OpenAI CFO Sarah Friar: IPO, AI Rivalries, New Device, and Spending $100B+ on Compute
频道: All-In with Chamath, Jason, Sacks & Friedberg
视频: https://allinchamathjason.libsyn.com/openai-cfo-sarah-friar-on-ipo-ai-rivalries-new-device-and-spending-100b-on-compute
原文语言: en
统计: 共 40 轮 · Sarah Friar 32 · Jason Calacanis 3 · Chamath Palihapitiya 2
[0:00]
OpenAI's CFO, Sarah Fryer.We've got to get right to it.You have just completed what I regard as the most successful fundraising round in history.We're going to raise actually north of $120 billion.We think AI is the biggest era that we've seen to date.We're just starting to understand what it's going to mean for global productivity.And with that, hopefully more affluence, better lives for everyone.Luck is whatever the preparation meets opportunity, but you've got to grab it.Long time listener, first time caller.Quite exciting to get to hang out with all the bros here.Hello.We weren't sure how to start this off, but I thought the best thing was to allow our erstwhilecrypto czar to maybe save comments.I saw an article today, I think it might have been in the Wall Street Journal, that the perceptionis that there's an advantage to IPOing earlier if you're an AI company.So now we know SpaceX is going.And then the question is, when are open AI and Anthropic going to go?
OpenAI 的首席财务官（CFO），莎拉·弗莱尔（Sarah Friar）。我们直接进正题。你刚刚完成了我认为是史上最成功的一轮融资。我们实际上会融到 1200 亿美元以上。我们认为 AI 是我们迄今见过的最大的一个时代。我们才刚开始理解它对全球生产力意味着什么。而随之而来的，希望是更多的富足、每个人更好的生活。所谓运气，就是准备遇上机会——但你得抓住它。我是老听众了，第一次打进来。能和这里的几位兄弟一起待着挺让人兴奋的。你好。我们本来不确定该怎么开场，后来我想最好的办法是让我们这位前任「加密沙皇」（指 David Sacks）先说两句。我今天看到一篇文章，我想可能是《华尔街日报》上的，说现在的普遍看法是：如果你是一家 AI 公司，早点上市（IPO）是有优势的。所以现在我们知道 SpaceX 要上市了。那问题就是：OpenAI 和 Anthropic 什么时候上？
[1:05] Sarah Friar
And I'm curious, how do you think about that?Do you think there is a little bit of a race on?Or you haven't made a decision about that yet?In the end, an IPO, I say this to the team all the time, it's a milestone.It is not a destination.Do not run your company as if that's some sort of destination.It's just another way to fundraise.We just did, you heard me on the sizzle reel, raise $122 billion in March.And that was to give ourselves maximum flexibility.I feel like my job as a CFO is create optionality for this, not just this company, but just thisera that we're living in.Sorry, Sarah.Was that point in fundraising, is that the biggest private republic up until the SpaceX IPO?
我很好奇，你怎么看这件事？你觉得这里面是不是有点在赛跑？还是说你们还没做决定？说到底，IPO——我经常跟团队讲——它是一个里程碑，不是终点。别把公司当成「以上市为终点」那样去经营。它只是另一种融资方式而已。我们刚刚就融了一笔，你们在片头集锦里听到了，3 月份融了 1220 亿美元。这么做是为了给我们自己最大的灵活性。我觉得我作为 CFO 的工作，就是为这家公司、其实也是为我们所处的这个时代，创造选择权（optionality，即让自己手上永远多几种可走的路）。抱歉打断，Sarah。那笔融资，在 SpaceX 上市之前，是不是史上最大的一笔私募融资？
[1:51] Sarah Friar
It is.It is by orders of magnitude.I think the largest IPO to date was the Sari Oramko, which was about $30 billion.So it is actually incredible that you're going to have potentially three IPOs at a scale thatwill be bigger even than 2001, that time frame, there was a lot that went on in the market too.But the market has grown.And by the way, the other thing going on in the market is like, if you look at buybacks,M&A, and so on, it's actually a lot of capital that keeps being returned back to shareholdersin cash.So there is a lot of money sitting on the sidelines.But in the spirit of the question, David, I think in the end, you'll be measured, right?
是的。而且是数量级上的领先。我记得迄今最大的一笔 IPO 是沙特阿美（Saudi Aramco），大约 300 亿美元。所以真的很不可思议：你可能会看到三家公司的 IPO，其规模甚至比 2001 年前后那一波还大——当然那个时间段市场上也发生了很多别的事。但市场本身已经长大了。顺便说，市场上还有一件事：如果你去看股票回购、并购这些，其实有非常多的资本在以现金形式不断返还给股东。所以有大量的钱在场外观望。不过就你这个问题的本意来说，David，我觉得最终你是会被「称重」的。
[2:32] Sarah Friar
In the end, the market is a weighing machine, not a popularity machine.No one remembers who went first, Google or Yahoo, Lyft or Uber.And I say that not because whether I want to be first or second, but I just think the pressloves a bit of drama.But in the end, we're going to have to build big, sustainable, durable companies.And fundraising will be a key component of doing exactly that.Sarah, breaking news.Oh, my God.So many people coming at me.Hi, Jason.I know.It is hard balancing four interviewers at the same time.It's okay.This is my world, by the way.So I'm good with this.Yes, Jason.Anthropic just confidentially filed their S1.So does that mean you're third place in terms of the filing?
说到底，市场是一台称重机，不是一台人气机器。没人记得是 Google 先上市还是 Yahoo 先上市，是 Lyft 先还是 Uber 先。我这么说不是因为我想当第一还是第二，我只是觉得媒体喜欢制造一点戏剧性。但最终，我们都得建成大的、可持续的、经得起时间的公司。而融资是做到这一点的关键一环。Sarah，突发新闻。我的天。这么多人一起冲我来。嗨，Jason。我知道，同时应付四个采访者挺难的。没事，这本来就是我的日常，所以我应付得来。请讲，Jason。Anthropic 刚刚秘密递交了 S-1（上市招股说明书）。那这是不是意味着，论递交顺序你们排第三？
[3:13] Jason Calacanis
It does not mean anything yet because you have to run now the gauntlet of the SEC.And who knows how long that takes for anyone?Yeah.Is there, though, a benefit to them going forward?And I think unpacking the rivalry with Anthropic is on everybody's minds.So I guess you can't talk too much about IPOs.So I'll just pivot to Anthropic was far behind.And now they've really, I think everybody would agree in the industry, now blown pastOpenAI in terms of developers and corporations.And it seems revenue.So how did that happen at OpenAI when you had such a tremendous lead?
