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:
如果这个故事听着耳熟，那是因为库克——显而易见——是利亚卡特·艾哈迈德（Liaquat Ahamed）今年早些时候出版的新书《1873》的核心人物。艾哈迈德毫不讳言铁路建设狂潮的崩塌与当下 AI 时刻之间的关联；这本书的第 0 页——比第 1 页还靠前——谈的就是如何折算金钱的数目，结尾这样写道：
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.
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：谷歌（Google）宣布将进行 850 亿美元的股权融资，其中包括向前面提到的伯克希尔·哈撒韦定向发行 100 亿美元。我当时在《谷歌资本公司》（The Google Capital Company）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:
至少就前沿而言，谷歌眼下不是任何人的押注对象。在 DeepMind CEO 德米斯·哈萨比斯（Demis Hassabis）离任（名义上升任董事长，但不再负责日常运营）、Gemini 联合负责人兼前首席科学家杰夫·迪恩（Jeff Dean）以及一众知名研究员相继出走之后，SemiAnalysis 直接宣判：《Gemini 没救了》（Gemini is Cooked）：
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.
到目前为止我们对 DeepMind 一直相当悲观。如果要为「他们未来仍能训练出真正的 SOTA 模型」强行找一个最硬的理由，大概会是这样：之前的班子显然行不通，靠原有领导层追上 Anthropic/OpenAI 的概率微乎其微；现在他们清了场，新团队可以从一张白纸开始，说不定还会收购雇佣 SSI 或 Thinking Machines 这类新锐实验室；有了新团队，追平前沿的概率反而是上升的。也许在某个平行世界里这会发生，但我们认为概率基本是零。谷歌的问题不在杰夫·迪恩，也不在诺姆·沙泽尔（Noam Shazeer），而在它极度官僚、慢得痛苦、战略上畏首畏尾的文化。别忘了，DeepMind 比 ChatGPT 早一年就有了 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: 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.
其实你可以说，问题也出在哈萨比斯和 DeepMind 自己身上。我在 Google I/O 之后的一篇更新里解释过：哈萨比斯对前沿的愿景与其他前沿实验室根本不同——他相信的是世界模型，而不只是文本和代码。我当时得出结论：这种愿景的产物是强调多模态的模型——与只输出文本的 Claude 形成对照——而且必须承认，编程能力远没有那么惊艳。这就引出这段题外话的真正要点：谷歌之所以被广泛认为在编程上落后于 Anthropic 和 OpenAI——尤其是既依赖模型、也同样依赖脚手架（harness）的长程智能体工作流——我认为原因可能很简单：它的研究团队另有优先事项。这就是为什么那场 keynote 的编程部分交给了 Antigravity 团队而不是 DeepMind，也是为什么哈萨比斯几乎没上台。
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.
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:
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:
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."
如果你听过谷歌云 CEO 托马斯·库里安的访谈，就知道他并不「AGI 上头」。比如在一个播客里，他说 TPU 成为「通用基础设施」是件大好事——可以服务城堡投资（Citadel）、美国能源部和一般的高性能计算客户。被问到为什么要把算力卖给 Anthropic——尽管对方正是 Gemini 的竞争对手——他说，这是谷歌作为「平台公司」的自然结果。
Kurian said the same thing to me in a Stratechery Interview:
> 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，我们拭目以待——有报道说谷歌自己的研究员已经在闹算力荒了。但总的结论是：无论谷歌是否还在争夺前沿，它都绝对在争夺 AI 基础设施的主导权。而在一个智能成为大宗商品的世界里，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《谁害怕中国模型？》（Who's Afraid of Chinese Models?）一文里，我在「前沿实验室对其他所有人」的语境下谈过大宗商品市场：在大宗商品市场里，边际成本不仅决定盈利能力，也决定生存资格；我当时论证了前沿实验室在任何给定单位的智能上，都有望拥有更优的成本结构。
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:
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?
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. 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.
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:
今天的公司高管和金融工程师已经不需要控制报纸了；多亏了新开的 X 账号，英伟达 CEO 黄仁勋（Jensen Huang）可以直接面对公众。他昨晚发布的一篇 X 文章写道：
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. 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. 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.
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（BlackRock）、黑石（Blackstone）、布鲁克菲尔德（Brookfield）、高盛（Goldman Sachs）和 KKR 达成合作，建立独立的融资平台，旨在于未来动员超过 5000 亿美元的第三方资本，支持 AI 基础设施建设。这是英伟达和 AI 行业的重要里程碑。我们已经从一个「公司买芯片、一个项目一个项目建数据中心」的时代，进入一个「AI 工厂可以像生产性基础设施一样被融资」的时代——有可复制的平台、有长期机构资本、有用算力创造收入的多元客户群。AI 已经到了拐点，正在从研究走向生产。AI 正在创造真实价值，而它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 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:
> 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.
