认识Snowflake Intelligence：与Sridhar Ramaswamy共同打造的企业级个性化智能代理

大家好，听众朋友们。

Hi, listeners.

欢迎回到《无先验》节目。

Welcome back to No Priors.

今天，我在这里与Snowflake的首席执行官Sridhar Ramaswamy对话，他同时也是Neva的创始人和谷歌广告的高级副总裁。

Today, I'm here with Sridhar Ramaswamy, the CEO of Snowflake, the former founder of Neva and the SVP of Google Ads.

我们将讨论他担任CEO的前十八个月，以及在这段时间内如何卓越地推动一家大型公司全面转向AI优先战略，企业AI的投资回报率在哪里，以及在AI时代云服务提供商和广告模式将发生怎样的变化。

We will talk about his first eighteen months of being CEO, the incredible execution over that time in shifting a company at scale to being AI first, where the enterprise ROI is, and what happens to the cloud service providers and to the ads model in the age of AI.

欢迎你，Sreedhar。

Welcome, Sreedhar.

Sara，非常高兴能再次回来。

Sara, really excited to be back.

能以老朋友和前同事的身份与你交谈，我感到非常愉快。

Well, pleasure it's to talk to you as an old friend and colleague.

上次我们交谈时，你正处在创业旅程中。

The last time we spoke, you were on the entrepreneurial journey

没错。

That's right.

仍在做搜索。

Doing Search Still.

你现在已经是Snowflake的CEO十八个月了。

You're now eighteen months into being CEO of Snowflake.

这十八个月非常充实。

It has been a very eventful eighteen months.

跟我们聊聊从接替Frank的职位开始，到最初几个月，再到你们今天所处的位置，这段历程是怎样的吧。

Tell us a little bit just about the journey from, you know, taking the mantle from Frank to, you know, the first few months to where you guys are today.

我认为市场已经以多种方式做出了反应。

I think the the market's reacted in many ways.

是的。

Yeah.

最近，市场对你们的执行能力反应极其积极，但这确实是一段不平凡的旅程。

Most recently, incredibly well to the execution, but it's it's been a journey.

没错。

That's right.

没错。

That's right.

Snowflake 一直是一家出色的产品公司。

Snowflake has always been an amazing product company.

Benoit 和 Theory 十多年前构想的原始产品远远领先于时代，并且风靡全球。

The original product that Benoit and Theory conceived of ten plus years ago was many years ahead of its time and it took the world by storm.

显然，他们实现了盛大的上市，那是当时规模最大的软件公司IPO。

And obviously they had the storied IPO, the biggest software IPO at that time.

我认为，公司对机器学习和人工智能等变化的反应有些迟缓，这确实是 Frank 自愿推动变革的原因——他敏锐地预感到，我们正进入一个产品层面更加动荡的时代，因此他希望由一位以产品为导向的人来领导公司。

I think what happened was the company was a little slow to reacting to changes from things like machine learning and AI, and that was a little bit of, honestly, the reason why Frank voluntarily pushed for the change because he felt presciently that we are headed into a time that was just a lot more tumultuous from a product perspective, and he wanted someone that was product first to be in charge of the company.

过去十八个月，我们一直在全力拥抱这场变革浪潮。

And the last eighteen months have really been about embracing that wave of change.

如果你回顾过去两年发生的变化，就会发现人工智能如何迅速渗透到我们日常生活的方方面面，以及事物变化的速度仍在不断加快。

And if you look back to what's happened in the last two years, it's crazy how much change with has respect to AI, how it's become commonplace every day in all of our lives, and then the speed at which things are still getting driven through.

我认为Snowflake最了不起的地方在于，公司拥抱了这一变革，实现了自我转型，并证明了我们不仅能在产品层面做到这一点——这一点本在预期之中，而且还在市场营销和整体市场策略方面进行了重大调整。

I think the really amazing thing about Snowflake is the company embraced this change, transformed itself, and then showed that not only can we do it from a product perspective, which one could have expected, but we've also done significant things to retool our marketing or go to market overall.

我认为这一转型过程令人惊叹，但你知道，时代有时会很艰难。

I think that transformation has been pretty amazing to watch, but, you know, times can be difficult.

去年，有很多怀疑者，但也有许多人，包括我，相信Snowflake已经创造的价值。我接受这份工作的一大原因，是因为在成为CEO之前，我与大量客户进行了交流。

Last year, there were a lot of doubters, but there were a lot of us who believed both in the value that Snowflake was already creating, and the reason I took this job was because I talked to a whole lot of customers before I became CEO.

他们都热爱Snowflake，这正是我接受这份工作的主要动力。

They all loved Snowflake, and that was a big motivation for me to take this job.

因此，我认为我们已经成功度过了那段时期，如今站在了企业级数据与人工智能的前沿。

So, I think we have sort of successfully ridden through that and are now at the cutting edge of data and AI for enterprises.

能够经历这段旅程，真是令人惊叹。

It's been an amazing journey to have gone through.

请谈谈你在前六个月里所做的方向调整和优先级排序，以及关于长期愿景的一些想法。

Walk me through just some of the like orientation prioritization you did in the first six months and then, you know, a little bit more about the long term vision here.

是的。

Yeah.

前六个月主要是进行了一些战术性调整，主要围绕责任落实。

The first six months were a lot of tactical changes, which primarily around accountability.

就像每一个经历火箭式增长、年增长率超过100%的公司一样，Snowflake在每个层级都实现了高度专业化，工程师开发的功能与使用该功能的客户之间相隔甚远，中间涉及了七到十个团队。

Like every company that goes through essentially a rocket ship phase of growth, growing at 100 plus percent year on year, Snowflake had basically specialized at every layer possible, and there was a very long distance between the engineer that did a feature and the customer that made use of that feature, and there were like seven to 10 layers of Teams that were involved.

当你拥有完美的产品市场契合度，并试图优化每个职能时，这种结构是可行的。

That works fine when you have perfect product market fit, and you're trying to optimize for every function.

你只是那个领先的云数据仓库。

You're just the winning cloud data warehouse.

就像

Just like

直接开着卡车冲进去。

Drive a truck through that.