这还什么都不意味着，因为接下来你得走完 SEC（美国证监会）那一关，而谁知道对任何一家公司来说这要花多久。是啊。不过，他们先走一步是不是有好处？我想，拆解一下你们和 Anthropic 之间的竞争关系，是所有人都关心的事。IPO 你可能不方便多谈，那我就转个话题：Anthropic 曾经远远落后，而现在——我想业内所有人都会同意——他们在开发者和企业客户上已经反超了 OpenAI，看起来收入也是。你们当年领先那么多，这是怎么发生的？
[3:54] Sarah Friar
How did Anthropic blow past you guys?So let's talk a little bit about a strategy.Our strategy is different, right?So we are building the AI layer, the infrastructure.And it's really important that there's a single foundation, but then with many interfaces out into the world.So ChatGPT is one to the consumer.Over 900 million people use ChatGPT weekly.And it's become the noun and the verb.It's how most people experience AI for the first time.Kind of fun fact, our economic research team just showed me the fastest growing continents now are Africa.Probably not totally surprising since it started a small base.Fastest growing languages are Azerbaijani and Kazakstani.What is it?
Anthropic 是怎么反超你们的？那我们稍微聊聊战略。我们的战略是不一样的。我们在建的是 AI 那一层，是基础设施。很重要的一点是：底下要有一个统一的基座，但要有很多个通向世界的界面。ChatGPT 是面向消费者的那个界面，每周有超过 9 亿人使用 ChatGPT。它已经变成了一个名词，也变成了一个动词。大多数人第一次接触 AI 就是通过它。一个有意思的数据：我们的经济研究团队刚给我看，现在增长最快的大洲是非洲——可能也不太意外，因为它是从很小的基数起步的。增长最快的语言是阿塞拜疆语和哈萨克语。那个叫什么来着？
[4:41] Sarah Friar
It's Kazakh.Which is kind of incredible to talk about where it's going.So multiple interfaces, ChatGPT, of course there's Codex.Just hit 5 million over the weekend.We're really proud of that.Coming from almost zero in January.5 million users.Go Codex.Help me prepare for this little special up here too.There's of course Frontier, our enterprise offering.And everything, every other way that we can get out there to reach businesses of all sizes.That is a very different strategy.We think that because it's served up on one model, there's a compounding element of advantage that comes from that.More users, more data, more ability to personalize.ChatGPT asks as a front door.As models get bigger, there's more efficiency.That should lower the overall cost to give you a token in the world.That should compound to higher gross margins.Ultimately more ways to pay for compute.And then access to compute is one of the really big competitive advantages at the moment.So, you know, we have to all run our own races.But we all have to recognize we're part of an ecosystem that also needs to bring people along collectively.Did you spread a little bit to then too many projects?
是哈萨克语。这本身就挺不可思议的，能看出它正在往哪里去。所以是多个界面：ChatGPT，当然还有 Codex——这个周末刚突破 500 万（用户），我们挺自豪的，1 月份的时候几乎还是零。500 万用户。Codex 加油。它还帮我准备了今天这个场子呢。当然还有 Frontier，我们的企业级产品，以及我们能触达各种规模企业的所有其他方式。这是一条非常不同的战略路径。我们认为，因为这些都跑在同一个模型上，会有一种复利式的优势叠加：更多用户、更多数据、更强的个性化能力。ChatGPT 就是那个总入口。模型越大，效率越高，这应该会拉低向全世界供应一个 token 的总成本，进而复利式地推高毛利率，最终也让「为算力付费」有更多种方式。而当下，能拿到算力本身就是一个非常大的竞争优势。所以我们各自跑各自的赛道，但我们都得承认：我们同属一个生态，这个生态还需要带上更多人一起走。你们是不是摊子铺得有点太开、项目太多了？
[5:51] Jason Calacanis
People were talking about this new gadget, Sora, and then maybe not enough focus on enterprise.Is that a fair assessment of if there was a mistake in the last year that was it?No, I think that the world loves to go to binarisms.Like, are you a consumer company, Sarah?
大家都在讨论这个新硬件、讨论 Sora，那是不是对企业市场的聚焦就不够了？如果说过去一年有什么失误，这个判断公平吗？不，我觉得这个世界特别喜欢往二元对立上靠。比如：Sarah，你们到底是一家消费公司，
[6:06] Sarah Friar
Are you an enterprise company?The reality is we're very much both.We're not one or the other.Right now, our revenue is getting pretty balanced, about 50-50.We are incredibly focused on the enterprise.Like, I spend so much of my time with, I mean, just even in the last week, I could tell you I've been to see Thermo Fisher in Boston.I was with a bunch of banks in New York.I was on the phone with travelers on Friday.I spent this morning on the phone with a tech company.It doesn't matter the vertical.People are really moving on AI right now.Our new head of revenue, Denise Dresser, in seat since December.She is a force of nature.And so I think the enterprise, broadly speaking, is really firing on all cylinders.But we don't want to leave the consumer behind.Remember, our mission at OpenAI is AGI for the benefit of humanity.Not for the benefit of humanity who can pay or for the benefit of humanity who live in an enterprise, but very broad-based.It's why we offer so much free, because we want people to get a taste.Once they get a taste of intelligence, the ability to come up a commitment curve is incredible.Our free users do about seven turns, seven questions a day.Our first paid tier do double that, about 15.
还是一家企业公司？现实是我们两者都很重，我们不是二选一。现在我们的收入已经相当均衡了，大概 50 对 50。我们对企业市场极其重视。我自己的时间——就说上周吧——我去波士顿见了赛默飞世尔（Thermo Fisher），在纽约见了一堆银行，周五和 Travelers 保险通了电话，今天上午又和一家科技公司通了电话。行业根本不重要，现在所有人都在往 AI 上动。我们新的收入负责人 Denise Dresser 去年 12 月上任，她是个狠角色。所以我觉得企业这条线整体上正在全速运转。但我们不想把消费者落下。别忘了 OpenAI 的使命是「让 AGI 造福全人类」——不是造福「付得起钱的人类」，也不是造福「在企业里上班的人类」，而是非常广泛的人群。这也是我们免费提供这么多东西的原因：我们希望人们先尝到一点甜头。一旦他们尝到智能的味道，那条「承诺曲线」（commitment curve，即用户一步步愿意投入更多的过程）往上走的速度是惊人的。我们的免费用户大约每天 7 轮对话、问 7 个问题；第一档付费用户是它的两倍，大约 15 轮。
[7:20] Chamath Palihapitiya
Our real paid tier, the plus, 20 bucks.Hopefully you're all on it or higher.About 3x.And pro, about 11x over a free user.So remember when you got your flip phone?And you're like, yeah, I don't know, it does make some calls.Now, that same phone, think of all the things it does for you.That's the path we're on with intelligence right now.Sorry, Jamal.You said something very influential.I think it was about 18 months ago, for a lot of us in the industry,where you framed a very simple economic trade-off, which was gigawatts to cash.And I think you said one gigawatt is roughly equivalent to about $10 billion a year of revenue to OpenAI.So comment number one was this one gigawatt equals $10 billion a year of revenue for you.But it's not just you, because you can probably extrapolate that to Anthropic and other folks, Gemini.But then you were really at the forefront of getting access to power and data centers and powered land.It seemed a little crazy.But now it looks like, hold on, there's a huge deficit of supply.Can you just unpack all of that and explain both the spectrum of where we are and then those specific economics and if that's changed?
