No Priors Ep. 23 | With Snowflake's CEO Frank Slootman
发布时间 2023-06-29 10:00:25 来源
Our guest today needs no introduction. Frank's Loomint is the legendary three time CEO of Data Domain, ServiceNow, and Snowflake, and one of the most looked up to leaders in technology for his relentless execution. We're excited to talk to him about what's on the horizon for Snowflake and how he looks at the AI opportunity.
我们今天的嘉宾无需介绍。Frank的Loomint是Data Domain、ServiceNow和Snowflake的传奇三位一体CEO，也是科技领域最受敬仰的领导者之一，因其不懈的实施能力而备受瞩目。我们很兴奋地与他讨论Snowflake未来的发展前景以及他对人工智能机遇的看法。
Frank, good to see you. Thanks for being here.
弗兰克，很高兴见到你。谢谢你能来参加。
Absolutely. Good to see you, sir.
当然。很高兴见到您，先生。
Let's start with just a little bit of personal background. You have had an amazing journey. You grew up in Holland, the first person in your family to go to college. What were you like as a kid and in college and how did you end up in product management and computing in the U.S.?
让我们从一点点个人背景开始。你经历了一段令人惊叹的旅程。你在荷兰长大，是家族中第一个上大学的人。你小时候和大学时代是怎样的？你是如何最终进入美国从事产品管理和计算领域的？
That's kind of a big, wide-ranging question. I sometimes have to go back and figure out what was the method to the madness because sometimes your life looks like a random walk. In other words, it's just a series of events that kind of go from one to the other. I was always relatively focused discipline kid. If I were to describe myself in almost any realm, whether it was school or sports or any of those things, it's just the nature of the beast, I would say. Definitely a bit of a chip on my shoulder, which I generally like in people. I don't need the reason to get up in the morning and have some that prove to the world or whoever. Those are all useful things.
这个问题有点大而广泛。有时候我不得不回头思考一下，弄清楚这些疯狂背后的方法，因为有时候你的生活看起来就像是随机行走。换句话说，它只是一系列事件，一件接着一件地发生。我一直是一个相对有目标、有纪律的孩子。如果我要描述自己在任何领域，无论是学校、运动或其他事情，那就是生活的本质，我会这么说。肩上总是有点压力，但我通常喜欢那样的人。我不需要理由早上起床，并向世界或其他人证明什么。这些都是有用的东西。
Obviously, I ended up in the U.S. because I think the U.S. is obviously much better. Maybe not obvious, but it's obvious to me that it's a much better canvas for people like me. Obviously, we see it all around us, right? People that come from all over the world here because they have far greater opportunity and they would have where they came from. It's certainly true for me. There's no doubt that I would have done where I came from, what I've done here. I'm very grateful having had that opportunity. I will tell younger people, it's very important where you decide to be. Don't just go where your friends are. To the point of choosing the right place, be it geography, yes, and thank you, America. My parents are also immigrants.
显然，我最终来到美国，因为我认为美国显然更好。也许对其他人来说并不明显，但对我来说，美国显然是一个更适合像我这样的人发展的地方。显然，我们身边都能看到，对各国人来说，到美国来是因为这里提供了更多的机会，而他们原本来自的地方则没有。对我来说也是如此。毫无疑问，如果我还在我原来所在的地方，我不会有今天的成就。我对有这种机会感到非常感激。我会告诉年轻人，选择待在哪里非常重要。不要只是跟随朋友的脚步。选择正确的地方，无论是地理位置还是社会环境，都是很重要的。谢谢你，美国。我的父母也是移民。
You talk about being on the right elevator and some of the companies you worked at weren't the hottest companies at the time when you joined. Tell us about those choices.
你谈到过选择正确的公司，而你加入的一些公司在当时并不是最热门的公司。请告诉我们一下你当时的选择。
I just use the analogy of the elevator because there's this aspect of opportunity and circumstances you can't change. It is what it is. You're going to be subject to it for better or for worse. Therefore, you need to choose carefully. Some people think that I can will my way to anything. That's not true. Your choices you make, like we just said, where are you going to be? What industry are you going to be? What company are you going to be? What people are you going to be with are all very formative. You have to make very careful choices because if you combine good choices with a great execution, you get the perfect cocktail for future opportunities and for having a successful sequence of experiences. It matters a whole lot. A lot and I talk to a lot of people joining entrepreneurial ventures and they're always trying to figure out where to go. That is often where their friends go and sometimes it's where investor friends will direct them.
我只是用电梯的类比来说明机会和环境的一种不可改变的特性。它是什么样子就是什么样子。不论是好是坏，你都将受其影响。因此，你需要谨慎选择。有些人认为我可以靠意志力达到任何目标。但这并不是真的。就像我们刚才说的，你所做的选择将决定你的去向、所从事的行业、将来的公司和你将与之相伴的人，这都会对你产生重要影响。你必须做出非常谨慎的选择，因为如果你把良好的选择与出色的执行相结合，你将获得未来机遇和成功经历的完美组合。这样的选择至关重要。我与很多加入创业企业的人交谈过，他们总是在努力找到前进的方向。通常他们会选择和朋友一起去的地方，有时也会听从投资者朋友的指引。
What advice would you have for people choosing that company in terms of the things you can't change?
关于那家公司中那些无法改变的因素，你对选择该公司的人有什么建议呢？
It's a great question. I get asked a couple of times a year to speak to graduating classes at early prominent business schools and all that sort of thing. They always ask me, is there one message that you have for the graduating class? I'm like, well, don't go working for some consulting firm out of school. I try to get a real job in the real economy building real products, selling real products. You really need to feel what it's like to be in the drive train of the economy as opposed to I'm just eating out of somebody else's trough and I sit on the vessel and glide along and feeling good about myself. You haven't really touched the real economy yet and I really wish that for people early on in their careers to feel the heat of competition and also the cold winds of threat of the markets that are disappearing because that's the real world. A lot of people choose jobs that are very removed from the real world. I don't think that's helpful for people's development in their careers.
这个问题非常棒。每年都有几次人们邀请我去早期知名商学院的毕业班演讲，问我是否有什么对毕业班的信息传达。我说，不要毕业后就去一家咨询公司工作。应该尽力在真实的经济中找到一份真实的工作，打造真实的产品，销售真实的产品。你真的需要感受一下作为经济中的一部分是什么感觉，而不仅仅是从别人的食槽中吃饭，坐在大船上滑行，然后为自己感到满意。你还没有真正接触到真实的经济，我真心希望那些刚入职场的人能够感受到竞争的热度，也能感受到市场消失的冷风，因为这才是真实的世界。很多人选择了与真实世界非常脱离的工作，我认为这对人们的职业发展并不有益。
How do you think about company versus industry versus role? Often when I talk to people as well, I advocate for the choose the right industry and then choose the best company in the industry and the role is secondary. Do you think that holds true or how would you suggest that people actually find their way?
你对公司、行业和角色有何看法？当我与人交流时，经常会提倡首先选择正确的行业，然后再选择行业中最好的公司，而角色次之。你认为这种观点是否正确？或者你对人们如何找到适合自己的选择有什么建议呢？
Yeah, I totally agree with that. I think the role is not that important. You'll have many roles. And roles come and go. And my first job, I took a role I really didn't want. But being an immigrant in this country, beggars couldn't be choosers and I had to, I figured, look, I'll get in there and I'll make my way from there. I was in a corporate planning group of like six people attached to the CEO of a large computer company. I was about as far a move from the real world as I could be. I didn't want that, but that's all I could get into. These were the hey days of affirmative action. We had a lot of picks.
是的，我完全同意这个观点。我认为角色并不那么重要。你会有很多个角色，而且这些角色会随时出现和消失。在我第一份工作中，我承担了一个我真的不想要的角色。但作为一个在这个国家的移民，当时无法挑剔，我明白，我可以进入那个角色，然后逐渐发展。当时我在一个负责公司规划的小组中，团队里有六个人，直接隶属于一家大型计算机公司的首席执行官。而我距离现实世界的距离就像天壤之别。我不想要那个角色，但那是我能进入的唯一机会。那时候是实施平权行动的黄金时期，我们有很多选择的机会。
And in hindsight, I was right because once I got in there, you spent two years doing typical MBA stuff, M&A and all the presentations were boards and all this kind of stuff. But then after that, they pretty much gave me whatever I wanted to do was fine with them and from there, I made my way.
事后看来，我是对的，因为一旦我进入那里，我花了两年时间做典型的工商管理硕士的工作，包括并购和所有的演示板等这些工作。但是在那之后，他们基本上允许我自由选择任何我想做的事情，并且从那时起，我就开始走向成功。
You've had three just amazing CEO jobs. So I believe you took data domain from less than 3 million in revenue through an IPO and a $2 billion acquisition by EMC. At service now, you took it from 75 million in revenue through an IPO and I think one and at $1.4 or $1.5 billion of revenue. And then Snowflake, of course, has just been an amazing run. And it's one of the really seminal companies in the data world. How do you go from step one to step two with all these things? And in particular, enjoying data domain, had an academic co-founder, I didn't have a product that was commercially scalable yet. Service now, you really turbocharged. Snowflake was growing, but it was spending a lot of cash. So what are the commonalities between those different experiences and more generally, what kind of drives you? What do you have to prove? You already had accomplished so much by the time you got to Snowflake. How do you keep going?
你曾担任过三个令人惊叹的首席执行官职位。所以我相信你将数据领域从不到300万美元的收入发展到了通过IPO和EMC的20亿美元收购。在服务现在，你将其从7500万美元的收入发展到了通过IPO和约14亿或15亿美元的收入。当然，Snowflake一直以来都非常出色。它是数据领域中的一家非常重要的公司之一。你是如何从第一步到第二步的呢？尤其是在享受数据领域时，拥有了一位学术共同创始人，但还没有一款商业可扩展的产品。服务现在，你真的推动了它的发展。Snowflake一直在增长，但也在大量投入现金。那么，这些不同经历之间有什么共同之处，更普遍地说，是什么驱使着你？你已经在到达Snowflake之前取得了很多成就，你是如何继续前进的？
So let me first sort of correct the record on that. They had no revenue, no customers, nothing. There were 15 people there. And when we first started to assert the product, it had one terabyte of usable space. Just imagine that. Okay, no, it wasn't one. You know, under around 30 megabytes a second. So it was useless for 99.9% of applications. So we're like, what are we going to do now? Why did you take the job? Well, I didn't know that. I'll tell you why I took the job. First of all, I got rejected numerous times for CEO opportunities. And the ones that they were interested in were like second and third string. And I know people really cautioned me at that time to hold out, do not go for a second third string. You know, daily, you need to have really good investors. We were a startup, one out of hundreds at the time. We now be walking the halls of NEA and Graille, and people look to me, who are you? What company is that? Oh, okay. We were a no name. And we were lectured on, you know, on other companies that in hindsight ended up being no name. So I mean, it's almost legendary how Jaya domain just manifested itself. And by the way, I live for that kind of drama. You know, it was great. But we didn't have product market fit. We just didn't. And, you know, I found a little bit of fit.