但另一方面，在AI领域，我们连下个月会发生什么都说不准，更别提明年了，这种结构就完全不合适了。

But on the other hand, if you're working in the world of AI where we can barely tell what's going to come out next month, forget next year, This is the wrong structure to have.

因此，我们围绕不同领域进行了大量重组，确保每个领域都有明确的责任人，比如在产品工程方面。

So, we did a lot of organizing by different areas, making sure that there were accountable people, for example, in product engineering.

这是最早的变化之一。

That was among the first changes.

按不同的产品领域进行组织，比如人工智能或核心数据仓库分析产品。

Organize into different product areas like AI or the core warehousing analytics product.

但我们也希望与我们的市场团队建立直接的联系，因此我们创建了这些专门团队，与产品、工程和市场部门紧密合作，将这些新产品推向市场。

But then we also wanted a straight line over to our go to market team, so we created these specialized teams that work closely with product and engineering and marketing to take these new products to market.

这是Snowflake早期阶段的重心，强调更快的迭代。

That was a lot of the early phase of Snowflake with an emphasis towards faster iteration.

这一直是我一生所信奉的理念：速度致胜。

This is something that I believed in all my life, which is speed wins.

迭代能力总是胜过精心规划的战略。

Ability to iterate always trumps carefully laid out strategies.

是的，你不该做蠢事，但另一方面，实现任何收益都需要大量迭代。

Yes, you shouldn't do dumb things, but on the other hand, realizing any kind of gain requires a lot of iteration.

因此，我们在这一方面做了多项调整，不仅加快了产品的开发速度，也加快了与客户的迭代节奏。

So, we made a number of changes on that side, both with respect to how quickly we created products, but also how quickly we iterated with customers.

我还会说，我们在AI领域花了一些时间来找到自己的最佳定位。

I would also say we took a little bit of time to find sort of our sweet spot in this AI space.

正如你所知，AI本身已经发生了很大变化。

As you know, that itself has evolved a lot.

我们不是云服务提供商。

We are not a CSP.

我们也不是基础模型实验室。

We are not a foundation lab.

那我们到底是什么？

So, what are we?

我们逐渐认识到自己是AI数据云，而不仅仅是数据云。

And there was that discovery of ourselves as the AI Data Cloud as opposed to the Data Cloud.

我认为，正是这种对自身价值的清晰产品洞察，为今年早些时候乃至我们今天即将讨论的内容奠定了基础。

And I think it's that kind of clear product insight into what value we add that is setting up the stage for the earlier parts of the year or even what we are about to talk about today.

现在你们正在发布Snowflake Intelligence。

Now you're announcing Snowflake Intelligence.

给我们讲讲这个，以及它如何融入更宏大的愿景。

Tell us about that and how it fits into the broader vision.

我的意思是，首先，在人工智能方面，正如我所说，我们必须认真审视自己。

I mean, first of all, when it came to AI, as I said, we had to look hard at ourselves.

去年年初，我们实际上走上了创建基础模型的道路。

Early last year, we actually went down the path of creating foundation models.

我们开发了一个可信的MOE模型。

We created a credible MOE model.

这是去年年初的事。

This was early last year.

但我们很快意识到，要与OpenAI或Anthropic这样的公司竞争将非常困难。

But we also quickly realized that our ability to compete with the likes of OpenAI or Anthropic was going to be really hard.

我们根本没有足够的资金来对这类事情进行有意义的投资。

We simply did not have the capital to be able to invest meaningfully in things like that.

因此，我们转向了另一个方向：AI如何极大地加速Snowflake中数据所能实现的功能？

So, we pivoted away from that into much more of a how does AI massively accelerate what can be done with data that is in Snowflake?

随着时间的推移，这将成为吸引更多数据进入Snowflake的原因，而我们现在正处于这一阶段。

And over time, can become a reason to bring more data into Snowflake, which is the phase that we are in now.

但我们的许多AI产品策略实际上非常务实。

But a lot of our AI product strategy was actually quite humble.

它并没有宣称我们要重新思考一切。

It didn't say we were going to rethink everything.

它指出，数量庞大的客户——大约全球一半的合格《财富》2000强公司都是Snowflake的客户。

It said enormous number of customers, something like half of the qualifying Fortune 2,000 companies on the planet are Snowflake customers.

他们最宝贵的数据都存储在Snowflake上。

They have their most valuable data on Snowflake.

那么，AI对这些意味着什么？

What does AI mean for that?

因此，我们系统性地投资于各种组件，无论是搜索还是文本转SQL，都以增加对人们已在Snowflake上进行的操作的价值。

So, we systematically invested in the components, whether it was search or whether it was text to SQL in ways that added on value to the things that people were already doing with Snowflake.

SI是一个智能代理平台，但它实际上是一个具有明确立场的智能代理平台。

And SI is an agentic platform, but it's actually an opinionated agentic platform.

许多由云服务提供商提供的智能代理平台会声称，你可以从任何地方引入数据。

A lot of agentic platforms, for example, from the CSPs will basically say, Oh, you can bring in data from anywhere.

可以想象任何你想要执行的工作流，一个代理就能通吃一切，这在理论上很美好，但在实践中，当你面对无穷无尽的可能性时，反而很难确定究竟该做什么。

Can imagine any kind of workflow that you want to do, and the one agent will rule them all, which is nice in theory, but in practice, when you have an infinity of things that you can do, it's also hard to figure out what you should actually do.

Snowflake Intelligence 非常专注于如何更快地从结构化或非结构化数据中创造价值。

Snowflake Intelligence is very focused on how do you create value from data, whether it's structured or unstructured, a whole lot faster.

因此，真正让我们兴奋的用例——坦白说，主要是内部用例——比如，如果我们把销售部门所有不同的仪表板整合成一个统一的界面，那会是什么样子？

And so, the kind of use cases that got us really excited, honestly, internal ones, were things like if we were to take all of the different dashboards that we used in sales and put it into one single interface, what could that be?

我们已经迭代了两三个版本，但最终催生了这个内部产品。

We had done two, three versions of this, but eventually that culminated in this internal product.

我们称之为 Raven，但它本质上是一个销售数据助手。

We call it Raven, but it's basically the Sales Data Assistant.

