Scale AI CEO on Meta's $14B deal, scaling Uber Eats to $80B, & what frontier labs are building next
Lenny Rachitsky: There's been a lot of talk these days about AI not delivering on the promise that we hear, especially at enterprises.
Lenny Rachitsky：最近有很多关于人工智能没有兑现我们听到的承诺的讨论，尤其是在企业中。
Jason Droege: These things take 6 to 12 months to get them truly robust enough where an important process can be automated.
Jason Droege：这些东西需要 6 到 12 个月的时间才能真正足够强大，从而可以实现重要流程的自动化。
Like with any of these major tech revolutions, headlines tell one story and then on the ground, laying broadband means you need to dig up every single road in America to lay it.
就像任何这些重大技术革命一样，头条新闻讲述一个故事，然后在地面上，铺设宽带意味着你需要挖掘美国的每一条道路来铺设它。
Someone's got to dig up the road or someone's got to run the undersea cable.
必须有人挖路或者铺设海底电缆。
Lenny Rachitsky: Is there anything you think people don't truly grasp or understand about where AI models are going to be in the next two, three years?
Lenny Rachitsky：对于人工智能模型在未来两三年内的发展方向，您认为人们有没有真正掌握或理解的地方？
Jason Droege: The general trend right now is going from models knowing things to models doing things.
Jason Droege：现在的总趋势是从模型了解事物到模型做事。
The next question becomes, what can it do for me?
下一个问题是，它能为我做什么？
How does the agent make decisions for you?
代理人如何为您做决定？
Lenny Rachitsky: Let's talk about Scale and this whole world of AI that you're in, you essentially pioneered data labeling, trading data, creating evals for labs.
Lenny Rachitsky：让我们来谈谈 Scale 和您所处的整个人工智能世界，您本质上是数据标签、交易数据、为实验室创建评估的先驱。
Jason Droege: 18 months ago, you would get a short story and it would say, "Is this short story better than this short story?" And now you're at a point where one task is building an entire website by one of the world's best web developers, or it is explaining some very nuanced topic on cancer to a model.
Jason Droege：18 个月前，你会收到一个短篇小说，它会说：“这个短篇小说比这个短篇小说更好吗？”现在，您面临的一项任务是由世界上最好的网络开发人员之一构建整个网站，或者向模型解释一些关于癌症的非常微妙的主题。
These tasks now take hours of time and they require PhDs and professionals.
这些任务现在需要花费数小时的时间，并且需要博士和专业人员。
Lenny Rachitsky: I've talked to a bunch of people that have worked with you over the years, and I heard a lot about just how high of a bar you set for new businesses.
Lenny Rachitsky：我和很多多年来与您共事的人交谈过，我听到很多关于您为新业务设定的标准有多高的消息。
Jason Droege: From an entrepreneurship standpoint, it truly is about what insight do I have?
Jason Droege：从创业的角度来看，真正重要的是我有什么洞察力？
Why am I so lucky to have this insight?
为什么我这么幸运能有这样的洞察力？
Why in a world of a million entrepreneurs who are thinking, who are smart, who are trying everything, why am I in the position where I likely have an insight that others do not?
为什么在一个有上百万企业家思考、聪明、尝试一切的世界里，为什么我可能拥有别人没有的洞察力？
Lenny Rachitsky: Today, my guest is Jason Droege.
Lenny Rachitsky：今天，我的客人是 Jason Droege。
Jason is the new CEO of Scale AI.
Jason 是 Scale AI 的新任首席执行官。
This is the first interview that he's done since taking over for Alex Wang after the Meta deal.
这是他在 Meta 交易后接替 Alex Wang 后接受的第一次采访。
Alex now leads the super intelligence team at Meta.
Alex 现在领导 Meta 的超级情报团队。
Prior to Scale, Jason co-founded a company with Travis Kalanick.
在加入 Scale 之前，Jason 与 Travis Kalanick 共同创立了一家公司。
Before, he started Uber, worked at a couple startups.
在此之前，他创办了 Uber，并在几家初创公司工作过。
Most famously, Jason launched and led Uber Eats, which went from an idea that he and his team had to what is now a multi-billion dollar run rate business and one that basically saved Uber during the pandemic when nobody was taking rides.
最著名的是，杰森创立并领导了 Uber Eats 优食，该服务从他和他的团队的一个想法发展成为现在价值数十亿美元的运行率业务，并且在大流行期间无人乘车时基本上拯救了 Uber。
This interview is following a theme that I've been following through a bunch of interviews, which is the evolution of how AI models actually gets smarter.
这次采访遵循了我在一系列采访中一直遵循的主题，即人工智能模型如何变得更加聪明的演变。
Along with scaling, compute and improving the actual model code, much of the improvements we're seeing in ChatGPT and Claude and every frontier AI model is these labs hiring experts to filling gaps in their knowledge and correcting their understanding of how things work, and basically showing them what good looks like in every domain that consumers are using models.
除了扩展、计算和改进实际模型代码之外，我们在 ChatGPT 和 Claude 以及每个前沿人工智能模型中看到的大部分改进都是这些实验室聘请专家来填补他们的知识空白并纠正他们对事物工作原理的理解，并基本上向他们展示消费者使用模型的每个领域的良好外观。
Lenny Rachitsky: Scale was the pioneer in this space.
Lenny Rachitsky：Scale 是这个领域的先驱。
They created the category, and in our conversation we talk about what is happening at Scale and just how this deal with Meta worked, what experts like doctors and software engineers are specifically doing to help models get smarter, how the whole market of data labeling and evals and data training has changed from when Scale entered the market to today, and also just how long will we need humans to keep helping AI get smarter.
他们创建了这个类别，在我们的对话中，我们讨论了 Scale 正在发生的事情，以及与 Meta 的交易是如何运作的，医生和软件工程师等专家具体做了哪些工作来帮助模型变得更智能，从 Scale 进入市场到今天，整个数据标记、评估和数据训练市场发生了怎样的变化，以及我们需要人类继续帮助人工智能变得更智能需要多长时间。
We also get into where Jason sees models going in the next few years because they have such a unique glimpse into the future.
我们还探讨了杰森对模型在未来几年的发展趋势，因为他们对未来有着独特的洞察力。
We also talk about a ton of really unique and really important product lessons from the course of Jason's career, including a bunch of advice on how to start a new business, both startups and within existing companies, and also a bunch of advice on hiring and leadership and so much more.
我们还讨论了 Jason 职业生涯中学到的大量非常独特且非常重要的产品经验教训，包括一系列关于如何在初创公司和现有公司内开展新业务的建议，以及一系列关于招聘和领导力等的建议。
Lenny Rachitsky: A huge thank you to Allen Penn and Stephen Chau for suggesting topics for this conversation.
Lenny Rachitsky：非常感谢 Allen Penn 和 Stephen Chau 为本次对话提出的主题。
If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube.
如果您喜欢这个播客，请不要忘记订阅并在您最喜欢的播客应用程序或 YouTube 中关注它。
It helps tremendously.
它有很大帮助。
And if you become an annual subscriber of my newsletter, you get a year free of 15 incredible products including Lovable, Replet, Bolt, n8n, Linear, Superhuman, Descript, Wispr Flow, Gamma, Perflexity, Warp, Granola, Magic Patterns, Raycast, ChatPRD, and Mobbin.
如果您成为我的时事通讯的年度订阅者，您将获得一年免费的 15 种令人难以置信的产品，包括 Lovable、Replet、Bolt、n8n、Linear、Super human、Descript、Wispr Flow、Gamma、Perflexity、Warp、Granola、Magic Patterns、Raycast、ChatPRD 和 Mobbin。
Head on over to lennysnewsletter.com and click product pass.
前往 lennysnewsletter.com 并单击产品通行证。
With that, I bring you Jason Droege.
接下来，我为您带来杰森·德罗奇。
Lenny Rachitsky: This episode is brought to you by Merge.
Lenny Rachitsky：本集由 Merge 为您带来。
Product leaders hate building integrations.
产品领导者讨厌构建集成。
They're messy, they're slow to build, they're a huge drain on your roadmap, and they're definitely not why you got into product in the first place.
它们很混乱，构建速度很慢，对您的路线图来说是巨大的消耗，而且它们绝对不是您首先进入产品的原因。
Lucky for you, Merge is obsessed with integrations.
