章节 1：2026 年展望——代理（Agents）与缩小能力差距

📝 本节摘要：

本节作为访谈的开篇，Vinod Khosla 提出了关于 AI 发展阶段的核心论点：2025 年是“氛围编码”（vibe coding）的成熟期，而 2026 年将是“代理”（Agents）和多智能体系统产生实质性影响的一年。讨论涵盖了从企业级应用（如自动化 ERP 系统）到消费者端（如复杂的旅行规划）的转变。Sarah Friar 进一步指出，当前的挑战在于“缩小能力差距”——即虽然用户已拥有强大的工具（好比法拉利），但尚在学习如何驾驭它，主要任务是将 AI 从单纯的问答机器人转化为能够执行具体任务的“任务工作者”。

[原文] [Andrew Mayne]: Hello, I'm Andrew Mayne, and this is the OpenAI Podcast. Today, our guests are Sarah Friar, CFO of OpenAI, and legendary investor Vinod Khosla of Khosla Ventures. In this discussion, we're going to talk about the state of the AI ecosystem, whether or not we're in a bubble, and how startups and investors can succeed as AI progresses.

[译文] [Andrew Mayne]: 大家好，我是 Andrew Mayne，这里是 OpenAI 播客。今天的嘉宾是 OpenAI 的首席财务官 Sarah Friar，以及 Khosla Ventures 的传奇投资人 Vinod Khosla。在本次讨论中，我们将探讨 AI 生态系统的现状，我们是否处于泡沫之中，以及随着 AI 的进步，初创企业和投资者如何取得成功。

[原文] [Vinod Khosla]: Unlike something like Netflix, where they're running so many hours in the day, I think of it much more like infrastructure, like electricity. Demand is limited, not by anything other than availability of compute today.

[译文] [Vinod Khosla]: 不像 Netflix 那样，人们一天只能看那么几个小时，我认为它（AI）更像是基础设施，就像电力一样。今天的需求仅受限于算力的可用性，而不受其他任何限制。

[原文] [Vinod Khosla]: I think the conversation we need to have is, what will people do? 2025 was about agents and vibe coding. Now it's 2026. What's the story of 2026? I think we matured in vibe coding in 2025. I don't think we've matured in agents.

[译文] [Vinod Khosla]: 我认为我们需要进行的对话是，人们将会做什么？2025 年是关于代理（agents）和氛围编码（vibe coding）的一年。现在是 2026 年。2026 年的故事是什么？我认为我们在 2025 年已经在氛围编码上成熟了。但我不认为我们在代理方面已经成熟。

[原文] [Vinod Khosla]: So agents, especially multi-agent systems, will mature to the point of having real visible impact. Whether you're an enterprise and you have multi-agent systems doing full tasks, like running an ERP system for you, doing all the reconciliation every day, accruals every day, tracking contracts every day.

[译文] [Vinod Khosla]: 因此，代理，特别是多智能体系统（multi-agent systems），将成熟到产生真实可见影响的程度。无论你是企业，拥有多智能体系统执行完整的任务，比如为你运行 ERP 系统（企业资源计划系统），每天进行所有的对账，每天处理应计项目，每天追踪合同。

[原文] [Vinod Khosla]: I think that on the enterprise side. But today, on the consumer side, you know, it's still a hassle to plan a trip. That's a multi-agentic thing that looks across a lot of different things from your food preferences to the restaurant reservation to airline schedules to your personal calendar. Those will start to mature, I think, a year from now. So I'm pretty excited about that.

[译文] [Vinod Khosla]: 我认为这是在企业方面。但在今天，在消费者方面，你知道，规划一次旅行仍然是个麻烦事。那是一个多智能体的事情，需要跨越很多不同的事物，从你的饮食偏好到餐厅预订，到航班时刻表，再到你的个人日历。我认为一年后这些将开始成熟。所以我对此非常兴奋。

[原文] [Vinod Khosla]: I think models in robotics and real world models that go well beyond robotics, like general intuition, will all start to happen in the next year. So I think those are areas to look for.

[译文] [Vinod Khosla]: 我认为机器人领域的模型，以及远超机器人范畴的现实世界模型，比如通用直觉，都将在明年开始出现。所以我认为这些是值得关注的领域。

[原文] [Vinod Khosla]: There's usual functions like memory in LLMs, continual learning in LLMs, reduction of the impact of hallucinations. Those are all areas I could go on. There's half a dozen areas in which AI doesn't do as well today that will start to be addressed.

[译文] [Vinod Khosla]: 还有一些常规功能，比如大语言模型（LLMs）中的记忆功能，大语言模型中的持续学习，以及减少幻觉（hallucinations）的影响。这些领域我还可以继续列举。大概有半打领域是 AI 目前做得还不够好，但将开始得到解决的。

[原文] [Sarah Friar]: Yeah. And I think at its baseline, what Vinod is saying is 26 is the beginning of closing this capability gap. So what we know is we've handed people massive intelligence, right? We've handed them the keys to the Ferrari, but they are only learning how to take it out on the road for the first time.

[译文] [Sarah Friar]: 是的。我认为从根本上说，Vinod 的意思是 2026 年是开始缩小这种能力差距的一年。我们所知道的是，我们已经把巨大的智能交给了人们，对吧？我们把法拉利的钥匙交给了他们，但他们才刚刚开始学习如何把它开上路。

[原文] [Sarah Friar]: We need to give consumers more and more easy ways to go from ChatGPT is just a chat bot call and response. Most people use it today just to ask questions. But how do we take it towards being a true task worker that books that trip for them or helps them get a second opinion on what they just heard from their doctor or enables them to create a menu for their diabetic child, right?

[译文] [Sarah Friar]: 我们需要给消费者提供越来越多简单的方法，让 ChatGPT 从仅仅是一个一问一答的聊天机器人转变过来。今天大多数人只是用它来问问题。但我们如何让它成为一个真正的任务工作者，为他们预订那次旅行，或者帮助他们对刚从医生那里听到的话获取第二意见，或者让他们能为患糖尿病的孩子创建一份菜单，对吧？

[原文] [Sarah Friar]: How do we help them really move from simple questions into actual outcomes that make my life better? And then on the enterprise side, it's that same continuum. How do we close the capability gap?

[译文] [Sarah Friar]: 我们如何帮助他们真正从简单的问题转向能够让生活变得更好的实际成果？然后在企业方面，这也是同一个连续体。我们如何缩小能力差距？

[原文] [Sarah Friar]: One of the things we know from our state of the enterprise AI and the enterprise report that our chief economist put out at the end of last year is on the frontier versus just even the median corporation. The average number of messages or the median is about 6x, which will tell you that 6x the usage from a company that's already on the frontier.

[译文] [Sarah Friar]: 我们从去年年底首席经济学家发布的《企业 AI 现状》报告中了解到的一件事是，关于前沿企业与中位数企业的对比。消息数量的平均值或中位数大约是 6 倍，这告诉你，一家已经在前沿的公司其使用量是（普通公司的）6 倍。

[原文] [Sarah Friar]: And we know that frontier isn't even pushed to its max. So for us, it's this focus of how do we help consumers move along that continuum to true agentic task working?

[译文] [Sarah Friar]: 而我们知道，即使是前沿也还没有被推到极限。所以对我们来说，重点在于我们如何帮助消费者沿着这个连续体移动，迈向真正的代理式任务工作？

[原文] [Sarah Friar]: And then for enterprises, how do we create a much more sophisticated, vertically specialized outcome for enterprises that allows them to go from maybe a very simple ChatGPT implementation the whole way to something that's transforming the most important part of their business.

[译文] [Sarah Friar]: 然后对于企业，我们如何为企业创造更复杂、垂直专业化的成果，让他们能够从可能非常简单的 ChatGPT 部署，一路发展到能够改变其业务最重要部分的某种东西。

[原文] [Sarah Friar]: For a healthcare provider, it might be their drug discovery process. For a hospital, it might be the time to admit a patient to get that patient back into the community. For a really large retailer, it might be just larger basket sizes, higher conversion rates, and much happier customers. So it's the basics of closing that capability gap.

[译文] [Sarah Friar]: 对于医疗保健提供商来说，这可能是他们的药物研发过程。对于医院来说，这可能是从接收病人到让病人回到社区的时间。对于一家超大型零售商来说，可能仅仅是更大的购物篮规模（客单量）、更高的转化率和更满意的客户。所以这就是缩小能力差距的基础。

[原文] [Vinod Khosla]: So I might add one other perspective. We've talked about the number of areas in which the technology will advance and capability will advance. I would venture to guess today, of the people using AI, whether it's personal or enterprise, some single digit percentage are even using 30% of the capability of the AI.

[译文] [Vinod Khosla]: 我想再补充一个视角。我们谈到了技术将进步和能力将提升的许多领域。我敢猜测，今天在使用 AI 的人中，无论是个人还是企业，只有个位数的百分比甚至用到了 AI 能力的 30%。

[原文] [Vinod Khosla]: So this percentage of people who are using 30% or 50%, let alone 80% of the AI's capabilities, will keep increasing. I think that's a 10-year journey before people learn to use AI.

[译文] [Vinod Khosla]: 因此，使用 30% 或 50%（更不用说 80%）AI 能力的人群比例将会持续增加。我认为在人们学会使用 AI 之前，这是一段为期 10 年的旅程。

请告诉我是否需要继续输出第二章节？

章节 2：采用曲线与医疗领域的深层变革

📝 本节摘要：

Vinod Khosla 深入剖析了“能力曲线”与“采用曲线”的区别，指出目前用户仅触及了 AI 潜力的冰山一角，这正如早期移动互联网用户未能预见到 Uber 或移动支付的出现一样。随后对话转向医疗领域，探讨 AI 如何将“专业知识商品化”，从而彻底改变医疗成本结构。尽管 66% 的美国医生已在使用 ChatGPT 辅助工作，且 AI 在病例分析上展现出超越人类的“百科全书式”能力（如 Sarah Friar 提到的罕见病诊断案例），但监管机构（如 FDA 和 AMA）的限制仍然是全面应用的主要障碍。

[原文] [Vinod Khosla]: I've seen this, some people kind of pundits confuse adoption curves for capability curves. And that's come up where you've seen people- So that's the point I'm making. And it's a force multiplier because today we have over 800 million using, ChatGPT today, 800 million consumers weekly using. But that number should be in the billions.

[译文] [Vinod Khosla]: 我见过这种情况，一些所谓的专家混淆了采用曲线（adoption curves）和能力曲线（capability curves）。这也是你看到人们——这就是我要表达的观点。它是一个力量倍增器，因为今天我们有超过 8 亿人在使用，ChatGPT 目前有 8 亿消费者每周在使用。但这个数字应该是数十亿。

[原文] [Vinod Khosla]: And then what percentage use are they using it for? It's like we've just turned electricity on in the home. We've wired up the home and they've turned on the lights, but they have no idea that they could now heat their home. They could cook, they could curl their hair, right? There's so many things you now can do.

