Inside OpenAI | Logan Kilpatrick (head of developer relations)

Logan Kilpatrick: Finding people who are high agency and work with urgency, if I was hiring five people today, those are 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 not needing to get 50 people's different consensus.

Logan Kilpatrick：寻找具有高度代理性且工作紧迫感的人，如果我今天要雇用五个人，这些是我会在人们身上寻找的两个最重要的特征，因为如果你拥有具有高度代理性的人并且不需要获得 50 个人的不同共识，那么你就可以征服世界。

They hear something from our customers about a challenge that they're having, and they're already pushing on what the solution for them is and not waiting for all the other things to happen that...

他们从我们的客户那里听到了他们所面临的挑战，并且他们已经在推动他们的解决方案是什么，而不是等待所有其他事情发生......

People just go and do it and solve the problem, and I love that.

人们只是去做并解决问题，我喜欢这样。

It's so fun to be able to be a part of those situations.

能够参与这些情况真是太有趣了。

Lenny: Today my guest is Logan Kilpatrick.

莱尼：今天我的客人是洛根·基尔帕特里克。

Logan is head of developer relations at OpenAI, where he supports developers building on open AI's, APIs and ChatGPT.

Logan 是 OpenAI 的开发者关系主管，他支持开发者在开放 AI、API 和 ChatGPT 上进行构建。

Before OpenAI, Logan was a machine learning engineer at Apple and advised NASA on their open source policy.

在 OpenAI 之前，Logan 是 Apple 的机器学习工程师，并为 NASA 的开源政策提供建议。

If you can believe it, ChatGPT 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.

如果你相信的话，ChatGPT 推出仅一年多，它改变了我们思考人工智能的方式以及它对我们的产品和生活的意义。

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 一直处于这一变革的最前线，每天都在帮助开发人员和公司弄清楚如何利用这些新的人工智能超能力。

Lenny: In our conversation, we dig into examples of how people are using ChatGPT and the new GPTs and other open AI APIs in their work and their life.

Lenny：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 ChatGPT 和新 GPT 以及其他开放 AI API 的示例。

Logan shares some really interesting advice on how to get better at prompt engineering.

洛根分享了一些关于如何更好地进行即时工程的非常有趣的建议。

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.

我们还了解了 OpenAI 的内部运作方式、他们如何快速交付以及他们在招聘人员中寻找的两个关键属性，此外，Logan 认为在其 API 上构建新产品和新初创公司的最大机会在哪里。

Lenny: 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.

Lenny：我们还稍微了解了 OpenAI 与董事会和 Sam Altman 以及所有这些人度过的非常戏剧性的周末，还有更多。

A huge thank you to Dan Shipper and Dennis Ing for some great questions, suggestions.

非常感谢 Dan Shipper 和 Dennis Ing 提出的一些很好的问题和建议。

With that, I bring you Logan Kilpatrick after a short word from our sponsors.

在我们的赞助商简短发言后，我为您带来了洛根·基尔帕特里克 (Logan Kilpatrick)。

Lenny: This episode is brought to you by Hex.

莱尼：这一集是由 Hex 为您带来的。

If you're a data person, you probably have to jump between different tools to run queries, build visualizations, write Python, and send around a lot of screenshots and CSV files.

如果您是一名数据人员，您可能必须在不同的工具之间切换来运行查询、构建可视化、编写 Python 以及发送大量屏幕截图和 CSV 文件。

Hex brings everything together.

十六进制将一切结合在一起。

Its powerful Notebook UI lets you analyze data in SQL, Python or no code in any combination and work together with live multiplayer and version control.

其强大的 Notebook UI 允许您使用 SQL、Python 或任何组合的无代码分析数据，并与实时多人游戏和版本控制一起工作。

And now Hex's AI tools can generate queries and code, create visualizations, and even kickstart a whole analysis for you, all from natural language prompts.

现在，Hex 的人工智能工具可以生成查询和代码、创建可视化，甚至为您启动整个分析，所有这些都可以通过自然语言提示进行。

It's like having an analytics copilot built right into where you're already doing your work.

这就像在您已经完成工作的地方内置了一个分析副驾驶。

Lenny: Then, when you're ready to share, you can use Hex's drag and drop app builder to configure beautiful reports or dashboards that anyone can use.

Lenny：然后，当您准备好共享时，您可以使用 Hex 的拖放应用程序构建器来配置任何人都可以使用的精美报告或仪表板。

Join the hundreds of data teams like Notion, All Trails, Loom, Mixpanel, and Algolia using Hex every day to make their work more impactful.

加入数百个数据团队，如 Notion、All Trails、Loom、Mixpanel 和 Algolia，每天使用 Hex，让他们的工作更具影响力。

Sign up today at hex.tech/lenny to get a 60-day free trial of the Hex team plan.

立即在 hex.tech/lenny 注册即可获得 Hex 团队计划的 60 天免费试用期。

That's hex.tech/lenny.

那是 hex.tech/lenny。

Lenny: This episode is brought to you by Whimsical, the iterative product workspace.

Lenny：本集由迭代产品工作区 Whimsical 为您带来。

Whimsical helps product managers build clarity and shared understanding faster with tools designed for solving product challenges.

Whimsical 利用专为解决产品挑战而设计的工具，帮助产品经理更快地建立清晰的思路并达成共识。

With Whimsical, you can easily explore new concepts using drag and drop wire frame and diagram components, create rich product briefs that show and sell your thinking, and keep your team aligned with one source of truth for all of your build requirements.

借助 Whimsical，您可以使用拖放线框和图表组件轻松探索新概念，创建丰富的产品简介来展示和推销您的想法，并使您的团队与满足您所有构建需求的单一事实来源保持一致。

Whimsical also has a library of easy-to-use templates from product leaders like myself, including a project proposal one pager and a go to market worksheet.

Whimsical 还拥有一个由像我这样的产品领导者提供的易于使用的模板库，其中包括一个寻呼机项目提案和一份上市工作表。

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尝试一下，看看用异想天开的方式建立清晰度是多么快速和容易。

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在 Whimsical.com/lenny 上注册，即可享受 Whimsical Pro 计划 20% 的折扣。

That's whimsical.com/lenny.

那是whimsical.com/lenny。

Lenny: Logan, thank you so much for being here and welcome to the podcast.

莱尼：洛根，非常感谢你来到这里，欢迎来到播客。

Logan Kilpatrick: Thanks for having me, Lenny.

Logan Kilpatrick：谢谢你邀请我，Lenny。

I'm super excited.

我非常兴奋。

Lenny: 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.

莱尼：我想从房间里的大象开始，我认为大象实际上正在离开房间，因为我认为这是几个月前的事情，但我仍然很好奇。

What was it like on the inside of OpenAI during the very dramatic weekend with the board and Sam and all those things?

在那个充满戏剧性的周末，董事会、Sam 以及所有这些事情，OpenAI 内部感觉如何？

What was it like?

是什么样子的？

And 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?

您是否可以分享一个故事，也许人们还没有听说过里面的情况，发生了什么？

Logan Kilpatrick: Yeah, it was definitely a very stressful Thanksgiving week.

Logan Kilpatrick：是的，这绝对是非常紧张的感恩节一周。

I think in broad context, OpenAI had been pushing for a really long time, since ChatGPT came out, and that was supposed to be one of the first weeks that the whole company had taken time away to actually reset and have a break.

我认为从更广泛的角度来看，自 ChatGPT 推出以来，OpenAI 已经推动了很长一段时间，这应该是整个公司花时间进行实际重置和休息的头几周之一。

So very selfishly, I was super excited to spend time with my family, all that stuff.

非常自私的是，我非常兴奋能与家人共度时光，等等。

And then, yeah, Friday afternoon we got the message that all of the changes were happening, and I think it was super shocking because I think, and this is a perspective a lot of folks share, everybody had and continues to have such deep trust in Sam and Greg and our leadership team that it was just very surprising.

然后，是的，周五下午我们收到消息，所有的变化都在发生，我认为这是非常令人震惊的，因为我认为，这是很多人都认同的观点，每个人都对萨姆和格雷格以及我们的领导团队有如此深的信任，这真是令人惊讶。

And we're also a very, as far as company cultures go, very transparent and very open.

就公司文化而言，我们也是一个非常透明和开放的公司。

So when there's problems or there's things going on, we tend to hear about them.

因此，当出现问题或发生事情时，我们往往会听到它们。

And 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, so very, very surprising.

再说一遍，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一些事情，所以非常非常令人惊讶。

Logan Kilpatrick: I think my being someone who's not based in San Francisco, I was, again, very selfishly kind of happy that it happened over the Thanksgiving break because a lot of folks actually had gone home to different places.

洛根·基尔帕特里克：我认为我是一个不在旧金山的人，我再次非常自私地高兴这件事发生在感恩节假期，因为很多人实际上已经回到了不同的地方。

So 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.

所以我感觉有点安慰，因为我知道我不是唯一一个不在旧金山的人，因为在那段时间里每个人都会亲自聚会做一堆事情并在一起。

So it was nice to know that there was a few other folks who were sort of out of the loop with me.

所以很高兴知道还有其他一些人与我不相干。

Logan Kilpatrick: I think the thing that surprised me the most was just how quickly everybody got back to business.

Logan Kilpatrick：我认为最让我惊讶的是每个人恢复工作的速度有多快。

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, like expecting, I don't know, something weird to be going on or happening.

感恩节后的下周，我飞往旧金山，我原本没有计划这样做，亲自与团队打交道，结果在周一早上，我走进办公室，就像在期待，我不知道，会发生什么奇怪的事情。

And really it was like people laser focused 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.

事实上，这就像人们全神贯注地回到工作岗位，我认为这说明了我们团队的能力以及每个人对实现我们正在实现的使命感到非常兴奋的人。

So I think that was, yeah, that was the most surprising thing of the whole incident.

所以我认为这是整个事件中最令人惊讶的事情。

I think a lot of companies would've 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.

我认为很多公司都有可能因此而真正脱轨一段不平凡的时间，而每个人都很快又回到了原来的状态，我喜欢这一点。

Lenny: I feel like it also maybe brought the team closer together.

Lenny：我觉得这也可能让团队更加紧密。

It feels like it was this kind of traumatic experience that may bring folks together because it was something they all shared.

感觉正是这种创伤性经历可以让人们走到一起，因为这是他们共同的事情。

Is there anything along those lines that's like, "Wow, things are a little different now?"

有没有类似这样的感觉：“哇，现在情况有点不同了？”

Logan Kilpatrick: One of my takeaways was I'm actually very grateful that this happened when it happened.

Logan Kilpatrick：我的收获之一是，我实际上非常感激这件事发生时的发生。

I think today the stakes are...

我认为今天的赌注是...

They're still relatively high.

他们的水平还是比较高的。

People have built their businesses on top of OpenAI.

人们已经在 OpenAI 之上建立了自己的业务。

We have tons of customers who love ChatGPT, so if something bad happens to us, we definitely impact our customers.

我们有大量喜欢 ChatGPT 的客户，所以如果我们发生不好的事情，我们肯定会影响我们的客户。

But on the world scale, somebody else will build a model if OpenAI disappeared and continue towards this progress of general intelligence.

但在世界范围内，如果 OpenAI 消失，其他人将会建立一个模型，并继续朝着通用智能的进步迈进。

I think fast-forward five or 10 years, if something like this would've happened and we hadn't gone through the hopeful upcoming work transformation and all those changes that are going to happen, I think it would've been a little bit, or potentially much worse, of an outcome.

我认为快进五年或十年，如果发生这样的事情，而我们没有经历充满希望的即将到来的工作转型以及所有将要发生的变化，我认为结果会有点，或者可能会更糟。

So I'm glad that things happened when the stakes are a little bit lower.

所以我很高兴事情发生在风险稍微低一点的时候。

Logan Kilpatrick: And I totally agree with you.

Logan Kilpatrick：我完全同意你的观点。

It's like the team has been growing so rapidly over the last year since I joined.

自从我加入以来，这个团队在过去的一年里发展得非常迅速。

It's been crazy to think about how many new folks there are, 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 ChatGPT, the launch of GPT-4, and for folks who weren't around for some of those launches, it was perhaps step day.

想想有多少新人真是太疯狂了，我真的认为这确实让人们走到了一起，因为大多数人，历史上的许多人，当我加入时，ChatGPT 的发布，GPT-4 的发布将我们所有人团结在一起，对于那些没有参加其中一些发布的人来说，这可能是一步日。

For folks who are on for dev day, it was probably this event.

对于那些参加开发日的人来说，这可能就是这个活动。

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 GPT five, whenever that comes, and stuff like that.

所以我认为我们举办的这些活动确实让公司跨职能地团结在一起，所以希望未来所有的活动都将是真正令人兴奋的事情，比如 GPT 5，无论何时到来，等等。

Lenny: Awesome.

莱尼：太棒了。

We're going to talk about GPT-5.

我们将讨论 GPT-5。

Going in a totally different direction, what is the most mind-blowing or surprising thing that you've seen AI do recently?

朝着完全不同的方向发展，您最近看到人工智能所做的最令人兴奋或令人惊讶的事情是什么？

Logan Kilpatrick: The things that are getting me most excited are these new interfaces around AI, like the Rabbit R-1.

Logan Kilpatrick：最让我兴奋的是这些围绕人工智能的新界面，比如 Rabbit R-1。

I don't know if you've seen that, but consumer hardware device, this company called TL Draw.

我不知道你是否见过，但是消费类硬件设备，这家公司叫做TL Draw。

I don't know if you've seen TL Draw.

不知道大家有没有看过《TL Draw》。

Lenny: I think.

莱尼：我想。

You sketch something and then it makes it as a website?

你画了一些东西然后把它做成一个网站？

Logan Kilpatrick: And that's only a small piece of what TL Draw 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 TL Draw folks a couple of days ago, really blows my mind to think about how chat is the predominant way that folks are using AI today.

Logan Kilpatrick：这只是 TL Draw 实际工作的一小部分，但是有所有这些与 AI 交互的新界面，我想几天前我正在与 TL Draw 人员进行对话，想到聊天如何成为当今人们使用 AI 的主要方式，这真的让我大吃一惊。

And I actually think, and this is my bulk case for the folks at TL Draw, I'm super excited for them to build what they're building, but they're sort of 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 filled in all the details and you might see a reference to a file and to a video and all of these different things.