所以我首先要纠正一下这个记录。他们没有收入，没有客户，什么都没有。那里有15个人。当我们第一次开始推销这个产品时，可用空间只有一TB。想象一下吧。好吧，不，不是一TB。大概在每秒30兆字节左右。所以对于99.9%的应用程序来说，它是无用的。所以我们在想，现在我们该怎么办呢？你为什么接这份工作？嗯，我之前申请过很多次CEO的职位，都被拒绝了。他们对我有兴趣的那些职位都是二三流的。当时有很多人都警告我坚持不要去接二三流的职位。你知道，你需要非常优秀的投资者。当时我们是数百家初创公司中的一家。现在我们可以在NEA和Graille的大厅里走动，人们问我，你是谁？你的公司是做什么的？哦，好的。我们以前是无名氏。我们曾被批评过，说我们是其他事后证明是无名氏的公司的模仿者。所以我可以说，Jaya domain的形成几乎是传奇般的。顺便说一句，我就是为这种戏剧而活的。那太棒了。但是我们的产品市场适配性不强。我们确实没有。但是，我找到了一点点适配性。
I remember, you know, meeting with a CIO company that has been acquired since by EMC. And they were testing the products. And the guy said to me, he said, you know, he said, that little product of yours was a real hero here on Friday. And I'm like, tell me more, do tell. But he explained that, you know, they had their email database, you know, backed up on our device and they had a mass corruption email database as happened back then. That's not the common anymore. And before o'clock in the Friday afternoon, and they're like, Oh, my God, we're going to be recovering from tape here. Oh, we can't long. We'll be sleeping on cards and blah, blah, blah. And then they remembered, Oh, we have it. We have a backup home desk. And by seven o'clock that evening, they were going home and obviously, you don't need to be a rocket scientist to figure out how to use case you can sell a few times more, right? So we stayed alive and we did do that $3 million step for a share. But I still remember doing the very first country with like a $5,000 service deal with Stanford University and they bitch and complain the whole way. I'm like, well, we've been doing great business. Yeah.
我记得，你知道的，曾经和一家后来被EMC收购的CIO公司见过面。他们当时在测试我们的产品。有个人对我说，你知道的，他说，你们的那个小产品在上周五真的很出色。我问他要细节，告诉我更多。然后他解释说，他们把他们的电子邮件数据库备份在我们的设备上，当时发生了大规模的电子邮件数据库损坏事件，虽然那不再普遍发生了。当时已经是下午四点钟了，他们开始慌了起来，觉得要从磁带恢复数据了，很麻烦。然后他们想起来了，哦，我们还有个备份在办公桌上。到了晚上七点，他们就放心回家了。显然，你不需要是个天才就能想象出有了这种实例，你就能多卖几次产品对吧？所以我们依然生意兴隆，获得了300万美元的订单。但我还记得和斯坦福大学做了第一次小额合作，只有5000美元的交易额，他们一直抱怨不满。我就想说，我们一直做得很好啊。是的。
You know, one of my, one of my favorite books, which I think is really a hidden gem in terms of go to market and sales and startups is tape sucks. And I think you get into very great tactical advice. It's lacking from a lot of other books that you get into different channel strategies and whether you should do them and partnerships and other things that I just don't think are addressed very well in a lot of business books.
你知道的，我最喜欢的一本书之一，我认为它在市场推广、销售和创业方面真的是一颗隐藏的宝石，它就是《胶带特别烂》。我觉得这本书提供了非常实用的建议。它与其他很多商业书籍不同，深入讨论了不同渠道策略、合作伙伴关系以及其他一些在其他书籍中很少涉及的问题。
And you've now written three books and we can come back to the question in terms of, you know, what continues to drive you and all the rest. What drives you to actually share knowledge that way and write a book?
你现在已经写了三本书了，我们可以回到这个问题，谈一谈是什么在驱动你，以及其他一切。是什么驱使你以这种方式去分享知识并写书？
It looks like with almost every formative experience that you've had. You know, I get an awful lot of inbound questions. You know, can we have coffee? Can you speak here? Can you do this? I'm like, I really can't because it's just that it'll become a full time job. So I'm like, look, I'll write a, and by the way, the data main book, the tape sucks. You know, I self published was home brew. And it's a very dense book, even though it doesn't have that many pages. You know, I don't spend a lot of time, you know, waxing poetic or having a lot of platitudes. That's sort of the difference between my writing and everybody else's. There's no filler. Everything. It's super dense. Everything that I write is, I find meaningful and worthwhile sharing. But it's really like these books all have different reasons.
看起来几乎每个你经历过的重要经历都是如此。你知道，我收到了很多问题。你知道，我们可以喝咖啡吗？你可以在这里演讲吗？你可以做这个吗？我真的做不到，因为这会成为一份全职工作。所以我就写了一本书，顺便说一句，这本书的录音质量很差，我自己出版的，手工制作的。尽管页数不多，但这本书非常密集。你知道，我不会花很多时间渲染诗意或说一些空话。这是我写作和其他人的区别所在。没有填充物。每一句都非常密集。我写的每一句话都有意义，值得分享。但这些书之间确实有不同的原因。
Okay. The last book that I wrote, I didn't want to write. Okay. Denise Pearson, our CMO really pushed me to write it. And she also made it easy for me to write it because I had a lot of help along the way. I wrote every word of it. Okay. In other words, it's not a, but I do have a ghost writer who just went through it. Is that lucky? You need examples here or nobody will understand this outside your business. You know, all that kind of commentary and explain this better. And so he helped me just make the book more consumable rather than this very narrow audience that we normally deal with.
好的。我写的最后一本书，其实我并不想写。好的。我们的首席营销官丹尼斯·皮尔逊真的推动我写下它。她也让我写起来很容易，因为我在这个过程中得到了很多帮助。书中的每个字都是我写的。好的。换句话说，并不是说我有一个幽灵写手来编辑它。这算是幸运吗？你需要在这里给出一些例子，否则在你的业务之外，没有人会明白。你知道的，所有那些评论和更好地解释这件事。所以他帮助我让这本书更具可读性，而不仅仅是针对我们通常面对的狭窄受众。
But the net of the reason why I wrote and put up was, you know, people said, hey, just like you just said, you've had three very successful experiences, different times, different markets, different technology, different competitive blah, blah, blah. You know, what's the secret sauce? And Americans always think there's a formula that can be extracted. And if I just have my hands on that, I can just do it too. Right? And I did a gratification type of thing. And the book is really the answer to the question of what do you guys do? What do you think explains the success in these companies? It's my answer. It's not that I'm trying to sell that to people at all. I don't care whether you agree with me or not. I'm just telling you what my best guess, my best take is on the answer to that question, right? The people sometimes go like, well, I don't agree with this. I don't care. I mean, we, I did kill customer success at every company I've been in. I think it's the biggest bullshit thing that goes on in Silicon Valley. It doesn't mean that I need you to agree with me. I'm just telling you what it is. Right?
但我写这本书并且发表的原因是，你知道的，人们说，嘿，就像你刚才说的，你已经有过三次非常成功的经历，不同的时间，不同的市场，不同的技术，不同的竞争等等。你知道的，有人总是认为有一种可以提取的公式。只要我掌握了那个，我也可以做到。对吧？我做了一种满足感的事情。这本书实际上是回答了这个问题：你们到底是怎么做到的？什么解释了这些公司的成功？这是我的答案。我并不是试图把这个卖给别人。我不在乎你是否同意我的观点。我只是告诉你，对于这个问题，我最好的猜测和看法是什么，对吗？有时候人们会说，我不同意这个观点。我不在乎。我在每个我所在的公司都成功地推广了客户成功这个概念。我认为这是硅谷上最大的胡扯。这并不意味着我需要你同意我的观点。我只是告诉你事实。
So one of the core messages in amped up is about the importance of urgency. And you talk a lot about how to create it, I guess maybe a more difficult question is why do you think a bunch of CEOs and leaders don't push for more urgency or higher standards? Well, I know you guys have been to a California DMV before. You want to see a lack of urgency? You know, this is what naturally happens to human beings. It's innate. We slow down to a glacial pace. And unless there are people who are going to drive tempo and pace and intensity and urgency, that's what leaders need to do because people naturally slow down. They're like, well, I need to be here anyways. And, you know, and sort of their mind is wandering off on their next vacation or what they're going to do on a weekend. And it's like, you know, you need to set, you know, high focus, high intensity, high preoccupation, you know, with what we're doing.
在《Amped Up》中，其中一个核心信息是关于紧迫性的重要性。你也谈到了如何创造紧迫感，我猜也许一个更难回答的问题是，为什么你认为一些CEO和领导者不去追求更大的紧迫感或更高的标准呢？嗯，我知道你们之前去过加利福尼亚的汽车管理局。你想看到没有紧迫感吗？你知道，这是人类的本性。我们会变得慢如冰川。除非有人推动节奏、速度、强度和紧迫感，这就是领导者需要做的，因为人们天然地会放慢脚步。他们会想，反正我必须在这里，而且他们的思维也开始游离到下一个假期或周末要做什么上。所以你需要设定高度专注、高度强度、高度专心于我们正在做的事情。
I mean, the people, something asked me, what's the message of your book? I'm like, read the title. Okay. That is the message. Look, there is an X factor. There's an enormous amount of room in the margin that is right under your nose. Okay. And you have the opportunity to take it up in the next meeting, in the next podcast, in the next email, in the next Slack message, you can take it up, you know, you can push the urgency, you can push the standards, right? You can push the alignment, right? You have all these opportunities. Are you taking them? It's an easy message, but it's really hard to have the mental energy to bring that to every single instance of the day, right? And that's the message of the book.