然后我们开始与早期客户合作，比如思科、Fanatics 或美国雪车国家队，来探索这对他们意味着什么。

Then we started working with early customers, whether it is a Cisco or Fanatics or the USA bobsled team to figure out what does this all mean for them.

核心主题依然是摆脱仪表板这类工具的僵化性。

The theme again is get away from the inflexibility of things like dashboards.

仪表板是对复杂数据表面的二维视图。

A dashboard is a two d view of a complex surface.

它无法轻易回答任何合理的人——你或我——可能会提出的诸多问题。

It just has no easy answers to the many questions that any reasonable person, you or I, is going to have off of that.

因此，我们希望创造一种摆脱二维思维模式的产品，为用户提供更灵活的访问方式，同时清楚自己的定位。

So, we wanted to create something that freed people of the two d style of thinking, was much more flexible in what it gave people access to, but also knew its place.

这并不是一个无所不能的通用智能代理平台。

This is not a general purpose agentic platform to do it all.

这是一个帮助人们更快从数据中实现价值的智能代理平台，能让人以有意义的方式快速获得数据价值。

This is an agentic platform that lets people realize value from data faster and is a great foundation for people to get value from data really quick in a meaningful way.

我认为这种有明确立场的AI框架对我们来说非常有帮助。

I think having this sort of an opinionated framework for AI has been super helpful for us.

用户如何使用Snowflake Intelligence？

How does a user consume Snowflake Intelligence?

是像我问一个问题，它就给我推送答案，或者为我自动生成仪表板吗？我该如何想象这种体验？

Is it like I ask a question, I get push and answer, it builds dashboards for me on like, how should I imagine that experience?

嗯。

Yeah.

所以，我们应该向您演示一下 Snowflake Intelligence。

So, we should show a demo of Snowflake Intelligence to you.

但没错，这是一个交互式界面。

But, yes, it's an interactive interface.

您可以提出问题。

You can ask questions.

我们提供了一组预设问题，以确保您在提问时不会遇到障碍。

There are a set of canned questions to make sure that you don't have block when it comes to being able to ask questions.

您可以问它：嘿，你有哪些数据集可以访问？

You can ask it to say, Hey, what data sets do you have access to?

你能回答哪些类型的问题？

What kind of questions can you answer?

它能很好地完成这些任务。

It'll do a perfectly reasonable job of that.

我们的目标是让公司每一位员工都能使用这个产品。

And our aspiration was for this product to be used by every single employee in the company.

这不是给会写 SQL 的人用的。

It's not for people who can write SQL

也不是给会写 SQL 的人用的。

or It's not for people that can write SQL.

我们希望它能成为每个人日常使用的工具。

Wanted it to be enough of a daily use product for every single person.

我参加的每一次客户会议，都会快速查看一下这位客户的最新动态。

There is not a single customer meeting that I'm going to have without quickly checking up on what's the latest with this customer.

因此，我之前提到的 Raven——我们的销售数据助手，不仅包含诸如我们与客户的合作关系、他们签署的合同类型、他们的使用情况等信息，还包括我们最近与他们的对话内容、这些对话的成果，以及是否存在任何待处理的问题。

And so, Raven that I talked about, our Sales Data Assistant, absolutely has things like what's our relationship with the customer, what kind of contract have they signed, what is their consumption looking like, but also things like what are the most recent conversations that we have had with them, what came out of these, are there any outstanding tickling issues.

因此，它涵盖了大量这类功能，就像许多优秀的产品一样，它具有广泛的适用性。

And so, it is a lot of that, and like many good products, there's breadth.

它为公司内众多人员带来了价值。

There's value driven to many, many people within a company.

另一方面，我们并不声称它是一个仪表盘。

On the other hand, we don't pretend it's a dashboard.

与Tableau或Sigma相比，你可以做更多事情，但那不是我们的目标，因为这个产品还能让你完成一些在仪表盘中难以实现的操作，专为所有业务用户设计。

There are more things that you can do with a Tableau or a Sigma than you can do with SI, but that's not the goal because this product also lets you do a bunch of things that you could not easily do in a dashboard and is really meant for any business user.

我们在所有AI产品中都非常重视信任，也就是说，我会告诉人们，我们需要像对待软件工程一样看待AI——这里有对错之分。

And we place a lot of trust and we place a lot of emphasis in all our AI products on trust, meaning I tell people we need to think of AI the same way we think about software engineering, which is there's a right and there's a wrong.

它不能是那种‘随缘’式的AI。

It cannot be this mode of like, YOLO, AI.

你可能会得到一些好的答案，也可能得到一些糟糕的答案。

You can get some good answers, some terrible answers.

那是你的问题。

It's your problem.

因此，我们非常强调，每推出一个新功能，都必须进行评估。

And so, we very much emphasize you need an eval for every single new thing that you're going to launch.

如果你想更换底层模型，就必须能够快速验证你没有破坏掉原本已经实现的功能。

If you want to change the underlying model, you need to be able to quickly verify that you didn't blow up on the things that you are already doing.

因此，我们希望它成为每位员工可信赖的产品，这对我们来说实际上是一个全新的方向，因为Snowflake在其整个历史中，一直是由数据团队用来在上面搭建仪表板，再提供给最终用户使用。

So, we want it to be the trustworthy product for every employee, which is actually a new thing for us, by the way, because Snowflake, pretty much all of its history, has always been used by the data team to slap a dashboard on top, which then gets exposed to end users.

这是一种非常不同的模式。

This is a very different motion.

这就是为什么我们正在开发身份提供商集成等功能，这样您就不必为公司中每个用户单独设置Snowflake账户。

This is why we are working on things like identity provider integration so that you don't have to set up Snowflake accounts for each of the many users you're going to have in your company.

我们还受益于订阅疲劳现象，因此Snowflake Intelligence本质上是一个按使用量计费的产品。

We are also aided by the fact that there is subscription fatigue, and so Snowflake Intelligence is very much a consumption product.

用户只为他们实际使用的内容付费，我们正在这方面尝试多种方法，以推动广泛而深入的采用，同时避免用户担心费用失控等问题。

People pay for what they consume, and we are experimenting with a bunch of things there in terms of how do we drive broad and deep adoption without having people worry about runaway costs and things like that.

