toward a high-end, intelligent, green, and integrated industrial structure. Looking at specific products, the output of 3D printing equipment, lithium-ion batteries, industrial robots, and other products emblematic of new quality productive forces surged. The average daily token call volume has reached hundreds of trillions, showcasing the vitality and potential of the digital and intelligent economies. Second, new growth drivers are accelerating to take on a leading role. Preliminary estimates suggest that new growth drivers—encompassing high-end manufacturing, the digital economy, and modern services—contributed over 40% to economic growth in the first half of the year. The economy's distinct shift toward a higher quality and more optimized structure is evident, and this overall trend is accelerating. For instance, industries related to artificial intelligence, such as integrated circuit manufacturing and intelligent vehicle equipment manufacturing, have all maintained high growth rates exceeding 30%, vividly illustrating the pace of China's industrial upgrading. Third, confidence on the investment front is on the rise. In the first half of the year, investment in high-tech industries grew by 4.6% year-on-year. Notably, investment in the manufacturing of aircraft, spacecraft, and equipment, computer and office equipment manufacturing, and information services grew by 23.3%, 8.1%, and 15.5%, respectively. Investment structure best reflects market expectations. The increasing "new economy content" in investments signals an acceleration in the replacement of old growth drivers with new ones. Investment in intellectual property products grew by 9.4%, indicating that enterprises are placing greater emphasis on R&D and innovation. This suggests that technological advancement is not simply about capacity expansion but about qualitative change driven by innovation. (II) Observing a Major Economy Requires Looking Beyond the Immediate Figures to the Long-term Trajectory. Behind the "new economy content" of the semi-annual report lies China's ongoing transformation from a global manufacturing hub to a global center of innovation. At the 17th Annual Meeting of the New Champions (Summer Davos), observers noted a new phenomenon: a host of unicorn companies are heading to China. They are establishing R&D centers, regional headquarters, and deeply integrating into China's innovation and industrial chains—shifting from "produced in China" to "created in China." So, why China? Economist Justin Yifu Lin, in his book *Demystifying the Chinese Economy, touched upon the theory of the "speed of technological change." He argues that the essence of the industrial revolution is not just the application of new technologies, but more fundamentally, the ever-accelerating pace of technological change. Since the mid-18th century, starting with the steam engine reshaping the textile industry, the snowball of technological change has grown, rapidly sweeping through industries like chemicals and