我的意思是，有人问我，你的书的信息是什么？我就像是，读标题吧。好吧，那就是信息。看着吧，有一个X因素。在你眼皮底下有很多的空间。好吧，你有机会在下一次会议中抓住它，在下一次播客中抓住它，在下一封电子邮件或Slack消息中抓住它，你可以抓住它，可以推动紧迫性，可以推动标准，对吧？你有所有这些机会。你会抓住它们吗？这是一个简单的信息，但是在每一天的每一个时刻都有能量把它带给你是非常困难的，对吧？这就是这本书的信息。
There's a lot of room there. There's a ton of room there and people don't realize it because, yeah, I've seen companies where, you know, you have to see MCU, they just think, I hired a bunch of people and I sit back and wait for greatness. They have no idea that they have to relentlessly drive, you know, every second of the day, every interaction and seek the confrontation because, you know, CEO jobs are insanely confrontational, which is not human nature. We don't like it. We are naturally confrontational. We avoid it. I mean, I had a founder CEO once, you know, every time somebody had to get fired, you know, he had a CFO do it and he stayed home that day because it's just so hard, right? And it's like, I don't have to disposition for it. We understand that, but there are people in the enterprise that have to do that stuff. Okay. That fully resonates.
那里有很大的发展空间。那里有非常多的发展空间，但人们没有意识到这一点，因为，嗯，我见过一些公司，他们认为只需要招聘了一大批人然后坐等伟大的出现。他们不知道自己必须要不断地推动，每一天每一次交互都要奋力前行，并寻求对抗，因为，你知道的，首席执行官的工作非常具有对抗性，而这并不符合人的本性。我们不喜欢这样，我们天生厌恶对抗，我们会回避它。我曾经有一位创始人兼首席执行官，每当有人需要被解雇时，他都让首席财务官去做，而他自己待在家里因为这实在是太难了，对吧？我没有这种处理能力。我们理解这一点，但是企业中有人必须要做这些事情。好的，我完全同意。
But another piece that strikes me is people are afraid, right, that they don't have the right people that they'll lose in the talent marketplace. If they push hard enough, their people will leave, right? What would you, how would you respond to that? No, they leave. They should leave. I mean, this is a great thing. You know, culture, shorts and sifts, you attract the right ones and you start losing the wrong ones. So it's actually quite perfect. If people are leaving, they're just not your DNA. They're not your blood type. And by the way, you need to create your blood type, you know, around you. Otherwise you're correct. You have nothing but conflict. I mean, I remember having people after two weeks to sit, you know what? I can't take the patient intensity as place anymore. It wasn't me personally. It was like everybody was like that. You know, they were all, you know, calling people out and driving these expectations they weren't used to. And they wanted to go home at 4pm and pick up the kids from school. I'm like, well, you need to go back to HP and sleep in your cubicle. This is not the place for you. So you need to, like culture can be incredibly helpful, you know, to a company. But culture is not a general thing. There's not such thing as general goodness. I mean, a culture needs to really enable your mission, right? And whatever enables your mission effectively is a good culture. There's no universal culture. That's good. You know, it depends on the type of leadership you have and type of business you have and you know, where you are in your journey and all this kind of stuff. But you know, culture is a very powerful thing because if you don't, if you don't fill the void, somebody else is going to, you know.
另一件让我印象深刻的事情是人们害怕，对吧，他们担心在人才市场上没有合适的人，他们会失去人才。如果他们再努力一些，自己的人就会离开，对吧？你如何回应这个问题？不，他们离开是好事。我的意思是，这是一件很好的事情。你知道，文化会进行筛选，你会吸引到正确的人，然后你会失去错误的人。所以，实际上还挺完美的。如果有人离开，那只是说明他们不是你们的体味。他们不是你们的血型。而且，顺便说一下，你需要在你周围创造你的血型。否则，你就只会充满冲突。我的意思是，我记得有些人在呆了两个星期后，坐在那里说：“你知道吗？我无法承受这种紧张的氛围了。这不是个人问题。每个人都是这样的。你知道吗？他们都在指责别人，对他们期望过高，这是他们不习惯的。而他们还想在下午4点回家接孩子上学。我告诉他们，你需要回想回想在惠普的日子，你需要待在那个隔间里睡觉。这不是适合你的地方。所以，你需要像文化那样非常有帮助，对一个公司来说。但文化不是一件普适的事情。没有所谓的普适好文化。我的意思是，一个文化需要真正支持你的使命，对吧？一切能有效支持你的使命都是好的文化。没有普遍适用的文化。这取决于你的领导方式，你的业务类型，你的发展阶段以及其他方面。但是你知道，文化是非常强大的，因为如果你不填补这个空白，别人就会来填补。
I want to switch over to talking about snowflake and then what's going on in AI. Can you just give our listeners a sort of snowflake 101? You know, what is the sort of scale and core innovation and use case of snowflake today? And we can talk about how the company has been evolving from warehousing to cloud, the data cloud and application platform in AI after that.
我想转换话题，谈谈Snowflake以及人工智能领域的最新动态。你能不能给我们的听众简单介绍一下Snowflake呢？你知道，现在Snowflake的规模、核心创新和应用情况是怎样的？然后我们可以谈谈公司如何从仓储向云存储、数据云和人工智能应用平台方面发展的。
Yeah, our founders probably would argue immediately with you that they were never warehousing play. So they sort of want to forgive me. Yeah, you're forgiven. And there's a reason for it because, you know, they were dealing with semi structured data right from the get go and sort of the workload types were more than just sort of batch analytical, you know, type of stuff, which is mostly associated with data warehouse. And that's also purely structured data. So there was always a broader scope and focus.
是的，我们的创始人可能立即与你争论，他们从未仓储玩耍。所以他们有点原谅我。是的，你被原谅了。也有原因，因为他们从一开始就处理半结构化数据，并且工作负载类型不仅仅是批量分析等与数据仓库大多相关的东西，而且这些数据还是纯粹的结构化数据。所以始终存在更广泛的范围和重点。
But our founders were two French guys. Long time, you know, Oracle CTO, technologists, architects, they were really responsible for making Oracle from the departmental level. You probably can't remember that far back, but Oracle at one point of time was the departmental platform to the enterprise platform that it became. So things like parallel, SQL, you know, we're all things that came from them. So they left and, you know, they want to reimagine database management, you know, for lack of a better word for cloud computing. In other words, they didn't want to carry technology forward or as little as they could. They want to reimagine. So, you know, building a database or a data platform, whatever you want to call it for cloud computing was very different than just sort of taking a Postgres SQL kernel forward and kind of hacking it up for the cloud. I'm being very unflattering here, but there's plenty of people that have done that.
但是我们的创始人是两个法国人。很长时间以来，你知道的，Oracle首席技术官、技术专家、架构师，他们真正负责将Oracle从部门级别发展为企业级平台。也许你记不得那么久远的事情了，但是在某个时间点，Oracle曾经是部门级平台，后来发展成了企业级平台。因此像并行SQL这样的东西都是从他们那里发展出来的。所以他们离开了，你知道的，他们想要重新想象数据库管理，换句话说，他们不想将技术继续前进，或者尽可能少地这样做。他们想要重新想象。所以，为云计算构建数据库或数据平台，无论你叫它什么，都与仅仅将Postgres SQL内核带入云平台并进行一些修改是非常不同的。我这里说话不太客气，但有很多人都这样做过。
So they did some really breakthrough things, you know, most notably that most people know is the separation of storage and compute. I mean, back in the day, people maybe not remember this, but, you know, I mean, you bought storage and computing combination. You can buy one without the other or isn't the world of cloud. You can come and do your compute and storage independent of each other. And of course, it became a consumption model, not right away, by the way, that was sort of an evolution. And, you know, obviously today is about a machine second or a compute second, but once for a while, I may have lost, you know, about a node and about a machine hour and all that. Now it's so incredibly fine-grained and granular that is completely different. But the other thing that they did is they took the control plane out of the cluster itself. So the clusters are now all stateless. You know, in other words, they're clueless, which is great because you can run tons of them, you know, concurrently, right? So there's not one master. The master lives outside of the clusters. So running jobs concurrently is another huge thing because in the world of data warehousing, just to use that word again, Sarah. And the reality was, you know, you have to beg for 230 AM timeslot three months from now because, you know, the cluster was consumed very quickly, very easily. Now it's like, there's no limit. So this is what I often tell an investor and it's like, I'm not creating them, man. I'm just enabling it. Okay. It's so pent up. It's insane. All right. And the architecture does that, right? And then I could also provision workloads either for economy, in other words, run as cheap as possible, or get run for performance blistering fast. And you could make these optimizations and choices. So this is this is beautiful stuff, right? Because we just we just opened up the demands in that legacy marketplace.
所以他们做了一些真正突破性的事情，你知道的，最显著的就是大多数人知道的存储和计算的分离。我的意思是，在过去的日子里，人们可能不记得这一点，但你知道，你以前要同时购买存储和计算的组合。现在你可以单独购买其中一个或者是云计算的世界。你可以随时独立地进行计算和存储。当然，这变成了一种消费模式，并不一定是立刻，这是一个演变过程。当然，今天是关于机器的第二个重点或者计算的第二个重点，但有一段时间，我可能忘记了有多少节点或者多少机器小时等等。现在它是如此的精细和细灵，完全不同了。但他们所做的另一件事是将控制平面从集群本身中分离出来。所以现在集群都是无状态的。也就是说，它们是不知情的，这很棒，因为你可以同时运行大量的它们，对吧？没有一个主节点，主节点存在于集群之外。所以并行运行作业是另一个巨大的优点，因为在数据仓库的世界中，就是再次使用你所说的那个词，萨拉。事实上，你不得不为三个月后凌晨2点30分的时间段乞求，因为集群很快就被使用完了，很容易就被使用完。现在没有限制了。这就是我经常告诉投资者的事情，就像我不是在创造它们，我只是在实现它们。好吧，需求非常巨大。不可思议。嗯，这就是这个架构所做的，对吧？我还可以根据经济需求或者说尽可能廉价地运行，或者以极快的速度来运行以获取性能。你可以进行这些优化和选择。所以这就很美好了，对吧？因为我们打开了传统市场上的需求。
And then, of course, we started migrating, you know, tear data databases. I mean, massive tear data plants. And by the way, I mean, it's still nearly anything of that because it's not easy to move those platforms at all. But you know, a ton of Hadoop, of course, which is sort of the, you know, what we used to call back data and now old data is big, so that the script doesn't make too much sense anymore.
