Inside OpenAI | Logan Kilpatrick (head of developer relations)

finding people who are high agency and work with urgency. If I was hiring five people today, like those are like some of the top two characteristics that I would look for in people because you can take on the world if you have people who have high agency and like not needing to get 50 people's different consensus. They hear something from our customers about a challenge that they're having and like they're already pushing on what the solution for them is and not waiting for all the other things to happen that like people just go and do it and solve the problem. I love that. It's so fun to be able to be a part of those situations. Today my guest is Logan Kilpatrick.

寻找具有高执行力并且迫切行事的人。如果我今天要招聘五个人，这两个特征是我会优先考虑的因为如果有执行力强且无需得到五十个人的不同共识的人，你就可以应对世界的挑战。当他们听到客户面临的挑战后，他们立即思考解决方案，而不需要等待其他事情的发生。他们只会主动去解决问题。我喜欢这种工作方式。能够参与其中是如此有趣。今天我邀请的嘉宾是Logan Kilpatrick。

Logan is head of developer relations at OpenAI, where he supports developers building on OpenAI's APIs and JatchyPT. Before OpenAI, Logan was a machine learning engineer at Apple and advised NASA on their open source policy. If you can believe it, ChatchyPT launched just over a year ago and transformed the way that we think about AI and what it means for our products and our lives. Logan has been at the front lines of this change and every day is helping developers and companies figure out how to leverage these new AI superpowers.

Logan是OpenAI的开发者关系负责人，在那里他支持开发者们构建基于OpenAI的API和JatchyPT。在加入OpenAI之前，Logan是苹果的机器学习工程师，并为NASA的开源政策提供建议。如果你能相信的话，ChatchyPT刚刚在一年前推出，彻底改变了我们对人工智能的看法，以及它对我们的产品和生活意味着什么。Logan一直站在这一变革的最前线，并每天都在帮助开发者和公司找到如何利用这些新的人工智能超能力的方法。

In our conversation, we dig into examples of how people are using ChatchyPT and the new and other OpenAI APIs in their work and their life. Logan shares some really interesting advice on how to get better at prompt engineering. We also get into how OpenAI operates internally, how they ship so quickly, and the two key attributes they look for in the people that they hire, plus where Logan sees the biggest opportunities for new products and new startups building on their APIs. We also get a little bit into the very dramatic weekend that OpenAI had with the board and Sam Altman and all of that, and so much more, a huge thank you to Dan Shipper and Dennis Yang for some great questions suggestions.

在我们的对话中，我们深入探讨了人们如何在工作和生活中使用ChatchyPT以及新的和其他OpenAI API的示例。Logan分享了一些关于如何在提示工程方面做得更好的非常有趣的建议。我们还讨论了OpenAI内部的运作方式，他们如何如此迅速地发布产品，以及他们在招聘员工时看重的两个关键特质，以及Logan认为建立在他们的API上的新产品和初创公司面临的最大机遇。我们还略微谈到了OpenAI与董事会和Sam Altman等人在周末发生的戏剧性事件，以及更多内容。非常感谢Dan Shipper和Dennis Yang提出的一些很棒的问题建议。

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Logan, thank you so much for being here and welcome to the podcast. Thanks for having me, Lenny. I'm super excited. I want to start with the elephant in the room, which I think the elephant is actually leaving the room because I think this is months ago at this point, but I'm still just really curious. What was it like on the inside of OpenAI during the very dramatic weekend with the board and Sam and all those things? What was it like? Is there a story maybe you could share that maybe people haven't heard about what it was like on the inside? What was going on? Yeah, it was definitely a very stressful, stressful Thanksgiving week. I think in broad context, OpenAI had been pushing for a really long time since TETCH-EBT came out, and that was supposed to be the first week, so the whole company had taken time away to actually reset and have a break. Very selfishly, I was super excited, spent time with my family, all that stuff. Then the afternoon, we got the message that all of the changes were happening.

Logan，非常感谢你在这里，欢迎来到这期播客节目。谢谢你邀请我，莱尼。我感到非常兴奋。我想先谈谈室内的大象，虽然我觉得这只大象可能已经离开了房间，因为我觉得这已经是几个月前的事情了，但我仍然非常好奇。在OpenAI内部在那个周末与董事会和Sam等人发生的非常激烈的事件中是什么感觉？当时的情况是怎样的？有什么故事可以分享的，也许是一些人还没听说过的，在内部是怎样的情况？是什么在发生？ 是的，在感恩节那周确实是非常紧张的一周。我想从宏观的背景来说，自从TETCH-EBT发布以来，OpenAI一直在努力，那本应该是第一周，所以整个公司都抽出时间来重新调整和休息。非常自私地，我当时非常兴奋，和家人在一起度过了时间，等等。然后下午，我们收到了所有这些变化正在发生的消息。

I think it was super shocking because I think, and this is a perspective a lot of folks here, everybody has had and continues to have such deep trust in Sam and Greg and our leadership team that it was just very surprising. As far as company cultures go, very transparent and very open. When there's problems, there's things going on. We tend to hear about them. Again, it was the first time that a lot of us had heard some of the things that were happening between the board and the leadership team. Very surprising.

我觉得这件事非常令人震惊，因为我认为，而且很多人都持有这种看法，大家对Sam、Greg和我们领导团队有着非常深厚的信任，所以这个情况实在是令人意外的。就公司文化而言，我们非常透明和开放。当出现问题时，大家通常会听说。再次，这是我们很多人第一次听说董事会和领导团队之间发生的事情。非常令人惊讶。

I think being someone who's not based in San Francisco, I was again, very selfishly happy that it happened over the Thanksgiving break because a lot of folks actually had gone home to different places. It felt like I had a little bit of comfort knowing I wasn't the only one, not in San Francisco because everybody was meeting up in person to do a bunch of stuff and be together during that time. It was nice to know that there was a few other folks who were who were out of the loop with me.

我觉得作为一个不在旧金山的人，我很自私地感到很开心它发生在感恩节假期，因为很多人实际上都回到了不同的地方。感觉到我并不是唯一一个不在旧金山的人，因为每个人都在当面见面，做各种事情，一起度过这段时间。知道还有一些其他人和我一样不在圈子里感觉真好。

I think the thing that surprised me the most was just how quickly everybody got back to business. I flew to San Francisco the next week after Thanksgiving, which I wasn't planning to do to deal with the team in person. And seeing literally Monday morning, I was walking into the office being expecting weird to be going on or happening or a day. And really, it was like people laser focus and back to work. And I think that speaks to the caliber of our team and everybody who's just so excited about building towards the mission that we're building towards. So I think that was the most surprising thing of the whole incident. I think a lot of companies would have had the potential to truly be derailed for some non-trivial amount of time by this. And everybody was just right back to it, which I love.

我认为最让我惊讶的事情是大家是多么迅速地回到工作状态。感恩节后的下周，我飞往旧金山，本来没有计划要亲自处理团队的事务。当我在星期一早上走进办公室时，我原以为会发生一些奇怪的事情或者发生一番混乱。但实际上，大家都非常专注，继续工作。我认为这证明了我们团队的素质，每个人都对我们正在追求的使命充满激情。所以我觉得这是整个事件中最令人惊讶的事情。我觉得很多公司可能会因此受到严重影响，需要花费相当长的时间来恢复正常。但每个人都立即回到了工作状态，这点我非常喜欢。

I feel like it also maybe brought the team closer together. It feels like it was a kind of traumatic experience that may bring folks together because it was something they all shared. Is there anything along those lines that's like, wow, things are a little different now? One of my takeaways was I'm actually very grateful that this happened when it happened. I think today the stakes are, they're still relatively high. People have built their businesses on top of OpenAI. We have tons of customers who love Chatsubiti. So if something that happens to us, we definitely impact our customers. But on the world scale, somebody else will build a model with OpenAI disappeared and continue towards this progress of general intelligence. I think fast forward, five or 10 years of something like this would have happened. And we hadn't gone through the hopeful upcoming word transformation and all those changes that are going to happen.

我觉得这也许也使团队更加团结。感觉这是一种有点创伤的经历，可能会让大家走得更近，因为这是他们共同经历的事情。有什么事情是类似的吗，让人感觉哇，现在有点不同了吗？我的一点收获是，我其实非常感激这件事发生在这个时候。我认为今天的赌注仍然相对较高。人们已经建立了基于OpenAI的业务。我们有很多喜欢Chatsubiti的客户。所以如果发生了什么事情，我们肯定会影响我们的客户。但在世界范围内，如果OpenAI消失了，其他人会建立一个模型并继续朝着普通智能的进步。我认为如果五到十年后像这样的事情发生了，而我们还没有经历过充满希望的未来改变和即将发生的所有变化，那就糟糕了。

I think it would have been a little bit, or potentially much worse of an outcome. So I'm glad that things happened when the stakes are a little bit lower. And I totally agree with you. It's like the team has been growing so rapidly over the last years since I joined. It's been crazy to think about how many new folks there are.

我认为结果可能会更糟一点，或者甚至更糟。所以我很高兴事情发生在风险较低的时候。我完全同意你的观点。就好像自从我加入团队以来，团队发展迅速。想想有多少新的成员加入，简直让人觉得不可思议。

And I really think that this really brought people together. Because most folks, historically, many of the folks when I joined, what kind of banded us all together, was the launch of GPT, the launch of GPT4. And for folks who weren't around for some of those launches, it was perhaps DevDay, from folks who were around for DevDaily, it was probably this event. So I think we've had these events that have really brought the company together cross-functionally. So hopefully all the future ones will be really exciting things like GPT5 whenever that comes and stuff like that. Awesome.

我真的认为这真的让人们团结起来。因为历史上，当我加入时，大多数人，许多人团结在一起的原因，是GPT的推出，GPT4的推出。对于那些没有参与过一些启动活动的人来说，可能是DevDay，对于那些参与了DevDaily的人来说，可能是这个活动。所以我认为我们有这些活动真正将公司跨职能部门团结在一起。希望所有未来的活动都会像GPT5这样令人兴奋，无论什么时候推出等等。太棒了。

We're going to talk about GPT5. Going in a totally different direction, what is the most mind-blowing or surprising thing that you've seen AI do recently? The things that are getting the most excited are these new interfaces around AI. The Rabbit R1, I don't know if you've seen that, but the consumer hardware device, this company called TLDRAW. I don't know if you've seen TLDRAW. I think you sketch something and then it makes it as a website. Yeah. And that's only a small piece of what TLDRAW is actually working on. But there's all of these new interfaces to interact with AI. And I think I was having a conversation with the TLDRAW folks a couple of days ago.

我们要谈论GPT5。走向完全不同的方向，你最近看到的人工智能做的最令人惊叹或意外的事情是什么？让我最兴奋的是围绕人工智能推出的这些新界面。像Rabbit R1这样的消费硬件设备，你见过吗？这家叫做TLDRAW的公司。我不知道你有没有见过TLDRAW。我觉得你可以草绘一些东西，然后它可以转化成网站。是的。这只是TLDRAW实际工作内容的一小部分。但有许多新的接口与人工智能互动。几天前我和TLDRAW的人交谈过。

It really blows my mind to think about how chat is the predominant way that folks are using AI today. And I actually think and this is my bulk case for the folks at TLDRAW. I'm super excited for them to build with their building, but they're building this infinite canvas experience. And you can imagine how, as you're interacting with an AI on a daily basis, you might want to jump over to your infinite canvas, which the AI has sort of filled in all the details and you might see a reference to a file and to a video and all of these different things. And it's such a cool way. It actually makes a lot more sense for us as humans to see stuff in that type of format than I think just listing out a bunch of stuff in chat. So I'm really, really excited to see more people.

想象聊天是如今人们主要使用人工智能的方式，实在让我大开眼界。我真的认为这正是TLDRAW公司的优势所在。我为他们能够构建这种无限画布体验感到兴奋。当你每天与人工智能互动时，你可能想跳转到无限画布，在那里AI已经填充了所有细节，你可能会看到文件和视频的参考，以及其他一切。这种方式太酷了。对于我们人类来说，在这种格式下看东西比在聊天中列出一堆东西更有意义。所以我真的很期待看到更多的人加入。

I think 2024 is the year of multimodal AI, but it's also the year that people really push the boundaries some of these new UX paradigms around AI. It's funny. I feel like chatbots, as a PM for many years, it feels like every brainstorming session we had about new features, it's like, hey, we should have built a chatbot to solve this problem. It's like the perennial like, oh, chatbot, for someone's going to suggest we do a chatbot. And now they're actually useful and working and everyone's building chatbots, a lot of them based on open AI APIs.

我认为2024年是多模态人工智能的年份，但也是人们真正推动围绕人工智能的一些新用户体验范例的边界的一年。有趣的是，作为多年产品经理，我觉得在关于新功能的头脑风暴中，每次都好像要建立一个聊天机器人来解决问题。就像一个永恒的问题，哦，聊天机器人，总有人会建议我们做一个聊天机器人。现在它们实际上是有用的，而且运作良好，每个人都在构建聊天机器人，其中很多基于开放的人工智能API。

There's not really a question there, but maybe the question I was going to get to this later is just when people are thinking about building a product like say, TL Draw, what should they think about where open AI is not going to go versus like, here's what open AI is going to do for us. We shouldn't worry about them building a version of TL Draw in the future. What's the kind of the way to think about where you won't be disrupted, essentially, by opening it, knowing also they may change their mind. That's a great question. I think like we're deeply focused on these like very, very general use cases, like the general reasoning capabilities, the general coding, the general writing abilities.

