SpaceX的真实价值之争，微软在智能代理数据战争中的新战场，Mercury首席执行官谈2亿美元融资

欢迎各位收看《The Information》的TITV节目。

Welcome everyone to The Information's TITV.

我是阿卡什·帕什里卡。

My name is Akash Pasricha.

今天是5月22日，星期五。

It is Friday, May 22.

在开始今天的节目之前，我想先介绍一下昨天《The Information》的一篇独家报道。

Before we get to today's show, I want to highlight some exclusive reporting from the information yesterday.

我的同事施里·马蒂发现，OpenAI第一季度的营收达到了57亿美元。

My colleague, Shri Mattee, found out OpenAI's first quarter financials, dollars 5,700,000,000 in revenue.

这个数字比Anthropic在同一时期多出了10亿美元。

That is $1,000,000,000 more than what Anthropic did in the same period.

Codex是推动OpenAI营收增长的关键因素。

Codex was a big driver for OpenAI's revenue.

更多细节都在这篇报道里。

There are more details in the story.

欢迎大家去了解一下。

I encourage you to check it out.

今天节目里，我们要聊企业之间的数据大战。

Today on the show, we are talking about corporate data wars.

微软最近在数据访问问题上做出了不少有争议的决定。

Microsoft has entered the fray with its own contentious decisions around data access.

我们还会在每周的《编辑精选》环节，深入分析SpaceX的估值。

We are also unpacking SpaceX's valuation on our weekly segment of the Editor's Cut.

我们会从底层数据和市场表现两个角度来做分析。

We're going to take both a bottoms up approach and also a market based approach to our analysis.

节目最后，我们会采访Mercury的联合创始人兼CEO。

We're going to close out the show with the co founder and CEO of Mercury.

这家公司刚完成了一轮大额融资。

The company just raised a big funding round.

我们会和他聊聊当前金融科技的现状，以及接下来的发展方向。

We're going to talk to him about the current state of fintech and what is next.

这期节目会很精彩，咱们直接开始吧。

It's going to be a fun show, so let's get right on into it.

微软最近在企业数据大战中又迈出一步，做出了一项引发争议的决定，惹恼了像Databricks这样的合作伙伴。

Microsoft has waded deeper into the corporate data wars with a contentious decision that has ruffled some feathers of certain companies that it partners with, like Databricks.

我请来我们的企业软件记者凯文·麦克拉林，带大家看看他今天早上发布的深度报道。

I want to bring on Kevin McLaughlin, our enterprise software reporter, to walk us through the inside reporting that he published this morning.

凯文，欢迎再次做客节目。

Kevin, welcome back to the show.

很高兴你今天能来。

It's great to have you here.

谢谢，阿卡什。

Thanks, Akash.

好的。

Okay.

那最近微软和Databricks之间到底发生了什么？

So what's going on with Microsoft and Databricks these days?

这件事大约一个月前发生的，但现在影响才逐渐显现。

So this is something that happened about a month ago, but the ramifications are now becoming clear.

主要是微软方面，抱歉。

Basically, Microsoft sorry.

上个月，Databricks 推出了一项功能，允许客户在 Databricks 里管理数据，然后通过微软的 Power BI 做可视化和图表分析。

Last month, Databricks launched the feature which allowed its customers to manage their data in Databricks and then do visualizations and charts and things in a Microsoft product called Power BI.

Databricks 在三月初上线了这个功能，但一个月后，微软突然切断了访问权限。

And now what happened was Databricks launched the feature in early March, and then a month later, Microsoft abruptly cut off access to it.

所以现在的情况是，这其实是一场关于所谓‘语义层’软件的争夺战，这个概念可能需要我解释一下。

And so, basically, what's happening is this is a battle for a type of software known as the semantic layer, which is gonna require some explanation.

这个过程有点

And it was a little

曲折

bit of a

做这个报道经历了不少波折，我就不在这儿细说了。

journey doing this story, so I won't try to attempt that here.

但简单来说，微软、Databricks 和 Snowflake 都希望客户用他们的软件来搭建语义层。

But basically, Microsoft wants customers to use its software to build their semantic layer, so does Databricks and and so does Snowflake.

所以每个人都想成为客户定义语义层的首选平台。

So everyone wants to be the place where customers define their semantic layer.

因此，现在正在上演的，正是我们一直报道并称之为‘AI数据战争’的这场新兴较量。

And so what's happening is this is an emerging battleground of what we've been writing about and what we've been calling the AI data wars.

我很乐意详细解释一下什么是语义层。

And I'm happy to go into sort of the the deeper explanation of what semantic layer is.

哦，我们当然要讲这个。

Oh, we're to do that.

这可是整个故事的核心。

I mean, think I mean, this core to the story.

不过总的来说，我们会逐一拆解这些概念。

But I mean, so broadly speaking here, so we are going to dig into each of these terms.