[译文] [Vinod Khosla]: 然后他们使用的比例是多少呢？这就像我们刚刚在家里接通了电源。我们给房子布好了线，他们打开了灯，但他们根本不知道现在可以用它来给房子取暖。他们可以做饭，可以烫头发，对吧？现在有很多事情可以做。

[原文] [Vinod Khosla]: An analogy I've used is that email didn't really get much better between 1990 and the year 2000. Neither did mobile, but usage went way up. And the problem wasn't like, well, we need better email. We need more better mobile. It's like people just need to learn all the things they could use it for.

[译文] [Vinod Khosla]: 我用过的一个类比是，电子邮件在 1990 年到 2000 年之间并没有真正变好多少。移动通信也是如此，但使用量却大幅上升。问题并不是说，我们需要更好的电子邮件，我们需要更好的移动通信。而是人们只需要学会他们可以用它来做的所有事情。

[原文] [Sarah Friar]: Right, yeah. And in a more sophisticated way, like mobile is always one that's interesting to me because when mobile took off, people just took their desktop websites and turned them into mobile. And they were really hard to scroll, but I guess you at least had them in your pocket.

[译文] [Sarah Friar]: 对，是的。而且以一种更复杂的方式来看，移动通信对我来说一直很有趣，因为当移动通信起飞时，人们只是把他们的桌面网站变成了移动版。它们真的很难滚动浏览，但我猜你至少把它们装进了口袋里。

[原文] [Sarah Friar]: But then you realized you had a GPS. So now you could have Uber and now you could do things with location or you had a camera at your fingertips. OK, so now, yeah, I can take photographs of all my friends, but I can also snap, you know, a check and deposit it into my bank account. Although we should fix the whole paper check thing. But that's an aside.

[译文] [Sarah Friar]: 但后来你意识到你有了 GPS。所以现在你可以拥有 Uber，现在你可以利用位置做事情，或者你指尖就有了一个摄像头。好的，所以现在，是的，我可以给所有的朋友拍照，但我也可以拍一张支票并将其存入我的银行账户。虽然我们需要解决整个纸质支票的问题。但这只是题外话。

[原文] [Sarah Friar]: It still seems like I can just take a photo of this and now I get money in my bank account. Yeah. But, you know, that all existed in the minute mobile was available to us. But just the ability for human ingenuity to come to work on it.

[译文] [Sarah Friar]: 看起来我只需拍张照片，钱就到我的银行账户了。是的。但是，你知道，这在移动设备可用的那一刻就已经存在了。但这只是人类智慧开始在上面发挥作用的能力。

[原文] [Andrew Mayne]: So I think you're right. I don't even know if we need more intelligence than we have today to vastly increase outcomes. But, of course, the models are going to keep getting more intelligent as well. You mentioned health, and that's one of the really kind of high stakes things we think about when it comes to just probably the most important thing.

[译文] [Andrew Mayne]: 所以我认为你是对的。我甚至不知道我们要大幅提高成果是否真的需要比今天更多的智能。但是，当然，模型也会继续变得更加智能。你提到了健康，这是我们在考虑最重要的事情时，真正涉及高风险的领域之一。

[原文] [Andrew Mayne]: And it's kind of fascinating to think about that just, you know, a few years ago, we got ChatGPT and we're using it for very simple applications. And now we're trusting with HIPAA compliant data. Do you look at that as sort of a marker of how fast or how well things have been accelerating? Are there other ones like that you think about to say, OK, now we know we're some new level?

[译文] [Andrew Mayne]: 想想看这真有点迷人，就在几年前，我们有了 ChatGPT，我们只用它来做非常简单的应用。而现在我们正信任它处理符合 HIPAA（健康保险流通与责任法案）标准的数据。你是否将其视为事情加速有多快或多好的一个标志？你是否还想到了其他类似的标志，让你觉得，好的，现在我们知道我们达到了某个新水平？

[原文] [Vinod Khosla]: Health is clearly one of those areas I've long believed will revolutionize health by making expertise be a commodity in all areas of health. The problem with health is regulatory. So first, there's constraints on what AI can do. And AI can't legally write a prescription, even if it's better than human beings at writing a prescription.

[译文] [Vinod Khosla]: 显然，通过将专业知识在所有健康领域变成一种商品（commodity），健康是我长期以来相信会发生革命性变化的领域之一。健康领域的问题在于监管。所以首先，AI 能做什么受到限制。AI 在法律上不能开处方，即使它在开处方方面比人类做得更好。

[原文] [Vinod Khosla]: That is not only the FDA, but it's actually beyond the FDA into the American Medical Association, institutionally controls that function. So they will be incumbent resistance in a lot of areas. I think we can talk about it if you like. But diagnosing is still a constraint because the FDA controls that. There's no AI approved as a medical device yet.

[译文] [Vinod Khosla]: 这不仅是 FDA（美国食品药品监督管理局），实际上还超出了 FDA，涉及控制该职能的机构——美国医学会（American Medical Association）。所以在很多领域会有既得利益者的阻力。如果你愿意，我们可以谈谈这个。但诊断仍然是一个限制，因为 FDA 控制着它。目前还没有 AI 被批准为医疗设备。

[原文] [Vinod Khosla]: So that all, fortunately, this administration is doing a very good job of moving quickly and taking the appropriate level of risk. So I'm pretty pleased to see what's happening there. On the health front, we see in our data 230 million people every week ask ChatGPT a health question. 66% of U.S. physicians say they use ChatGPT in their daily work.

[译文] [Vinod Khosla]: 幸运的是，这届政府在快速行动和承担适当风险方面做得很好。所以我很高兴看到那里发生的事情。在健康方面，我们在数据中看到每周有 2.3 亿人向 ChatGPT 咨询健康问题。66% 的美国医生表示他们在日常工作中使用 ChatGPT。

[原文] [Sarah Friar]: I'll tell you at a personal level, my brother is an HDU doctor in the U.K. So his job is, right, you hit the ER, they don't know how to triage you, so they send you to him. You kind of don't want to show up to him. He's expected to have. He's very good, though. He's very good at what he does, but it means you're not in good shape.

[译文] [Sarah Friar]: 我可以在个人层面上告诉你，我的哥哥是英国的一名 HDU（高度依赖病房）医生。所以他的工作是，当你到了急诊室，他们不知道如何对你进行分诊，就会把你送到他那里。你不太会想见到他。虽然他被期望拥有……他非常优秀。他在他的工作上非常出色，但这意味你的状况不太好。

[原文] [Sarah Friar]: But he's expected to have almost an encyclopedic knowledge of every disease that ever existed. So I always give the example, he works in Aberdeen in Scotland. If you showed up with malaria, he will not think of that. That is not in his pattern recognition. And yet, that could have happened. I don't know. You went on vacation somewhere. You got bitten by a mosquito.

[译文] [Sarah Friar]: 但他被期望拥有对每一种曾存在的疾病几乎百科全书式的知识。所以我总是举这个例子，他在苏格兰的阿伯丁工作。如果你出现疟疾症状，他不会想到那个。那不在他的模式识别中。然而，那是有可能发生的。我不知道。你去某个地方度假了。你被蚊子咬了。

[原文] [Sarah Friar]: boom, you're showing up in an ER room in Aberdeen. What ChatGPT can do or what the model can do is really act as a great augmentation to the doctor, which is why I think 66% of them are using it. And that number is only growing, right? You know, it's probably already much higher.

[译文] [Sarah Friar]: 嘣的一声，你出现在阿伯丁的急诊室里。ChatGPT 能做的，或者模型能做的，实际上是作为医生的强大增强工具，这就是为什么我认为 66% 的医生都在使用它。而且这个数字只会在增长，对吧？你知道，现在可能已经高得多了。

[原文] [Sarah Friar]: And so I think it's just a great example of where something like health, we're getting the benefit of our doctors being able to have always the latest research in front of them, always the known interactions, say, between someone's drug regime and what they're living through and experiencing as individuals. But it also puts some independence back into consumers' hands.

[译文] [Sarah Friar]: 所以我认为这只是一个像健康这类领域的绝佳例子，我们从中受益，医生能够随时获得最新的研究成果，随时了解已知的相互作用，比如某人的药物治疗方案与他们作为个人正在经历的生活状况之间的关系。但这同时也把一些独立性交还到了消费者手中。

[原文] [Sarah Friar]: So now I get the opportunity to, ahead of time, do some research on what my symptoms might be saying so I can have a much more educated conversation with my doctor. It allows me to maybe get a second opinion or know that I want to go ask for a second opinion. It also, we go very fast to, you know, these extreme places. But just even things like, hey, I've got 20 minutes a day to exercise. I know I'm suffering from type 1 diabetes. What could I do in 20 minutes?

[译文] [Sarah Friar]: 所以现在我有机会提前做一些研究，了解我的症状可能意味着什么，这样我就能与医生进行更有见地的对话。这让我可能获得第二意见，或者知道我想要去寻求第二意见。而且，我们很快就会谈到那些极端的情况。但即使是一些小事，比如，嘿，我每天有 20 分钟锻炼时间。我知道我患有 1 型糖尿病。在这 20 分钟里我能做什么？

[原文] [Sarah Friar]: Or my daughter has an interesting issue with the food she eats. And so it used to be a super just frustrating thing to go to a restaurant even because we'd have to almost ask the server so many questions. And now we can photograph a menu, chat suggests what are likely the best dishes for her to order. And then we can have a bit more of a terser conversation, but a bit more productive on what's going to work.

[译文] [Sarah Friar]: 或者我女儿在饮食方面有个棘手的问题。以前去餐厅甚至是一件非常令人沮丧的事情，因为我们几乎不得不问服务员很多问题。而现在我们可以拍下菜单，聊天机器人会建议她点哪些菜最好。然后我们可以进行更简短但更有成效的对话，确认哪些菜是可行的。

[原文] [Sarah Friar]: And it has just changed how we think about just eating. Takes it away from all about the food to why we're going out for dinner together. And so I think there are all these just examples of something like health. It's already happening and it's going to keep getting better and better. And then to Vinod's point, I think regulatory environment is going to have to catch up.

[译文] [Sarah Friar]: 这改变了我们对饮食的看法。把重点从仅仅关注食物转移到了我们为什么一起出去吃饭。所以我认为这些都是健康领域的例子。它已经在发生，而且会变得越来越好。然后回到 Vinod 的观点，我认为监管环境必须跟上。

[原文] [Sarah Friar]: It's no matter what kind of system you're under, the cost of medical care is exceeding the GDP of every country, the rate at which increases. And it seems like we needed AI. We needed it now. And, you know, it can be helpful. And as you pointed out, it's the first time the cost of medical intelligence has dropped year over year.

[译文] [Sarah Friar]: 无论你在什么样的体制下，医疗保健成本的增长速度都超过了每个国家的 GDP 增长速度。看起来我们需要 AI。我们现在就需要它。而且，你知道，它会很有帮助。正如你指出的，这是医疗智能的成本第一次逐年下降。

请告诉我是否需要继续输出第三章节？

章节 3：算力经济学——OpenAI 的商业魔方战略

📝 本节摘要：

在本节中，OpenAI 首席财务官 Sarah Friar 揭示了算力投入与收入增长之间的“硬核”相关性（例如 2 吉瓦算力对应超 200 亿美元营收），并解释了为何必须为 2028 年后的需求提前布局基础设施。她提出了精彩的“魔方理论”（Rubik's Cube）：OpenAI 的战略构建在基础设施（多芯片/多云）、产品（Sora/ChatGPT）和商业模式（订阅/广告/企业版）三个维度上。通过“旋转魔方”，公司可以灵活组合不同要素（如低延迟芯片+代码生成+高阶订阅，或图像生成+广告模式）以最大化战略期权。Vinod Khosla 补充强调，当前的需求几乎是无限的，唯一的限制因素仅仅是算力的供给，而非价格。

[原文] [Andrew Mayne]: But that comes with a lot of demand for compute. And we have a lot more questions, you know, that we want to have answered. And certainly people can see the need for more compute, but the scale and scope at which OpenAI is investing in compute is incredibly huge. You know, we're talking, you know, numbers that are just really hard to fathom. How does OpenAI determine that need? You know, what are the metrics you're looking at to think that, like, yes, we need to spend this much?