幸运的是，Merge 痴迷于集成。
With a single API, B2B SaaS companies embed merge into their product and ship 220 plus customer facing integrations in weeks, not quarters.
通过单个 API，B2B SaaS 公司将合并嵌入到他们的产品中，并在几周内（而不是几个季度）交付 220 多个面向客户的集成。
Think of Merge like plaid, but for everything B2B SaaS.
将 Merge 想象为格子图案，但适用于所有 B2B SaaS。
Companies like Mistral AI, Ramp and Dorada use Merge to connect their customers as accounting, HR, ticketing, CRM, and file storage systems to power everything from automatic onboarding to AI-ready data pipelines.
Mistral AI、Ramp 和 Dorada 等公司使用 Merge 将其客户作为会计、人力资源、票务、CRM 和文件存储系统连接起来，为从自动入职到人工智能就绪数据管道的一切提供支持。
Even better, Merge now supports the secure deployment of connectors to AI agents with a new product so that you can safely power AI workflows with real customer data.
更好的是，Merge 现在支持通过新产品将连接器安全部署到 AI 代理，以便您可以使用真实的客户数据安全地为 AI 工作流程提供支持。
If your product needs customer data from dozens of systems, Merge is the fastest, safest way to get it.
如果您的产品需要来自数十个系统的客户数据，合并是最快、最安全的获取方式。
Book and attend a meeting at merge.dev/lenny and they'll send you a $50 Amazon gift card.
在 merge.dev/lenny 上预订并参加会议，他们将向您发送一张 50 美元的亚马逊礼品卡。
That's merge.dev/lenny.
那是 merge.dev/lenny。
Lenny Rachitsky: This episode is brought to you by Figma, makers of Figma Make.
Lenny Rachitsky：本集由 Figma（Figma Make 的制作者）为您带来。
When I was a PM at Airbnb, I still remember when Figma came out and how much it improved how we operated as a team.
当我在 Airbnb 担任产品经理时，我仍然记得 Figma 的问世以及它对我们团队运作方式的改善。
Suddenly, I could involve my whole team in the design process, give feedback on design concepts really quickly, and it just made the whole product development process so much more fun.
突然之间，我可以让我的整个团队参与到设计过程中，非常快速地提供设计概念反馈，这使得整个产品开发过程变得更加有趣。
But Figma never felt like it was for me.
但 Figma 从来不觉得它适合我。
It was great for giving feedback and designs, but as a builder, I wanted to make stuff.
它非常适合提供反馈和设计，但作为一名构建者，我想要制作东西。
Lenny Rachitsky: That's why Figma built Figma Make.
Lenny Rachitsky：这就是 Figma 打造 Figma Make 的原因。
With just a few prompts, you can make any idea or design into a fully functional prototype or app that anyone can iterate on and validate with customers.
只需几个提示，您就可以将任何想法或设计制作成功能齐全的原型或应用程序，任何人都可以与客户进行迭代和验证。
Figma Make is a different kind of vibe coding tool.
Figma Make 是一种不同类型的氛围编码工具。
Because it's all in Figma, you can use your team's existing design building blocks, making it easy to create outputs that look good and feel real and are connected to how your team builds.
因为一切都在 Figma 中，所以您可以使用团队现有的设计构建块，从而轻松创建看起来不错、感觉真实并与团队构建方式相关的输出。
Stop spending so much time telling people about your product vision and instead show it to them.
不要再花太多时间告诉人们你的产品愿景，而是向他们展示它。
Make code-backed prototypes and apps fast with Figma Make.
使用 Figma Make 快速制作代码支持的原型和应用程序。
Check it out at figma.com/lenny.
请访问Figma.com/lenny 进行查看。
Jason, thank you so much for being here and welcome to the podcast.
杰森，非常感谢您来到这里，欢迎来到播客。
Jason Droege: Yeah, thanks for having me.
Jason Droege：是的，谢谢你邀请我。
Excited to be here.
很高兴来到这里。
Lenny Rachitsky: As I was researching your background and prepping for this podcast, I learned a really interesting fun fact about you that I don't think a lot of people know.
Lenny Rachitsky：当我研究你的背景并准备这个播客时，我了解到一个关于你的非常有趣的事实，我认为很多人都不知道。
So Travis Kalanick, he had a startup before Uber.
特拉维斯·卡兰尼克（Travis Kalanick）在 Uber 之前就已经创办了一家初创公司。
It was called Scour.
它被称为冲刷。
It was a peer-to-peer file sharing app, and then I think got shut down.
这是一个点对点文件共享应用程序，然后我认为它被关闭了。
You were his co-founder.
你是他的联合创始人。
This was the early part of your career.
这是你职业生涯的早期阶段。
I'm guessing there are hours of stories we could talk about during this experience.
我猜在这次经历中我们可以谈论几个小时的故事。
So let me just ask you this one question.
那么让我问你这个问题。
What's just a lesson that has stuck with you from that experience that you've taken with you to future places you've worked and built product at?
从您带到未来工作和开发产品的地方的经历中，您学到的教训是什么？
Jason Droege: I mean, there's so many lessons.
Jason Droege：我的意思是，有很多教训。
I like to pick one.
我喜欢选一个。
I think that the main lesson is that in business and in startups, everything's negotiable.
我认为主要的教训是，在商业和初创企业中，一切都是可以协商的。
I think that's the main thing.
我认为这是最重要的。
Because we were 19 at the time, 19, 20 at the time, we built this search engine in a dorm room and we were running it out of the dorm room and our first URL was scour.cs.ucla.edu.
因为我们当时 19 岁，当时 19 岁、20 岁，所以我们在宿舍里构建了这个搜索引擎，并在宿舍外运行它，我们的第一个 URL 是 scour.cs.ucla.edu。
These things were not necessarily in fractions at the time, but we were just being practical.
这些东西在当时不一定是分数，但我们只是比较实际。
It was basically a project that we had started, and so we built the search engine and people started using it and we thought we would get in trouble, but it turned out the computer science department was excited about it even though we had basically parked a domain on their servers and we were using our own computers in the dorms to serve up this website and product.
这基本上是我们已经启动的一个项目，所以我们构建了搜索引擎，人们开始使用它，我们认为我们会遇到麻烦，但事实证明，计算机科学系对此感到兴奋，尽管我们基本上在他们的服务器上停放了一个域，并且我们在宿舍中使用自己的计算机来提供这个网站和产品。
Jason Droege: And then, when we got into financing, the financing process was fascinating, and this is where the everything is negotiable lesson came from, which is, it was Ron Burkle and Mike Ovitz, who are the initial investors in the business.
Jason Droege：然后，当我们进入融资领域时，融资过程非常有趣，这就是一切都是可以协商的教训的来源，即罗恩·伯克和迈克·奥维茨是该业务的初始投资者。
We were in LA, so we were at UCLA, so we were not quite wired into the entire Sand Hill Road scene.
我们在洛杉矶，所以我们在加州大学洛杉矶分校，所以我们不太了解整个沙山路场景。
And as we were doing the deal, the terms kept changing on us.
当我们进行交易时，我们的条款不断变化。
We thought you went and raised money and it was like, "Oh, we'll get a few million dollars at a $5 million valuation." This is back when that was actually a series A valuation.
我们以为你去筹集了资金，然后就像是，“哦，我们将以 500 万美元的估值获得几百万美元。”这实际上是 A 轮估值的情况。
And then over the course of the deal, it was like, "We're doing the deal.
然后在交易过程中，就像是，“我们正在做这笔交易。
We're not doing the deal.
我们不做这笔交易。
Oh, you should give us 50% of the company.
哦，你应该给我们公司50%的股份。
Oh, you should give us 75% of the company.
哦，你应该给我们公司75%的股份。
Oh, if you want to sign the document today, this person's going to show up for breakfast and if you don't sign today and give us 80% of the company, the person's not going to show up."
哦，如果你今天想签署文件，这个人就会出现吃早餐，如果你今天不签署并给我们公司 80% 的股份，这个人就不会出现。”
Jason Droege: It was just completely wild, the things that we saw from day one of what can happen in business, and we thought there was a way to do things, and at a very young age we realized there is no way to do things.
