企业AI能为繁荣付出代价吗？

企业AI能为繁荣付出代价吗？

人工智能的积累正在对企业最终愿意支付什么情报做出巨大的赌注。 在我们今年夏天关于预测AI泡沫的研究中，我们提出，一个值得观察的关键指标是企业客户的生产性利用。

具体地说，我们当时的称，如果在生产性货币化实现之前基础设施供应赶上，泡沫爆发的风险就会增加到我们认为危险的程度。 我们预测，当时的供应限制和健康的资本筹资胃口可以为软着陆创造条件。 我们把2029年定为风险最高的一年。

而带来我们下一个明显的问题：主流企业内部必须发生什么情况才能使其AI投资成为经济上的自筹资金？Michael Burry对Anthropic PBC的分析凸显了Anthropic所做的承诺的规模和相互联系。 托尔斯滕·斯洛克·融资(英语：Torsten Slok 融资)的阿波罗研究质疑今天经济效益的展现范围有多广。 而个人电脑和互联网时代的历史提醒我们，生产率的提高并不总是立即的，强大的技术可以与最初看来需要时间的低投资回报共存。

我们认为，软着陆之路贯穿于企业运作方式的变化，具体来说就是它们所做的工作，它们需要的人，它们的规模如何，以及它们的客户最终要支付的价值。 欢迎收看"断裂分析"第332集：企业AI能为繁荣付出代价吗？.

今天加入我们的是克劳福德·德尔普雷特现任PSG公平公司高级顾问。 数十年来，克劳福德在技术转型上占有一席之地，他带来目前对软件企业如何适应AI,AI-本土公司如何形成，新兴的运营模式是什么样子的看法。 克劳福德欢迎很高兴见到你。

嘿，很高兴见到你，戴夫。

AI正在改变经济学的地方

Dave Vellante：从这段录音来看，也只有937人。 这有很多原因。 当然，我们昨晚从大型超规模公司得到的收入并不高。

Google对广告的失望以及AWS的卡普克斯支出和负现金流是罪魁祸首，但超大尺度机却在云中摇摆不定。 首先让我们从你所看到的开始，从你的观点来看，AI正在怎样改变经济学呢？

克劳福德·德尔普雷特(Crawford Del Prete)：嗯，AI正在很多不同的领域改变经济学，对吧？因此，我们开始看到生产力提高的早期迹象，但我们还没有看到这些迹象。 我所看到的是，公司开始自动化工作流程。 他们开始以不同的方式使用人工智能。 而这些方式是你最初会期望的方式，对不对？所以我们看到了一个大动作向编码。 我们看到客户服务大步前进但是现在我们开始进入许多其他领域，个别任务在组织内部开始自动化。

我们开始看到AI每天在组织中的影响，同样，工作由数百项，有时是数千项任务组成。 现在，我们开始自动化这些任务，但我们未必改变工作方式，所以我认为这解释了一些不一致之处，我认为我们将在一分钟内讨论一下我们所看到的生产率数字。

资助大赦国际的建设

戴夫·韦兰特：谢谢你。 在PC时代，你和我切断了我们的牙齿。 当然，我们知道摩尔定律就是一切。 我们了解到价格迅速下跌。 技术总是通货紧缩。 现在技术是通货膨胀。 记住，你我经常跟随磁盘驱动器市场，客户会想锁定。 他们希望有一个远期定价合同。

克劳福德·德尔普雷特：是的。

Dave Vellante：根据摩尔定律，历史衰落是沿着曲线走的。 今天你想锁定当前价格，因此，你可以看到各种疯狂的混乱。 Anthropic S-1本周上映，文件中肯定有一些正数。 当然，每个人都在谈论收入增长。 2026年上半年到160亿美元，但我真正突出的是克劳福德资产负债表称公司拥有1 320亿美元的流动资产，而美国只有两家非银行——据报道，这些银行的现金比Anthropic多。 当然，这是字母和伯克郡。

记得，他们拥有约1400亿美元的融资。 所以，让这个沉淀观众一点点。 它基本上已经燃烧了100亿美元以达到320亿美元的收入运行率。 但大问题当然是围绕所承诺的能力的经济回报。

迈克尔·布利(Michael Burry)写了"卡珊德拉无链"——顺便说一句，他的作品是无与伦比的。 他的想法经常被阴阳化他因为整个GPU的生命周期受到打击，人们把他开除，但他的研究是深入而独特的。 这个图是显示环绕Anthropic的相互联系的融资和商业关系。 他从S -1中把这一切拉到一起，你可以看到，在各种不同的安排中，5,370亿美元的多年计算承诺。 包括最低购买协议、租赁协议、交货能力等。 当然，这不是当前的债务，而是他们承诺要做的事情。

