LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

Odaily星球日报Pubblicato 2024-01-30Pubblicato ultima volta 2024-01-30

Introduzione

全球股票基金和新兴市场基金上周大幅净流入,中国股票基金流入规模创历史新高,但机构对中国股票的仓位依然偏低,本周注目美国多家科技巨头财报,波动性或加大。

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

摘要

  • 中国市场上周最值得关注,一系列政策/传闻支持了中国股票市场和大宗商品价格,人民币也大幅升值。这似乎改变了人们对中国资产的负面情绪。

  • 全球股票基金和新兴市场基金上周大幅净流入,中国股票基金流入规模创历史新高。但机构对中国股票的仓位依然偏低。

  • 美国四季度 GDP 数据、制造业 PMI 数据、个人消费支出数据好于预期。但标普 500 和美债收益率均小幅上涨,显示市场更看重经济增长而非利率。

  • 英特尔业绩主要数字超预期,但股票遭到重挫,反映投资者对高估值股票的挑剔态度。

  • 中国 11 月份增持美债,可能因为中美关系和解及美债价格趋涨所致。

  • 本周注目美国多家科技巨头财报,波动性或加大。另外关注各国央行政策走向,及美国 1 季度政府债发行计划的影响。

中国市场

上周最值得关注的是中国市场,一系列政策/传闻支持了中国股票市场和大宗商品价格,人民币也在前半周对美元大幅升值 500 点(7.19-7.14 也是时隔三周首次收涨),中国股市触底反弹的情绪正在升温,K 线上快速杀跌后走出三连阳的行情也是技术分析派喜闻乐见的底部形态。

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

背景

  • 周二有关潜在 2 万亿股市救助计划的传闻,如果这个计划成真,有望改变低迷的人民币资产的情绪。这笔资金可能会使用国有企业存放在海外的现金,相当于中国内地股市自由流通市值的 8% 左右。

  • 中国央行行长潘功胜周三意外宣布, 2 月 5 日下调存款准备金率 0.5 个百分点=向市场提供长期流动性人民币 1 万亿元,以及 25 bp 的“定向降息”。中国人民银行行长在发布会上突发宣布降准和降息等重大消息,且赶在收盘前发布,在中国金融历史上非常罕见,凸显中国资产连续大跌之际,中国监管提振全球投资者信心非常急迫。(由于市场一直预计央行要降息,之前预期扑空导致“股灾”,监管压力山大,)

  • 周三,国务院国资委表示,将进一步研究将市值管理纳入中央企业负责人业绩考核,引导央企负责人更加重视所控股上市公司的市场表现。这意味着央企加大市场化增持、回购、分红等真金白银的操作将变成政治任务,后续估计也会传导到地方上市企业,在此背景下,周四“中字头”股票掀起涨停潮,高股息、低估值的企业成为买入热点。

随着经济和股市陷入困境,政策制定者采取了一系列行动,但没有一个被认为是足够的。押注于火箭筒式刺激措施的投资者——就像全球金融危机期间看到的那样——却被冷落了。国家基金购买 ETF、降低股票交易印花税以及限制新股上市等措施最多只提供了短暂的反弹。

但当局最近加大了支持力度,人们希望这一次可能会有所不同。本周股市罕见地连续三天上涨。这不是中国政府第一次在股市暴跌时大举救市。2015 年,中国也动用了各种国有控股基金向股市投入巨资,最近还买入中国股票 ETF。

策略

关于投资策略,除了国企概念,与指数概念,一类投资人认为应坚守电动汽车和半导体等板块,因为无论政府是否出台大规模刺激政策,这些行业都已经具备充足的发展后劲。

还有一种观点认为应该去港股捡便宜,那里被情绪错杀的标的更多,例如李嘉诚的投资公司长和(CK Hutchison)就是一个很好的例子:该公司有一半左右的收入来自欧洲,涉及港口和电信等多个行业。来自中国内地和香港的销售额只占该公司总销售额的 14% ,因此,中国内地和香港经济的不景气对该公司的影响不大,并且股息收益率为 7.2% ,但长和股价目前的市盈率只有五倍。