Jason Droege：这完全是疯狂的，我们从第一天起就看到了商业中可能发生的事情，我们认为有一种方法可以做事，但在很小的时候我们就意识到没有办法做事。
There is just the way that you can negotiate your way through the world, which I actually think influenced Travis heavily and then me later heavily at Uber in terms of if you can imagine it and it makes sense and you can align incentives, then it can happen.
有一种方式可以让你通过谈判走遍世界，我实际上认为这种方式对特拉维斯以及后来我在 Uber 的影响很大，如果你能想象它并且它有意义，并且你可以调整激励措施，那么它就可以实现。
But there is no way.
但没有办法。
And to learn that at 19 or 20 years old I think was highly imprinting.
我认为在 19 或 20 岁时了解到这一点是非常深刻的。
Lenny Rachitsky: That is an amazing lesson.
Lenny Rachitsky：这是一个很棒的教训。
What happened to Scour?
斯库尔怎么了？
It got shut down, I think.
我想它已经被关闭了。
What happened there?
那里发生了什么？
Jason Droege: Well, yeah, so basically what Scour was was it was a multimedia search engine and then peer-to-peer file sharing network.
Jason Droege：嗯，是的，基本上，Scour 是一个多媒体搜索引擎，然后是点对点文件共享网络。
But what it was used for was finding free content.
但它的用途是寻找免费内容。
And at the time, the laws were on this were pretty ambiguous because we weren't, mix tapes were legal, but this was like a hyperversion of that.
当时，有关这方面的法律相当模糊，因为我们不是，混合磁带是合法的，但这就像是一个超级版本。
But we were eventually sued for a quarter of a trillion dollars.
但我们最终被起诉，索赔 25 万亿美元。
So I guess if you're going to experience something that's potentially as life devastating as that, doing it when you're, I think we were 21 or 22 at the time is the time to do it, but it was just this very cold splash of water about how the real world really works, because the MBAA and the RAA were the ones who sued us, the entertainment industry sued us or the associations that represent the entertainment industry, and then they settled it for $1 million.
所以我想如果你要经历一些可能对你的生活造成毁灭性的事情，那么当你在的时候去做这件事，我想我们当时21岁或22岁是时候去做这件事，但这只是关于现实世界如何运作的一个非常冰冷的泼水，因为MBAA和RAA是起诉我们的人，娱乐行业起诉我们或代表娱乐行业的协会，然后他们以100万美元和解。
Jason Droege: So we're like, "Wait, you wanted a quarter of a trillion dollars and then you settle for $1 million." And of course they were just trying to drive us in a bankruptcy, drive us out of the market, and these are established companies.
Jason Droege：所以我们会说，“等等，你想要 25 万亿美元，然后你就满足于 100 万美元。”当然，他们只是想让我们破产，把我们赶出市场，而这些都是老牌公司。
So we're like, "If these guys don't have a playbook to follow, they just make up numbers, then wow, how should we navigate the rest of our lives?"
所以我们会想，“如果这些人没有可遵循的剧本，他们只是编造数字，那么哇，我们应该如何度过余生呢？”
Lenny Rachitsky: Let's talk about Scale and this whole world of AI that you're in.
Lenny Rachitsky：我们来谈谈 Scale 以及您所处的整个人工智能世界。
This is the first interview that you're doing since taking over CEO at Scale.
这是您接任 Scale 首席执行官以来进行的第一次采访。
I'm honored to have you here to talk through this stuff.
我很荣幸能邀请到你来讨论这个问题。
This is also the first interview you're doing since the whole Meta deal, which is very complicated, confused a lot of people.
这也是自整个 Meta 交易以来您第一次接受采访，这非常复杂，让很多人感到困惑。
So I'm just curious to hear the current state of Scale, what people should know.
所以我只是想知道 Scale 的现状，以及人们应该知道什么。
For example, what's your relationship with Meta?
例如，你和 Meta 是什么关系？
What's your relationship with Alex?
你和亚历克斯是什么关系？
What is the current state of Scale?
Scale目前的状况如何？
Jason Droege: Yeah, so Scale is a fully independent company.
Jason Droege：是的，所以 Scale 是一家完全独立的公司。
The transaction was Meta invested a little bit over $14 billion to get 49% of the company, non-voting stock, didn't take a new board seat.
此次交易中，Meta 投资略高于 140 亿美元，获得了该公司 49% 的股份（无投票权股票），但并未占据新的董事会席位。
Alex fills the board seat.
亚历克斯填补了董事会席位。
So the board is the same, the governance is largely the same.
因此，董事会是相同的，治理结构也基本相同。
There's no preferential access to anything that Meta has.
对 Meta 拥有的任何内容都没有优先访问权。
There's no preferential relationship.
不存在优惠关系。
I mean, we've had a longstanding relationship with Meta on the data side of the business for a long time and even on some business development related things to maybe working on things in government together, et cetera.
我的意思是，我们在业务数据方面与 Meta 建立了长期合作关系，甚至在一些业务开发相关的事情上也与 Meta 建立了长期的合作关系，甚至可能一起在政府部门合作，等等。
And so, those might get bigger just as we're closer now, but there's nothing that prevents us from doing things with other parties and they have no access to anything that they wouldn't have had otherwise.
因此，随着我们现在距离越来越近，这些可能会变得更大，但没有什么可以阻止我们与其他各方合作，而且他们无法获得任何他们原本无法获得的东西。
All the privacy still in place, all the data security still in place that was there before.
所有隐私仍然存在，所有数据安全仍然存在。
Jason Droege: And in fact, only about 15 people went over in the transaction.
Jason Droege：事实上，只有大约 15 人参与了此次交易。
So Scale has about 1,100 employees or so now, and we have two major businesses.
所以Scale现在大概有1100名左右的员工，我们有两个主要业务。
Each of those businesses, each of them has hundreds of millions of revenue.
这些企业每一个都有数亿的收入。
So we have two unicorns inside the company today that sustains.
所以今天我们公司内部有两家独角兽能够维持下去。
The business has grown every month since the deal happened, which I've read, the reporting is not consistently reported.
自从交易发生以来，业务每个月都在增长，我读过，但报道的报道并不一致。
We haven't talked about it, so this is part of getting the word out and we're excited to continue to build, deliver data, and do what we did before.
我们还没有讨论过这个问题，所以这是宣传的一部分，我们很高兴能够继续构建、交付数据并做我们之前所做的事情。
Lenny Rachitsky: So the company today, independent, its own company.
Lenny Rachitsky：所以今天的公司是独立的，有自己的公司。
Alex, just to be clear, he works at Meta now.
Alex，需要澄清的是，他现在在 Meta 工作。
He's no longer at Scale.
他已经不在Scale了。
Jason Droege: Yeah, that's right.
杰森·德罗奇：是的，没错。
Excuse me, I should have talked about that more.
对不起，我应该多谈一下这个。
Lenny Rachitsky: I think that's really interesting.
Lenny Rachitsky：我认为这真的很有趣。
So basically, it was an investment.
所以基本上，这是一项投资。
Some people left to join Meta, the company continues, you're running the ship.
有些人离开加入 Meta，公司继续，你正在经营这艘船。
Let's talk about this whole space that you guys essentially pioneered, I don't know best way to call it, data labeling, training data, creating evals for labs.
让我们来谈谈你们本质上开创的整个领域，我不知道最好的称呼，数据标签，训练数据，为实验室创建评估。
You guys were at this before anyone even knew this was a thing.
在没有人知道这是一件事之前，你们就已经这么做了。
I know even Scale pivoted into this market from other things.
我知道 Scale 也从其他方面转向了这个市场。
I think there was a bunch of stuff they tried with self-driving cars and all these things, and then it's like, "Oh shit, AI labs need this data."
我认为他们在自动驾驶汽车和所有这些东西上尝试了很多东西，然后就像，“哦，糟糕，人工智能实验室需要这些数据。”
Lenny Rachitsky: One of the main stories I've been hearing is, and I've had a bunch of CEOs from this space on the podcast, is that there's been this big shift from the way, from what Scale had pioneered and had been doing for a long time, which is generalists, low-cost labor training.
Lenny Rachitsky：我听到的一个主要故事是，我在播客上听过很多来自这个领域的首席执行官，就是 Scale 开创并长期以来一直在做的方式发生了巨大的转变，即通才、低成本劳动力培训。
From that to now, labs mostly need experts, lawyers, doctors, engineers doing training, writing evals, things like that.
