Google 增发 850 亿美元（含向伯克希尔定向发 100 亿）：股权稀释股东但不增加公司风险；TPU 成本优势让它在'算力变商品'的世界里利润弹性最大。
DeepMind 换血：Hassabis 退居、Jeff Dean 离开，SemiAnalysis 称'Gemini 凉了'；但最大赢家可能是 Google Cloud——TPU 出货超 20% 直接卖给 Anthropic。
黄仁勋联合 Apollo/贝莱德/黑石等设 5000 亿融资平台，把 AI 工厂包装成'可投资资产类别'，英伟达兜底 25% 残值——作者视之为用避险资金承担未定价的风险。
批判性阅读：Thompson 长期看空英伟达护城河；铁路没有现金流，而 AI 云已有真实收入，1873 类比可能过度简化。
On January 1, 1870, Jay Cooke, hailed as an American hero for his role in financing the Union effort in the Civil War, signed a contract that would, if you squint, lead to world war.
In 1864, Congress had created the Northern Pacific Railway Company with the goal of linking the Great Lakes and Puget Sound with tracks that would eventually run from Duluth to Tacoma; the charter included 40 million acres of land adjacent to the proposed line in exchange for accomplishing the build-out. For the ensuing six years, however, Northern Pacific struggled to secure financing, even as the Union Pacific and Central Pacific railroads built towards each other, driving the golden spike linking Sacramento and Omaha in May 1869.
Northern Pacific had approached Cooke about funding in 1866, but lacked the generous federal guarantees that undergirded Union Pacific and Central Pacific (which, it should be noted, led to an incredible amount of graft); Cooke, himself no stranger to the financial power of the federal government, wasn't interested. Ultimately, however, Northern Pacific gave him an offer he couldn't resist: a commission of 12 percent on every bond, and $200 of Northern Pacific stock for every $1,000 in bonds he sold.
Cooke soon found that his institutional peers agreed with his earlier refusal, and weren't interested in his bonds, so he leaned on the same tactics he honed selling war bonds: appeals to patriotism, control of the media, and promises of railroad fortunes, backed by industrial-scale distribution. At the peak Cooke employed 1,500 salespeople and funded 1,300 newspapers (through a combination of advertising and direct payments) with a brand burnished by the Civil War. Retail investors could already buy railway bonds; Cooke made them his primary funding mechanism.
This was, to be certain, an incredible innovation. It used to be the case that if you couldn't get loans from the government or from banks, you couldn't get much money at all. The problem was that Northern Pacific's capital needs were endless, and by September 1873, as credit tightened worldwide thanks to a crash on the Vienna stock exchange and the demonetization of silver, Cooke, who had been funding Northern Pacific from deposits in between bond issuances, could find no more buyers. The subsequent bankruptcy of Jay Cooke & Company triggered the Panic of 1873, culminating in endless railroad bankruptcies across the country, a multi-year depression, multi-decade deflation, and, one could argue, the financial conditions that made Europe, four decades later, into a tinder box.
Northern Pacific did eventually finish their line, by the way, with multiple bankruptcies along the way; ultimately, they were one of four railroads that were merged to form the Burlington Northern Railroad. Burlington Northern would eventually merge with the Atchison, Topeka and Santa Fe Railway to form BNSF Railway; Berkshire Hathaway would purchase the parent corporation in 2009.
If this story sounds vaguely familiar it might be because Cooke is — for obvious reasons — a central character in Liaquat Ahamed's new book, 1873, released earlier this year. Ahamed is not shy about drawing a link between the collapse of the railroad buildout and the current AI moment; the book's very first page — even before page 1 — is about translating sums of money, and concludes thusly:
如果这个故事听起来隐约耳熟，那是因为库克——原因不言自明——正是利亚夸特·艾哈迈德（Liaquat Ahamed）今年早些时候出版的新书《1873》的核心人物。艾哈迈德毫不讳言要把铁路建设潮的崩塌与当下的 AI 时刻联系起来；这本书的第 0 页——比第 1 页还靠前——谈的就是金额的换算，并得出如下结论：
In order to grasp the true significance of sums of money that relate to the economic situation of whole countries — such as the size of the indemnity imposed on France after the Franco-Prussian war — it is most useful not simply to make allowances for changes in the cost of living but instead to adjust for changes in the size of economies. To translate such figures into comparable 2026 magnitudes, multiply by a factor of 1,200. Thus the $500 million that went into U.S. railway bonds annually during the boom years of the early 1870s would today be the equivalent of $600 billion, roughly what is projected to be invested by major tech companies in 2026.