持续性

鉴于中国股市估值偏低,短期反弹非常合理。但这种反弹能否持续,最终还取决于政府是否愿意通过更多的财政和货币宽松政策来提振实体经济,对比欧美咱们这里不只是工具本身的问题,还有 ZZ 环境的问题,例如去年央行划归中央金融委员会管理,削弱中国人民银行和证监会等政府机构的权力。所以大家即便知道只有这几招,但能否落地的信心不能维持的话,反弹持续性会很差。

现在政府接连已经放松一些城市的购房限制,并小幅下调了利率,但这仍然让大部分市场参与者感到失望,相当一部分人认为中国经济的快速放缓需要下猛药,就像各国政府过去所做的那样。但高层不想搞强刺激的原因也可以理解,这类措施虽然会提振增长,但也会推高债务、助长长期不稳定、还有分配不均、产业转型放慢、汇率大幅贬值等等问题。

如下图所示,自去年 9 月份以来,中国基准利率 LPR 已连续第五个月保持不变, 1 年期和 5 年期以上 LPR 分别报 3.45% 和 4.2% ,但中国的 CPI 接近 0 或者负数,这让这样的利率显得十分具有“限制性”,这对于经济下行的环境来说显得不可理喻,对比英美等经济还在扩张的国家也就是最近几个月利率才高于通涨。

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

不过尽管人民币在宣布 RRR 下调时走强(同时还有支持外汇稳定性的评论),但其未能保持这些涨幅,在随后的交易中可能是因为进一步宽松预期增加所致。总的来说这是非常有趣的博弈,宽松的货币政策如果能带领人民币资产走出负循环,未必导致汇率走弱:

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

未来个人的观点是偏乐观的,因为发达国家普遍已经结束了加息周期,最快在 3 月开启降息周期的话,会给中国施展更多宽松措施留出空间。

美国市场

上周有远强于预期的第四季度 GDP 数据(3.3% ),同时个人消费支出 (PCE) 走软(核心 3 M 2% ,整体 3 M 1.7% ),初请失业金人数上升超预期,耐用品数据低于预期,制造业 PMI 重回到 50 以上(最弱的一环也恢复扩张了),结果是美元小幅走强, 10 美债收益率先跌后涨基本走平,短久期收益率降幅更大, 2-10 利差倒挂已经从去年最高 1% 缩窄至 0.21% ,收益率曲线“正常化”成为市场热议主题。

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

标普 500 指数上周继续刷新历史新高, 10 年期美国国债收益率几乎横盘在 4.14 附近。10 Y 今年开盘在 3.87% 已上升 27 个基点,SPX 今年也上涨了 2.4% ,尽管收益率走高。利率与股票的相关性逆转是时隔半年来首现,这样的情况显示了市场如今更关注增长而不是利率:

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

美股最近两周风格积极,趋向周期、增长、大盘:

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

最近市场上有一些声音在讨论收益曲线正常化如何影响股票,尤其是考虑到股票与债券收益之间最近的负相关性。然而,经济增长对于股票回报的影响比收益曲线的变动更为重要。在经济增长强劲时期,无论收益曲线是陡峭还是平缓,股票通常都能获得最大回报。只要美国经济避免了衰退,即便正常化后的收益曲线也会带来积极回报。

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

目前高盛预计美联储今年将实施 5 次 25 个基点的减息, 2 年期收益率将在年底降至 3.7% , 10 年期将在 2024 年底维持在当前水平的 4.0% ,如果成真的话利率曲线将恢复正常化,在此基础上如果是债券多头在短久期券类上的确定性会更高。