但说到底，这本质上是微软以某种方式限制 Databricks 的访问权限，而这只是你长期报道的那场企业数据大战和AI代理数据访问之争的一部分。

But I mean, this is an issue of basically Microsoft blocking access in certain ways to Databricks, this being part of the broader corporate data wars, AI agent data access story that you've written about for quite some time now.

是的，大多数财富500强企业都在用Databricks、Snowflake和微软的产品。

Yeah, this has to do with basically, most of the Fortune 500 uses Databricks, Snowflake, and Microsoft.

所以现在的情况是，作为客户，你可以用这三家供应商各自的功能，也可以全都在一家搞定。

And so what's happening is you can kind of do things as a customer using all three of those providers, or you can just do them all in one provider.

微软一向喜欢说，嘿，

And so Microsoft sort of has a history of of saying, hey.

你所有需求都能在我们的产品里完成，那为什么不呢？

You know, you can do everything within our products, and so why wouldn't you?

结果就是，大家都想成为客户定义语义模型的首选平台。

And then it's sort of what's happening is that everyone wants to be the place where some customers define semantic models.

所以我不会说这是 outright 封锁，但关键是，怎么让客户更方便地把所有事情都留在一个生态体系里完成？

So I wouldn't say it's like blocking per se, but it's sort of like how do you make it easier for customers to kind of do everything within one ecosystem?

我知道这个回答有点复杂，不过好吧。

And so I know that's kind of a complex answer to your question, but Okay.

那我们不妨再深入一点。

Maybe let's try to go a little deeper here then.

所以你能不能用最简单的话给我们解释一下，这个语义层到底是什么？

So explain to us in the simplest possible terms, mean, is the semantic layer?

咱们不妨从一个例子说起，比如公司用企业软件来管人力资源。

Maybe let's think about it from a perspective of we use enterprise applications for our HR stuff.

这个我们还挺熟悉的。

We have a good idea of that.

我就看到一些东西，比如仪表盘，对吧？

So, I mean, I see what I see, you know, a dashboard, for example.

那这个仪表盘背后，语义层到底指的是什么？

What would be the semantic layer behind that?

然后我们再聊聊，真正的竞争点到底在哪。

And then we can get to sort of what's the battleground here.

好。

Okay.

假设你是一家公司。

Let's say you're a company.

数据从零售收银系统、传感器设备等各种来源源源不断地流入，都是原始数据。

You have raw data that's coming in from retail point of sale systems, from sensor devices, from all sorts of places.

这些原始数据本身是模糊不清的。

And so that raw data is inherently ambiguous.

比如，有关于收入的数据，但收入可能指净收入、毛收入，或者发票收入。

So for example, there's data about revenue, but that could mean net revenue, gross revenue, invoice revenue.

语义层的作用，就是让公司对‘收入’到底指什么、各个类别怎么定义，达成统一共识，避免财务部门自定义一套，市场部门又自定义另一套。

And so the semantic layer allows companies to come to common definitions of what revenue means and what those categories mean so that you don't have finance creating their own definitions and marketing creating their own definitions.

语义层其实已经存在很久了。

So semantic layers have been around for a long time.

它一直是商业智能软件或数据可视化工具的核心部分，而Power BI就是这类工具。

They're a core part of what's known as business intelligence software or data visualization, which is what Power BI is.

Power BI 是目前最广泛使用的这类产品之一。

And Power BI is one of the most widely used products for this purpose.

现在，语义层的重要性不仅限于此，AI代理同样需要这些明确的定义。

And so the semantic layers are now becoming very important, not only for that, but AI agents need to have these definitions as well.

所以这就像对数据类型达成一致，比如对收入、支出、金额这些概念的统一定义？

So it's like a standardization of the data type of thing, like an agreement on the definition of revenue of expenses, dollars, stuff like that?

对。

Yeah.

这些是标准化的数据定义，让不同部门和公司都能基于同一套标准工作，避免各自为政。

They're standard definitions of data from which different departments and companies can work so that again, so everyone's not going off in their own directions.

最终结果就是报表和可视化图表的准确性更高了。

And the end result is more accuracy for the the reports, for the visualizations.

但后来发现，AI代理同样需要这些定义，一旦有了它们，代理不仅更准确，运行成本还能更低。

But it turns out that AI agents really need these definitions as well, and they can actually be much more accurate and even cheaper to run if agents have these definitions.

现在，我来简单讲讲语义层的基础知识。

Now, and I guess some semantic layer 101 here.

这些定义是每个公司都独一无二的吗？

Would these definitions be unique to each company sort of thing?

比如一家公司对某个收入细分的定义，确实会和其他公司不同。

How one company defines this segment of revenue, that would be unique.

是的。

Yeah.

如果你是客户，正在用 Databricks、Snowflake 或微软的产品，你肯定不想在每个系统里都重新定义一遍这些内容。

If you're a customer and you're using Databricks, Snowflake, Microsoft, you don't wanna have to define those definitions in each product.