[译文] [Andrew Mayne]:但这伴随着对算力的巨大需求。而且我们有很多问题想要得到解答。当然，人们能看到对更多算力的需求，但 OpenAI 在算力上的投资规模和范围是极其巨大的。你知道，我们要讨论的数字简直让人难以置信。OpenAI 是如何确定这种需求的？通过看哪些指标让你们觉得，是的，我们需要花这么多钱？

[原文] [Sarah Friar]: So, first of all, we are trying to make sure we stay investing in compute to match the pace of our revenue. And we've seen a really strong correlation between in-period compute and in-period revenue. I'll give you an example. If you just go back in 23, 24, and 25, our compute was 200 megawatts, 600 megawatts, when it ended last year at 2 gigawatts.

[译文] [Sarah Friar]: 首先，我们试图确保我们在算力上的投资与我们的收入增长步伐相匹配。我们看到了当期算力与当期收入之间存在非常强的相关性。我举个例子。如果你回顾 23、24 和 25 年，我们的算力分别是 200 兆瓦、600 兆瓦，而去年年底达到了 2 吉瓦。

[原文] [Sarah Friar]: Against that, and it's really easy because the numbers match up, we exited 23 at 2 billion in ARR. So 200 megawatts, 2 billion. We exited 24 at 6 billion. So 6 billion, 600 megawatts. And we exited last year a little over 20 billion, 20 billion, two gigawatts. Actually, it's been accelerating. So that's just even if you look at the slope of the line, it says more compute, more revenue.

[译文] [Sarah Friar]: 对比之下，这很容易看出来，因为数字是匹配的：我们以 20 亿美元的年度经常性收入（ARR）结束了 23 年。所以是 200 兆瓦对应 20 亿。我们以 60 亿结束了 24 年。所以是 60 亿对应 600 兆瓦。而在去年结束时，我们的收入略高于 200 亿，也就是 200 亿对应 2 吉瓦。实际上，这一直在加速。所以，即使你只看这条线的斜率，它也说明了：更多的算力意味着更多的收入。

[原文] [Sarah Friar]: Now, there is definitely a timing mismatch because I have to make decisions today about making sure we have compute in not even 26 or 27, but 28, 29 and 30. Because if I don't put in orders today and don't give the signal to create data centers, it won't be there, right? Today, we feel absolutely constrained on compute. There are many more products that we could launch, many more models that we would train, many more multimodality things we would explore if we had more compute today.

[译文] [Sarah Friar]: 当然，这里确实存在时间错配，因为我今天必须做出决定，以确保我们在 28、29 和 30 年（甚至不是 26 或 27 年）有算力可用。因为如果我今天不下订单，不发出建立数据中心的信号，到时候算力就不会存在，对吧？今天，我们感觉在算力上绝对受到了限制。如果我们今天有更多算力，我们可以推出更多产品，训练更多模型，探索更多多模态的东西。

[原文] [Sarah Friar]: So, for example, even in the last year, I think the overall hardware investments globally has gone up by something like $220 billion. That's just how much actual spending has gone up. If you look at chips, chip forecasts have gone up similarly about $334 billion. So it's not just OpenAI. The signal from the whole environment is AI is real. We are in a paradigm shift. We need to invest to give people the intelligence they need to do all the things we just talked about, for example.

[译文] [Sarah Friar]: 所以，举个例子，即使在去年，我认为全球硬件总投资也增加了大约 2200 亿美元。这只是实际支出的增长量。如果你看芯片，芯片的预测也类似地增加了大约 3340 亿美元。所以这不仅仅是 OpenAI。来自整个环境的信号是：AI 是真实的。我们要处于一个范式转移中。我们需要投资，以便为人们提供他们所需的智能，来做我们刚才讨论的所有事情。

[原文] [Sarah Friar]: So back inside of OpenAI, we do spend a lot of time going very deep on what is our demand signal in consumer, in enterprise, in developers. We think about what's the mosaic first at the base, like on an infrastructure layer, how do we create max optionality? So we want to be multi-cloud, multi-chip, and that gives us an interesting layer at the infrastructure layer.

[译文] [Sarah Friar]: 回到 OpenAI 内部，我们确实花了很多时间深入研究我们在消费者、企业和开发者方面的需求信号是什么。我们首先考虑底层的拼图，比如在基础设施层，我们如何创造最大的选择权（optionality）？所以我们想要实现多云、多芯片，这在基础设施层给了我们一个有趣的层面。

[原文] [Sarah Friar]: One tick up at the product layer, we also want to become more multidimensional. So we used to just be one product, ChatGPT. Today we are ChatGPT for consumer with all of the blades inside it, healthcare and so on. ChatGPT for work, but we also have Sora as a new platform. We have some of our transformational research projects.

[译文] [Sarah Friar]: 往上一层是产品层，我们也想变得更加多维。以前我们只有一个产品，ChatGPT。今天我们有面向消费者的 ChatGPT，里面包含了所有细分功能（blades），比如医疗保健等。还有面向工作的 ChatGPT，但我们也有作为新平台的 Sora。我们还有一些变革性的研究项目。

[原文] [Sarah Friar]: One tick up, we also then have a business model ecosystem that's becoming much more multidimensional. Began with a single subscription because we'd launched ChatGPT and we needed a way to pay for the compute. We now have multiple price points. First ChatGPT subscriber, by the way. I love you for that. Multiple subscriptions. We went to the enterprise and had SaaS-based pricing. We have credit-based pricing now for places where high value is being found. People want to pay more to get more. We're beginning to think about things like commerce and ads.

[译文] [Sarah Friar]: 再往上一层，我们的商业模式生态系统也变得更加多维。最开始只有一个单一的订阅，因为我们推出了 ChatGPT，我们需要一种方式来支付算力费用。现在我们有多个价位。（Andrew 插话：顺便说一句，我是第一个 ChatGPT 订阅者。）我因此爱你。多种订阅。我们进入了企业市场，采用了基于 SaaS 的定价。对于发现高价值的地方，我们现在有基于积分（credit-based）的定价。人们愿意付更多钱来获得更多。我们开始考虑像商业和广告这样的事情。

[原文] [Sarah Friar]: And then, of course, longer term, I like models like, for example, would we do licensing models to really align? Let's say in drug discovery, if we licensed our technology, you have a breakthrough. That drug takes off and we get a licensed portion of all its sales. It's great alignment for us with our customer. So if you think about those three tiers, I actually think of it like a Rubik's Cube. So we went from a single block, one CSP, Microsoft, one chip, one product, one business model, to now a whole three-dimensional cube.

[译文] [Sarah Friar]: 然后，当然从长远来看，我喜欢一些模式，比如，我们会不会做授权模式来真正实现利益一致？比方说在药物研发中，如果我们授权了我们的技术，你取得了突破。那种药火了，我们从其所有销售额中获得一部分授权费。这对我们和客户来说是非常好的利益一致。所以如果你考虑这三个层级，我实际上把它看作一个魔方（Rubik's Cube）。所以我们从单一的积木块——一个云服务提供商（Microsoft）、一种芯片、一个产品、一种商业模式——变成了现在一个完整的三维立方体。

[原文] [Sarah Friar]: And one of the things I love about a Rubik's Cube, I'm probably not getting the number exactly right, but I think it has 43 quintillion different states it can be in. It always blew my mind when I was in university. So now just think about that cube spinning. So we pick a low latency chip going alongside something like coding that's 5x the pace that people expect. We can charge a high end subscription for that. So it's almost like you line up the cube and you get three colors on one side.

[译文] [Sarah Friar]: 关于魔方我喜欢的一点是，我可能记不太清确切数字，但我认为它有 4300 亿亿（quintillion）种不同的状态。上大学时这一点总是让我感到震撼。所以现在想象一下转动那个魔方。我们选择一款低延迟芯片，配合像代码生成这样速度是人们预期 5 倍的东西。我们可以为此收取高端订阅费。这就好比你把魔方对齐，在一面上凑齐了三种颜色。

[原文] [Sarah Friar]: We could spin the cube again and say low latency chip, faster image gen, more free users come in. But that creates more inventory for ultimately perhaps an ads platform. So you can start to see how the goal in the last 12 months has been creating more and more strategic options that allow me to keep paying for the compute we need to really achieve our mission, AGI for the benefit of humanity.

[译文] [Sarah Friar]: 我们可以再次转动魔方，比如低延迟芯片，更快的图像生成，吸引更多免费用户进来。但这为最终可能的广告平台创造了更多的库存。所以你可以开始看到，过去 12 个月的目标一直是创造越来越多的战略选项，让我能够持续支付我们需要真的实现使命——造福全人类的通用人工智能（AGI）——所需的算力费用。

[原文] [Sarah Friar]: So, you know, the way to simplify that, demand is limited not by anything other than availability of compute today. Whether it's Sora or more broadly. And then there's price elasticity, where demand is infinite for compute. So I think that's the way to think about it. We haven't even started to exercise the price elasticity lever. It's just we can't fulfill demand. And it's limited by compute.

[译文] [Sarah Friar]: 所以，你知道，简单来说，今天的需求仅受限于算力的可用性，而不受其他任何限制。无论是 Sora 还是更广泛的领域。而且还存在价格弹性，算力的需求是无限的。所以我认为这就是思考它的方式。我们甚至还没有开始动用价格弹性这个杠杆。只是我们无法满足需求。而这受限于算力。

请告诉我是否需要继续输出第四章节？

章节 4：破除“泡沫”论——从股价波动到真实生产力

📝 本节摘要：

针对外界对 AI “泡沫”的质疑，Vinod Khosla 提出了犀利的观点：泡沫通常由贪婪与恐惧驱动的股价来衡量，但衡量技术真实价值的唯一指标应该是 API 调用量（即实际使用需求），正如当年互联网泡沫期间流量并未减少一样。Sarah Friar 随后以 OpenAI 财务部门为例，生动演示了 AI 如何替代繁重的人力工作（如合同审查），不仅避免了单纯的“加人”策略，还提升了工作的战略价值。Vinod 补充了 Slash 等初创公司的案例，指出虽然生产力革命已经发生，但目前仅分布在少数前沿企业中（“未来已来，只是分布不均”），大规模的普及才刚刚开始。

[原文] [Vinod Khosla]: So all the people talking about bubbles and things, I think, are on the wrong track. They have no sense of how large this change is and how much more demand elasticity there's a need for API calls.