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

美银策略称美债任何下跌都是买入机会,建议在 10 年期国债收益率超过 4.1-4.15% 时增加久期(即买入国债),而当利率在 3.85-3.9% 左右时减持。

明星企业方面,因特尔业绩主要数字超预期,但一季度业绩指引远不及分析师预期,尤其是备受关注的数据中心业务不及预期,显示出英特尔在捍卫该行业一哥地位上力不从心。财测不佳导致英特尔周四盘后跌超 10% ,也侧面反映出现在投资者在高估值企业面前挑剔的态度:

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

具体来看:

每股收益 0.63 美元,高于预期 0.45 美元

收入 154.1 亿美元,超出预期 151.7 亿美元

调整后营业收入 25.8 亿美元,高于预期 21 亿美元

调整后营业利润率为 16.7% ,超出预期的 13.9% 

调整后毛利率 48.8% ,也超出预期 46.5% 

然而,英特尔预计第一季度的营收区间为 122 亿美元至 132 亿美元,远不及分析师的平均预测为 142.5 亿美元。预计一季度调整后每股收益将为 13 美分,不及分析师预计的 34 美分。

英特尔预计一季度毛利率为 44.5% ,略低于分析师预期的 45.5% ,显示出英特尔芯片工厂的效率不佳。相比之下,在 2019 年之前,英特尔的毛利率经常远超 60% 。

中国增持美债

美国财政部公布 2023 年 11 月国际资本流动报告(TIC)显示,截至 2023 年 11 月底,中国持有美债规模达到 7820 亿美元,较 10 月环比增持 124 亿美元。这意味着中国结束连续七个月抛售美债趋势,令美债总持仓规模从 2009 年 5 月以来的最低值有所回升。

在业内人士看来,中国增持美债,可能受到两大因素影响,一是当月中美元首会晤令中美关系有所改善,二是 2023 年 11 月美联储释放明确的加息周期结束信号令美债收益率大幅下跌(美债价格趋涨),吸引不少国家纷纷抄底美债获利。

值得注意的是,TIC 数据显示,在美债前十大持有国家和地区里,除了开曼群岛在 2023 年 11 月减持 47 亿美元美债,其他国家和地区均选择增持美债。

金融市场普遍认为,美联储加息周期结束或成为众多国家增持美债的最重要驱动力。

资金流

根据 EPFR 数据,全球股票基金的净流入在截至 1 月 24 日的一周内表现强劲(与上一周相比,增加了 180 亿美元,而上一周为负 9 亿美元)。美国股票基金继续推动 G 10 股票基金的净正向流入。在新兴市场中,对中国大陆的资金流入达到了历史水平,总额约为 120 亿美元,这是自 2015 年以来最大的周度资金流入。值得注意的是,这些资金流入几乎完全由国内投资者驱动,暗示着“国家队”的支持:

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

根据高盛的客户数据,在市场救助计划的消息下,周二中国股票出现大规模净买入,周三和周四,在 Prime book 上继续看到中国股票的净买入,尽管按名义价值计算的购买速度相对于周二有所放缓。从 1 月 23 日到 1 月 25 日,中国股票累计净购买额超过了过去五年中任何连续 3 天期间的数值(+ 4.4 Z score),这是由多方购买推动的。

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

最近超过 70% 的净买入活动由个股推动,这表明它可能具有持续力。所有 11 个中国行业在周二至周四都出现了净买入,其中以消费者耐用品、工业、通信服务和金融为主导。

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

总体而言,无论是对冲基金还是共同基金,在中国股市的整体仓位,仍然处于非常低的水平。尽管最近有净买入行为,但高盛 Primebook 上对中国股票的总配置和净配置都处于近 5 年来的低点。与此同时,根据 EPFR 数据,全球范围内的共同基金在 2023 年底时对中国的配置比例为 5.5% ,创下过去十年来最低水平。

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

美股方面高盛客户最近净杠杆率快速抬升:

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

经资金流变化不大:

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

按行业资金流出可选消费,流入 TMT 和周期:

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

德银统计口径下,整体股票仓位水平上周大幅上升至六个月来的最高水平(79 百分位),此前 12 月中旬以来一直在一个狭窄范围内波动。尽管仓位明显偏高,但还未达到极端水平。