Microsoft CEO Satya Nadella is certainly aware of the connection: he cited 1873 as "the book to be read" on the company's recent earnings call. Perhaps it's not a coincidence, then, that Microsoft, alone amongst the hyperscalers, still boasts substantial free cash flow — $19.6 billion last quarter. Microsoft is the one hyperscaler still abiding by the dictum used to deny the existence of a bubble: its CapEx isn't funded by debt.
微软 CEO 萨提亚·纳德拉显然清楚这层关联：他在公司最近的财报电话会上把《1873》称为"必读之书"。因此，微软成为超大云厂商中唯一仍保有可观自由现金流的一家——上季度 196 亿美元——恐怕并非巧合。微软是唯一还遵守那条"不存在泡沫"论证前提的大厂：它的资本开支不靠举债。
This was, believe it or not, a defense that could be used for nearly all of Big Tech a year ago; then, between September and November, Oracle, Meta, Alphabet, and Amazon issued a combined $80 billion in debt for building out infrastructure. That was only the beginning: after raising a combined $108 billion in all of 2025, these four companies have, as of July 7, already raised $194 billion this year. Unsurprisingly, spreads are rising, and 86% of the bonds issued this year are already trading at higher yields than at issuance. Cover for recent issuance has fallen to less than 2x, from 5x in February.
The real shock, however, came at the beginning of June, when Google announced it would raise $85 billion in equity, including a special $10 billion issuance to the aforementioned Berkshire Hathaway. I wrote at the time in The Google Capital Company:
然而，真正的冲击发生在 6 月初：Google 宣布将通过股权融资 850 亿美元，其中包括向前面提到的伯克希尔·哈撒韦定向增发 100 亿美元。我当时在《The Google Capital Company》一文中写道：
It is worth noting that $10 billion is a relatively small amount of money to both companies. To that end, perhaps the primary utility is as a signaling mechanism. On Google's side, the signal is that the expected demand is actually far greater than anyone thinks, and that the company is ready and willing to fund supply using all means at its disposal, including equity; for them Berkshire Hathaway's investment is an endorsement of this view and a validation of the wisdom of the investment. And, on the flip side, if the signal is correct, then Berkshire Hathaway is getting a deal and putting its cash flow machines to work building the future.
Implicit in this analysis was that there was enough compute capacity in the world to be bought; what happens, however, when and if there isn't? What if the ultimate battle — the one that determines who gets compute — becomes a matter of who can bring the most cash to bear? And what if that advantage compounds, such that the company with the most cash capacity ends up with the most compute capacity (which we already know they will sell, in addition to using themselves) driving the ability to generate more cash? In that world, what company would be your best bet?
Google right now is no one's bet, at least in terms of the frontier. After the departure of DeepMind CEO Demis Hassabis (technically promoted to chairman, but no longer in charge of day-to-day operations) and Gemini co-lead and former Chief Scientist Jeff Dean, along with a host of other prominent researchers, SemiAnalysis declared that Gemini is Cooked:
然而眼下，没人在押注 Google——至少就前沿模型而言。在 DeepMind CEO 戴密斯·哈萨比斯（名义上升任董事长，实则不再掌管日常运营）、Gemini 联合负责人兼前首席科学家杰夫·迪恩，以及一大批知名研究员相继离开后，SemiAnalysis 直接宣布"Gemini 完蛋了"：
For all intents and purposes, we believe DeepMind is no longer a frontier lab. We said as much a few months ago to our Tokenomics clients due to large numbers of departures from their reinforcement learning teams and poor compute allocation. Google will continue meandering on and releasing models, but their odds of reaching SOTA again have dropped to zero.