[译文] [Vinod Khosla]: 所以我认为所有那些谈论泡沫之类的人，都走错了方向。他们根本没意识到这种变化有多大，以及对 API 调用的需求弹性有多大。

[原文] [Andrew Mayne]: As one of OpenAI's earliest investors, you made a bet early on. You saw where this was headed, but you saw the dot-com bubble. You watched what happened there, but you've also seen other things, the mobile revolution. You've seen this happen with other areas. And you mentioned the term broad. And is that sort of where your conviction comes from, is just how many different areas it touches?

[译文] [Andrew Mayne]: 作为 OpenAI 最早的投资者之一，你很早就下了注。你看到了它的发展方向，但你也经历过互联网泡沫。你看着那里发生了什么，但你也见过其他事情，比如移动革命。你见过这种情况在其他领域发生。而且你提到了“广泛”这个词。这是否就是你信念的来源，仅仅因为它触及了那么多不同的领域？

[原文] [Vinod Khosla]: Look, when we invested, we had one simple metric. There was no projections to look at, no product plans to look at, no ChatGPT to look at. It was very simply the idea. If we develop anywhere near close to human intelligence, let alone supersede human intelligence, its impact is going to be huge. So it was this hand-wavy approach, like the consequences of success are really going to be consequential. So why not try that?

[译文] [Vinod Khosla]: 听着，当我们投资时，我们只有一个简单的指标。没有预测可以看，没有产品计划可以看，没有 ChatGPT 可以看。这仅仅是一个想法。如果我们开发出的东西接近人类智能，更不用说超越人类智能，它的影响将是巨大的。所以这是一种很笼统的方法，就像是成功的后果真的会非常重大。所以为什么不试试呢？

[原文] [Vinod Khosla]: There's also this funny notion of bubble. people equate bubble to stock prices, which has nothing to do with anything other than fear and greed among investors. So I always look at bubbles should be measured by the number of API calls. Or in the dot-com bubble, which people refer to, it should be amount of internet traffic, not by what happened to stock prices because somebody got overexcited or underexcited and in one day they can go from loving NVIDIA to hating NVIDIA because it's overvalued.

[译文] [Vinod Khosla]: 还有一个关于泡沫的可笑观念。人们把泡沫等同于股价，而股价除了反映投资者的恐惧和贪婪外，与任何事情都无关。所以我总是认为泡沫应该用 API 调用数量来衡量。或者在人们提到的互联网泡沫中，应该用互联网流量来衡量，而不是看股价发生了什么，因为有人可能过度兴奋或过度悲观，一天之内他们就可以从热爱 NVIDIA 变成讨厌 NVIDIA，只因为它被高估了。

[原文] [Vinod Khosla]: Those gyrations aren't reality. The reality is the underlying number of API calls. If you look at internet traffic during the dot-com bubble, prices may have gone up violently and gone down violently. There's no bubble detected in internet traffic. I would almost guarantee you, you won't see the bubble in number of API calls. And if that's your fundamental metric of what's the real use of your AI, usefulness of AI, demand for AI, you're not going to see a bubble in API calls. What Wall Street tends to do with it, I don't really care.

[译文] [Vinod Khosla]: 那些波动不是现实。现实是底层的 API 调用数量。如果你看看互联网泡沫期间的互联网流量，价格可能剧烈上涨又剧烈下跌。但在互联网流量中检测不到泡沫。我几乎可以向你保证，你不会在 API 调用数量中看到泡沫。如果那是你衡量 AI 真实用途、AI 有用性、AI 需求的基本指标，你就不会在 API 调用中看到泡沫。至于华尔街怎么处理它，我真的不在乎。

[原文] [Vinod Khosla]: I think it's mostly irrelevant. Great for press articles because press has to fill their column inches, but it's not reality. So prices of things aren't reality or stock prices, private company valuation. The reality is what's the actual demand for AI, which is the number of API calls.

[译文] [Vinod Khosla]: 我认为这大多是无关紧要的。这对新闻文章来说很棒，因为媒体必须填满他们的版面，但这不是现实。所以事物的价格、股票价格或私有公司估值都不是现实。现实是 AI 的实际需求是多少，也就是 API 调用的数量。

[原文] [Sarah Friar]: Right. And I think if I hark back to that moment where you were looking at 1999, The value people were getting from the internet at the time was actually very, it was so young, so nascent that you couldn't really see how it was changing their lives. I do think that with AI, it's happened so fast, that change. It's very real.

[译文] [Sarah Friar]: 对。如果我回想你提到的 1999 年那个时刻，当时人们从互联网获得的价值实际上非常……它是如此年轻，如此初级，以至于你真的看不出它如何改变人们的生活。我确实认为对于 AI，这种变化发生得太快了。它是非常真实的。

[原文] [Sarah Friar]: Like as a CFO, forget about being the CFO of OpenAI, but as a CFO, what I see happening in my organization is truly taking tasks that previously I would have kept having to add more and more people doing fairly mundane things. Like let's take something like revenue management. So in a team that does revenue management, one of the things they do every day is they have to download all the contracts that we signed the day before or through the week.

[译文] [Sarah Friar]: 比如作为一名 CFO——忘掉我是 OpenAI 的 CFO 这回事——仅仅作为一名 CFO，我在我的组织中看到的是真正接管了那些以前我不得不通过不断增加人手来处理的相当琐碎的任务。比如拿收入管理来说。在一个负责收入管理的团队中，他们每天要做的一件事就是必须下载我们前一天或一周内签署的所有合同。

[原文] [Sarah Friar]: And they have to read all of those contracts to make sure there's no terms sitting in it that are unexpected, that are effectively non-standard terms. Because a non-standard term means that there could be a revenue recognition change that has to happen. And that's a very big deal for a finance team. That's the number one thing usually your auditors come in to audit you on.

[译文] [Sarah Friar]: 他们必须阅读所有这些合同，以确保里面没有意料之外的条款，也就是实际上非标准的条款。因为一个非标准条款意味着可能需要进行收入确认上的变更。这对财务团队来说是一件非常重要的大事。这通常是审计师来审计你的第一件事。

[原文] [Sarah Friar]: The pace at which we are growing, right, that number of contracts every day is going up in multiples. So my only choice in a pre-AI world would have been hire more people. And imagine what those people's jobs are like. You come to work every day and you read a contract and then you read the next one and the next one. It is so mundane and such drudgery. And it's not why people went to school and learned about the accounting field or thought about being a finance professional. But that's kind of the job we hand them as an entry-level job.

[译文] [Sarah Friar]: 考虑到我们的增长速度，对吧，每天的合同数量都在成倍增加。所以在前 AI 时代，我唯一的选择就是雇佣更多的人。想象一下那些人的工作是什么样的。你每天来上班，读一份合同，然后读下一份，再读下一份。这太单调了，简直是苦差事。这不是人们去上学学习会计领域或想成为财务专业人士的原因。但这却是我们交给他们的入门级工作。

[原文] [Sarah Friar]: Today, using our own tools here at OpenAI, I now have overnight, all of those contracts are pulled out of a system. They are put into a tabular database, the Databricks database in our case. The agent or the intelligence is able to go through. It shows me exactly what is non-standard and why. It suggests what, therefore, the rev-rec is.

[译文] [Sarah Friar]: 今天，在 OpenAI 内部使用我们自己的工具，我可以让所有这些合同在一夜之间被从系统中提取出来。它们被放入一个表格数据库中，在我们的案例中是 Databricks 数据库。代理或智能系统能够检查一遍。它准确地向我展示什么是其非标准的以及原因。它建议收入确认（rev-rec）应该是什么。

[原文] [Sarah Friar]: But it also suggests the insight, which is, you know, should this term even be here? Did the salesperson just give away something they shouldn't have? In which case, you know, I go and I coach them. Is it actually telling me something about my business that's starting to shift? In which case, this non-standard term actually should become a standard term. And I'm actually, what I'm experiencing is a shift in my business model.

[译文] [Sarah Friar]: 但它也提供了洞察，比如，你知道，这个条款甚至应该出现在这里吗？是不是销售人员刚刚放弃了他们不该放弃的东西？如果是这种情况，你知道，我会去指导他们。还是说这实际上告诉我关于我业务的一些正在开始转变的事情？如果是这种情况，这个非标准条款实际上应该变成标准条款。而我实际上经历的是商业模式的转变。

[原文] [Sarah Friar]: which might actually be a good thing, or perhaps I want to find a different way to help get the customer what they're looking for, the salesperson what they're looking for, but maintain my revenue recognition, my current business model, right? So I know my more junior entry-level people are over on the right of that discussion, and they're kind of refinding the job they loved. That to me is why it's not a bubble, because the value is real and tangible.

[译文] [Sarah Friar]: 这实际上可能是一件好事，或者也许我想找到一种不同的方式来帮助客户得到他们想要的，帮助销售人员得到他们想要的，但同时维持我的收入确认和当前的商业模式，对吧？所以我知道我的那些初级员工现在处于那个讨论的正确一侧（更有价值的一侧），他们可以说重新找到了他们热爱的工作。这对我来说就是为什么这不是泡沫的原因，因为价值是真实且有形的。

[原文] [Sarah Friar]: It also means I probably can have a smaller team. I can have a much more high performing team, a much higher morale on my team, better retention rates. All of these I can put into numbers to say my business is now healthier. And I think that's the piece when the press is trying to lead with the bubble conversation or whatever. They just miss that we are investing with demand, if anything, behind demand at the moment.

[译文] [Sarah Friar]: 这也意味着我可能只需要一个更小的团队。我可以拥有一支绩效高得多的团队，团队士气高得多，保留率更好。所有这些我都可以量化成数字，说明我的业务现在更健康了。我认为这就是当媒体试图引导泡沫话题时所缺失的部分。他们只是忽略了我们是在随需求投资，如果说有什么的话，目前投资甚至落后于需求。

[原文] [Vinod Khosla]: A bubble to me suggests you're investing ahead of demand and there's going to be a gap. And you look at productivity numbers, they're going up in the companies that are adapting AI, especially the newer set of tech oriented companies. The numbers are just absolutely amazing. So one of my favorites is a little company called Slash. About 150 million ARR. They have one person in accounting, only a controller, because they adapted an AI-oriented ERP system. They replaced NetSuite with it.

[译文] [Vinod Khosla]: 对我来说，泡沫意味着你在需求之前投资，并且将会出现缺口。而你看看生产力数据，在那些适应 AI 的公司里，尤其是较新的科技导向型公司，数据正在上升。这些数字简直令人惊叹。我最喜欢的例子之一是一家叫 Slash 的小公司。大约 1.5 亿的年度经常性收入（ARR）。他们在会计方面只有一个人，只有一个财务总监，因为他们采用了以 AI 为导向的 ERP 系统。他们用它替换了 NetSuite。

[原文] [Vinod Khosla]: But it's just amazing what they can do. And the CEO was apologizing to me. He might have to hire a second person. And they're moving really rapidly. I just saw a story someone replaced 10 SDRs with one SDR in AI, essentially that the one SDR remaining supervises.