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

按投资者类型看,主观投资者仓位最近上升明显,其已经上升至 87 百分位,而系统性策略的仓位继续稳步攀升至 73 百分位。在各个行业中,科技行业(排名第 73 百分位)的仓位进一步上升,并且是唯一明显超过历史平均水平的行业。

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

比特币现货 ETF 上周每日均处于净流出状态,资金流入贝莱德的 IBIT 和富达的 FBTC 等基金未能跟上市场退出灰度的 GBTC 的速度,上市两周来总净流入为 8 亿美元左右也就是 1.7 ~ 2 万个 BTC,考虑到 GBTC 的抛压,总体依然能有 8 亿流入,这已经是颇为积极的信号,BTC 价格上周也大涨 5% :

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

市场情绪

美国银行牛熊指标:从 5.5 上涨至 6.0 , 2021 年 7 月以来最高,因股票大量流入、强劲的股市广度(7% 上升至 44% )和强劲的信贷市场。

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

高盛的机构情绪连续三周回落后再次上升:

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

AAII 牛熊差值从极值回落:

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

CNN 恐惧贪婪指数重回“极度贪婪”区间:

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

本周关注

财报

本周标普 500 中 32% 的公司将发布财报,投资者将关注 AI 给哪些公司带来了实际的收益,包括 AMD、Alphabet、微软(周二)以及 Meta、亚马逊和苹果(周四)的财报出炉,波动性势必增加,INTEL 的跳水已经为我们展示了投资者如今有多么挑剔。

央行

本周继续关注央行的动向,美联储将于周三公布决议,英国央行和瑞典央行则将在周四公布决议。周二,日本央行还会发布本周会议的意见摘要。

鉴于 3 个月和 6 个月年化通胀已经低于 2% 的目标,央行也可能通过将声明中的剔除一些鹰派措辞(additional policy firming )增加移除一些鸽派措辞(通胀接近目标)来承认这一进展。

在新闻发布会,鲍威尔很可能被问到 3 月会否降息,届时看他是否能清晰的回答这个问题,如果断然拒绝(边走边看概率更大)可能对市场产生打压。另外可能会被问及自 12 月会议以来金融状况的大幅宽松、通胀率下降以及 QT 政策的变化,尤其注意 QT 削减的预期是否会被鲍威尔确认——也就是官员不只是在“谈论”而是在“制定计划”,根据历史来看至少需要 2 次会议才能出台具体计划,而 RRP 工具的余额可能在 3 月就耗尽。

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

总的来说,目前关于降息的预期有些分裂,经济学家调查认为是 6 月降息,市场定价是 3 月,FOMC 可能需要做一些努力,从 12 月会议上“仅” 3 次降息的鹰派倾向转变为与市场预期更加一致的情况,否则市场可能借此机会通过回调来对其预期。

尽管近几周除了股市外的外汇和利率市场价格已经有所调整,但风险仍然偏向于过度定价:

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

另外就是领先通涨指标——供应链成本压力已经持续走高,该指标领先年化核心 PCE 约六个月,如果中国也能开始复苏对供应链价格的压力只会更大,今年晚些时候通胀反扑的阴影仍然存在,Bloomberg 策略师 Simon White 就写到“PCE 可能证明美联储三月份降息是合理的,但这将是愚蠢的”。

LD Capital宏观周报(1.29):抢筹中国,挑剔的投资者遇上科技股,万亿新债计划出炉

今年迄今为止美元和美债收益率走强主要是由于去年最后一个月市场过于自信以及联储终于自己确认转向,市场出现了一些卖事实。这种调整趋势可能在本周还是有可能继续,因为目前看来 FOMC 官员们的态度还没有像市场定价那样进展。另外,美国数据持续领先其他发达国家,这意味着市场很可能无法像欧洲央行那样推断出更明确的宽松倾向,美元依旧没有拿得出手的对手。