然后，当然，我们开始迁移数据数据库，你知道的，泪水数据数据库。我的意思是，大规模的泪水数据系统。顺便说一句，这几乎一点也不容易，因为迁移这些平台非常困难。不过，我们用了大量的Hadoop，这是我们以前所称的后备数据，现在老数据变得很重要，所以那个脚本已经不太有意义了。
You know, and all the cloud era and on and on and on, tons of work, all SQL server. I mean, so that's that's what we've been doing. But you know, when I started, you know, the, the, the tagline, if you will, the positioning or core message was this is the date, this is the data warehouse built for the club. That was an off-lax message. And I'm like, okay, well, I'm going to stick with that. Because, you know, you, you, you're tanked yourself with a brushed pretty soon. You can't get it off you, which is pretty much what happened to us. I mean, you just started on.
你知道的，云时代以及接下来接下来接下来，大量工作，全部是SQL服务器。我的意思是，这就是我们一直在做的。但是你知道的，当我开始的时候，口号，如果你愿意这样说，定位或核心信息是这是一个为云计算而建立的数据仓库。这是一个不入流的信息。而我，我决定坚持这一点。因为你知道的，过不了多久，你就会跟着这个想法倒霉。这就是我们遇到的情况。我的意思是，你才刚刚开始。
Like so here we go again. I have an allergic reaction every time I hear data warehouse him because to me, it's just a type of workload. Now it's no longer a market. It's no longer an industry where and, you know, cloud data management platforms, you know, are, and certainly we are, you know, we're, we're seeking to become full spectrum workload capable, meaning from the most batch analytical to the most streaming online transactional, you know, massive, you know, scale and, and, and, and extremely low latency from, from what you're used to and, you know, to be type of environments.
就这样，我们再来一次。每次听到数据仓库的时候，我都会有过敏反应，因为对我来说，它只是一种工作负载。现在它不再是一个市场，也不再是一个行业，云数据管理平台（包括我们自己）正努力成为全方位负载能力的平台，从批量分析到实时在线交易，都能应对，能够处理大规模、低延迟的环境。
And the reason is we don't want the whole premise behind the data cloud is that the work comes to the data. The data does not go to the work. Now why does that matter? You know, because historically the data has always been pumped around to go to the work where you get massive siloing of the data. You don't even have to work at it. You're going to get siloing, you know, whether you try or not, because you have a new app, you get a new silo, you know, because it comes with its own database, right?
原因是我们不希望整个数据云的前提是工作来到数据那里。数据不会去到工作那里。为什么这很重要呢？因为从历史上看，数据总是被传输到工作地点，导致数据被大规模孤立。你甚至不需要努力，也会发生数据孤立，因为你有一个新应用程序，就会得到一个新的数据孤立，因为它自带自己的数据库，对吧？
And the siloing prevents you from really fully exploiting the potential that, that lies within your data because there's no walls that exist between them. So the notion of a data cloud is kind of a really new data strategy element in the mix. And we advocate really hard. I mean, I've started to see a large bank. It says, don't go resilowing your roles in the cloud. You end up with the same set of problems you have right now. And your data science, ML, AI, et cetera, teams are going to be, you know, very frustrated, you know, trying to overlay and blend that data and fine tune and train and all these fancy things we do now, you know, with data.
而且，数据的专门化使您无法充分利用数据中潜在的可能性，因为它们之间没有墙壁。因此，数据云的概念是混合中的一种全新的数据战略要素。我们非常推崇这一点。我的意思是，我开始看到一个大型银行说，不要在云中进行专门化的角色设置。您最终会面临与目前相同的一系列问题。您的数据科学、机器学习、人工智能等团队将感到非常沮丧，因为他们试图叠加和混合数据，进行微调、训练和一些我们现在用数据做的高级操作。
So we know we're trying to create an unfettered data universe data orbit. That's much bigger than your enterprise, by the way, because this is really an ecosystem, right? You have data providers, you know, in the world of financial services, you know, fact said, Bloomberg and S&P and all these things. So in hedge fund, they have hundreds and hundreds, you know, data flows, you know, coming in. So you really need to think of data management as a much broader orbit than just your enterprise. And so in the world of artificial intelligence or general intelligence around data, the ability to mobilize data, you really need to have a data called strategy.
所以我们知道自己试图创建一个不受限制的数据宇宙数据轨道。顺便说一下，这要比你的企业大得多，因为这实际上是一个生态系统，对吧？在金融服务领域，你们有数据提供商，像FactSet、彭博和标普等等。所以在对冲基金领域，他们有成百上千的数据流进来。所以你真的需要将数据管理视为比你的企业更广泛的范围。所以在人工智能或围绕数据的智能领域，能够调动数据的能力，你真的需要有一种被称为数据策略的策略。
That's also why we are multi-cloud capable, because we don't think, you know, we can have a data cloud in a single public, in a single public cloud platform. By definition, you can't, right? So that's really the strategy. And obviously, things have taken off a lot. But there have been multiple iterations in the journey, you know, of snowflake, I mean, started off, started off just moving legacy, you know, systems for the cloud and taking advantage of the elasticity and the economics and the provisioning all these things. But now it's much more broadly work, look capable and that's the journey that goes on and on.
这也是为什么我们具备多云能力的原因，因为我们认为我们不能仅仅在一个公共云平台中拥有一个数据云。根据定义，你是不可以的，对吧？所以这确实是我们的策略。显然，事情发展了很多。但在snowflake的旅程中，经历了多次迭代，起初仅仅是将传统系统迁移到云端，并利用弹性、经济性和提供各种功能。但现在，它更广泛地具备了工作能力，这是一个不断发展的旅程。
The other thing that has changed is no longer a database world, you know, historically a database was just, you know, a platform that was self-contained and it had standard interfaces like ODBC and JDBC that the application used to access the data. Now it's like, well, wait a second, you know, we don't want to operate that way anymore, because you're bridging the government's perimeter. So the application needs to execute inside the perimeter of the platform, not outside. So we have a programability platform called Snow Park, okay? And then that's where, you know, all the applications left. We have native application framework, all these kinds of things. So now you're looking at a very different platform environment, very different layers stacked and historically what we've had in the on-premise stack that we've grown up with.
另一件改变的事情是不再是一个数据库的世界，你知道，历史上，数据库只是一个自包含的平台，并且有标准接口，比如ODBC和JDBC，应用程序用来访问数据。现在，情况已经改变了，因为我们不再希望以这种方式操作，因为你正在跨越政府的边界。所以应用程序需要在平台的范围内执行，而不是在外面。所以我们有一个叫做Snow Park的可编程平台，可以让所有的应用程序留在那里。我们有本地应用程序框架等等。所以现在你可以看到一个非常不同的平台环境，和我们在本地部署的栈上所拥有的历史性差异很大。
It was kind of as short as the story is I can tell you. That's really a great background and obviously SNFIC has accomplished amazing things and really become central now to the enterprise data world and ecosystem.
我可以告诉你，这个故事很短。实际上，这是一个很棒的背景，并且SNFIC显然已经实现了令人惊讶的成就，现在真正成为企业数据世界和生态系统的核心。
How do you think about what's shifting in AI? Because I think we went from a world where we had almost like this older version of AI models, CNNs and RNNs and things like that, where people during old-school natural language processing or other things. And then more recently, we've had this big breakthrough wave of generative AI and it felt like the starting gun for that to some extent was really when chat GPT came out about six months ago. And then GPT-4 came out maybe three months ago and then suddenly everybody started building applications against this.
你如何看待人工智能发生的变革？因为我认为我们从几乎只有类似于旧版的AI模型，如CNN和RNN等，以及过去的传统自然语言处理等方面的技术，变成了最近一次重大突破的生成式人工智能浪潮。而在某种程度上，这波浪潮的开端实际上可以追溯到大约六个月前chat GPT问世的时候。然后大约三个月前，GPT-4问世，突然之间所有人都开始基于此构建应用程序。
How has that been showing up or has that been showing up yet in terms of the AI use cases that you see in the enterprise or your customer requests or has anything really shifted yet in terms of, you know, the broader enterprise ecosystem that you deal with just given it often it takes six months for an enterprise to plan something if it's a very large business. And so I feel like the last few months or last two quarters have just been a lot of big companies kind of planning against what to do.
在您所见过的企业的AI使用案例中，这种情况已经显现出来了吗？或者这种情况已经出现了吗？另外，您所处理的更广泛的企业生态系统中是否发生了任何变化，考虑到如果是一家非常大的企业，规划一项计划通常需要六个月的时间。因此我觉得最近几个月或者最近两个季度大公司只是在计划要做什么。
Yeah, you know, first of all, large language models are about language. Okay, surprise. But and it's a huge deal because, you know, I was taught the basics of COBOL when I was in school and COBOL stood for common business oriented language. Well, there was nothing common or business oriented about it. It was extremely cryptic syntax and all that. Compared to assembler and machine code, it was amazingly, you know, the syntax was amazingly comprehensible. So it's all relative, you know, in the 80s, we had SQL, which was back then, you know, also positioned as something that mere mortals could use to query data. So this is all about what, how and what is your relationship with data, right?
是的，首先，大型语言模型是关于语言的。好吧，这不出奇。但这是一件大事，因为你知道，当我在学校的时候，我学过COBOL的基础知识，COBOL代表通用商业定向语言。然而，它既不通用，也与商业无关。它的语法极其晦涩难懂。与汇编语言和机器代码相比，它的语法令人惊讶地易于理解。所以这都是相对的，你知道，在80年代，我们有了SQL，那时它也被定位为普通人可以用来查询数据的工具。所以这一切都与你对数据的关系以及如何处理数据有关，对吧？
And over the years that has, you know, evolved, but it's been immensely frustrating, you know, for people to get, you know, access to data in the form that they want and there's a lot of that hot and there's a lot of standardized reporting and dashboarding, all this kind of stuff. But it's been difficult. So, you know, going to natural language is like, it's like the last mile here. And that is an enormous thing. I mean, the effect on demand will be just enormous because every mortal, if you're semi literate, maybe you're not even literate, you can just talk, you know, you can get value from data. Wow. That is an incredibly, you know, big deal.