你描述Raven或销售助手代理的用例时，听起来像是我经常被推销的某种应用程序。

The way you describe Raven or the Sales Assistant Agent use case, it sounds like an application or like a lot of applications that I get pitched.

那么，今天你如何在数据、代理系统和应用程序之间划清界限呢？

Like, how do you draw the line between like data and agent system and app today?

回到我关于执行的观点，我对这些界限的存在持完全无情感的态度——根本不存在明确的分界线。

Back to my point about execution, I tend to be like completely emotionless about where the There's no strong line.

好的。

Okay.

当前最强劲的潮流所在。

Where the strongest current is.

好的。

Okay.

另一方面，如果我们自认为是SAP或Salesforce，那就太荒谬了。

On the other hand, it's absurd if we think we are SAP or Salesforce.

我们不是。

We are not.

管理着价值一千亿美元的供应链生态系统并使用复杂软件供应商的人，不会说：‘我要用SI，我不需要那个了。’

Somebody managing a $100,000,000,000 supply chain ecosystem with a complicated software provider isn't saying, Hey, I'm going to use SI and I don't need that.

那根本不是我们的目标。

That's really not the goal.

但另一方面，我认为这种智能代理系统与纯软件之间的界限，必将变得极其模糊。

But on the other hand, I think the line between what an agentic system like this is going to be and what pure software is going to be, absolutely is going to be bloody.

我能想到很多简单的使用场景。

I can imagine a lot of easy use cases.

比如，我们的销售团队经常需要更新Salesforce，因为我们强制他们在每次使用场景转换时都要更新。

Like, my sales team has to go update Salesforce quite often because we force them to update whenever there's use case transition and stuff like that.

这能通过API来实现吗？

Can that be done with APIs?

当然可以。

Absolutely.

你能否通过我们的HR代理在Workday上提交休假申请？

Should you be able to file a vacation on top of Workday using our HR agent?

我认为这算是一个合理的需求。

I would say that's sort of a reasonable thing.

因此，我们非常采取这种机会主义的方法，但同样要基于价值和优势行事，而不是仅仅凭空野心，因为我认为那行不通。

And so, we very much take this approach of be opportunistic, but again, operate from a position of value and strength and not just on naked ambition because I think that doesn't work out.

但另一方面，如果你专注于价值创造意味着什么，这些用户真正想要的是什么，我认为这样会更加持久。

But if on the other hand, you focus on what does value creation mean, what do these users really want, I think that tends to be much more durable.

所以，你描述的是你们组织中迅速实施的一系列变革，对吧，无论是在

So, you're describing a bunch of changes for the organization that you executed on very rapidly, right, both in

总觉得太慢了。

Always feels entirely too slow.

是的。

Yes.

我一直觉得你相当缺乏耐心，但就规模而言，这已经很快了。

I have always experienced you to be quite impatient, but, you know, for scale seems pretty fast.

从领导力的角度来看，你具体采取了什么策略来加速推进、传达新的方向并让团队跟上步伐？

How do you what is one tactical thing you were doing are doing from a leadership perspective in terms of like move faster or communicate new direction internally and get people on board?

因为，你知道，Snowflake的愿景比以前更广阔、更不同了。

Because, know, broader is and different vision for Snowflake than before.

变革很难。

Change is hard.

你必须承认这一点，要让大量人员改变行为模式是极其困难的。

You have to acknowledge that, and driving behavioral changes from lots of people is incredibly difficult.

我们对变革的推行持谨慎态度。

We were measured about how we rolled out changes.

例如，最初的变革包括领导层调整、对齐以及更明确的责任分工。

For example, among the first changes were leadership and alignment changes and clearer accountability.

这些变革在几个季度内就完成了，因为你面对的人数不多。

That happened within a few quarters because you're not dealing with as many people.

是的，你会把团队组织在他们之下，但我想说，这种变革后来被称为战争室或小组模式，即产品、工程和市场团队协同工作。

Yes, you organize the teams under them, but I would say that change also what we then call the war room or the pod model of product and engineering and our go to market functions will all work together.

这同样是早期的变革，是在小范围内实施的，没有对大量人员造成干扰。

That was again an early change, and it was done with small groups of people without necessarily disrupting lots of people.

我还会提到其他方面。

I would say other things.

例如，向我们的工程师推出编码助手。

For example, rolling out coding agents to our engineers.

这是一个项目。

That was a project.

不是每个人都愿意做。

Not everybody wants to do it.

有些人持怀疑态度。

Some people are skeptical.

有些人则不。

Some people are not.

我非常支持将自下而上与自上而下的方法结合起来。

I'm a big fan of combining bottoms up with tops down approaches.

以编码代理为例，我们的杰出创始人贝诺瓦对编码代理着迷，他推动工程师采用编码代理的成效，远超过我说的任何话。

The example with coding agents is that Benoit, our wonderful founder who fell in love with coding agents, did more to drive coding agent adoption with engineers than any number of words from me.

你必须找到合适的人，找到倡导者。

You sort of have to find the right people, find the champions.

我的看法是，每个大型组织都有这样一些富有远见、充满好奇心、愿意周末加班来研究如何做事的人。

My take is that every large organization has these forward thinking, curious, I'm going to work over the weekends to figure out how to do something kind of people.

你需要找到他们。

You need to find them.

你需要鼓励他们。

You need to encourage them.

你需要提升他们，并利用他们来推动变革。

You need to elevate them and use that to drive change.

自上而下的变革可能有帮助，但真正需要来自自下而上的视角。

Top down change can be helpful, but it really needs to come from a bottoms up perspective.

我们已经将编码代理推广给所有解决方案工程师，他们对此感到兴奋，因为这大大缩短了创建演示所需的时间。

We've rolled out coding agents to all of our solution engineers, and they are excited because that just dramatically lowered the amount of time it takes to create a demo.

通常，演示内容都是预先准备好的，不一定能针对特定客户进行定制，但现在我们可以这样说：好吧，我们知道伊拉德和莎拉在他们的播客中可能会使用什么样的数据。

Usually, demos used to be canned and they were not always customizable to a particular customer, but we can now be like, okay, we know the kind of data we think Ilad and Sara are going to have as part of their podcast.