非农

周五的美国一月非农就业报告不是关于最新就业数字,季节性调整对市场的影响可能更大,因为过去的数字连续被大幅下修。

财政部发债计划

财政部将于周一公布未来两个季度的融资预估,并于周三提供拍卖规模的详细信息。财政部自己上季度时的预期是 8160 亿,美银预估美国净借款规模为 9700 亿美元,德银预估预计为 7970 亿美元。如果债务供应数字需要超过 1 万亿美元将对美国国债的上涨势头产生影响。自去年 7 月政府宣布第三季度借款需求高于预期后,引发美国国债债券抛售,美国财政部的季度再融资报告就一直备受关注。

不过过去两周美债拍卖的结果意外的还不错,财政部经历上次超发赢得广泛批评之后,也表示充分了解市场对增发的担忧,并且愿意采取行动来安抚市场,在本次拍卖计划中不太能传出供给方面的意外。有分析预期财政部这次可能宣布启动回购计划的计划,涉及回购流动性较低的债务,并增加流动性最强的当前债券的发行,旨在改善市场的流动性。另外,要关注本次是的增发否更倾向于短期国债发行而不是长期,因为目前长期国债市场表现相对更差。

Letture associate

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

In three days, Google lost two AI legends. On June 18, Noam Shazeer, co-author of the seminal "Attention is All You Need" paper and Gemini co-lead, left for OpenAI. Just 48 hours later, John Jumper, 2024 Nobel laureate and AlphaFold lead, departed DeepMind for Anthropic. This follows Andrej Karpathy joining Anthropic in May. These moves highlight a structural trend: top AI talent is concentrating at mission-driven, pre-IPO firms like OpenAI and Anthropic, while Google becomes a primary source. The exodus stems from a core mission mismatch. Google's ad-centric model often subordinates AI research to product and revenue goals, creating friction for pioneers like Shazeer, who returned in 2024 only to leave again. In contrast, OpenAI and Anthropic offer singular focus on pushing AI boundaries, whether towards AGI or safety-aligned models, which deeply appeals to top researchers like Jumper. Financial incentives amplify the pull. With both OpenAI and Anthropic nearing IPO, employees stand to gain immensely from equity, an upside Google's mature stock cannot match. Furthermore, the 2023 merger of Google Brain and DeepMind, intended to consolidate strength, has instead created cultural tension and slowed the path from research to product, as evidenced by Gemini's pace. This talent redistribution is reshaping the AI landscape. While Google retains vast data and compute resources, its true crisis is the quiet, continuous loss of the people who define the field's future. The real moat in AI is not infrastructure, but the concentration of brilliant minds—a battle Google is currently losing.

marsbit15 min fa

Two Legends Lost in Three Days: Is Google's AI Talent Dam Cracking?

marsbit15 min fa

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

Beyond the familiar performance charts like MMLU-Pro and MMMU, which major AI models strive to ace, stands a key "examiner": Chinese-Canadian researcher Wenhu Chen. An assistant professor at the University of Waterloo and founder of TIGERLab, Chen addresses the crucial need for more rigorous AI evaluation. As models like GPT-4 began scoring near-perfect results on older benchmarks like MMLU, it became difficult to distinguish their true capabilities. In response, Chen introduced MMLU-Pro in 2024, featuring harder, more reasoning-focused questions with more answer choices, successfully reintroducing meaningful performance gaps. His work extends to multi-modal evaluation with MMMU and its enhanced version, MMMU-Pro. These benchmarks test a model's ability to understand and reason with complex information from images, charts, and text across diverse academic subjects, exposing the significant challenges even top models face in genuine comprehension. Chen's background in complex QA, table reasoning, and his experience at Google DeepMind on projects like Gemini inform his approach. He understands that effective benchmarks must anticipate how models might "cheat" by memorizing data or avoiding visual analysis. His lab also actively researches video understanding and generation models (e.g., UniVideo, Vamba), ensuring his evaluation work is grounded in practical model-building challenges. Now at Meta's Super Intelligence Lab, Chen continues his focus on multi-modal data and evaluation, representing the deep yet often unseen contributions of Chinese talent in shaping the fundamental tools of the AI industry.