这里实际上并没有一个问题，但也许我打算稍后会提出的问题是，当人们考虑构建像TL Draw这样的产品时，他们应该考虑哪些方面是开放AI不会涉足的，而不是像这样说，开放AI对我们将会做什么。我们不应该担心他们将来会建立一个TL Draw的版本。如何想到哪些方面不会被开放AI打扰，基本上，要知道他们可能会改变主意。这是一个很好的问题。我认为我们专注于这些非常非常普遍的用例，比如一般的推理能力，一般的编码，一般的写作能力。

I think where you start to get into some of these like very vertical applications, and I think a great example of this is it's actually like Harvey. I don't know if you've seen Harvey, but it's this legal AI use case where they're building custom models and tools to help lawyers and people at that legal firms and stuff like that. And that's a great example of like, our models are probably never going to be as capable as some of the things that Harvey's doing, because like, our goal and our mission is really to solve this like very general use case. And then people can do things like fine tuning and build all their own custom UI and product features on top of that. And I think that's the, I have a lot of empathy and a lot of excitement for people who are building these very general products today.

我认为在涉及一些非常垂直的应用领域时，有一个很好的例子就是像Harvey这样的。我不知道你是否看过Harvey，但它是一个法律人工智能使用案例，他们正在构建定制模型和工具，以帮助律师和法律公司的人们等等。这是一个很好的例子，我们的模型可能永远不会像Harvey做的那样有能力，因为我们的目标和使命真的是解决这个非常普遍的用例。然后人们可以进行微调，并在此基础上构建他们自己定制的用户界面和产品功能。我对那些今天构建这些非常普遍产品的人们充满同情和激动。

I talked to a lot of developers who are building just general purpose assistance and general purpose agents and stuff like that. I think it's cool and it's a good idea. I think the challenge for them is they are going to end up directly computing against us in those spaces. And I think there's there's enough room for a lot of people to be successful. But like, it to me, like you shouldn't be surprised when, you know, we end up launching some like general purpose agent product, because like, again, we're sort of building that with GPT today and versus like, we're not going to launch like some of these like very verticalized products like we're not going to launch like an AI sales agent. Like, that's just not what we're building towards and companies who are and have some domain specific knowledge.

我和许多开发人员交谈过，他们正在构建通用助手和通用代理等产品。我觉得这很酷，是个不错的主意。我认为他们面临的挑战是最终会直接与我们在这些领域竞争。我认为有足够的空间让很多人取得成功。但是，对我来说，当我们推出一些类似通用代理产品时，你不应该感到惊讶，因为我们今天是在用GPT建立这方面的产品，而我们不会推出像AI销售代理这样的垂直产品。那不是我们努力构建的方向，而那些具有某些领域专业知识的公司可能推出这些产品。

And they're really excited about that problem space. Like, they can go into that and leverage our models and like, end up continuing to be on the cutting edge without having to like do all that R&D effort themselves. Got it. So the advice I'm hearing is get specific about use cases. And that could be either models that are tuned to be especially useful for a use case like sales, or make an interface or experience solving a more specific problem.

他们对这个问题领域感到非常兴奋。他们可以进入其中，利用我们的模型，继续保持领先地位，而不必自己进行所有的研发工作。明白了。所以我听到的建议是要具体了解使用情况。这可以是调整为特定用例（例如销售）的模型，也可以是设计解决更具体问题的界面或体验。

And I think if you're going to try and solve this like, very general, like if you're going to try to build like the next general assistant to compete with something like Chatching D, like it has to be so radically different. Like, people have to really like, wow, this is solving like these 10 problems that I have with Chatching D. And therefore I'm going to go and try your new thing. Otherwise, like, you know, we're just putting a ton of engineering efforts and research effort into making that like an incredible product.

我认为如果你要尝试解决这个问题，就要像解决一个非常普遍的问题一样，如果你要尝试建立下一个像Chatching D这样的普遍助手来竞争，就必须要有根本性的不同。就好像，人们必须真正地感到，哇，这个解决了我对Chatching D有的这十个问题。因此我会去尝试你的新产品。否则，我们只是把大量的工程努力和研究努力投入到打造一个令人难以置信的产品上。

And it's just going to be like the normal challenges of building companies. It's just hard to compete against somebody like that. Awesome. Okay, that's great. I was going to get that later, but I'm glad we touched that. I imagine that's on the minds of many developers and founders kind of along the same lines. There's a lot of talk about how chat GPT and GPTs and many of the tools you guys offer are going to make a company much more efficient.

这将会成为建立公司时面对的常规挑战。要和这种对手竞争确实很困难。太棒了。好的，这很棒。我本来打算稍后再谈到这个问题，但我很高兴我们碰到了。我想许多开发者和创始人的想法可能跟这个类似。很多人都在讨论Chat GPT和GPTs以及你们提供的许多工具将如何让公司更加高效。

They don't need as many engineers, data scientists, PMs, things like that. But I think it's also hard for companies to think about what should we actually like, what can we actually do to make our company more efficient? I'm curious if there's any examples that you can share of how companies have built to say a GPT internally to do something so that they don't have to spend engineering hours on it, or generally just used OpenAI tooling to make their business internally more efficient.

他们不需要那么多的工程师、数据科学家、产品经理之类的人才。但我觉得公司很难考虑到我们实际上应该做些什么，我们能做些什么来让我们的公司更有效率？我很好奇是否有一些例子可以分享，即公司如何内部构建GPT来做某些事情，以便他们不必花费工程师的时数，或者简单地利用OpenAI的工具使他们的业务在内部更加高效。

Yeah, that's a great question. I wonder if you can put this in the show notes or something like that, but there's a really great Harvard Business School study about, and I forgot, which consulting for a major was like, Boston Consulting or something like that, but it might have been one of the other ones. And they talk about the order of magnitude of efficiency gained for those folks who are using AI tools.

是的，这是一个很棒的问题。我想知道你是否可以把这放在节目笔记里或者类似的地方，但是有一项非常棒的哈佛商学院的研究，我忘了是哪家咨询公司，可能是波士顿咨询公司或者其他公司之一。他们谈到了那些使用人工智能工具的人所获得的效率增长的数量级。

And I think it was chat GPT specifically in those use cases that they were using, comparatively against folks who aren't using AI. I'm really excited also just as this more time passes between the release of this technology for us to get more empirical studies. Because I feel this for myself, as somebody who's an engineer today.

我认为在那些使用情况中，他们使用的是特定的GPT聊天机器人，与不使用人工智能的人相比。随着时间的推移，我对释放这种技术后我们获得更多实证研究感到非常兴奋。因为作为一个今天的工程师，我也有这种感觉。

I use chat GPT and I can ship things way faster than I would be able to. I don't have any good metrics for myself to put a specific number on it, but I'm guessing people are working on those studies right now. I think engineering is actually one of the highest leveraged things that you could be using AI to do today.

我使用聊天GPT，比我自己能够做得更快地发货。我没有任何很好的指标来准确地衡量，但我猜测人们现在正在进行这些研究。我认为工程实际上是你今天可以使用AI进行的最高效的事情之一。

I'm really unlocking probably on the order of at least a 50% improvement, especially for some of the lower hanging fruit software engineering tasks. The models are just so capable at doing that work. And it's crazy to think, and I'm guessing actually GitHub probably has a bunch of really great studies that publish around co-pilot. And you could use those as an analogy for what people are getting from chat GPT as well.

我真的觉得至少有50%的改进，特别是对于一些较为简单的软件工程任务。这些模型在完成这些工作时表现得非常出色。想想真的很疯狂，我猜GitHub可能已经发布了很多关于Co-Pilot的研究，你可以把这些作为人们从Chat GPT中获得的东西的类比。

But those are probably the highest leveraged things. I think now with GPTs, people are able to go in and solve some of these more tactical problems. I think one of the general challenges with chat GPT is it gives a decent answer for a lot of different use cases, but oftentimes it's not particular enough to the voice of your company or the nuance of the work that you're doing.

但这些可能是影响最大的事物。我认为现在有了GPT，人们能够解决一些更具体的问题。 我认为聊天GPT的一个普遍挑战是，它为许多不同的用例提供了一个合理的答案，但往往并不足够贴近您公司的声音或您正在做的工作的细微差别。

I think now with GPTs, and people who are using the teams in chat GPT and enterprise in chat GPT, I can actually build those things, incorporate the nuance of their own company and make solving those tasks much more domain specific. So we literally just launched GPTs a couple of months ago.

我认为现在有了GPT（生成式预训练模型），以及正在使用聊天GPT团队和企业GPT聊天的人，我实际上可以构建这些东西，融入他们公司的细微差别，并使解决这些任务更具领域特定性。所以我们真的只是在几个月前推出了GPT。

So I don't think there's been any good, public success stories. But I'm guessing that success is happening right now at companies. And hopefully we'll hear more about that in the months that come as folks get super excited about sharing those case studies. I'll share an example.

所以我觉得目前还没有什么好的、公开的成功故事。但我猜想一些公司正在取得成功。希望随着大家对分享这些案例研究变得非常兴奋，我们会在未来几个月听到更多相关情况。我想分享一个例子。

So I have this good friend named Dennis Yang. He works at Chime. And he told me about two things that they're doing at Chime that seem to be providing value. One is he built a GPT that helps write ads for Facebook and Google, just big gives you ideas for ads around. And so that takes a little load off the marketing team or the grow team.

我有一个好朋友叫Dennis Yang。他在Chime工作。他告诉我Chime正在做的两件事似乎提供了价值。其中一件就是他建立了一个GPT来帮助撰写Facebook和Google的广告，可以给出广告创意。这样一来，市场团队或增长团队的压力就会减轻一些。

And then he built another GPT that delivers experiment results, kind of like a data scientist with like, here's the result of this experiment. And then you could talk to it and ask for like, hey, how much longer do you think we should run this for? Or what might this imply about our product and things like that? And I think it's really like you said, is there anything else that comes to mind just like things you've heard people do just like, wow, that was a really smart way of so I get there's like engineering, copilot type tooling, is there anything else that comes to mind just to give people a little inspiration of like, wow, that's an interesting way I should be thinking about using some of these tools.

然后他建立了另一个GPT，用于提供实验结果，有点像一个数据科学家，就像，“这个实验的结果是这样的。”然后你可以和它交流，询问，“你认为我们应该再运行多久？这对我们的产品可能意味着什么？”我觉得就像你说的那样，还有没有其他一些让人印象深刻的事情，就像你听到别人做的那种聪明的方式，让我明白有像工程师的副驾驶般的工具，还有没有其他的东西让人想到，来给人一些灵感，让他们知道，哇，这是一个有趣的思考如何使用这些工具的方式。

I've seen some interesting GPTs around like the planning use cases, like you want to do like, okay, our planning for your team or something like that. There's I just actually saw some retweet it like literally yesterday. I've seen some cool like venture capital ones of like doing diligence on like a deal flow, which is kind of interesting, like getting some different perspectives. I think all of those like horizontal use cases where like you can bring in a different personality and like get perspective on different things. I think is really cool. Like I've personally used in a GPT, the private GPT that I use myself that like helps with some of the like planning stuff for different quarters and like just making sure that I'm being consistent in how I'm framing things like driving back to like individual metrics stuff that like when people do planning like they often miss and like are bad at then it's been super helpful for me to like have a GPT to like force me to think about some of those things.

我看到一些有趣的GPT用例，比如计划使用案例，比如你想要像这样做，好吧，我们的团队的计划或类似的事情。我昨天确实看到一些转发的，感觉相当酷，比如在风险投资上进行尽职调查，这有点有趣，可以获得不同的观点。我觉得所有这些横向使用案例，你可以引入不同的个性，获得不同的视角，我觉得真的很酷。我个人使用了一个私人的GPT，用于帮助我处理不同季度的计划以及确保我在构思事物时保持一致，回到个体指标等方面，通常人们在计划时常常忽视并且做得不好，这对我非常有帮助，因为有了GPT，迫使我思考一些事情。

Wait, can you talk more about this? What does this GPT do for you and how do you, what do you feed it? Yeah, there's I forgot what article I saw online, but it was like some article that was talking about like what are the best ways to like set yourself up for success in planning. And I took a bunch of the like I'll see if I can make a public after this and send you a link, but took a bunch of the examples from that and went in and put some of those suggestions into the GPT. And then when now when I do any of my planning of like I want to build this thing, I put it through and have it like generate a timeline generate all the specifics of like what are the metrics and success that I'm working for like who might be some important cross-functional stakeholders like include in the planning process, all that stuff and it's been it's been helpful.

等等，你能再详细谈谈这个吗？这个 GPT 对你来说有什么作用，你是如何，你投入什么内容的？是的，我忘了我在网上看到了哪篇文章，但是有一篇文章在讲述如何为规划成功做好准备的最佳方法。我从中挑选了一些例子，我会在之后尝试将其公开并发送给你链接，我将这些建议输入到了 GPT 中。现在，当我进行任何规划时，比如我想要建设某个东西，我都会输入进去，并让它生成一个时间表，生成所有具体的内容，比如我正在追求什么指标和成功标准，哪些跨职能利益相关者可能是重要的，需要在规划过程中包括他们的所有内容，这对我非常有帮助。

Wow, that is very cool. That would be awesome if you made a public and if we do we'll link to it and we'll make it the number one most popular GPT in the store. I love it. Going in a slightly different direction, there's this whole genre of prompt engineering. It feels like it's one of these really emerging skills. I actually saw a startup hiring a prompt engineer when I saw the startups I've invested in and I think that's gonna blow a lot of people's minds that there's this huge job that's emerging and I know the idea is this won't last forever that in theory AI will be so smart. You don't need to really think about how to be smart about asking it for things you needed to do but can you just describe this idea of what is prompt engineering this term that people might be hearing and then even more interesting and just like what advice do you have for people to get better at writing prompts for say CHAD, GPT or through the API in general?