但事实上，现在正是这么在做的。

But essentially, that is what is happening.

所以我们说的这场竞争，就是有一些公司正在努力制定语义层的标准。

And so when we say the battle, there are companies out there that are trying to develop sort of standards for the semantic layer.

你只需要定义一次，就能在多个不同产品里通用。

So you can do it once and you can use it across a number of different products.

目前有一个由 Salesforce 和 Snowflake 领导的组织或联盟，正试图建立统一标准，让客户不用在各个产品里重复做同样的工作。

But there's an organization or coalition being led by Salesforce and Snowflake, which is attempting to define a standard so that customers don't have to reproduce this work across different products.

明白了。

Got it.

好的。

Okay.

既然我们现在对语义层有了更清楚的理解，那我们再回头看看它。

So now that we have a better understanding of what the semantic layer is, so then let's go back to it.

你说它不是一个独立的模块，那微软到底是怎么阻止Databricks访问这个功能的呢？

So you said that it's not a block, so to speak, but what exactly is Microsoft doing then to make it more difficult to Databricks to access this?

问题到底出在哪里？

What's the issue here?

Databricks推出了一项测试功能，说只要你是他们的客户，就可以在Databricks里定义语义层，然后用这些定义在Power BI里生成图表和可视化内容。

So Databricks launched a beta feature that said, Okay, if you're a Databricks customer, you can define your semantic layers inside of Databricks, and then you can use them to create charts and visualizations in Power BI.

但几周后，微软突然停止支持这个功能，连个解释都没给。

And then a a few weeks later, Microsoft, without explaining why, blocked stop supporting that feature.

对那些正在用这个功能的Databricks客户来说，他们的报表一夜之间就全不见了。

So for Databricks customers who were using it, the reports just sort of vanished overnight.

全都用不了了。

They stopped working.

后来，为了说明情况，微软表示他们对这个功能的稳定性和准确性有顾虑。

Subsequently, and for our story, Microsoft explained that they had concerns over reliability and accuracy with the feature.

他们担心Databricks的客户在Power BI里用这个功能时，得到的结果不准确。

So they they were concerned that customers of Databricks would use this feature in Power BI and they wouldn't get accurate results.

最后客户可能会把责任怪到微软头上。

And then at the end of the day, customers might blame Microsoft for that.

这个解释是有道理的。

And so it's a valid explanation.

后来，一位微软高管说，我们不会在Power BI里支持第三方语义模型，因为太复杂了。

And later, a Microsoft executive explained that, look, we're not gonna we're not gonna support third party semantic models in Power BI because it's complex.

对客户来说，其实也没太大必要这么做。

There's not really much, reason to do that for customers.

从技术角度讲，这理由站得住脚，但背后可能也有商业考量。

Again, a valid explanation on technical terms, but perhaps one that also has a business motivation as well.

所以，这事儿确实挺棘手的。

So, it's kind of this thorny issue that's happening.

而这些公司本来都是紧密合作的伙伴。

And these are all partners that work closely with each other.

这些公司并不是像我们之前说的那样，互相设卡收费，抢夺数据。

So these are not like rival companies, like, putting up tollbooths around their data like we've been talking about.

这比那要微妙得多。

This is a little more subtle than that.

但归根结底，语义层才是新的竞争焦点

But at the end of the day, the semantic layer is the new battleground

AI领域的竞争焦点。

for AI.

所以，凯文，我想聊聊这一点。

So Kevin, I want

继续说下去。

to go on that.

这跟我们之前讲的那些公司互相设卡、抢地盘的故事不一样，更像是合作伙伴之间在关系上玩一些微妙的花样。为什么这件事值得我们特别关注？

This is not the story that we've been talking about, the rival companies putting up the toll gates, like you say, and it's more partners that are developing nuances to the relationship, why is this something that we ultimately should be paying attention to?

对终端用户来说，这件事背后到底有什么样的利害关系？

What are the stakes here in terms of what this could mean end customers?

每个人都希望AI代理更准确，运行成本也更低。

Everyone wants AI agents to be more accurate and even cheaper to run.

语义层属于一种叫作上下文的数据，厂商们最近总在提这个，有些产品甚至直接把‘上下文’当名字用了。

Semantic layer is part of this type of data known as context that you're hearing vendors talk a lot about, context, and some products even have the name context in them.

上下文就像是AI代理的营养餐，让它能更精准地运作。

Context is the the rich food that agents makes that makes them operate more accurately.

如果你掌握了上下文，并把它提供给代理，代理就不必花那么多精力去推理了，这样能减少token消耗，运行成本也更低。

And so, if you have the context and you you wanna give that context to an agent, it even means that the agent doesn't have to reason as much before making decisions, which means they're consuming less tokens and they're cheaper to run as well.