真正的付费档，也就是每月 20 美元的 Plus——希望在座各位都至少订了这一档——大约是 3 倍。Pro 档大约是免费用户的 11 倍。还记得你拿到翻盖手机的时候吗？你会想：「嗯，也就能打打电话吧。」而现在，同样是一部手机，想想它为你做了多少事。我们和智能的关系，现在正走在这条路上。抱歉，Chamath。大概 18 个月前，你说过一句对我们行业里很多人都很有影响力的话。你当时把经济账框成了一个很简单的换算：吉瓦（GW，算力对应的电力规模）换现金。我记得你说 1 吉瓦大致相当于 OpenAI 每年约 100 亿美元的收入。所以第一点是：1 吉瓦 = 每年 100 亿美元收入。但这不只对你们成立，你大概可以外推到 Anthropic、Gemini 这些人身上。第二点是，你当时非常早地就冲到最前面去抢电力、抢数据中心、抢带电的土地，当时看着有点疯狂。但现在看，等一下——供给缺口大得惊人。你能把这些都拆开讲讲吗？讲讲我们现在处在什么位置、那套具体的经济账是什么，以及它有没有变化？
[8:29] Sarah Friar
So first of all, yes, compute is a very scarce resource at the moment.I mean, what we see in our business, we're going up that kind of vertical wall of demand right now.And there's just not enough tokens available.So I'm very grateful that I got to work alongside Greg and Sam.I think we're very prescient on this.And last year, we were definitely taking some arrows in the back about why are they out there buying all this compute?
首先，是的，算力现在是极度稀缺的资源。我们在自己的业务里看到的是：需求正在贴着一堵垂直的墙往上走，可用的 token 就是不够。所以我非常庆幸能和 Greg（Brockman）、Sam（Altman）一起共事，我觉得他们在这件事上非常有先见之明。去年我们背后确实挨了不少箭：「他们干嘛在外面买这么多算力？」
[8:54] Sarah Friar
And I think, thank God we did, because in 26, we still won't have enough compute.Where are we on the compute continuum?There's kind of choke points everywhere.And I think they will continue to move back and forth.I mean, you all talk about this and know this as well as anyone here.Whether it's energy, first and foremost, land power, how we get regulatory environments such that we can build quickly.When you get into the racks and chips themselves, clearly, do we have enough in that supply chain?
而我现在想说，谢天谢地我们买了，因为到了 26 年，我们的算力还是不够。我们在算力这条连续谱上到底走到哪儿了？到处都是卡点，而且这些卡点还会来回移动。这些你们几位聊得也很多、知道得跟谁都不差。首先是能源，然后是土地、电力，以及怎么让监管环境允许我们快速开建。再往里到机架和芯片本身，供应链里的量显然够不够也是问题。
[9:27] Sarah Friar
Memory spike is on at the moment.Access to great talent.Do we have enough people coming through our education system?I really worry about this right now.I'm a trustee at Stanford, and I see that we need to keep the focus on education and science.And then trust.I mean, I actually put that as part of the supply chain.Sam right now is in Saline, Michigan.He's going to be cutting the ribbon in about two hours, so you are getting a sneak preview.But they told me it was okay to say it in the room.That will be sticking shovels in the ground on a one gigawatt data center, which is part of our Oracle complex.It's really important there on the trust side that we don't leave communities behind.I spent seven years of my life working at Nextdoor, doing the hard work of what it means to be local.And you cannot tell people from top down what they need, because they will tell you, thank you, but no thank you.I will tell you what I need.And so in a data center like that, we're actually spending a lot of time in the community saying,number one, we're not going to raise your electricity bills.We're going to pay for our infrastructure and our power.It will not be the rate payer that has to pay.Number two, we're going to bring jobs, 2,500 union jobs, good jobs, like electricians, HVAC, and so on.
内存（memory）现在正在涨价。还有优秀人才的获取：我们的教育体系输送的人够不够？这一点我现在真的很担心。我是斯坦福的校董，我看到我们必须持续把重心放在教育和科学上。然后是信任——我其实把信任也算作供应链的一部分。Sam 现在人在密歇根州的塞莱恩（Saline），大约两小时后他要去剪彩，所以你们这算是提前剧透了——不过他们跟我说在这个房间里可以讲。那边要动土开建一座 1 吉瓦的数据中心，属于我们和 Oracle 那个园区的一部分。在信任这一面上，非常重要的一点是不能把社区落下。我人生中有七年在 Nextdoor 工作，做的就是「什么叫本地」这种苦活。你不能自上而下地告诉人们他们需要什么，因为他们会告诉你：「谢谢，但不必了，我需要什么我自己告诉你。」所以在这样一座数据中心里，我们其实花了大量时间在社区里说：第一，我们不会抬高你们的电费，我们自己的基础设施和用电我们自己付钱，不会让缴电费的居民来承担；第二，我们会带来就业，2500 个工会岗位，都是好岗位，电工、暖通空调（HVAC）这类。
[10:42] Sarah Friar
We are going to pay our taxes, a billion dollars in taxes just for that data center into Michigan.And on top of that, we're going to invest $45 million going into education for codex credits.To do what you all talked about this weekend is like anyone who's not like coming in facile to their new job.I have teenagers using codex.It would be like I would never hire a finance person who didn't know how to use Excel.And I pretty much probably wouldn't hire a finance person today that doesn't know how to use a tool like codex.So when I think about investment, we're having to invest ahead of demand.That means we need to both be able to find all of the compute and all the pieces and then pay for it.So that goes back to your capital question on IPO.And then on the other side on the economics, look, the economics do continue to get better.They're getting better on multiple fronts.I think we are doing a better job of actually showing true value to our customers.And I think you get beyond kind of a cost plus type pricing into something that feels more akin to the value being created.Now, scarcity of tokens helps because it's causing a bit of a compression in cost.Talk about that and just like without specific names, where you know the landscape exists today in terms of all the power that's available and all the demand that exists across everybody.