哇，这太酷了。如果你做一个公开的GPT，那将是太棒了，我们会链接到它并让它成为商店里最受欢迎的GPT之一。我喜欢这个想法。稍微换个方向说，有这样一整个类型的提示工程。感觉这是一项新兴的技能之一。我实际上看到了一家初创公司在招聘一个提示工程师，当时我看到我已经投资的初创公司，我觉得这将让很多人大开眼界，原来会有这么一个新兴的重要工作。我知道这个想法不会永远持续下去，在理论上AI会变得如此智能，你不需要去思考如何聪明地要求它做事情了，但是你能描述一下什么是提示工程这个人们可能会听到的术语吗，还有更有趣的是你对于帮助人们更好地为CHAD、GPT或者总体API撰写提示的建议是什么？

Yeah, this is such an interesting space and I think it's like another space where I'm excited for people to do like more scientific and empirical studies about because there's like so much like gut feeling best practices that like maybe aren't actually true in a certain ways. I think the reason that prompt engineering exists and comes up at all is because the models are so inclined because of the way that they're trained to give you just an answer to the question that you asked. Crap in, crap out. If you ask like a pretty like basic question, you're gonna get a pretty basic response. They're actually the same thing. It's true for humans and you can think of a great example of this. When I go to another human and I ask like how's your day going, they say, hey, it's going pretty good. Like literally absolutely zero detail, no nuance, like not very interesting at all versus again, if you have some context with the person, if you have a personal relationship with them, I'm going to ask you, hey, Lenny, how's your day going? Like how did the last pond, Chasco, etc, etc. Like you just have a little bit more context and agency to go and answer my question.

是的，这是一个非常有趣的领域，我觉得这就像另一个空间，我很期待人们进行更多科学和实证研究，因为有很多像是凭直觉的最佳实践，可能在某种程度上并不真实。我认为晋级工程存在并被提出的原因是，模型通常倾向于根据训练方式给出对你所提问问题的答案。垃圾进, 垃圾出。如果你问一个相当基本的问题，你就会得到一个相当基本的回答。实际上，这对人类也是一样的。你可以想象一个很好的例子。当我去问另一个人说，你今天过得如何，他们会说，嘿，还不错。完全没有细节，没有细微差别，不太有趣，与此相反，如果你和这个人有点背景，有个人关系，我会问，嘿，莱尼，你今天过得如何？最近的事，查斯科的湖，等等。你就有一点背景和主动权来回答我的问题。

I think this is like prompt engineering, my whole position on this is like, prompt engineering is a very human thing. Like when we want to get some value out of a human, we do this prompt engineering. We try to effectively communicate with that human in order to get the best output. And the same thing is true of models. And I think it's like, again, because we're using a system that appears to be really smart, we assume that it has all this context, but it's really like, you know, imagine a human, human level of intelligence, but like literally no context, like it has no idea what you're going to ask it. It's never met you before. It has no idea who you are, what you do, what your goals are. And like, it's the reason that you get super generic responses sometimes is because people forget they need to put that context into models.

我认为这就像是提示工程，对此我整个立场是，提示工程是一个非常人性化的事物。就像当我们想从一个人身上得到一些价值时，我们就会进行提示工程。我们试图有效地与那个人沟通，以获得最佳输出。对模型也是同样的情况。我认为这就像，再次，因为我们使用了一个看起来非常聪明的系统，我们假设它具有所有这些背景知识，但事实上，你知道，想象一个人，具有人类水平的智力，但实际上没有任何背景知识，就像它完全不知道你要问它什么。它从来没有见过你，不知道你是谁，你做什么，你的目标是什么。有时你收到非常通用的回复是因为人们忘记了他们需要将背景知识输入模型中。

So I think the thing that is going to help solve this problem. And we already kind of do this in the context of Dali. So when you go to the image generation model that we have Dali, and you say, I want a picture of a turtle, what it does is it actually takes that description, it says, I want a picture of a turtle, and it changes it into this high fidelity, like, you know, generate a picture of a turtle with a shell with a green background and, you know, lily pads and the water and all this other, it adds all this fidelity, because that's the way that the model is trained. It's trained on examples with super high fidelity. This will happen with text models. You can imagine a world where you go into chat, you're being you say, write me a blog post about AI, it automatically will go and be like, let me generate a much higher fidelity description of what this person really wants, which is, you know, generate me a blog post about AI that talks about the trade-offs between these different techniques and some example use cases and references, some of the latest papers, and it does all that for you. And then you with the user will hopefully be able to be like, yep, this is kind of what I wanted. Let me edit this. Let me edit this here.

所以我认为能够帮助解决这个问题的东西。在达利的背景下，我们实际上已经有所尝试了。当你去使用我们的图像生成模型达利，并说，我想要一张海龟的图片时，它实际上会将这个描述转化成高保真度的结果，比如说生成一张有贝壳、绿色背景、荷叶和水的海龟图片，它会加入所有这些细节，因为这正是模型的训练方式。它是在高保真度的示例上进行训练的。这种情况也会在文本模型中出现。你可以想象一个世界，在那里你进入聊天室，你说，帮我写一篇关于人工智能的博客文章，它会自动去生成一个更高保真度的描述，即是，生成一篇关于人工智能的博客文章，讨论这些不同技术的权衡和一些示例用例和参考文献，以及一些最新的论文。它会为你完成所有这些。然后你作为用户最终会能够说，是的，这就是我想要的。让我来编辑一下。

And again, the inherent problem is like, we're lazy as humans. We don't want to type off. We don't really want to type what we mean. And I think AI systems are actually going to help solve some of that problem. So until that day, what did what can people do better when they're prompting say chat GPT? And I'll give you an example. Tim Ferriss suggested this really good idea that I've been stealing, which is when you're preparing for an interview, go to chat GPT. And I'm and so I did this for you. I was like, Hey, I'm interviewing Logan Kilpatrick. He's a head of developer relations at OpenAI on my podcast. Give me 10 questions to ask him in the style of Tyler Cowan, who I think is the best interviewer. He's so good at just like very pointed original questions. So what advice would you have for me to improve on that prompt to have better results? Because the questions were like fine. They're great. They're like interesting enough, but they were like, holy shit, these are incredible.

而且，根本的问题是，作为人类，我们懒惰。我们不想输入太多文字。我们并不真的想要输入我们的意思。我认为AI系统实际上将有助于解决这个问题的一部分。所以在那一天到来之前，人们在提示聊天GPT时可以做得更好吗？我给你举个例子。蒂姆·费里斯提出了一个我一直在借鉴的好主意，就是当你为面试做准备时，去找chat GPT聊天。所以我就是这样做的，我说：“嘿，我要采访Logan Kilpatrick，他是OpenAI的开发者关系主管，我要在我的播客上采访他。给我10个问题，以泰勒·考温的风格提问，我觉得他是最好的采访者。他非常擅长提出非常直接和独特的问题。那么你对我有什么建议，让我改进提问，获得更好的结果？因为那些问题很好，足够有趣，但我惊讶地发现，它们简直太棒了。

So I guess what advice would you give me in that example? Yeah, that's a great example. We're like thinking in context of like who it is that you're asking questions about like, I'm probably not somebody who has enough information about me on the internet, where like the model actually has been trained and like knows the nuances of my background. I think there's like probably like much more famous guests where like it might be that there's enough context on the internet to answer the question. So like you actually have to do some of that work. You need to say like if you're using browse with Bing, for example, you could say like, here's a link to Logan's blog and like some of the things that he talked about, like here's a link to his Twitter, like go through some of his tweets, go through some of his blogs and like see what his interesting perspectives are that we might want to surface on the blog or something like that.

在这个例子中，你会给我什么建议呢？对，这是一个很好的例子。我们需要考虑提问对象的背景，我可能不是一个在互联网上有足够信息的人，因此模型实际上已经被训练，知道了我的背景的细微差别。我觉得可能有更著名的嘉宾，他们在互联网上有足够信息来回答问题。因此，你实际上需要做一些工作。举个例子，如果你使用必应浏览器，你可以说：“这是Logan的博客链接，他谈论了一些事情，这是他的Twitter链接，请浏览他的一些推文和博客，并看看他的有趣观点，我们可能想要在博客上展示。”

And again, giving the model enough context to answer the question. I think again, that prompt actually might work really well for somebody who like has it like if you were interviewing like Tom Cruise or something like that, sort of you has a lot of information about them on the internet, it probably works a little bit better. So the advice there is just give more context. It doesn't tell you, hey, I don't actually know that much about Logan. So give me some more information. It's just like, here I go. Here's a bunch of good questions. Exactly. Like it wants to like it so deeply wants to answer your question. Like it doesn't care that it doesn't have enough context. It's like the most eager person in the world you could imagine to answer the question. And without that context, it's just hard to do to give up anything of value. If we got T-shirts printed, they should say like context is all you need. Context is the only thing that matters. Like it's it's such an important piece of getting a language model to do anything for you.

并且，再次给予模型足够的背景信息来回答问题。我认为，那个提示实际上可能对某些人特别有效，比如像汤姆·克鲁斯这样的人，如果你在网上对他们有很多信息，那可能会更好一些。所以建议是给予更多的背景信息。它不会告诉你，嘿，我实际上并不了解洛根那么多。请给我更多信息。它只是说，让我来。这里有一堆好问题。确切地说，它想要回答你的问题，就像它非常渴望回答你的问题。它并不在乎自己是否有足够的背景信息。它就像是你能想象到的世界上最热切的人来回答问题。没有这个背景信息，它很难提供有价值的内容。如果我们印T恤衫，它们应该写着“背景就是一切”。背景就是唯一重要的事情。它是让语言模型为你做任何事情如此重要的一部分。

Any other tips just as people are sitting there, maybe they're good. They have chat to PT open right now as they're crafting a prompt. Is there anything else that you'd say would help them have better results? We actually have a prompt engineering guide which folks should go and check out and have some of these examples. It depends on sort of the order of magnitude of like how much performance increase you can get. There's a lot of like really small silly things. Like adding a smiley face increases the performance of the model. Like telling the you know, you've seen I'm sure folks have seen like a lot of these like silly examples. Like telling the model to like take a break and then answer the question. All these kinds of things.

当人们坐在那里时，可能会有其他建议。在他们制作提示的过程中，他们可能打开了PT对话。您是否还有其他建议可以帮助他们取得更好的结果？实际上，我们有一个提示工程指南，大家应该去查看一些示例。这取决于你能获得多大程度的性能提升。有很多很小的愚蠢的事情。比如添加一个笑脸会提高模型的性能。就像告诉模型休息一下，然后回答问题。所有这些都是种种。

And again, if you think about it, it's because the corpus of information that that's trained these models is the same things is that humans have sent back and forth to each other. So like you telling a human like when I go take a break and then I come back to work like I'm fresher and I'm able to answer questions better and like do work better. So very similar things are true for these models. And again, when I see a smiley face of the end of someone's message, like I feel empowered that like this is going to be a positive interaction and I should like be more inclined to give them a great answer and spend more effort on the thing that they asked me for. Wow. Wait. So that's a real thing. If you had a smiley face, it might give you better results. Again, it's like the challenge with all this stuff is like it's very nuanced. And and it's also like it's a small jump in performance. You could imagine like on the order of like one or two percent, which for a few sentence answer is like might not even be a discernible difference. Again, if you're generating like an entire saga of text, like the smiley face like could actually make a material difference for you, but for like something small and textual and it might not.

再次，如果你仔细想一想，这是因为这些模型训练的信息语料库是人类彼此之间来回发送的相同内容。就像你告诉一个人，当我休息一下然后回来工作时，我会感觉更加清新，能够更好地回答问题并做出更好的工作。对于这些模型来说，情况也是如此。再次，当我看到某人消息末尾的笑脸时，我会感到鼓舞，觉得这将是一次积极的互动，我应该更倾向于给他们一个很好的答案，并花更多精力处理他们要求的事情。哇，等等。所以这是真的。如果有一个笑脸，可能会得到更好的结果。再次，所有这些都很微妙。它也是一个性能上的小跳跃。你可以想象，大约是一到两个百分点的提升，对于几句话回答来说，可能甚至无法察觉到这种差异。再次，如果你产生了一整套的文本，像是一个小故事，笑脸可能真的能为你带来实质性的影响，但对于一些小的文字内容，可能没有。

Okay. Good tip. Amazing. Okay. We've talked about GPTs. I think maybe might be helpful to describe what is what is this new thing that you guys launched? GPTs. And I'm curious just how it's going this because this is a really big change and element of open AI now with this idea that you could build your own like kind of mini and I'm almost explaining it, your mini open chat GPT. And then people can I think you can pay for it, right? Like you can charge for your own GPT or is it all for you right now? It's all for you right now. Okay. It's all for you. Okay. In the future, I imagine people will be able to charge. So there's this whole store now. Basically, it's that whole app store that you guys have launched. How's it going? What's happening? What surprised you there? What should people know? Yeah, it's going great. And again, historically, the thing that you would have to do, let's say, for example, you have like a really cool chat to be to use case, what you would have to do to share it with somebody else is like actually go in and like start the conversation with the model, like prompted to do the things that you wanted to. And then you would share that link with somebody else before the action has actually happened and be like here, now you can like essentially finish this conversation with chat GPT that I started. So GPT is kind of changes this where you take all that important context, you put it into the model to begin with, and then people can go and like chat with essentially a custom version of chat GPT. And the thing that's really interesting is you know, you can upload files, you can give it custom instructions, you can add all these different tools, like a code interpreter is built in, which allows you to like do like math, essentially, you have browsing built in image generation built in, you can also like for more advanced use cases, if you're a developer, you can like connect it to external API. So you can connect it to the notion API or Gmail or all these different things, like have it actually take actions on your behalf. So there's there's so many cool things that people are unlocking. And what's been most exciting to me actually is like the non developer persona is now empowered to like go and solve these like really, really, really more challenging problems by giving the model enough context on what that problem is to be able to solve it going back to like context is all you need, like this is very true in the context of GPTs. And if you've given enough context, like you can solve much more interesting problems. There's so many things that I'm excited about with this, like I think monetization when it comes to the store later this quarter, I think is going to be extremely exciting. Like when people can get paid based on who's using their GPTs, that's going to be a huge unlock and like open a lot of people's eyes to the to the opportunity here. I also think like continuing to push on making more capabilities accessible to GPTs for people who can't code is really exciting. Like having to even for me as like a someone who was a software engineer, like it's not super easy to like connect the notion API or the Gmail API to my GPT. And like really, I'd love to just be able to like one quick sign in with Gmail and then all of a sudden, it's like my Gmail is accessible or like someone else can sign in with their Gmail and make it accessible. So I think over time, like all those types of things will come. But today, it's really like custom prompts is essentially like one of the biggest value ads with GPTs.