Crawford，我有个问题要问你。 只要回答他们全部，或任何你最有知名度。 首先，你有资本供应商、基础设施供应商和模范公司。 他们都是互相关联的。 那又有什么关系？你的证券公司花得更多吗？还是因为他们拥有FOMO，当然，你们的新初创企业正在像一艘火箭飞船一样以更少的劳动力成长，它们可能给我们指明通往未来的道路。 你对这一切有什么想法？

克劳福德·德尔普雷特(Crawford Del Prete)：你看，戴夫，你和我一直围绕着这个行业，我们从未有过一个你拥有一个相互联系的行业，这个行业是建立在昨天的建筑之上的，因为新的建筑必须重新定义。 所以一旦它被重新定义，它就必须被建造。 所以，你把世界连在一起了，现在你有了称，那么，你的堆栈是错的。 因此，我们必须建立一个新的堆栈，而这个新的堆栈将创造应用程序和使用，将立即达到100%的容量。

因此，我认为，这种循环融资是基本上同时重建全世界堆栈的一种方式。 所以你问：在最终用户客户的需求方面，我们看到了什么？我会告诉你，这是一个令人不满意的答案，但它取决于顾客，对吧？

我本周跟一位顾客说称对我说：“我无法掌握足够的计算能力。” 我推倒了称，“等等。 “不，不，不，你不明白。 所有我能从勒诺沃购买的能力，所有我能从HPE购买的能力，所以他们愿意得到像OEM一样的回报。 这些显然是非常，非常量性的客户。 但是，在很多这样的地方，需求是真正无厌的。

其他的客户开始关注象征性的开支。 我在那些客户所看到的，这将会非常，非常真实地反映我们25年前发展出的东西，他们正在研究工作量，他们开始研究AI工作量，他们开始教人们说：“嘿，你知道吗？那个Opus 4.8型号，不错的型号可能不需要用5. 因此，他们开始为具体的工作量而鼓动模型。

因此，我们与客户的旅程开始之际，但我会告诉你，这看起来就像20年前你和我的服务器市场，在那里，你最终会有不同的模型处理不同的工作量，包括开源和开放式模型，这些模型位于数据中心内或云层内。 现实是，但我会告诉你，我没有看到一吨客户说，“我必须撤退，”因为那是关于竞争力的。 也得给我的团队成员一些工具

Dave Vellante：我们确实在IDC将工作日的香肠切得很薄，Intel的建筑今天下降了30%。

克劳福德·德尔普雷特：是的。

Dave Vellante：基本上，通用x86被吸收到加速计算中。 我想个人电脑显然是人们错过的但Intel在GPU并没有真正的强势地位。 而AMD正在吞噬它在个人电脑中的市场份额。 这确实是一个难题。 以你的例子来看那个顾客的货币化，对吧？这就是为什么他们希望尽可能的计算，对不对？

克劳福德·德尔普雷特(Crawford Del Prete)：这位顾客一天到晚都在货币化，并说，“你看，我可以通过赋予我的用户更多的能力来创新，他们可以做他们以前无法做的事情。 它们可以产生分析。 他们能够创造出他们以前从未创造过的可达到的目标。” 因此，我要说，这些类型的客户是存在的，它们存在于规模上。 我觉得客户在谁愿意花和谁不愿意花之间并不存在大的二分法。 每个人都愿意花钱。 它真的只是下到他们拥有的资源数量。

现在，你问了第二个问题，那就是：公司花费更多是因为AI正在改善经济，还是因为他们必须这样做才能保持竞争力？今天没有软件公司不为AI投资。 他们必须这样做。 他们必须提高产品竞争力，我每天都在看这些东西。

一个相邻的、大型的、规模化的平台，可以用人工智能，吸收你的区域，接下来又出现了一个无所不在的问题 — — 我们可以暂时讨论一下 — — 客户感应码可以吗？他们能创造出一个软件产品而不是发放许可证或使用它吗？所以每个人都在投资。 如果你没有在AI上投资——这个类别中有几家公司，但非常，非常，非常少——那么你可能正在写你自己的死刑判决。

戴夫·韦兰特：对。 而你们作为资本分配者，对投资一个不投资于AI的公司并不感兴趣。

克劳福德·德尔普雷特：正是如此。

从个人生产力到业务成果

Dave Vellante：好，所以我们要努力把这个圈子整齐起来，因为我们看到了Anthropic和其他人所打击的这些关系，他们是如何资助建设的。 但是，我们今天的前提是，这些技术最终的可行性将取决于客户能够从这种能力中产生何种价值。 克劳福德只是说

本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。

Can enterprise AI pay for the boom?

The artificial intelligence buildout is making an enormous bet on what businesses will eventually be willing to pay for intelligence. In our research this summer on forecasting the AI bubble, we argued that a key indicator to watch is productive utilization by enterprise customers.