我实际上认为，这是我对 TL Draw 人员的大宗案例，我对他们构建他们正在构建的东西感到非常兴奋，但他们正在构建这种无限画布体验，你可以想象，当你每天与人工智能交互时，你可能想跳到你的无限画布，人工智能已经填充了所有细节，你可能会看到对文件和视频以及所有这些不同事物的引用。

Logan Kilpatrick: And it's such a cool way.

Logan Kilpatrick：这是一种很酷的方式。

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.

我认为，作为人类，以这种格式查看内容实际上比在聊天中列出一堆内容更有意义。

So I'm really, really excited to see more people.

所以我真的非常非常兴奋能见到更多的人。

I think 2024 is the year of multimodal AI, but it's also the year that people really push the boundaries of some of these new UX paradigms around AI.

我认为 2024 年是多模式人工智能的一年，但这也是人们真正突破人工智能周围的一些新用户体验范式界限的一年。

Lenny: It's funny.

莱尼：这很有趣。

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," or, "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 OpenAI APIs.

我觉得聊天机器人，作为多年的产品经理，感觉就像我们每次关于新功能的头脑风暴会议，就像，“嘿，我们应该建立一个聊天机器人来解决这个问题。”就像常年出现的“哦，聊天机器人”或者“有人会建议我们做一个聊天机器人”，现在它们实际上很有用并且正在工作，每个人都在构建聊天机器人，其中很多都基于 OpenAI API。

Lenny: 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?

Lenny：这并不是真正的问题，但也许问题是，当人们考虑构建像 TL Draw 这样的产品时，他们应该考虑什么？

Where OpenAI is not going to go versus here's what OpenAI is going to do for us.

OpenAI 不会去的地方与 OpenAI 将为我们做的事情。

We shouldn't worry about them building a version of TL Draw in the future.

我们不应该担心他们将来会构建 TL Draw 的版本。

What's the way to think about where you won't be disrupted essentially by OpenAI, knowing also they may change their mind?

在知道 OpenAI 可能会改变主意的情况下，如何考虑在哪些方面不会从本质上受到 OpenAI 的干扰？

Logan Kilpatrick: That's a great question.

Logan Kilpatrick：这是一个很好的问题。

I think we're deeply focused on these very, very general use cases like the general reasoning capabilities, the general coding, the general writing abilities.

我认为我们非常关注这些非常非常普遍的用例，例如一般推理能力、一般编码、一般写作能力。

I think where you start to get into some of these very vertical applications...

我认为你从哪里开始进入这些非常垂直的应用程序......

And I think a great example of this is actually Harvey.

我认为哈维就是一个很好的例子。

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 legal firms and stuff like that.

我不知道你是否见过 Harvey，但这是一个法律人工智能用例，他们正在构建自定义模型和工具来帮助律师和律师事务所的人员以及类似的东西。

And that's a great example of our models are probably never going to be as capable as some of the things that Harvey's doing because our goal and our mission is really to solve this 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.

这是一个很好的例子，我们的模型可能永远不会像 Harvey 所做的一些事情那样有能力，因为我们的目标和使命实际上是解决这个非常普遍的用例，然后人们可以做一些事情，比如微调，并在此基础上构建所有自己的自定义 UI 和产品功能。

Logan Kilpatrick: I have a lot of empathy and a lot of excitement for people who are building these very general products today.

Logan Kilpatrick：我对今天正在构建这些非常通用的产品的人们充满同情和兴奋。

I talk to a lot of developers who are building just general purpose assistants and general purpose agents and stuff like that.

我和很多开发人员交谈过，他们只是构建通用助手和通用代理以及类似的东西。

I think it's cool and it's a good idea.

我认为这很酷而且是个好主意。

I think the challenge for them is they are going to end up directly competing against us in those spaces and I think there's enough room for a lot of people to be successful, but to me you shouldn't be surprised when we end up launching some general purpose agent product because, again, we're sort of building that with GPTs today and versus we're not going to launch some of these varied verticalized products.

我认为他们面临的挑战是他们最终将在这些领域与我们直接竞争，而且我认为很多人都有足够的成功空间，但对我来说，当我们最终推出一些通用代理产品时，你不应该感到惊讶，因为我们今天正在使用 GPT 来构建它，而我们不会推出一些不同的垂直产品。

We're not going to launch an AI sales agent.

我们不会推出人工智能销售代理。

That's just not what we're building towards.

这不是我们的目标。

And companies who are and have some domain specific knowledge and they're really excited about that problem space, they can go into that and leverage our models and end up continuing to be on the cutting edge without having to do all that R&D effort themselves.

那些拥有一些特定领域知识并且对这个问题空间感到非常兴奋的公司，他们可以深入研究并利用我们的模型，最终继续处于领先地位，而无需自己进行所有研发工作。

Lenny: Got it.

莱尼：明白了。

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.

因此，我听到的建议是具体了解用例，这可能是经过调整以对销售等用例特别有用的模型，也可能是制作界面或体验解决更具体的问题。

Logan Kilpatrick: And I think if you're going to try and solve this very general, if you're going to try to build the next general assistant to compete with something like ChatGPT, it has to be so radically different.

Logan Kilpatrick：我认为，如果你要尝试解决这个非常普遍的问题，如果你要尝试构建下一个通用助理来与 ChatGPT 之类的东西竞争，它必须是截然不同的。

People have to really be like, "Wow, this is solving these 10 problems that I have with ChatGPT, and therefore I'm going to go and try your new thing." Otherwise we're just putting a ton of engineering efforts and research effort into making that an incredible product, and it's just going to be the normal challenges of building companies.

人们必须真的会说，“哇，这解决了我在 ChatGPT 上遇到的 10 个问题，因此我要去尝试你的新东西。”否则，我们只是投入大量的工程工作和研究工作来使其成为令人难以置信的产品，而这将成为建筑公司的正常挑战。

It's just hard to compete against something like that.

很难与这样的东西竞争。

Lenny: Awesome.

莱尼：太棒了。

Okay, that's great.

好的，太好了。

I was going to get to that later, but I'm glad we touched on that.

我本来打算稍后再谈这个问题，但我很高兴我们谈到了这个问题。

I imagine that's on the minds of many developers and founders.

我想这是许多开发人员和创始人的想法。

Kind of along the same lines, there's a lot of talk about how ChatGPT and GPTs and many of the tools you guys offer are going to make a company much more efficient.

类似地，有很多关于 ChatGPT 和 GPT 以及你们提供的许多工具将如何提高公司效率的讨论。

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 can we actually do to make our company more efficient.

他们不需要那么多的工程师、数据科学家、产品经理等，但我认为公司也很难思考我们实际上可以做些什么来提高公司的效率。

I'm curious if there's any examples that you can share of how companies have taken built a, 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?

我很好奇是否有任何例子可以分享一下公司如何在内部构建一个 GPT 来做某事，这样他们就不必在上面花费工程时间，或者通常只使用 OpenAI 工具来提高他们内部业务的效率？

Logan Kilpatrick: Yeah, that's a great question.

Logan Kilpatrick：是的，这是一个很好的问题。

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...

我想知道你是否可以将其放在展会笔记或类似的内容中，但是哈佛商学院有一项非常出色的研究...

And I forgot which consulting firm they did it with.

我忘了他们是和哪家咨询公司合作的。

Maybe it was like Boston Consulting or something like that, but it might've been one of the other ones.

也许它就像波士顿咨询公司或类似的公司，但也可能是其他公司之一。

And they talk about the order of magnitude of efficiency gain for those folks who are using AI tools, I think it was chat GPT specifically in those use cases that they were using, comparatively against folks who aren't using AI.

他们谈论了那些使用人工智能工具的人的效率增益的数量级，我认为特别是在他们使用的那些用例中，与不使用人工智能的人相比，这是聊天 GPT。

I'm really excited, also, just as just more time passes between the release of this technology, for us to get more empirical studies.

我也很兴奋，随着这项技术的发布间隔时间越来越长，我们可以获得更多的实证研究。

I feel just for myself, as somebody who's an engineer today, I use ChatGPT and I can ship things way faster than I would be able to.

我只是为自己感到，作为当今的一名工程师，我使用 ChatGPT 并且我可以比我更快地交付东西。

Logan Kilpatrick: 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.

Logan Kilpatrick：我自己没有任何好的指标来给出具体的数字，但我猜人们现在正在从事这些研究。

I think engineering is actually one of the highest leverage things that you could be using AI to do today and really unlocking, probably on the order of at least a 50% improvement, especially for some of the lower hanging fruit software engineering tasks.

我认为工程实际上是你今天可以使用人工智能做的、真正解锁的最高杠杆的事情之一，可能至少有 50% 的改进，特别是对于一些容易实现的软件工程任务。

The models are just so capable at doing that work.

这些模型非常有能力完成这项工作。

And it's crazy to think...

而且想想就很疯狂...

And I'm guessing, actually, GitHub probably has a bunch of really great studies they publish around copilots and you could use those as an analogy for what people are getting from ChatGPT as well.

我猜，实际上，GitHub 可能发布了很多关于副驾驶的非常出色的研究，您也可以用这些来类比人们从 ChatGPT 中获得的信息。

But those are probably the highest leverage things.

但这些可能是杠杆率最高的事情。

Logan Kilpatrick: I think now with GPTs, people are able to go in and solve some of these more tactical problems.

Logan Kilpatrick：我认为现在有了 GPT，人们就能够介入并解决一些更具战术性的问题。

I think one of the general challenges with ChatGPT 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.

我认为 ChatGPT 的一般挑战之一是它为许多不同的用例提供了不错的答案，但通常它对于您公司的声音或您正在做的工作的细微差别不够具体。

And I think now with GPTs and people who are using the teams in ChatGPT and Enterprise in ChatGPT, they can actually build those things, incorporate the nuance of their own company, and make solving those tasks much, much more domain specific.

我认为现在有了 GPT 以及使用 ChatGPT 中的团队和 ChatGPT 中的企业团队的人们，他们实际上可以构建这些东西，融入自己公司的细微差别，并使解决这些任务变得更加特定于领域。

So we literally just launched GPTs a couple of months ago, 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 to come as folks get super excited about sharing those case studies.

所以我们几个月前才刚刚推出了 GPT，所以我不认为有任何好的公开成功故事，但我猜现在公司正在取得成功，希望我们在未来几个月能听到更多关于这方面的信息，因为人们对分享这些案例研究感到非常兴奋。

Lenny: I'll share an example.

莱尼：我将分享一个例子。

So I have this good friend, his name's 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.

我有一个好朋友，他的名字叫 Dennis Yang，他在 Chime 工作，他告诉我他们在 Chime 所做的两件事似乎提供了价值。

One is he built a GPT that helps write ads for Facebook and Google just gives you ideas for ads to run, and so that takes a little load off the marketing team or the growth team.

一是他构建了一个 GPT，可以帮助为 Facebook 编写广告，而 Google 只是为您提供运行广告的想法，因此这减轻了营销团队或增长团队的负担。

And then he built another GPT that delivers experiment results, kind of like a data scientist, with here's the result of this experiment.

然后他建立了另一个 GPT 来提供实验结果，有点像数据科学家，这是这个实验的结果。

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.

然后你可以与它交谈并询问诸如“嘿，你认为我们应该运行这个多久”，或者“这对我们的产品可能意味着什么”之类的问题。

And I think it's really-

我认为这真的是——

Lenny: Like you said.

莱尼：正如你所说。

Is there anything else that comes to mind?

还有其他想到的吗？

Just things you've heard people do just like, "Wow, that was a really smart way of...

就像你听到人们做的事情一样，“哇，这是一种非常聪明的方式......

" So I get there's engineering, co-piloting type tooling.

“所以我得到了工程副驾驶类型的工具。

Is there anything else that comes to mind?

还有其他想到的吗？

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."

只是为了给人们一些启发，比如“哇，这是一种有趣的方式，我应该考虑使用其中一些工具。”

Logan Kilpatrick: I've seen some interesting GPTs around the planning use cases, like you want to do OKR planning for your team or something like that.

Logan Kilpatrick：我在规划用例方面看到了一些有趣的 GPT，比如你想为你的团队进行 OKR 规划或类似的事情。

I just actually saw somebody tweet it literally yesterday.

我昨天才看到有人发推文。

I've seen some cool venture capital ones of doing diligence on a deal flow, which is kind of interesting, and getting some different perspectives.

我见过一些很酷的风险投资公司对交易流程进行尽职调查，这很有趣，并获得了一些不同的观点。

I think all of those horizontal use cases where you can bring in a different personality and get perspective on different things I think is really cool.

我认为所有这些横向用例都可以带来不同的个性并获得对不同事物的看法，我认为这真的很酷。

I've personally used a GPT, the private GPT that I use myself that helps with some of the planning stuff for different quarters, and just making sure that I'm being consistent in how I'm framing things like driving back to individual metrics, stuff that, when people do planning, they often miss in our data, and then it's been super helpful for me to have a GPT to force me to think about some of those things.

我个人使用过 GPT，这是我自己使用的私人 GPT，它有助于为不同的季度制定一些计划，并确保我在制定诸如返回个人指标之类的事情时保持一致，当人们进行计划时，他们经常会在我们的数据中忽略这些内容，然后拥有 GPT 来迫使我思考其中一些事情对我来说非常有帮助。

Lenny: Wait, can you talk more about this?

莱尼：等等，你能多谈谈这个吗？

What does this GPT do for you and what do you feed it?

这个 GPT 对你有什么作用，你又给它提供什么？

Logan Kilpatrick: Yeah, I forgot what article I saw online, but it was some article that was talking about what are the best ways to set yourself up for success in planning.

Logan Kilpatrick：是的，我忘记了在网上看到的哪篇文章，但有一篇文章讨论了让自己在规划中取得成功的最佳方法是什么。

And I took a bunch of the...

我还拿了一堆...

I'll see if I can make it 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 now when I do any of my planning of I want to build this thing, I put it through and have it generate a timeline, generate all the specifics of what are the metrics and success that I'm looking for, who might be some important cross-functional stakeholders to include in the planning process, all that stuff, and it's been helpful.

我会看看我是否可以在此之后将其公开并向您发送一个链接，但是从中获取了一堆示例，然后将其中一些建议放入 GPT，然后现在当我进行任何我想要构建这个东西的规划时，我将其完成并让它生成一个时间表，生成我正在寻找的指标和成功的所有细节，谁可能是一些重要的跨职能利益相关者，包括在规划过程中，所有这些东西，这很有帮助。

Lenny: Wow, that is very cool.

莱尼：哇，那太酷了。

That would be awesome if you made it public.