好的。好建议。惊人。好的。我们已经谈论过GPT。我觉得也许描述一下你们推出的这个新东西可能会有帮助。GPT。我很好奇这个是怎么运作的，因为这真的是open AI现在的一项很大的变革和元素，就是你们可以建立自己的一种像迷你开放式聊天GPT。然后人们可以，我觉得你们可以收费，对吧？你们现在全部免费吗？现在全部免费。好的。目前你们都免费。将来也许人们会可以收费。现在基本上是整个商店，就是你们推出的整个应用程序商店。进展如何？发生了什么？有什么让你们惊讶的？人们应该知道些什么？是的，一切进展顺利。再说起历史上，假设你有一个非常酷的聊天使用场景，要与别人分享，你需要做的是实际上进入并开始与模型对话，促使它做你想要的事情。然后你会在这个对话动作实际发生之前，与别人分享那个链接，说：“现在你可以用我开始的chat GPT完成这段对话。”GPT有一定的改变，你把所有重要的内容放入模型中首先，然后人们可以通过一个定制版本的chat GPT与之交谈。而最有趣的是，你们可以上传文件，给它自定义指令，增加各种不同的工具，像内置的代码解释器，允许你做数学计算，基本上你还有内置的浏览，图像生成功能，还可以进行更高级的用例，如果你是开发者，你可以将其连接到外部API。所以你可以连接到notion API或Gmail或所有这些不同的东西，实际上让它代表你采取行动。人们正在探索很多很酷的东西。对我来说最令人兴奋的是，非开发者现在有能力去解决这些更具挑战性的问题，只需给模型足够的上下文来解决这个问题。回到上下文就是一切，这在GPT的上下文中是非常正确的。如果你给足够的上下文，你可以解决更有趣的问题。我对此非常兴奋，我认为通过商店来实现赚钱，这个季度后期对我来说会非常令人兴奋。当人们可以根据谁在使用他们的GPT来获得报酬时，这将是一个巨大的机会，打开很多人的视野。我也认为，继续努力让更多功能对于不能编程的人来说更容易访问GPT也很令人兴奋。就像对我来说，作为一个软件工程师，连接到notion API或Gmail API也不太容易。我真的希望能快速登录Gmail，然后突然间我的Gmail就可以访问，或者别人可以使用他们的Gmail登录并使其可访问。所以我认为随着时间的推移，所有这些都将实现。但今天，定制提示基本上是GPT的最大附加值之一。

Awesome. I have it pulled up here on the Undifferent Monitor. And Canva has the top GPT currently. And I was trying to play with it as you're chatting just to see, I was going to make a big banner that said it's the context stupid. And it doesn't I'm not doing some right, but I'm not paying that much attention to it because we're talking. But this is very cool. Just maybe a final question there is there a GPT that you saw someone built that was like, wow, that's amazing. That's so cool. Something that surprised you. And I'll share one. That was really cool. But is there anything that comes to mind and ask that? I think my my instinct is the Zapier, all of the stuff that Zapier has done with GPTs is like the most useful stuff that you can imagine. You're like, you can go so far with what and I don't know how it's like packaged for Zapier's GPT right now, but like you can actually as a third party developer integrate Zapier without knowing how to code into your GPT. So like they're they're pushing a lot of this stuff. And then basically like all 5000 connections that are possible with Zapier today, you can bring into your GPT and like essentially enable it to do anything. So I'm incredibly excited for Zapier and for people who are building with them because like there's so many things that you can unlock using that platform. So I think that's how we like the most the most exciting thing to me for people who aren't who aren't developers.

太棒了。我在Undifferent Monitor 上看到它了。Canva 目前拥有顶尖的 GPT。在你聊天的时候，我试着玩一下它，看看是否能制作一个大型横幅，上面写着“这是上下文，蠢瓜”。我没有完全弄明白，但我没那么在意，因为我们在交谈。但这非常酷。也许最后一个问题是，你是否见过有人构建的令人惊讶的 GPT？有什么令你感到惊讶的事情吗？我分享一个我觉得很酷的。但你有没有想到的事情？我觉得我本能地认为 Zapier 所做的一切，都是最有用的。你能够用 Zapier 的 GPT 做到很多事情，你可以把 Zapier 的 GPT 集成到你的 GPT 中，而不需要懂编程。所以他们正在推动很多这样的事情。基本上，你可以把今天在 Zapier 中可能的 5000 个连接都带入你的 GPT 中，使其能够做任何事情。所以我对 Zapier 和那些与他们一起构建的人感到非常兴奋，因为有很多东西可以利用他们的平台。所以我认为这是对一般非开发人员来讲最令人兴奋的事情。

Awesome. Zapier is always in there getting there connecting things. Yeah, they're great. So the one that I had in mind. So I had a buddy mine, Seki, who's the CEO of a company called Runway built this thing called Universal Primer, which helps you learn. It's described as learn everything about anything. And he basically I think is kind of this secretic method of helping you learn stuff. So it's like explain how transformers work in LLMs. And then it just kind of goes through stuff and then asks you questions, I think, and kind of helps you learn new concepts. And I think it's the number two education GPT. I love that. Seki's incredible.

太棒了。Zapier总是在那里连接各种事物。是的，他们很棒。我有一个想法。我有一个朋友叫Seki，他是一个叫Runway的公司的CEO，他开发了一个叫Universal Primer的东西，可以帮助你学习。它被描述为学会任何事物的一切。我认为他基本上是利用了一种神奇的方法来帮助你学习东西。比如，它会解释LLMs中变压器的工作原理，然后会逐步介绍内容，然后提问，帮助你学习新概念。我认为它是排名第二的教育GPT。我很喜欢这个。Seki 真是不可思议。

So yes, it's true. Let me tell you about a product called Arcade. Arcade is an interactive demo platform that enables teams to create polished on brand demos in minutes. Telling the story of your product is hard and customers want you to show them your product, not just talk about it or gait it. That's why product for teams such as Atlassian, Carta, and Retool use Arcade to tell better stories within their home pages, product change logs, emails, and documentation. But don't just take my word for it. Quantum metric, the leading digital analytics platform created an interactive product to a library to drive more prospects.

是的，这是真的。让我来告诉你一个叫做Arcade的产品。Arcade是一个互动演示平台，让团队能够在几分钟内创建出外观精致符合品牌形象的演示。讲述产品故事很难，客户想要看到您的产品，而不仅仅是听您谈论或描述它。这就是为什么像Atlassian、Carta和Retool这样的团队使用Arcade来在他们的主页、产品更新日志、电子邮件和文档中讲述更好的故事。但不要只是相信我的话。Quantum Metric，领先的数字分析平台，创建了一个交互式产品库，以吸引更多的潜在客户。

With Arcade, they achieved a 2x higher conversion rate for demos and saw five times more engagement than videos. On top of that, they built a demo 10 times faster than before. Creating a product demo has never been easier. With browser based recording, Arcade is the no-code solution for building personalized demos at scale. Arcade offers product customization options, designer approved editing tools, and rich insights about how your viewers engage every step of the way, ready to tell more engaging product stories that drive results, head to Arcade.Software.Lenny and get 50% off your first three months. That's Arcade.Software.Lenny.

他们通过Arcade获得了演示文稿的转化率提高了两倍，比视频的参与度提高了五倍。此外，他们比以往快了十倍地制作了演示文稿。创建产品演示从未如此简单。通过基于浏览器的录制，Arcade是建立个性化演示文稿的无代码解决方案。Arcade提供产品定制选项、经设计师批准的编辑工具，并深入了解您的观众在每个步骤中如何参与，准备好讲述更引人入胜的产品故事来推动结果，赶快到Arcade.Software.Lenny并享受首三个月半价优惠。就在Arcade.Software.Lenny。

I want to talk about just what it's like to work at OpenAI and how the product team operates and how the company operates. So you worked at your two previous companies were Apple and NASA, which are not known for moving fast. And now you're OpenAI, which is known for moving very fast, maybe too fast for some people's taste as we saw with the whole board thing. And so what I'm curious is just what is it that OpenAI does so well that allows them to build and ship so quickly, and it's such high a bar. Like is there a process or a way of working that you've seen that you think other companies should try to move more quickly and ship better stuff? There's so many interesting trade-offs and all this tension around how quickly companies can move.

我想谈谈在OpenAI工作的感受，以及产品团队和公司的运作方式。你之前在苹果和NASA工作过，这两家公司都不以快速行动著称。现在你在OpenAI工作，OpenAI以行动迅速而著称，也许有些人觉得速度太快了，就像我们在整个董事会事件中看到的那样。我很好奇的是，OpenAI是如何做到能够快速构建和交付产品，并且质量非常高。他们有什么优秀的方法或工作流程让他们如此迅速地行动，并达到如此高的标准。你有没有看到一些让其他公司应该尝试更快地行动和交付更好产品的方法或工作方式？对于公司如何快速行动，有很多有趣的取舍和紧张局势。

I think for us, again, if you think about Apple as an example, if you think about NASA as an example, just like older institutions, like lots of overtime, the tendency is think slow down. There's additional checks and balances that are put in place, which sort of drag things down a little bit. So we're young and a new company, so we don't have a lot of that institutional legacy barriers that have been put in place.

我认为对我们来说，如果以苹果和NASA为例，就像许多老机构一样，时间会让人变得保守。会有额外的检查和平衡措施，这会使事情变得有些缓慢。所以我们是一家年轻的新公司，没有很多已经设立的机构遗留障碍。

I think the biggest thing, and there's a good SAM tweet somewhere in the ether about this from I think 2022 or something like that. But like finding people who are high agency and work with urgency is like one of the most, you know, if I was hiring five people today, like those are like some of the top two characteristics that I would look for in people because it's you can take on the world if you have people who have high agency and like not needing to either like, you know, get 50 people's different consensus because like you have people who you trust with high agency and they can just go and do the thing.

我认为最重要的事情，我记得在2022年左右，SAM在某个地方发过一条推文谈到这个话题。但是找到那些拥有高效执行力和紧迫感的人是最重要的。如果我今天要雇佣五个人，我会优先考虑这两个特质，因为如果你拥有高效执行力的人，你可以征服整个世界，他们无需得到其他50个人的一致同意，因为你相信他们的执行力，他们能够快速行动并完成任务。

I think is like one of the most, it is the most important thing. I'm pretty sure if you if you were to distill it down and like I see this and folks that I work with, like folks are so high agency, like they see a problem and they go and tackle it. Like they hear something from our customers about a challenge that they're having and like they're already pushing on what the solution for them is and not like waiting for all the other things to happen that like I think traditional companies are sort of stuck behind because they're like, oh, let's check with all these like seven different departments.

我觉得这是最重要的事情之一。我相信，如果你把它归纳起来，就像我看到与我一起工作的人一样，他们非常有行动力，看到问题就会去解决。他们听到客户遇到的挑战，就会立即思考解决方案，而不是等待传统公司往往会受阻的所有其他事情发生，因为他们会说：“让我们与这七个不同部门核实一下”。

So like, you know, try to get feedback on this. Like people just go and do it and solve the problem. And I love that. It's so fun to be able to be a part of those situations. That is so cool. I really like these two characteristics because I haven't heard this before as the two maybe the two most important things you guys look for high agency, high urgency to give people a clear sense of what these actually look like when you're hiring. He's shared maybe this example of customer service, someone's hearing a bug and then going to fix it.

所以，你知道，尽量在这件事上获得反馈。有些人就是立即行动并解决问题。而我喜欢那种感觉。能够成为这些情况的一部分真的很有趣。这太酷了。我真的很喜欢这两个特质，因为我以前从未听说过，也许这两个是你们在招聘时最看重的高主动性和高紧迫性，这样可以为人们提供一个清晰的概念，当你在招聘时究竟寻找什么样的人才。他也许分享了一个客服问题的例子，有人听到了一个bug然后去修复它。

Is there anything else that can illustrate what that looks like high agency and then similar question on urgency other than just like move move ship ship. I think like the assistance API that we released for dev day like we continued to get this feedback from developers that people wanted these higher levels of abstraction on top of our existing API's and like a bunch of folks on the team just like came together and were like, let's let's put together what the plan would look like to build something like this. And then very quickly came together and actually built the actual API that now powers so many people's assistant applications that are out there.