我们会依法纳税，光这一座数据中心就要给密歇根州交 10 亿美元的税。除此之外，我们还要投 4500 万美元用于教育，形式是 Codex 的使用额度。就是为了做你们这个周末聊的那件事——毕竟今天不会用这些工具的人，进新岗位是不顺手的。我自己家的青少年孩子都在用 Codex。这就好比，我绝不会雇一个不会用 Excel 的财务；而今天我基本上也不会雇一个不会用 Codex 这类工具的财务。所以当我思考投资时，我们必须在需求到来之前就先投。这意味着我们既要能找到所有的算力和所有环节，还要付得起钱。这就回到你关于 IPO、关于资本的问题了。而在经济账那一面，成本确实在持续变好，而且是在多条战线上同时变好。我认为我们在向客户展示真实价值这件事上做得更好了：你会从「成本加成」式的定价，走向一种更贴近「所创造价值」的定价。而 token 的稀缺其实也帮了忙，因为它在成本端造成了一点压缩。能不能讲讲这个——不用点具体的名字——就以今天你所了解的格局，所有人手上的电力供给和全部需求之间到底是什么关系？
[12:01] Sarah Friar
Yep.What's going to happen over the next year just at the current course and speed of what is available?Of the data centers that's available, of the tokens that's available, of the infrastructure that's available for everybody.Because, you know, I told this story last week, but, you know, I'll use Anthropic.And one of the frustrating things is at some point it just says, you know, 1030.It's like, all right, see you at 230.Yeah.And that's not a viable experience.Right.And in fairness to ChatGPT, actually, I've never had that with.Yeah, we're quite generous with our tokens.And again, on purpose, we're trying to drive access so people understand.Because if you're on that free tier, not actually getting the latest model, but we're trying to put it in your hands so you get a sense for it, by the way.Because, you know, if you're a kid doing homework, like I think about when I grew up and the Encyclopedia Britannica showed up at the front door in Northern Ireland in a tiny little community in the middle of the Troubles.It was like the clouds parted.And so we want to make sure that people get that feeling, by the way.But the landscape right now, in 26, if you want to buy more compute, good luck to you.
对。在当前的走势和速度下，未来一年会发生什么？就现有的数据中心、现有可用的 token、现有对所有人开放的基础设施而言。因为，我上周讲过这个故事：拿 Anthropic 举例吧，让人沮丧的一点是，用着用着它就说——现在 10 点半，那好，我们 2 点半见。对，这不是一个可行的体验。是的。而公平地说，ChatGPT 我其实从来没遇到过这种情况。是的，我们在 token 上相当慷慨。而且是有意的，我们想推动可及性，让人们理解它。因为如果你在免费档，其实拿不到最新的模型，但我们还是想把它放到你手上，让你先有个感觉。因为你想，如果你是个写作业的孩子——我就想起我小时候，《大英百科全书》送到我家门口，那是在北爱尔兰一个很小的社区，正处在「动荡年代」（the Troubles）当中，那感觉就像云开了一样。所以我们希望人们能获得那种感受。但说到当前的格局：在 26 年，如果你还想买更多算力，祝你好运。
[13:07] Sarah Friar
Like, tell me, because I don't know where else to find it.I mean, as you know.Elon has some.Well, I was going to say, Elon, ironically, ended up being the one person that had too much compute in a way.But good job on, like, figuring out how to sell that off.In 27, it's pretty limited as well, frankly.Now, there's a couple of things shifting around.When we talk about compute, there's training that mostly still all happens here in the United States for USG reasons, for making sure that a national asset, in effect, is happening on US soil.For inference, we want that to be global.And I think particularly in an agentic world, you want much more kind of real time, even for things like Sora and video, which, by the way, yeah, we have, you know, we had to make a really tough choice because we didn't have enough compute.And it uses a lot.Right now, yeah, video does.But video is not over.Like, in particular, when you start to think about where AI is taking us into more multimodality.So, remember, we've all been taught by the last generation of technology to talk with our thumbs.It's a disease.You walk around.Everyone's looking down.They don't look up anymore.Teenagers sit on my sofa at night and talk to each other with their thumbs.
你告诉我在哪能买到，因为我真不知道还能上哪儿找。你们也知道的。Elon 手上有一些。我正想说，讽刺的是，Elon 最后成了那个某种意义上算力买多了的人。不过他把多出来的卖掉这事干得漂亮。到了 27 年，坦白说也相当紧张。不过有几件事在起变化。我们说算力的时候，训练目前基本还都发生在美国本土，出于美国政府（USG）方面的考虑，要确保这项事实上的国家资产是在美国国土上进行的；而推理（inference，即模型上线后每次回答问题的计算）我们希望是全球化的。我觉得尤其是在一个 agentic（智能体）的世界里，你会需要更接近实时的算力，甚至像 Sora 和视频这类也是——顺带一提，我们当时不得不做了一个非常艰难的取舍，因为算力不够，而视频很吃算力。现在确实是这样，视频很费。但视频这件事没有结束，尤其当你开始想 AI 正把我们带向更强的多模态（multimodality）时。你想，上一代技术把我们都训练成了「用大拇指说话」的人。这是一种病。你走在街上，所有人都低着头，没人抬头看了。我家的青少年晚上坐在我沙发上，彼此之间用拇指打字聊天。
[14:22] Sarah Friar
I'm like, who are you talking to?And my son will be like, him.I'm like, okay, talk.Multimodality is here.Hopefully, I think you all talked about it this weekend.You're talking to your tool.I talk to Codex every day.And so, that is changing rapidly.But that is going to need much more kind of real time compute because it's an odd experience if I was talking to Jamaf.And you're building with Johnny I have this puck in these earpieces.So, maybe tell us a little bit about that process.You've admitted it now.If I tell you it's in your piece, Johnny will come and steal my teenage son.I might give it to him.Give him to him.No, but you do believe that there should be some.We're changing into a consumer substrate that I cannot tell you what it is.But by the end of this year, we will unveil it.But you've seen it and you've tried it.I have seen it.I've tried it.I am a hand talker.Right now, I'm sitting on my hands.Is it paradigm shift?
我说：你在跟谁聊？我儿子会说：跟他。我说：好吧，那你们说话啊。多模态已经来了。我希望——我想你们这个周末也聊到了——你现在是在「跟你的工具说话」。我每天都在跟 Codex 讲话。所以这件事在快速变化。但这会需要多得多的实时算力，因为如果我在跟 Chamath 说话（却有延迟），那体验就很怪。你们正在和 Jony Ive 一起做东西——我听说是一个圆盘状的小装置加上耳塞。能不能讲讲这个过程？你这算是承认了啊。要是我告诉你耳塞里是什么，Jony 会跑来把我家青春期的儿子偷走。那我可能就把他给他了。给他吧。不过你确实相信应该有某种……我们正在转向一种新的消费级载体，具体是什么我不能告诉你，但今年年底之前我们会揭晓它。可是你已经见过、也试过了。我见过，我试过。我是个说话爱比手势的人，现在我正坐在自己手上（忍着不说）。它算是范式转变吗？
[15:10] Sarah Friar
Did you have an iPhone moment?Yeah, when you used it, was it like having an iPhone for the first time?It's very...What Johnny and team are really good at is bringing humanity to devices.And I don't really know how to explain that well.But when you see it, you feel it.It feels natural in some way?