这种数据的好处非常多，所以未来几个月，你肯定会越来越多地听到语义层这个说法。

And so there's there are lots of benefits to this type of data, and that's why semantic layer, you're going to be hearing a lot more about it in the coming months.

因为只要客户和公司不断加强对数据访问权限的管控，模型的性能要求和算力消耗就会随之变化。

Because this is really I mean, as long as these customers or as long as these companies are developing more and more guardrails, I guess, around, you know, what data is accessible or not, that will then have an implication on how good the models need to be, how much compute is consumed.

如果我没理解错的话，这正是这些公司最终能盈利的关键起点。

And so this is actually one of the starting points here of what could very much be these companies ending up turning a profit in the end, if I'm understanding you correctly.

没错，当然。

Sure, sure.

就像我之前说的，现在有个行业联盟正在努力让这个问题变得无关紧要。

And like I said before, there's this sort of industry coalition that's working to sort of make this a non issue.

我相信你接下来还会听到更多关于这些开放举措的消息，毕竟最终，封闭式的做法只会拖慢进度，这在企业软件领域我们已经见过很多次了。

And I think that you're going to be hearing more about these open efforts as well, because at the end of the day, the proprietary approach is gonna slow things down, and we've seen this many times before in enterprise software.

这就是为什么开源软件会存在。

That's why open source software exists.

所以，关于语义层是该走封闭路线还是开放路线，这肯定会成为一个热门话题。

And so, you know, the proprietary versus open debate in regards to semantic layer, it's going to be a hot topic.

它可能不会成为每个科技新闻读者都关注的头条，但相信我，这真的非常重要。

It might not be, you know, headline news for everyone who reads tech news, but trust me, is very important.

不过我们这个节目就喜欢聊这个，所以才请你来，帮我们把这事理清楚。

But we like to talk about it here on the show, so that's why we like to have you here, helping us make sense of it.

凯文，非常感谢你来做客。

Kevin, I want to thank you for coming on.

这位是凯文·麦克劳林，我们《信息》杂志的企业软件记者。

That is Kevin McLaughlin, our Enterprise Software Reporter, here at The Information.

SpaceX的S1带来了新的数据，让大家都在琢磨：这家公司到底该值多少钱？

SpaceX's S1 gave the world some new data to Crunch as we all seek to answer the question, what should the company really be worth?

除此之外，这周还有不少与IPO相关的重磅新闻。

Outside of that, this week, there were also a number of other big IPO related headlines.

为了深入聊聊这些，本周的《编辑精选》我请来了马丁·皮尔斯和梅雷迪思·马齐利。

To unpack all of that, in this week's edition of The Editor's Cut, I want to bring on Martin Peers and Meredith Mazzilli.

欢迎两位。

Welcome to you both.

很高兴你们能来。

It's great to have you here.

嘿，阿卡什。

Hey, Akash.

嘿，阿卡什。

Hey, Akash.

最近怎么样？

How's it going?

我挺好的。

I'm doing well.

我不知道能不能得出确切答案，但至少能听到几种不同的看法。

So I don't know if we'll get an answer here, but we'll get some versions of an answer.

SpaceX到底值多少钱？

What is SpaceX worth?

这正是大家讨论的焦点。

Is the topic of discussion.

马丁，你写过这方面的东西。

Martin, you wrote about this.

你在昨晚的简报里用了自下而上的方法来评估这家公司，我觉得是这样。

You took a bit of a bottoms up approach, I would say, to valuing the company in last night's briefing.

我想请你详细说说，你是怎么得出这个估值的。

And I want you to walk you through how you were thinking about coming to your valuation.

好的。

Sure.

我先说一下，梅雷迪思待会儿肯定也会这么说，所以我提前讲清楚。

Now, I will just say, as Meredith, I'm sure, is going to say, so let me preempt her.

哎呀，别这样。

Oh, no.

我特别觉得，SpaceX的价值就是市场愿意出多少钱，这事儿真挺让梅雷迪思烦的。

I really annoy her, that SpaceX is worth whatever people are willing to pay for it.

咱们先把这事儿放一边。

So let's just get that aside.

我真正想做的，是把那些关于去火星、去月球的迷雾、戏剧性和神话都扫干净，就看SpaceX到底有什么实实在在的东西。

But really what I wanted to do was to try to clear away all the murk and the drama and mythos around flying to Mars, flying to the moon, all this sort of stuff that Elon loves to talk about, and just look at what actually is in SpaceX.

它本质上就是一个电信公司。

What it really is, is a telecom firm.

在全球范围内出租或销售卫星宽带终端，让人能通过它们连上互联网。

Rents or sells satellite broadband terminals to people around the world and they can get access to the internet from them.

这么说吧，它就跟有线电视运营商差不多，是个很普通的生意。

That means it's basically like a cable operator or so that's a fairly plain vanilla business.

