吴说获悉，去中心化 AI 协议 Prime Intellect 宣布完成 1.3 亿美元 A 轮融资，由 Radical Ventures 领投，NVIDIA Ventures、Intel Capital、Dell Technologies Capital 等参投，总融资额超过 1.5 亿美元。本轮资金将用于构建开放超级智能堆栈，涵盖算力、大规模强化学习、环境、沙盒、评估和部署等。

👇本周前五加密融资事件 | 7.5 - 7.10 1⃣ Prime Intellect - 1.3亿美元 去中心化人工智能平台@PrimeIntellect 完成 1.3 亿美元 A 轮融资，估值达到 10 亿美元。融资由 Radical Ventures @radicalvcfund领投，英伟达@nvidia风投、Intel Capital @intelcapital等参投。 2⃣ Gauntlet - 1.25亿美元 DeFi 资产管理与风险分析公司 Gauntlet @gauntlet_xyz完成 1.25 亿美元融资，由日本金融集团 SBI 控股独家投资，融资于今年 6 月完成，具体估值未披露。 3⃣ EDX Markets - 7600万美元 机构加密货币交易平台 EDX Markets 在 C 轮融资中获得 7600 万美元，由日本金融集团SBI Holdings 领投 4⃣ Mercado Bitcoin - 2000万美元 Tether 宣布向@MercadoBitcoin 的战略增长融资轮投资 2000 万美元。Mercado Bitcoin 是一家受监管的巴西链上金融服务平台，拥有 450 万用户，已发行超过 20 亿雷亚尔的代币化资产，并在巴西和欧洲拥有超过 10 个牌照。 5⃣ QIZ Security - 1700万美元 量子安全初创公司 QIZ Security 宣布完成 1700 万美元种子轮融资，由 Bessemer Venture Partners 和 Merlin Ventures 领投，Evolution Equity Partners、Qbeat Ventures、Singtel Innov8 和 Qino Cyber Capital 参投。

吴说获悉，投资机构 CoinFund 创始人 Jake Brukhman 表示，随着 Anthropic 开始遵守美国 AI 出口管制要求，AI 模型的中心化及监管控制趋势进一步显现。他认为，去中心化 AI 网络可作为制衡力量，而关键在于利用全球分散的 GPU 资源开展前沿模型训练。Brukhman 指出，包括 Gensyn、Prime Intellect、Pluralis 和 Nous Research 等团队正研究分布式训练方案，以降低对大型科技公司的依赖；其中 Pluralis 还尝试通过拆分模型权重建立去中心化 AI 的商业模式。