从那时到现在，实验室主要需要专家、律师、医生、工程师进行培训、撰写评估等。
I'm curious just what you're seeing, how that's impacting you guys, where you think things are heading, what people should know about this whole market of data training data.
我很好奇你们所看到的，这对你们有何影响，你们认为事情的发展方向，人们应该了解整个数据训练市场的哪些内容。
Jason Droege: Yeah, totally.
杰森·德罗奇：是的，完全可以。
I think the current positioning out there from competitors is just bogus.
我认为竞争对手目前的定位是虚假的。
So I'll start with that and then maybe talk a little bit about, I'll explain what I mean by that in a second.
所以我将从这一点开始，然后可能会谈论一些，我会在一秒钟内解释我的意思。
But I think it's important to just give 30 seconds on what the history of Scale is and what's the thread going back to 2016.
但我认为用 30 秒的时间介绍 Scale 的历史以及追溯到 2016 年的线索是很重要的。
So Alex had this insight in very early days that the important thing to models was data.
因此，亚历克斯很早就认识到，对于建模而言，最重要的是数据。
And I think he was 19 or 20 years old at the time as well.
我想他当时也只有 19 岁或 20 岁。
And so, he's like, "Okay, well what business would I create around this?" And the business that he created around it was, okay, let's do labeling for autonomous vehicles, because if you label the data that they have, the cars do better.
所以，他说，“好吧，那么我会围绕这个创建什么业务呢？”他围绕它创建的业务是，好吧，让我们为自动驾驶汽车贴上标签，因为如果你为它们拥有的数据贴上标签，汽车就会做得更好。
And then, that wave turned into the computer vision wave, which we have a relationship with the Department of Defense where we do labeling for them, and that was in 2020.
然后，那股浪潮变成了计算机视觉浪潮，我们与国防部有关系，我们为它们做标签，那是在 2020 年。
Jason Droege: And then, you move forward and the models have gotten better over this period of time.
Jason Droege：然后，你继续前进，模型在这段时间里变得更好了。
And so, as models get better, they need different types of data.
因此，随着模型变得更好，它们需要不同类型的数据。
So we've constantly been adapting to the type of data that models need to be successful.
因此，我们一直在不断适应模型成功所需的数据类型。
And so, then the gen AI wave hit, and this went through the moon or to the moon.
于是，新一代人工智能浪潮袭来，这股浪潮穿过了月球，或者到达了月球。
And so, as part of that, that industry is changing constantly too.
因此，作为其中的一部分，该行业也在不断变化。
So it is correct that when the models came out two or three years ago, I mean we remember using them, they would hallucinate all the time, they would get basic answers wrong, they didn't know which poem was better, this poem or that poem.
因此，当模型在两三年前出现时，我的意思是我们记得使用它们，他们会一直产生幻觉，他们会得到错误的基本答案，他们不知道哪首诗更好，这首诗还是那首诗，这是正确的。
And that was the state of labeling a couple years ago.
这就是几年前标签的状态。
And things have changed quickly and we've changed with it.
事情变化得很快，我们也随之改变。
And now the state for everyone, and we've been at the forefront of all of this, is expert data labeling, more sophisticated tasks.
现在，对于每个人来说，我们一直处于这一切的最前沿，那就是专家数据标记，以及更复杂的任务。
Jason Droege: So to give you a sense of what the task was 18 months ago, I've been here about 13 months.
Jason Droege：为了让您了解 18 个月前的任务是什么，我已经在这里工作了大约 13 个月。
So I was interviewing and I remember seeing it.
所以我在面试时我记得看到过它。
You would get a short story and it would say, "Is this short story better than this short story?" And then you would edit it and be like, "Yeah, it would be better if it was this," and you would give some preference ranking to it.
你会收到一个短篇故事，它会说：“这个短篇故事比这个短篇故事更好吗？”然后你会编辑它，然后说，“是的，如果是这个就更好了”，然后你会给它一些偏好排名。
It was pretty basic 18 months ago, and you had the rise of some experts, but the models were so far behind that they needed just even the basic stuff they needed.
18 个月前，这还是相当基础的，后来出现了一些专家，但模型远远落后，以至于他们只需要他们需要的基本东西。
And now, you're at a point where a task is, one task is building an entire website by one of the world's best web developers, or it is explaining some very nuanced topic on cancer to a model.
现在，你正面临一项任务，一项任务是由世界上最好的网络开发人员之一构建整个网站，或者是向模型解释一些关于癌症的非常微妙的主题。
And these tasks now take hours of time and they require PhDs and professionals.
这些任务现在需要花费数小时的时间，并且需要博士和专业人员。
Jason Droege: So to give you a stat to back this up, 80% of the people that we have on our expert network have a bachelor's degree or greater, which is very contrary to some of the positioning that's out there and some of the understanding of this industry.
Jason Droege：所以给你一个统计数据来支持这一点，我们的专家网络中 80% 的人拥有学士学位或更高学位，这与现有的一些定位和对这个行业的一些理解是非常相反的。
About 15% have a PhD that's greater, and we have PhDs on the network earning significant amounts of money doing labeling, contributing their expertise to these models.
大约 15% 的人拥有更高级别的博士学位，我们网络上的博士通过做标签工作赚取了大量金钱，为这些模型贡献了他们的专业知识。
So we've been doing expert data labeling ever since the models need it.
因此，自从模型需要它以来，我们就一直在进行专家数据标记。
I mean, this game is keeping in touch with the researchers, knowing what they need, coming up with ideas internally.
我的意思是，这个游戏与研究人员保持联系，了解他们需要什么，在内部提出想法。
In some ways, we drove this because we were seeing that the models were not sufficient in more expert ways.
在某些方面，我们推动了这一点，因为我们发现这些模型在更专业的方面还不够。
And so, we would go to the model builders and say, "Hey, we noticed that this is a problem.
因此，我们会去找模型构建者说：“嘿，我们注意到这是一个问题。
If you would like to fix it, this cadre of experts can do that for you." So the counter positioning is out there, but I think that's just what competitors say sometimes.
如果你想修复它，这群专家可以为你做到。”因此，反击定位是存在的，但我认为这正是竞争对手有时所说的。
It has nothing to do with reality.
这与现实无关。
Lenny Rachitsky: Okay.
莱尼·拉奇茨基：好的。
That was extremely interesting.
这非常有趣。
So what I'm hearing is yes, there has been a big shift to labs need more expert folks involved in training, labeling, writing evals.
所以我听到的是，是的，实验室已经发生了巨大的转变，需要更多的专家参与培训、标记、编写评估。
You guys are very aware of that and have evolved with that.
你们非常清楚这一点，并随之发展。
One of the, I don't know, allegations I guess in the market is that it's hard to find these experts.
我不知道，市场上的一种指控是很难找到这些专家。
So all these companies have their proprietary network of experts and how they find them.
因此，所有这些公司都有自己专有的专家网络以及如何找到专家。
Is there anything you could share about just how you guys go about that because that feels like the hardest part is finding these experts and keeping them from other companies?
关于你们是如何做到这一点的，您有什么可以分享的吗？因为感觉最困难的部分是找到这些专家并让他们远离其他公司？
Jason Droege: They are hard to find.
Jason Droege：它们很难找到。
You have to have many, many tactics.
你必须有很多很多的战术。
So we get, as you would expect, there's not one way you do it.
因此，正如您所期望的，我们没有一种方法可以做到这一点。
The largest way is that they refer each other because when you are enjoying what you're doing and you are using your expertise to contribute to AI, which is pretty cool.
最大的方式是他们互相推荐，因为当你享受你正在做的事情并且你正在利用你的专业知识为人工智能做出贡献时，这非常酷。
If you're a PhD on this pretty specific topic and you're using a model and you're frustrated that, oh, it doesn't interact with me in the way that I want, this is a paid way to have an outlet for that and to make hundreds or thousands of dollars doing that.
如果你是这个非常具体的主题的博士，并且你正在使用一个模型，并且你感到沮丧，哦，它没有以我想要的方式与我互动，那么这是一种付费方式，可以为此提供一个出口，并通过这样做赚取数百或数千美元。
And so, a lot of times they refer each other.
因此，很多时候他们会互相推荐。
Jason Droege: We also have campus programs where we will literally go onto the campus and talk to the professors, talk to the students, ask about who would like to do this type of work.