多年来，这一情况已经发展了，但这对人们来说非常令人沮丧，他们很难以他们想要的形式获取数据，而且有很多热门的和标准化的报告和仪表盘等等。但这很困难。因此，使用自然语言就像是最后一公里那样。这是很大的一件事。我是说，对需求的影响将会是巨大的，因为每个人，即使你只是半文盲，甚至是不识字的人，都可以通过说话来从数据中获得价值。哇，这真是一件了不起的事情。
But, you know, the generative aspect in terms of content generation does very cool when you're trying to plant a trip to Yellowstone, but when you're in the enterprise, you're dealing with structured proprietary data. And you know, they're not planning trips to Yellowstone. They're going to, you know, they're going to ask really hard questions like an insurance, for example, they may say, you know, we had disproportionate, you know, bodily injury claims in Florida and the surrounding states didn't have it. You know, A, what explains that B, we're going to have it again next quarter and C, what do we do about it? Do we stop underwriting? We change our pricing and blah, blah, blah, blah. Believe me, you're not going to get the answer to that question. It's a large language model. So you got to sort of separate the issues of, you know, text to speak on all that, you know, which I think are incredibly valuable from going to structured proprietary data because that's a very different realm.
但是，你知道，在生成内容方面，当你试图计划一次去黄石的旅行时，那种生成的方面非常酷。但是当你在企业中处理结构化的专有数据时，情况就不同了。你知道，他们不是在计划去黄石的旅行。他们会提出非常困难的问题，比如关于保险，他们可能会说，你知道，我们在佛罗里达和周边州遭受了不成比例的人身伤害索赔。A，是什么解释了这个现象？B，下个季度我们还会遇到这个问题吗？C，我们该怎么解决？我们停止承保吗？我们改变价格等等。相信我，你不会从这个大型语言模型中得到答案。所以你必须将语言生成和其他相关问题分开来看，我认为它们非常有价值，但要区分开来与处理结构化的专有数据，因为那是一个非常不同的领域。
So, you know, the way I'm trying to think about it right now is, yeah, we have language models, but we're going to see all kinds of other models. We're going to see business models. Okay. Because the question I just asked, you need to understand business models. I mean, one of the big things that just to stick with insurance for a second, one of the biggest things in insurance in a specific type of insurance, like auto insurance, auto insurance is SEO and progressive and labor to mutual and all these people, you know, telemetry data is number one through 10 for them.
所以，你知道的，我现在试着思考的方式是，是的，我们有语言模型，但我们将会看到各种其他模型。我们将会看到商业模型。好的。因为我刚刚问的问题，你需要理解商业模型。我是说，对于保险业而言，保险业的一个重要部分，比如汽车保险，汽车保险对于SEO、Progressive和Labor to Mutual等所有这些人来说，遥测数据是他们的前10名最重要的因素。
Or it's limited as the device you get in your car and it knows when you're speeding and all this kind of stuff. And by the way, that's how they now price risk and they're capable of lowering their prices yet increasing their profits because of their extremely sophisticated and refined use of that data. That data is extremely productive, you know, in terms of, you know, what the claims are going to be. And it's the difference between winners and losers and people who make money and people who don't make money. So that's that level of, and by the way, that's not even AI. That's just machine learning and a really data driven. And that's already in broad use in other insurance companies.
或它的限制就是你在车上得到的设备，它知道你何时超速以及所有这些。顺便说一句，这就是他们现在定价风险的方式，他们能够降低价格，同时增加利润，因为他们对数据的极其复杂和精细的使用。你要知道，那些数据在理赔方面非常有生产力。这是赢家和输家、赚钱和不赚钱之间的区别。而且，这甚至还不是人工智能，只是机器学习和对数据的深度运用。其他保险公司已经广泛使用这种技术了。
That's that is sort of, you know, where this is all going. And I need to be able to ask questions that analysts might take weeks and months, you know, to bring in McKinsey or Boehner, whoever, you know, to kind of study, you know, problems, right?
这就是，你知道的，一切发展的方向。而我需要能够提出一些分析师可能需要花几个星期甚至几个月的问题，你知道的，为了引进像麦肯锡或伯纳这样的单位，来研究问题，对吧？
The systems will be able to start giving you insight into those kinds of questions. That's really where we live, you know, proprietary structured enterprise data. That's a totally different realm, you know, and, you know, and by the way, you couple that with language problems and the natural language. Yeah, that's pretty powerful.
这些系统将能够开始向您提供对这些问题的深入了解。你知道的，那真的是我们生活的领域，专有的结构化企业数据。那是一个完全不同的领域，你知道的，而且，顺便说一句，再加上语言问题和自然语言处理。是的，这是非常强大的。
Sorry, Marvell movies, you know, the retro systems. That's a nice model. But imagine in medical, we have diagnostic models, you know, and we have all these different, you know, levels of intelligence that we can build. That as long as they have the data, I mean, they're going to be insanely lightening fast providing insight.
很抱歉，Marvell电影，你知道的，那些复古的系统。那是一个不错的模型。但是想象一下在医疗领域，我们有诊断模型，你知道的，我们可以建立各种不同水平的智能模型。只要它们拥有数据，我指的是，它们将会以惊人的速度提供洞察力。
You know, we acquired this company called Neva, you know, very recently, very excited about bringing the expertise into the company because, you know, they're search experts. And I'm a search junkie. I mean, 25 years ago, I mean, I was at that search in earlier on in my life because it's such a huge thing, you know, I just can't help myself, I'm always and the search is so addicting because it lets you start to explore everything that's known and ever been written or published or opinionated about and sort of process all that information.
你知道吗，我们最近收购了一家叫Neva的公司，你知道，我们非常兴奋地将他们的专业知识引入到公司中来，因为他们是搜索专家。而我自己也是个搜索迷。我是说，25年前的时候，我在我的生活中就参与了搜索，因为这是一件非常了不起的事情，你知道，我就是控制不住自己，总是对搜索着迷，因为它让你开始探索所有已知的、被写下或发表过的、或是代表某种观点的一切内容，并对所有这些信息进行处理。
But the problem with search is it has no context, right? It just matches on strings. And, you know, if you search on snowflake, you might get the company, you might get the weather, you might get the social phenomenon because it doesn't know, it just knows the word. And it's incredibly and so enrichment and context is really the name of the game and the world of data, right?
但问题是搜索没有上下文，对吗？它只是匹配字符串。你知道的，如果你搜索"snowflake"，你可能会得到这个公司的信息，可能会得到天气情况的结果，也可能会获得有关社会现象的结果，因为搜索引擎并不知道具体需要什么，它只知道这个词。在数据的世界中，丰富和上下文非常重要。
We always like to say one attribute can make a data attribute go from being mundane to being high octane because of the context of the create all of a sudden becomes wildly insightful and impactful and predictive and all these kinds of things.
我们经常说一个属性可以使一个数据属性从平庸变得充满活力，因为它的上下文突然变得深入洞察、具有影响力和预测性以及其他种种特点。
So, you know, in order for search, you know, to get that context and become stateful is going to be a normal step forward and, you know, check and search, you know, it all becomes one natural language conversation after a while.
所以你知道，为了搜索，获取上下文并实现状态性将是一次正常的前进步骤，你知道，经过一段时间的检查和搜索，这将逐渐变成一次自然语言对话。
So, you combine that, you know, with having this new levels of intelligence specific to industries or just subject matters. You know, I think that's really where there's a world of opportunity waiting to unfold still and I'm uncertain that it will, you know?
所以，你将那些针对特定行业或者主题的全新智能水平结合起来。我认为，这才是充满着机会等待着绽放的地方，不过我不确定它是否会发生，你知道吗？
Yes, you know, Anivo is a dear former portfolio company. Do you imagine that the snowflake like interface for users changes a great deal over the next, you know, five, ten years in terms of like supporting more natural language or broader user set?
是的，你知道，Anivo是一家亲爱的前投资组合公司。你有没有想象过在接下来的五年或十年里，Anivo的界面会像雪花般的改变，以支持更自然的语言或更广泛的用户群体？
Yeah, both of those things. You know, I think that there still will be a future for BI companies business intelligence sort of tableau's lookers of world. And, you know, dashboarding is done for a number of reasons, sometimes it's just, you know, basically providing data in the consumable format.
是的，两者都是。你知道的，我认为商业智能公司（如tableau和lookers）仍将有未来。而且，你知道的，仪表板制作有很多原因，有时只是为了以可消化的格式提供数据。
But it's also done because it's a way to basically tell people this is how I want you to look at the data. This is how I want you to understand. So, there is sort of a guiding element to dashboarding. Not all analysis is ad hoc based.
但这也是因为这是一种基本告诉人们：“这是我希望你们看待数据的方式”。这是我希望你们理解的方式。因此，仪表板的目的也有一种引导性的元素。并非所有的分析都是临时性的。
Now, a lot of it is. And, you know, for ad hoc, you know, nothing is going to be better than natural language. At least, I'm already using it. You know, we push sales force data into what we call snowhouse. That's our internal snowflake data that's pushed everything into. And it's just incredibly easy to use already commonly available services and have, you know, a conversational relationship with that data, you know, or my two top reps in this country or debt market or it is industry.
现在，有很多都是这样的。而且，你知道的，对于特别目的而言，没有什么比自然语言更好了。至少，我已经在使用它了。你知道的，我们将销售数据推送到我们称之为雪屋的内部雪花数据中心。这使得使用已经普遍可用的服务并且与数据进行对话变得非常容易，不管是与我在这个国家的两个顶级销售代表、债务市场还是行业有关。
No, it spits it out in the fraction of a second. But a beautiful graph attached to it and all that. It's very dictating because it just like search, right? You just keep going and going and going. And it becomes like a whole journey.
不，它在几分之一秒内将其呈现出来。但是它附带了一个漂亮的图表等等。它非常具有指导性，因为它就像搜索一样，对吧？你只需要继续前进，不断探索。这就变成了一次完整的旅程。
So, yeah, I definitely democratize access. Anybody semi-literate will be able to get, you know, way more value than they ever imagined from the data. And it will change, you know, how products get used. I mean, BI will not be the same. I think I see that as severely affected by this evolution.