Furthermore, the biggest beneficiary of today's news is neither Anthropic nor OpenAI—it's Google Cloud. Whereas Gemini and GCP used to desperately fight for compute allocation, it's now clear that Thomas Kurian won. We expect GCP revenue growth to meaningfully accelerate as a result.
We've obviously been quite bearish on DeepMind thus far, and if we had to steelman the case for why they'll still be able to train a true SOTA model in the future, it would go something like the following: The current setup clearly wasn't working. With the existing leadership team, their odds of catching up to Anthropic/OpenAI looked extremely slim. Now that they've cleaned house, the new guys can start from a blank slate. Maybe they'll even acqui-hire a neolab like SSI or Thinking Machines. With this new team, their odds of catching up to the frontier actually increase.
Perhaps there's some world in which this happens, but we think the odds are basically zero. The issue with Google was not Jeff Dean nor Noam Shazeer, but rather their extremely bureaucratic, painfully slow, and strategically timid culture. Remember that DeepMind had an AI chatbot 1 year before ChatGPT but was not allowed to release it due to fears of disrupting their core business.
也许某个平行世界里这会发生，但我们认为概率基本为零。Google 的问题不在杰夫·迪恩，也不在诺姆·沙泽尔，而在于那种极度官僚、慢得痛苦、战略上畏首畏尾的文化。别忘了，DeepMind 比 ChatGPT 早一年就做出了 AI 聊天机器人，却因为怕冲击核心业务而不被允许发布。
Actually, you could make the case the problem was also Hassabis and DeepMind. I explained in an Update after Google I/O how Hassabis' vision of the frontier was fundamentally different from the other frontier labs because he believed in world models, not just text/code, and concluded:
其实你也可以论证，问题同样出在哈萨比斯和 DeepMind 自己身上。我在 Google I/O 之后的一篇更新里解释过：哈萨比斯对前沿的设想与其他前沿实验室根本不同——他信仰的是世界模型，而不只是文本/代码。我当时总结道：
What falls out of [Hassabis' vision] are models with multimodality — in contrast to Claude, which outputs text only — and, it must be said, not nearly as impressive coding capabilities. This gets at the point of this entire digression: I think it's possible that the reason Google is widely considered to be behind both Anthropic and OpenAI in terms of coding, particularly long-running agentic workflows that depend just as much on the harness as the model itself, simply comes down to their research team having other priorities. That's why the coding parts of this keynote fell on the Antigravity team, not DeepMind, and why Hassabis was barely on stage.
From this perspective, last week's events are less surprising, and were arguably foretold at I/O: Hassabis might be right about world models being the path to AGI, but Google has run out of patience in terms of letting him find out; Google co-founder Sergey Brin is reportedly deeply involved and closely allied with Koray Kavukcuoglu, the new DeepMind CEO, and I wouldn't be surprised if the company is pivoting to Anthropic's more text- (and thus code-) centered approach.