Jason Droege：我们还有校园项目，我们会真正走进校园，与教授交谈，与学生交谈，询问谁愿意做此类工作。
And then, of course, there's the more traditional scaled ways of LinkedIn and places like that.
当然，还有 LinkedIn 等更传统的扩展方式。
But the best ones come from these grassroots and referral networks.
但最好的来自这些草根和推荐网络。
And the only way you get that is providing a great experience to these people, because these people, they're doing it partly for money, but they're also doing it because they think that their contribution to the AI models is important and interesting, and in many times it solves a problem for them.
实现这一目标的唯一方法就是为这些人提供良好的体验，因为这些人这样做的部分原因是为了钱，但他们这样做也是因为他们认为自己对人工智能模型的贡献是重要且有趣的，而且在很多时候这为他们解决了问题。
Lenny Rachitsky: So something that I've been seeing on Twitter just this week as I was preparing for this is there's the information headline.
Lenny Rachitsky：本周，当我为此做准备时，我在 Twitter 上看到了一条信息标题。
This came out and this mirrored something that Brendan from Workhorse said that over time the entire economy is going to move towards just reinforcement learning and everyone's just training AI is basically the jobs that will be left.
这一结果反映了 Workhorse 的 Brendan 所说的，随着时间的推移，整个经济将转向强化学习，而每个人只训练人工智能基本上就是剩下的工作。
Thoughts on that?
对此有何想法？
Is that where you think things are going?
你认为事情的发展方向是这样的吗？
Is there another perspective?
还有另一种观点吗？
Jason Droege: Reinforcement learning is very important, and I think this is a broader comment about the move to environments.
Jason Droege：强化学习非常重要，我认为这是对环境迁移的更广泛的评论。
There's these things called RL environments that effectively are sandboxes for AI agents to play in to accomplish a goal so that they can learn how to accomplish that goal.
有一些称为强化学习环境的东西，它们实际上是人工智能代理在其中实现目标的沙箱，以便它们能够学习如何实现该目标。
We've been doing this for over a year.
我们这样做已经一年多了。
So for example, you have a Salesforce instance.
例如，您有一个 Salesforce 实例。
How does an AI agent navigate that instance?
AI 代理如何导航该实例？
That instance has data that it needs to recognize, it has configurations.
该实例具有它需要识别的数据，它具有配置。
Salesforce is a highly configurable product.
Salesforce 是一款高度可配置的产品。
It has configurations, it needs to understand how to navigate.
它有配置，它需要了解如何导航。
You're asking the agent to do a business process that needs very high reliability, and then the agent needs to know, "Hey, if I can't accomplish what I'm going to accomplish, or I think if there's a low accuracy of what I'm about to accomplish, how do I pop it up to a human being for feedback so I can get guidance?"
您要求代理执行一个需要非常高可靠性的业务流程，然后代理需要知道，“嘿，如果我无法完成我要完成的任务，或者我认为如果我要完成的任务的准确性较低，我如何将其弹出给人类以获取反馈，以便我可以获得指导？”
Jason Droege: All of those things need to be trained and there's no alchemy to it.
Jason Droege：所有这些都需要训练，而且没有炼金术。
You just have to put the AI agent in an environment that represents what a human being would be doing.
你只需要把人工智能代理放在一个代表人类会做什么的环境中。
And you can imagine the number of environments in the world and the number of goals within each environment is enormous.
你可以想象世界上的环境数量以及每个环境中的目标数量是巨大的。
So the question is, and the research that we have done over the past year to try to be a good partner to our model builders, our model builder customers, is how generalizable is each individual task or each individual environment.
所以问题是，我们在过去一年中所做的研究是为了成为我们的模型构建器和模型构建器客户的良好合作伙伴，即每个单独的任务或每个单独的环境的通用性如何。
So if you imagine the world of environments of software systems, configurations, data types, sizes, user counts, complexities, it's like the permutations are endless.
因此，如果您想象一下软件系统、配置、数据类型、大小、用户数量、复杂性的环境世界，就会发现排列是无穷无尽的。
So what you need is you need a strategy that allows a lab to collect data that is generalizable enough across a broad spectrum of use cases so that they don't have to collect 45 trillion combinations of what should the agent do in this particular situation.
因此，您需要的是一种策略，允许实验室收集在广泛的用例中足够通用的数据，这样他们就不必收集代理在这种特定情况下应该做什么的 45 万亿种组合。
Jason Droege: So sometimes the work and the data is highly generalizable, and by generalizable I mean you have it accomplished in a simple way.
Jason Droege：所以有时工作和数据是高度可概括的，我所说的可概括是指你可以用一种简单的方式完成它。
The task might be find the meeting on my calendar for my interview with Lenny, and the agent goes and it looks through all my calendar and then it pops it out, very simple example.
任务可能是在我的日历上找到与 Lenny 面谈的会议，然后代理会查看我所有的日历，然后将其弹出，非常简单的示例。
That needs to be generalizable to any calendar search potentially or potentially any calendar action.
这需要推广到任何可能的日历搜索或可能的任何日历操作。
And the more generalizable it is, the more valuable the data is.
数据的普遍性越强，数据就越有价值。
So our job is to provide the most valuable data to model builders that accomplishes the goal of making agents as useful as possible for their end users.
因此，我们的工作是向模型构建者提供最有价值的数据，以实现让代理尽可能为其最终用户提供帮助的目标。
Lenny Rachitsky: I love that you've been sharing these examples of what this stuff is specifically that these people are doing, the data you're providing to labs.
Lenny Rachitsky：我喜欢你分享这些例子，说明这些人正在做什么，以及你向实验室提供的数据。
So just to mirror back a few of the examples you've shared, one is an engineer building a website, sharing the code essentially with the model.
因此，回顾一下您分享的一些示例，其中一个是构建网站的工程师，本质上与模型共享代码。
And here's how I would do it.
我将这样做。
And in that example, is it just like here's the code or is it a recording of them building it?
在那个例子中，它是像这里的代码一样还是他们构建它的记录？
What is the data?
数据是什么？
Jason Droege: It could be both.
Jason Droege：可能两者都有。
So in some cases, it's just the website and here's an example, and then they design it.
所以在某些情况下，这只是网站，这是一个示例，然后他们进行设计。
In some cases, it needs to be annotated in such a way that's like, I made this decision for this reason or this decision for that reason, or here's how I would think about it.
在某些情况下，需要以这样的方式进行注释：我出于这个原因做出了这个决定，或者出于这个原因做出了这个决定，或者这就是我的想法。
So it depends on what the model builders are trying to accomplish.
因此，这取决于模型构建者想要实现的目标。
And so, it can get quite nuanced in terms of what they're trying to train on.
因此，他们想要训练的内容可能会变得非常微妙。
Jason Droege: So it's not like here's a website and then it's created doing websites.
Jason Droege：所以这不像是一个网站，然后创建的网站。
It's like, here's a website, here's why I made this decision, here's why I didn't make this decision, or here's a broken website and here's why it's broken if they're trying to accomplish, I don't know, a debugging tool for a website builder or something like that.
就像，这是一个网站，这就是我做出这个决定的原因，这就是我没有做出这个决定的原因，或者这是一个损坏的网站，这就是为什么如果他们试图完成（我不知道）网站构建器的调试工具或类似的东西，它就会损坏。
Lenny Rachitsky: And another example you shared is a short story where it's like, here's one short story, here's another I imagine generated by a model.
Lenny Rachitsky：您分享的另一个例子是一个短篇故事，就像，这是一个短篇故事，这是另一个我想象由模型生成的故事。
And then it's like, which is better, and then how would you make it better?
然后就会想，哪个更好，然后你会如何让它变得更好？
The other example you just shared is a Salesforce agent where it's like, Hey, book a meeting with a prospect and then teach it how that happens.
您刚刚分享的另一个例子是 Salesforce 代理，它会说，嘿，与潜在客户预约一次会议，然后教它如何进行。
I love just how concrete these are because it's like, okay, I get it.
我喜欢这些具体的内容，因为它就像，好吧，我明白了。
This is the stuff that these companies do.
这就是这些公司所做的事情。
Is there another maybe one or two examples just to give people a sense of what this data looks like?
是否还有一两个例子可以让人们了解这些数据是什么样子？
Jason Droege: Absolutely.
杰森·德罗奇：当然。
I can actually give you an example from, so we have two sides of our business.