黄仁勋的论点是：基于英伟达的 AI 工厂具有可替代性（fungible），这保护了残值；CUDA 又让 AI 工厂随时间变得更好，延长其经济寿命。按他的说法：
> 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、还可以被重新部署。
Thus the attempted formalization of a new investment structure:
于是，就有了这场把一种新投资结构正式化的尝试：
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 云有算力需求，却还没有按所需的规模和成本获得融资、从而快速建设的渠道。这就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 同时也是全球领先的基础设施投资者，在承销长寿命的生产性资产方面经验深厚。我们要一起打造可复制的融资平台，帮助 AI 生态建成它需要的工厂。
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 们扮演的角色，是投资级债券市场之外的新资金来源。在这个意义上，这套拟议中的结构与谷歌的股权融资有些相似：都是一种在债券之外锁定资金的办法。但区别是鲜明的：股权融资稀释的是投资者的上行空间，不给公司增加风险；而这套结构是通过找到愿意承担风险的新资金池，保住了英伟达自己的利润率。
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:
> 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).
英伟达更大的问题由来已久；我在 2024 年就写过：
> 在「从前」，也就是 ChatGPT 发布之前，英伟达围绕自己的 GPU 建起了相当可观的（免费）软件护城河；当时不清楚的是，到底谁会来用这些软件。今天则相反：这些 GPU 的用例非常明确，而且用例发生在比 CUDA 框架高得多的层级上（也就是模型之上）。这一点，再加上寻找英伟达平替的巨大激励，意味着逃离 CUDA 的压力和可能性都达到了历史最高（尽管对更底层的工作——尤其是训练——来说，那一天还很遥远）。
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).
今天的局面更麻烦：Anthropic 和 OpenAI 看起来正在甩开对手，而 Anthropic 已经多年不依赖 CUDA，OpenAI 至少在推理上也在朝这个方向走。如果赢的是这两家，英伟达的利润就会被挤压——事实上，那个兜底条款的含义就是：挤压已经开始了（不用说，这正是黄仁勋的第一篇 X 文章是一封为开放模型辩护的公开信的原因）。
冒险生意Risky Business
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. 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.
这一切最终可能不会让英伟达损失什么：如果 AI 收入真正起飞，债券市场会重新敞开，公司们终究会回到用自由现金流为基础设施投资融资的老路上。但眼下正是危险地带：超大规模厂商正在把债券市场用到极限，而谷歌至少已经开始动用股权。当英伟达通过新型融资机制参与竞争——这些机制归根结底动用的是保险浮存金、养老基金和其他长期负债，也就是与它合作的那些资管公司的看家资产——其中的风险（而且不像股权那样逐日盯市）要高得多。
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's too late.
过去三个季度，快手广告增速持续滑坡，背后源于双重压制：一是流量基本面走弱，与广告变现高度相关的 DAU 几乎停滞，MAU 扩张更多带来低频用户短暂回流，难以转化为广告价值；二是宏观消费环境疲软，电商等重点行业预算承压，尽管短剧营销投放同比翻倍、AIGC 营销素材消耗大增，但这部分不足以填补传统广告降速的缺口。
上述离职者是否属于不可替代的核心角色，目前尚不可考究，但视频生成领域对顶尖人才的争夺，已经是不争的事实。随着 AI 视频行业的爆发，各大公司都在疯狂挖 AI 人才，可灵的核心研发人员早已成为猎头的“重点狩猎对象”。
为了留住人才，快手不得不为可灵团队单独设立期权池。据虎嗅了解，可灵 AI 独立融资时同步采纳了三项股份参与计划，股权激励上限设为 15%，可灵 CEO 盖坤持有 3% 股权加十倍投票权。但股权激励与人才流动之间的赛跑仍在进行，激励机制的搭建速度，需要跟得上模型竞争的烈度；否则，激励就会沦为“画饼”，一旦无法达到预期，人才流失会愈发严重。
最后，可灵 AI 分拆独立融资，被视为快手估值重构的关键一步，但从股权结构看，融资完成后快手仍持有 68.33% 的经济权益，保持绝对控制权，可灵的财务报表依然并表。
这意味着，可灵 AI 的亏损会继续体现在快手的利润表上，capex 层面的投入压力，快手仍需承担；而融资到账的 30 亿美元进入可灵账户，与快手母公司的利润和现金流并无直接关系。
通俗点说，这就是分家不分灶：可灵 AI 名义上独立了，但亏的钱还是算在快手头上，融来的钱却只能用于可灵自身发展。