marsbit26 min fa

Behind the AI Report Card, Lies a Chinese 'Exam Setter'

marsbit26 min fa

Alliance Co-founder's Letter to Entrepreneurs: Written at the Moment Cursor Sold for $600 Billion

Alliance Co-founder's Letter to Entrepreneurs: On Cursor's $60 Billion Sale Many aspiring founders see massive exits like Cursor's $60B sale and wonder why they can't achieve the same, often concluding opportunities are exhausted. But great companies aren't built in obvious, crowded spaces. Cursor, like Stripe, Figma, and Shopify before it, started with a non-consensus belief about the future. Before ChatGPT, they believed AI would transform knowledge work. They focused on a genuinely exciting domain, became their own customer, and obsessed over power users. Their journey involved years of "glass-chewing" effort before the market was ready. The pattern is consistent: identify a long-term technological shift, find a missed entry point, and execute for years before the trend becomes obvious. First-generation products (PayPal, Adobe, Amazon) prove a market exists. Second-generation winners (Stripe, Figma, Shopify) rebuild that market around new insights, technology, or changing customer behaviors. Founders must identify their phase in the cycle. Early entrants like Coinbase or Cursor focus on making new technology usable for power users. Later entrants find the "yin" to the established "yang"—the blind spots incumbents miss as they grow distant from individual users. The key is deep market immersion. Use every product in your space. Talk to users. Build an audience. Stop looking for ideas and start *seeing* them everywhere. Then, choose one. The idea must offer a 10x improvement or solve a "hair-on-fire" pain point—something severe enough that users are already crafting workarounds. When building, avoid feature bloat. Ask: why would someone switch? Great startups rarely force new behaviors; they improve familiar workflows with drastically lower friction (e.g., Cursor forked VS Code instead of creating a new editor). Distribution is the underestimated moat. Before product-market fit, achieve distribution-market fit. How do customers discover new tools? Founders like those at Airbnb, Stripe, and Cursor did unscalable, manual work to recruit early users. The final, unteachable ingredient is resilience. Cursor built for years pre-market, faced rejection, and persisted. So did Airbnb, Nvidia, and Rain (which launched post-FTX collapse). The lesson isn't that these founders were smarter, but that they stayed in the game long enough for their insights to compound. Framework: Spot technological cycles. Cultivate unique insight. Obsess over your market. Talk to customers. Find a hair-on-fire problem. Build the simplest wedge. Win your distribution channel. Above all, don't quit when it gets hard. Most people won't do these things consistently. The few who do build the next generation of great companies. Go build.

marsbit30 min fa

Alliance Co-founder's Letter to Entrepreneurs: Written at the Moment Cursor Sold for $600 Billion

marsbit30 min fa

Weekly Editor's Picks (0613-0619)