让我们为他们专门创建一个使用合成数据集的演示。

Let's create a demo with synthetic data sets just for that.

我认为这就是我们获得的能力。

I think that's the kind of ability that we have gotten.

变革是困难的。

Change is hard.

当你我初次相遇并开始合作时，你是一名投资者，那时你是否曾担任过其他角色？这些身份的转变是否改变了你作为大规模领导者或CEO的方式？

When you and I first met and got to work together, you were an investor, then you were Did an either one of those rules change the way you are a leader at scale or a CEO?

我认为这些经历是累积性的。

I think these things are accretive.

它们以你当时或从未意识到的方式，为你的能力增添了价值。

They add on to things in ways that you don't always appreciate like then or ever.

事实就是如此。

It is what it is.

我总是向家人抱怨我花了十年时间做研究、攻读博士学位。

I always complain to my family about the ten years that I spent doing research and getting a PhD.

我当时觉得那是浪费时间，但其实并不是。

I was like, that was a waste of time, but not really.

例如，攻读博士学位教会你专注于想法，教你如何清晰地表达它们。

For example, doing a PhD teaches you to focus on ideas, teaches you to focus on how do you convey them crisply.

你常常会花大量时间撰写四行的摘要，但事实证明，能够以简单的方式传达想法是非常强大的。

You often spend like enormous amounts of time writing four line abstracts, but it actually turns out that's incredibly powerful to be able to convey ideas in an easy way.

内瓦是我生命中最艰难、最令人心碎的经历之一。

Neva is among the hardest and most heartbreaking experiences of my life.

事情就是这样。

It is what it is.

有时候你太早了，但另一方面，我可能学到了更多关于拼搏的知识，对成功不再那么理所当然，也更多地了解了社交、营销或其他这些你在谷歌时会习以为常的事情。

Sometimes you are too early, but on the other hand, I probably learned more about hustling, took success far less for granted, learned more about social or marketing or any of these other things that you kind of take for granted if you're at Google.

在谷歌，无论你做什么，我第一次在谷歌的发布——完全由我一个人在三个月内完成——被《纽约时报》报道了。

At Google, whatever you did, my first launch at Google, which was entirely my work for three months, one person, was covered by The New York Times.

好的。

Okay.

嗯。

Yeah.

所以，你一有任何新东西，就能立即获得规模效应。

So, you just have immediate scale with any

新的分发渠道。

new You distribution.

你的想法，你的产品。

Your ideas, your products.

创业让你意识到，这其实很特别。

Doing a startup makes you realize that that's actually special.

所以，我认为，当我思考什么是拼搏所需时，我带入了很多这样的经验。

And so, I think, you know, I bring quite a lot of that when it comes to what does it take to hustle?

什么是成功所需？

What does it take to win?

老实说，我认为谷歌和Neva的经历让我变得更加感恩自己的工作。

Honestly, I think both the Google and the Neva experiences make me, you know, somebody that's just a lot more grateful for my job.

我们之前谈过，当你做大事时，不得不处理很多你并不想处理的事情，这让我对这些事更加感恩，因为能在Snowflake这样的地方工作，产生我们这样的影响力，实在是一种特权。

We talked earlier about how you have to deal with like a bunch of stuff that you don't really want to deal with when it comes to like doing something big I'm that you a lot more gracious about that because it is just such a privilege to be at a place like Snowflake, to be having the kind of impact that we have.

我记得你在峰会后告诉我的话。

I remember what you told me after Summit.

你说了类似这样的话：‘谢谢你邀请我参加你的摇滚演唱会。’

You said something along the lines of, Thank you for inviting me to your rock concert.

当时，为了参加我们在纽约贾维茨中心举办的一场小型会议，排队等候的人群排了超过两个街区长。

There was a line that was more than two blocks long of people waiting to get into Javits Center in New York for this little conference that we were doing.

我对这样的事情更加心怀感激。

I'm a lot more grateful for things like that.

这没什么普通的。

There's nothing ordinary about it.

我认为，你可能是世界上研究科技巨头博弈论与战略的顶尖专家，因为你曾经领导过巨头、对抗过巨头，如今又建立在巨头之上，对吧？

You, I think, are perhaps the world's expert on like game theory and strategy with the tech elephants because you have led the elephant, fought the elephant, and now, you know, are built on top of the elephant, right?

所以我觉得这个比喻在某个时候就失效了。

And so I think this is analogy broke down at some point.

但就你在云服务提供商之上构建Snowflake的经验而言，无论是对你今天而言，还是对那些像你一样正在构建基础模型的人而言。

But in terms of the experience of building Snowflake on the cloud service providers and like both for you today and then the analogy for anybody building on foundation models as you are as well.

是的。

Yeah.

你是怎么看待这个问题的？

Like, how do you think about that?

那么，有什么框架可以用来创造持久的价值呢？

Like, what's a framework for like creating durable value there?

我认为产品与市场的契合仍然是神奇的。

I think product market fit continues to be magical.

这正是Snowflake存在的原因。

It's the reason that Snowflake exists.

想想看。

Think about it.

三大超大规模云服务商非常希望像掌控其他领域一样掌控数据领域。

The three hyperscalers would love to just own the data space like they own any other space.

但偏偏有了Snowflake。

But yet there's Snowflake.

还有Databricks。

There's Databricks.

因此，这种救赎性的价值非常独特，我们都应该对创造这种‘瓶中闪电’所需的条件保持极大的谦逊。

And so, that sort of redeeming value is quite unique, and we should all have a lot of humility about what it takes to create that lightning a bottle.

话虽如此，我认为像OpenAI这样的模型公司非常有趣，因为它们正处于这样一个发展阶段：它们真的觉得自己无所不能。

Having said that, I think the model companies, especially OpenAI, is super interesting because they are in that phase of their growth where they literally, like, they don't think they can not do anything.

是的，没错。

Yes, yeah.

我常开玩笑说，这些公司就像是尚未遇到海洋的帝国。

I joke to people that these are like empires that have not met their oceans just yet.

因此，你确实需要关注它们眼前可能面临的路径。

And so, I think you do have to pay attention to what is likely to be in their immediate path.