如果你公开的话那就太棒了。

And if you do, we'll link to it and we'll make it the number one most popular GPT in the store.

如果您这样做，我们将链接到它，并使其成为商店中最受欢迎的 GPT。

Lenny: Going in a slightly different direction, there's this whole genre of prompt engineering.

Lenny：稍微不同的方向，有整个类型的即时工程。

It feels like it's one of these really emerging skills.

感觉这是真正新兴的技能之一。

I actually saw a startup hiring a prompt engineer, one of the startups I've invested in, and I think that's going to blow a lot of people's minds that there's this new job that's emerging.

我实际上看到一家初创公司正在招聘一名快速工程师，这是我投资的初创公司之一，我认为这会让很多人大吃一惊，因为这个新工作的出现。

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 need it to do.

我知道这个想法不会永远持续下去，理论上人工智能会非常聪明，你不需要真正考虑如何聪明地要求它做你需要它做的事情。

But can you just describe this idea of what is prompt engineering, this term that people might be hearing?

但是您能否描述一下人们可能听到的“即时工程”这个概念？

And then even more interestingly, just what advice do you have for people to get better at writing prompts for, say, ChatGPT or through the API in general?

更有趣的是，您对人们如何更好地为 ChatGPT 或通过一般 API 编写提示有什么建议？

Logan Kilpatrick: Yeah, this is such an interesting space, and I think it's another space where I'm excited for people to do more scientific empirical studies about, because there's so much gut feeling, best practices that maybe aren't actually true in a certain way.

Logan Kilpatrick：是的，这是一个非常有趣的领域，我认为这是另一个我很高兴人们能够进行更多科学实证研究的领域，因为有太多的直觉和最佳实践在某种程度上可能并不真实。

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 ask.

我认为即时工程存在并出现的原因是因为模型非常倾向于，因为它们的训练方式，只为你提供你所问问题的答案。

Crap in crap out.

废话进废话。

If you ask a pretty basic question, you're going to get a pretty basic response.

如果您问一个非常基本的问题，您将得到一个非常基本的答复。

And actually the same thing is true for humans, and you can think of a great example of this.

事实上，对于人类来说也是如此，你可以想出一个很好的例子。

When I go to another human and I ask, "How's your day going," they say, "It's going pretty good."

当我去找另一个人时，我问“你今天过得怎么样”，他们说：“一切都过得很好。”

Logan Kilpatrick: Literally, absolutely zero detail, no nuance, not very interesting at all versus, again, if you have some context with a person, if you have a personal relationship with them and I ask you, "Hey Lenny, how's your day going?

洛根·基尔帕特里克（Logan Kilpatrick）：从字面上看，绝对是零细节，没有细微差别，一点也不有趣，相反，如果你与一个人有一些背景，如果你与他们有个人关系，我问你，“嘿莱尼，你今天过得怎么样？

How did the last podcast go," et cetera, et cetera, you just have a little bit more context and agency to go and answer my question.

上一个播客进展如何，”等等，等等，你只是有更多的背景和机构来回答我的问题。

I think this is prompt engineering.

我认为这是即时工程。

Logan Kilpatrick: My whole position on this is prompt engineering is a very human thing.

Logan Kilpatrick：我对此的整体立场是，即时工程是一件非常人性化的事情。

When we want to get some value out of a human, we do this prompt engineering.

当我们想从人类身上获得一些价值时，我们就会进行这种即时工程。

We try to effectively communicate with that human in order to get the best output.

我们尝试与该人进行有效沟通，以获得最佳输出。

And the same thing is true of models.

模型也是如此。

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 imagine a human level intelligence but literally no context.

我认为这就像，因为我们正在使用一个看起来非常聪明的系统，我们假设它具有所有这些上下文，但这真的就像想象人类水平的智能，但实际上没有上下文。

It has no idea what you're going to ask it.

它不知道你要问它什么。

It's never met you before.

它以前从未见过你。

It has no idea who you are, what you do, what your goals are.

它不知道你是谁、你做什么、你的目标是什么。

And it's the reason that you get super generic responses sometimes is because people forget they need to put that context in the model.

这就是有时你得到超级通用响应的原因是因为人们忘记了他们需要将上下文放入模型中。

Logan Kilpatrick: 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.

Logan Kilpatrick：所以我认为这将有助于解决这个问题，我们已经在 Dali 的背景下做到了这一点，所以当你进入我们拥有的图像生成模型时，Dali，你说，“我想要一张乌龟的图片”，它所做的就是它实际上接受了这个描述。

It says, "I want a picture of a turtle," and it changes it into this high fidelity, like generate a picture of a turtle with a shell, with a green background and lily pads in the water and all this other.

它说，“我想要一张乌龟的图片”，然后它会将其更改为高保真度，就像生成一张带壳乌龟的图片，背景为绿色，水中有睡莲叶，等等。

It adds all this fidelity because that's the way that the model is trained.

它增加了所有这些保真度，因为这就是模型的训练方式。

It's trained on examples with super high fidelity.

它经过超高保真度示例的训练。

This will happen with text models.

文本模型会发生这种情况。

Logan Kilpatrick: You can imagine a world where you go into ChatGPT and 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 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 at the user will hopefully be able to be like, "Yep, this is kind of what I wanted.

Logan Kilpatrick：你可以想象这样一个世界：你进入 ChatGPT，然后说：“给我写一篇关于人工智能的博客文章。”它会自动变成这样，“让我生成一个关于这个人真正想要的东西的更高保真度的描述，这会为我生成一篇关于人工智能的博客文章，讨论这些不同技术和一些示例用例之间的权衡，并引用一些最新的论文，”它会为你完成所有这些，然后你作为用户将希望能够像，“是的，这就是我想要的。

Let me edit this.

让我编辑一下。

Let me edit this here."

让我在这里编辑一下。”

Logan Kilpatrick: And again, the inherent problem is we're lazy as humans.

Logan Kilpatrick：再说一次，固有的问题是我们作为人类是懒惰的。

We don't want to type all...

我们不想输入所有...

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.

我们并不是真的想输入我们的意思，我认为人工智能系统实际上将帮助解决其中一些问题。

Lenny: So until that day, what can people do better when they're prompting, say ChatGPT?

Lenny：所以直到那一天之前，人们在ChatGPT 等提示下可以做得更好吗？

And I'll give you an example.

我给你举个例子。

Tim Ferris suggested this really good idea that I've been stealing, which is when you're preparing for an interview, you go to chat GPT.

Tim Ferris 提出了一个我一直在偷的非常好的主意，那就是当你准备面试时，你去聊 GPT。

And so I did this for you.

所以我为你做了这件事。

I was like, "Hey, I'm interviewing Logan Kilpatrick, he is head of developer relations at OpenAI, on my podcast.

我当时想，“嘿，我正在我的播客上采访 Logan Kilpatrick，他是 OpenAI 开发者关系主管。

Give me 10 questions to ask him in the style of Tyler Cowen," who I think is the best interviewer.

请给我 10 个问题，以泰勒·考恩 (Tyler Cowen) 的方式问他，我认为他是最好的面试官。

He is so good at just very pointed original questions.

他非常擅长提出非常尖锐的原创问题。

So what advice would you have for me to improve on that prompt to have better results?

那么，您对我有什么建议可以改进该提示以获得更好的结果？

The questions were fine.

问题问得很好。

They're great.

他们很棒。

They're interesting enough, but they weren't like, "Holy, these are incredible." So I guess what advice would you give me in that example?

它们很有趣，但他们并不喜欢，“天哪，这些太不可思议了。”所以我想在这个例子中你会给我什么建议？

Logan Kilpatrick: Yeah, that's a great example where thinking in context of who it is that you're asking questions about.

Logan Kilpatrick：是的，这是一个很好的例子，在上下文中思考你所问的问题是谁。

I'm probably not somebody who has enough information about me on the internet where the model actually has been trained and knows the nuances of my background.

我可能不是一个在互联网上拥有足够关于我的信息的人，模型实际上已经接受过培训并且了解我背景的细微差别。

I think there's probably much more famous guests where it might be that there's enough context on the internet to answer the questions.

我认为可能有更多著名的客人，互联网上可能有足够的背景来回答这些问题。

You actually have to do some of that work.

你实际上必须做一些工作。

You need to, say if you're using browse with Bing, for example, you could say, "Here's a link to Logan's blog and some of the things that he's talked about.

例如，如果您使用必应浏览，您需要说，“这是 Logan 博客的链接以及他谈论的一些内容。

Here's a link to his Twitter.

这是他的 Twitter 的链接。

Go through some of his tweets, go through some of his blogs and see what his interesting perspectives are that we might want to surface on the blog," or something like that.

浏览他的一些推文，浏览他的一些博客，看看他的有趣观点是什么，我们可能想在博客上展示”，或者类似的东西。

Logan Kilpatrick: It's, again, giving the model enough context to answer the question.

Logan Kilpatrick：这再次为模型提供了足够的背景来回答问题。

I think, again, that prompt actually might work really well for somebody who has it, if you were interviewing Tom Cruise or something like that, somebody who has a lot of information about them on the internet.

我再次认为，如果你正在采访汤姆·克鲁斯或类似的人，这个提示实际上可能对那些在互联网上拥有大量有关他们的信息的人来说非常有效。

It probably works a little bit better.

它的效果可能会好一些。

Lenny: So the advice there is just give more context.

莱尼：所以建议只是提供更多背景信息。

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 you go.

它不会告诉你，“嘿，我实际上对洛根了解不多，所以请给我更多信息。”就像，“给你。

Here's a bunch of good questions."

这里有很多好问题。”

Logan Kilpatrick: Exactly.

洛根·基尔帕特里克：没错。

It wants to.

它想要。

It so deeply wants to answer your question.

它非常想回答你的问题。

It doesn't care that it doesn't have enough context.

它并不关心它没有足够的上下文。

It's 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 anything a value.

这是你能想象到的世界上最渴望回答这个问题的人，如果没有这样的背景，就很难做到，也很难赋予任何东西价值。

If we got t-shirts printed, they should say, "Context is all you need.

如果我们印制 T 恤，他们应该说：“上下文就是你所需要的。

Context is the only thing that matters." It's such an important piece of getting a language model to do anything for you.

背景是唯一重要的事情。”对于让语言模型为你做任何事情来说，这是非常重要的一部分。

Lenny: Any other tips?

莱尼：还有其他建议吗？

Just as people are sitting there, maybe they have ChatGPT open right now as they're crafting a prompt, is there anything else that you'd say would help them have better results?

就像人们坐在那里一样，也许他们在制作提示时打开了 ChatGPT，您认为还有其他什么可以帮助他们获得更好的结果吗？

Logan Kilpatrick: We actually have a prompt engineering guide, which folks should go and check out.

Logan Kilpatrick：我们实际上有一个快速的工程指南，人们应该去看看。

It has some of the examples.

它有一些例子。

It depends on the order of magnitude of how much performance increase you can get.

这取决于您可以获得多少性能提升的数量级。

There's a lot of really small silly things, like adding a smiley face, increases the performance of the model.

有很多非常小的愚蠢的事情，比如添加笑脸，可以提高模型的性能。

I'm sure folks have seen a lot of these silly examples, but telling the model to take a break and then answer the question, all these kinds of things.

我相信人们已经见过很多这样愚蠢的例子，但是告诉模型休息一下然后回答问题，所有这些事情。

And again, if you think about it, it's because the corpus of information that's trained these models is the same things that humans have sent back and forth to each other.

再一次，如果你仔细想想，这是因为训练这些模型的信息库与人类相互发送的信息是相同的。

So you telling a human, "When I go take a break and then I come back to work, I'm fresher and I'm able to answer questions better and do work better," so very similar things are true for these models.

所以你告诉一个人，“当我休息一下然后回来工作时，我会更有活力，我能够更好地回答问题并做得更好，”所以对于这些模型来说非常相似的事情是真实的。

And again, when I see a smiley face at the end of someone's message, I feel empowered that this is going to be a positive interaction and I should be more inclined to give them a great answer and spend more effort on the thing that they asked me for.

再说一遍，当我在某人的信息末尾看到一个笑脸时，我感到有力量，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，并在他们要求我做的事情上花更多的精力。

Lenny: Wow, wait.

莱尼：哇，等等。

So that's a real thing.

所以这是真的。

If you had a smiley face, it might give you better results.

如果你有一张笑脸，可能会给你带来更好的结果。

Logan Kilpatrick: Again, it's like the challenge with all this stuff is it's very nuanced and it's also it's a small jump in performance.

Logan Kilpatrick：再说一次，所有这些东西的挑战都是非常微妙的，而且性能上也有一个小跃升。

You could imagine on the order of one or 2%, which for a few sentence answer might not even be a discernible difference.

您可以想象 1% 或 2% 左右，对于几句话来说，这甚至可能不是明显的差异。

Again, if you're generating an entire saga of texts, the smiley face could actually make a material difference for you, but for something small and textual it might not.

再说一遍，如果您要生成完整的文本传奇，笑脸实际上可能会对您产生重大影响，但对于小而文本的内容可能不会。

Lenny: Okay, good tip.

莱尼：好的，很好的建议。

Amazing.

惊人的。

Okay, we've talked about GPTs I think maybe might be helpful to describe what is this new thing that you guys launched, GPTs, and I'm curious just how it's going.

好的，我们已经讨论了 GPT，我想这可能有助于描述你们推出的新事物 GPT，我很好奇它进展如何。

This is a really big change and element of OpenAI now with this idea that you could build your own mini, and I'm almost explaining it, your mini open ChatGPT and then people can...

这是 OpenAI 的一个非常大的变化和元素，现在你可以构建自己的迷你，我几乎正在解释它，你的迷你打开 ChatGPT，然后人们可以......

I think you can pay for it.

我想你可以付钱。

You can charge for your own GPT or is it all free right now?

您可以为自己的 GPT 收费还是现在全部免费？

Logan Kilpatrick: It's ll free right now.

Logan Kilpatrick：现在免费。

It's all free.

一切都是免费的。

Lenny: Okay.

莱尼：好的。

In the future I imagine people will be able to charge.

我想未来人们将能够充电。

So there's this whole store now.

所以现在就有了整个商店。

Basically it's the whole app store that you guys have launched.

基本上这是你们推出的整个应用程序商店。

How's it going?

怎么样了？

What's happening?

发生什么事了？

What surprised you there?

那里什么让你感到惊讶？

What should people know?