automobiles, ultimately redrawing the geographical map of great power competition. Looking at China today, the trajectory of accelerating technological change is equally clear. A leading enterprise can drive an entire industry, which in turn can boost a whole region. These burgeoning industrial clusters, growing from saplings to forests, not only enhance production efficiency and invigorate market vitality but also effectively improve development quality and resilience. For example, specialized and sophisticated "little giant" enterprises above a designated scale in Beijing, through deep cultivation of innovation chains, supply chain collaboration, and international expansion, have become "connecting points" and "accelerators" for the dual circulation strategy. More importantly, emerging industrial clusters possess powerful spillover effects. The rapid rise of new energy vehicles is not only reshaping the automotive industry but also driving transformations in chips, software, and energy networks, allowing more sectors to gain value from efficiency improvements. The swift advancements in AI and biomedicine are sparking a "gentle qualitative change" in people's livelihoods, significantly enhancing the sense of fulfillment and well-being through smarter, more affordable products and more livable environments. (III) Looking from the First Half to the Full Year, China's Development Momentum Remains Positive. Of course, during this critical period of transitioning between old and new growth drivers, China's economy still faces lingering issues and new challenges. Some core areas are still grappling with "bottleneck" technologies, certain high-tech industries face external risks of "decoupling" and supply chain disruptions, and "involution"-style competition affects the new energy market ecosystem. However, most of these are issues arising from development and transition, and they can be addressed with effort. The supporting conditions and fundamental trends for long-term economic improvement remain unchanged. China's economic journey toward a newer, higher-quality model is itself a process of encountering new problems and solving them along the way. By maintaining confidence, proceeding steadily, and balancing both qualitative improvements and quantitative growth, China's industries are poised to be brimming with dynamism, and the Chinese economy will continue to advance steadily and sustainably. #China# #Jiangxi# #JiangxiEconomy# #世界经济看中国# #赣出新精彩#

经济发展局和企业发展局的战略投资平台SG Growth Capital将与美国风险投资公司Santé Ventures合作，成立全新平台Santé Accel Singapore，为新加坡医疗器械、生物科技及健康科技企业提供创业孵化和投资支持，并培养本地创业人才。

Some news! I've joined @StandX_Official as Growth Lead. 做了五年多crypto VC，投过不少项目，也见证了它们从0到1的过程——但一直是在投资人视角看数字，现在想换个位置，自己下场试试。 PerpDEX是一个非常卷的赛道，但也是crypto里少数有真实用户和真实收入的赛道（正因为如此才卷）。StandX吸引我的首先是团队的迭代速度：主网上线四个月，已经发布了3个SIP，积累了235K+用户，$100M+ TVL，和$57B交易额。 更重要的是团队在坚持做创新而不是fork。以Block Trade（SIP-1）为例：我一开始还以为它只是"大宗交易"，但实际上远不止于此——它支持任意Size的区块交易，是连接链与StandX结算引擎的跨链执行基建。在不影响订单簿价格发现的前提下，为包括机构在内的所有用户提供了传统CLOB难以实现的执行策略。我能看到在这个基础上扩展到更多衍生品类型的巨大空间。 但真正让我下定决心的，是和Founders @StandX_AG 的几次长谈。他们有非常硬核且充满色彩的过往经历，做事风格却出奇地务实和低调。在这个浮躁的行业里，这种纯粹的Builder气质太少见了。 很兴奋能加入这个团队一起迎接挑战。接下来会在这里持续分享赛道观察、增长实践，以及从VC转operator的真实体感。 欢迎大家来交流，DM open 🫡