例如，我认为从这个角度看，编码代理特别有趣，因为很明显，Anthropic和OpenAI都会全力以赴，打造最顶尖的编码代理。

So, for example, I think coding agents are particularly interesting from this perspective because it is very clear that both Anthropic and OpenAI are going to be laser set on having the best one that there is.

所以，我认为应该思考这些公司的潜在发展轨迹，它们真的有资格获胜吗？

So, I think thinking about what is the likely trajectory of these companies and do they really have a right to win?

还是说，它们的模式足够独特，以至于你根本不必担心？

Or is it something that is different enough that you don't really have to worry about?

比如，谷歌就停止了这一信息。

Google, for example, stopped that information.

天知道我花了多少时间试图进入购物、航空订票或酒店这类实体领域。

God knows I spent enough time trying to get into physical things like shopping or airline purchases or hotels.

我们并没有真正成功，因为我们本质上缺乏信息领域之外的核心能力。

We didn't really succeed cause we didn't have core competence really in some fundamental way beyond the world of information.

我认为，发现OpenAI或Anthropic这类公司的边界将会非常有趣，但我相信我们可以合理推测出它们未来可能涉足的许多领域。

I think it's going to be fascinating to discover what that kind of a boundary is for an OpenAI or Anthropic, I but think there are lots of areas that can be reasonably guessed at with respect to where they're going to go.

我认为这是一条艰难的路径，如果你只是在这些模型之上堆叠一系列提示词，那将是一个有问题的领域。

I think that's the one that's tough and early patterns of if you are, for example, a set of prompts on top of one of these models, that's a problematic space to be in.

你必须真正增加价值。

You kind of need to add value.

我也经常思考我们与这些模型公司的区别在哪里。

I also think a lot about what differentiates us from these model companies.

这是一个他们可能感兴趣的领域吗？

Is this an area that they're likely to be interested in?

我们如何确保自己所增加的价值具有足够的独特性？

How do we make sure that we have distance with respect to what we add?

我们与这些伙伴合作开发的产品所面临的紧迫感和变革，都将我们推向了数据平台这一持久类别；但与此同时，如今没有任何软件公司能对自己的市场地位感到安心，我认为这一点或许与我刚才说的任何事情一样重要。

And a lot of the urgency and change in the products we create in collaboration with these folks, it all take us towards the data platform as a durable category, but this is also a time where literally no software company can feel secure about their position in the sun, and I actually think that that perhaps is just as important as anything else that I just said.

要有这种心态：我们必须持续赢得信任。

To have that orientation of like, we need to continue to earn it.

我们必须持续赢得信任。

We to continue to earn it.

如果我们从云服务提供商身上学到了什么，那就是他们拥有无限的预算。

And if there is anything that all of us have learned, say from the CSPs, it is that they have infinite budgets.

他们拥有无限的耐心。

They have infinite patience.

除非你不断创新并保持领先，而不仅仅是暂时领先，否则你将面临不确定性。

And unless you innovate and stay ahead, not just be ahead, but stay ahead, you will be in doubt.

我认为，对于像Snowflake这样的公司而言，在当前的环境中，这是一个非常值得铭记的宝贵教训。

I think that's another really useful lesson to remember as a company like Snowflake navigates the current role.

这让我深有共鸣，尤其是在我想到我最喜爱的公司之一的创始CEO时——那家公司如今已上市，而我曾认为它牢不可破。

And this resonates hugely with me both on the dimension of like, I'm thinking about, you know, one of the founder CEOs of one of my favorite companies that's now a public company that I thought was unassailable.

还有，说到人工智能，我不愿成为那种动不动就说‘这改变了所有一切’的人，但这些人确实多年以来第一次感到了威胁。

And, you know, AI, I hate to be the person to be like, well, this changes everything, but they feel threatened for the first time in many years.

我认为，作为一位软件公司的首席执行官，这如今是一种相当普遍的体验。

I think that's a pretty common experience right now as a software CEO.

尤其是在技术环境如此瞬息万变的情况下，防御力是靠实践建立的，而不是靠战略规划出来的。

I think especially when the technical environment is so fluid, defensibility is built, not strategized.

没错。

That's correct.

它需要每天都在不断构建。

It's built every single day.

你必须持续前进。

You have to keep moving.

关于数据云，我有一个问题：即使你没有云服务提供商那样的预算，你仍然有能力制定多年期的规划。

One of the questions I would have on the data cloud is like, even if you don't have the CSP's budget, you do have the ability to make multiyear plans.

你加入Snowflake，是因为作为一名技术专家，你看到了它未来远不止是一个数据云的愿景。

And you, you know, joined Snowflake because you saw like a vision for it to be much more than the data cloud as a technologist.

当你展望三到五年后，你希望人们如何看待Snowflake，无论是从生态系统角度还是客户使用方式来看？

Like, when you look out three to five years, like, how do you expect people to think of Snowflake, both, you know, in the ecosystem and then customers to use it most?

我们的核心优势在于数据平台层。

Our core strength comes in that data platform layer.

我有时内部会说，我们要陪伴客户从数据诞生到获得洞察的全过程，从数据最初形成到有人从中获得洞见并反馈回系统。

I sometimes internally talk about being there for our customers from inception to insight, from when data is first conceived to when somebody gets an insight that feeds back into that system.

事实上，我第一次会见CEO或首席数据官时的主张是：本世纪的伟大公司，比如谷歌或Meta，是更偏向数据驱动的公司，而非纯粹的产品驱动型公司，这一点远超以往任何公司。

In fact, the pitch that I make to CEOs or CDOs that I meet for the first time is really that the great companies of this century, a company like Google or Meta, were more data first companies than like purely product first companies than pretty much any others compared to before.

你制造汽车，然后才加装一些仪器来确保它不会出事故，或者记录维修情况。

You built cars, and then you kind of did an instrumentation to make sure that it didn't crash or what the maintenance records for that was.

就连产品本身，你也想想看。

Even products, think about it.

如果你在90年代开发了Adobe Photoshop，你会做大量研究，开发出产品，然后把光盘寄给各种用户，再等待反馈回来。

If you built something like Adobe Photoshop in the '90s, you did a bunch of research, you built the product, and then you sent CDs over to various people, and then you waited for some feedback to come back.