人们应该知道什么？

Logan Kilpatrick: Yeah, it's going great, and again, historically the thing that you would have to do, let's say for example, you have a really cool ChatGPT use case, what you would have to do to share it with somebody else is actually go in and start the conversation with the model, prompt it 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 essentially finish this conversation with ChatGPT that I started.

Logan Kilpatrick：是的，一切进展顺利，再说一遍，从历史上看，这是您必须做的事情，例如，您有一个非常酷的 ChatGPT 用例，您需要做的就是与其他人共享它，实际上是进入并开始与模型的对话，提示它做您想做的事情，然后您将在操作实际发生之前与其他人共享该链接，就像，现在您基本上可以与我的 ChatGPT 完成此对话开始了。

Logan Kilpatrick: So GPT 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 chat with essentially a custom version of ChatGPT.

Logan Kilpatrick：所以 GPT 改变了这一点，你将所有重要的上下文放入模型中，然后人们就可以使用 ChatGPT 的自定义版本进行聊天。

And the thing that's really interesting is you can upload files, you can give it custom instructions, you can add all these different tools.

真正有趣的是你可以上传文件，你可以给它自定义指令，你可以添加所有这些不同的工具。

Like a code interpreter is built in, which allows you to do math.

就像内置的代码解释器一样，它允许您进行数学运算。

Essentially you have browsing built in, image generation built in.

本质上你已经内置了浏览功能，内置了图像生成功能。

You can also, for more advanced use cases if you're a developer, you can connect it to external APIs so you can connect it to the Notion API or Gmail or all these different things, and have it actually take actions on your behalf.

如果您是开发人员，对于更高级的用例，您还可以将其连接到外部 API，这样您就可以将其连接到 Notion API 或 Gmail 或所有这些不同的东西，并让它实际代表您采取操作。

Logan Kilpatrick: So there's so many cool things that people are unlocking.

Logan Kilpatrick：人们正在解锁很多很酷的东西。

And what's been most exciting to me, actually, is the non-developer persona is now empowered to go and solve these really, really, really more challenging problems by giving the model enough context on what that problem is to be able to solve it.

事实上，最令我兴奋的是，非开发人员角色现在能够通过为模型提供足够的背景信息来解决这些真正、真正、真正更具挑战性的问题，从而能够解决问题。

Going back to context is all you need, this is very true in the context of GPTs, and if you give it enough context, you can solve much more interesting problems.

回到上下文就是你所需要的，这在 GPT 的上下文中是非常正确的，如果你给它足够的上下文，你可以解决更多有趣的问题。

Logan Kilpatrick: There's so many things that I'm excited about with this.

Logan Kilpatrick：有很多事情让我感到兴奋。

I think monetization, when it comes to the store later this quarter, I think is going to be extremely exciting when people can get paid based on who's using their GPT.

我认为，当涉及到本季度晚些时候的商店时，货币化将会非常令人兴奋，因为人们可以根据谁使用他们的 GPT 来获得报酬。

That's going to be a huge unlock and open a lot of people's eyes to the opportunity here.

这将是一个巨大的释放，让很多人看到这里的机会。

I also think continuing to push on making more capabilities accessible to GPTs for people who can't code is really exciting.

我还认为，继续推动为不会编码的人提供更多 GPT 功能确实令人兴奋。

Even for me as someone who is a software engineer, it's not super easy to connect the Notion API or the Gmail API to my GPT, and really I'd love to just be able to one click sign in with Gmail and then all of a sudden it's like my Gmail is accessible, or someone else can sign in with their Gmail and make it accessible.

即使对于我这样一个软件工程师来说，将 Notion API 或 Gmail API 连接到我的 GPT 也不是一件容易的事，而且我真的很希望能够一键登录 Gmail，然后突然间我的 Gmail 就可以访问了，或者其他人也可以使用他们的 Gmail 登录并使其可访问。

So I think over time all those types of things will come, but today it's really custom prompts is essentially one of the biggest value adds with GPTs.

因此，我认为随着时间的推移，所有这些类型的事情都会出现，但今天，真正的自定义提示本质上是 GPT 最大的增值之一。

Lenny: Awesome.

莱尼：太棒了。

I have it pulled up here on different monitor and Canva has the top GPT currently, and I was trying to play with it as you're chatting just to see.

我把它拉到了不同的显示器上，Canva 目前拥有顶级的 GPT，我试图在你聊天时玩它，只是为了看看。

I was going to make a big banner that said, "It's the context stupid," and it doesn't.

我本来打算制作一个大横幅，上面写着“这是愚蠢的背景”，但事实并非如此。

I'm not doing something right, but I'm not paying that much attention to it because we're talking, but this is very cool.

我没有做正确的事情，但我没有太关注它，因为我们正在谈论，但这非常酷。

Just maybe a final question there.

也许只是最后一个问题。

Is there a GPT that you saw someone built that was like, "Wow, that's amazing.

您是否看到有人构建了一个 GPT，就像“哇，这太棒了。

That's so cool," something that surprised you?

太酷了，”有什么让你惊讶的事情吗？

And I'll share one that was very cool, but is there anything that comes to mind when I ask that?

我将分享一个非常酷的东西，但是当我问这个问题时，我会想到什么吗？

Logan Kilpatrick: I think my instinct is the Zapier.

Logan Kilpatrick：我认为我的直觉是 Zapier。

All of the stuff that Zapier has done with GPTs is the most useful stuff that you could imagine.

Zapier 使用 GPT 所做的所有事情都是您能想象到的最有用的事情。

You can go so far with what...

你可以走这么远……

And I don't know how it's packaged for Zapier's GPT right now, but you can actually, as a third party developer, integrate Zapier without knowing how to code into your GPT.

我现在不知道它是如何为 Zapier 的 GPT 打包的，但实际上，作为第三方开发人员，您可以将 Zapier 集成到您的 GPT 中，而无需知道如何编码。

So they're pushing a lot of this stuff, and then basically all 5,000 connections that are possible with Zapier today, you can bring into your GPT and essentially enable it to do anything.

所以他们正在推动很多这样的东西，然后基本上今天 Zapier 可能实现的所有 5,000 个连接，你都可以带入你的 GPT 并基本上使它能够做任何事情。

So I'm incredibly excited for Zapier and for people who are building with them so many things that you can unlock using that platform.

因此，我对 Zapier 以及与他们一起构建如此多可以使用该平台解锁的东西的人们感到非常兴奋。

So I think that's probably the most exciting thing to me for people who aren't developers.

所以我认为对于非开发人员来说这可能是最令我兴奋的事情。

Lenny: Awesome.

莱尼：太棒了。

Zapier's always in there, getting in there connecting things.

Zapier 总是在那里，参与连接事物。

Lenny: So the one that I had in mind, so I had a buddy of mine, , who's the CEO of a company called Runway built this thing called Universal Primer which helps you learn.

Lenny：所以我想到了一个，所以我有一个朋友，他是一家名为 Runway 的公司的首席执行官，他开发了一种名为 Universal Primer 的东西，可以帮助你学习。

It's described as, "Learn everything about anything," and basically, I think, it's kind of this Socratic method of helping you learn stuff.

它被描述为“学习任何事物的一切”，基本上，我认为，这是一种帮助你学习东西的苏格拉底式方法。

So it's like, "Explain how transformers work in LMs," and then it just kind goes through stuff and then asks you questions, I think, and helps you learn new concepts.

所以这就像“解释 Transformer 如何在 LM 中工作”，然后它只是简单地介绍一下内容，然后问你问题，我认为，并帮助你学习新概念。

And I think it's the number two education GPT.

我认为这是排名第二的教育 GPT。

Logan Kilpatrick: I love that.

洛根·基尔帕特里克：我喜欢这个。

is incredible, so...

太不可思议了，所以……

Lenny: Yes, it's true.

莱尼：是的，确实如此。

Let me tell you about a product called Arcade.

让我告诉你一个叫做Arcade的产品。

Arcade is an interactive demo platform that enables teams to create polished, on-brand demos in minutes.

Arcade 是一个交互式演示平台，使团队能够在几分钟内创建精美的品牌演示。

Telling the story of your product is hard and customers want you to show them your product, not just talk about it or gate it.

讲述你的产品的故事很困难，客户希望你向他们展示你的产品，而不仅仅是谈论它或限制它。

That's why Product Four teams such as Atlassian, Carta and Retool use Arcade to tell better stories within their homepages, product change logs, emails and documentation.

这就是为什么 Atlassian、Carta 和 Retool 等产品四团队使用 Arcade 在其主页、产品变更日志、电子邮件和文档中讲述更好的故事。

Lenny: But don't just take my word for it.

莱尼：但不要只相信我的话。

Quantum Metric, the leading digital analytics platform created an interactive product tour library to drive more prospects.

领先的数字分析平台 Quantum Metric 创建了一个交互式产品游览库，以吸引更多潜在客户。

With Arcade, they achieved a 2X higher conversion rate for demos and saw five times more engagement than videos.

借助 Arcade，他们的演示转化率提高了 2 倍，参与度是视频的五倍。

On top of that, they built the demo 10 times faster than before.

最重要的是，他们构建演示的速度比以前快了 10 倍。

Creating a product demo has never been easier.

创建产品演示从未如此简单。

With browser-based recording Arcade is the no-code solution for building personalized demos at scale.

通过基于浏览器的录制，Arcade 是用于大规模构建个性化演示的无代码解决方案。

Lenny: Arcade offers product customization options, designer approved editing tools and rich insights about how your viewers engage every step of the way.

Lenny：Arcade 提供产品定制选项、设计师认可的编辑工具以及有关观众如何参与每一步的丰富见解。

Ready to tell more engaging product stories that drive results?

准备好讲述更具吸引力的产品故事来推动成果了吗？

Head to arcade.software/lenny and get 50% off your first three months.

前往 arcade.software/lenny，前三个月可享受 50% 的折扣。

That's arcade.software/lenny.

那是 arcade.software/lenny。

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.

Lenny：我想谈谈在 OpenAI 工作的感觉以及产品团队如何运作以及公司如何运作。

So you worked at...

那么你曾在...

Your two previous companies were Apple and NASA, which are not known for moving fast.

你之前的两家公司是苹果公司和美国宇航局，这两家公司并不以行动迅速而闻名。

And now you're at OpenAI, which is known for moving very fast, maybe too fast for some people's taste, as we saw it with the whole board thing.

现在你在 OpenAI，它以发展速度非常快而闻名，对于某些人来说可能太快了，正如我们在整个董事会上看到的那样。

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 at such a high bar?

所以我很好奇的是，OpenAI 为何做得如此出色，让他们能够如此快速地以如此高的标准构建和发布？

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?

您是否认为其他公司应该尝试更快地采取行动并交付更好的产品的流程或工作方式？

Logan Kilpatrick: Yeah, there's so many interesting trade-offs and all of this tension around how quickly companies can move.

Logan Kilpatrick：是的，有很多有趣的权衡，以及围绕公司行动速度的所有紧张关系。

I think for us, again, if you think about Apple as an example, if you think about NASA as an example, just older institutions, lots of...

我再次认为，对于我们来说，如果你以苹果公司为例，如果你以美国宇航局为例，只是老机构，很多......

Over time, the tendency is things slow down.

随着时间的推移，事情的发展趋势会变慢。

There's additional checks and balances that are put in place, which drags things down a little bit.

还有额外的制衡措施，这会稍微拖慢事情的进展。

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.

所以我们是一家年轻的新公司，所以我们没有太多已经设置的制度遗留障碍。

Logan Kilpatrick: 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 finding people who are high agency and work with urgency is one of the most...

Logan Kilpatrick：我认为最重要的事情是，我认为 2022 年或类似的事情上，Sam 在某处发布了一条很好的推文，但找到具有高度代理性和紧迫感的人是最重要的事情之一......

If I was hiring five people today, those are 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 not needing to either get 50 people's different consensus, because you have people who you trust with high agency and they can just go and do the thing, I think, is one of the most...

如果我今天要雇用五个人，这些就是我在人身上寻找的两个最重要的特征，因为如果你拥有具有高度代理性的人，并且不需要获得 50 个人的不同共识，那么你就可以承担这个世界，因为你拥有你信任的具有高度代理性的人，他们可以直接去做我认为是最……的事情之一。

It is the most important thing, I'm pretty sure, if you were to distill it down.

我很确定，如果你把它提炼出来的话，这是最重要的事情。

Logan Kilpatrick: And I see this in folks that I work with.

Logan Kilpatrick：我在与我一起工作的人身上看到了这一点。

Folks are so high agency.

人们的代理能力如此之高。

They see a problem and they go and tackle it.

他们看到问题就会去解决它。

They hear something from our customers about a challenge that they're having and they're already pushing on what the solution for them is and not waiting for all the other things to happen that I think traditional companies are stuck behind because they're like, "Oh, let's check with all these seven different departments to try to get feedback on this." People just go and do it and solve the problem.

他们从我们的客户那里听到了关于他们所面临的挑战的信息，他们已经在推动他们的解决方案是什么，而不是等待所有其他事情发生，我认为传统公司被困在了后面，因为他们会说，“哦，让我们与所有这七个不同的部门核实，试图获得对此的反馈。”人们只是去做并解决问题。

And I love that.

我喜欢这样。

It's so fun to be able to be a part of those situations.

能够参与这些情况真是太有趣了。

Lenny: That is so cool.

莱尼：那太酷了。

I really like these two characteristics because I haven't heard this before.

我真的很喜欢这两个特性，因为我以前从未听说过。

Those are the two, maybe the two most important things you guys look for, high agency, high urgency.

这就是两个，也许是你们寻找的两个最重要的东西，高代理性，高紧迫性。

To give people a clear sense of what these actually look like when you're hiring, you shared maybe this example of customer service.

为了让人们清楚地了解您在招聘时的实际情况，您可能分享了这个客户服务的示例。

Someone's hearing a bug and then going to fix it.

有人听到了一个错误，然后打算修复它。

Is there anything else that can illustrate what that looks like, high agency?

还有什么可以说明高级代理的情况吗？

And then a similar question on urgency other than just move, move, move, ship, ship, ship.

然后是一个关于紧迫性的类似问题，而不仅仅是移动、移动、移动、船、船、船。

Logan Kilpatrick: I think the assistants API that we released for dev day, we continue to get this feedback from developers that people wanted these higher levels of abstraction on top of our existing APIs, and a bunch of folks on the team just came together and were like, "Hey, 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.