[译文] [Vinod Khosla]: 但他们能做的事情简直令人惊叹。那位 CEO 还在向我道歉，说他可能不得不雇佣第二个人。他们发展得非常快。我刚看到一个故事，有人用一个 AI 销售开发代表（SDR）替换了 10 个 SDR，实际上剩下的那一个 SDR 负责监督。

[原文] [Vinod Khosla]: I've been hearing two stories about where instead of hiring somebody that's in an area that doesn't create growth, they can now then, when they hire, hire people that are creating a lot more growth for the company. And that's why you're seeing a lot of these tech companies just build so fast. You know that old phrase, the future is here now, but it's not evenly distributed.

[译文] [Vinod Khosla]: 我一直听到两种故事，与其雇佣某个不能创造增长领域的人，他们现在可以在招聘时，雇佣那些能为公司创造更多增长的人。这就是为什么你看到很多这类科技公司发展得如此之快。你知道那句老话，“未来已来，只是分布不均”。

[原文] [Vinod Khosla]: Yes. I see all these single points of huge productivity gains and efficiency gains or agility gains, the ability to move faster. But very small percentage of the people in the world or in the U.S. or worldwide have adapted these or even know they exist. And so this issue back to demand, I think these ideas, some of these examples will spread to everybody over time. And you'll see an exponential growth of adoption of these technologies. That's why I don't think demand is the question.

[译文] [Vinod Khosla]: 是的。我看到了所有这些巨大的生产力提升、效率提升或敏捷性提升（更快行动的能力）的单点案例。但在美国或全世界，只有极小比例的人适应了这些或者甚至知道它们的存在。所以回到需求这个问题，我认为这些想法、其中一些例子会随着时间推移传播给每个人。你会看到这些技术的采用呈指数级增长。这就是为什么我不认为需求是个问题。

[原文] [Sarah Friar]: Yeah. Vinod is absolutely spot on. I think McKinsey did a study that showed for companies that are more in the top quartile, their productivity as measured by any kind of financial metric you would pull is up in the 27 to 33 percent. Like that's a really meaningful jump.

[译文] [Sarah Friar]: 是的。Vinod 说得完全正确。我想麦肯锡做过一项研究，显示对于那些处于前四分位数的公司来说，无论你用哪种财务指标来衡量，他们的生产力都提高了 27% 到 33%。那是一个真正有意义的飞跃。

[原文] [Sarah Friar]: I think where you were going is it doesn't just mean fewer employees overall. There's definitely a place to kind of shift people over into more growth oriented jobs. I was hiking this weekend with someone who runs a very large consulting company that you all would know of. And he was talking about how his and his what he thinks of more his back end systems. The leader there is now talking about her organization as people plus agents.

[译文] [Sarah Friar]: 我认为你想表达的是，这并不一定意味着员工总数减少。肯定有一个空间可以将人们转移到更多以增长为导向的工作中。这个周末我和一个经营着一家你们都知道的大型咨询公司的人去徒步旅行。他在谈论他的后端系统。那里的领导者现在把她的组织描述为“人加代理”。

[原文] [Sarah Friar]: And she has a one to five ratio, one person to five agents. But on the front end, they're actually back out rehiring to grow because clients need more help now to think about deploying AI. So it's actually shifting back, I would say, to the jobs people want to do, not the jobs that maybe were just open to them because more and more of the world had become this kind of, you know, so much information that people were parsing it. Now we're finally back to a machine and agent intelligence parsing it.

[译文] [Sarah Friar]: 她有一个 1 比 5 的比例，一个人对应五个代理。但在前端，他们实际上又开始重新招聘以实现增长，因为客户现在需要更多帮助来考虑如何部署 AI。所以我认为这实际上是在转回人们想要做的工作，而不是那些以前不得不做的工作，因为世界变得充斥着如此多的信息，需要人们去解析它。现在我们终于回到了由机器和代理智能来解析它的状态。

请告诉我是否需要继续输出第五章节？

章节 5：商业模式之辩——广告、信任与订阅制未来

📝 本节摘要：

随着对话深入，话题转向了消费者关心的商业模式。针对引入广告可能引发的信任危机，Sarah Friar 强调 OpenAI 95% 的用户为免费用户，符合“造福全人类”的使命，并承诺广告将与模型生成的“最佳答案”严格区分。关于订阅制，她预测未来用户会像订阅多种媒体一样同时订阅多个 AI 模型（即“多栖”现象），但模型的“记忆”功能将是留住用户的关键壁垒。最后，Sarah 提出了一个精妙的类比：AI 不会像 Netflix 那样受限于用户每天的闲暇时间，而是像“电力”一样作为基础设施融入生活的方方面面，因此其需求是全天候且无限的。

[原文] [Andrew Mayne]: I want to touch back on the consumer side. You mentioned ads. And certainly the argument we made that with ads, you can increase the benefits to people. You can provide more services, more AI.

[译文] [Andrew Mayne]: 我想回过头来谈谈消费者方面。你提到了广告。当然，我们提出的论点是，通过广告，你可以增加给人们带来的利益。你可以提供更多的服务，更多的 AI。

[原文] [Andrew Mayne]: You can help pay for the compute and people get more out of those tiers with that. But that brings up the question, though, of trust. And when people think about AI initially even asking questions, people worried about what does ChatGPT do with my information? Once you have ads in play, people worry about that because it's often just a big question of how does that affect the rest of the product and the org?

[译文] [Andrew Mayne]: 你可以借此支付算力费用，而人们也能从中获得更多层级的服务。但这引发了信任问题。最初人们考虑 AI 甚至只是问问题时，就会担心 ChatGPT 会如何处理我的信息？一旦引入广告，人们就会担心这个，因为通常这只是一个关于它如何影响产品其他部分和整个组织的大问题。

[原文] [Sarah Friar]: Yeah. So I think you started in the right place, which is today 95 percent of our users use our platform for free on the consumer side. And that's absolutely where our mission is, right? AGI for the benefit of humanity, not the benefit of humanity who can pay, right? So access is very important.

[译文] [Sarah Friar]: 是的。我认为你的切入点很对，那就是今天在消费者端，我们 95% 的用户是免费使用我们平台的。这绝对是我们使命的所在，对吧？造福全人类的 AGI（通用人工智能），而不是只造福那些付得起钱的人类，对吧？所以这种可访问性非常重要。

[原文] [Sarah Friar]: From an ads perspective, I think, number one, we have to just make sure everyone understands you're always going to get the best answer the model can provide you, not the paid for answer. And I think other platforms have fallen back into that where you're not sure is this a sponsored link or is this truly the best outcome? We have a North Star, which is that the model will always give you the best answer.

[译文] [Sarah Friar]: 从广告的角度来看，我认为，第一，我们必须确保每个人都明白，你永远会得到模型能提供的最佳答案，而不是被付费推广的答案。我认为其他平台已经退化到了那种地步，你不确定这是赞助链接还是真正的最佳结果？我们有一个北极星指标，那就是模型将始终给你最佳答案。

[原文] [Sarah Friar]: I think the second thing to understand is that there can be a lot of utility in ads. So we want to make sure people know when it is an ad that they're working with. But for example, if I do a search for a weekend getaway to pick your favorite city, I don't know, San Diego, an ad for Airbnb might actually be very helpful.

[译文] [Sarah Friar]: 我认为第二点需要理解的是，广告其实可以很有用。所以我们要确保人们知道他们正在接触的是广告。但举个例子，如果我搜索周末去你最喜欢的城市度假，比如圣地亚哥，Airbnb 的广告实际上可能会非常有帮助。

[原文] [Sarah Friar]: And you might even want to have a discussion with the ad or with the advertiser in that case in a ChatGPT setting that's very rich, but you're clear that it's in an advertising setting. And I think this is where there has to be more innovation on what feels endemic to the platform, not just kind of the old world of stick banner ads on things.

[译文] [Sarah Friar]: 在这种情况下，你甚至可能想在 ChatGPT 的场景下与广告或广告商进行讨论，这种体验是非常丰富的，但你会清楚这是在广告场景中。我认为这就是需要在什么感觉是平台原生（endemic）的方面进行更多创新的地方，而不仅仅是那种到处贴横幅广告的旧世界做法。

[原文] [Sarah Friar]: And I think the third and final thing for me is, again, there always has to be a tier where advertising doesn't exist. So we give the user some choice and some control. But we're very mindful of your data. When we released Health, we were very clear your data is off to one side. It's not being used to train on and so on. And I think we just need to keep giving users that kind of that trust is everything for OpenAI and that we're going to stand by those principles, even when it comes to things like ads.

[译文] [Sarah Friar]: 我认为对我来说第三点也是最后一点是，同样，必须始终存在一个没有广告的层级。这样我们给用户一些选择和控制权。但我们非常在意你的数据。当我们发布健康（Health）功能时，我们非常明确地表示你的数据是被隔离在一边的。它不会被用来训练等等。我认为我们只需要继续给用户这种感觉，即信任对 OpenAI 来说就是一切，即使涉及到像广告这样的事情，我们也会坚守这些原则。

[原文] [Andrew Mayne]: On the consumer side, is it going to be a world where you're going to have a lot of subscriptions to different AI services?

[译文] [Andrew Mayne]: 在消费者方面，这会不会变成一个你要订阅许多不同 AI 服务的世界？

[原文] [Vinod Khosla]: I think you'll have every model. Most people will have more than one subscription. Media is a good example. Most people have more than one subscription media. And so that's a good proxy for consumer behavior. Different people will pick different choices, including free choices, which is ad-supported media too. So even the same services you can get for pay or for free. I think you'll see a wide range of diversity.

[译文] [Vinod Khosla]: 我认为你会拥有各种模型。大多数人会有不止一个订阅。媒体就是一个很好的例子。大多数人有不止一个媒体订阅。所以那是消费者行为的一个很好的参照。不同的人会做出不同的选择，包括免费的选择，也就是广告支持的媒体。所以即使是同样的服务，你也可以付费获得或免费获得。我认为你会看到广泛的多样性。

[原文] [Sarah Friar]: How do you think about, though, the expense of going to a different platform? So I like ChatGPT memory. I'm finding it more and more helpful because as I ask about one thing, it remembers something we talked about maybe weeks ago, months ago.

[译文] [Sarah Friar]: （注：此处原文 Speaker 应为 Andrew Mayne 提问）不过，你是怎么看待转换到不同平台的成本的？比如我喜欢 ChatGPT 的记忆功能。我发现它越来越有帮助，因为当我问一件事时，它记得我们可能几周前、几个月前谈论过的事情。

[原文] [Sarah Friar]: Pulse, which is today not widely distributed, but it's the way I wake up in the morning now. It's amazing. It's so amazing. And when you start connecting it to things like your calendar, so it's not just saying, you know, you say are very interested in AI data centers, which clearly it must think I'm the most boring person on earth because this is what I see a lot of.

[译文] [Sarah Friar]: Pulse（目前尚未广泛发布，但我现在早上就是用它醒来的）太棒了。真的太棒了。当你开始把它连接到像你的日历这样的东西上时，它就不只是说，你知道，你说你对 AI 数据中心很感兴趣——显然它肯定认为我是地球上最无聊的人，因为我经常看这些。

[原文] [Sarah Friar]: But it also says, hey, on your calendar, you're going to be sitting down with Vinod today. You know, remember a couple of these things like it's so helpful. But if I am multi homing, I'm losing the benefit, which is not the same as if I subscribe to the Wall Street Journal, The Economist and The New York Times. They're not really losing out if I go read in other places in the same way or I'm not losing out.