AI，泡沫即飞轮？ MIT经济学家 Ricardo Caballero 在最新工作论文《Speculative Growth and the AI "Bubble"》中提出了一个非常有意思的观点： 真正的问题不是 AI 是不是泡沫，而是泡沫本身能否创造未来的基本面。 传统金融认为，估值来自基本面。未来现金流决定今天的价格。如果价格远远高于现金流，那就是泡沫。这几乎是所有价值投资、DCF模型以及有效市场理论共同遵循的逻辑。 Caballero则把因果关系补充成了一个闭环。价格不仅反映未来，也塑造未来。高估值带来融资能力，融资能力带来资本形成，资本形成提高生产率，生产率最终又改善未来现金流，于是原本看似脱离基本面的估值，反而成为未来基本面形成的一部分（有点像索罗斯的反身性？）。 论文认为，当估值能够影响投资时，价格上涨本身就可以帮助创造未来的基本面。 这一逻辑会在 AI 上成立的关键在于，AI 不是传统意义上的资本。 普通资本遵循边际收益递减。建更多工厂，最终会遇到需求不足、产能过剩，资本回报越来越低。 但 Caballero 认为，AI 更接近一种能够持续扩张的"劳动型资本"。GPU、模型、Agent 并不仅仅增加机器数量，而是在不断增加整个经济中的有效劳动。论文中直接将 AI 建模为能够执行原本由劳动完成任务的资本，因此资本增加的同时，劳动能力也同步扩大，资本收益递减被明显削弱。 如果继续深究，还有更加重要的发现：AI 投资改变了收入分配。 越来越多收入流向资本所有者，而资本所有者天然拥有更高的储蓄倾向。储蓄增加意味着长期资金供给增加，长期利率下降，更大的资本存量反而更容易被整个经济承载。论文称之为 Funding Feedback。资本形成越多，未来融资成本越低；融资成本越低，又进一步支持更多资本形成。整个系统开始出现正反馈，而不是传统增长模型里的负反馈。 于是经济开始出现两个完全不同的长期均衡。 一个世界里，AI 投资始终不足，资本形成缓慢，生产率长期维持低增长。 另一个世界里，AI 获得持续融资，大规模建设数据中心、GPU、模型和 Agent，最终形成新的高资本、高生产率均衡。 真正有意思的是，高资本均衡虽然存在，却无法仅靠理性市场自动到达。论文证明，从今天这个低资本状态出发，即使所有投资者都是完全理性的，也不会主动跳到那个更好的未来。原因很简单。今天没有足够资本，就不会有未来的高增长；没有未来高增长，今天就不会有高估值；没有高估值，也就没有资本形成。整个系统陷入自我锁定。 泡沫恰恰打破了这个循环。 高估值让企业能够融资，融资建设更多 GPU，训练更大的模型，部署更多 Agent，最终真正提高整个经济的生产率。泡沫不是长期均衡，而是通向长期均衡的桥梁。 这也是论文为什么反复强调 Fragility。真正的问题从来不是泡沫会不会破，而是泡沫会不会破得太早。如果资本还没有形成，融资就停止，那么整个 AI 建设就会中断，未来增长也随之消失。如果在泡沫破裂之前已经完成了足够多的数据中心、模型、Agent 和基础设施建设，那么即使估值最终回归正常，高资本均衡依然能够维持。论文明确指出，关键不是修正是否发生，而是修正是否发生得过早。 互联网就是一个典型例子。2000 年互联网泡沫彻底破裂，但光纤、服务器、软件、数据中心和互联网人才全部保留下来。泡沫消失了，互联网革命却真正开始了。AI 很可能也是类似过程，只不过留下来的不只是网络，而是智能本身。 不过，我认为 Caballero 的框架还能再向前推一步。 论文把 AI 建模成"可复制的劳动"，但现实中的 AI 正越来越接近"可复制的科研人员"。如果 AI 不仅能够替代劳动，还能够参与科研、写代码、设计芯片、发现新材料、研发新模型，那么它改变的不只是生产函数，而是创新函数。 过去，创新能力主要取决于科学家数量、工程师数量以及优秀人才数量，因此重大技术革命通常需要几十年积累，这也是康波周期长期存在的重要原因。并不是经济天然每六十年发生一次革命，而是创新资源本身增长太慢。 AI 第一次开始打破这一约束。 未来的创新能力，不再只是依靠人脑（Human brain），而可能是 Human + Millions of AI Agents。更进一步，创新能力甚至可能只依赖 ai（算力）。 算力持续增长，创新能力也持续增长。创新第一次变成了一种可以资本化、规模化扩张的生产要素。 如果再结合今天正在快速发展的 Coding Agent、Research Agent、自动科研以及递归自我改进（RSI），这个反馈会变得更强。更多 AI 带来更快科研，更快科研产生更好的模型，更好的模型继续提高科研效率，形成真正意义上的 Intelligence Flywheel。创新速度本身开始加速，而不仅仅是生产效率提高。 这也是为什么我一直认为，AI 的经济回报很可能符合 "Slowly, Then Suddenly"。 今天大家看到的是 GPU 投资、模型训练、数据中心建设，ROI 看起来并不高，于是很多人开始怀疑 AI 是不是泡沫。但这些投资真正购买的，并不是今天的利润，而是未来的智能资本。当模型能力跨越某个临界点，大规模 Agent 开始进入企业，劳动替代开始发生，生产率可能出现非线性的跃迁，过去几年看似过高的估值，也开始真正兑现。 这意味着，Caballero 所提出的反馈环路： 估值 → 投资 → 资本形成 → 基本面 未来很可能进一步演化为： 估值 → 投资 → 算力 → 智能 → 创新 → 更多 Ideas → 更高生产率 → 更高利润 → 更高估值 这里真正形成正反馈的不只是资本，而是整个社会的创新能力。 如果这一过程成立，那么 AI 带来的变化可能不仅仅是一次新的技术革命，而是改变了技术革命本身的产生机制。 历史上的康德拉季耶夫长波之所以持续四五十年，很大程度上并不是经济规律决定的，而是因为创新资源始终稀缺：科学家有限、研发能力有限、知识扩散缓慢。AI 正在改变这一前提。 未来，我们或许看到的不是一个越来越短的康波，而是在同一个 AI 平台上持续涌现多个产业革命：AI 药物、AI 材料、AI 芯片、AI 机器人、AI 生物制造……创新开始工业化，技术革命开始连续发生。 如果说熊彼特让创新成为增长的核心，罗默让知识成为增长的核心，那么 RSI 与 Caballero 共同指向的，可能是下一阶段增长理论的核心命题： 之前的熊彼特的经济周期理论，依靠破坏式创新，破坏式创新，依靠人脑和偶尔出现的天才；而 AI，第一次让这样的天才本身成为可以投资、可以批量制造、可以不断增强、而且还可以不断自我强化的资本。 从这个角度来看目前无论多大的泡沫，在指数级增长的创新面前，都可能会被很快消化。