数据过去总是被当作缓慢的附属品，但搜索广告却很神奇，因为用户与广告互动的行为几乎在几分钟内就能反馈回系统，影响其运行，我的数据团队规模和产品团队一样大。我告诉客户，我们希望成为所有类型数据的忠实伙伴。

Data was always like a slow afterthought, but search ads, for example, was magical because the behavior, what people did interacting with these ads went back into influencing what happened to that system pretty much like in a few minutes, and my data teams were as large as the product teams, and what I tell our customers is that we want to be that companion for all different kinds of data.

我们希望他们能拥有谷歌和Meta等世界级公司的专业能力，因此我们认为AI是推动这类变革的强劲加速器，因为如今首席执行官们突然意识到，这不仅仅关乎数据现代化。

We want them to have the expertise that the Google and the metas of the world have, and so we see AI as a massive accelerant for things like that because all of a sudden CEOs now realize, wait, this is not just about data modernization.

这不仅仅关乎我能运行更多查询，或者编写一个机器学习算法。

This is not just about me being able to run more queries or perhaps code up a machine learning algorithm.

这可能会影响我的业务运营方式。

This could influence how my business operations work.

这可能会影响我对效率这一概念的理解。

This could influence what efficiency means for me as a category.

因此，我认为这就是AI带给我们的顺风车，因为数据的价值得到了极大的提升。

So, I think that's the tailwind that we have from AI because the value of data just got vastly elevated.

这就是我们的愿景，关于云服务提供商，我的看法是，像Snowflake这样以数据为先、而非以一系列服务为先并强调简洁与集成的公司，你可以在Snowflake上创建任意规模的数据库，并且它能在公司内部完全共享。

That's our aspiration, and with respect to the CSPs, my take is that a company like Snowflake, which comes data first as opposed to a set of services first with an emphasis on simplicity and integration, You can create as large a database as you like on Snowflake, and it'll be completely shareable within the company.

它也能与你的合作伙伴完全共享。

It'll be completely shareable to your partners.

当我们谈论Snowflake中的AI时，它绝非事后附加的功能。

And when we talk about AI in Snowflake, it's not as an afterthought.

它将与您之前建立的所有治理机制协同工作，我们认为这种集成方法具有持久性，因为随着时间推移，购买原始计算和存储资源并编写代码来解决问题的方式从未变得更容易。

It will work with all of the governance that you have put before, and that kind of an integrated approach we think will have durability because over time, the idea of buying say, raw compute and storage and writing code in order to solve a problem, it never gets easier.

因此，我们处于更高的抽象层次。

And so, we are at a higher level of abstraction.

在人工智能出现之前，这正是我选择加入Snowflake的主要原因。

Pre AI, this was my main thesis for why I wanted to be part of Snowflake.

我说过，一个尤其跨越云服务提供商的数据平台，必须赢得这种地位，它有潜力变得和云服务提供商本身一样庞大。

I said a data platform that especially spans CSPs has the right, you have to earn it, has the right to be as large as a CSP itself.

因此，这大致是我加入这家公司的原因，也是我们对未来中期愿景的构想。

And so, that was roughly why I joined the company, our medium term vision for what we want to be.

正如我所说，我认为人工智能极大地加速了如何更快地从数据中获取价值，或如何更快地更好地利用数据做出行动。

And as I said, AI, I think, is a massive accelerant on how do you get value from data faster or how do you get better at acting on data quicker.

当你思考当今整体的数据格局时，传统上存在于Snowflake中的数据是其中之一。

When you think about the overall data landscape today, there's the data that's traditionally been in Snowflake.

然后，还有你们所做的投资以及新宣布的合作伙伴关系。

And then, like, you know, the investments you've made and new partnerships you're announcing.

你能解释一下，为什么现在要和SAP以及其他一些合作伙伴合作吗？

Can you explain, like, why SAP and some of the other partners you're working with now?

这是个好问题。

Yeah, this is a good question.

我认为，有一段时间，Snowflake对世界的看法是以Snowflake为中心的。

I think for a while, Snowflake had a Snowflake centric view of the world.

很多人从SAP、Workday或Salesforce导入数据。

Plenty of people brought in data from SAP or from Workday or from Salesforce.

但越来越多的情况是，这些公司都意识到这些数据极具价值。

But what is increasingly happening is that all of these companies realize that this is incredibly valuable data.

这并不完全是他们的数据。

It's not quite their data.

这是客户数据，但他们明白它很有价值，同时也意识到软件与服务、软件与数据之间的界限在很大程度上已经变得模糊，而我在谷歌从事支付等领域的经历中学到的一点是合作伙伴心态。

It's customer data, but they understand that it is valuable and they also understand that this line between software and services and software and data is also blurry in a pretty meaningful way, and the one quality that I learned from Google working in areas like payments, which is all about partnerships, was that partnership mentality.

那就是如何选择一组合作伙伴，并共同创造价值？

Was how do you pick a set of folks and figure out how you can create value together?

这种合作模式最早在我们与微软的关系中得到体现，但当时并不理想，因为他们与Databricks有第一方合作关系，始终在纠结Fabric是答案还是Snowflake是答案。

Among the earliest places where this went to work was in our relationship with Microsoft, which was okay, but not that great because they had a 1P relationship with Databricks and they were always kind of conflicted about is Fabric the answer or Snowflake the answer?

当然，你是知道的。

Of course, you know this.

萨提亚是打造成功合作关系的高手。

Satya is the master of how to create winning partnerships.

因此，我们从他身上学到了一些经验，关于如何

And so, we took a lesson from kind And how

摆脱他们的束缚。

to get out of them.

如何根据需要调整合作关系。

How to adjust them as you need to.

因此，过去几年我们一直在与他们推进合作关系。

So, we've been working on a partnership with them for the past couple of years.

这既包括与Fabric等产品的集成，也包括公司之间的协作方式。

This is both product integration with things like Fabric, but also how do the companies work together.

我认为，与十八个月前相比，我们现在的情况好得多。当时，我们确实意识到我们会与一些客户竞争，但这没关系；同时，我们也会与另一批客户合作，比如Azure加Snowflake能带来显著优势——这与我们对AWS的态度一致，我们也在与GCP探讨类似的安排。

I think we're in a much better place now compared to say eighteen odd months ago where, yes, there is an understanding that we will compete with some customers, and that's fine, and we will collaborate on a whole set of other customers where, let's say, Azure plus Snowflake is a strict positive, which is the same, again, the same attitude that we have with AWS, and we are working on a similar sort of arrangement with GCP.