从这个视角看，上周的人事变动就不那么意外了——甚至可以说在 I/O 大会上已有预告：哈萨比斯关于"世界模型是通往 AGI 之路"的判断也许是对的，但 Google 已经没有耐心让他验证下去了；据报道，联合创始人谢尔盖·布林深度介入，并与新任 DeepMind CEO 科雷·卡武克丘奥卢关系密切。如果 Google 就此转向 Anthropic 那种更以文本（也即代码）为中心的路线，我一点也不会惊讶。
Google 的基础设施豪赌Google's Infrastructure Bet
What is fascinating about Google's position is that these machinations do not necessarily mean the Berkshire Hathaway bet was a bad one; indeed, it's arguably good news. This is what the SemiAnalysis article was driving towards, and it's a point I made last week about Google's recent earnings:
Google 处境的有趣之处在于：这些内部运作未必意味着伯克希尔押错了注——实际上，这甚至可以说是好消息。这正是 SemiAnalysis 那篇文章的落脚点，也是我上周评论 Google 最新财报时讲过的观点：
The story seems to be very similar to last quarter, with even more Google Cloud growth: 82% year-over-year (compared to 63% last quarter, and 32% a year ago), with 36% margins (compared to 33% last quarter, and 21% a year ago). I wondered then how much of this growth was actually Anthropic, and while we didn't get clear confirmation this quarter, I thought this answer from CEO Sundar Pichai on the earnings call about why Google needs to rent 3rd-party capacity was notable:
故事和上个季度如出一辙，只是 Google Cloud 的增速更猛了：同比增长 82%（上季度 63%，一年前才 32%），利润率 36%（上季度 33%，一年前 21%）。我当时就好奇，这增长里有多少其实来自 Anthropic；这个季度虽然没有得到明确证实，但 CEO 桑达尔·皮查伊在财报电话会上关于"Google 为什么要租用第三方算力"的一段回答，很值得玩味：
I think on the bridge deal, the main thing I would say is, look, there are — on the margin, there are very, very large customers of ours on Cloud who we are trying to support them through this extraordinary moment. And the incremental opportunities they are bringing to us, while a short‑term cost over a few months may be very high, in the lifetime of the deal, as we bring more capacity on, is highly ROI‑positive. So those are factors we are taking into account. So are you willing to take upfront a six‑month deal to be able to serve the customer in what is a multiyear opportunity where the margins and the returns are very, very attractive over that multiyear horizon? So hopefully that gives some color on how we've thought about those opportunities.
More than 20% of total TPU shipments from 3Q26 to 4Q27 are being sold directly to Anthropic. This is excluding the hundreds of thousands of TPUs GCP already rents to Anthropic today, and the many hundreds of thousands more they've committed to rent to Anthropic and Meta over the next 6 quarters…
If you've ever listened to an interview of Google Cloud CEO Thomas Kurian, you know he is not AGI pilled. In one podcast, for example, he argued that it's great for TPUs to become "general purpose infrastructure" that supports customers like Citadel, the Department of Energy, and generic high performance computing. And when asked why he was selling compute to Anthropic despite them competing with Gemini, he said this was the natural consequence of Google being a "platform company."
如果你听过 Google Cloud CEO 托马斯·库里安的访谈，就知道他并不"嗑 AGI 的迷幻药"。比如在一个播客里，他说 TPU 成为"通用基础设施"是天大的好事——可以服务 Citadel（城堡投资）、能源部以及各类高性能计算客户。当被问到"Anthropic 明明在和 Gemini 竞争，你为什么还把算力卖给他们"时，他说这是 Google 作为"平台公司"的自然结果。
Kurian said the same thing to me in a Stratechery Interview:
库里安在接受我的 Stratechery 专访时也说了同样的话：
We sell different parts of our stack. One of the things people don't realize is we monetize many different parts of the stack in different ways. Like Anthropic, there's a lot of labs that use our stack — in fact, most of the large AI labs use our stack. So if somebody uses TPUs to either to train their model or to use it for inference, we're monetizing that part of the stack, that gives us resources to then fund our R&D and other investments. Some of the labs use our TPU and our Gemini model, others may use our TPU and then buy our cybersecurity protection for their models. So as a platform player, we have to allow our technology to be monetized in as many ways as possible and we don't see it as a zero sum.
We'll see how zero sum compute actually is — there are reports Google's researchers have been starved for compute — but the overall takeaway is that whether or not Google is competing for the frontier, they are absolutely competing to dominate AI infrastructure. And, in a world where intelligence is a commodity, TPUs in particular are a big deal.
算力到底是不是零和，我们走着瞧——有报道说 Google 自己的研究员都在闹算力饥荒——但总体的结论很清楚：不管 Google 还争不争前沿模型，它绝对在争夺 AI 基础设施的统治权。而在一个智能变成大宗商品的世界里，TPU 的分量尤其重。
Last month, in Who's Afraid of Chinese Models?, I talked about commodity markets in the context of frontier labs versus everyone else; in commodity markets marginal costs are determinative of not just profitability but also viability, and I made the case that the frontier labs are well-positioned to have superior cost structures for any given unit of intelligence.