Logan Kilpatrick：我认为我们在开发日发布的助手 API 中，我们不断从开发人员那里得到反馈，人们希望在我们现有的 API 之上实现更高级别的抽象，团队中的一群人聚集在一起，说：“嘿，让我们把计划放在一起，构建这样的东西。”然后很快就聚集在一起，实际构建了实际的 API，现在为很多人的助手应用程序提供支持。

And I think that's a great example of it wasn't this top down, 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 knowing that they can come together as a team and solve these problems really quickly.

我认为这是一个很好的例子，它不是自上而下的，哦，有人坐在那里，“哦，让我们做这五件事，”然后，“好吧，团队，去做吧。”这就好像人们真的看到了这些即将出现的问题，并且知道他们可以作为一个团队聚集在一起并快速解决这些问题。

And I think the assistants API, and there's like 1,001 other examples of teams taking agency and doing this, but I think that's a great one at the top of my head

我认为助理 API 以及大约 1,001 个其他团队采取代理并执行此操作的示例，但我认为这是我脑海中最棒的示例

Lenny: That makes me want to ask.

莱尼：这让我想问。

Just how does planning work at OpenAI?

OpenAI 的规划到底是如何运作的？

So in this example is just like, "Hey, we think we need to build this.

所以在这个例子中就像，“嘿，我们认为我们需要构建这个。

Let's just go and build it." I imagine there's still a roadmap and priorities and goals and things that that team had.

我们就去建造它吧。”我想团队仍然有路线图、优先事项、目标和事情。

How does road mapping and prioritization and all of that generally work to allow for something like that?

路线图和优先级以及所有这些通常如何工作以允许类似的事情发生？

Logan Kilpatrick: I think this is one of the more challenging pieces at OpenAI.

Logan Kilpatrick：我认为这是 OpenAI 最具挑战性的作品之一。

There's so many.

有这么多。

Everyone wants everything from us, and today, especially, in the world of ChatGPT and how large and well-used our API is, people will just come to us and say, "Hey, we want all of these things." I think there's a bunch of core guiding principles that we look at.

每个人都想从我们这里得到一切，而今天，尤其是在 ChatGPT 的世界里，以及我们的 API 的规模和使用情况，人们会来找我们说：“嘿，我们想要所有这些东西。”我认为我们关注了一系列核心指导原则。

One, going back to the mission, is this actually going to help us get to AGI?

一，回到任务，这真的会帮助我们实现通用人工智能吗？

So there's a huge focus on there's this potential shiny reward right in front of us, which is optimize user engagement, or whatever it is.

因此，我们非常关注摆在我们面前的潜在闪亮奖励，即优化用户参与度或其他任何东西。

And is that really the thing?

真的是这样吗？

Maybe the answer is yes.

也许答案是肯定的。

Maybe that is what is going to help us get to AGI sooner, but looking at it through that lens I think is always the first step of deciding any of these problems.

也许这将帮助我们更快地实现通用人工智能，但我认为从这个角度来看它始终是解决任何这些问题的第一步。

Logan Kilpatrick: I think, on the developer side, there's also these core tenets of reliability like, "Hey, it would be awesome if we had additional APIs that did all these cool things like new endpoints, new modalities, new abstractions, but are we giving customers a robust and reliable experience on our API?" And that's often the first question.

Logan Kilpatrick：我认为，在开发人员方面，还有这些可靠性的核心原则，比如“嘿，如果我们有额外的 API 来完成所有这些很酷的事情，比如新端点、新模式、新抽象，那就太棒了，但是我们是否在我们的 API 上为客户提供了强大而可靠的体验？”这通常是第一个问题。

And I think there have been times where we've fallen short on that, and there was a bunch of other things that we've been thinking about doing and 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.

我认为有时候我们在这方面做得不够，而且我们一直在考虑做很多其他事情，并真正将焦点和优先权带回到可靠性部分，因为到最后，如果他们不能强大而可靠地使用它，没有人会关心你是否有很棒的东西。

Logan Kilpatrick: So there's these core tenets.

Logan Kilpatrick：所以有这些核心原则。

And I think, again, we have very, other than all the principles about how we're making the decision, I think the actual planning process is pretty standard.

我再次认为，除了关于我们如何做出决定的所有原则之外，我认为实际的规划过程是相当标准的。

We come together.

我们走到一起。

There's H1 Q1 goals.

有 H1 Q1 目标。

We all sprint on those.

我们都朝着这些方向冲刺。

I think the real interesting thing is how stuff changes over time.

我认为真正有趣的是事物如何随着时间的推移而变化。

You think we're going to do these very high level things and new models, new modalities, whatever it is.

你认为我们会做这些非常高水平的事情和新模式、新方式，无论是什么。

And then as time goes on, there's all of this turmoil and change, and it's interesting to have mechanisms to be like, "Hey, how do we update our understanding of the world and our goals as everything the ground changes underneath of us as is happening in the craziness of the AI space today?"

随着时间的推移，出现了所有这些混乱和变化，有趣的是，有这样的机制：“嘿，当我们脚下的一切发生变化时，我们如何更新我们对世界和目标的理解，就像今天疯狂的人工智能领域所发生的那样？”

Lenny: It's interesting that it sounds a lot like most other companies.

莱尼：有趣的是，它听起来很像大多数其他公司。

There's H1 planning.

有 H1 规划。

There's Q1 planning.

有第一季度的计划。

Are there metrics and goals like that that you guys have OKRs or anything like that?

你们有 OKR 之类的指标和目标吗？

Or is it just, Here we're going to launch these products?"

或者只是，我们要在这里推出这些产品？”

Logan Kilpatrick: I think it's much higher level.

Logan Kilpatrick：我认为这是一个更高的水平。

I actually don't think OpenAI is a big OKR company.

我其实并不认为 OpenAI 是一家 OKR 大公司。

I don't think teams do OKRs today and I don't have a good understanding of why that's the case, whether or not.

我认为现在的团队没有制定 OKR，而且我也不太明白为什么会出现这种情况，无论是否如此。

I don't even know if OKRs are still the industry.

我什至不知道 OKR 是否仍然是这个行业。

You're probably talking to a lot more folks about who are making those decisions.

您可能正在与更多人谈论谁在做出这些决定。

So I'm curious.

所以我很好奇。

Is that something that you're seeing from folks?

这是你从人们那里看到的吗？

Is it still common for people to do OKRs?

人们做 OKR 的情况还常见吗？

Lenny: Yeah, absolutely.

莱尼：是的，绝对是。

Many companies use OKRs, love OKRs.

许多公司都使用 OKR，并且热爱 OKR。

Many companies hate OKRs.

许多公司讨厌 OKR。

I'm not surprised that OpenAI is not an OKR driven company.

对于 OpenAI 不是一家 OKR 驱动的公司，我并不感到惊讶。

Along those lines, I don't know how much you can share about all this stuff, but how do you measure success for things that you launch?

沿着这些思路，我不知道你能分享多少关于所有这些东西，但你如何衡量你推出的东西的成功？

I know there's this ultimate goal, AGI.

我知道有一个终极目标，AGI。

Is there some way to track we're getting closer?

有什么办法可以追踪我们的距离吗？

What else do you guys look at when you launch, say DPT Store or assistants or anything that's like, "Cool, that was exactly what we're hoping for." Is it just adoption?

当你们推出产品时，你们还会看什么，比如 DPT 商店或助手或任何类似“酷，这正是我们所希望的”之类的东西。难道只是领养吗？

Logan Kilpatrick: Yeah, adoption is a great one.

Logan Kilpatrick：是的，收养是一件很棒的事。

I think there's a bunch of metrics around revenue, number of developers that are building on our platform, all those things.

我认为有很多关于收入、在我们平台上开发的开发人员数量等指标。

And a lot of these, and I don't want to dive...

还有很多这样的，我不想潜水......

I'll let Sam or someone else on our leadership team go more into details, but I think a lot of these are actual abstractions towards something else.

我会让 Sam 或我们领导团队中的其他人详细介绍，但我认为其中很多都是对其他事物的实际抽象。

Even if revenue is a goal, it's like revenue is not actually the goal.

即使收入是一个目标，但收入实际上并不是目标。

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.

收入是获得更多计算能力的指标，这实际上可以帮助我们获得更多 GPU，以便我们可以训练更好的模型并真正实现目标。

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 OpenAI just wants to make money," and it's like, "Well, really money is the mechanism to get better models so that we can achieve our mission." And I think there's a bunch of interesting angles like that as well.

因此，在所有这些中间层中，即使我们说某件事是目标，你在真空中听到这个，你会想，“哦，OpenAI 只是想赚钱”，就像，“好吧，钱确实是获得更好模型的机制，以便我们能够实现我们的使命。”我认为还有很多类似的有趣角度。

Lenny: I don't know if I've heard of a more ambitious vision for a company, to build artificial general intelligence.

莱尼：我不知道我是否听说过一家公司有一个更雄心勃勃的愿景，即打造通用人工智能。

I love that.

我喜欢那个。

I imagine many companies are like, "What's our version of that?" Before we leave this topic, is there anything else that you've seen OpenAI do really well that allows it to move this fast and be this successful?

我想很多公司都会问，“我们的版本是什么？”在我们离开这个主题之前，您是否还看到 OpenAI 在其他方面做得非常好，使其能够如此快速地发展并取得如此成功？

You talked about hiring people with higher agency and high urgency.

您谈到了雇用具有更高代理性和高度紧迫性的人员。

Is there anything else that's just like, "Oh wow, that's a really good way of operating?" I imagine part of it's just hiring incredibly smart people.

还有其他类似的事情吗？“哇哦，这真是一个很好的操作方式？”我想其中一部分就是雇用非常聪明的人。

I think that's probably an unsaid thing, but yeah, anything else?

我认为这可能是一件没有说出来的事情，但是，是的，还有什么吗？

Logan Kilpatrick: 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 OpenAI has such a slack heavy culture and it really...

Logan Kilpatrick：我认为使用 Slack 有一个不小的好处，我认为这可能是有争议的，也许有些人不喜欢 Slack，但 OpenAI 有着如此浓厚的 slack 文化，它真的......

The instantaneous real time communication on Slack is so crucial.

Slack 上的即时实时通信至关重要。

And I just love being able to tag in different people from different teams and get everybody coalesced.

我只是喜欢能够标记来自不同团队的不同人员并让每个人联合起来。

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 someone a Slack message sometimes than it would be to walk over to their desk because they're on Slack and they're going to be using it.

因此，每个人都始终使用 Slack，因此，即使您处于远程位置，或者您在不同的团队或在不同的办公室，公司文化的很多内容都根植于 Slack 中，它使我们能够真正快速地协调，有时向某人发送 Slack 消息实际上比走到他们的办公桌上更快，因为他们在 Slack 上并且他们将使用它。

Logan Kilpatrick: And I saw, 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 and, "I don't even look at the time thing on my phone anymore because I don't want to know how long I'm using Slack," but I'm sure the Salesforce people are looking at the numbers and they're like, "This is exactly what we wanted."

Logan Kilpatrick：我看到了，如果你看到了最近对 Sam 和 Bill Gates 的采访，Sam 正在谈论 Slack 如何成为他手机上最常用的应用程序，并且“我什至不再看手机上的时间，因为我不想知道我使用 Slack 多长时间”，但我确信 Salesforce 人员正在查看这些数字，他们会说，“这正是我们想要的。”

Lenny: I also love Slack.

Lenny：我也喜欢 Slack。

I'm a big promoter Slack.

我是 Slack 的大推动者。

I think there's a lot of Slack hate, but such a good product.

我认为 Slack 有很多人讨厌，但它是一个很好的产品。

I've tried so many alternatives and nothing compares.

我尝试了很多替代方案，但没有任何一个可以比较。

I think what's interesting about Slack for you guys is one of the...

我认为 Slack 对你们来说有趣的地方之一是……

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.

你不知道那里的某个人是否只是一个 AGI，那实际上并不是一个在公司工作的人。

Logan Kilpatrick: I know there are real people.

Logan Kilpatrick：我知道有真实的人。

There is no AGIs yet.

目前还没有 AGI。

But I think, yeah, even Slack is building a bunch of really cool AI tools, which I'm excited to...

但我认为，是的，甚至 Slack 也在构建一堆非常酷的人工智能工具，我很高兴……

And that's why there's so much cool AI progress.

这就是人工智能取得如此多酷炫进展的原因。

And at the end of the day, it's so exciting, from being a consumer of all these new AI products.

归根结底，作为所有这些新人工智能产品的消费者，这真是令人兴奋。

Google's a great example.

谷歌就是一个很好的例子。

I'm so happy that Google is doing really cool AI stuff because I'm a Google Docs customer and I love using Google Docs.

我很高兴 Google 正在做非常酷的 AI 东西，因为我是 Google Docs 的客户，而且我喜欢使用 Google Docs。

I like a bunch of their other products, and it's awesome that people are building such useful things around these models.

我喜欢他们的许多其他产品，人们围绕这些模型构建如此有用的东西真是太棒了。

Lenny: How big is the OpenAI team at this point, whatever you can share?

Lenny：OpenAI 团队目前有多大，您可以分享一下吗？

Just to give people a sense of the scale.

只是为了给人一种规模感。

Logan Kilpatrick: Yeah, I think the last public number was something around like 750 near the end of last year, 780 or something like that near the end of last year.

Logan Kilpatrick：是的，我认为上一个公开数字大约是去年年底的 750 个左右，去年年底的 780 个左右。

And we're still growing so quickly, so I won't be the messenger to share the specific updated numbers, but the team is growing like crazy and we're also hiring across all of our engineering teams and PM teams, so folks who are interested, would love to hear from folks who are curious about joining.

我们的增长速度仍然如此之快，所以我不会分享具体的更新数字，但团队正在疯狂地发展，我们还在招聘所有的工程团队和 PM 团队，所以有兴趣的人很乐意听到有兴趣加入的人的来信。

Lenny: Maybe one last question here.

莱尼：也许还有最后一个问题。

So you're growing, maybe getting to 1,000 people, clearly still very innovative and moving incredibly fast.

所以你正在成长，可能会发展到 1,000 人，显然仍然非常创新，而且发展速度快得令人难以置信。

Is there anything you've seen about what OpenAI does well to enable innovation and not slow down new big ideas?

您是否发现 OpenAI 在推动创新且不拖慢新的伟大创意方面有何出色表现？

Logan Kilpatrick: Yeah, there's a couple of things, one of which is the actual research team, who seed most of the innovation that happens at OpenAI, is intentionally small.