Weekly Editor's Picks (0613-0619): Market Insights & Analysis This weekly digest curates in-depth analysis often lost in the information flow, focusing on key insights across macro trends, investment, and technology. **Macro & Geopolitics:** With the Strait of Hormuz reopening and military conflict shifting to negotiation, markets are pivoting from "war shock" to "supply restoration." Trades include shorting crude risk premiums, longing airlines/tourism, Asian energy importers, and bond duration, while shorting inflation expectations. LNG, fertilizer, and chemical chains are also being repriced. **Investment & VC:** Ray Dalio advises against betting on concentrated AI giants dominating indices, advocating for diversified portfolios of high-quality, low-correlation assets instead. Analysis covers the 4-year crypto cycle, predicting the core surviving product by 2029 will be asset trading markets. Current BTC metrics suggest a potential bottoming zone, presenting a patient accumulation window. SpaceX's high-profile IPO at a $2.1T valuation faces scrutiny over fundamentals, with key watchpoints being its likely inclusion in the Nasdaq index and Q2 earnings. Concerns are raised about potential "gamma squeeze" and systemic risks if its narrative-driven valuation gets amplified by passive index funds. Robinhood (HOOD) is noted for breaking its high correlation with crypto, bolstered by its stock trading and new underwriting business. **Web3 & AI:** A warning highlights ~$1.8T in off-balance-sheet AI infrastructure commitments (purchase commitments, leases) as a potential systemic risk if AI monetization lags. AI models are being used for World Cup predictions, adding a new layer for betting markets. A cost breakdown of a $20 AI subscription reveals the supply chain from model companies to cloud, GPUs, and power. **Prediction Markets:** The emergence of prediction market "concept stocks" is noted, with Robinhood developing its own platform, Rothera, signaling a shift from market competition to a "channel war" for user access. **CeFi & DeFi:** The SpaceX IPO tested perpetual contract mechanisms for pre-IPO assets, highlighting challenges in handling corporate actions like stock splits on-chain. The de-pegging of STRC (Strategy's preferred share) to ~$89 reflects market concerns over MicroStrategy's capital structure and BTC-backed leverage model. BlackRock's covered-call Bitcoin ETF (BITA) offers yield but caps upside, appealing to yield-seeking institutions. **Ethereum:** An opinion piece argues Ethereum's core strength is its vast developer community and composability, solidifying its role as the default operating system for the financial internet. **Weekly Hot Topics:** Include the US-Iran deal reopening the Strait of Hormuz, Fed's hawkish hold, Anthropic restricting model access, SpaceX acquiring Cursor, and a humorous stock surge for "Liuliumei" due to its "LLM" ticker.

marsbit35 min fa

Weekly Editor's Picks (0613-0619)

marsbit35 min fa

Alliance's Co-Founder's Letter to Entrepreneurs: Written on the Occasion of Cursor's $60 Billion Sale

In this letter to entrepreneurs, Alliance reflects on the success of Cursor's $60 billion sale to Elon Musk, using it as a case study to counter the misconception that opportunities in crowded fields like AI or crypto are exhausted. The piece argues that great companies like Cursor, Stripe, Figma, and Shopify are not built by geniuses with perfect ideas, but by founders who start with a non-consensus belief about the future and build for years before that future becomes obvious to everyone. They identify long-term shifts, find overlooked entry points, and execute relentlessly. The framework for success involves: 1. **Identifying your place in the technology cycle**: Early-stage opportunities focus on making new tech usable for power users (e.g., Coinbase, Cursor). Later-stage opportunities involve finding the "yin" to an existing "yang"—the blind spots of first-generation players (e.g., Stripe vs. PayPal, Figma vs. Adobe). 2. **Cultivating unique insights**: Immerse yourself deeply in the market. Use every product, talk to users, and build an audience. Insights will emerge naturally from deep engagement. 3. **Finding a "hair-on-fire" problem**: Look for a 10x improvement or a severe, urgent pain point. The strongest signal is people already building clumsy workarounds. 4. **Building a focused MVP**: Don't just add features because you can. Ask why users would abandon their current tool for yours. The best startups rarely force new behaviors; they improve familiar workflows with drastically lower friction. 5. **Winning a distribution channel**: Distribution is often the moat. Before product-market fit, achieve channel-market fit. Find where your customers are and build an engine to reach them, even through unscalable, manual efforts initially. 6. **Persistence**: The final, unteachable ingredient is resilience. Success stories like Cursor, Airbnb, and Nvidia involved years of grinding, rejection, and perseverance when the path forward seemed unclear. The conclusion is that there is no secret. Most people fail to consistently execute these steps over the long term. The few who do build the companies that define the next era. The world is yours to create.

链捕手40 min fa

Alliance's Co-Founder's Letter to Entrepreneurs: Written on the Occasion of Cursor's $60 Billion Sale

链捕手40 min fa

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