所以，是的，我肯定要使访问普及化。任何半文盲的人都能够获取，你知道的，比他们想象中更有价值的数据。它将改变产品的使用方式。我的意思是，商业智能将不再相同。我认为我看到它受到这种演变的严重影响。
You made another acquisition of a company called Streamlet that I think we're also both familiar with. Can you talk about the rationale for that?
你又收购了一家叫Streamlet的公司，我想我们对它也都很熟悉。你能谈谈这样做的理由吗？
Streamlet is a company that does visualization animation, you know, for Python applications, but specifically in the world of machine learning. The problem with machine learning is if you're not a programmer, it's pretty damn hard to consume, you know, what it is and how it works. But Streamlet is almost reflexively reached for by Python programmers to basically make a machine learning model consumable by a general business user.
Streamlet是一家从事可视化动画的公司，你知道，在Python应用程序中，但是特别是在机器学习的世界里。机器学习的问题在于，如果你不是一位程序员，那么很难理解它是什么以及如何工作。但是Python程序员几乎本能地会去使用Streamlet，以便让一般的商业用户能够更好地理解和使用机器学习模型。
You can manipulate the variables and it just redraws everything. Visualization animation. And that's the reason that we acquired Streamlet is a, you know, that's certainly we have to have visualization animation. And by the way, this also touches the world of BI because a lot of people use Streamlet, you know, for the same reason that they would use BI type of products, but this is just much more, you know, specific to all kinds of reporting and use cases and dashboarding.
你可以操作变量，系统会重新绘制所有内容。这是为什么我们收购Streamlet的原因，你知道的，我们必须拥有可视化动画。顺便说一下，这也涉及到商业智能的领域，因为很多人使用Streamlet，出于与使用商业智能产品相同的原因，但它更加具体，适用于各种报告、用例和仪表盘。
So what we wanted to do with Streamlet is to bring it inside Snowflake. We call it Streamlet in Snowflake. And the reason is you need to have that hardcore trusted sanctioned governance perimeter because otherwise people will not allow the business to use these kind of applications. Governance is a really big deal because the data needs to be sanctioned and trusted and the business should not be able to get in trouble with the data. And that's really what we try to do with Snowflake. We are a hardcore enterprise grade platform.
所以我们希望将Streamlet引入Snowflake中。我们在Snowflake中称之为Streamlet。原因是您需要有严密可靠的治理边界，否则人们不会允许业务使用这类应用。治理是一件非常重要的事情，因为数据需要得到认可和信任，业务不应因数据而陷入麻烦。这正是我们在Snowflake中努力实现的目标。我们是一个非常强健的企业级平台。
It is really hard. I mean, you can bring Python to your data in two weeks time. But the problem is, you know, people are downloading libraries every couple of weeks to their hearts content and people have no idea what kind of risks they are exposed to in terms of exfiltration and all that. We spent two years, you know, making Python non-porous and it was an enormous effort to do that. But you know, you go to large financial institutions, like we're not going to let Python anywhere near our core data. It's just not even a conversation.
这真的很困难。我的意思是，你能在两周时间内将Python应用于你的数据。但问题是，你知道，人们每隔几周就会下载他们心爱的库，但他们完全不知道在数据泄露等方面会遇到什么样的风险。我们花了两年的时间，你知道，将Python做得非常完善，这是一项巨大的努力。但你知道的，像大型金融机构这样的机构，绝不会允许Python接触我们的核心数据。这根本就不是一个讨论的问题。
And we're like, well, we're going to do it in a way that, you know, the people that use Python, there are many, obviously, but they can do it in a way that they don't violate and create exposures to the enterprise. So that's really the role that we play. We talk about governance a lot. We talk about data quality a lot. And we get into this conversation. I don't know how many times a day because in the world of AI, if you don't have highly organized, optimized, sanctioned, and trusted data, what do you want, you know, your models to do this kind of train on a data lake? I call it a landfill, you know, you have no idea what the hell is in there. You know, everybody dumps their stuff in there. You're going to go train on that. It's just absurdity.
而且我们会这样做，你知道，那些使用Python的人，显然有很多，但是他们可以以一种不违背并创建企业曝光的方式来使用它。这就是我们所扮演的角色。我们经常谈论治理。我们经常讨论数据质量。我们进行了很多这样的对话。我不知道一天要进行多少次，因为在人工智能的世界里，如果你没有高度组织、优化、认可和值得信赖的数据，你想让你的模型在一个数据湖中进行训练，你会想要什么呢？这就像在垃圾填埋场上进行训练一样，你不知道他妈的有什么。每个人都把他们的东西倒进去。你会去在那上面进行训练。这太荒谬了。
So you're having highly organized, optimized, sanctioned data is really it's a prerequisite for old and people publish what they call data products. I'm sure you've heard that term before. A data product is essentially enough, taking data, you know, out of a lake and I've created into a trusted, optimized, understood object that I can now give to the business and stand behind. That's really the role that you data officer to to make the data, you know, trusted, organized and optimized. And also that the business can get in trouble, you know, with the data because there's no good or or because they're reaching all kinds of security and compliance, you know, aspects of using data.
所以，你要进行高度组织、优化和认可的数据，这实际上是对于老年人和人们出版他们所谓的数据产品的基本要求。我相信你之前听过这个术语。数据产品基本上是将数据从湖泊中取出，并转化为一个可信、优化和被理解的对象，现在可以交给业务部门并加以支持。这真正是你作为数据官员的角色，使数据变得可信、组织良好和优化。同时，业务部门可能会在使用数据时陷入麻烦，因为可能存在一些安全和合规方面的问题，没有良好的解决方法，或者由于涉及各种安全性和合规性方面的问题。
So that's stream was really important to us. The great thing about this and open source project, so you know, people so many people out there are reaching for when they want to publish something. And you know, we're like, okay, we're going to bring that insight the enterprise perimeter and make it high trust.
这个流媒体对我们来说非常重要。这个开源项目的好处是，很多人都希望使用它来发布内容。我们打算将这一洞察力引入企业范围，并使其更加可信赖。
I go back to sort of the journey you described from not just a data warehouse, but only data warehouse is a first workload to, you know, broadly, you know, more online analytics, other workloads, applications that sit inside snowflake with, you know, unified data. What are the what are the biggest challenges you guys face in making that vision come true? Is it convincing people to like move to, you know, customers to an entirely new architecture is it building the ecosystem? Is it just supporting the workloads? Because it's a very big rewrite of sort of enterprise architecture overall.
我回过头来回顾一下你所描述的旅程，不仅仅是一个数据仓库，而只有数据仓库是首要工作负荷，你知道，广义上来说，更多的是在线分析、其他工作负荷、应用程序，这些都是在雪花中集成的，你知道，通过统一的数据来支持。在实现这个愿景上，你们面临的最大挑战是什么？是说服人们转移到全新的架构上吗？是构建生态系统吗？还是仅仅是支持工作负荷？因为这涉及到企业整体架构的重大改写。
Yeah, but it's, you know, we are rewriting anyways because of our migration to cloud. It's like the most disruptive thing ever. And yeah, look, you know, when I was at service now, we basically had a non-premise architecture that we hosted in the cloud. By the way, I'm not being, you know, unduly critical here. I mean, because it was very useful that we were, you know, a single tenant platform and had all kinds of advantages. And we were able to manage it really well through massive standardization and things like that.
是的，但我们因为迁移到云端而不得不重新编写。这简直是令人震惊的事情。而且你知道，在我在ServiceNow时，我们基本上有一个非本地架构，一直在云端托管。顺便说一句，我并不是在此无端批评。我的意思是，我们是一个独占的平台，拥有各种优势，这非常有用。我们可以通过大规模标准化等方式很好地管理它。
Now, I'll give you an example. You know, all the federal business that we had at service now was all on premise oracle because, you know, you could not get in there with the clouds hosted solutions. By the way, you still can't.
现在，我会给你一个例子。你知道，我们在服务现在的所有联邦业务都是基于本地Oracle系统的，因为你知道，你无法使用云托管的解决方案进入那个系统。顺便说一句，现在你仍然无法这样做。
I mean, the certifications in federal are so insanely demanding. You know, federal is a very small part of our business because we spent, we're in the process for years and years and years to meet those standards. It's very, very hard, right? But we are a pure cloud implementation. We can't run on premise.
我的意思是，联邦政府的认证要求太过苛刻。你知道，联邦政府在我们业务中只占很小的一部分，因为我们花了好几年的时间来符合这些标准。这非常非常困难，对吧？但我们是一个纯粹的云实施公司，无法在本地运行。
I get asked that by people, you know, like, I mean, I can't even conceive of it, you know, the way snowflake works, right, because of common deers, you know, resources. It's not a, it's not a machine-centric platform, you know.
我被人问到这个问题，你懂的，我的意思是，我甚至无法想象，你知道的，雪花是如何工作的，这是因为常见的资源，而不是一个以机器为中心的平台。
So it is a big change. There's no doubt. And as I said earlier, you know, we fight the siloing of data because we're that kind of a company from a data strategy standpoint, really tell people you need a different data strategy for the cloud. Do not continue with what you've been doing because you've created a massively proliferated bunker silo world and it will not serve you in the world of AI and machine learning and the level of data science.
因此，这是一个巨大的变化。毫无疑问。就像我之前说的，你知道的，我们与数据隔离进行斗争，因为我们是那种数据战略角度的公司，真的要告诉人们，您需要为云端制定不同的数据战略。不要继续做您一直在做的事情，因为您已经创建了一个极其扩散的堡垒隔离世界，而在人工智能、机器学习和数据科学方面，它将无法为您提供服务。
If you want to drive intelligence from data, you're going to be in a world hurt if you keep siloing the data. And we tell that to application developers to ISVs and say, look, don't have your own data container. Okay, because instinctively application development, I went at my own data layer hanging underneath it. I'm like, you know what? It's you're going to hate it because A, it has no value to what you do because you're not a data management expert. It's just a utility function, you know, for you.