Logan Kilpatrick：是的，有一些事情，其中之一是实际的研究团队，他们为 OpenAI 发生的大部分创新提供了种子，但故意规模很小。

Most of the growth that OpenAI has seen is around our customer facing roles, our engineering roles to provide the infrastructure for ChatGPT and things like that.

OpenAI 的大部分增长都是围绕我们面向客户的角色、为 ChatGPT 提供基础设施的工程角色以及类似的事情。

The research team is, again, intentionally kept small and there's all of this talk.

研究团队再次有意保持较小规模，这就是所有这些讨论。

And it's really interesting.

这真的很有趣。

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 open AI researchers, but 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.

我刚刚看到我们的一位研究人员发来的这条帖子，他谈到在一个研究人员受到 GPU 容量限制的世界中，开放人工智能研究人员就是这种情况，其他地方的研究人员也是如此，你添加的每一位新研究人员实际上都会导致研究小组的净生产力损失，除非该人以如此深刻的方式提升其他所有人的水平，从而提高效率。

Logan Kilpatrick: If you just add somebody who's going to go and 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.

Logan Kilpatrick：如果你只是添加一个人去解决一些完全不同的研究方向，你现在必须与那个人共享你的 GPU，而其他人现在的实验速度会变慢。

So it's a really interesting trade-off that research folks have that I don't think product folks...

所以这是一个非常有趣的权衡，研究人员有，但我不认为产品人员......

If I add another engineer to our API team or to some of the ChatGPT teams, you can actually write more code and do more.

如果我向我们的 API 团队或某些 ChatGPT 团队添加另一位工程师，您实际上可以编写更多代码并做更多事情。

And that's actually a net beneficial improvement for everybody.

这实际上对每个人来说都是一个净有益的改进。

And that's always not the case in the case of researchers, which is interesting, in A GPU constrained world, which hopefully we won't always be in.

但研究人员的情况却并非如此，这很有趣，在 GPU 受限的世界中，希望我们不会总是陷入这样的情况。

Lenny: I want to zoom out a bit and then there's going to be a couple follow-up questions here.

莱尼：我想稍微缩小一下范围，然后这里会有几个后续问题。

Where are things heading with OpenAI?

OpenAI 的发展方向是什么？

What's in the near future of what people should expect from the tools that you guys are going to have in launch?

在不久的将来，人们对你们即将推出的工具有何期望？

Logan Kilpatrick: Yeah, new modalities.

Logan Kilpatrick：是的，新的方式。

I think ChatGPT continuing to push all of the different experiences that are going to be possible.

我认为 ChatGPT 会继续推动所有可能的不同体验。

Today, ChatGPT is really just text in, text out or, I guess three months ago, it was just text in, text out.

今天，ChatGPT 实际上只是文本输入、文本输出，或者，我猜三个月前，它只是文本输入、文本输出。

We started to change that with now you can do the voice mode and now you can generate images and now you can take pictures.

我们开始改变这一点，现在你可以进行语音模式，现在你可以生成图像，现在你可以拍照。

So I think continuing to expand the way in which you interface with AI through ChatGPT is coming.

因此，我认为通过 ChatGPT 继续扩展与人工智能交互的方式即将到来。

Logan Kilpatrick: I think GPTs is our first step towards the agent future.

Logan Kilpatrick：我认为 GPT 是我们迈向代理未来的第一步。

Again, today when you use A GPT, it's really you send a message, you get an answer back almost right away, and that's kind of the end of your interaction.

再说一次，今天当你使用 GPT 时，实际上是你发送一条消息，你几乎立即得到答复，这就是你的交互的结束。

I think as GPTs continue to get more robust, you'll actually be able to say, "Hey, go and do this thing and just let me know when you're done.

我认为，随着 GPT 不断变得更加强大，您实际上可以说：“嘿，去做这件事，完成后告诉我。

I don't need the answer right now.

我现在不需要答案。

I want you to really spend time and be thoughtful about this."

我希望你真正花时间思考这个问题。”

Logan Kilpatrick: And again, if you think back to all these human analogies, that's what we do as humans.

Logan Kilpatrick：再说一次，如果你回想一下所有这些人类类比，那就是我们作为人类所做的事情。

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.

当我要求他们为我做一些有意义的事情时，我不期望有人立即去做并立即给我答案。

So I think pushing more towards those experiences is what is going to unlock so much more value for people.

因此，我认为更多地推动这些体验将为人们释放更多价值。

Logan Kilpatrick: And I think the last thing is GPTs as this mechanism to get the next few hundred million people into ChatGPT and into AI.

Logan Kilpatrick：我认为最后一件事是 GPT 作为这种机制，让接下来的几亿人进入 ChatGPT 和人工智能。

So I think if you've had conversations with people who aren't close to the AI space, oftentimes you talk about, even if they've heard of ChatGPT...

因此，我认为，如果您与不熟悉人工智能领域的人进行过交谈，即使他们听说过 ChatGPT，您也会经常谈论……

A lot of people haven't heard of ChatGPT, but if they have, they show up in ChatGPT and they're like, "I don't really know what I'm supposed to do with this blank slate.

很多人没有听说过 ChatGPT，但如果听说过，他们会出现在 ChatGPT 中，然后他们会说，“我真的不知道应该用这张白纸做什么。

I can kind of do anything.

我可以做任何事。

It's not super clear how this solves my specific problem."

目前还不清楚这如何解决我的具体问题。”

Logan Kilpatrick: But I think the cool thing about GPTs is you can package down like, "Here's this one very specific problem that AI can solve for you and do it really well," and I can share that experience with you and now you can go and try that GPT, have it actually solve the problem and be like, "Wow, it did this thing for me.

Logan Kilpatrick：但我认为 GPT 的酷之处在于，你可以将其打包下来，比如“这是人工智能可以为你解决的一个非常具体的问题，并且做得非常好”，我可以与你分享这一经验，现在你可以去尝试 GPT，让它真正解决问题，然后说，“哇，它为我做了这件事。

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." So 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 unlock for them.

我可能应该花时间研究另外五个问题，看看人工智能是否也能解决这些问题。”所以我认为会有更多的人上网并开始使用这些工具，因为非常狭窄的垂直工具对他们来说将是一个巨大的解锁。

Lenny: So in that last case, a classic horizontal product problem where it does so many things and people don't know what exactly it should do for them.

莱尼：所以在最后一个例子中，这是一个典型的横向产品问题，它做了很多事情，但人们不知道它到底应该为他们做什么。

So that makes a ton of sense.

所以这很有意义。

Just being a lot more template oriented, use case specific, helping people onboard makes tons of sense.

只要更加面向模板、特定于用例，帮助船上的人就很有意义。

A common problem for so many SaaS products out there.

这是许多 SaaS 产品的常见问题。

The other ones you mentioned, which was really interesting, basically more interfaces to more easily interact with OpenAI voice.

你提到的其他确实很有趣，基本上是更多的接口，可以更轻松地与 OpenAI 语音交互。

You mentioned audio and things like that.

你提到了音频之类的东西。

That makes tons of sense.

这很有道理。

And then this agents piece where the idea is, instead of just it's a chat, it's like, "Hey, go do this thing for me."

然后这个特工的想法就是，不仅仅是聊天，就像是，“嘿，去帮我做这件事。”

Lenny: Kind of along those lines, GPT-5, we touched on this a bit.

Lenny：沿着这个思路，GPT-5，我们稍微谈到了这一点。

There's a lot of speculation about the much better version.

关于更好的版本有很多猜测。

People just have these wild expectations, I think, for where GPT is going.

我认为，人们只是对 GPT 的发展抱有疯狂的期望。

GPT-5 is going to solve all the world's problems.

GPT-5 将解决世界上所有的问题。

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.

我知道您不会告诉我它何时发布以及它将做什么，但我从一位朋友那里听说，当您今天构建产品时，您应该朝着 GPT-5 的未来发展，而不是基于今天 GPT-4 的限制。

So to help people do that, what should people think about that might be better in a world of GPT-5?

因此，为了帮助人们做到这一点，人们应该考虑什么在 GPT-5 的世界中可能会更好？

Is it just faster?

只是速度更快吗？

It's just smarter?

它只是更聪明吗？

Is there anything else that might be like, "Oh wow, I should really rethink how I'm approaching my product?"

还有其他什么可能会像“哦哇，我真的应该重新考虑如何处理我的产品吗？”

Logan Kilpatrick: 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.

Logan Kilpatrick：如果人们看过我们在 3 月份 GPT-4 发布时发布的 GPT-4 技术报告，就会发现 GPT-4 是我们训练的第一个模型，我们可以根据我们要投入的计算量事先可靠地预测该模型的功能。

We did a scientific study to show like, "Hey, this is what we predicted and here is what the actual outcome was." So it'll be, one I think, just as somebody who's interested in technology, but interesting to see does that continue to hold for GPT-5, and hopefully we'll some of that information whenever that model comes out.

我们做了一项科学研究来表明，“嘿，这就是我们的预测，这就是实际结果。”所以，我认为，就像对技术感兴趣的人一样，但有趣的是看到 GPT-5 是否继续适用，希望每当该模型出现时我们都会得到一些信息。

Logan Kilpatrick: I also think you can probably draw a few observations.

Logan Kilpatrick：我还认为您可以得出一些观察结果。

One of them, which is GPT-4 came out.

其中之一，就是GPT-4问世了。

The consensus from the world is, "Everything is different.

世界的共识是：“一切都不同了。

All of a sudden everything is different.

突然一切都不同了。

This changes the world.

这改变了世界。

This changes everything." And then slowly but surely, we come back to reality of like, "This is a really effective tool and it's going to help solve my problems more effectively."

这改变了一切。”然后慢慢地但肯定地，我们回到现实，“这是一个非常有效的工具，它将帮助更有效地解决我的问题。”

Logan Kilpatrick: And I think that is the, undoubtedly, the lens in which people should look at all of these model advancements, like GPT-5 is surely going to be extremely useful and solve some whole new echelon of problems.

Logan Kilpatrick：我认为，毫无疑问，人们应该从这个角度来看待所有这些模型的进步，比如 GPT-5 肯定会非常有用，并解决一些全新的问题。

Hopefully it'll be faster.

希望会更快。

Hopefully It'll be better in all these ways, but fundamentally, the same problems that exists in the world are still going to be the same problems.

希望所有这些方面都会变得更好，但从根本上来说，世界上存在的相同问题仍然是相同的问题。

You now just have a better tool to solve those problems.

您现在有了一个更好的工具来解决这些问题。

Logan Kilpatrick: And I think 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.

Logan Kilpatrick：我认为回到垂直用例，我认为解决非常具体的用例的人们现在将能够更有效地做到这一点。

I don't think that's going to...

我不认为这会...

People have these unrealistic expectations that GPT-5 is going to be doing back flips in the background in my bedroom while it also writes all my code for me and talks on the phone with my mom or something like that.

人们有一些不切实际的期望，认为 GPT-5 会在我卧室的后台进行后空翻，同时它还会为我编写所有代码并与我妈妈通电话或类似的事情。

Logan Kilpatrick: I'm like, "That's not the case." It's just going to be this very effective tool, very similar to GPT-4, and it's also going to be become very normal very quickly.

洛根·基尔帕特里克：我想，“事实并非如此。”它将会是一个非常有效的工具，与 GPT-4 非常相似，而且它也会很快变得非常正常。

And I think that is actually a really interesting piece.

我认为这实际上是一个非常有趣的作品。

If you can plan for the world where people become very used to these tools very quickly, I actually think that's an edge, and assuming that this thing is going to absolutely change everything, and in many ways I think it's actually a downside, is the wrong mental framing to have of these tools as they come out.

如果你能够为人们很快习惯这些工具的世界做好计划，我实际上认为这是一个优势，并且假设这个东西绝对会改变一切，并且在很多方面我认为这实际上是一个缺点，当这些工具出现时，他们的心理框架是错误的。

Lenny: Kind of along these lines, you guys are investing a lot into B2B offerings.

Lenny：按照这个思路，你们在 B2B 产品上投入了大量资金。

I think half the revenue, last I heard, was B2B and then half is B2C.

我上次听说，我认为一半的收入来自 B2B，一半来自 B2C。

I don't know if that's true, but that's something I heard.

我不知道这是否属实，但这是我听到的。

What is it that you get if you work with opening AI as a company, as a business?

如果你作为一家公司、一家企业致力于开放人工智能，你会得到什么？

What does it unlock?

它能解锁什么？

Is it just called OpenAI Enterprise?

它只是叫 OpenAI Enterprise 吗？

What's it called and what do you get as a part of that?

它叫什么名字？你能从中得到什么？

Logan Kilpatrick: Yeah, so I think a lot of our B2B customers are using the API to build stuff.

Logan Kilpatrick：是的，所以我认为我们的很多 B2B 客户都在使用 API 来构建东西。

So I think that's one angle of it.

所以我认为这是其中的一个角度。

I think if you're a ChatGPT B2B customer, we sell Teams, which is the ability to get multiple subscriptions of ChatGPT packaged together.

我认为，如果您是 ChatGPT B2B 客户，我们会销售 Teams，这是将 ChatGPT 的多个订阅打包在一起的能力。

We also have an enterprise version of ChatGPT.

我们还有企业版的 ChatGPT。

There's a bunch of enterprise-y things that enterprise companies want, around SSO and stuff like that, related to ChatGPT Enterprise.

企业公司想要很多企业级的东西，比如 SSO 以及与 ChatGPT Enterprise 相关的类似东西。

Logan Kilpatrick: I think the coolest thing is actually being able to share some of these prompt templates and GPTs internally.

Logan Kilpatrick：我认为最酷的事情实际上是能够在内部共享其中一些提示模板和 GPT。

So 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.

同样，您可以使用与解决公司问题相关的所有信息来制作非常适合您公司的自定义内容，并在内部共享这些信息。

And to me, that's like you want to be able to collaborate with your teammates on the cool things you create using AI.

对我来说，这就像你希望能够与你的队友合作，利用人工智能创造出很酷的东西。

So that's a huge unlock for companies.

所以这对公司来说是一个巨大的释放。

I think that those are the two biggest value adds.

我认为这是两个最大的附加值。

There's higher limits and stuff like that on some of those models, but I think being able to share your very domain specific applications is the most useful thing.

其中一些模型有更高的限制和类似的东西，但我认为能够共享您的特定领域的应用程序是最有用的事情。

Lenny: I think if you're a company listening and you think a lot of employees are using ChatGPT, basically the simplest thing you could do is just roll it up into a business account with single sign-on and that probably saves you money and makes it easier to coordinate and administer.