我认为软件提供商的情况有所不同。

I think the software providers are different.

正如我所说，他们明白世界正在变化。

As I said, they understand that the world is changing.

因此，对于SAP这样的公司，我们正在更深入地思考如何实现‘1+1>3’的效果。

So, with folks like SAP, we are actually thinking much harder about what is that one plus one equals three.

对于SAP，我认为这将是双向的数据共享，但我们也能否在分析、AI和智能代理领域展开合作，让客户更容易基于SAP数据构建这些应用？

With SAP, I think it's going to be absolutely bidirectional data share, but can we also collaborate in the area of analytics and AI and agents and make it easier for people to create these on top of SAP data.

我认为这也能成为我们拓展更多企业的杠杆点，因为你知道，SAP在全球拥有极其广泛的影响力。

I think that can also become a leverage point for us to expand out to more companies because, as you know, SAP has incredible presence throughout the globe.

所以，我认为这代表了我们对合作关系思考的成熟。

So, I think it represents a maturing of how we think about partnerships.

我们绝对希望与其他几位关键合作伙伴也开展类似的合作。

We absolutely want to do this with a few other key folks.

这不是每家公司都能做到的事情，但我认为这种合作心态——共同创造价值——将使我们受益匪浅，并有望为我们带来更多的业务和利润。

This is not the kind of thing that you can do with every company, but I think that partnership mentality, create value together, is something that'll stand us in good stead and hopefully also be profitable for us with respect to generating more business.

我想用两个我经常被问到的问题来结束，我认为你更有能力回答这些问题。

I want to close out with two of the most common questions I get that I think you are more prepared to answer.

第一个问题是，每当我与企业客户交谈时，他们最先提出的两个问题之一就是：对我公司而言，AI的最高投资回报率应用场景有哪些？

The first is just, you know, with every enterprise customer I talk to, one of the first two questions is going to be, where are the highest ROI use cases for AI for my business?

你经营着一家大型企业。

You run a large business.

你为大型企业提供服务。

You serve large businesses.

那么，你的优先级排序是怎样的？

Like, what is your stack rank here?

你认为人们应该如何应对这个问题？

How do you think people should address it?

如今每家公司都拥有一支技术团队。

Every company now has a set of technologists.

即使他们不是软件公司，也需要应对技术。

Even if they're not software companies, they need to deal with technology.

我认为编码代理可能是最容易实现投资回报的，因为它们能加快新项目的进度，让技术不再神秘，让更多人能够使用。

I would say that coding agents are probably the easiest ROI just in terms of making new projects faster, demystifying technology so that more people can get at it.

正如我之前所说，我们是编码代理的重度使用者，我们正在开发将成为Snowflake一部分的编码代理，因为我们希望让人们更容易使用Snowflake本身。

As I said earlier, we are large users of coding agents, and we're working on coding agents that are going to be part of Snowflake because we want to make it easier for people to be able to use Snowflake itself.

对别人有利的，对Snowflake也有利。

What's good for other people is also good for Snowflake.

我认为像客户支持这样的领域，完全符合‘这是一个人类知识库’的模式。

I think absolutely areas like customer support, which fits the pattern of here is a repository of human knowledge.

当AI无法完成某项任务时，这里还有一个简单的备选方案，而且AI模型能够轻松接入语音和文字提问并生成回答。

Here is an easy backup in case the AI cannot do something, plus his ability for AI models to effortlessly tap into voice, into typed questions, and generate answers.

这是一个明显具有完整投资回报潜力的领域。

That's an area where there is clearly a whole set of ROI.

更快、更便捷、更无缝地访问数据，尤其是当这种访问无需支付每人每月50美元的许可证费用时，这是另一个容易实现投资回报的方面，这也是我们推动Snowflake Intelligence以实现数据访问民主化的原因之一。

Faster, easier, more seamless access to data, especially when it's combined with, I don't have to pay for $50 per user per month license, is another easy ROI item, and that's part of kind of our motivation for Snowflake Intelligence of democratizing data access.

这些领域中的回报几乎是 guaranteed 的，但换一种思路是，对于许多公司来说，过早地过度关注回报也是一个坏主意，因为你不希望第一步就迈出100英尺。

These are among the areas where it's just more or less guaranteed ROI, But the other way to think about this is I think obsessing about a lot of ROI too quickly is also a bad idea for many companies because you don't want your first step to be a 100 feet.

你应当做许多小事，逐步证明其价值。

You want to do a lot of little things that sort of prove value.

人们可以通过使用ChatGPT——即使是免费版——以及类似工具，在许多日常事务中获得大量价值。

People can get plenty of value from using ChatGPT's, even the free ones, and tools like that for many day to day things that we do.

我认为，公司越能消除使用AI的神秘感，就越有可能找到获取价值的方法，因为你承担了风险。

I think the more companies demystify what it is to use AI, I think the more chance they have of figuring out how to get value because you take the risk.

我非常注重你射门的次数有多少。

I place a lot of emphasis on how many shots do you take on goal?

你能同时快速运行多少个项目，以便感受变化的格局？

How many projects can you run very, very quickly so that you get a feel for what's the landscape of change?

销售数据助手之前有过三个版本。

The Sales Data Assistant had three versions that came before it.

这个版本成功了。

This one stuck.

就是这个。

This one.

嗯，实际上，它们都保留了下来。

Well, actually, they all stuck.

它们都不断添加了越来越多的功能。

They all added more and more things on top.

第一个版本仅专注于赋能。

The first one was just on enablement.

第二个版本更多地涉及客户信息。

The second one was a little bit more about customer information.

我们还有一个名为客户360的应用。

We also had an app called Customer three sixty.

它是一个Streamlit应用，一个Python应用，你可以从中获取这类信息。

It was a streamlet app, so Python app that you could get that kind of information from.

所有这些最终汇聚成了销售助手，整合了所有这些功能。

All of these then culminated into the Sales Assistant, which is all of these things combined.