如果你想从数据中获取智能，如果你继续将数据孤立起来，你将感到痛苦。我们告诉应用程序开发者和 ISV（独立软件供应商），不要拥有自己的数据容器。因为在应用程序开发中，本能反应是在我的数据层下面创建属于自己的东西。但你知道吗？你会讨厌它，因为首先，这对你的工作没有任何价值，因为你不是数据管理专家。它只是一个为你提供实用功能的工具。
But then, you know, you're out of silo and the customer is now frustrated because they're going to start pushing that data into snowflake. And now we have pipelines and ETL process and all this kind of stuff and latency issues, governance issues, all this kind of stuff.
但是，你知道的，你已经离开了储料塔，现在客户感到沮丧，因为他们要开始将这些数据推送到Snowflake中。而且现在我们还有数据管道、ETL流程以及各种延迟问题、治理问题等等。
So we just announced that this relationship with Blue Yonder, for example, said, Hey, we're going to fully replatform, you know, on snowflake because in the world of supply chain management, that's really important because we need to have visibility, you know, across all the entities that make up a supply chain. We only do that when you have a single data universe. And when you have all these containers, it's impossible. That's why supply chain management has never been platform because the data problem was unsolvable, literally, you know.
我们刚刚宣布与Blue Yonder建立了这种合作关系。例如，我们说：“嘿，我们打算在Snowflake上进行完全重新平台化”，因为在供应链管理领域，这非常重要，我们需要在供应链的所有组成部分之间实现可见性。只有当你拥有一个统一的数据宇宙时，才能做到这一点。而当你拥有所有这些容器时，这是不可能的。这就是为什么供应链管理从未成为一个平台，因为数据问题是无法解决的，不言而喻。
So these and the other thing is the supply chain management. I mean, they run these extremely demanding and analytical processes, right? And they run many, many, many times, you know, you know, you know, per minute per hour. And they are very, very commanding of resources, right? So again, this is where, you know, our style of computing is very, very desirable, right? Because I can run the process. I can run them as fast as I need to. I can run as many as I want concurrently.
所以这些和其他事情是供应链管理。我的意思是，他们运行这些极为严苛和分析性的流程，对吧？他们运行很多很多次，你知道的，每分钟每小时，非常消耗资源，对吧？所以再说一遍，你知道的，我们的计算方式非常有吸引力，对吧？因为我可以运行这个流程。我可以按需要运行得很快。我可以同时运行任意数量的流程。
So all these new architectural things are lending themselves really to use cases that have been there for generation. But, you know, supply chain management is an e-mails spreadsheet business. I mean, they're still living in a world of Microsoft 30 years ago. That's insane, right? Because it's one of those use cases that should have been extremely optimized. But it isn't, right? So yeah, you're going to be doing a re-platform, re-architecting, and re-imagining. That's how we did it. So it's like, is it a re-imagination of data management for cloud computing? But as we get through our journey, it's looking more and more different than what it used to look.
所以所有这些新的建筑技术实际上都适用于已经存在了几代的用例。但是，供应链管理还是在使用电子邮件和电子表格。我的意思是，他们还停留在30年前的微软时代。这太疯狂了，对吧？因为这是一个本应被极度优化的用例。但事实并非如此，对吧？所以是的，你将需要重新平台化、重新架构和重新设想。这就是我们的做法。因此，这是对云计算数据管理的重新构想吗？但是随着我们的进展，它看起来越来越不同于过去。
You mentioned some very large-scale evolutions in terms of just the data world there. What are some of the other future directions that you're most excited about or the big thrust that you see coming in terms of data?
你提到了关于数据世界的一些非常大规模的进展。除此之外，你对未来哪些方向最感兴趣，或者你认为数据领域即将出现哪些重大推动力？
Data is going to redefine whole industries, okay? And that's what I find the most interesting. And the reason I say that is, first of all, you know, nine out of ten conversations I have with customers are not technology and architecture and all that, and migrations. It's about industry use cases.
数据将重新定义整个行业，明白吗？这就是我最感兴趣的地方。我之所以这样说，首先是因为，你知道的，我与客户交流的九成不是关于技术、架构和迁移等内容，而是关于行业应用案例。
It's about call centers. It's about, you know, making medicine predictive, for example, because everybody knows, you know, healthcare is economically, you know, not viable at the scale that we need to deliver it. And so data can make it, you know, predictive and prescriptive, right? If we have enough data, you know, we can tell who is at risk for what disease, when, and what they need to do. All data driven, this is not, well, this is not somebody's opinion. The data just, data doesn't have opinions, okay? This is what it is. And it gives you the accuracy to go with it for the more depth and breadth of data that you have and the more debt certain that stuff becomes.
这是关于呼叫中心的问题。你知道，比如说让医学变得有预测性，因为大家都知道，你知道，按照我们需要提供的规模来看，医疗保健在经济上不可行。因此，数据可以使其具有预测性和处方性，对吧？如果我们有足够的数据，你知道，我们可以知道谁有什么疾病的风险，何时出现，以及他们需要做什么。这都是数据驱动的，不是某人的观点。数据不会有观点，明白吗？这就是现实情况。并且，你拥有更深度和广度的数据，就越能提供准确性，并且对这些数据越确信。
And this is how healthcare will become much more effective, obviously, because you don't need no longer reacting to disease and symptoms, but you're getting ahead of it. And every healthcare institution, you know, that we talked to in the customer of ours, this is where they want to go. This is where they need to go. They don't want to treat disease. They want to prevent it. And they want to anticipate it.
这就是医疗保健变得更有效的方式，显然是因为你不再需要对疾病和症状做反应，而是能提前预防。我们与客户接触的每个医疗机构，都希望走向这个方向，这也是他们需要走向的方向。他们不希望治疗疾病，而是希望预防它，并能提前预料到它的发生。
So it will change, you know, healthcare is an industry, but, you know, I just mentioned, you know, auto insurance is a similar type of example. In the world of pharma, you know, it takes from average 12 years to, you know, to bring a drug to market. Well, then you got five years left before your patent runs out. What if I could, could compress that by one, two or three years? Now you've changed the economics of the entire industry, right?
所以它会改变，你知道，医疗保健是一个产业，但是，你知道，我刚才提到的，汽车保险是一个类似的例子。在制药业中，你知道，平均需要12年时间，才能将一种药物推向市场。那么，在你的专利权到期之前，只剩下五年时间。如果我能够将这个时间缩短一年、两年或者三年，那么整个行业的经济就会发生改变，对吗？
So you know, data is far more important to you, how the economics and how the industry functions and people still realize.
所以你知道，对你来说，数据比经济和行业运作更为重要，人们依然意识到这一点。
Yeah. How do your investments in R&D reflect this? Or what are the big areas of thrust that you have right now from an R&D small perspective?
是的。你们的研发投资如何反映这一点？或者说，从研发的小角度来看，你们现在的主要重点领域是哪些？
The hardest part, you know, for us is, you know, I have to massively enable, we have to massively enable this platform to be incredibly broadly and capable, not broadly, but also in depth because if it doesn't do what people need to do or it doesn't do it well, they're going to say like, well, forget it, we'll just pump the data over here. And now we're back to, you know, fragmenting and siloing the data. So if we have the data, we have to enable the workloads, okay? We have to. And that's really hard. That's really hard to me.
我们最困难的部分，你知道的，对我们来说是，你知道的，我必须大幅度地使这个平台变得非常广泛和功能强大，不仅仅广泛，而且还要深入，因为如果它不能满足人们的需求，或者不能做得很好，他们会说，算了，我们就把数据传输到另外一个地方。现在我们又回到了数据的分割和孤立。所以如果我们有数据，我们必须使工作负载有效。我们必须这样做。这对我来说真的很难。
You mentioned some of the workload types, but we do things like global search, okay? Because in the world of cybersecurity, you know, that's incredibly important because a lot of cybersecurity companies that, you know, they're a partner of ours, they are running on the data club. Because they couldn't sell to their customers yet another database container because we didn't want it. They said, look, bring the data here and then we can combine it with all these other data sources, you know, vulnerability and all. And then, you know, our analysts can search one day in the universe instead of 15 of them and try in their head to figure out what does it all mean and do something with it.
你提到了一些工作负载类型，但我们在做全球搜索，好吗？在网络安全领域，这非常重要，因为很多网络安全公司都是我们的合作伙伴，他们正在使用数据库集群。因为我们不想再给他们卖另一个数据库容器。他们说，把数据带到这里，我们可以与其他数据源结合起来，比如漏洞等。然后，我们的分析师就可以在宇宙中进行一天的搜索，而不是15天，并且试图通过他们的头脑搞清楚这一切的意思并加以处理。
Yeah, I'm definitely saying a lot of people right now building in terms of snowflake apps so that they can just maintain the data locally within a snowflake instance for a customer, but then provide enriched functionality on top of that or access to that data in ways that are really performant and combined with what the company is trying to do more broadly.
是的，我绝对说现在有很多人正在构建雪花应用，以便他们可以在雪花实例中仅在本地维护数据，然后在此基础上提供丰富的功能或以非常高效的方式访问这些数据，并与公司更广泛的目标相结合。
So I think that's been a really great innovation for the industry.
所以我认为这对行业来说是一项非常伟大的创新。
I guess one last question is just around the macro shift. So obviously we've gone from a zero-interest rate environment where everybody was just buying software like crazy to a world where people are cutting SaaS budgets increasingly, they're rethinking spend.
我想最后一个问题就在宏观转变的附近了。显然，我们已经从利率为零的环境转变为人人都疯狂购买软件的世界，而现在人们正在不断削减SaaS预算，重新审视开支。
Does the macro environment change your point of view on consumption or credit-based pricing or how you think about the pricing and economic model in the snow regime?
宏观环境是否会改变你对消费或基于信用的定价或对雪地制度中定价和经济模式的看法呢？
Yeah, not really. You know, we have different stakeholders that have different opinions on this. Investors, of course, love it when you have customers over a barrel and you can keep a gun to their head and they're going to pay no matter what. I don't particularly like that.
是的，不完全是这样。你知道，我们有不同的利益相关者对此持有不同意见。投资者当然喜欢当客户完全依赖我们并且无论如何都会支付费用时，我们可以对他们施加压力。但我个人并不喜欢这样做。
You know, when I was at ServiceNow, I always felt that it was not an equitable relationship that we had with our customers because oftentimes, you know, they would sign up with us for many millions of dollars and it took them nine months to even get in production. They were paying for older users all this time. How was that equitable?