[译文] [Sarah Friar]: 但它也会说，嘿，在你的日历上，你今天会和 Vinod 坐下来谈谈。你知道，记住这几件事，这太有帮助了。但如果我同时使用多个平台（multi-homing），我就失去了这种好处，这和我订阅《华尔街日报》、《经济学人》和《纽约时报》是不一样的。如果我去其他地方阅读，它们并不会真的损失什么，或者我也不会损失什么。

[原文] [Vinod Khosla]: Yeah. So I do think memory is an important question, whether there'll be one per wear or more than one per wear of the models. On each model, there'll be multiple services that may offer different tradeoffs. So even whether you're talking health or media, even on the OpenAI models, there's multiple people providing services. So that's what I was thinking of multi-homing. But obviously, I don't think OpenAI will be 100% of the market.

[译文] [Vinod Khosla]: 是的。所以我确实认为记忆是一个重要的问题，关于每个用户是只用一个还是多个模型。在每个模型上，会有多种服务提供不同的权衡取舍。所以无论你说的是健康还是媒体，即使在 OpenAI 的模型上，也有多方在提供服务。这就是我所想的多平台使用。但显然，我不认为 OpenAI 会占据 100% 的市场。

[原文] [Andrew Mayne]: I think it's hard for people to wrap their heads around because like Netflix is a great company, but there's only so many hours on the planet that people can watch Netflix. Right. And mobile is great. Right. I only I only need so many minutes of mobile per week or whatever to do that with AI and intelligence. You can have more intelligence. I can buy more and get better answers and do this. And I think that's I think I'm still trying to wrap my head around about where where that goes.

[译文] [Andrew Mayne]: 我认为人们很难理解这一点，因为像 Netflix 是家伟大的公司，但地球上人们每天能看 Netflix 的时间只有那么多小时。对。移动设备也很棒。对。我每周只需要那么多分钟的移动设备时间，或者不管用 AI 和智能做什么。你可以拥有更多智能。我可以买更多，得到更好的答案，做这些事。我想我还试图弄清楚这会走向何方。

[原文] [Sarah Friar]: So I think unlike something like Netflix, where they're running so many hours in the day, I think of it much more like infrastructure, like electricity. How much electricity do you use in the day? I don't know. I walked into a room today and there was a fan blowing. It was really nice. It cooled it down. There are lights on around us right now. There's so many. I charged my phone overnight and it worked for me all day.

[译文] [Sarah Friar]: 所以我认为不像 Netflix 那样，人们一天只能看那么几个小时，我认为它（AI）更像是基础设施，就像电力一样。你一天用多少电？我不知道。今天我走进一个房间，有个风扇在吹。真的很舒服。它让房间降温了。现在我们周围开着灯。有很多电器。我彻夜给手机充电，它就能为我工作一整天。

[原文] [Sarah Friar]: So I think that the state we live in today is much more I call on ChatGPT, I invoke it, as opposed to intelligence just being baked in. Like I think this will be the big change over the next couple of years. You'll kind of look back almost it'll feel a little toy like that we used to do this thing. And instead it just is everywhere around us.

[译文] [Sarah Friar]: 所以我认为我们今天生活的状态更多的是我呼叫 ChatGPT，我调用它，而不是智能已经融入（baked in）其中。我认为这将是未来几年的巨大变化。你会回过头看，甚至会觉得我们以前做这件事的方式有点像玩具。取而代之的是，它就在我们周围无处不在。

[原文] [Sarah Friar]: And so it's not really quite answering the question you're asking, but it's that I don't get so caught up that there's only so many hours for people to do things. Because I feel like almost everything I do in life requires intelligence because I'm walking around, hopefully with some intelligence up here. And if I can get that augmented, I think it's going to surprise us.

[译文] [Sarah Friar]: 所以这并没有完全回答你问的问题，但我并不会纠结于人们只有那么多小时可以做事。因为我觉得我生活中做的几乎每一件事都需要智能，因为我四处走动，希望脑子里有点智能。如果我能让这种智能得到增强，我认为结果会让我们大吃一惊。

请告诉我是否需要继续输出第六章节？

章节 6：多模态交互与信息过滤的新时代

📝 本节摘要：

对话转向了 AI 的多模态能力（Multimodal）。Sarah Friar 分享了她利用 AI 视觉能力的一个“可爱”案例：拍照让 ChatGPT 朗读《经济学人》的文章。她指出，就像手机教会人类“用拇指说话”一样，AI 正在开启一个能听、能看、能说的新交互时代。Vinod Khosla 则从宏观角度补充，互联网时代导致了信息的“大爆炸”，超出了人类的处理极限；而 AI 的本质价值在于“过滤”——将无限的信息缩减为对每个人最相关的有限内容，从而极大地提升人类时间的利用效率。

[原文] [Sarah Friar]: As we were talking before we got started, you said about on your phone when you suddenly discovered you had a flashlight and a camera. It is, you say that, it's so obvious. And yet with ChatGPT, every time I discover kind of a, what feels like almost a slightly cute use case, I'm so blown away by it.

[译文] [Sarah Friar]: 正如我们在开始之前谈到的，你提到在手机上当你突然发现你有手电筒和相机时的情形。确实，你说出来觉得很明显。然而对于 ChatGPT，每当我发现一个感觉几乎有点可爱的小用例时，我都会被它深深震撼。

[原文] [Sarah Friar]: Like yesterday morning, I do love The Economist. I wanted to read the editorial. I didn't really have a ton of time because I was running upstairs to get ready. So I took a photograph of the editorial because they're very good. They put it on one page. And I asked ChatGPT to read it to me. And it did it. And I was like, oh, my God, this is awesome.

[译文] [Sarah Friar]: 比如昨天早上，我很喜欢《经济学人》。我想读那篇社论。但我真的没有太多时间，因为我正跑上楼准备出门。所以我拍了一张社论的照片，因为他们做得很好，把社论放在一页纸上。然后我让 ChatGPT 读给我听。它做到了。我当时就想，天哪，这太棒了。

[原文] [Sarah Friar]: So I just think there are all these moments where we're just getting started. And multimodal, I think, is probably the biggest because phones taught us to talk with our thumbs. And I think this new world we're moving into, there's going to be new hardware that just really help us understand that we can talk, we can listen, we can see, we can write.

[译文] [Sarah Friar]: 所以我真的认为在所有这些时刻中，我们才刚刚开始。我认为多模态（multimodal）可能是最大的变化，因为手机教会了我们用拇指说话。而我认为在这个我们正在进入的新世界里，会有新的硬件真正帮助我们理解我们可以说话、可以倾听、可以看、可以写。

[原文] [Sarah Friar]: We can do all of these things in a very human way that we're just scratching the surface of.

[译文] [Sarah Friar]: 我们可以用一种非常人性化的方式做所有这些事情，而我们目前只是触及了皮毛。

[原文] [Vinod Khosla]: So let me give you a different frame on that. I agree with all of that. If you look at what we talked about the internet earlier and the bubble associated with it, but what the internet did is give you access to a lot more stuff, whether it was media, YouTube videos, or TikTok, or you name it, information of any sort.

[译文] [Vinod Khosla]: 让我给你一个不同的视角。我同意所有这些观点。如果你看看我们早些时候谈到的互联网以及与之相关的泡沫，互联网所做的是让你能接触到更多的东西，无论是媒体、YouTube 视频、TikTok，还是你说的任何东西，任何种类的信息。

[原文] [Vinod Khosla]: But it's expanded it to the point where no human can actually use the internet fully. I think of AI as given you're limited to 8,000 some hours a day, some of which is meant for sleeping. It'll make your time much more efficient. So the internet exploded information available to you to the point where no human can use it.

[译文] [Vinod Khosla]: 但它已经扩展到了没有任何人能真正充分利用互联网的地步。我认为 AI 的作用在于，鉴于你（每年）只有 8000 多个小时（注：原文口误说成每天），其中一些还要用来睡觉，它会让你的时间变得更有效率。所以互联网让可用的信息爆炸性增长，到了没人能用得过来的地步。

[原文] [Vinod Khosla]: And I think what AI will do is filter it to make your every hour the most effective hour if you know how to use it. So intelligence will reduce the world to what is most relevant to you personally. And I may have a different set of priorities than Sarah. So I think of intelligence as summarizing the world to the most relevant things for me and the most relevant things to her, which are different.

[译文] [Vinod Khosla]: 而我认为 AI 将要做的是过滤它，如果你知道如何使用的话，这会让你的每一小时都成为最有效率的一小时。所以智能会将世界缩减为对你个人最相关的内容。我和 Sarah 可能有一套不同的优先级。所以我认为智能就是将世界总结为对我来说最相关的事物和对她来说最相关的事物，而这是不同的。

[原文] [Vinod Khosla]: So I think that's where there's almost unlimited capacity for intelligence to be used to reduce information when the Internet exploded information.

[译文] [Vinod Khosla]: 所以我认为这就是在互联网让信息爆炸之后，利用智能来减少信息拥有几乎无限能力的地方。

请告诉我是否需要继续输出第七章节？

章节 7：企业级市场与创业公司的生存空间

📝 本节摘要：

随着话题转向企业级市场，Sarah Friar 指出 OpenAI 正通过“消费者倒逼企业”（类似当年的 iPhone 效应）的策略取得胜利。她强调企业应用正从简单的全员 ChatGPT 部署，深化为与特定垂直领域（如能源公司的地震数据分析）的深度结合。针对创业者普遍的焦虑——“OpenAI 是否会挤占所有空间？”，Vinod Khosla 和 Sarah 给出了定心丸：95% 的高价值数据仍深藏在企业防火墙后。创业公司的真正机会在于处理复杂的业务流（如采购审批中的权限管理）和构建垂直领域的“护城河”，这些是通用大模型无法独自完成的。

[原文] [Andrew Mayne]: We've talked a lot about the consumer side, and it feels like OpenAI is very much winning the consumer side. Question comes up about enterprise, and how is OpenAI going to compete and win in that area?

[译文] [Andrew Mayne]: 我们已经谈了很多关于消费者方面的内容，感觉 OpenAI 在消费者方面确实是大赢家。关于企业方面的问题随之而来，OpenAI 将如何在那个领域竞争并获胜？

[原文] [Sarah Friar]: So I think we're already winning in this area. What I see is, you know, 90% of corporations are saying they either are using OpenAI or intend to use over the next 12 months, right?

[译文] [Sarah Friar]: 我认为我们在这个领域已经赢了。我看到的是，你知道，90% 的企业表示他们正在使用 OpenAI 或者打算在未来 12 个月内使用，对吧？

[原文] [Sarah Friar]: I think the second is Microsoft and Microsoft's using our technology. So I actually think we have, this is where the consumer is a really potent part of the enterprise flywheel.

[译文] [Sarah Friar]: 我认为第二点是微软，微软正在使用我们的技术。所以我实际上认为我们拥有……这就是消费者成为企业飞轮中非常有力的一部分的地方。

[原文] [Sarah Friar]: So as I said earlier, when someone, you know, you back in the day when you first started bringing your iPhone to work and corporates didn't want you to do that, you just discovered you can't say no to the tidal wave that is consumer preference.