你有没有那种「第一次拿到 iPhone」的时刻？对，你用它的时候，是不是像第一次用 iPhone？这个很……Jony 和他的团队真正擅长的，是把人性带进设备里。我不太知道该怎么把这一点解释好。但当你看到它，你会感觉到。是那种很自然的感觉吗？
[15:29] Sarah Friar
It feels very natural, but it feels very lovable.Really?And I can't really explain what that emotion is.It's intimate in some way in terms of not taking your phone out.And it's seamless is what I've heard from people who played with it.Technology can be very mechanistic.But we all know great design just makes everything fade away.It's what, at the time, the simple is hard job set.This story, just going back to the earlier question, so putting on the CFO hat, help usunderstand the capital allocation model that you use.Because a lot of businesses over the last decade, two decades, that have kind of been these outsizedreturners have found some unique way to deploy capital at a higher ROC than anyone else.And then you end up plowing all your capital into that higher ROC bucket.What is that for you guys?
感觉非常自然，但更是「讨人喜欢」（lovable）。真的吗？我也说不清那是一种什么情绪。它有某种亲密感，就是那种「不用把手机掏出来」的感觉。玩过的人跟我说的词是「无缝」。技术可以非常机械。但我们都知道，伟大的设计会让一切都消失于无形，就像当年乔布斯说的：把东西做简单，是很难的。回到刚才那个问题——戴上 CFO 的帽子，帮我们理解一下你们用的资本配置模型。因为过去一二十年里，很多回报特别惊人的生意，都是找到了某种独特的方式，把资本部署到比别人更高的资本回报率（ROC）上，然后就把所有资本都往那个高回报的桶里灌。对你们来说，那个桶是什么？
[16:24] Sarah Friar
And how do you think about that portfolio approach to having more of these kind of big returnershots?And is there an engine where that gets better over time?There has to be.Because in the end, the durable, high-value companies created in this era, I don't thinkthey're not going to be magical.They're going to look like the great companies of prior eras.They're going to create customer value.Starts with the customer and really helps the customer do something different, better,more revenue, more efficiency, right?
你怎么看待这种「组合式」的打法，去多押几个这样的大回报机会？有没有一个引擎能让它随时间越来越好？必须得有。因为说到底，这个时代里被创造出来的那些经久的高价值公司，我不认为它们会靠魔法。它们会长得像过去每个时代里的伟大公司：创造客户价值。从客户出发，真正帮客户做成一件不一样的事、做得更好、赚更多收入、更高效率。
[16:55] Sarah Friar
Thermo Fisher wants to be able to get patient screening done faster so they get FDA approvalfaster.That's really important.Like, if you have a form of cancer where you have weeks to live, the difference betweena breakthrough in four weeks and two weeks can literally be life or death.They also have, I'm going to misquote this, but something like 30,000, 38,000 people inthe field selling those amazing, like if you walk into any lab in the country, you'll justsee Thermo Fisher plastered all over every device.Those people want to be more efficient going to work.Like, the fastest takeoff of codecs within OpenAI right now is actually in our go-to-marketteam.Our devs are there, but like if you look at the pace of growth, kind of month over month,it's all in GTM.So they want more productivity out of their GTM team.And of course, they're doing things in areas like finance, which I get really excited about.So customer value first.From that, now you need to get to a great gross margin.So how do you get to a great gross margin?
赛默飞世尔希望更快完成患者筛选，这样就能更快拿到 FDA 批准。这非常重要——如果你得的是某种只剩几周命的癌症，突破发生在四周后还是两周后，字面意义上就是生与死的差别。他们还有——我可能会记错数字——大概三万到三万八千名一线销售人员在卖那些设备；你走进这个国家的任何一间实验室，都会看到每台仪器上贴满赛默飞世尔的标。这些人希望上班时更高效。其实现在 Codex 在 OpenAI 内部起飞最快的地方，恰恰是我们的 GTM（go-to-market，市场与销售）团队。我们的开发者当然在用，但要看逐月增长的速度，全在 GTM。所以客户希望自己的 GTM 团队产出更多。当然他们也在财务这类领域做事情，这让我特别兴奋。所以第一步是客户价值。从这里出发，接下来你需要有一个很好的毛利率。那怎么做到好毛利率？
[17:54] Sarah Friar
You're looking at like the cost of revenue.The main input is compute.The good news on compute is that there is a massive deflationary curve on cost, right?From chat GPT 5 to 5.4, I think the deprecation cost was something like 97%.It's kind of an amazing curve.Actually, I'm slightly.From 4 to 5.4, it was 97%.But that happened in like two years.That's kind of wowing, right?
你要看的是收入成本（cost of revenue，即为了产生收入直接付出的成本），主要投入就是算力。算力上的好消息是，成本存在一条巨大的通缩曲线。从 ChatGPT 5 到 5.4，我记得成本降幅大概是 97%，这条曲线相当惊人。其实我说得不太准——是从 4 到 5.4，降了 97%。但这是在大约两年里发生的。这挺让人惊叹的，对吧？
[18:21] Sarah Friar
That's incredible.Even our newest model, if you look at 5.5 that we just released, we're trying to nowtranslate that back to the customer.So we actually raised prices on 5.5, 2x.But if you look at what the cost of the customer is, they're probably still getting a break ofabout 20% to 30% cost reduction per token because it's just much more efficient per token.So there's a lot to do in that envelope.And part of making a capital allocation decision is having to, if you make it on today's costprofile, you actually might misprice the outcomes.You have to lean in a little on the cost profile.And then as we think about the bills, yeah, you are having to make, like really, my focustoday on compute is what's the compute I can buy for 28 onwards?
太不可思议了。哪怕是我们刚发布的最新模型 5.5，我们现在也在努力把这条曲线还给客户。我们确实把 5.5 的价格提了一倍（2 倍），但如果你看客户实际的成本，他们每个 token 大概还是省了 20% 到 30%，因为单 token 的效率高得多。所以这个空间里还有很多事可做。而做资本配置决策时有一点很关键：如果你按今天的成本结构来做决策，你其实可能会把结果定错价。你得在成本结构上往前多探一点身子。然后当我们考虑要建什么时，我今天在算力上的关注点其实是：我能为 28 年及以后买到什么算力？
[19:07] Sarah Friar
Like that Michigan data center in Saline, I don't think we will be getting compute outof it until probably end of 27, early 28.So that's where you're starting to make your bets.And in fact, where I feel most short of compute right now is starting to look at 30, 31, 32.So you're having to create a business model.Now, the good news is each year goes by, we get more confidence in the build.We're seeing it massively outperform.And so that's giving us more and more confidence.And the market is coming towards us much more.So how are you making the compute need forecast multiple years out?