它现在也开始涉足云计算了。

It's also moving into cloud computing.

它把一部分AI算力租给了像Endtopic这样的公司，据我所知，目前就这一家。

It's rented out some of its AI computing capacity to various companies like Endtopic, I guess that's the only one.

不过它表示未来还想扩大这方面的业务。

Although it said it wants to do more of that.

它在X平台上还做着一小块广告业务。

And it's got a small advertising business in X.

当然，它也搞火箭发射。

So, course, it also launches the rockets.

人们最关注的可能就是这一块，但实际上它在公司整体里占比并不大。

And that is maybe one of the businesses that people are most focused on, but it's actually one of the smaller parts of the business.

所以我做了件事，就是把这三项业务分别找了一个公开市场上最接近的对标公司，看看它们的股价倍数，再用这些倍数乘上SpaceX的收入数据，还结合了第一季度的数字，估算了一下今年这三块业务可能的业绩，最后算出来一个估值——我给得已经非常慷慨了，SpaceX的估值大概在700亿左右，等等，不对，是500亿？不对，我搞错了。

So, what I did was I just looked at each of these three and I looked at the closest comparison in the public market and I looked at where those companies are trading and I applied the multiples of which they are trading to the revenue figures that we got from SpaceX and I tried to also calculate based on the first quarter numbers what those businesses might do this year and I came up with a number that suggested that the most generous, and I'm being very generous, valuation for SpaceX would be around 700 I think it was 50 no, sorry.

大概是700亿。

It was around 700.

7000亿美元，是的。

That 700,000,000,000, yeah.

对，我觉得你算到了60亿到80亿美元。

Yeah, I think you got to $6.06 $8,000,000,000

我有点困惑是因为我试了几个不同版本，一直得调整一些数据。

The reason I'm a bit confused is I went through various different versions and had to keep adjusting some things.

所以是600亿到7000亿美元。

Okay, so $600 to $700 700,000,000,000.

不是1.7万亿美元。

It's not 1,700,000,000,000

有些人，你昨天在TITV上请的那位Russ Gerber，比我还懂，他说值500亿。

Some people, and you had someone on TITV yesterday, Russ Gerber, who's much smarter than I am, who said it's worth 500.

我也不反对这种说法。

And I wouldn't argue with that.

这完全取决于你怎么算，以及拿它跟谁比。

It really depends on how you do the calculations and what you compare it to.

但我们就假设它的价值在那个范围内，比如五千亿、七千亿这样的水平。

But let's say it's worth somewhere of that order, 500, 700, something like that.

说它值一万七千五百亿美元，这简直太离谱了。

The idea that it's worth $1,750,000,000,000 is crazy.

但那多出来的部分只是

But that extra is just

好的，好的，好的，好的，好的。

Okay, okay, okay, okay, okay.

所以我们达成了共识。

So we got it.

我明白你的逻辑，我觉得这个分析非常出色，内容也很到位，我建议大家都去读一读。

I follow your logic, and I will say to folks that it is a great analysis and a good piece, so I encourage everyone to read it.

不过，梅雷迪思，现在我想听听你的看法。

But Meredith, I want to come to you now.

我想问你，你是怎么支持一万七千亿这个估值的？或者至少，你能解释一下投资者是怎么得出这个数字的吗？

I guess I'm going to ask you how to justify the 1,700,000,000,000 valuation or at least provide another perspective as to how you think investors might get there.

是的，我不会坐在这儿说SpaceX就该值两万亿美元之类的话，但我想指出，把各个部分加总起来只是评估估值的一种方式，并不能反映全部情况。

Yeah, look, so I'm not going to sit here and argue that SpaceX should be worth 2 gajillion dollars or whatever, but I will point out that I do think the sum of the parts is just one way to model valuations and it doesn't capture the whole picture.

所以，我觉得我们不该太把这种算法当真，把它当成判断价值的黄金标准。

So I don't think we should put too much stock in that as you know, the golden number of what something should be worth.

就像马丁说的，你是在把每个独立业务的价值加在一起，然后硬凑成一个总数。

You know, as Martin explained, you're kind of adding together what all these individual businesses should be worth on their own and kind of squishing them together.

但SpaceX的情况完全不同。

But with SpaceX, it's actually very different.

和其他卫星公司或移动运营商不一样，它的业务高度垂直整合。

You know, unlike a lot of other satellite companies or mobile operators, they're a lot more vertically integrated.

它的发射服务直接把卫星送入轨道。

You know, their launches put their satellites into orbit.

能这么做的人少之又少。

And there's very few other companies that can do that.

单从这一点来看，我觉得把各部分加总的方法确实有点问题，抱歉，马丁。

So just on that basis, I think there is a bit of an issue, sorry, Martin, with the sum of the parts analysis.

这还没算上星舰在太空部署数据中心这些事呢。

And that's before we get to even Starship data centers in space, all of that.