你知道，在我在ServiceNow的时候，我总是觉得我们与客户之间的关系并不公平，因为很多时候，你知道的，他们会与我们签约，投入数百万美元，却要花上九个月的时间才能进入生产状态。他们这段时间都在为旧用户付费。这样公平吗？
So one of the things that I really liked about Snowflake in cloud computing and consumption models and the elasticity is that we pay for what you use. It's a utility model. And, you know, is that painful? Sometimes? Yes.
在云计算和消费模式以及弹性方面，我对Snowflake最喜欢的一点是我们按实际使用付费。它采用了一个公共事业模式。但是，你知道吗，这有时会带来痛苦？是的。
I talked to the CIO of a bank last week and he said, you know, this is my bank's growing 3%. Snowflake's growing 22%, you know, and it's that that can't go on forever.
上周我和一家银行的首席信息官交谈，他说：“你知道，我的银行的增长率是3%。而这个叫Snowflake的公司的增长率是22%，不过你知道，这种情况是不可能永远持续下去的。”
You know, the CFO gets in there and he goes, he starts going bullshit on everybody and saying like, hey, people, you know, they basically say this is the size of your bread bogs live with it. You're not going to get a new contract. But it's and then people need to go back to the drawing work.
你知道，首席财务官进来后开始对每个人大放厥词，说：“嘿，你们现在所拥有的就是这块面包的大小，就这样向它适应吧。你们不会得到新合同。”但是人们需要回去重新起步。
Okay, it's a very fine-grained thing because you can go into snowflake work and I say, okay, I'm going to downgrade the provision on this. I'm going to run this less frequently. I'm going to change the retention period on data. You can do all these things to lower your consumption of storage and compute.
好的，这是一个非常具体的事情，因为你可以参与雪花计划，我说，好吧，我要降低这个项目的配额。我要更少地运行这个项目。我要改变数据的保留期限。你可以做所有这些事情来降低你的存储和计算消耗。
Does that hurt us sometimes? Yes. But it's a value to the customer because, you know, if you're an assassin, encryption model, they got to wait for the next drill before they can start cutting over limp here. Whereas with us, you can do it in near real time. Investors don't like it. I understand because they love it on the way up. They just hate it on the way down.
这样偶尔会对我们造成伤害吗？是的。但对于顾客来说，这是一项价值，因为你知道，如果你是一个刺客，加密模式，他们必须等待下一次机会才能开始削减这边的干肢。而与我们一起，您可以几乎实时地完成它。投资者不喜欢这种情况。我理解，因为他们喜欢上涨时的情况，但他们讨厌下跌时的情况。
Yeah, so I guess related to that, a lot of the people who tune in to know priors are people who are running their own companies right now. And there are different stages. You know, we have everything from early stage startup CEOs to executives at larger companies, researchers, engineers, et cetera. And one of the big questions of their mind right now is how to manage differently through this, you know, economic downturn or the shift in spend or the shift in the macro environment.
是的，所以我猜与此相关的是，很多关注先前情况的人现在都在经营自己的公司。他们所处的不同阶段来自各种各样的企业，既有初创企业的CEO，也有大公司的高管、研究人员和工程师等等。他们现在心中的一个重要问题就是如何通过这个经济衰退、支出转变或宏观环境变化来做出不同的管理方式。
You obviously are known as a CEO who is very good at making tough choices and, you know, prioritizing in both good times and bad times. How should people think through managing differently in this changing economic environment? What are the first things people should do? You know, I mean, I see all these layoffs, you know, with Amazon, Meta and Google and all this kind of stuff. And we don't do layoffs because we don't wait until there is a huge happen. We're always pruning the tree, so to speak, right? So we don't have to do it as some massive event that is super unsettling. You know, management of resources is something that should be happening on a daily basis, not just performance, but also, you know, bringing supply and demand in sync with each other, alignment. That should be happening constantly.
你显然是众所周知的一位CEO，擅长做出艰难决策，无论是在好时候还是坏时候，你都能很好地选择优先事项。在这个不断变化的经济环境中，人们应该如何思考管理问题？人们应该首先做什么？你知道的，我看到亚马逊、Meta和谷歌等公司都在进行大规模裁员。而我们不会等到发生大事件才进行裁员。我们总是像修剪树一样经常清理冗余。这样我们就不需要让它成为一个巨大的、令人不安的事件。资源管理不仅仅应该发生在绩效评估中，还应该包括供需的同步和对齐。这应该是不断发生的事情。
But the culture sort of evolves over the years where it's just unfathomable. That's a word where you just, they can't conceive of being so confrontational that I'm going to take somebody out of a job. So we just look the other way until we get a crisis and then we start to ripen out, you know, tens of thousands of people. I just don't think that's fair as well as effective, right? I mean, in this is the reason my world doesn't change all that much because I was already doing it. So these are just more sort of management practices and ways of thinking about, you know, how you run things, you know, rather than, oh, gosh, we have economics that way now. We need to change everything we're doing. No, you don't. You just need to run things, you know, like you always, by the way, people are not used to living in downjurbs, you know, when you've been around longer, it's like, hey, they come around.
然而，这种文化随着时间的推移演变得难以理解。"无法理解"是一个词，意思是他们无法想象如此对抗性，以至于我要解雇某人。因此，我们只是假装不看见，直到出现危机，然后开始裁员数以万计的人。我不认为这样公平，也不有效，对吧？我的世界并没有发生太大的变化，因为我本来就在这么做。所以这些只是更多的管理实践和思维方式，用于思考如何经营事物，而不是像"Oh，天啊，我们现在经济这样了。我们需要改变我们所做的一切。"不，你不需要。你只需要像以前一样经营事物。顺便说一句，人们不习惯生活在衰退中，但当你经历了更长时间时，他们就会明白的。
Okay, this part of life. And by the way, let's, let's, let's, you know, let's double down, triple down, put our game phase on, put our boots on, you know, we're into fight now. This is actually going to be a lot. I will say it's going to be a lot of fun. This is where it really happens, right? So in other words, you can get up for it. You know, we need to use amp things up. That's what you're doing. People are growing up like, oh, they only know, you know, that the trees grow into the heavens. Trees don't grow into the heavens. Okay. They don't. Everybody needs to grow up a little bit, you know, and just get a leash on reality and say, look, this is this part of life, you know, do I have to start rethinking everything? Because economically, things are now, you know, different. Yeah, do some degree. Yes. I mean, we're, we're scrutinizing productivity much harder in sales organizations. You know, we might be a little bit quicker on the trigger. All that kind of stuff for startups, obviously, you know, raising money is a whole different ballgame and you guys are in that world. So they definitely need to think harder.
好的，这是生活的一部分。顺便说一下，让我们加倍努力，三倍努力，全力以赴，做好准备，你知道的，我们现在要准备战斗了。这真的会非常有趣。这才是真正发生的地方，对吧？换句话说，你可以做好准备。你知道的，我们需要提升一下氛围。这就是你正在做的事情。人们正在成长，他们只知道树木长到天上。树木并不长到天上。好了，每个人都需要成熟一点，认清现实，说，看，这是生活的一部分，我需要重新思考一切吗？因为经济上，现在情况有所不同。是的，在某种程度上是的。我们对销售组织的生产力进行更严格的审查。你知道的，我们可能会更加迅速地做出决策。对于创业公司来说，筹集资金显然是完全不同的游戏规则，而你们正处在这个世界中。所以他们绝对需要更加深入地思考。
I mean, when I was a day at a main, we would best be going to company from one fundraising milestone to another. That's how it was back then. That hasn't been the way it's been. I mean, you know, in recent years, people have never had to raise money or run businesses that way to prepare themselves for a fundraising milestone. They've never done it before. Why are you sure? You know, because that's how you stay alive. I mean, fundraising is oxygen for a company, you know?
我的意思是，在我从事主要工作的时候，我们通常会在不同的筹款里程碑之间不断前进。那是过去的情况。现在情况已经不再如此。我的意思是，在最近的几年里，人们从来没有为了筹款里程碑而筹集资金或经营企业。他们以前从未这样做过。你为什么这么确定呢？因为这是确保生存的方式。我是说，筹款对于一家公司来说就像氧气一样重要。
Yeah, basically, I think gravity turned back on and everybody's like, you know, I'm just realizing it. Yeah.
是的，基本上，我认为重力重新生效了，每个人都在意识到这一点。是的。
Frank, this is a great conversation. Is there anything that we missed that you think would be useful or interesting to talk about?
弗兰克，这是一次很棒的对话。你觉得我们可能漏掉了什么内容，你认为讨论起来会很有用或者有趣吗？
Well, we've already talked about amping things up and that's always, you know, when we have conversation like this and a lot of people are listening to it, I just, I just, I'm trying to get people to say, you know, my next meeting, my next message, my next encounter, my next situation, I'm going to amp it up because it's just a choice that you make.
嗯，我们已经谈过增强事物的内容，当我们像这样进行对话并且有很多人在听的时候，我只是，我只是试图让人们明白，你知道的，我的下一次会议、下一条信息、下一次相遇、下一个情况，我将会增强它们，因为这只是一个你所做的选择。
And you know, don't be afraid, you know, that people will react poorly to it. They won't. The good people will actually love it. And especially if you're in the leadership role and who isn't, you know, this is, this is really what people want. They want to inject energy and focus and intensity and quality so that the whole place starts to feel, you know, exciting, you know, and it's not like, oh, it's just four o'clock or five o'clock or whatever.
你知道的，不要害怕，你知道，人们对此反应不佳。实际上并非如此。好的人们实际上会喜欢它。特别是如果你在领导角色中，谁不是呢，你知道，这真的是人们想要的。他们想要注入能量、专注、强度和质量，使整个地方开始变得令人兴奋，你知道，它不再只是四点钟或五点钟或任何其他时刻。
No, right? It's much easier to live in an energized environment than one that's devoid of energy, you know? I love it. It's a very, it's a very courageous message.
不是吗？你知道，生活在充满活力的环境中比毫无活力的环境要容易得多，对吗？我喜欢这样。这是一条非常勇敢的信息。
Thanks for doing this, Frank.
谢谢你做这个，弗兰克。
You bet. I love it.
没错。我喜欢它。