[译文] [Sarah Friar]: 正如我之前所说，就像当年你第一次把 iPhone 带去上班，而公司不希望你那样做时，你发现你无法拒绝消费者偏好这一巨大浪潮。

[原文] [Sarah Friar]: So something I'm already using that I've already got in my pocket and I get to work, my expectation is work is at least as good, if not better. And so that's what's helped drive our actual enterprise business, the fastest company ever to get to 1 million businesses on a platform. And we did that in about a year and a half.

[译文] [Sarah Friar]: 所以既然有些东西我已经在使用，已经装在口袋里了，当我开始工作时，我的期望是工作用的工具至少要一样好，如果不是更好的话。这就是推动我们实际企业业务发展的原因，我们是历史上最快在平台上拥有 100 万家企业的公司。我们在大约一年半的时间里就做到了。

[原文] [Sarah Friar]: But where to from here? Because clearly we're just scratching the surface. So some of it is certainly meeting customers in terms of their vertical so that we talk to them in their language. And we learned this art of enterprise selling, which is let me not tell you all about my products, but let me understand your problem.

[译文] [Sarah Friar]: 但接下来去向何方？因为显然我们才刚刚触及皮毛。所以其中一部分当然是在垂直领域满足客户，以便我们用他们的语言与他们交谈。我们学会了企业销售的艺术，那就是不要让我告诉你关于我产品的一切，而是让我了解你的问题。

[原文] [Sarah Friar]: Like, what is your board forcing on you, Mr. and Mrs. CEO? What is the thing your customers most want that you can't deliver? OK, let's start putting intelligence against that. We can then drop that down into some light vertical specialization to quite heavy vertical specialization.

[译文] [Sarah Friar]: 比如，CEO 先生或女士，董事会正在强迫你们做什么？你的客户最想要但你无法提供的东西是什么？好的，让我们开始将智能应用到那上面。然后我们可以将其落实到一些轻度的垂直专业化，甚至是相当重度的垂直专业化。

[原文] [Sarah Friar]: things like RLing models that are very pertinent to a use case. Like let's say in an energy company, it might be really understanding that particular oil well or all the seismic data they have to say, what's the recovery we're going to get out of this gas field? Like that is deep specialization.

[译文] [Sarah Friar]: 比如像强化学习（RLing）模型这样与用例非常相关的东西。比方说在一家能源公司，可能需要真正理解那个特定的油井或他们拥有的所有地震数据，来判断我们将从这个气田获得多少采收率？那就是深度专业化。

[原文] [Sarah Friar]: And then I think it gets the whole way to some of these big transformational research projects that we've begun, where we're actually almost taking over someone's whole business and helping them rethink it in a smarter, faster, better way that ultimately drives their key business metrics. So it's a journey.

[译文] [Sarah Friar]: 然后我认为这会一直延伸到我们已经开始的一些大型变革性研究项目，在这些项目中，我们实际上几乎接管了某人的整个业务，并帮助他们以更智能、更快速、更好的方式重新思考它，最终推动他们的关键业务指标。所以这是一段旅程。

[原文] [Sarah Friar]: I think most corporates have started with wall-to-wall ChatGPT. That's an easy starting point. They've done some coding. And in many cases, a lot of coding. Like when I talk to corporates, CEOs are starting to say things like 60% of all my production code was built by an agent.

[译文] [Sarah Friar]: 我认为大多数企业都是从全员部署（wall-to-wall）ChatGPT 开始的。这是一个简单的起点。他们做了一些编码。在很多情况下，是大量的编码。比如当我与企业交谈时，CEO 们开始说这样的话：“我所有的生产代码中有 60% 是由代理构建的。”

[原文] [Sarah Friar]: And I'm like, you didn't even know what production code meant 12 months ago. But now you're saying that. That's good because it means you're tracking it. But on agents, it's just starting. We only see about 14% of all customers when you go out and just survey U.S. corporates are using something agentic today, 14%. When I just explained what's happening in my finance organization. So I think we are just getting going.

[译文] [Sarah Friar]: 我当时想，你 12 个月前甚至不知道生产代码是什么意思。但现在你在说这个。这很好，因为这意味着你在追踪它。但在代理方面，这仅仅是个开始。当你去调查美国企业时，我们看到只有大约 14% 的客户今天正在使用某种代理功能，只有 14%。就像我刚才解释的我的财务组织中正在发生的事情。所以我认为我们才刚刚开始。

[原文] [Sarah Friar]: But I couldn't be more excited about the opportunity. It's huge.

[译文] [Sarah Friar]: 但我对这个机会感到无比兴奋。它是巨大的。

[原文] [Andrew Mayne]: Okay. But if I'm a startup and I look at everything OpenAI is doing, I might be asking, is there room for me? What do I get to do?

[译文] [Andrew Mayne]: 好的。但如果我是一家初创公司，看着 OpenAI 正在做的一切，我可能会问，还有我的空间吗？我能做什么？

[原文] [Vinod Khosla]: Look, models will keep getting better and do more and more. But I do believe there's lots of room to build on top. You know, no one company can do everything on the planet. There's billions of people who are working whose job AI can help with. I don't think OpenAI will specialize in everyone.

[译文] [Vinod Khosla]: 听着，模型会不断变得更好，能做的也会越来越多。但我确实相信在上面有很多构建的空间。你知道，没有一家公司能做地球上的所有事情。有数十亿人在工作，AI 可以帮助他们的工作。我不认为 OpenAI 会专门针对每个人。

[原文] [Vinod Khosla]: So I think the careful thing to do is be clear where the models will go, OpenAI or others, and what they will be able to do. And how do you use that best to then specialize into a more interesting world? Some sort of specialization where you add something that's additional to the base models.

[译文] [Vinod Khosla]: 所以我认为谨慎的做法是弄清楚模型（无论是 OpenAI 还是其他人的）会走向何方，以及它们将能够做什么。然后你如何最好地利用这一点，从而专门化进入一个更有趣的世界？某种专门化，即你在基础模型之上添加一些额外的东西。

[原文] [Vinod Khosla]: And frankly, just intelligence isn't the only thing to provide a solution. There's lots of other stuff that goes around solution beyond intelligence. So I think there's lots of opportunity to build on top of these models. And the more powerful they get, the number of opportunities to add to it dramatically increases.

[译文] [Vinod Khosla]: 坦率地说，仅有智能并不是提供解决方案的唯一要素。除了智能之外，解决方案周围还有很多其他东西。所以我认为在这些模型之上构建有很多机会。而且它们变得越强大，添加到上面的机会数量就会急剧增加。

[原文] [Sarah Friar]: How do you think about, so I think a lot about use cases where there's already a lot of data that's being aggregated, perhaps by that startup, by that company, that, you know, today, I think 95% of the world's information actually sits behind corporate firewalls, university firewalls, and so on.

[译文] [Sarah Friar]: 你怎么看这个问题？我经常思考那些已经聚集了大量数据的用例，也许是由那家初创公司或那家公司聚集的。你知道，今天我认为世界上 95% 的信息实际上位于企业防火墙、大学防火墙等等之后。

[原文] [Sarah Friar]: So there's, even though we talk about the vast training that's occurred, again, we're just getting going. But I think companies that have already built businesses that have aggregated that data have access to it. And then on top of that, have managed complex workflows.

[译文] [Sarah Friar]: 所以，尽管我们谈论已经进行了大规模的训练，但我们同样才刚刚开始。但我认为那些已经建立了聚合这些数据的业务的公司拥有访问权。然后在此基础上，他们管理着复杂的工作流。

[原文] [Sarah Friar]: So I often give the example of our procurement system. Procurement system per se, not that complicated. But what it does very well is it understands things like delegation of authority. So it knows what the board has approved in terms of approval limits.

[译文] [Sarah Friar]: 所以我经常举我们采购系统的例子。采购系统本身并不那么复杂。但它做得很好的一点是它理解诸如“授权”（delegation of authority）之类的事情。所以它知道董事会在审批限额方面批准了什么。

[原文] [Sarah Friar]: So it knows that when this software contract comes in, it's over X amount, so only I can approve it. Or if it's beneath that, but it knows a VP can approve it. It doesn't know that Andrew's a VP, but it knows to touch the HRS system and check what's his level.

[译文] [Sarah Friar]: 所以它知道当这个软件合同进来时，如果金额超过 X，那么只有我能批准。或者如果低于那个金额，它知道副总裁（VP）可以批准。它虽然不知道 Andrew 是副总裁，但它知道去连接人力资源（HRS）系统并检查他的级别是什么。

[原文] [Sarah Friar]: And so the whole procurement flow can happen in a way where I have compliance and governance and hopefully makes just the whole company run faster. Those are places I get interested for startups. So where have you got access to unique data with a complex workflow?

[译文] [Sarah Friar]: 因此，整个采购流程可以在我有合规性和治理的情况下进行，并希望使整个公司运行得更快。这些是我对初创公司感兴趣的地方。那么你在哪里拥有独特的数据访问权限以及复杂的工作流？

[原文] [Sarah Friar]: It feels like there's more of a moat around that, that we want to work alongside you. But, you know, the general purpose model is not going to do all of that itself.

[译文] [Sarah Friar]: 感觉那周围有更多的护城河，我们希望与你们并肩工作。但是，你知道，通用模型不会自己做所有这些事情。

[原文] [Vinod Khosla]: Yeah, no, I completely buy that. I think there's lots of opportunity. I've seen quite a few startups around just permissioning around data.

[译文] [Vinod Khosla]: 是的，不，我完全同意。我认为有很多机会。我已经看到不少围绕数据权限管理的初创公司。

[原文] [Sarah Friar]: Yeah. Like who can do access to what information.

[译文] [Sarah Friar]: 是的。比如谁可以访问什么信息。

[原文] [Vinod Khosla]: For example, I've seen a whole bunch of startups around customizing to each company the models for their history and their priorities. And the agent, the whole identity side of agents, I think we're just starting to understand both the risk that can happen when you have agents talking to agents talking to agents, but then also how are you going to permission that and then start to think about like agentic commerce, like the complexity that's coming is also quite big.

[译文] [Vinod Khosla]: 举个例子，我看到一大堆初创公司围绕着根据每家公司的历史和优先级来定制模型。还有代理，关于代理的整个身份方面，我认为我们才刚刚开始理解当代理与代理对话再与代理对话时可能发生的风险，以及你将如何对其进行权限管理，然后开始思考像代理商业（agentic commerce）这样的事情，即将到来的复杂性也相当大。

[原文] [Vinod Khosla]: So to suggest there's no more opportunity as a startup, I think it's never been probably more interesting or fun to be a startup.

[译文] [Vinod Khosla]: 所以如果说作为一家初创公司已经没有机会了，我认为现在作为一家初创公司可能从未像现在这样有趣或好玩。

[原文] [Andrew Mayne]: Yeah. I think there's more opportunities than there have ever been. What are you looking for now? What gets you excited when you talk to a company?

[译文] [Andrew Mayne]: 是的。我认为现在的机会比以往任何时候都多。你现在在寻找什么？当你与一家公司交谈时，什么让你感到兴奋？

[原文] [Vinod Khosla]: Well, the hardest thing is great people, always. But I think the other thing that has been in short supply is agency, where people sort of have the agency to make things happen. That, again, comes down to people, but there's so much opportunity.