就说密歇根塞莱恩那座数据中心，我认为我们大概要到 27 年底、28 年初才能从它那里拿到算力。所以那才是你现在下注的地方。而实际上，我现在感觉最缺算力的时间点，已经开始看向 30 年、31 年、32 年了。所以你必须为此建一套商业模型。好消息是，每过一年，我们对这些建设的信心都更足，我们看到（需求）大幅超出预期，这让我们越来越有底，市场也在朝我们靠拢。那你们是怎么把多年之后的算力需求预测出来的？
[19:45] Sarah Friar
Accounting for all of the architectural and model advancements that are happening where,call it value or utility per unit of power is going up.And help us understand how you kind of estimate that, given that there's a lot of technologydevelopment going on that has a high kind of variance to it.Yeah, yeah.So we do have to make multiple assumptions, both on the compute itself.So we assume right now that compute actually on a per gigawatt is getting more expensivebecause power is getting more expensive, memory is getting more expensive, and so on.However, the intelligence that we get on the other side out because of the deprecation onthe chip side is more than making up for that.So in terms of a per unit sold to a customer, it should actually get a lot less expensive forthe customer.So that's just the chip.Exactly.That's just the chip itself.We don't try to overestimate on the model side because sometimes, like 5.5 is an incrediblygood model on the efficiency side.But if you look at something like 5.4, the prior model, it was a really large pre-trainedmodel.It was very expensive.It was actually hard to serve.And sometimes we want to do that really big pre-trained moment.And then we take multiple model turns to be able to kind of drive down on the cost side.
要把正在发生的所有架构和模型进步都算进去——也就是说，每单位电力所产生的价值或效用是在上升的。帮我们理解一下你们怎么估这件事，毕竟技术演进的方差非常大。是的，是的。所以我们确实得做多个假设，一是算力本身：我们现在假设按每吉瓦算，算力其实是在变贵的，因为电更贵、内存更贵等等。然而，我们在另一端拿到的智能，因为芯片侧的成本折旧下降，是绰绰有余地把这部分抵消掉的。所以按卖给客户的每单位算，对客户来说应该会便宜得多。这只是芯片这一层。没错，只是芯片本身。在模型这一侧我们不会做过高的估计，因为有时候——比如 5.5 在效率上是个特别好的模型；但你看 5.4，就是上一代模型，它是一个非常大的预训练模型，非常贵，其实也很难服务。而我们有时候就是想要那种「超大规模预训练」的时刻，然后再用好几轮模型迭代把成本压下来。
[21:02] Sarah Friar
I mean, in the near term, like in 26 and 27, I clearly build a model that's bottoms up.So I know what my products are.I have a sense of what the pricing will be.You know, consumer, P times Q.How many wows do I think I have?
在近期，比如 26 和 27 年，我显然是自下而上（bottoms up）建模型的。我知道我有哪些产品，我大致知道定价会是多少。消费端就是 P 乘以 Q（价格 × 数量）。我觉得我会有多少周活用户？
[21:19] Sarah Friar
I can see what the shape of the line is.How many of them will subscribe?Advertising coming in is also still related to how many weekly actives, how many dailies,how many messages, and so on.So you can do actually a pretty good model job in 26 and 27.That said, the shape of the line keeps taking us by surprise to the upside.When you get into the outer years, you're actually looking more at the compute you've bought andalmost just doing an algorithm the other way that's saying this amount of compute shouldequate somewhat to this amount of revenue.I don't know for certain exactly where it will all come from.Like a year ago, I built a model for investors that showed agentic revenue.And the story was, we're going to have this thing.We're going to be in the agentic era.We're going to hand it to a developer.With natural language, they're going to be able to build.And we think they will pay upwards of maybe $2,000 a month for it.Which is kind of laughable in hindsight.But nobody believed.They were like, I don't even know what she's talking about.There's no way that will happen in $2,000 a month.Remember when people were losing their minds over ChatGPT Pro being at $200?
我能看出那条曲线的形状。其中有多少会转成订阅？进来的广告收入也和周活、日活、消息量这些相关。所以 26 和 27 年其实可以做出一个相当不错的模型。话虽如此，那条曲线的形状还是不断地往上超出我们的预期。等到更远的年份，你其实更多是看你已经买下的算力，几乎是反过来做一个算法：这么多算力大致应该对应这么多收入。我并不确切知道这些收入具体会从哪里来。比如一年前，我给投资人做了一个模型，里面有一条 agentic（智能体）收入。故事是这样的：我们会有这么个东西，我们会进入 agentic 时代，我们把它交给一个开发者，他用自然语言就能构建东西，我们认为他们会愿意为此每月付高达约 2000 美元。现在回头看这个数字简直可笑（因为太保守了）。但当时没人信，他们说：我都不知道她在讲什么，每月 2000 美元绝无可能。还记得当年 ChatGPT Pro 定价 200 美元时，大家都要疯了吗？
[22:27]
Like, oh my God.No one will ever pay for that.Yeah.So why $122 billion?Does it take you to 2031, 2032?Like, how do you get the calculus on the capital needs as you do that modeling?Maybe even more specific.So the estimates I've seen is that to stand up one gigawatt of AI compute costs about $50 billion.That's right.Land, power, shell, chips, everything.All in around $50 billion.Do you have to front all of that money when you create a new data center?
就那种「天呐，永远不会有人为这个付钱」。是啊。那为什么是 1220 亿美元？这笔钱够你们撑到 2031、2032 年吗？你们是怎么把资本需求算出来的？说得再具体点：我看到的估算是，立起 1 吉瓦的 AI 算力大约要花 500 亿美元。没错。土地、电力、厂房外壳、芯片，全部算进去，大约 500 亿美元。你建一座新数据中心时，这笔钱是不是得全部先垫出去？
[22:58] Sarah Friar
Or how much of it do you do?How much of it can you get debt for?Does 100 billion raise only get you two gigawatts?Or does it get you five?Like, what does it get you?It's a great question.So if you look at our compute strategy, and it's crazy how fast the world has changed.So just two years ago, we were literally one.We had one CSP we worked with, Microsoft, Azure.We sat on one chip, NVIDIA.We had one product, ChatGPT.One price point, $20 a month.So I often use a Rubik's Cube as kind of my metaphor.So we were like one cube in the bottom.Today, if you look at our strategy, it's been to go, first of all, multiple CSPs.CSPs, because what CSPs do for us, in effect, is they shift CapEx into OpEx.So you pay as you get the revenue, so as you're actually utilizing the data centers.So in effect, we are writing somewhat on their ability to build CapEx and financing.So today, we sit on top of every CSP, Oracle, CoreWeave, Microsoft, GCP, AWS, and a bunch of small neoscalers.On the chip side, we've also gone for a program of being multi-chip, because we want to make sure you're always on the frontier.I think if you're only on one chip, there's just inherently a moment where you can't be on the frontier, because there's some leapfrogging that happens.