除了像移动般快速行动外，还有什么能够展示高度机构性的特点？还有关于紧急性的类似问题吗？我觉得我们在开发者日发布的辅助API就很好地体现了这一点，我们继续从开发者那里得到反馈，他们想要在我们现有API之上建立更高级的抽象层。团队中的许多人聚在一起，就像说“让我们一起制定建立这样一种计划的具体样貌”，然后很快就集思广益，实际上建立了现在为许多人的助手应用提供动力的实际API。

And I think that's a great example of like, you know, it wasn't like this like top down like, oh, someone's sitting there being like, oh, let's do these five things. And then like, okay, team, go and do that. It's like, people really seeing these problems that are coming up and like knowing that they can come together as a team and like solve these problems really quickly. And I think the assistance API and there's like a thousand and one other examples of teams taking agency and doing this. But I think that's a great one at the top of my head. That makes me want to ask just how does planning work at OpenAI?

我认为这是一个很好的例子，就像，你知道的，它并不是像这样从上而下地，哦，有人坐在那里说，哦，让我们做这五件事。然后，好的，团队，去做吧。事实上，人们真的看到了这些问题的出现，知道他们可以作为一个团队一起解决这些问题，而且解决得很快。我认为辅助 API 以及其他许多团队采取主动行动并实现这一点的例子，还有无数例子。但就我个人而言，我认为这是一个很好的例子。这让我想要问一下 OpenAI 的计划如何工作？

So in this example, it was just like, hey, we think we need to build this, let's just go and build it. Imagine there's still a roadmap and priorities and goals and things that that team had. How does road mapping and prioritization and all of that generally work to allow for something like that? I think this is one of the more challenging pieces at OpenAI. Like there's so many like everyone wants everything from us. And like today, especially in the world of JAGIBT and how large and well used are API is like, people will just come to us and say like, hey, we want all of these things.

在这个例子中，就像是，嘿，我们觉得我们需要建立这个，让我们就去建立它。想象一下，团队还有路线图、优先事项和目标等等。那么，路线图和优先级排序以及所有这些工作通常是如何工作，以便实现这样的目标呢？我认为这是OpenAI最具挑战性的部分之一。就像每个人都希望从我们这里得到一切一样。特别是在JAGIBT世界中，我们的API的规模如此之大且被广泛使用，人们会直接来找我们说，嘿，我们想要所有这些东西。

I think there's like a bunch of like core guiding principles that we look at. Like one, going back to the mission, like is this actually like going to help us get to AGI? So there's a huge focus on like, you know, there's this like potential shiny reward right in front of us, which is like, you know, like optimize user engagement or whatever it is. And like, is that really the thing like maybe the answer is yes, like maybe that is what is going to help us get to AGI sooner. But like looking at it through that lens, I think is like always the first step of deciding any of these problems.

我认为有一些核心的指导原则，我们要考虑。比如，回到使命上，这个实际上会帮助我们实现AGI吗？所以我们非常关注这一点，你知道，现在面前有一个潜在的诱人回报，比如优化用户参与度或其他什么事情。这真的就是重点吗？也许答案是肯定的，也许这确实会帮助我们更快地实现AGI。但是从这个角度来看待问题，我认为总是决定任何这些问题的第一步。

I think on the developer side, there's also these like core tenets of like reliability like, hey, you know, it would be awesome if we had additional API that did all these cool things like new new endpoints, new modalities, new abstractions, but like, are we giving customers a robust and reliable experience or API? And like that's often like the first question. And I think there have been times where we fall in short on that. And like, you know, there was a bunch of other things that we've been thinking about doing and like really bringing the focus and priority back to that reliability piece. Because at the end of the day, nobody cares if you have something great, if they can't use it robust and reliably. So there's like these core tenets. And I think like, again, we have like very other than all the principles about how we're making the decision or think like the actual planning process is like pretty standard, like we come together, there's like H1, Q1 goals, we all sprint on those.

我认为在开发者这一方面，也有一些核心原则，比如可靠性，就是说，嘿，你知道的，如果我们有额外的API，做了所有这些很酷的事情，比如新增端点、新增模式、新增抽象层，那将会非常棒。但是，我们是否在为客户提供健壮可靠的体验或API？这通常是第一个问题。我认为有时我们在这方面做得不够好。你知道的，我们一直在考虑做很多其他事情，现在真的要把焦点和优先级转回到可靠性这一点上来。因为到头来，如果用户无法稳定可靠地使用某个东西，那么这个东西再好也没用。所以有一些核心原则。再者，除了我们做决定的所有原则之外，我认为实际的规划过程是非常标准的，比如大家聚在一起，设定上半年、第一季度的目标，然后我们都全力以赴。

I think the real interesting thing is like, how stuff changes over time. Like you think we're going to do these like very high level things and like, you know, new models, new modalities, whatever it is. And then like as time goes on, there's like all of this turmoil and change. And it's interesting to have like mechanisms to be like, Hey, how do we, how do we update our understanding of the world and our goals as everything sort of the ground changes underneath of us as is happening in the craziness of the AI space today. It's interesting that it sounds a lot like most other companies.

我认为真正有趣的事情是，事物随着时间的推移发生了变化。你会认为我们将会做这些非常高级的事情，比如新模型、新形式等等。然后随着时间的推移，会出现各种动荡和变化。拥有机制去思考，嘿，我们该如何更新我们对世界的理解以及我们的目标，因为一切似乎正在发生变化，就像今天在人工智能领域的疯狂中所发生的那样。有趣的是，这听起来很像大多数其他公司。

There's H1 planning, there's Q1 planning. Are there metrics and goals like that that you guys have? Okay, ours or anything like that? Or is it just here, we're going to launch these products? I think it's like much higher level. I actually don't think open AI is like a big okay, our company. Like I don't think teams do okay, ours today. And I don't have a good understanding of like, why that's the case? Whether or not I don't even know. Okay, ours are like still the industry. You're probably talking to a lot more folks about like, yeah, who are making those decisions.

有H1规划，有Q1规划。你们有类似的指标和目标吗？好的，我们有吗？或者只是这里，我们要推出这些产品？我觉得这更像是高层次的规划。我其实不认为开放AI是一个很大的公司。我不认为团队们今天在做什么。我对为什么会这样没有一个很好的理解。我甚至不知道，好的团队仍然是行业的一部分。你可能会和更多人交流，他们正在做出这些决定。

So I'm curious, is that something that you're seeing for folks like, is it still common for people to do OTRs? Yeah, absolutely. Many companies use their cares, love OTRs. Many companies hate OTRs. I am not surprised that open AI is not an OTR driven company. Along those lines, I don't know how much you can share about all the stuff. But how do you measure success for things that you launch? I know there's this ultimate goal, AGI. Is there some way to track we're getting closer? What else do you guys look at when you launch say, DPT store or systems or anything? That's like, cool. That was exactly what we're hoping for. Is it just adoption?

所以我很好奇，像你这样的人是否仍然经常进行OTR（全职远程工作）？是的，绝对的。许多公司喜欢他们的员工在OTR上工作，也有许多公司讨厌OTR。我不奇怪开放AI并不是一个以OTR为驱动力的公司。在这方面，我不知道你能分享多少关于所有这些东西。但是你是如何衡量你们发布的东西的成功的呢？我知道有这个终极目标，AGI。有没有一种方法可以追踪我们是否越来越接近？当你们发布像DPT Store或系统之类的东西时，你们还关注什么？那个很酷。那正是我们所希望的。仅仅是采用率吗？

Yeah, adoption is a great one. I think there's a bunch of metrics around revenue, a number of developers that are building on our platform, all those things. A lot of these, and I don't want to dive, I'll let Sam or someone else on our leadership team go more into the details. I think a lot of these are actual abstractions towards something else. Even if revenue is a goal, it's like revenue is not actually the goal. Revenue is a proxy for getting more compute, which is then actually what helps us get towards getting more GPUs so that we can train better models and actually get to the goal. So there's all these intermediate layers where even if we say something is the goal and you hear that in a vacuum and you're like, oh, well, open AI, I just want to make money. It's like, well, really money is the mechanism to get better models so that we can achieve our mission. I think there's a bunch of interesting angles like that as well.

是的，采用是一个很好的指标。我认为围绕收入、在我们平台上构建的开发者数量等等有很多指标。很多这些，我不想深入探讨，我会让山姆或我们领导团队中的其他人更详细地阐述。我认为很多这些实际上是指向其他事物的抽象概念。即使收入是一个目标，但实际上收入并不是真正的目标。收入是获取更多计算资源的一种代理，而这实际上有助于我们获得更多的GPU，这样我们就可以训练更好的模型，实现真正的目标。因此，即使我们说某个事物是目标，如果你在一个真空环境中听到这个，你可能会觉得，哦，OpenAI只是想赚钱。其实，钱只是获取更好模型的机制，这样我们就可以实现我们的使命。我认为还有很多有趣的角度可以探讨。

I don't know if I've heard of a more ambitious vision for a company to build artificial general intelligence. I love that. I imagine many companies are like, what's our version of that? Before we leave this topic, is there, is there anything else that you've seen open AI do really well that allows it to move this fast and be this successful? You talked about hiring people with higher agency and high urgency. Is there anything else that's just like, oh, wow, that's a really good way of operating? Imagine part of it's just hiring incredibly smart people. Like, I think that's probably an unsight thing, but yeah, anything else.

我不知道是否听过一个更雄心勃勃的愿景，让一家公司建造人工通用智能。我喜欢这个想法。我想许多公司都在考虑，我们的版本是什么？在我们离开这个话题之前，你是否看到开放AI做得非常好的其他事情，让它能够如此迅速地发展并取得成功？你谈到了招聘具有更高主动性和迫切性的人。还有其他任何事情让你印象深刻，觉得他们运营得非常好吗？我想部分原因可能是他们雇佣了非常聪明的人。就像，我认为这可能是一个不言自明的事情，但是，还有其他方面吗？

I think there's a non-trivial benefit to using Slack. And I think maybe that's controversial, and maybe some people don't like Slack, but opening such a Slack-heavy culture. And it really, the instantaneous real-time communication on Slack is so crucial. And I just love being able to tag in different people from different teams and get everybody coalesced. So everybody is always on Slack. So even if you're remote or you're on a different team or in a different office, so much of the company culture is ingrained in Slack and it allows us to really quickly coordinate where it's actually faster to send them into Slack messages sometimes than it would be to walk over to their desk because they're on Slack and they're going to be using it.

我认为使用Slack有很大的好处。可能有些人对此持有不同意见，可能有些人不喜欢Slack，但建立这样一个以Slack为主的文化是非常重要的。在Slack上的即时实时沟通非常关键。我喜欢能够在Slack中标记不同团队的人并让所有人团结在一起。所以每个人都会在Slack上。即使你是远程工作或者在不同团队或不同办公室，公司文化的很多方面都融入了Slack中，这让我们能够迅速协调，有时发送一个Slack消息甚至比走到他们的办公桌旁还要快，因为他们在Slack上并且会去使用它。

If you saw the recent Sam and Bill Gates interview, but Sam was talking about how Slack is his number one most used app on his phone. I don't even look at the time saying, I'm like, I don't want to know how long I'm using Slack, but I'm sure the sales force people are looking at the numbers and they're like, this is exactly what we wanted. So I also love Slack. I'm a big promoter of Slack. I think there's a lot of Slack hate, but it's such a good product. I've tried so many alternatives and nothing compares. I think what's interesting about Slack for you guys is one of the, you don't know if someone in there is just an AGI. That is not actually a person that's just there working at the company. I know they're real people. There's no AGIs yet, but I think like, yeah, even Slack is building a bunch of really cool AI tools, which I'm excited to. That's why there's so much cool AI progress. At the end of the day, it's so exciting from being a consumer of all these new AI products. Google's a great example. I'm so happy that Google's doing really cool AI stuff because I'm a Google Docs customer. I love using Google Docs and a bunch of their other products. It's awesome that people are building such useful things around these models.

如果你看到最近的山姆和比尔·盖茨的访谈，山姆谈到Slack是他手机上使用最频繁的应用。我甚至不看时间，我说，我不想知道我使用Slack的时间有多长，但我肯定销售团队在看数字，他们会说，这正是我们想要的。我也很喜欢Slack。我是Slack的忠实支持者。我觉得Slack受到了很多批评，但它是一个很好的产品。我试过很多替代品，但没有一样能比得上Slack。我觉得对你们来说Slack有趣的地方之一是，并不清楚里面是否有人工通用智能，那并非是真正在公司工作的人。我知道他们是真实的人，还没有人工通用智能，但我觉得，即使是Slack也在开发很多很酷的AI工具，我对此感到兴奋。这也是为什么会有这么多酷炫的AI进展。最终，作为所有这些新AI产品的消费者，这真是令人兴奋。Google是一个很好的例子。我很高兴Google在进行很酷的AI研究，因为我是 Google Docs 的用户。我喜欢使用Google Docs和他们的其他很多产品。人们围绕这些模型构建这么有用的东西真的太棒了。

How big is the opening AI team at this point, whatever you can share, just to give people sense of the skill? Yeah, I think the last public number was something around 750 near the end of last year, 780 or something like that near the end of last year. We're still growing so quickly. I won't be the messenger to share the specific update. The team is growing like crazy and we're also hiring across all of our engineering teams. Folks are and npm teams. Folks are interested. We'll have to hear from folks who are curious about joining. Maybe one last question here. You're growing, maybe getting to 1,000 people, clearly still very innovative and moving incredibly fast. Is there anything you've seen about what OpenAI does well to enable innovation and not slow down new big ideas? Yeah, there's a couple of things. One of which is the actual research team who see the most of the innovation that happens at OpenAI is intentionally small. They're not like most of the growth that OpenAI is seen as around customer-facing roles. Our engineering roles to provide the infrastructure to protect CBT and things like that. The research team is intentionally kept small. There's all of this talk. It's really interesting. I just saw this thread from one of our research folks who was talking about how in a world where you're constrained by the amount of GPU capacity that you have as a researcher, which is the case for OpenAI researchers, and also researchers everywhere else, each new researcher that you add is actually a net productivity loss for the research group unless that person is up-leveling everyone else in such a profound way that it increases the efficiency.