上个月在《Who's Afraid of Chinese Models?》一文里，我在"前沿实验室 vs 其他所有人"的框架下讨论过大宗商品市场的逻辑：在商品市场里，边际成本决定的不只是盈利能力，更是生存能力；我当时论证，对于任一单位智能，前沿实验室完全有条件做到更优的成本结构。
That cost structure, at least for now, includes the cost of renting compute, and it seems likely that TPUs are cheaper than Nvidia GPUs; Anthropic may have built for TPUs (and Amazon's Trainium chips) because only Google and Amazon had the wherewithal to fund them, but at this point that ability may very well be a significant advantage. The fact that Anthropic is straight up buying TPUs for its own data centers (converting compute costs from marginal costs to capital costs) suggests that is the case.
What is notable is how amenable Google is to share, even at the price of needing to issue equity. This, however, fits the Berkshire Hathaway model that I wrote about in The Google Capital Company:
值得注意的是，Google 是多么愿意分蛋糕——哪怕代价是要增发股权。而这，恰好符合我在《The Google Capital Company》里写过的伯克希尔模式：
One of the businesses Berkshire Hathaway used the See's profits for was on the opposite end of the spectrum in terms of capital utilization: BNSF Railway. Railways require a lot of capital to operate; BNSF consumed $3.8 billion last year; they also make a lot of money: BNSF's net income was $5.5 billion on revenue of $23.4 billion. To put that in perspective, the total amount that Berkshire Hathaway has made from See's Candies is probably less than $3 billion (the last disclosure was "over $2 billion" in 2019), i.e. less than BNSF made last year…
In fact, you can make the case that Abel is actually just replaying Buffett's strategy, only this time Berkshire Hathaway is See's Candies, and Google is BNSF. At the end of last quarter Berkshire Hathaway had $373 billion in cash, and $25 billion in free cash flow in 2025. How many companies could actually employ that cash in a way that generated a high rate of return?
事实上可以这么说：阿贝尔不过是在复刻巴菲特的策略——只不过这一次，伯克希尔自己扮演了喜诗糖果，而 Google 扮演 BNSF。截至上季度末，伯克希尔手握 3730 亿美元现金，2025 年自由现金流 250 亿美元。放眼全球，有几家公司能把这笔钱用出高回报率？
It's hard to imagine a better option than Google. The company is not only investing in AI, but has optionality in terms of outcomes: its Services business benefits from the investment, it is in contention at the model layer with Gemini, and it can sell capacity to the frontier labs. Moreover, that capacity has a sustainable cost advantage because of TPUs, which means that in a world where compute becomes a commodity — as hard as that is to imagine right now — Google is the hyperscaler that is poised to make the most profit.
很难想象还有比 Google 更好的去处。这家公司不只在投 AI，而且在各种结局里都有期权价值：服务业务受益于这笔投资，Gemini 在模型层有竞争力，算力还能卖给前沿实验室。更重要的是，拜 TPU 所赐，它的算力拥有可持续的成本优势——这意味着，在一个算力变成大宗商品的世界里（尽管现在很难想象），Google 将是赚钱最多的那家超大云厂商。
Notice that I didn't say margin; if that were Google's concern they would almost certainly be making different choices. Profit, however, is an absolute number, and Google is bringing everything to bear — first its cash flow, then its debt, and now its equity — on making money from the infrastructure build-out.
注意，我说的是"利润"，不是"利润率"——如果 Google 在意的是后者，它的选择几乎肯定完全不同。利润是个绝对数字，而 Google 正倾其所有——先动现金流，再发债，现在连股权也用上——铁了心要从这轮基础设施建设里赚钱。
英伟达的"可投资资产类别"Nvidia's Investable Asset Class
Today corporate executives and financial engineers don't need to control newspapers; thanks to his new X account, Nvidia CEO Jensen Huang can go straight to the public. From an X Article posted last night:
今天的企业高管和金融工程师已经不需要控制报纸了——英伟达 CEO 黄仁勋新开了 X 账号，可以直接面对公众发声。以下摘自他昨晚发布的一篇 X 长文：
NVIDIA AI Factory Compute Is Becoming an Investable Asset Class. Today, we announced partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to establish independent financing platforms designed to mobilize over $500 billion of third-party capital to support the buildout of AI infrastructure over time.