还是说你们自己出一部分？有多少可以用债务融资？1000 亿美元的融资是只够两吉瓦，还是能撑到五吉瓦？它到底能买到什么？这是个好问题。你看我们的算力战略——世界变化之快简直离谱。就在两年前，我们真的只有「一」：一个合作的云服务商（CSP），微软 Azure；坐在一种芯片上，英伟达；一个产品，ChatGPT；一个价位，每月 20 美元。所以我常用魔方（Rubik's Cube）来打比方，我们当时就像是底层的一个小方块。今天再看我们的战略：首先是多云服务商。CSP 对我们的作用，实际上是把资本开支（capex，先花大钱建资产）转成运营开支（opex，用多少付多少）——你是在拿到收入、真正用到数据中心的时候才付钱。所以某种程度上我们是搭在他们做资本开支和融资的能力上。所以今天我们坐在每一家 CSP 之上：Oracle、CoreWeave、微软、GCP、AWS，还有一批小的新兴云厂商（neoscalers）。在芯片这一侧，我们也走了多芯片路线，因为我们要确保自己始终处在前沿。我认为如果你只绑一种芯片，就必然会有某个时刻你没法待在前沿，因为总会发生某种蛙跳式的反超。
[24:17] Sarah Friar
So today, NVIDIA remains our absolute priority partner.They have the frontier chip.Our next big trading run in the fall will be done on Vera Rubens.We're really excited about that.And now we're plotting kind of the Feynman series that's coming.But we also now have chips in the pipeline from AMD.Cerebris is already online.It's been an incredible low-latency chip, great for devs, for example, that want real-time coding.And there's our own chip that we're working on with Broadcom.And then beyond that, there's other ways we've diversified.So now think about that RubikCube.It's become much more multidimensional, and it allows us to effectively utilize investment-grade CSPs in order to be able to go fast and push it back to be more OpEx, not CapEx.Now, we are starting to shift gears into more of a built-to-suit type environment.We announced a data center we're building with SoftBank Energy down in Texas.That's the beginning of something that's beyond a CSP.There's a little bit more CapEx required there.And then finally, I think, as the world progresses, remember, we've done all that just in two years.The reason I like a Rubik's Cube is, again, please chat GPT this, but I think a Rubik's Cube has something like a quintillion different forms it can come up with.
所以今天，英伟达仍然是我们绝对的首选合作伙伴，他们有前沿芯片。我们今年秋天的下一次大规模训练会跑在 Vera Rubin 上，我们对此非常期待。现在我们也在筹划即将到来的费曼（Feynman）系列。但我们同时也有来自 AMD 的芯片在管线里。Cerebras 已经上线了，它是一款低延迟表现极好的芯片，比如对想要实时写代码的开发者就特别合适。还有我们和博通（Broadcom）一起在做的自研芯片。除此之外，我们还有其他的多元化方式。所以再想想那个魔方，它已经变得多维得多了，这让我们能有效地利用投资级（investment-grade）的云服务商，从而跑得快、并且把开支尽量推回到运营开支而不是资本开支。现在我们也开始换挡，走向更多「定制自建」（built-to-suit）的模式。我们宣布了和软银旗下能源公司（SB Energy）在得州合建的一座数据中心，那是超越 CSP 模式的一个开始，那里需要多一点资本开支。最后我想说，随着世界继续演进——记住，我们这一切都是在短短两年里做到的。我喜欢用魔方这个比喻还有个原因：你可以让 ChatGPT 帮你查一下，我记得一个魔方大概有百亿亿（quintillion）种不同的组合形态。
[25:31] Sarah Friar
And so it just gives us a lot of optionality.So remember what I said, my job is maximum optionality.And in a moment where I'm not yet an investment-grade type of entity where I can go get lower-cost debt financing, being able to work with partners to do that is really important.Do you think that in five years from now, the stack is just merged together?
所以它给了我们很大的选择空间。记得我说过的：我的工作就是把选择权最大化。而在我们还不是一个「投资级」主体、还拿不到更低成本债务融资的这个阶段，能和合作伙伴一起把这件事做成，就非常重要。你觉得五年之后，这个技术栈会不会就整个融在一起了？
[25:52] Chamath Palihapitiya
What do I mean?In traditional or historical markets, you'd have NVIDIA sell the chips, but that's all they do.And then you'd have Microsoft just run a cloud.That's all they would do.And then you would have a consumer app.That's all you would do.But now we see everybody doing everything.You guys have silicon that you're spinning.You have models that you make.You may or may not eventually decide that you need to be some form of a neocloud yourself.If you look at NVIDIA, they have incredible silicon, but they also have their own open-source models.They're increasingly becoming an off-taker.Google is a cloud company first, but they also have a chip.Now they have models.So it's all merging.If that continues to happen, does that make the competitive landscape simpler or easier?
我什么意思呢？在传统的、或者说历史上的市场里，英伟达卖芯片，他们就只干这个；微软就只跑一朵云，就只干这个；然后有一个消费级应用，你就只干那个。但现在我们看到的是所有人都在干所有事。你们自己在做芯片，你们做模型，你们可能最终也会决定自己要成为某种形态的新型云（neocloud）。你看英伟达，他们有极强的芯片，但也有自己的开源模型，而且越来越成为算力的承购方（off-taker）。Google 首先是家云公司，但他们也有芯片，现在也有模型。所以一切都在融合。如果这种融合继续下去，竞争格局是会变得更简单，还是更容易？
[26:38] Sarah Friar
I mean, I think where everyone is trying to make sure they reside is the layer that is closest to the customer,where usually you take the largest portion of the profits of the ecosystem, right?No one wants to find themselves abstracted away.Absolutely.Absolutely.And so that's why today, when I think about our positioning, it comes back to where I started.Why we want to be that AI intelligence layer is because a year ago, people talked about the commoditization of the LLMs.And frankly, it's gone the opposite.Because as you start building an agentic layer, and we've all started to use this word harness, but the harness is what brings the context, the memory.I have in my codex, I have a whole ginormous memory file where it knows I'm me.It knows I'm the CFO of OpenAI.It knows how I like to write things, how I like to say things.It knows what I'm interested in.It actually also knows that I'm a mom.I'm teenagers.I mean, it just carries all this memory.And that makes the model more powerful for me.Now, think about what happens when that memory and that context is brought into an actual enterprise environment.So now, it's not just even about the data that resides there.But I always think about the intuition of, like, back when I worked on Wall Street, right?
我觉得每个人都想确保自己待在离客户最近的那一层，因为通常那一层能拿走整个生态里最大比例的利润，对吧？没人希望自己被抽象掉、被隔在后面。完全同意，完全同意。所以今天我思考我们的定位时，会回到我一开始说的：我们之所以想做那个「AI 智能层」，是因为一年前大家都在说大语言模型（LLM）会被商品化，而坦白讲，事情走向了相反的方向。因为当你开始构建 agentic 那一层——我们现在都开始用 harness（外壳/骨架，指把模型套起来、给它接上上下文和工具的那层工程）这个词——harness 带来的正是上下文和记忆。我的 Codex 里有一个巨大无比的记忆文件，它知道我是我，知道我是 OpenAI 的 CFO，知道我喜欢怎么写东西、怎么表达，知道我对什么感兴趣，它甚至知道我是个妈妈、家里有青春期的孩子。它就这样承载着所有这些记忆，这让模型对我而言更强大。那你再想想，当这份记忆和上下文被带进一个真实的企业环境里会发生什么。到那时，重点甚至已经不只是那里存着什么数据了。我总会想到一种「直觉」：我当年在华尔街工作的时候，
[27:58] Sarah Friar
There was all the data in the world that told you what a stock should do post an earnings call.But, give me one second, then you called your trader, and the trader would be like, yeah, stock's not going up, Sarah.And I'm like, what are you talking about?