在这一点上AI团队有多大，你能分享什么，只是为了让人们了解团队的技能水平吗？是的，我认为去年年底公布的数字大约是750左右，去年年底大约是780左右。我们仍在迅速增长。我不会成为分享具体更新的信使。团队快速增长，我们也在所有工程团队中招聘。人们对此感兴趣。我们需要听听那些对加入感兴趣的人。也许最后一个问题。你们正在成长，也许会增加到1000人，显然仍然非常创新并且移动迅速。有关于OpenAI如何能够促进创新而不让新的大想法放慢速度的任何发现吗？是的，有几点。其中之一是实际研究团队，他们看到OpenAI发生的大部分创新都是故意保持很小规模的。他们不像OpenAI看到的大部分增长是围绕着面向客户的职位。我们的工程职位提供了基础设施来保护CBT等等。研究团队被故意保持很小规模。有很多谈论。这很有趣。我刚刚看到一个我们研究人员连线讨论，谈到在一个研究员被GPU容量所限制的世界中，这对于OpenAI的研究员和其他地方的研究人员都是如此，每增加一个新的研究员实际上对于研究小组来说是净生产力的损失，除非这个人以如此深刻的方式提升了其他人，从而增加了效率。

If you just add somebody who's going to tackle some completely different research direction, you now have to share your GPUs with that person, and everyone else is now slower on their experiments. They're really interesting trade-off that research folks have that I don't think like products folks like I add another engineer to our API team or to some of the chat GPT teams, you can actually write more code and do more. That's actually a net beneficial improvement for everybody. That's always not the case in the case of researchers, which is interesting. In a GPU constraint world, which hopefully we won't always be in.

如果你只是添加一个将要处理完全不同研究方向的人，你现在必须与他人分享你的GPU，并且其他人在实验中变得更慢。研究人员有一个非常有趣的权衡，我不认为产品人员会喜欢，比如我向我们的API团队或一些聊天GPT团队添加另一个工程师，实际上可以编写更多代码并做更多事情。这实际上是对每个人都是一个净好的改进。在研究人员的情况下，情况并非总是如此，这很有趣。在GPU受限的世界中，希望我们不会总是置身其中。

I want to zoom out a bit, and then there's going to be a couple of follow-up questions here. Where are things heading with OpenAI? What's in the near future of what people should expect from the tools that you guys are going to have in lunch? Yeah, new modalities. I think chat GPT continuing to push all of the different experiences that are going to be possible. Today, chat GPT is really just text in-text out. I guess three months ago, it was just text in-text out, we started to change that with now. You can do the voice mode and you can generate images, and now you can take pictures.

我想要放大一点，接下来会有几个跟进问题。OpenAI的发展方向是什么？人们可以从你们即将推出的工具中期待什么？是的，新的模式。我认为Chat GPT将继续推动所有可能的不同体验。现在，Chat GPT实际上只是文本输入文本输出。我想大概三个月前，它还只是文本输入文本输出，我们开始改变现状。现在你可以进行语音模式，可以生成图像，甚至可以拍照。

I think continuing to expand the way in which you interface with AI through chat GPT is coming. I think GPT is our first step towards the agent future. Again, today when you use a GPT, you send a message, you get an answer back almost right away. That's kind of the end of your interaction. I think as GPT's continue to get more robust, you'll actually be able to say, hey, go and do this thing. Just let me know when you're done. I don't need the answer right now. I want you to really spend time and be thoughtful about this.

我认为通过与聊天GPT的接口扩展方式，继续与人工智能互动已经在不断发展。我认为GPT是我们迈向未来智能代理的第一步。今天当你使用GPT时，你发送一条消息，几乎立刻就会得到答复。这种互动基本上就结束了。我认为随着GPT的不断强大，实际上你将能够说，嘿，去做这件事情。等你做完了再告诉我。我并不需要现在就得到答案。我希望你能花费时间深思熟虑。

Again, if you think back to all these human analogies, that's what we do with humans. I don't expect somebody when I ask them to do something meaningful for me to do it right away and give me the answer back right away. I think pushing more towards those experiences is what is going to unlock so much more value for people. I think the last thing is GPT's as this mechanism to get the next few hundred million people into chat GPT and into AI.

再次，如果你回想起所有这些与人类类比的情况，这就是我们对待人类的方式。当我要求别人为我做有意义的事情时，我并不指望他们立刻就给我答案。我认为更加倾向于这些经验将为人们带来更多的价值。我认为最后一点是将GPT作为让接下来的几亿人进入聊天GPT和人工智能的机制。

I think if you have conversations with people who aren't close to the AI space, oftentimes you talk about, even if they've heard of chat GPT, a lot of people haven't heard of chat GPT, but if they have, they're like, they show up in chat GPT and they're like, I don't really know what I'm supposed to do with this. This blokes late. I can kind of do anything. It's not super clear how this solves my specific problem. But I think the cool thing about GPT's is you can package down, here's this one very specific problem that AI can solve for you and do it really well. I'm like, I can share that experience with you.

我认为，如果你和那些不熟悉人工智能领域的人交谈，通常你会谈到，即使他们听说过聊天GPT，很多人还是没听说过，但如果他们听说过，他们可能会说，如果他们在聊天中使用GPT，他们可能会说，我不知道我应该怎么处理这个。这似乎有点晚了。我可以做任何事情。这并不是很清楚如何解决我的具体问题。但我认为GPT的酷之处在于，你可以将这个问题明确化，告诉你人工智能能够很好地解决一个非常具体的问你，并且做得非常出色。我可以和你分享这种经验。

Now you can go and try that GPT, have it actually solve the problem and be like, wow, it did this thing for me. I should probably spend the time to investigate these five other problems that I have to see if AI can also be a solution to those. I think so many more people are going to come online and start using these tools because very narrow, vertical tools are what's going to be a huge amount for them. In the last case, a classic horizontal product problem where does so many things and people don't know what exactly it should do for them.

现在你可以去尝试那个GPT，让它真正解决问题，并且感叹，哇，它为我做到了这件事。我应该花时间去研究一下我面临的其他五个问题，看看人工智能是否也可以是一个解决方案。我觉得会有很多人上线并开始使用这些工具，因为非常狭窄、垂直的工具对他们来说将是一个巨大的帮助。在最后一种情况下，是一个经典的水平产品问题，它可以做很多事情，但人们不清楚它到底应该为他们做些什么。

So that makes a ton of sense. Just being a lot more template oriented, use case specific, helping people on board makes a tons of sense. Common problem for so many sales products out there. The other ones you mentioned, which is really interesting, basically more interfaces to more easily interact with opening eye voice, you mentioned audio and things like that. That makes tons of sense. And then this agent's piece where the idea is instead of just it's a chat, it's like, it good to do this thing for me.

所以这就变得非常合理了。更多地关注模板，特定用例，帮助用户上手，这是非常合理的。这是许多销售产品所面临的常见问题。你提到的其他方面也很有趣，基本上是更多接口以更容易地与开放式语音交互，你提到了音频等等。这也很有道理。然后再谈到这个代理部分，想法是它不仅仅是一个聊天，而是好像“帮我做这件事”。

Kind of along those lines, GPT-5, we touched on this a bit. There's a lot of speculation about the much better version. People just have these wild expectations, I think, for where GPT is going. GPT-5 is going to solve all the world's problems. I know you're not going to tell me when it's launching and what it's going to do, but I heard from a friend that there's kind of this tip that when you're building products today, you should build towards a GPT-5 future, not based on limitations of GPT-4 today.

在这些方面，GPT-5，我们稍微提及了一下。关于这个更好版本，有很多猜测。我认为人们对GPT将去往何方有着一些疯狂的期望。据说GPT-5将解决世界上所有问题。我知道你不会告诉我它何时上线以及它会做什么，但我从朋友那里听说，建议你在构建产品时，应当朝着GPT-5的未来发展方向努力，而不是基于GPT-4的现有局限性。

To help people do that, what should people think about that might be better in a world of GPT-5 is it just like it's faster, it's just smarter, is there anything else that might be like, oh, wow, I should really think I'm approaching my product. If folks have looked through the GPT-4 technical report that we released back in March when GPT-4 came out, GPT-4 was the first model that we trained where we could reliably predict the capabilities of that model beforehand based on the amount of computes that we were going to put into it.

为了帮助人们做到这一点，人们应该考虑在GPT-5的世界里可能会更好的是什么？它仅仅只是更快，更聪明吗？还有其他可能的因素，让人们觉得，哇，我真的应该考虑我正在开发的产品。如果大家看过我们在3月份发布的GPT-4技术报告，GPT-4是我们训练的第一个模型，我们可以可靠地预测出该模型的能力，这是基于我们将要投入的计算量来进行的预测。

We did a scientific study to show, hey, this is what we predicted, and here is what the actual outcome was. It'll be one, I think, just as somebody who's interested in technology, but interestingly, does that continue to hold for GPT-5? Hopefully, we'll share some of that information whenever that model comes out. I also think you can probably draw a few observations, one of them, which is GPT-4 came out, the consensus from the world is everything is different. All of a sudden, everything is different. This changes the world, this changes everything, and then slowly but surely we come back to reality of this is a really effective tool, and it's going to help solve my problems more effectively. I think that is the undoubtedly the lens in which people should look at all of these model advancements.

我们进行了一项科学研究来展示，嘿，这是我们预测的结果，这是实际结果。作为一个对技术感兴趣的人，我认为这将是一个有趣的发现，但有趣的是，这个结果是否继续适用于GPT-5呢？希望我们能在那个模型推出的时候分享一些信息。我也认为你可以从中得出一些观察，其中之一是，GPT-4面世时，世界的共识是一切都不同了。突然间，一切都不同了。这改变了世界，改变了一切，然后慢慢地我们回到现实，这只是一个非常有效的工具，它将更有效地帮助我解决问题。我认为这无疑是人们应该以这种视角来看待所有这些模型进展的透镜。

GPT-5 is going to be extremely useful and solve some whole new echelon of problems. Hopefully, it'll be faster. Hopefully, it'll be better on all these waves, but fundamentally, the same problem that exists in the world are still going to be the same problems. You now just have a better tool to solve those problems. Going back to vertical use cases, I think people who are solving very specific use cases are just now going to be able to do that much more effectively. I don't think that's going to—people have these unrealistic expectations that GPT-5 is going to be doing backflips in the background of my bedroom while it also writes all my code for me and talks in the phone with my mom or something like that. It's not the case. It is just going to be this very effective tool, very similar to GPT-4.

GPT-5将会非常有用，解决一些全新层次的问题。希望它会更快速。希望它能在各个方面都更好，但基本上，世界上存在的问题仍然会是同样的问题。现在你只是有了一个更好的工具来解决这些问题。回到垂直用例，我认为那些正在解决非常具体用例的人现在将能够更有效地做到这一点。我不认为这会…人们对GPT-5有这些不切实际的期望，认为它会在我卧室的背景里做空翻，同时也能为我写代码，与我妈妈通电话之类的。这不是事实。它只会是一个非常有效的工具，与GPT-4非常相似。

It's also going to become very normal very quickly. That is actually a really interesting piece if you can plan for the world where people become very used to these tools very quickly. I actually think that's an edge. Assuming that this thing is going to absolutely change everything, in many ways, I think it's actually a downside. It's the wrong mental framing to have of these tools as they come out. Along these lines, you guys are investing a lot into B2B offerings. I think half the revenue last I heard was B2B and then half is B2C. I don't know if that's true, but that's some hired. What is it that you get if you work with OpenAI as a company, as a business? What is the lock? Is it just called OpenAI Enterprise? What's it called? What do you get as a part of that? I think a lot of our B2B customers are using the API to build stuff. I think that's one angle of it.

这种情况也会很快变得非常正常。如果你能够计划一个人们对这些工具非常快速习惯的世界，那实际上是一个非常有趣的部分。我认为这是一个优势。假设这件事情会彻底改变一切，在很多方面，我认为这实际上是一个不利因素。这种错误的心态框架会导致工具推出时的错误看法。在这方面，你们正在向B2B产品投资很多。据我所知，至少一半的收入来自B2B，另一半来自B2C。我不知道这个说法是否正确，但听到的是这样。作为一家公司、一家企业与OpenAI合作，会得到什么？这是一把钥匙吗？是否就称为OpenAI企业版？得到的是什么？我想我们很多B2B客户都在使用API来构建产品。我认为这是其中的一个方面。

I think if you're a ChachieBT B2B customer, we sell Teams, which is the ability to get multiple subscriptions of ChachieBT packaged together. We also have an enterprise version of ChachieBT. There's a bunch of enterprising things that enterprise companies want around like SSO and stuff like that related to ChachieBT Enterprise. I think the coolest thing is actually being able to share some of these prompt templates and GBTs internally. Again, you can make custom things that work really well for your company with all of the information that's relevant to solving problems at your company and share those internally. To me, you want to be able to collaborate with your teammates on the cool things you create using AI. That's a huge unlock for companies. Those are the two biggest value ads. There's higher limits and stuff like that on some of those models. I think being able to share your very domain specific applications is the most useful thing.