我实际上可以给你举一个例子，所以我们的业务有两个方面。
One, we supply data to model builders.
第一，我们向模型构建者提供数据。
We sell the data, and then the other is we actually do solutions.
我们出售数据，然后我们实际上做解决方案。
We sell applications and services to healthcare systems, insurance systems, et cetera.
我们向医疗保健系统、保险系统等销售应用程序和服务。
I actually think it would paint a more colorful picture if I gave you an example of one of those because it involves data, but it involves the use of data, the manipulation of data for a very, very specific goal.
事实上，我认为如果我给你一个例子，它会描绘出一幅更加丰富多彩的图画，因为它涉及数据，但它涉及数据的使用，为了一个非常非常具体的目标而操纵数据。
And so, one example there is we work with a healthcare system and health systems have lots of problems.
举个例子，我们与医疗保健系统合作，而卫生系统存在很多问题。
This particular healthcare system has experts that see very rare cases on a regular basis.
这个特殊的医疗保健系统有专家定期处理非常罕见的病例。
So you go there only if no one else can figure out your problem, and there's a huge backlog.
因此，只有当其他人无法解决您的问题并且积压的工作量很大时，您才去那里。
So there's a productivity element to this implementation tier.
因此，这个实施层有一个生产力要素。
Jason Droege: So there's a huge backlog.
Jason Droege：所以有大量的积压。
They want to be able to see more patients, they want to be able to provide better care, and they want to prevent the number of revisits because they want to give the accurate diagnosis day one and what the treatment should be.
他们希望能够看到更多的患者，他们希望能够提供更好的护理，他们希望减少复诊次数，因为他们希望在第一天就给出准确的诊断以及应该采取的治疗措施。
Well, to do this today without the help of AI, the doctor really needs to read 200 to 300 pages of documentation and it's rolled into one document, but in different formats.
好吧，今天要在没有人工智能帮助的情况下做到这一点，医生确实需要阅读 200 到 300 页的文档，并将其汇总成一个文档，但格式不同。
And so, if you're a doctor, how are you going to read 200 or 300 pages of everything?
那么，如果您是一名医生，您将如何阅读 200 或 300 页的所有内容呢？
So what they do is they do the best they can.
所以他们所做的就是尽力而为。
They scan it, they ask a nurse to look at it, they ask maybe a more junior doctor to take a look at this case.
他们扫描它，他们请护士查看，他们可能会请一位资历较浅的医生查看此病例。
They want to treat the patient well, obviously this is why they became a doctor.
他们想好好对待病人，显然这就是他们成为医生的原因。
And then, they go into the room and they talk to the person and then they make a diagnosis.
然后，他们进入房间并与该人交谈，然后做出诊断。
Jason Droege: Well, we basically built a tool that will read that document for them and point out the top 5 to 10 things that they should take into consideration, either allergies that might not be obvious is one example where we actually, we picked up on an allergy that a patient had that would not have been obvious from reading the document and that allergy actually would've had a conflict with the medication that they were going to be prescribed.
Jason Droege：嗯，我们基本上构建了一个工具，可以为他们阅读该文件，并指出他们应该考虑的前 5 到 10 件事，或者可能不明显的过敏就是一个例子，实际上，我们发现患者有一种过敏，这种过敏在阅读文件时不会很明显，而且过敏实际上会与他们要开的药物发生冲突。
And so, the AI tool basically pulled out this correlation that would've even been hard for a human being to do.
因此，人工智能工具基本上消除了人类很难做到的这种相关性。
To make this tool better and better, you get to a certain limit with the models off the shelf, and actually the people inside of this healthcare system have to do their own labeling.
为了使这个工具变得越来越好，现成的模型会受到一定的限制，实际上这个医疗保健系统内部的人员必须自己做标签。
Jason Droege: So we talk about labeling for model builders, but we are starting to see the labeling move into enterprises and into governments because you can only get so far with off the shelf plus rag plus some fine-tuning based on recorded data.
Jason Droege：所以我们谈论模型构建者的标签，但我们开始看到标签进入企业和政府，因为你只能用现成的加上破布加上一些基于记录数据的微调来达到这一点。
One thing people often miss about these systems is we assume because you hear these numbers of like, "Oh, this bank in just 200 petabytes of data a year or whatever fantastical number." What we miss is is that the right data?
人们经常错过这些系统的一件事是我们的假设，因为你听到这些数字，比如“哦，这家银行每年只有 200 PB 的数据或任何奇妙的数字。”我们错过的是正确的数据吗？
Which of that data is useful to the models?
哪些数据对模型有用？
And most of it is not useful.
而且大部分都是没有用的。
Some of it is, but a lot of what we do when we're talking about knowledge work, when we're talking about making judgment is human judgment based on synthesizing how would this doctor in this case or how would this banker in this case make this decision and how would they make decision in the context of their overall enterprise?
其中一些是，但是当我们谈论知识工作时，当我们谈论做出判断时，我们所做的很多事情都是人类的判断，基于综合这个案例中的医生或银行家将如何做出这个决定，以及他们将如何在整个企业的背景下做出决定？
And that might be different bank to bank, healthcare system to healthcare system, because of the culture, the objectives, the incentives, et cetera.
由于文化、目标、激励措施等原因，银行与银行、医疗保健系统之间可能有所不同。
And so, we're getting to the point now where we see that digitizing judgment, human judgment, true subject matter, deep expertise is becoming a bottleneck that we're unblocking for our customers.
因此，我们现在看到，数字化判断、人类判断、真实主题、深厚的专业知识正在成为我们为客户扫清障碍的瓶颈。
Lenny Rachitsky: That's really interesting.
Lenny Rachitsky：这真的很有趣。
It's like the spectrum went from just low skill generous labor to experts to now the specific expert at this one company who needs to do this work, this labeling.
就好像这个范围从低技能的慷慨劳动力到专家，再到现在这家公司需要做这项工作、这个标签的特定专家。
Jason Droege: Absolutely.
杰森·德罗奇：当然。
I mean understanding what, there's this broad narrative.
我的意思是理解什么，有这个广泛的叙述。
We have two narratives.
我们有两种叙述。
We have the AGI, everything is just going to become AGI, and then there's the skeptics, which is like, "Hey, this is all bunk, this is a bubble, et cetera." And of course, my view is most things are kind of like there's truth in between and some of the extreme parts of the extreme probably correct, but the reality is is that it's very hard to get machine critical use cases in agentic systems where agents are talking to agents to a level of accuracy that is necessary to accomplish a goal.
我们有通用人工智能，一切都将成为通用人工智能，然后就有怀疑论者，他们会说，“嘿，这都是废话，这是一个泡沫，等等。”当然，我的观点是，大多数事情都有点像中间存在真理，极端的一些极端部分可能是正确的，但现实是，在代理系统中很难获得机器关键用例，在代理系统中，代理与代理之间的对话达到了实现目标所需的准确性水平。
And one of the main issues is that a one document, think about the problem of even understanding a document, a document that reads the exact same words in company A will have a different meaning and importance in company B.
其中一个主要问题是，一份文件，想想甚至理解一份文件的问题，一份在A公司读到完全相同文字的文件在B公司会有不同的含义和重要性。
So how do you have a system that knows that?
那么如何拥有一个知道这一点的系统呢？
So this is all got to be built.
所以这一切都必须建立。
So if you're going to make good decisions.
所以如果你要做出正确的决定。
Lenny Rachitsky: This is a good segue to this question that is always on people's minds when they look at companies like yours and the other folks in the space is just how long do we need people to be doing this?
Lenny Rachitsky：这是对这个问题的一个很好的延续，当人们看到像你们这样的公司和该领域的其他人时，他们总是会想到这个问题，即我们需要人们这样做多久？
At what point will AI be smart enough to do it themselves?
人工智能什么时候才会足够聪明，能够自己做这件事？
I know your incentives are to say we'll never run out of people because it's aligned with your growth, but just how should we think about just why do we need people, I don't know, in 10 years?
我知道你的动机是说我们永远不会缺少人才，因为这与你的成长是一致的，但我们应该如何思考为什么我们需要人才，我不知道，十年后？
How long do we need these experts telling AI things it doesn't know?
我们需要这些专家告诉人工智能它不知道的事情多久？
Jason Droege: First off, the history of data labeling is a history of new beginnings.