世界上所有的数据都能告诉你一只股票在财报电话会之后应该怎么走。但是——等我一下——然后你打给你的交易员，交易员会说：「是啊，Sarah，这股票不会涨。」我说：你在说什么呢？
[28:12] Sarah Friar
Like, all the numbers say it did this, did this, did this.And he's like, yeah, no, but I know this fund is under pressure, and they need to sell down their book,and that is going to kill the stock for the next week.That is the intuition of an enterprise.Like, it's the best example I always think of because I came out of a financing world.But there's this intuition in every walk of life.And that's where I think the models are now getting very connected to the memory and context and intuition of your company.And that's what gets CEOs and C-suite really excited because they're like, okay, now I really see how this is going to add value to drive my revenue line, my top line.But also, you know, I can think about it as an efficiency play as well.And so back to what you're asking, I think what people want to make sure is they stay as close to that value as possible.And be flexible enough to pivot as you need to.If you want to need.We have to wrap.Yeah.But.Sorry, Jason.It's quite all right.It's been wonderful, and you've been so great with the details.One final detail question.Rapid fire.Three greatest consumer businesses of our lifetime.iPhone, Meta, Advertising Network, and Google's Advertising Network.
所有数字都说它该这样、这样、这样啊。他说：「对，但我知道有只基金现在压力很大，他们必须把仓位卖掉，这会把这只股票压死一个星期。」这就是一家企业的直觉。因为我是从金融圈出来的，所以我总拿这个当最好的例子；但其实每一行都有这种直觉。而这正是我认为模型现在正在接上的东西——接上你们公司的记忆、上下文和直觉。这就是让 CEO 和高管层真正兴奋起来的地方，他们会说：好，我现在真的看到这东西怎么给我的收入、我的营收线创造价值了；同时我也可以把它当成一个提效的手段。所以回到你的问题：我觉得大家想确保的，是自己尽可能贴近价值所在，同时保持足够的灵活性，需要转身的时候能转身。我们得收尾了。是的。但是——抱歉，Jason。完全没关系。今天太精彩了，你在细节上讲得特别好。最后一个细节问题，快问快答。我们这辈子最伟大的三个消费生意：iPhone、Meta 的广告网络、Google 的广告网络。
[29:20] Jason Calacanis
Two of those three are ad-based, and even Apple has a sprinkling of ad ads.Haven't heard you talk about ads much.People tell me they're seeing some ads in the experiment, in the free version.What is your commitment to the ad version?
这三个里有两个是靠广告的，连苹果也撒了一点广告进去。但我们没怎么听你谈广告。有人跟我说，他们在实验版、在免费版里看到了一些广告。你们对广告版本的投入决心到底是什么？
[29:32] Sarah Friar
You guys got a little trolled by Anthropic during the Super Bowl.Oh, you're going to have ads.But is ads the solution to making this free for the world?Yeah.So first of all, on the ad front, you know, we want to stick by our principles.We want to make sure that you know you're always getting the best result based on the model, not by something that was sponsored.So that has to hold true.And I think the second thing is that we'll always provide a free, a tier, sorry, an ad-free tier for people that just don't want ads.But with that said, if you took, if you took, Fiji says this really well, if, you know, Google and Meta had a baby, it would be ChatGPT.Because what you have in Google search, and by the way, we know we have at least 11% of the search market.It's a lot more because actually when you do a Google search and the page refreshes, that counts as one.In ChatGPT, when you do a whole conversation where you might ask 50 questions, that also only counts as one.So in reality, we have a much higher portion.Very high intent.That is great for advertisers because I'm effectively telling you what I'm doing, right?
你们在超级碗期间还被 Anthropic 小小地嘲了一下——「哦，你们要上广告了」。但广告是不是「让这东西对全世界免费」的解法？是的。首先，在广告这件事上，我们希望守住自己的原则：我们要确保你永远知道，你拿到的是模型给出的最好结果，而不是被赞助买来的结果，这一条必须成立。第二点是，我们会始终提供一个无广告的档位，给那些就是不想看广告的人。话虽如此，Fiji（Fiji Simo）有句话说得特别好：如果 Google 和 Meta 生了个孩子，那就是 ChatGPT。因为在 Google 搜索里——顺带一提，我们知道自己至少占了搜索市场的 11%，实际上比这多得多：你在 Google 上搜一次、页面刷新一次，就算一次；而在 ChatGPT 里，你可能在一整段对话里问 50 个问题，那也只算一次，所以实际占比要高得多。而且意图非常明确，这对广告主来说太好了，因为我等于是在直接告诉你我要干什么。
[30:36] Sarah Friar
I want really cool shoes to sit on the stage.I'm telling you what I want to go buy.In Meta's case, right, they use this, like, people like you sort of intent so they have the demographic.We have more than that because we have memory, right?
我想要一双很酷的鞋，好穿着上台——我是在告诉你我要买什么。而在 Meta 那边，他们用的是「和你相似的那类人」这种意图推断，所以他们有的是人群画像。我们比这更多，因为我们有记忆。
[30:50] Sarah Friar
I just told you it knows who I am.So imagine putting memory and context next to intent.You should have a very potent ad platform, which gives you an ability to offer up massive access to the world writ large because now you can pay for it.And I think back to a question you asked, Freeberg, like, if you look at the revenue per token right now, if I was optimizing only for today, I would give every token to the API.Right.Every token to the API.Order of magnitude more than to the consumer.However, I told you we're playing our own game.We have a strategy where we believe there's an AI infrastructure layer, a utility like electricity.And in a future state, you'll want to be able to serve the world writ large.Consumers, small businesses, large enterprises, governments.That's our strategy.Ladies and gentlemen, the CFO of OpenAI, Sarah Fryer.Well done.Fabulous.Good job.
我刚说了，它知道我是谁。所以想象一下，把记忆和上下文摆在意图旁边，你就应该能得到一个非常强大的广告平台——而这又让你有能力把巨大的可及性提供给整个世界，因为现在有人替它买单了。这也回到你之前问的一个问题，Friedberg：如果看现在每个 token 的收入，假如我只为今天做最优化，我会把每一个 token 都给 API。对，每个 token 都给 API，那比给消费者高一个数量级。但是，我跟你们说过，我们打的是自己的那盘棋。我们的战略是相信存在一个 AI 基础设施层，像电力一样的公用事业；而在未来的状态里，你会希望能服务整个世界——消费者、小企业、大企业、政府。这就是我们的战略。女士们先生们，OpenAI 的首席财务官，Sarah Friar。精彩。太棒了。干得漂亮。
[31:53]
Bye.
再见。