英伟达 AI 工厂算力正在成为一种可投资的资产类别。今天，我们宣布与 Apollo、贝莱德、黑石、布鲁克菲尔德、高盛和 KKR 达成合作，共同设立独立的融资平台，旨在逐步撬动超过 5000 亿美元的第三方资本，支持 AI 基础设施建设。
This is a major milestone for NVIDIA and the AI industry. We have moved from an era in which companies bought chips and built data centers project by project to one in which AI factories can be financed as productive infrastructure — with repeatable platforms, long-term institutional capital and a diverse customer base that uses compute to create revenue.
这是英伟达和整个 AI 行业的重要里程碑。我们已经从"企业买芯片、一个项目一个项目建数据中心"的时代，迈入"AI 工厂可以像生产性基础设施一样融资"的时代——可复制的平台、长期机构资本、以及一个用算力创造收入的多元客户群体。
AI has reached an inflection point. It is moving from research into production. AI is creating real value, and the infrastructure behind it is becoming one of the world's most productive assets. In AI, compute is revenue.
AI 已经到达拐点，正在从研究走向生产。AI 在创造真实的价值，其背后的基础设施正在成为全球最具生产力的资产之一。在 AI 的世界里，算力就是收入。
Huang argues that Nvidia-based AI factories are fungible, protecting residual value, and that CUDA makes AI factories better over time, extending their economic value; according to Huang:
黄仁勋的论点是：基于英伟达的 AI 工厂具有可互换性（fungible），这保护了残值；而 CUDA 让 AI 工厂随时间增值，延长其经济寿命。用他的话说：
These are the characteristics of an investable infrastructure asset: it produces revenue, serves a broad market, improves in performance over time and can be redeployed.
这些正是一种可投资基础设施资产的特征：它能产生收入、服务广阔市场、性能随时间提升、还可以重新部署。
Thus the attempted formalization of a new investment structure:
于是，一套新投资结构的正式化尝试应运而生：
The demand for AI infrastructure is extraordinary. But access to capital is uneven. Many great AI companies, enterprises and AI clouds have demand for compute but do not yet have access to financing at the scale or cost required to build quickly. That is why we are partnering with the world's leading long-term capital providers. Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR are also among the world's leading infrastructure investors, with deep expertise in underwriting long-lived, productive assets. Together, we are creating repeatable financing platforms to help the AI ecosystem build the factories it needs.
AI 基础设施的需求是非凡的，但获得资本的机会并不均等。许多优秀的 AI 公司、企业和 AI 云厂商有算力需求，却拿不到足够规模、足够低成本的融资来快速建设。这正是我们与全球领先的长期资本提供方合作的原因。Apollo、贝莱德、黑石、布鲁克菲尔德、高盛和 KKR 同时也是全球顶尖的基础设施投资者，在为长寿命、生产性资产承保方面拥有深厚专长。我们正共同打造可复制的融资平台，帮助 AI 生态系统建起它所需要的工厂。
What Apollo et al. are, are new sources of capital beyond the investment grade debt markets. In that sense this proposed structure is somewhat akin to Google's equity issuance: a way to secure funding beyond bonds. The difference, however, is stark: whereas equity dilutes the upside for investors without adding risk to the company, this structure preserves Nvidia's margins by finding new pools of capital willing to bear risk.
Apollo 们本质上是投资级债券市场之外的全新资金来源。从这个意义上说，这套结构与 Google 增发股权有点像：都是在债券之外找钱。但差别是本质性的：股权融资稀释的是投资者的上行收益，不给公司增加风险；而这套结构保住的是英伟达的利润率——办法是找到一批新的、愿意承担风险的资金池。
It's not a total free ride for Nvidia: the company is backstopping opportunities with up to 25% residual-value based financing, suggesting that Huang believes his "investable asset class" pitch much more than the market does. That is, in a certain sense, a price cut, as the goal is to reduce the cost of capital for entities building data centers with Nvidia chips, by putting Nvidia's profits on the line for uncertain investments.