[译文] [Vinod Khosla]: 嗯，最难得的总是优秀的人才。但我认为另一件一直短缺的东西是能动性（agency），即人们拥有某种让事情发生的能动性。这归根结底还是关于人，但机会实在是太多了。

[原文] [Vinod Khosla]: I think traditional things like knowing a space or experiencing space is much less relevant now. It's more agency.

[译文] [Vinod Khosla]: 我认为像了解一个领域或拥有该领域经验这类传统的事情现在相关性低多了。更多的是关于能动性。

请告诉我是否需要继续输出第八章节？

章节 8：机器人技术与未来的通缩经济

📝 本节摘要：

访谈的最后部分将视线投向了更长远的未来——物理世界中的 AI。Vinod Khosla 做出惊人预测：15 年内，机器人产业的规模将超越今天的汽车产业。Sarah Friar 补充认为，机器人的“杀手级应用”可能不是复杂的叠衣服，而是解决“孤独流行病”的陪伴功能，特别是针对老龄化人口。对话在 Khosla 对“极度通缩经济”（massively deflationary economy）的构想中结束：当劳动力和专业知识的成本趋近于零，初级医疗和教育将变得免费，人类的生活水平底线将大幅提升，尽管住房成本仍是待攻克的难题。

[原文] [Andrew Mayne]: We've not talked about the whole new world of robotics and real world models and all that. That's a whole space by itself that we probably don't have time for.

[译文] [Andrew Mayne]: 我们还没谈到机器人技术和现实世界模型那个全新的领域。那本身就是一个完整的领域，我们可能没时间细聊了。

[原文] [Vinod Khosla]: Well, do we? We've got time for that. I've got plenty of time. I'd love it. I want to go there.

[译文] [Vinod Khosla]: 嗯，是吗？我们要谈那个。我有的是时间。我很乐意谈。我想谈谈那个。

[原文] [Andrew Mayne]: Yeah, because we talked about where we're headed here. And you famously talked about kind of the role of 2050 and things are moving fast. Models are getting faster and more capable. And where do you see things like robotics headed?

[译文] [Andrew Mayne]: 好，因为我们刚才谈到了未来的方向。你曾著名的谈论过关于 2050 年的角色，而且事情发展得很快。模型变得越来越快，能力越来越强。你认为像机器人技术这样的领域会走向何方？

[原文] [Vinod Khosla]: Well, I think two years ago when I gave a talk at TED, I said the robotics business, both bipedal and other robots will be a larger business in 15 years than the auto industry is today.

[译文] [Vinod Khosla]: 嗯，我想两年前我在 TED 演讲时说过，机器人产业，包括双足机器人和其他机器人，在 15 年内的规模将超过今天的汽车产业。

[原文] [Vinod Khosla]: We think of auto industry as one of the larger businesses on the planet. And this other thing will be larger. I don't think there's very many automotive companies who are thinking of the world that way. They're thinking about how to use a robot in their assembly line. Not that that business is larger than their current business, all driven by the intelligence of robots.

[译文] [Vinod Khosla]: 我们认为汽车产业是地球上最大的产业之一。而这另一个东西将会更大。我认为没有多少汽车公司是这样思考世界的。他们在想如何在装配线上使用机器人。而不是意识到那个由机器人智能驱动的业务规模会比他们现在的业务还要大。

[原文] [Vinod Khosla]: So massive opportunities for startups there. And we are seeing a lot of activities.

[译文] [Vinod Khosla]: 所以那里有巨大的创业机会。我们已经看到了很多活动。

[原文] [Sarah Friar]: Yeah. And I think sometimes we underestimate. So when you think about robots in the home, right? People, very fertile area, no one's really had a breakthrough, though. There's so many different issues around the complexity.

[译文] [Sarah Friar]: 是的。我认为有时我们要么低估了。比如当你想到家庭机器人时，对吧？这是一个非常肥沃的领域，虽然还没人真正取得突破。围绕其复杂性有很多不同的问题。

[原文] [Sarah Friar]: Actually, sometimes the more time I spend in AI, They actually, the more respect I have for the human condition in a way, because our ability to move around the world and do, you know, if you watch like the people in robotics getting so excited about a robot folding clothes, you know, perhaps my 18 year old, I'd be just as excited about.

[译文] [Sarah Friar]: 实际上，有时我在 AI 领域花的时间越多，我在某种程度上就越对人类的生存状态感到敬畏，因为我们在世界上移动和做事的能力……你知道，如果你看那些机器人领域的人对机器人叠衣服感到如此兴奋，虽然如果是我的 18 岁孩子（叠衣服），我可能也会同样兴奋。

[原文] [Sarah Friar]: But for the average human, I assume they can fold clothes. But I think the hello world of robotics. But you do get a little stuck in your head that they have to somehow be a human. But it turns out there may just be these breakthrough moments, like, for example, companionship in the home.

[译文] [Sarah Friar]: 但对于普通人来说，我假设他们会叠衣服。但这被认为是机器人技术的“Hello World”（入门测试）。但你确实容易陷入一种思维定势，认为它们必须在某种程度上像人一样。但事实证明，可能会有一些突破性时刻，比如，家庭陪伴。

[原文] [Sarah Friar]: Right. We have an aging population. What's one of the biggest? You know, we talk about epidemics in the world. Loneliness is probably one of the biggest epidemics. What does someone living alone, maybe has just lost a spouse, value most?

[译文] [Sarah Friar]: 对。我们有人口老龄化问题。最大的问题之一是什么？你知道，我们谈论世界上的流行病。孤独可能是最大的流行病之一。一个独居的人，也许刚刚失去了配偶，最看重什么？

[原文] [Sarah Friar]: Just someone to converse with in a way that feels intuitive and human. We see people using ChatGPT more and more for this conversation. But is there a humanoid-esque breakthrough where it turns out you don't need it to make coffee or full clothes or do the dishes, although that would be good too.

[译文] [Sarah Friar]: 只是一个可以交谈的对象，以一种直观且人性化的方式。我们看到人们越来越多地使用 ChatGPT 进行这种对话。但会不会有一种拟人化的突破，结果发现你并不需要它来煮咖啡、叠衣服或洗碗——尽管那样也很好。

[原文] [Sarah Friar]: But it might just be something a little bit more simple that still adds a lot of value and is just the first crawl, walk, run of this kind of future that Vinod is talking about where that whole complex is X times more valuable ever than we saw in automotives.

[译文] [Sarah Friar]: 但它可能只是某种更简单的东西，却仍然增加了巨大的价值，这只是 Vinod 所说的那种未来的“爬、走、跑”的第一步，那个整体综合体的价值将是我们以前在汽车行业看到的 X 倍。

[原文] [Andrew Mayne]: I think that it's interesting because we can sort of think of kind of like our present and put robots in places and do things like that. It's really hard to think of when you really have extremely low cost labor manufacturing, etc. And then the world you can build from there because, you know, we can look at that's a good solution for now.

[译文] [Andrew Mayne]: 我认为这很有趣，因为我们可以某种程度上基于现在来思考，把机器人放在某些地方做些事情。但真的很难想象，当你真正拥有极低成本的劳动力制造等能力时，你能以此构建一个什么样的世界。因为，你知道，我们可能会觉得那对现在来说是个好方案。

[原文] [Andrew Mayne]: But when the cost of building a wonderful state of the art assisted living facility where you can put a bunch of people together, the cost drops. I think that's the thing I have. The hardest problem is for me is to really think, what does it really mean when you lower the cost? We've lowered the cost of intelligence. What does it mean we really lower the cost of labor?

[译文] [Andrew Mayne]: 但当建造一个极好的、最先进的辅助生活设施（养老院），你可以把一群人安置在一起，而成本却下降了。我认为那就是我的困惑。对我来说最难的问题是真正去思考，当你降低成本时这真正意味着什么？我们已经降低了智能的成本。如果我们真的降低了劳动力的成本，那意味着什么？

[原文] [Vinod Khosla]: Well, my personal view, sometime probably towards the end of the next decade, you'll see a massively deflationary economy. Because labor will be near free. Expertise will be near free. Most functions will be almost zero cost.

[译文] [Vinod Khosla]: 嗯，我个人的观点是，大概在下一个十年结束时的某个时候，你会看到一个极度通缩的经济（massively deflationary economy）。因为劳动力将近乎免费。专业知识将近乎免费。大多数职能的成本将几乎为零。

[原文] [Vinod Khosla]: How it exactly plays out, a little hard to tell. How purchasing power versus production of goods and services plays out. But I expect we'll see a hugely deflationary economy at a level people aren't planning on.

[译文] [Vinod Khosla]: 具体如何演变有点难说。购买力与商品服务生产之间的关系如何演变也难说。但我预计我们会看到一个人们尚未计划到的巨大通缩经济水平。

[原文] [Vinod Khosla]: So there's social aspects of adaption of AI that hasn't been handled yet. I think the conversation we need to have is what will people do? I get asked that a lot. How will people make a living?

[译文] [Vinod Khosla]: 所以 AI 的适应还有一些社会层面尚未处理。我认为我们需要进行的对话是：人们将会做什么？我经常被问到这个问题。人们将如何谋生？

[原文] [Vinod Khosla]: I think the minimum standard of living governments can assure people is going to be much, much higher without needing to earn an income. I mean, I can't imagine much better primary care, like 10x more primary care than today, doesn't happen for a dollar a month.

[译文] [Vinod Khosla]: 我认为政府能向人们保证的最低生活标准将会大大提高，甚至不需要赚取收入。我的意思是，我无法想象如果每月只需一美元，怎么会不出现比今天好 10 倍的初级医疗保健（primary care）。

[原文] [Vinod Khosla]: I have a hard time imagining how that happens. It will be true, it costs almost nothing to have free primary care, free education. Almost AI tutors for every person, personal tutors for every child. That's already happening. So there's a set of services that will be free.

[译文] [Vinod Khosla]: 我很难想象那（如果不发生）是怎么回事。这将成为现实，拥有免费的初级医疗、免费的教育几乎不需要任何成本。几乎每个人都有 AI 导师，每个孩子都有私人导师。这已经在发生了。所以有一系列服务将会是免费的。

[原文] [Vinod Khosla]: There's some hard nuts to crack. Housing is the hard one. You know, for people in the bottom half of the U.S. population, they spend 40 some percent of their income on housing and food. So there's some hard nuts. But I do think both are addressable by robotics and better approaches.

[译文] [Vinod Khosla]: 还有一些难啃的骨头。住房就是那个难题。你知道，对于美国底层那一半的人口来说，他们 40% 左右的收入花在住房和食物上。所以确实有些难题。但我确实认为这两者都可以通过机器人技术和更好的方法来解决。

[原文] [Andrew Mayne]: Well, this has been a very interesting conversation. I'm excited to see where things are headed. Thank you both for joining us here on the podcast.

[译文] [Andrew Mayne]: 嗯，这是一次非常有趣的对话。我很兴奋看到事情将如何发展。谢谢你们两位来到播客。

[原文] [Vinod Khosla / Sarah Friar]: Thank you. Thank you.

[译文] [Vinod Khosla / Sarah Friar]: 谢谢。谢谢。