一旦你开始考虑这些，那就更像是一种情景推演了。

And once you start looking at that, that really just starts becoming more of a scenario analysis.

如果这种情况真的发生，我们该给它赋予多大的可能性？

So if this happens, what weight do we assign to this happening?

我们又该给它估值多少？

What value did we assign to that?

我并不是说外界流传的那个IPO估值就一定对，但确实有别的思路可以看待这个问题。

Again, I'm not saying that the big number that is out there for the IPO is necessarily the right number, but there are different ways to think about it.

另一个我想和你聊聊的话题，马丁，马丁，你对

So the other topic that I wanted to talk to you, Martin, Martin, anything to

把各部分简单相加只是其中一种方式，而且并不全面。

know, some of the parts is one, you know, only one thing and it's not doing that.

没错。

That's true.

这确实没错。

It's definitely true.

但你知道，业务多元化的公司通常估值都会打个折扣。

But you know, companies that have many different businesses usually trade at a discount to that analysis.

所以你可以说，SpaceX 的价值可能在 500 亿甚至更低。

So you could say that SpaceX is worth 500 or even less.

我的意思是，反正吧。

I mean, anyway.

还更低？

Even less?

你打算降到什么地步？

How low are we going go here?

我也不清楚。

I don't know.

但问题是，我们根本不知道这些事什么时候会发生。

But I mean, look, the issue, the big problem is that we don't know when any of these things will happen.

你真的愿意买一样东西吗？就好像你在店里买东西，店员跟你说：这东西能用，但我也不知道啥时候能用。

And do you really want to buy something when it's almost as if you were buying something in a store and the person says to you, this will work, but I don't know when.

所以你花大价钱买一辆车，结果这车不能马上开，得等将来某一天才可能跑起来。

And so you're paying an awful lot for like buying a car, you're paying a huge amount for the car and it won't work immediately, but at some point in the future

等等，这是风险投资。

Wait, it's venture capital.

这是风险投资。

It's venture capital.

风险投资本来就是这么回事。

That's what a venture capital investment is.

你得明白这一点。

Point this out.

你这是在

You're

买一辆车，却付了三倍的价钱，只因为承诺说，等它真能开的时候，会跑得特别快。

buying a car, so you're paying three times the price of the car and on the promise that when it does eventually work, it'll drive really fast.

但问题就在这儿。

But here's the problem.

我们根本不知道它什么时候能成，甚至能不能成。

We have no idea if it will ever work and when it will.

这就是我对这次IPO的看法。

That's my analogy for this IPO.

梅雷迪思，你是说有点像什么？

Meredith, it's kind of what?

这其实就是给股市投资者提供了一个当风投的机会。

It's just offering stock market investors a way to be VCs in some ways.

是的。

Yeah.

更准确地说，其实你有一个真正能运作的业务。

Mean, maybe the more accurate sum of the parts is you have this functional real business.

这个说法你没法反驳。

I think that's you can't argue with that.

然后还有一块是类似风投的部分，附着在上面，也需要考虑进去。

And then there is this venture like, part of it that is on top of it and thinking too.

怎么把这两部分结合起来呢？

And how to combine those together?

马丁，我们还是回到刚才说的，应该是6878亿美元，我刚才说错了。

Well, Martin, let's just go back to I mean, so $68 $678,000,000,000 I should say.

你大概给我们讲讲，这笔钱是怎么分配的？空间业务、通信业务，还有人工智能业务，各自占了多少？

Just walk us through, roughly speaking, how did that break down in terms of what you got for the space business, the connectivity business, and then the AI business?

最后最大的一块是哪部分呢？

What was the biggest in the end for

最大的当然是空间业务，这其实有点奇怪。

Well, the biggest was the space business, and that's a bit of a strange thing.

因为虽然空间业务的实际收入并不多，但我拿它和一家叫火箭实验室的公司做对比，我记得是这个名字，它的估值高得离谱。

It's because even though the space business actually revenue from that is fairly small, I was using as a comp a company called Rocket Lab, I think it's called, which is trading at an extraordinarily high valuation.

我觉得它才是和SpaceX最接近的对标公司。

It's the closest comp to SpaceX, which I thought was the right one.

我觉得，考虑到马斯克打算实现的前景，给它一个很高的估值也说得通。

And I also thought, well, given the promise of what Elon is hoping to do, maybe it's fair to apply a very high valuation.

为什么当

Why when

我算出这个总数的时候，其实已经非常慷慨了，比其他人给出的数字都要高。

I come up with this overall number, it's very generous and it's higher than what other people have actually put on it.

我注意到这一点，梅雷迪思，很想听听你的看法。

And I found it interesting, Meredith, maybe I'd love your reaction to this.

关于AI业务部分，也就是xAI，马丁刚才算出的估值是1650亿美元，这个数字是基于它目前产生的收入。

So the AI part of the business, xAI, that Martin valued here, I mean, number that he got to was $165,000,000,000 and that was based on the revenue that it had generated.