That guarantee is downstream from Google's (and soon Amazon's) aggressiveness: why build a data center with Nvidia chips if you can buy TPUs or Trainiums (Nvidia chips are likely better, but if the constraint on new data centers is capital, lower up-front prices may matter more than token efficiency).
Nvidia's bigger problem is one that has been apparent for a long time; I wrote back in 2024:
英伟达还有一个更大的问题，其实早已显现。我在 2024 年就写过：
In the before-times, i.e. before the release of ChatGPT, Nvidia was building quite the (free) software moat around its GPUs; the challenge is that it wasn't entirely clear who was going to use all of that software. Today, meanwhile, the use cases for those GPUs is very clear, and those use cases are happening at a much higher level than CUDA frameworks (i.e. on top of models); that, combined with the massive incentives towards finding cheaper alternatives to Nvidia, means both the pressure to and the possibility of escaping CUDA is higher than it has ever been (even if it is still distant for lower level work, particularly when it comes to training).
在旧时代——也就是 ChatGPT 发布之前——英伟达围绕 GPU 构筑了相当可观的（免费）软件护城河；当时的问题是，到底谁会来用这些软件，并不明朗。而今天，这些 GPU 的用途再清晰不过，只是这些用途发生在比 CUDA 框架高得多的层级上（也就是模型之上）；再加上"寻找英伟达平替"的巨大利益动机，逃离 CUDA 的压力和可能性都达到了历史新高（尽管对更底层的工作——尤其是训练——来说，逃离依然遥远）。
The situation today, with Anthropic and OpenAI appearing to pull away, is even more problematic: Anthropic has not been dependent on CUDA for years, and OpenAI is moving in that direction, at least for inference. If those companies win then Nvidia's profits will be squeezed — indeed, the implication of that backstop is they already are (this, needless to say, is why Huang's first post was an open letter in defense of open models).
This might not cost Nvidia anything in the end: if AI revenues truly take off, then the debt markets will open back up, and ultimately companies will go back to funding infrastructure investment through free cash flows. Right now, however, is the danger zone, as hyperscalers blow through the debt markets and Google at least starts to tap equity.
这套安排最终可能不会让英伟达损失一分钱：如果 AI 收入真的起飞，债券市场会重新敞开，公司们终将回到用自由现金流为基础设施投资的正轨。但眼下正是危险地带——超大云厂商正在打穿债券市场，而 Google 至少已经开始动用股权。
To the extent Nvidia competes through novel funding mechanisms that, at the end of the day, draw on things like insurance floats and pension funds and other long-run liabilities that are the bread and butter of the asset managers the company is partnering with, the risk — unmarked, unlike equity — is considerably higher.
That's why I started with 1870 and Cooke's ill-fated agreement with Northern Pacific. Yes, the upside the deal afforded Cooke was incredible, but it was incredible for a reason: it was very risky, and pioneering new funding mechanisms only served to spread the pain when it all blew up. It's one thing to spend all of your free cash flow; it's another thing to tap the debt markets. And, beyond that, it's a completely new nerve-racking thing to bring safety-seeking assets to bear. AI better deliver before it was too late.
最近我们复盘了去年「2025 AI Best Ideas」提出的20个关键预测，发现绝大部分关于技术方向与格局演化的AI预测已经兑现。而站在当下看2026年这个关键时间节点，市场已经显现出了更明显的分歧：Gemini 3发布后，Google能否保持长期领先？OpenAI是否有机会在2026年实现逆转？在AI入口竞争中，是操作系统占优，还是超级APP更具潜力？
因此我们组织了一场「2026 AI Best Ideas」社群讨论，AI researchers、创业者、产品经理和一二级投资人围绕2026年AI公司竞争格局、AI应用与Agent形态、算力与infra瓶颈，以及AI在具体行业中的落地路径等关键问题，展开了一次深入的讨论。