Specifically, we said at the time, if infrastructure supply catches up before productive monetization materializes, the risk of a bubble popping increases to what we felt were dangerous levels. We forecast that the supply constraints and healthy capital funding appetites at that time could provide conditions for a soft landing. And we pegged 2029 as the highest-risk year.

And that brings us to the next obvious question: What has to happen inside mainstream enterprises for their AI investments to become economically self-funding? Michael Burry’s analysis of Anthropic PBC highlights the scale and interconnectedness of the commitments that Anthropic is making. Apollo’s research by Torsten Slok raises questions about how broadly the financial benefits are showing up today. And the history of the personal computer and internet eras reminds us that productivity gains aren’t always immediate, and that powerful technologies can coexist with what initially seem to be poor investment returns that take time to appear.

We think the path to a soft landing runs through changes in how businesses operate, specifically the work that they do, the people that they need, how they scale and the value that their customers are ultimately going to pay for. Welcome to Breaking Analysis, episode 332: Can enterprise AI pay for the boom?

Joining us today is Crawford Del Prete. He’s the former president of IDC and now senior adviser at PSG Equity. For decades, Crawford has had a front-row seat on technology transitions, and he brings current perspectives on how software businesses are adapting to AI, how new AI-native companies are forming, and what the emerging operating model looks like. Crawford, welcome. Good to see you.

Crawford Del Prete: Hey, great to see you, Dave.

Where AI is changing economics

Dave Vellante: As of this recording, the Nasdaq is down almost 1,000 points, so it’s getting worse by the minute. It’s not quite at 1,000, it’s only down 937. There are a lot of reasons there. Of course, we had earnings last night from the big hyperscalers which were not well received.

Google’s disappointment in advertising and AWS’ capex spend and negative cash flow were the culprits, but the hyperscalers are cranking in cloud. Anyway, let’s start with what you’re seeing, specifically in the market, but also inside businesses. Where is AI changing economics from your point of view?

Crawford Del Prete: Well, AI is changing economics in so many different areas, right? So we’re starting to see the early signs of productivity gains, but we’re not yet seeing them in the numbers. And what I’m seeing is that companies are starting to automate workflows. They’re starting to use AI in different ways. And those ways are ways that you would expect initially, right? So we saw a big move toward coding. We saw a big move toward customer service. But now we’re starting to get into a lot of the other areas where individual tasks are starting to get automated within organizations.

And as that happens, it becomes very uneven in terms of how we’re starting to see the impact of AI in organizations on an everyday basis. Again, jobs are made up of hundreds, sometimes thousands of tasks. And now we’re starting to automate those tasks, but we haven’t necessarily changed the way work is wired in an organization. So I think that makes for some of the inconsistencies that I think we’ll talk about in a minute in terms of the productivity numbers that we’re seeing.

Funding the AI buildout

Dave Vellante: Thank you for that. You and I cut our teeth during the PC era. And of course, we learned Moore’s Law was everything. We learned about the rapidly declining prices. Tech was always deflationary. Now tech is inflationary. Remember, you and I used to follow the disk drive markets, and customers would want to lock in. They’d want to have a forward pricing contract.

Crawford Del Prete: Yes.

Dave Vellante: Historic declines followed a curve according to Moore’s Law. Today you want to lock in current prices because they’re going up. So you’re seeing all kinds of crazy dislocations. The Anthropic S-1 came out this week, and there were definitely some positive numbers in the document. Everybody, of course, talks about the revenue growth. We’ve never seen anything like it before: 14x in the first half of 2026 to $16 billion. But what really stood out to me, Crawford, was the balance sheet. The company says they’ve got $132 billion in liquid assets, so cash and equivalents. And there are only two nonbanks in the U.S. — and this has been reported — that have more cash than Anthropic. That’s, of course, Alphabet and Berkshire.

And remember, they’ve raised about $140 billion. So let that sink in for the audience a bit. It basically has burned $10 billion to get to a $32 billion revenue run rate. But the big question, of course, is around the economic return on that capacity that’s being committed.

Michael Burry, who writes Cassandra Unchained — his work, by the way, is unparalleled. His ideas are often pooh-poohed. He got sort of slammed for the whole GPU lifecycle, and people dismiss him, but his research is deep and unique. And this diagram shows the interconnected financing and commercial relationships around Anthropic. He pulled this all together from the S-1, and you see it’s $537 billion of multiyear compute commitments across all kinds of different arrangements. You’ve got take-or-pay deals, minimum purchase agreements, lease deals, capacity that’s conditioned upon delivery, stuff like that. Of course, this isn’t current debt, but it’s stuff that they’ve committed to.