Lenny：我认为，如果您是一家倾听的公司，并且您认为很多员工都在使用 ChatGPT，那么基本上您能做的最简单的事情就是将其汇总到具有单点登录的企业帐户中，这可能会节省您的资金，并使协调和管理变得更加容易。

Logan Kilpatrick: Yeah, there's also a bunch of security stuff too, like if you want to control.

Logan Kilpatrick：是的，还有很多安全方面的东西，比如你想控制的话。

You don't want people to use certain GPTs from the GPT store because you're worried about security or privacy and stuff like that.

您不希望人们使用 GPT 商店中的某些 GPT，因为您担心安全或隐私等问题。

You don't want your private data going in places.

您不希望您的私人数据出现在其他地方。

It makes a lot of sense to sign up for that so that you have a little bit more control over what's happening.

注册这个项目很有意义，这样您就可以更好地控制正在发生的事情。

Lenny: Okay, got it.

莱尼：好的，明白了。

There's a launch happening tomorrow, I think, after we're recording this.

我想，在我们录制完这个之后，明天就会有一次发射。

Can you talk about what is new, what's coming out?

您能谈谈有哪些新内容、即将推出的内容吗？

I think this is going to come out a couple weeks after recording, but just what should people know that's new that's coming out from OpenAI tomorrow in our world?

我认为这将在录制后几周发布，但人们应该知道明天 OpenAI 在我们的世界中发布的新内容是什么？

Logan Kilpatrick: Yeah, updated, so there's a few different things.

Logan Kilpatrick：是的，更新了，所以有一些不同的东西。

A couple of quick ones are updated GPT-4 Turbo model, updated the preview model that we released that dev day.

一些快速的更新是 GPT-4 Turbo 模型，更新了我们在开发日发布的预览模型。

There's an updated version of that.

有一个更新版本。

It fixes this, if folks have seen online people talking about this sort of laziness phenomenon in the model.

如果人们看到网上有人谈论模型中的这种懒惰现象，它就解决了这个问题。

We improve on that and it fixes a lot of the cases where that was the case.

我们对此进行了改进，它修复了很多这样的情况。

So hopefully the model will be a little bit less lazy.

所以希望模型能少一点懒惰。

The big thing is the third generation embeddings model.

最重要的是第三代嵌入模型。

So we were talking off-camera before recording about all of the cool use cases for embedding.

因此，在录制所有很酷的嵌入用例之前，我们在镜头外进行了讨论。

So if folks have used embeddings before, it's essentially the technology that powers many of these question and answering with your own documentation or your own corpus of knowledge.

因此，如果人们以前使用过嵌入，那么它本质上是为许多此类问题提供支持的技术，并用您自己的文档或您自己的知识库来回答。

And like you were saying, you actually have a website where people can ask questions about recordings of the podcast.

正如您所说，您实际上有一个网站，人们可以在其中询问有关播客录音的问题。

Logan Kilpatrick: Yeah, lennybot.com.

Logan Kilpatrick：是的，lennybot.com。

And my assumption was that lennybot.com is actually powered by embedding.

我的假设是 lennybot.com 实际上是由嵌入提供支持的。

So you take all of the corpus of knowledge.

所以你掌握了所有的知识库。

You take all the recordings, your blog post.

你拿走所有的录音，你的博客文章。

You embed them, and then 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, something that's true from your knowledge base.

你将它们嵌入，然后当人们提出问题时，你实际上可以进去看看问题和知识库之间的相似性，然后为某人的问题提供答案并引用经验事实，这是你的知识库中真实的东西。

And this is super useful and people are doing a ton of this.

这是非常有用的，人们正在做很多这样的事情。

It's like trying to ground these models in reality, in what they know to be true.

这就像试图将这些模型扎根于现实，扎根于他们所知道的真实情况。

We know all the things from your podcast to be at least something that you've said before and to be true in that sense, and we can bring them into the answer that the model is actually generating in response to a question.

我们知道您播客中的所有内容至少是您之前说过的内容，并且在这个意义上是真实的，并且我们可以将它们带入模型针对问题实际生成的答案中。

So that'll be super cool.

所以那会非常酷。

Logan Kilpatrick: And these new V3 embeddings models, again, state-of-the-art performance, the cool thing is actually the non-English performance has increased super significantly.

Logan Kilpatrick：这些新的 V3 嵌入模型同样具有最先进的性能，最酷的事情实际上是非英语性能显着提高。

I think historically, people really were only using embeddings for...

我认为从历史上看，人们实际上只是将嵌入用于......

It only worked really well for English, and I think now you can use it across so many new languages because it's just so much more performant across those languages and it's five times cheaper as well, which is wonderful.

它只适用于英语，而且我认为现在您可以在许多新语言中使用它，因为它在这些语言中的性能要高得多，而且价格也便宜五倍，这真是太棒了。

There's no better feeling than making things cheaper for people.

没有比让人们的东西变得更便宜更好的感觉了。

I love it.

我喜欢它。

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.

我想现在你可以嵌入，我很确定，就像 62,000 页文本只需 1 美元，这是非常非常便宜的。

So lots of really cool things that you can do with embeddings and exciting to see people invent more stuff.

您可以通过嵌入做很多非常酷的事情，并且很高兴看到人们发明更多东西。

Lenny: What a deal.

莱尼： 真是太好了。

Final question before we get to a very exciting lightning round.

在我们进入非常激动人心的闪电回合之前，最后一个问题是。

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, GPT-4, all the other APIs?

假设您是一家大公司的产品经理，甚至是创始人，您认为他们利用你们正在构建的技术、GPT-4 以及所有其他 API 的最大机会是什么？

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.

人们应该如何思考，“这就是我们应该如何真正考虑在现有产品或新产品中利用这种力量”，无论你想朝哪个方向发展。

Logan Kilpatrick: Yeah, I think going back to this theme of new experiences is really exciting to me.

Logan Kilpatrick：是的，我认为回到新体验这个主题对我来说真的很令人兴奋。

I think consumers are just going to be...

我认为消费者将会...

You are going to have an edge on other people if you're providing AI that's not accessible in a Chatbot.

如果您提供聊天机器人无法访问的人工智能，您将比其他人更具优势。

People are using a ton of chat and it's a really valuable service area.

人们正在使用大量的聊天工具，这是一个非常有价值的服务领域。

It's clearly valuable because people are using it.

它显然很有价值，因为人们正在使用它。

But I think products that move beyond this chat interface really are going to have such an advantage.

但我认为超越聊天界面的产品确实会有这样的优势。

And also, thinking about how to take your use case to the next level.

此外，还要考虑如何将您的用例提升到一个新的水平。

I've tried a ton of chat examples that are very, 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."

我尝试过大量非常非常基础的聊天示例，但我想，“实际上，这应该走得更远，真正从头开始构建您的核心体验。”

Logan Kilpatrick: 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.

Logan Kilpatrick：我使用过这个产品，它允许您从本质上管理或查看围绕某些主题和类似内容在网上发生的对话。

So I can go and look online.

这样我就可以去网上看看。

What are people saying about GPT-4?

人们对 GPT-4 有什么看法？

And what I just said out loud, "What are people saying about GPT-4," is the actual question that I have.

我刚才大声说的“人们对 GPT-4 有何看法”是我真正的问题。

And in a normal product experience today, I have to go into a bunch of dashboards and change a bunch of filters and stuff like that.

在今天的正常产品体验中，我必须进入一堆仪表板并更改一堆过滤器和类似的东西。

And what I really want is just ask my question.

我真正想要的只是问我的问题。

What are people doing?

人们在做什么？

What are people saying about GPT-4?

人们对 GPT-4 有什么看法？

Get an answer to that question in a very data grounded way.

以非常基于数据的方式获得该问题的答案。

Logan Kilpatrick: And I've seen people solve part of this problem where, oh, they'll be like, "Oh, well here's a few examples of what people are saying and, well, that's not really what I want.

Logan Kilpatrick：我见过人们解决了这个问题的一部分，哦，他们会说，“哦，这里有一些人们所说的例子，但这并不是我真正想要的。

I want this summary of what's happening." And I think it just takes a little bit more engineering effort to make that happen.

我想要一份关于正在发生的事情的摘要。”我认为只需要多一点工程努力就能实现这一点。

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."

但我认为这就像神奇的解锁，“哇，这是一个令人难以置信的产品，我将继续使用”，而不是“是的，这有点用，但我真的想要更多。”

Lenny: Awesome.

莱尼：太棒了。

I'll give a shout-out to a product.

我会对一个产品大声喊叫。

I'm not an investor, but I know the founder, called visualelectric.com, which I think is doing exactly this.

我不是投资者，但我认识其创始人，名为 Visualelectric.com，我认为该公司正是这样做的。

It's basically a tool specifically built for creatives, I think specifically graphic design, to help them create imagery.

它基本上是一个专门为创意人员（我认为特别是图形设计）构建的工具，以帮助他们创建图像。

So there's like Dali, 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 rate until you have the thing that you need.

显然，就像达利一样，但这将它提升到了一个全新的水平，它是一种画布，无限的画布，你可以生成图像，编辑，调整它们，并继续评价，直到你拥有你需要的东西。

Visualelectric.com.

Visualelectric.com。

Logan Kilpatrick: I'm going to try that out.

Logan Kilpatrick：我要尝试一下。

Is it similar to Canva?

和Canva类似吗？

Lenny: It's even more niche, I think, for more sophisticated graphic design, I think, is the use case.

莱尼：我认为，对于更复杂的图形设计来说，这是更利基的用例。

But I'm not a designer, so I'm not the target customer.

但我不是设计师，所以我不是目标客户。

But I will say my wife is a graphic designer.

但我会说我的妻子是一名平面设计师。

She had never used AI tools.

她从未使用过人工智能工具。

I showed her this and she got hooked on it.

我给她看了这个，她就着迷了。

She paid for it without even telling me that she was going to become a paid customer, and she created imagery of our dog and all this art.

她甚至没有告诉我她将成为付费客户就付了钱，她创造了我们的狗和所有这些艺术品的图像。

And now it's like on our TV.

现在就像我们的电视上一样。

The art she created is now sitting...

她创造的艺术现在正坐在......

It's like we have a frame TV, and that's the image on our TV.

就像我们有一个框架电视，这就是我们电视上的图像。

So anyway...

所以无论如何...

Logan Kilpatrick: I love that.

洛根·基尔帕特里克：我喜欢这个。

What was it called again?

又叫什么来着？

Lenny: Visualelectric.com.

莱尼：Visualelectric.com。

Anyway, anything else you wanted to touch on or share before we get to a very exciting lightning round?

不管怎样，在我们进入激动人心的闪电回合之前，您还有什么想谈或分享的吗？

Logan Kilpatrick: I've made this statement a few times online and other places, but for people who have cool ideas that they should build with AI, this is the moment.

Logan Kilpatrick：我已经在网上和其他地方多次发表过这样的声明，但是对于那些有很酷的想法应该使用人工智能来构建的人来说，现在就是时候了。

There are so many cool things that need to be built for the world using AI.

有很多很酷的东西需要使用人工智能为世界构建。

And again, if I or other folks on the team at OpenAI can be helpful in getting you over the hump of starting that journey of building something really cool, please reach out.

再说一遍，如果我或 OpenAI 团队的其他人可以帮助您克服困难，开始构建真正酷的东西，请与我们联系。

The world needs more cool solutions using these tools, and would love to hear about the awesome stuff that people are building.

世界需要使用这些工具的更酷的解决方案，并且很乐意听到人们正在构建的很棒的东西。

Lenny: I would've asked you this at the end, but how would people reach out?

莱尼：我本来会在最后问你这个问题，但是人们如何联系呢？

What's the best way to actually do that?

实际做到这一点的最佳方法是什么？

Logan Kilpatrick: Twitter, LinkedIn.

洛根·基尔帕特里克：Twitter、LinkedIn。

My email should be findable somewhere.

我的电子邮件应该可以在某个地方找到。

I don't want to say it and then get slammed with a bunch of email.

我不想说出来然后被一堆电子邮件猛烈地攻击。

You should be able to sign my email, if you need it, online somewhere.

如果需要的话，您应该能够在网上某个地方签署我的电子邮件。

But yeah, Twitter and LinkedIn is usually the easiest place.

但是，是的，Twitter 和 LinkedIn 通常是最简单的地方。

Lenny: And how do they find you on Twitter?

莱尼：他们如何在 Twitter 上找到你？

Logan Kilpatrick: It's just Logan Kilpatrick, or I think my name shows up as Logan.GPT or-

Logan Kilpatrick：这只是 Logan Kilpatrick，或者我认为我的名字显示为 Logan.GPT 或-

Lenny: Yeah.

莱尼：是的。

Awesome.

惊人的。

Okay.

好的。

And we'll link into it in the show notes.

我们将在演出笔记中链接到它。

Amazing.

惊人的。

Well Logan, with that, we've reached a very exciting lightning round.

好吧，洛根，至此，我们进入了非常激动人心的闪电回合。

Are you ready?

你准备好了吗？

Lenny: First question, what are two or three books that you've recommended most to other people?

莱尼：第一个问题，你向其他人推荐最多的两三本书是什么？

Logan Kilpatrick: I think the first one is one that I read a long time ago and came back to recently, is the One Room Schoolhouse by Sal Khan.

Logan Kilpatrick：我想第一本是我很久以前读过的，最近又读到了，是 Sal Khan 的《One Room Schoolhouse》。

Incredible.

极好的。

Yeah, I don't want...

是的，我不想...

It's the lightning round so I won't say too much, but incredible story and AI is what is going to enable Sal Khan's vision of a teacher per student to actually happen.

这是闪电轮，所以我不会说太多，但令人难以置信的故事和人工智能将使萨尔·汗的“每个学生都有一个老师”的愿景真正实现。

So I'm really excited about that.

所以我对此感到非常兴奋。

And the other one is, that I always come back to, is Why We Sleep.

另一个是我经常回想的，那就是我们为什么睡觉。

Sleep science are so cool.

睡眠科学太酷了。

If you don't care about your sleep, it's one of the biggest up-levels that you can do for yourself.

如果您不关心自己的睡眠，那么这是您可以为自己做的最大的提升之一。

Lenny: What is a favorite recent movie or TV show that you really enjoyed?

莱尼：您最近最喜欢的电影或电视节目是什么？

Logan Kilpatrick: I'm a sucker for a good inspirational human story.