今天谈谈学术近亲繁殖。 十年前，我特别羡慕本硕博八年贯通的人。 那时候觉得，在一所学校从青涩呆到成熟，校园里每一棵树、每一栋教学楼都刻满回忆，甚至连食堂阿姨都认识你，是一件极其浪漫且幸福的事情。 看着国内很多高校推行的长周期培养项目，总觉得这种长情的陪伴代表着学术的极致深耕。 直到后来，本科到美国的学术体系里，才慢慢发现，本硕博都在同一个学校对于学术层面其实是一个极其明显的隐性扣分项。 现代科学作为一套全球化生态，有着它非常冷酷且底层的运行逻辑。 本硕博如果都在同一个系甚至同一个课题组，你大概率会完美继承导师的思维盲区、研究范式，甚至连说话的腔调都如出一辙。 科学的突破往往发生在不同知识领域的交叉碰撞边界，长期的近距离同质化，会把一个人的学术视野牢牢锁死在舒适区里。 你可能会问：苏姿丰不就是MIT本硕博吗？ 其实，学术界，尤其是高校教职招聘对学术血统纯正性的焦虑，很多时候是为了防止圈子固化和思想近视。 而工业界，像芯片研发这种极度看重工程落地与执行力的领域对出身的教条没那么敏感，它只认你能打出什么样的硬仗。 在北美学术圈层，招聘市场的硬性排他上的局限是存在。因为在顶尖大学的教职招聘中，名校普遍遵循避嫌和引血原则，即极少直接留用自己的博士毕业生。 委员会更看重你是否有过多重异质环境的生存能力（比如本科在A、博士在B、博后在C）。 如果简历从头到尾只有一个校名，往往会被默认缺乏跨圈层交流和独立开辟新阵地能力。 从系统论角度，科学研究本质上是一个抗拒熵增、追求信息持续输入的系统。 人类，天然喜欢低熵的熟悉感，本硕博留在原地确实省去了所有社会适应成本； 但我想，一个具有生命力的学者，必须不断把自己扔进未知的、充满异质性的外部环境中，去对抗思维的僵化。 一个人的知识来源、导师网络、评价体系、合作网络和研究范式长期来自同一个局部生态，是很危险的。 不过我之前跟同学聊，比如一个人本硕博都在 MIT，但期间： 本科做理论物理； 博士转 CS； 去 Stanford 做博后； 与欧洲实验室长期合作； 最后研究一个完全不同的问题； 他的学术基因可能比一个本科 A、硕博 B、但一直在同一个课题组的人更加多样。 反过来，一个人本科 A、博士 B、博后 C，但每一步都沿着同一个导师网络、同一个范式走，也未必真正实现了 intellectual diversification。 所以我会把学术近亲繁殖重新定义为知识与评价机制的局部闭环。 我不知道大家是否观察到现在的一种非常普遍的情况：所有人都非常聪明，但所有聪明人都在同一个坐标系里聪明。 这和之前谈非平衡系统时的思路非常接近。 我认为，一个局部系统如果没有足够的外部扰动，就会越来越接近自己的稳定吸引子。 这里，我也想引入我那位朋友的观点。 她认为近亲繁殖也并非全是坏事，因为科学进步同样需要高度连续性。 假设一个人本科四年换一次范式，硕士换一次，博士换一次，博后再换一次，所有东西都浅尝辄止。他的 entropy 很高，但未必有 deep work。 她的论据是，伟大的科学家往往需要在一个问题上连续投入十年、二十年。 所以学术成长其实存在一个很漂亮的张力，即局部深耕与外部扰动的动态结合。 没有深耕，只有漂泊，会没有理论积累。 没有扰动，只有深耕，容易形成范式锁定。 其实到这里，我觉得学术圈和演化系统很像：mutation 太低，stagnation；mutation 太高，无法积累。 判断一个系统是否有生命力，其实需要看它是否在稳定遗传与持续变异间维持动态平衡。 一个具有生命力的学者，必须不断把自己扔进未知的、充满异质性的外部环境中，去对抗思维的僵化。 或者，我会更进一步，把它理解成一种更一般的科学观： 优秀学者，要去主动寻找能够让自己暴露于认知误差的环境。 本科 → 硕士 → 博士 → 博后 → 教授 上面这个路径只是表象。 具有原创性的人，人生轨迹应该是： A领域 → B领域 → 某个偶然问题 → C领域 → 又回到A → 创造此前不存在的交叉点。 他的大脑里存在多个互相竞争的模型，而一个现象出现的时候，他不会只有一种解释框架。物理学家可能用动力系统看它；计算机科学家可能用算法复杂度看它；生物学家可能用演化看它；经济学家可能用激励机制看它。 而原创性经常就产生于这些模型在同一个人的脑子里发生碰撞的瞬间。 回到我最初的议题，关于本硕博在一个学校和学术近亲繁殖问题。 其实本硕博同校不好只是表象。 我要强调的是，科学需要积累，但科学家不能只继承。知识来源过于单一，以至于一个人逐渐失去了发现自己认知边界的能力的情况，是需要随时警惕的。 从这个意义上说，学术近亲繁殖，其实是一个知识系统的遗传多样性问题。

#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#

新版的 GLM 5.2 基本稳稳站住全球开源模型第一的位置了，作为开源模型可以排到全球前三。上一个这么牛逼的还是 DeepSeek，给 AI 圈带去一点震撼。Claude 4.6 之后提升就没那么大了，4.6 也是 AI 编程的可用线。前端时间巨头们开始对 AI 投入算总账，几十亿美金的 AI 账单，现在用开源的GLM 5.2自己部署，就能立即省下高额的 AI 账单，Claude 的收入端可能要大受影响了。 中美顶尖 AI 的差距大概也就是半年左右，另一方面也说明大模型的护城河没那么深，半年时间的差距并非不可逾越，Anthropic 的万亿估值显然是挺虚的。Claude 年初最辉煌的时候，总有人会说 Claude 多牛逼，迭代速度多强，我去看了一下 zcode 的迭代速度基本是一天一更，这次zcode3.0+GLM5.2 真的和 codex 使用起来没什么太大差距了。 还有中国为什么一直追不上，主要是因为算力资源的限制，假如算力限制放开怕是已经 4:6 开了。可怜的智谱算力一直都不足，难以想象要是有充足的算力且模型是闭源的情况下，难以想象收入会高到什么程度。 The new version of GLM 5.2 basically stand firmly in the first position of the global open source model, as an open source model can be ranked in the world's top three. The last one was DeepSeek, bringing a little shock to the AI circle. After Claude 4.6, the promotion was not so big, and 4.6 was also an available line for AI programming. The front-end time giants are starting to take a general ledger of AI investment, billions of dollars in AI bills, and now deploying themselves with the open source GLM 5.2 can immediately save the high AI bills, and Claude's revenue side may be greatly affected. The gap between the top AI of China and the United States is about half a year, on the other hand, it also shows that the moat of the big model is not so deep, the gap in half a year is not insurmountable, the Anthropic trillion valuation is obviously quite empty. When Claude was at its most brilliant at the beginning of the year, some people would always say how awesome Claude was and how fast the iteration speed was. I went to see that the iteration speed of zcode was basically one change every day. This time zcode3.0 GLM5.2 was really not much different from codex. There is also a reason why China has been unable to catch up, mainly because of the limitation of computing resources. If the limitation of computing power is released, it may have already been opened at 4:6. Poor intellectual spectrum computing power has been insufficient, it is difficult to imagine if there is sufficient computing power and the model is closed source, it is difficult to imagine how high the income will be.