So Crawford, I’ve got a bunch of questions for you. Just answer them all, or whatever you have best visibility on. First of all, you’ve got capital providers and infrastructure suppliers and model companies. They’re all sort of interconnected. So how much of a concern is that? Are your portfolio companies spending more? Is it because AI is showing better return, or is it because they have FOMO and they’ve gotta remain competitive? And of course, your new startups are going like a rocket ship and growing with less labor, and they potentially show us the way to the future. What are your thoughts on all that?

Crawford Del Prete: So look, Dave, you and I have been around this industry forever, and we’ve never been at a point where you’ve got an interconnected industry that is built on yesterday’s architecture because a new architecture that came along had to be redefined. And so once it was redefined, it had to be built. And so you’ve connected the world, and now you’ve said, well, the stack that you had was the wrong stack. So we’re going to have to build a new stack, and that new stack is going to create applications and usage that will immediately be at 100% capacity.

So this circular financing is, in my opinion, a way to basically rebuild the stack around the world all at the same time. And so you ask the question: What are we seeing in terms of demand from end-user customers? And I would tell you, it’s an unsatisfying answer, but it depends on the customer, right?

So I talked to one customer this week that said to me, “I cannot get my hands on enough compute capacity.” And I pushed back and said, “Wait a minute.” “No, no, no, you don’t understand. All the capacity that I could buy from a Lenovo, all the capacity I could buy from an HPE, from a Dell — I’ll take it all.” And so they’re willing to get as much as an OEM is willing to give them. And these are obviously very, very quantitative-oriented customers. But again, the demand in a lot of these places is truly insatiable.

Other customers are starting to look at token spend. And what I’m seeing in those customers — and this will ring very, very true to things that you and I developed 25 years ago — they’re looking at workloads, and they’re starting to look at AI workloads, and they’re starting to teach people to say, “Hey, you know what? That Opus 4.8 model, not a very bad model. Probably don’t need to use the 5.0 on the extra effort and peel through six times as many tokens.” So they’re starting to agitate models for specific workloads.

And so we’re kind of at the beginning of that journey with customers, but I’ll tell you, this is going to look just like you and I looked at the server market back 20 years ago, where eventually you’re going to have different models for different workloads, and that’s going to include open-source and open-weight models that are inside of a data center or inside of a cloud. The reality is, it’s all over the map. But I will tell you that I’m not seeing tons of customers saying, “I’ve got to pull back,” because it’s about competitiveness. I’ve got to be competitive, and I’ve got to give my team members the tools to achieve things they never could achieve before with AI.

Dave Vellante: We did slice the workload salami pretty thin at IDC, didn’t we? And to your point about this whole new architecture you’re seeing at Intel today, Intel’s down 30% today.

Crawford Del Prete: Yes.

Dave Vellante: Basically, general-purpose x86 is kind of being absorbed into accelerated computing. I guess PCs are obviously what folks missed. I don’t know how they missed it, but Intel doesn’t really have a strong position in GPUs. And AMD is eating away at its market share in PCs. And so that’s a real conundrum. In your quant example, that customer’s monetizing, right? That’s why they want as much compute as possible, right?

Crawford Del Prete: That customer is monetizing all day long and is saying, “Look, I can innovate by giving my users more capacity, and they can do things they never could do before. They can create analysis. They can create deliverables that they never were able to create before.” And so I would say that those kinds of customers exist, and they exist at scale. And I feel like there is not this big dichotomy between customers in terms of who’s willing to spend and who’s not willing to spend. Everybody’s willing to spend. It really just comes down to the amount of resources that they have.

Now, you asked the second question, which is: Are companies spending more because AI is improving their economics, or because they have to do it to remain competitive? And I will tell you that whether it’s a portfolio company or it’s not, there is no software company today that is not investing for AI. They have to. They’ve got to do it to increase their product set to be competitive, because there are many, many competitive factors that never existed before. And these are all things that I’m looking at every single day.

There’s a whole discussion around: Can an adjacent, large, at-scale platform, by using AI, absorb your area, the area you’re innovating in? And then separately, there’s the omnipresent question of — and we can talk about this for a while — can customers vibe code? And can they create a software product as opposed to licensing it or using it? So everybody is investing. And if you’re not investing in AI — there are a few companies in that category, but very, very few — then you’re probably writing your own death sentence.

Dave Vellante: Right. And you, as a capital allocator, aren’t that interested in investing in a company that’s not investing in AI.

Crawford Del Prete: Exactly.

From individual productivity to business results

Dave Vellante: Okay, so we’re going to try to square this circle, because we see these relationships that Anthropic and others have struck, how they’re funding the buildout. But our premise today is that the viability of those ultimately is going to depend on what value end customers can create from that capacity. Crawford just laid out that they can’t get

Auto-fetched and translated by AI Atlas. All rights belong to the original author.