准确说是基于它从Anthropic那里预期能获得的收入。

Well, based on the number that they will generate from Anthropic.

对。

Right.

但我想问问你，梅雷迪思，xAI在这里其实是三个板块里最小的，而它恰恰是——

But my question for you, Meredith, mean, XAI here, I mean, would be the smallest of the three segments here, and it's the one that Well, yeah.

我的意思是

I mean

Sarlink值大约150亿。

The Sarlink is worth about 150.

哦，小多了。

Oh, smaller.

女孩们都在忽略X。

Girls are forgetting X.

它只值3亿。

It's worth 3.

咱们不妨想想，埃隆买这个花了多少钱？

Let's just think, how much did Elon pay for that?

对，44亿。

Yeah, 44.

就在桥底下。

Right under the bridge.

四百四十亿美元。

44,000,000,000.

他毁掉了价值四百一十亿美元的资产。

He's destroyed $41,000,000,000 worth of value.

这位就是投资者们还愿意把大笔钱投给SpaceX的人。

This is the guy that investors want to give all this money to for SpaceX.

埃隆在毁灭价值这方面很在行，创造价值的时候偶尔也行。

Elon is very good at destroying value as well as sometimes creating it.

别忘了这一点。

Let's not forget that.

好的。

Okay.

最后一个话题。

Last topic.

梅雷迪思，我想快速听听你对整个IPO市场的看法，因为这周我们看到的只是其中一个IPO相关的新闻。

Meredith, I do want to get your take here quickly on the IPO market at large, because this was just one IPO related headline that we saw this week.

我们还听说了OpenAI正在加速推进相关事宜。

We also had the news that OpenAI is fast tracking things.

Aura已经悄悄提交了上市申请。

We saw Aura has filed confidentially.

Blockchain.com又重新出现在我们的IPO追踪名单里了，以前我们可是一直盯着这家公司的。

Blockchain.com is back in the mix as a company that you and I track very closely on our IPO tracker once upon a time.

它已经

It's been

在市场上游荡一阵子了。

floating around there for a while, yeah.

你怎么看这个情况？

What do you make of this?

这个窗口期会持续很久吗？

Is this a window that's going to last a long time?

接下来会怎样，确实值得观察，如果真演变成强者愈强、弱者愈弱的局面，今年可能有三家超级规模的IPO要登场。

So it will be interesting to see how this plays out, and if this really turns into a have and have not situation, we do have potentially three mega IPOs coming this year.

还有那些消费类公司和加密货币领域的中小型IPO，不过那边会怎样还不确定。

And then we have these smaller IPOs from consumer companies, crypto, though we'll see what happens there.

但大型IPO会不会把所有资金和注意力都吸走，让这些小公司根本没机会？

But do the big IPOs really just take all the attention money out of the picture for these smaller IPOs?

我觉得现在消费类公司其实挺有优势的，因为它们算是‘抗AI’的标的，你知道吧？

I think that with consumer companies, there is an argument right now that they're also well positioned because they're kind of like the AI proof play, right?

AI再厉害，总不能让人不戴AuraRing吧？

You know, like AI is not going to get rid of the fact that you need to wear an AuraRing.

它可能会影响一些软件或者附加功能，但不会动摇根本。

It might impact maybe some of the software, other add ons.

有些消费品牌确实挺抗AI的，不太可能出现SaaS行业全面崩盘的情况。

But there is an argument that some of these consumer brands are pretty AI proof, and we're not going to see a SaaS pocalypse there.

至于区块链公司，我真猜不透他们在想什么。

Blockchain, I honestly do not know what they're thinking.

还有几家加密货币公司几个月前，甚至去年底就悄悄提交了申请，到现在还卡在那里。

We have a couple other crypto companies that actually filed confidentially a few months ago, I think even late last year that are still just sitting there.

加密货币市场现在跌了。

The crypto market is down.

他们可能只是在留后路，但这个举动确实有点出人意料。

They could just be positioning for optionality, but that one was a a bit out of left field.

我能说说对Oura的不同看法吗？

Can I just make the counterargument on Oura?

我同意，如果看同类竞品，Garmin今年股价涨了18%。

And I do agree that if you look at the comp on that one, I think it's Garmin, its stock is up 18% this year.

所以你可能会觉得Oura的机会不小。

So you might say that Oura has a pretty good chance.

但谷歌刚推出了自己的健康追踪设备。

But Google has just come out with its own version of this sort of health tracking product.

我其实刚下单买了一个。

I've just ordered it actually.

这个设备只要100美元，那Oura卖多少钱？

It cost $100 Oura costs how much?

400美元，还得再付一笔费用。

$400 And you have to pay some fee.

我觉得还是别选Oura。

Think I would avoid Oura.

但它是戒指啊。

Well, but it's a ring.