Jason Droege：首先，数据标签的历史是一部新起点的历史。
Autonomous vehicles do not need as much data labeling as they did in the past.
自动驾驶汽车不再需要像过去那样多的数据标签。
I mean, Scale is a company that believes that data will always be important at the point at which you don't need external data, human data in models.
我的意思是，Scale 公司相信，当你不需要外部数据、模型中的人类数据时，数据始终很重要。
I think we've gotten to a level of advancement in the world that is almost like unfathomable because you're effectively saying that no new human skill and no new human knowledge is important enough to put into these models.
我认为我们已经达到了世界上几乎不可思议的进步水平，因为你实际上是在说，没有新的人类技能和新的人类知识足够重要到可以放入这些模型中。
That feels like pretty far out there.
感觉好像离那里很远。
And so, for a business like ours, we're constantly looking at how do you build operations that can constantly find the new needs and then work with the contributor network we call the experts contributors to unearth that data, to unearth that information.
因此，对于像我们这样的企业，我们不断地研究如何构建能够不断发现新需求的运营，然后与我们称为专家贡献者的贡献者网络合作来挖掘数据、挖掘信息。
And sometimes it's new people, sometimes within our existing base we find that existing people have expertise that we didn't know about that maybe wasn't useful to a model a year ago, but now is useful.
有时是新人，有时在我们现有的基础上，我们发现现有的人拥有我们不知道的专业知识，这些专业知识可能对一年前的模型没有用，但现在很有用。
Jason Droege: So this is a constant progression of getting more and more data into these models.
Jason Droege：因此，这是将越来越多的数据输入这些模型的持续过程。
Yes, we are financially incentivized to believe that humans will always be in the loop, but that's not just a business belief, it is a personal belief.
是的，我们在经济上受到激励，相信人类将永远参与其中，但这不仅仅是一种商业信念，更是一种个人信念。
These systems need to work for us, and if these systems work for us, then we will need to be on the loop or in the loop on any of the decisions that these systems make.
这些系统需要为我们工作，如果这些系统为我们工作，那么我们将需要参与或参与这些系统做出的任何决策。
As to the broader point around labor, which I think comes up around white collar apocalypse and these things that come up, I'm definitely on the more maybe practical side of this, possibly just because of my nature, possibly because I see what's going on on the ground actually in these customers where supposedly this transformation is going to happen in the next one to two years.
至于围绕劳工的更广泛的观点，我认为这是围绕白领末日和出现的这些事情而出现的，我绝对站在更实际的一边，可能只是因为我的本性，可能是因为我看到了这些客户实际上正在发生的事情，据说这种转变将在未来一到两年内发生。
And I just think that it might happen.
我只是认为这可能会发生。
The space is moving super fast, but I don't think it's going to happen.
这个空间正在快速发展，但我认为这不会发生。
Jason Droege: It is definitely not going to happen in the next year.
Jason Droege：明年绝对不会发生这种情况。
The idea that it happens in the next two years I think is very far-fetched, but nothing's impossible here.
我认为这种在未来两年内发生的想法非常牵强，但在这里没有什么是不可能的。
And long-term, I think that if you go back through, I don't know, pessimist archive or whatever, these accounts that post, the radio was invented and then all of this will be eliminated.
从长远来看，我认为，如果你回顾一下，我不知道，悲观主义档案或其他什么，这些发布的帐户，收音机被发明了，然后所有这些都将被消除。
There will be change, but the change, I think humans are very good at adapting.
会有变化，但对于变化，我认为人类非常善于适应。
So I think what we're underestimating in all of the doom and gloom is we believe in human adaptability.
所以我认为，在所有的厄运和悲观情绪中，我们低估了我们对人类适应性的信念。
We as a company are highly adaptable and I think the history of technology has shown that people are adaptable.
我们作为一家公司具有很强的适应能力，我认为技术的历史已经表明人们具有很强的适应能力。
Lenny Rachitsky: I really like that takeaway.
Lenny Rachitsky：我真的很喜欢那个外卖。
I'm an optimist as well, so I'm always looking for reasons to be optimistic.
我也是一个乐观主义者，所以我总是寻找乐观的理由。
I want to follow that thread before I get there, something very tactical I want to ask about is evals seems to be coming up a lot, especially with companies in your space.
我想在到达那里之前遵循该线索，我想问的一些非常战术性的问题是评估似乎出现了很多，尤其是对于您所在领域的公司。
I'm still learning a lot about just what this all is, especially in your market.
我仍然对这一切有很多了解，尤其是在你们的市场中。
How much of what you or experts are providing are evals versus other types of data?
与其他类型的数据相比，您或专家提供的有多少是评估数据？
Jason Droege: A lot of it's evals, and within enterprise customers and government customers, it's mostly evals because somebody's got to establish the benchmark for what good looks like.
Jason Droege：很多都是评估，在企业客户和政府客户中，大部分是评估，因为必须有人为良好的外观建立基准。
That's the simple way to think about evals.
这是思考评估的简单方法。
What does good look like and do you have a comprehensive set of evals so that the system knows what good looks like?
好的是什么样子？您是否有一套全面的评估，以便系统知道好的是什么样子？
It's as simple as that.
就这么简单。
Lenny Rachitsky: So in the case maybe of the healthcare example you shared, essentially this doctor would be sitting there looking at all these reports, creating evals that are like, this is what this should be discovering in this report, in this record.
Lenny Rachitsky：因此，在您分享的医疗保健示例中，本质上这位医生会坐在那里查看所有这些报告，创建类似的评估，这就是应该在这份报告、这份记录中发现的内容。
Is that a way to think about it?
这是一种思考方式吗？
Jason Droege: Yeah, that's a very big part of it, which is what does good look like?
Jason Droege：是的，这是其中非常重要的一部分，这就是优秀的样子？
Jason Droege: I have to reduce things down to simple terms.
Jason Droege：我必须将事情简化为简单的术语。
Lenny Rachitsky: It's interesting you say good versus correct.
Lenny Rachitsky：有趣的是，你说“好”与“正确”。
Is that a specific term you like to use good versus just this is the correct answer.
这是您喜欢使用的特定术语“好”还是“这”才是正确的答案。
Jason Droege: I didn't intentionally use that word, but these are probabilistic systems and so depending upon...
Jason Droege：我不是故意使用这个词的，但这些是概率系统，因此取决于......
Yeah, so I can get into some nuance here about the right types of problems that AI is good at solving.
是的，所以我可以在这里深入探讨人工智能擅长解决的问题类型的一些细微差别。
So if you have a human process that is 10 or 20% accurate or 10 or 20% liked, AI is awesome.
因此，如果你的人工流程准确率达到 10% 或 20%，或者喜欢度达到 10% 或 20%，那么人工智能就非常棒。
Because if you get to 50, 60, 70, 80% accurate, you're in the money, you're in the green, everybody's happy.
因为如果你的准确率达到 50%、60%、70%、80%，你就赚到钱了，你就赚到了钱，每个人都很高兴。
Now, the system then has to know, hey, for the remainder, how do I make sure that humans are involved for the remainder of the decision making?
现在，系统必须知道，嘿，对于剩下的事情，我如何确保人类参与剩下的决策？
But from a net value add standpoint, the humans are pumped in that scenario.
但从净增值的角度来看，人类在这种情况下会受到刺激。
Jason Droege: If you have a human process, a workflow that is 98% accurate, and you expect an AI system to get you the remaining 2%, not totally there yet.
Jason Droege：如果你有一个人工流程，一个准确率高达 98% 的工作流程，并且你希望人工智能系统能够为你提供剩下的 2%，但还没有完全实现。
And so, when I say what does good look like?
那么，当我说什么是好的？
A lot of the processes and a lot of the things that people are asking these systems to do and systems for us to build are making judgments on their behalf.
人们要求这些系统做的很多流程和很多事情以及我们构建的系统正在代表他们做出判断。
And so, just like we would ask a human being, "Hey, what do you think we should do in this scenario?" What you're looking for is you're looking for the best recommendation or course of action given the current information.
所以，就像我们问一个人一样，“嘿，你认为在这种情况下我们应该做什么？”您正在寻找的是根据当前信息寻找最佳建议或行动方案。
Lenny Rachitsky: To you, this is so obvious and to people in your market that I think a lot of people think about AI being trained on just here's a bunch of data, check it out, learn everything you can from all of human history and all of written record.