我认为，如果你是ChachieBT B2B客户，我们提供的是团队套餐，可以订购多个ChachieBT的订阅服务。我们还有ChachieBT的企业版。企业公司通常需要一些与ChachieBT企业版相关的企业级功能，比如SSO等。我认为最酷的事情实际上是能够在内部共享一些提示模板和GBT。再次强调，您可以根据公司解决问题所需的相关信息，打造适合公司的自定义功能，并在内部共享。对我来说，您应该能够与团队合作，共同使用AI创建出来的精彩产品。这对公司来说是一个巨大的收益。这是两个最大的附加值。在某些模型上还有更高的限制等内容。我认为，能够共享专属领域的应用程序是最有用的事情。

I think if you're a company listening and you think a lot of employees are using ChachieBT, basically the simplest thing you could do is just roll it up into a business account with single sign-on that probably saves you money and makes it easier to coordinate and administer. There's also a bunch of security stuff too. If you want to control, you don't want people to use certain GBTs from the GBT store because you're worried about security or privacy and stuff like that. You don't want your private data going in places. It makes a lot of sense to sign up for that so that you have a little bit more control over what's happening. There's a launch happening tomorrow, I think, after recording this. Can you talk about what is new, what's coming out? I think this is going to come out a couple of weeks after recording, but just what should people know that's new, that's coming out from opening AI tomorrow in our world?

我认为，如果你是一家公司在倾听，而且你认为很多员工在使用ChachieBT，基本上你可以做的最简单的事情就是将它整合到一个商业账户中，使用单一登录。这样可能会为您省钱，使协调和管理变得更加容易。还有一堆安全相关的事情。如果你想控制，你不希望人们从GBT商店使用特定的GBT，因为你担心安全或隐私等问题。你不希望你的私人数据进入某些地方。注册一下是很有道理的，这样你就能对发生的事情有更多的控制。我觉得在录制后的明天会有一个发布会。你能谈谈有什么新东西，即将推出的产品吗？我想这篇文章会在录制后的几周内发布，但人们应该知道从明天开始在我们的世界中会有什么新的、即将推出的项目？

Yeah, there's a few different things. A couple of quick ones are updated GBT for TurboModel, the preview model that we released at DevDay, there's an updated version of that. It fixes this if folks have seen online, people talking about this laziness phenomenon in the model. We improve on that and it fixes a lot of the cases where that was the case. Hopefully the model will be a little bit less lazy. The big thing is the third generation embeddings model. We were talking off-camera before recording about all of the cool use cases from embeddings. It's used in embeddings before. It's essentially the technology that powers many of these questions in answering with your own documentation or your own corpus of knowledge. You were saying you actually have a website where people can ask questions about recordings of the podcast.

是的，有几个不同的事情。一些快速的更新是为TurboModel更新GBT，这是我们在DevDay发布的预览模型，现在有更新版本了。它修复了在线讨论中提到的“懒惰现象”在模型中存在的问题。我们改进了这一点，修复了许多出现这种情况的情况。希望模型会变得不那么懒惰。重要的是第三代嵌入模型。我们在录制前在摄像机外讨论了嵌入的所有酷炫用例。以前也使用了嵌入。这本质上是支持许多回答问题的技术，使用您自己的文档或知识库。你说你实际上有一个网站，人们可以在那里询问关于播客记录的问题。

Lennybot.com, check it out. Yeah, Lennybot.com. My assumption was that Lennybot.com is actually powered by embeddings. You take all of the corpus of knowledge, you take all the recordings, your blog posts, you embed them. When people ask questions, you can actually go in and see the similarity between the question and the corpus of knowledge and then provide an answer to somebody's question and reference an empirical fact, like something that's true from your knowledge base. I'm like, this is super useful and people are doing a ton of this.

Lennybot.com，看看吧。是的，Lennybot.com。我认为Lennybot.com实际上是由嵌入式技术驱动的。你将所有的知识语料库、录音和博客文章嵌入其中。当人们提出问题时，你可以实际查看问题与知识库之间的相似性，然后为某人的问题提供答案并引用一个经验事实，例如你的知识库中的真实信息。我觉得这非常有用，而且很多人正在尝试这样做。

Trying to ground these models in reality in what they know to be true. We know all the things from your podcast to be at least something that you've said before to be true in that sense. We can bring them into the answer that the model is actually generating in response to a question. That'll be super cool. These new V3 embeddings models, again, state of the art performance, the cool thing is actually the non-English performance has increased super significantly. Historically, people really were only using embeddings only worked really well for English. I think now you can use it across so many new languages because it's just so much more performing across those languages. It's like five times cheaper as well, which is wonderful. There's no better feeling than making things cheaper for people. I love it. I think now it's like you can embed, I'm pretty sure it was like 62,000 pages of text for $1, which is very, very cheap. Lots of really cool things you can do with embeddings and excited to see people embed more stuff.

尝试将这些模型植根于他们认为真实的现实情况中。我们知道你的播客里的所有东西至少是你以前说过的真实的东西。我们可以将它们纳入模型在回答问题时实际生成的答案中。这将非常酷。这些新的V3嵌入模型，再次展现了最先进的性能，令人兴奋的是非英文性能实际上有了很大提升。从历史上看，人们实际上只使用嵌入在英语中表现良好。我觉得现在你可以在许多新语言中使用它，因为在这些语言中的性能更加强大。而且价格还便宜了五倍，这太棒了。没有比让东西更便宜更让人开心的事情了。我喜欢。我想现在你可以用1美元嵌入大约62,000页的文本，这非常便宜。嵌入有很多很酷的功能，我很期待看到人们嵌入更多东西。

What a deal. Final question before we get to a very exciting lightning round. Say you're a product manager at a big company or even a founder, what do you think are the biggest opportunities for them to leverage the tech that you guys are building, GPT4, all the other APIs? How should people be thinking about, here's how we should really think about leveraging this power in our existing product or new product whichever direction you want to go? Yeah, I think going back to this theme of new experiences is really exciting to me. I think consumers are just going to be like, you're going to have an edge on other people. If you're providing AI that's not accessible in a chat bot, people are using a ton of chat. And it's a really valuable service area. It's clearly valuable because people are using it. But I think products that move beyond this chat interface really are going to have such an advantage and also thinking about how to take your use case to the next level.

多好的交易。在我们进行非常激动人心的闪电回合之前的最后一个问题。假设你是一家大公司的产品经理，甚至是创始人，你认为最大的机会是什么，可以利用你们正在构建的技术，如GPT4和所有其他API呢？人们应该如何思考，这是我们应该如何真正利用现有产品或新产品中的这种力量的方式？是的，我觉得回到新体验这个主题对我来说真的很令人兴奋。我认为消费者会感到兴奋，因为如果你提供的AI不仅仅是在一个聊天机器人中可以访问的话，那么你就会比其他人更具竞争优势。人们正在大量使用聊天工具。这是一个真正有价值的服务领域。显然，人们正在使用它，所以它是有价值的。但我认为，产品如果超越了这种聊天界面，就会有如此大的优势，还要考虑如何将您的用例提升到更高层次。

I've tried a ton of chat examples that are very basic and providing a little bit of value to me. But I'm like, really, this should go much further and actually build your core experience from the ground up. I've used this product that allows you to essentially manage or view the conversations that are happening online around certain topics and stuff like that. So I can go and look online. What are people saying about GPT-4? And what I just said out loud, what are people saying about GPT-4 is the actual question that I have. And in a normal product experience, I have to go into a bunch of dashboards and change a bunch of filters and stuff like that. And what I really want is just ask my question, what are people saying about GPT-4 and get an answer to that question in a very data-grounded way?

我已经尝试了很多基本的聊天示例，虽然对我有一点价值，但我认为这些还可以更进一步，实际上可以从基础开始构建你的核心体验。我使用了一个产品，可以让你管理或查看在线讨论特定主题等内容。因此我可以上网查看，人们对GPT-4有什么看法？刚才我大声说出来的问题“人们对GPT-4有什么看法？”就是我真正想要的问题。在正常的产品体验中，我必须进入一堆仪表板并更改一堆过滤器之类的东西。而我真正想要的是只要问出自己的问题“人们对GPT-4有什么看法？”并以一种非常数据基础的方式得到答案。

I've seen people solve part of this problem where I'll be like, oh, here's a few examples of what people are saying. And well, that's not really what I want. I want this like summary of what's happening. And I think it just takes a little bit more engineering effort to make that happen. But I think it's like, that is the magical unlock of like, wow, this is an incredible product that I'm going to continue to use instead of like, yeah, this is kind of useful, but I really want more. Awesome. I'll give a shout out to a product. I'm not an investor, but I know the founder called visualelectric.com, which I think is doing exactly this. It's basically tools specifically built for creatives, I think specifically graphic design, to help them create imagery. So, you know, there's like Dolly, obviously, but this takes it to a whole new level where it's kind of this canvas, infinite canvas that you could just generate images, edit, tweak them, and continue to iterate until you have the thing that you need visual, like, I'm not trying to similar to Candor.

我看到有人解决了这个问题的一部分，就像说，这是人们在讨论的一些例子。但实际上，这并不是我想要的。我想要的是对正在发生的事情的概括。我认为只需要更多的工程投入才能实现这一点。但我觉得这就像是一个神奇的发现，哇，这是一个令人难以置信的产品，我将继续使用它，而不仅仅是说，这有点有用，但我真的想要更多。太棒了！我想向一个产品致敬。我并不是投资者，但我知道一个名为visualelectric.com的创始人，我认为他们正好做到了这一点。基本上，这是专门为创意人士构建的工具，我认为主要是图形设计，以帮助他们创建图像。所以，你知道，当然有Dolly，但这把它提升到了一个全新的水平，就像一个无限的画布，你可以轻松生成图像，编辑、微调它们，然后持续迭代，直到你拥有你需要的视觉。这有点类似于Candor。

It's it's even more niche, I think, for more sophisticated graphic design, I think is the use case. But I'm not a designer. So, I'm not I'm not the target customer, but I will say my wife is a graphic designer. She'd never use AI tools. I showed her this and she got hooked on it. She paid for it without even telling me that she is going to become a paid customer. And she just started she created the imagery of our dog, all in all this art. And now it's like on our TV. She the art she created is now sitting. It's like a wave of frame TV. And that's the image on our TV. So anyway, I love that. What was it called again? Visual electric calm.

我认为，对于更复杂的图形设计，它更加专业，我认为这是使用案例。但我不是设计师。所以，我不是目标客户，但我会说我的妻子是一名平面设计师。她从未使用过人工智能工具。我向她展示了这个，她就迷上了。她付了钱，甚至没有告诉我她要成为付费客户。她开始创建我们的狗的形象，所有这些艺术品。现在就像放在我们的电视上。她创作的艺术品现在就在那里。就像一台框架电视。那就是我们电视上的画面。总之，我喜欢这个。它叫什么来着？ Visual electric calm。

Anyway, anything else you wanted to touch on or share before we get to a very exciting lightning round. I've made this statement a few times on my other places, but like for people who are have cool ideas that they should build with AI, like, this is the moment, like there are so many cool things that need to be built for the world using AI. And like, again, if I or other folks on the team at over that can be helpful in like giving you over the hump of like starting that journey of building something really cool, like please reach out like they're just the world needs more cool solutions using these tools and would love to hear about like the awesome stuff that people are building. I would have asked you this at the end, but how would people reach out? What's the best way to actually do that? Twitter LinkedIn might my email should be signed to bull somewhere. I don't want to say it and I could stand with a bunch of emails like you should be able to sign my email if you needed online somewhere. But yeah, Twitter and LinkedIn is usually like the easiest place. And how do they find your Twitter? It's just Logan to Patrick or I think my name shows up as Logan dot chbq or looking gpt. artificial Logan K. Yeah, awesome. Okay. And we'll link to in the show notes. Amazing. Well, Logan with that, we reached our very exciting lightning round. Are you ready? First question, what are two or three books that you recommended most to other people?

无论如何，在我们进行非常激动人心的闪电回合之前，您还想谈论或分享其他任何事吗？我在其他地方多次提到过这个声明，但是对于那些有很酷想法的人，他们应该利用AI来构建，这是时机，世界上有很多需要使用AI构建的很酷的东西。再次强调，如果我或团队中的其他人可以帮助您开始构建真正酷的东西的旅程，比如帮助您解决一些问题，那请联系我们，世界需要更多使用这些工具的酷解决方案，并且很乐意听到人们正在构建的很棒的东西。我本来会在最后问你这个问题的，但人们应该如何联系你？实际上，最好的方式是什么？Twitter LinkedIn可能需要在某处注册我的电子邮件。我不想说出来，否则可能会收到一大堆电子邮件。如果需要，在线上应该能找到我的电子邮件。但是，Twitter和LinkedIn通常是最容易的地方。他们怎样找到您的Twitter呢？只需搜索Logan to Patrick，或者我的名字显示为Logan.chbq或lookingpt. artificial Logan K. 是的，很棒。好的。我们将在节目笔记中提供链接。很棒。好的，有了Logan，我们来到了非常激动人心的闪电回合。您准备好了吗？第一个问题，您向其他人推荐最多的两到三本书是什么？

I think the first one that I read a long time ago and came back to recently is the one room schoolhouse I sell con. Incredible. Yeah, I don't want to it's a lightning round. So I won't say too much. But like incredible story and AI is what is going to enable style cons vision of like a teacher per student to actually happen. So I'm really excited about that.

我认为我很久以前读过的第一本书，最近又回来看的是我在卖掉的那间小学教室。令人难以置信。是的，我不想说太多，因为这本书太精彩了，而且人工智能将会实现风格控的愿景，每位学生都有属于自己的老师。所以我对此感到非常兴奋。

And the other one is that I always come back to was why we sleep. I sleep, sleep and science are so cool. If you don't care about your sleep, like it's one of the biggest up levels that you can do for yourself.