Logan Kilpatrick：我很喜欢励志的人类故事。

So I watched, with my family recently over the holidays, this Gran Turismo movie, and it's a story about someone who, a kid from London, who grew up doing SIM racing, which is a virtual race car, and did this competition, ended up becoming a real professional race car driver through some competition.

所以我最近在假期里和家人一起看了这部《Gran Turismo》电影，这是一个关于一个来自伦敦的孩子的故事，他从小就玩SIM赛车，这是一辆虚拟赛车，参加了这场比赛，最终通过一些比赛成为一名真正的职业赛车手。

And it's just really cool to see someone go from driving a virtual car to driving a real car and competing in the 24-hour Le Mans and all that stuff.

看到有人从驾驶虚拟汽车变成驾驶真正的汽车并参加 24 小时勒芒耐力赛等，真是太酷了。

Lenny: I used to play that game and it was a lot of fun, but I don't think I have any clue how to drive a real car, race car.

莱尼：我以前玩过那个游戏，很有趣，但我不认为我有任何线索如何驾驶一辆真正的汽车，赛车。

So that's inspiring.

这很鼓舞人心。

Do you have a favorite interview question that you'd like to ask candidates that you're interviewing?

您有最喜欢的面试问题想问正在面试的候选人吗？

Logan Kilpatrick: Yeah, I'm always curious to hear what people's...

Logan Kilpatrick：是的，我总是很好奇人们的想法……

The thing that they so strongly believe that people disagree with them on.

他们如此坚信人们不同意他们的事情。

Lenny: What do you look for in an answer that seems like, Wow, that's a really good signal?"

莱尼：你想在一个看起来像“哇，这真是一个很好的信号”的答案中寻找什么？”

Logan Kilpatrick: Oftentimes, it's just an entertaining question to ask in some sense, but it's also...

Logan Kilpatrick：通常，从某种意义上来说，这只是一个有趣的问题，但它也是......

It's interesting to see what somebody's deeply held strong belief is, I think.

我认为，了解某人深深持有的坚定信念是什么是很有趣的。

And not to judge whether or not I believe in that, but just curious to see why people feel that way.

并不是要判断我是否相信这一点，只是好奇为什么人们会有这样的感觉。

Lenny: What is a favorite product that you've recently discovered that you really like?

Lenny：您最近发现并且真正喜欢的最喜欢的产品是什么？

Logan Kilpatrick: On the narrative of sleep, I have this really nice sleep mask from this company called...

Logan Kilpatrick：关于睡眠的叙述，我有一个来自这家公司的非常好的睡眠面罩，叫做......

Not being paid.

没有得到报酬。

I just say this, but it's called Manta Sleep or something like that.

我只是这么说，但它被称为“曼塔睡眠”或类似的东西。

It's a weighted sleep mask and it feels incredible when I...

这是一款加重睡眠面罩，当我...

I don't know.

我不知道。

Maybe I just have a heavy head or something like that, but it feels good to wear a weighted sleep mask at night.

也许我只是头重或类似的东西，但晚上戴加重睡眠面罩感觉很好。

I really appreciate it.

我真的很感激。

Lenny: I have a competing sleep mask that I highly recommend.

莱尼：我有一款睡眠面膜，我强烈推荐。

I'm trying to find it.

我正在努力寻找它。

I've emailed people about it a couple of times in my newsletter for gift guides.

我已经在我的礼品指南时事通讯中多次向人们发送了有关此事的电子邮件。

Lenny: Okay.

莱尼：好的。

My favorite is called the Waoaw Sleep Mask, W-A-O-A-

我最喜欢的是 Waoaw 睡眠面膜，W-A-O-A-

Lenny: W-A-O-A-W.

莱尼：W-A-O-A-W。

I'll link to it in the show notes.

我将在演出笔记中链接到它。

It makes a lot of room.

它腾出了很多空间。

It's very large and there's space for your eyes, so your eyelashes and whatever eyes aren't pressed on, and it just fits really nicely around the head.

它非常大，有足够的空间容纳您的眼睛，因此您的睫毛和任何眼睛都不会受到挤压，并且它非常适合头部周围。

And my wife, we both wear eye masks at night.

我和我的妻子，我们晚上都戴着眼罩。

It just, speaking of sleep, really helps us sleep.

只是，说到睡眠，它确实有助于我们睡眠。

It doesn't have the weight-ness piece, so it might be worth trying, but everyone I've recommended this to is like, "That changed my life.

它没有减肥功能，所以可能值得尝试，但我推荐过的每个人都会说，“这改变了我的生活。

Thank you for helping me sleep better." And so we'll link to .

谢谢你帮助我睡得更好。”因此我们将链接到 .

Lenny: Look at us.

莱尼：看看我们。

So adult.

这么大人了。

Two more questions.

还有两个问题。

Do you have a favorite life motto that you often come back to share with friends or family, either in work or in life?

您是否有最喜欢的人生格言，并且经常在工作或生活中与朋友或家人分享？

Logan Kilpatrick: Yeah, I've got it.

Logan Kilpatrick： 是的，我明白了。

It's on a Post-It note that I...

这是在便利贴上，我...

Right behind my camera and it's "Measure in hundreds." I love this idea of measuring things in hundreds, and it's for folks who are at the beginning of some journey.

就在我的相机后面，上面写着“以数百为单位”。我喜欢这种以数百为单位来衡量事物的想法，它适合那些刚刚开始旅程的人。

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.

我总是和人们交谈，他们会说，“是的，我尝试过这个方法，但没有成功。”如果你的心理模型是以数百为单位来衡量的，“我以数百为单位来衡量”，那么你在某件事上失败了五次，那么你失败并尝试了零次。

And I love that.

我喜欢这样。

It's such a great reminder that everything in life is built on compounding and multiple attempts at stuff.

这是一个很好的提醒，生活中的一切都是建立在复合和多次尝试之上的。

And if you don't try enough times, you're never going to be successful at it.

如果你尝试的次数不够多，你就永远不会成功。

Lenny: I love that.

莱尼：我喜欢这个。

I could see why you are successful at OpenAI and why you're a good fit there.

我可以理解为什么您在 OpenAI 取得成功以及为什么您非常适合那里。

Final question.

最后一个问题。

So I asked ChatGPT for a very silly question.

所以我向 ChatGPT 问了一个非常愚蠢的问题。

"Give me a bunch of silly questions to ask Logan Kilpatrick, head of developer relations at OpenAI," and I went through a bunch.

“给我一堆愚蠢的问题，让我去问 OpenAI 开发者关系主管 Logan Kilpatrick，”我回答了一堆问题。

I have three here, but I'm going to pick one.

我这里有三个，但我要选一个。

If an AI started doing standup comedy, what do you think would be its go-to joke or funny observation about humans?

如果人工智能开始制作单口喜剧，你认为它最喜欢的笑话或关于人类的有趣观察会是什么？

Logan Kilpatrick: I think today, I think if you were to do this, I think the go-to question would be something along the, "So an AI walks into a bar," and likely because, again, it's trained on some distribution of training data, and that's the most common joke that comes up, and that's probably...

Logan Kilpatrick：我认为今天，如果你要这样做，我认为首要问题将是“人工智能走进酒吧”，很可能是因为它再次接受了某些训练数据分布的训练，这是最常见的笑话，这可能是......

I wonder if you came up with a joke right now, whether or not that would show up in one of the examples.

我想知道你现在是否想出了一个笑话，是否会出现在其中一个示例中。

Lenny: I love it.

莱尼：我喜欢。

What would be the joke though?

但这会开什么玩笑呢？

We need the joke.

我们需要这个笑话。

We need the punchline.

我们需要笑点。

I'm just joking.

我只是开玩笑。

I know you can't come up with amazing-

我知道你想不出惊人的-

Logan Kilpatrick: That's what we have ChatGPT for.

Logan Kilpatrick：这就是我们拥有 ChatGPT 的目的。

Lenny: We're already irrelevant.

莱尼：我们已经无关紧要了。

Amazing.

太棒了。

Logan, thank you so much for being here.

洛根，非常感谢你来到这里。

Two final questions, even though you've already shared this information, but just for folks to remind them.

最后两个问题，尽管您已经分享了这些信息，但只是为了提醒人们。

Where can folks find you if they want to reach out and ask you more questions?

如果人们想联系您并向您询问更多问题，可以在哪里找到您？

And how can listeners be useful to you?

听众如何对你有用？

Logan Kilpatrick: Yeah, Twitter and LinkedIn, Logan Kilpatrick or Logan.GPT on Twitter.

Logan Kilpatrick：是的，Twitter 和 LinkedIn、Logan Kilpatrick 或 Twitter 上的 Logan.GPT。

Please shoot me messages.

请给我留言。

I get a ton of DMs from people and it's always really, really interesting stuff.

我从人们那里收到了大量的私信，它们总是非常非常有趣的东西。

I think the thing that I would love to have help on is if people find bugs and things that don't work well in ChatGPT, I oftentimes see people be like, "This thing didn't work really well." And the key, and I think we as OpenAI need to do a better job of messaging this to people, but having shared chats or actual, tangible, reproducible examples, are the two things that we need in order to actually fix the problems that people have.

我认为我希望得到帮助的事情是，如果人们发现 ChatGPT 中的错误和不能很好地工作的东西，我经常看到人们会说，“这个东西不能很好地工作。”关键是，我认为我们作为 OpenAI 需要更好地向人们传达这一信息，但是共享聊天或实际的、有形的、可复制的示例是我们真正解决人们所遇到的问题所需要的两件事。

The model laziness was a good example where it was hard to figure out what was going on because people would be like, "Oh, the model's lazier," but it's hard to figure out what were the prompts they were using.

模型惰性就是一个很好的例子，很难弄清楚发生了什么，因为人们会说，“哦，模型更惰性”，但很难弄清楚他们使用的提示是什么。

What was the examples, all that stuff?

例子是什么，所有这些东西？

So send those examples as you come up on things that don't work well and we'll make stuff better for you.

因此，当您遇到效果不佳的问题时，请发送这些示例，我们将为您改进。

Lenny: Amazing.

莱尼：太棒了。

And I'll also just remind people, if you're listening to this and you're like, "Oh, okay, cool.

我也会提醒人们，如果你正在听这个并且你会说，“哦，好吧，酷。

A lot of cool ideas for OpenAI and ChatGPT," what you need to do is actually just go to chat.openai.com and try this stuff out.

OpenAI 和 ChatGPT 有很多很酷的想法，”您所需要做的实际上只是访问 chat.openai.com 并尝试一下这些东西。

There's a lot of just theorizing, but I think once you actually start doing it, you start to see things a little differently.

有很多只是理论，但我认为一旦你真正开始这样做，你就会开始以不同的方式看待事物。

And at this point, every day I'm in there doing something, like asking for ideas for questions, doing research on a newsletter post, and it's just a tab I'm always coming back to.

在这一点上，我每天都在那里做一些事情，比如征求问题的想法，对时事通讯帖子进行研究，这只是我总是会回来的一个选项卡。

And I know there's a lot of people just talking about this sort of thing, and I just want to remind people.

我知道有很多人在谈论这类事情，我只是想提醒人们。

Just go.

走吧。

Sign in.

登录。

Play with it.

玩它。

Ask it questions on something you're working on and just see how it goes and keep coming back to it.

就你正在做的事情向它提出问题，然后看看进展如何，然后不断地回来处理它。

Is there anything else you want to share along those lines to inspire people to give this a shot?

您还有什么想分享的，以激励人们尝试一下吗？

Logan Kilpatrick: I love it.

洛根·基尔帕特里克：我喜欢它。

I think the phrase of people being worried about humans being replaced by AI, and I've seen this narrative online, that it's not AI that's going to replace humans.

我认为人们担心人类被人工智能取代的说法，我在网上看到过这样的说法，即人工智能不会取代人类。

It's other humans that are being augmented and using AI tools that are going to be more competitive in a job market and stuff like that.

其他人类正在被增强并使用人工智能工具，这些工具将在就业市场等方面更具竞争力。

So go and try these AI tools.

所以去尝试这些人工智能工具吧。

This is the best time to learn.

这是学习的最佳时机。

You're going to be more productive and empowered in your job and the things that you're excited about.

在你的工作和你感兴趣的事情上，你会变得更有效率、更有力量。

So yeah, excited to see what people use ChatGPT for.

是的，很高兴看到人们使用 ChatGPT 的用途。

Lenny: And then you can expense your account.

莱尼：然后你就可以支出你的账户了。

I think it's 10 or 20 bucks a month.

我觉得每个月也就10、20块钱吧。

A lot of companies are paying for this for you, so ask your boss if you can just have it expensed and make sure you use the latest version.

许多公司都会为您支付此费用，因此请询问您的老板是否可以将其费用化并确保您使用最新版本。

Anyway, Logan, thank you again so much for being here.

不管怎样，洛根，再次非常感谢你来到这里。

Logan Kilpatrick: This is awesome, Lenny.

Logan Kilpatrick：这太棒了，Lenny。

Thanks for having me in.

谢谢你让我进来。

Thoughtful questions.

深思熟虑的问题。

Hopefully those weren't all from ChatGPT.

希望这些不全部来自 ChatGPT。

Lenny: Nope, only the last one.

莱尼：不，只有最后一个。

I did have a bunch of others I had in the belt or in the pocket.

我的腰带或口袋里确实有很多其他的东西。

I don't know what the metaphor is.

我不知道这个比喻是什么。

In the back pocket, that's the metaphor, but I did not get to them because we had enough great stuff.

在后面的口袋里，这就是比喻，但我没有拿到它们，因为我们有足够多的好东西。

So no, that was all me.

所以不，那是我的全部。

Human AI.

人类人工智能。

Lenny: I love it.

莱尼：我喜欢。

Lennybot.com, check it out.

Lennybot.com，请查看。

Okay, thanks Logan.

好的，谢谢洛根。

Bye everyone.

再见大家。

Thank you so much for listening.

非常感谢您的聆听。

If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app.

如果您发现这很有价值，您可以在 Apple Podcasts、Spotify 或您最喜欢的播客应用程序上订阅该节目。

Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast.

另外，请考虑给我们评分或留下评论，因为这确实有助于其他听众找到播客。

You can find all past episodes or learn more about the show at lennyspodcast.com.

您可以在 lennyspodcast.com 上找到所有过去的剧集或了解有关该节目的更多信息。

See you in the next episode.

下一集见。