我的意思是，它就是个戒指。

I mean, it's a ring.

它可不是那种运动手环。

It's not the risk band.

那戒指挺笨重的。

It's bulky ring.

戴上戒指

Puts a ring

是这个意思吗？

on that?

好的。

Okay.

这更像是谷歌在进军Whoop市场，而不是三星，我觉得没错，三星本来是冲着戒指类产品的。

This is more of a Google taking on a whoop than it is on the Samsung, I think if I'm not mistaken, mean, Samsung was going after the ring category

是啊，市场已经饱和了。

as Yeah, the sell over.

好的。

Okay.

行吧。

All right.

嗯，

Well,

你听好了，我可是第一个说的。

You heard it we will from me first.

Oura已经过时了。

Oura is over.

谷歌。

Google.

我忘了那东西叫什么了。

Whatever the I've forgotten what it is now.

好的。

All right.

我很期待看到这件事最后会怎样发展。

Well, I look forward to seeing how that plays out.

我觉得这跟根源有关。

I think it's a roots thing.

感谢你们两位前来做客。

I want to thank you both for coming on.

这位是马丁·皮尔斯，这位是梅雷迪思·马齐利，都是《信息》杂志的编辑。

That is Martin Peers and Meredith Mazzilli, two of our editors here at The Information.

金融科技公司Mercury在D轮融资中筹集了2亿美元，估值达到52亿美元。

Fintech company Mercury raised $200,000,000 in Series D funding at a $5,200,000,000 valuation.

这轮融资由TCB领投，还包括安德森·霍洛维茨、红杉资本、Kotu以及众多其他投资方。

The funding round was led by TCB and also included Andreessen Horowitz, Sequoia and Kotu, and a whole host of other investors.

现在邀请到我们的嘉宾，也是金融科技公司Mercury的联合创始人兼首席执行官伊马德·阿赫ун德，来聊聊当前金融科技领域的现状。

Joining me now to talk about the current state of play in fintech is co founder and CEO, Immad Akhund.

伊马德，欢迎来到节目。

Immad, welcome to the show.

很高兴你能来。

It's great to have you here.

是的，谢谢邀请我。

Yeah, thanks for having me.

你最近怎么样？

How are you doing?

我挺好的。

I'm doing well.

你呢？

How are you?

我挺好的。

I'm good.

我挺好的。

I'm good.

这周特别忙，不过总算把所有公告和相关事项都收尾了。

It was a really busy, busy week, but we're just wrapping it up, up all the announcements and things like that.

你们连续两年都做融资公告，是吧？

So you've done this is back to back years for funding announcements, right?

我记得去年你们估值大概是35亿左右，对吧？

You mean you were just valued at three point something, I think, last year, right?

是的，去年差不多这个时候，我们从红杉资本融资，估值是35亿美元。

Yeah, so last year, about twelve months ago, we raised from Sequoia at a 3,500,000,000 valuation.

去年我们取得了不少显著进展。

We had a lot of really strong progress last year.

今年第一季度是我们有史以来增长最强劲的季度，申请量比前年同期高出两倍半。

Actually, Q1 this year was our strongest kind of growth quarter we've ever had, and it was 2.5 X higher applications compared to Q1 the year before.

所以我们觉得可以趁这股势头，顺势再筹一轮资。

So we thought we'd kinda, you know, jump on that momentum and raise around.

第一季度增长最猛，那收入大概到什么水平了？

So strongest quarter yet in Q1, so what does that turn out to be in terms of revenue?

去年第三季度我们公布过营收，年化是六亿五千万美元。

We announced our revenue last year in Q3, $650,000,000 annualized.

之后我们还在持续增长。

We've continued growing.

我们没公布新数字，但大部分指标都增长了五成以上。

We're not announcing a new number, but we're seeing 50% plus kind of growth on most of our metrics.

我们明显感觉到，AI催生了新一轮创业热潮。

And we're really feeling like AI's created this kind of extra boom of entrepreneurship right

现在是六月。

now, So June.

我试试看能不能大概估算一下，现场做个预测。

I'm going to see if I can ballpark Can do live

算过这笔账吗？

math there?

你说大多数指标增长了50%，那按这个趋势，今年Q3的收入可能就要接近十亿美元的年化水平了？

June, you said 50% growth on most metrics, which if we take revenue So what, Q3 this year, you'll be trending around a billion maybe annual run rate?

天啊，希望能超过更多。

Wow, hopefully more.

希望更多，好的。

Hopefully more, okay.

你们正在一步步接近了。

But you're getting there, maybe.

我猜得差不离吧？

Am I in the right ballpark?

现在还不能公布，但等我们达到下一个里程碑时，一定会正式宣布。

We're not announcing it right now, but yeah, will announce it when we hit the next milestone for sure.

所以你们账上又多了两亿美元。

Okay, so you've 200,000,000 more in the bank.