另一个我总是想过的问题是为什么我们需要睡觉。睡眠和科学实在太酷了。如果你不关心你的睡眠，那就无法达到自我提升的最大程度。

What is the favorite recent movie or TV show that you really enjoyed? I'm a sucker for like a good inspirational human story. So I watched with my family recently over the holidays this Gran Turismo movie. And it's a story about somebody who like this kid from London who grew up like doing like sim racing, which is like a virtual race car and did this competition ended up becoming like a real professional race car driver through some competition.

你最近真的很喜欢的一部电影或电视节目是什么？我特别喜欢那种能激励人心的人文故事。最近在假期里，我和家人一起观看了这部《极速狂飙》电影。讲述了一个来自伦敦的孩子，小时候就喜欢模拟赛车比赛，最终通过比赛成为了真正的职业赛车手的故事。

And it's just like really cool to see. Yeah, someone go from driving a virtual car to driving a real car and like competing in the 24 hour lemons and all that stuff. I used to play that game and it was a lot of fun. But yeah, I think any clue how to drive a real car race car. So that's inspiring.

看到这样的情景真的很酷。有人能够从驾驶虚拟汽车转变为驾驶真实汽车，参加24小时柠檬赛等比赛，这真是令人兴奋的事情。我曾经玩过那个游戏，真的很有乐趣。但是，我不确定如何驾驶真实赛车。所以这是令人鼓舞的。

Do you have a favorite interview question they'd like to ask candidates that you're interviewing? Yeah, I'm always curious to hear what people's like the thing that they so strongly believe that people disagree with them on. What do you look for in an answer that seems like, wow, that's a really good signal. I'm oftentimes it's just an entertaining question to ask in some sense, but it's also it's interesting to see like what somebody's like deeply held strong belief is.

在你面试候选人的时候，你有没有一个最喜欢问的面试问题？是的，我总是很好奇听听人们的强烈信仰是什么，而其他人可能不同意他们的意见。你在候选人的回答中寻找什么信号，让你感到，哇，这是一个非常好的信号。这个问题通常只是一种有趣的提问方式，但也很有意思看到某人内心深处坚定的信念是什么。

I think that's it. And you know, not not to like judge whether or not I believe in that, they're like just curious to like see why why people feel that way.

我想就是这样。而且你知道，不是要判断我是否相信那个，只是好奇为什么人们会有那种感觉。

What is a favorite product that you've recently discovered that you really like a kind of narrative of sleep. I have this, I have this really nice sleep mask from this company called and they're not being paid to say this, but it's called like meant, meant to sleep or something like that. It's a weighted sleep mask and it feels incredible when I, I don't know, maybe I just have a heavy head or something like that, but it feels it feels good to wear a weighted sleep mask.

最近你发现的喜爱产品是什么？说说你与睡眠的故事吧。最近我发现了一个很喜欢的产品，来自一家名为的公司，我并没有被支付来说这些话，但是它叫像‘ meant to sleep’ 等等。这是一个带有重量的睡眠眼罩，戴起来感觉很棒。也许是因为我头重，但是戴重量的睡眠眼罩感觉很好。

And I really appreciate it. I have a competing sleep mask that I highly recommend. I'm trying to find it. It's an I've emailed people about it a couple times in my newsletter for gift guns. Okay, my favorite is called the Wawa sleep mask. W a like about it. Oh, a W a O a W.

我真的很感激。我有一款非常推荐的竞争睡眠眼罩。我正在尝试找到它。我已经在我的通讯中给人们发过几次关于它的邮件，用于赠送礼物。好的，我最喜欢的是叫做Wawa睡眠眼罩。我非常喜欢它的一切。哦，W a O a W。

I'll link to it in the show. It makes a lot of room. It's like very large and in their space for your eyes. So like your eyelashes and whatever eyes are impressed on. And it's just it just fits really nicely around the head. And my wife be both wearing masks at night to just speaking of sleep really helps to sleep. Yeah, it's not like I love it. Yeah, it doesn't have the weightiness piece. So it might be worth trying. But everyone I've recommended this to you is like that changed my life. Thank you for helping me sleep better.

我会在节目中提到它。它有很多空间。它就像非常大，在你的空间里，让你的眼睛有很大的活动空间。像你的睫毛和眼睛印象深刻的东西。它非常舒适地围绕在头部。我和我的妻子晚上都戴着面罩，说实话，这对睡眠真的很有帮助。是的，我很喜欢它。它没有很重的感觉。也许值得一试。但是每个我推荐它的人都说它改变了我的生活。谢谢你帮我睡得更好。

And so we'll link to put a little bit of sleep mask. Look at us.

所以我们将戴上一点睡眠眼罩。看着我们。

Two more questions. Do you have a favorite life motto that you often come back to share with friends or family either in work or in life? Yeah, I've got it on a posted note that I write behind my my camera and it's measuring hundreds. I love this idea of measuring things in hundreds.

另外两个问题。你有一个喜欢的人生座右铭吗？你经常与朋友或家人分享，无论是在工作中还是生活中？是的，我把它写在我相机后面的便利贴上，那就是“以百计量”。我喜欢以百计量事物的这个理念。

And it's for folks who are like at the beginning of some journey. I talk to people all the time. They're like, yeah, I've tried this thing and it hasn't worked. And if your mental model is to measure in hundreds, I measure in hundreds the five times that you failed at something you failed and tried zero times.

这是为那些处于旅程初期的人设计的。我经常和人们交谈。他们会说，是的，我尝试过这件事，但它没成功。如果你的思维模式是以百计量，那么我会以百计量你在某件事上失败了五次，实际上你尝试了零次。

And I love that. It's like such a great reminder that everything in life is like built on compounding and multiple attempts at stuff. And if you don't try enough times like you're never going to be successful at it. I love that. I could see why you're successful at OpenAI and why you're a good fit there.

我很喜欢这一点。这就像是一个很好的提醒，生活中的一切都是建立在复利和多次尝试的基础上的。如果你不尝试足够多次，你永远不会成功。我喜欢这一点。我能理解为什么你在OpenAI很成功，为什么你很适合那里。

Final question. So I asked OpenAI I asked chat GPT for a very silly question. Give me a bunch of silly questions to ask Logan, Kilpatrick, head of developer relations at OpenAI. And I went through a bunch I have three here, but I'm going to pick one.

最后一个问题。所以我问了OpenAI，我问了聊天GPT一个非常愚蠢的问题。给我一堆愚蠢的问题，我要问OpenAI的开发者关系主管Logan Kilpatrick。我列了一堆问题，这里有三个，但我只会挑选一个。

If an AI started doing standup comedy, what do you think would be it's go to joke or funny observation about humans? I think today, I think if you were to do this, like I think the go to question would be something along like the so an AI walks into a bar and likely because again, it's trained on some distribution of training data. And like, that's like the most common joke that comes up. And that's probably like, I'm wondering if you came up with a joke right now, whether or not that would shell up in one of the examples.

如果人工智能开始做脱口秀喜剧，你觉得它会用什么样的笑话或有趣的观察来讽刺人类？我认为，如果今天真的发生这种情况，一个常见的问题可能是“所以一个人工智能走进了一个酒吧”，因为它是根据某种训练数据分布进行训练的。那可能是最常见的笑话之一。我在想，如果你现在想出一个笑话，它是否会出现在其中的一个例子里。

I love it. What would be the joke though? We need the joke. We need the punch line. I'm just joking. I know you can't come up with amazing. That's what we have shot you before. We're really irrelevant. Amazing. Logan, thank you so much for being here. Two final questions, even though you've already shared this information, but just for folks to remind them, work and folks find you if they want to reach out and ask you more questions. And how can listeners be useful to you? Yeah, Twitter and LinkedIn, Logan, Kilpatrick or Logan dot GBT on Twitter, please, please shoot me messages. I get a ton of DMs from people.

我喜欢它。不过笑话会是什么呢？我们需要那个笑话。我们需要那个笑点。我只是开玩笑的。我知道你无法想出惊人的笑话。那是我们以前对你开枪的原因。我们真的是毫无关联的。太棒了。Logan，非常感谢你在这里。最后两个问题，尽管你已经分享了这些信息，但为了提醒大家，工作在哪里可以找到你，如果他们想要联系你并问你更多问题？听众们如何对你有所帮助？是的，Twitter和LinkedIn上，Logan Kilpatrick或者Twitter上的Logan GBT，请给我发消息。我收到很多来自别人的私信。

And it's always like really, really interesting stuff. I think the thing that I would love to have held on is like, if people find bugs and things that don't work well in chat to be too, like I oftentimes like see people be like, this thing didn't work really well. And the key and I think we as OpenAI did do a better job of like messaging this to people. But having like shared chats or like actual like tangible reproducible examples are like the two things that we need in order to like actually fix the problems that people have.

这总是非常非常有趣的东西。我想我希望能够保留住的是，如果人们发现聊天中存在的错误和不好的地方，我经常看到人们说这个东西并不运行得很好。我认为我们作为OpenAI应该做得更好一点，向人们传达这个信息。但是，我们需要共享聊天记录或者实际的可复制的例子，这两点是我们需要的，这样我们才能真正解决人们遇到的问题。

Like the model laziness was a good example, where it was kind of hard to figure out what was going on because people would be like, God, the model is lazier. But like, it's hard to figure out like what were the prompts they were using? What was the examples, all that stuff? So send those examples as you come up on things that don't work well and we'll make stuff better for you. Amazing.

就像模型"懒惰"是一个很好的例子，这种情况下很难确定发生了什么，因为人们会说，上帝，这个模型更懒了。但是，很难确定他们使用的提示是什么？他们使用的例子是什么？所有这些东西都是什么？所以当你遇到不顺利的事情时，请告诉我们这些例子，我们会为您做的更好。太棒了。

And I'll also just remind people, if you're listening to this and you're like, Oh, okay, cool. A lot of cool ideas for OpenAI and chat GPT. What you need to do is actually just go to chat. The chat.openai.com and try the stuff out. There's a lot of just like theorizing. But I think once you actually start doing it, you start to see things a little differently. And at this point every day, I mean, there's doing something like asking for ideas for questions, doing research on a newsletter post. And it's just like a tab I'm always coming back to.

我还想提醒大家，如果你正在听这个并且感觉很酷，对OpenAI和Chat GPT有很多酷炫的想法。你需要做的实际上是去chat.openai.com并尝试这些东西。有很多只是纯理论的想法。但我想一旦你开始实际操作，你会开始以不同的方式看待事物。每天都在做像请求问题的想法，研究新闻稿，这些都是我总是回来看的标签。

And I know there's a lot of people just like talking about this sort of thing. And I just want to remind people just like, go sign in, play with it, ask a question, then let me work it on and just see how it goes and keep coming back to it. Is there anything else you want to share along those lines to inspire people to give this a shot? I love it. I think that the phrase of like, you know, people being worried about humans being replaced by AI. And I've seen this narrative online that it's like, it's not AI that's going to replace humans. It's like other humans that are being augmented and like using AI tools that are like going to be more competitive than a job market and stuff like that.

我知道有很多人在谈论这种事情。我只是想提醒大家，去注册一下，试着玩一下，提一个问题，让我来解决，然后看看结果，不断回来尝试。你还想分享什么来鼓励人们尝试一下吗？我喜欢这个。我觉得有人担心人类被人工智能取代的说法。我在网上看到过这样的故事，不是人工智能会取代人类，而是其他人类会被增强，使用人工智能工具，会在职场上更具竞争力。

So go and try these AI tools. Like this is the best time to learn. Like you're going to be more productive and like empowered in your job and the things that you're excited about. So yeah, exciting to see what people use Chachid be T for. And then you can expense your account. I think it's 10 or 20 bucks a month. A lot of companies are paying for this for you. So ask your boss if you can just have it expense to make sure you use the latest version. Anyway, Logan, thank you again so much for being here. This is awesome.

所以去尝试这些人工智能工具吧。现在是学习的最佳时机。通过这些工具，你将会在工作中更加高效和有动力，同时也能更好地处理你感兴趣的事情。所以，很期待看到人们如何使用Chachid be T。然后你可以报销你的账户。我想每月费用大概是10到20美元。很多公司会为此付费。所以问问你的老板是否可以报销这笔费用，确保你使用的是最新版本。总之，Logan，再次非常感谢你的到来。这太棒了。

I mean, thanks for having me and thoughts, low questions. Hopefully those weren't all from Chachid be T. Nope. Only the last one. I did have a bunch of others I was had in the in the belt or in the pocket, I don't know the metaphors in the back pocket. That's the metaphor. But I did not get to them because we had enough great stuff. So no, that was all me. Human. Thank you. Thanks, Logan. Lenny dot a I love it. Lenny bot.com. Check it out. Okay. Thanks, Logan. Bye everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify or your favorite podcast app.

我是说，谢谢你邀请我来同时分享我的想法，很棒的问题。希望这些问题并不全是Chachid提出的。不是的，只有最后一个是。我确实还有很多其他问题，我准备好了，但我没有时间问到，因为我们已经有了足够精彩的内容。所以不，那些问题都是我自己准备的。谢谢你们。谢谢，Logan。Lennybot.com，我很喜欢。请查看它。好的，谢谢，Logan。再见大家。非常感谢你们的收听。如果你觉得有价值，可以在Apple Podcasts、Spotify或你喜欢的播客应用上订阅我们的节目。

Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lenny's podcast.com. See you in the next episode.

另外，请考虑给我们评分或留下评论，因为这真的有助于其他听众找到这个播客。您可以在lenny's podcast.com找到所有过往的集数或了解更多关于节目的内容。我们下期节目再见。