Lenny Rachitsky：对你来说，这对于你所在市场的人们来说是如此明显，我认为很多人都认为人工智能是根据这里的一堆数据进行训练的，检查一下，从所有人类历史和所有书面记录中学习你能学到的一切。
But what's wild is basically people are sitting around teaching AI things it doesn't know, filling gaps.
但最疯狂的是，人们基本上坐在一起教人工智能它不知道的东西，填补空白。
That's how AI is getting smarter now.
这就是人工智能现在变得越来越聪明的原因。
There's no more real data for it to feed on.
没有更多的真实数据可供它使用。
It's just like, here's what I don't know, or here's what an expert found you're wrong.
就像，这是我不知道的，或者这是专家发现你错的。
I'm going to teach you this.
我要教你这个。
And the fact that it scales and that's keeping models improving is so mind-boggling.
它的可扩展性以及不断改进模型的事实是如此令人难以置信。
Jason Droege: Yes.
杰森·德罗奇：是的。
No, yeah, I agree.
不，是的，我同意。
I mean, like with any of these major tech revolutions, the headlines tell one story and then on the ground, laying broadband means you need to dig up every single road in America to lay it.
我的意思是，就像任何这些重大技术革命一样，头条新闻讲述一个故事，然后在地面上，铺设宽带意味着你需要挖掘美国的每一条道路来铺设它。
There is the, yeah, it's as simple as that.
是的，就是这么简单。
Someone's got to dig up the road or someone's got to run the undersea cable.
必须有人挖路或者铺设海底电缆。
There's always some operational chiseling that's going on in all of these industries.
所有这些行业中总会发生一些运营上的凿刻。
I mean, if you think about how magical these models are, they're remarkable that if you've been in technology long enough, it blows my mind even today that they get the punctuation right consistently.
我的意思是，如果你想想这些模型有多么神奇，你会发现它们非常了不起，如果你在技术领域工作了足够长的时间，即使在今天，它们始终保持正确的标点符号也让我大吃一惊。
I mean, that sounds like almost daft to say at this point in the market, but if you were to go back three years and think about that from a technological standpoint, a lot of things that we think are trivial now are very sophisticated, and it's a combination of, I mean, the real answer is it's a combination of computational power, model improvement, and data, and all three are getting better at once.
我的意思是，在市场的这个阶段，这听起来几乎是愚蠢的，但如果你回顾三年，从技术的角度思考这一点，我们现在认为微不足道的很多事情都非常复杂，而且它是一个组合，我的意思是，真正的答案是它是计算能力、模型改进和数据的组合，而且这三者同时变得更好。
Lenny Rachitsky: Let's follow that thread.
Lenny Rachitsky：让我们跟随这条线索。
You've been at Scale for a long time, CEO for, you said, 13 months.
CEO，你在 Scale 工作了很长时间，你说，有 13 个月了。
I feel like you see a lot more about where things are heading because you work with labs on things they haven't even announced yet.
我觉得你对事情的发展方向有了更多的了解，因为你与实验室合作研究他们尚未宣布的事情。
You see more than most people, and I know there's only so much you can share about what companies are doing, but just is there anything you think people don't truly grasp or understand about where AI models are going to be in the next two, three years?
你比大多数人看到的都多，我知道你能分享的关于公司正在做的事情只有这么多，但你认为人们有没有真正掌握或理解人工智能模型在未来两三年内将走向何方？
Jason Droege: Look, there's so much talk.
Jason Droege：看，有很多讨论。
I think it depends on how much X or news you consume.
我认为这取决于你消费了多少X或新闻。
So I think it's like what sort of our perspective.
所以我认为这就像我们的观点是什么样的。
The general trend right now is going from models knowing things to models doing things.
现在的总趋势是从模型知道事情到模型做事。
And we're pushing the boundaries of knowledge, like the benchmarks that we put out and that others put out are showing that the knowledge that these models have is getting, it's quite robust.
我们正在突破知识的界限，就像我们推出的基准和其他人推出的基准表明，这些模型所拥有的知识正在变得非常强大。
And then, the next question becomes, well, what can it do for me?
然后，下一个问题就变成了，它能为我做什么？
And as soon as you get into that world, that's where the environments we were talking about start to come into play.
一旦你进入这个世界，我们所说的环境就开始发挥作用。
How do you navigate a Salesforce instance?
如何导航 Salesforce 实例？
How do you navigate a healthcare system?
您如何驾驭医疗保健系统？
How do you navigate even a weather app on your phone, and how does the agent make decisions for you?
您如何导航手机上的天气应用程序，以及代理如何为您做出决定？
Jason Droege: We're just getting into the beginning of that.
Jason Droege：我们才刚刚开始。
It'll be very interesting to see how quickly that happens.
看看这种情况发生的速度有多快将会非常有趣。
And I think that's where a lot of the speculation has a wide variance because we're at the beginning of it.
我认为这就是很多猜测存在很大差异的地方，因为我们才刚刚开始。
People take different trajectories on how that's going to improve.
人们对如何改进采取不同的轨迹。
And so, if you take a trajectory of the most aggressive trajectory, which is like, oh, it's actually going to be quite easy to train on these things, and then it's just a change management exercise in the economy, which by the way, change management exercises are not to be underestimated.
因此，如果你采取最积极的轨迹，就像，哦，实际上很容易在这些事情上进行培训，然后这只是经济中的变革管理练习，顺便说一句，变革管理练习不可低估。
Jason Droege: There's still people in the world without an email address.
Jason Droege：世界上仍然有人没有电子邮件地址。
And so, the adoption curve then becomes a human and policy issue, not a technological issue.
因此，采用曲线就变成了人力和政策问题，而不是技术问题。
We're not there from the technology standpoint, but I do think in the next two to three years, if I take the bait and have to make a guess is the technology will get to a point where it will push the change management and policy makers to say like, "Oh, what do we do with this because it's getting pretty close?" That's probably two or three years away.
从技术的角度来看，我们还没有做到这一点，但我确实认为在接下来的两到三年内，如果我上钩并必须猜测技术是否会达到这样的程度，它将推动变革管理和政策制定者说：“哦，我们该怎么办，因为它已经非常接近了？”那可能是两三年之后的事了。
Lenny Rachitsky: There's been a lot of talk these days about AI not delivering on the promise that we hear, especially at enterprises.
Lenny Rachitsky：最近有很多关于人工智能没有兑现我们听到的承诺的讨论，尤其是在企业中。
There's this MIT study that just showed that there's all these pilots that people are excited about and then they don't work and companies aren't adopting these tools.
麻省理工学院的一项研究表明，人们对所有这些试点感到兴奋，但它们不起作用，公司也没有采用这些工具。
There's data showing engineers are not actually as productive with tools.
有数据显示，工程师实际上使用工具的效率并不高。
It actually slows them down sometimes.
有时它实际上会减慢他们的速度。
You work with a ton of companies implementing all kinds of AI.
您与大量实施各种人工智能的公司合作。
What are you seeing on the ground?
你在地上看到什么？
What kind of gains are you seeing?
您看到了什么样的收获？
Do you feel like it's overhyped, underhyped?
你是否觉得它被夸大了，或者被低估了？
Jason Droege: There's a lot of hype out there, and our job is to actually build products that work, that deliver value for our customers and figure out where the rubber hits the road.
Jason Droege：有很多炒作，我们的工作是实际制造出有效的产品，为我们的客户提供价值，并弄清楚橡胶在路上的位置。
And to get a sophisticated, my healthcare example is one, we do other sophisticated workflows, claims management for insurance companies.
为了得到一个复杂的，我的医疗保健例子就是一个，我们为保险公司做了其他复杂的工作流程，索赔管理。
This is a financial decision that's happening, but it's an automatable process.
这是一个正在发生的财务决策，但它是一个自动化的过程。
But basically what happens is the POCs get to 60 or 70% of the way there, and the human mind goes, oh, the rest is no big deal.
但基本上发生的情况是 POC 达到了 60% 或 70%，人们的想法是，哦，剩下的没什么大不了的。
But it's like uptime in data centers where every nine is an order of magnitude investment in terms of reliability, backups, et cetera.
但这就像数据中心的正常运行时间一样，其中每九个就意味着在可靠性、备份等方面的一个数量级的投资。
One nine is basically a web server in a dorm room like we had at UCLA, and then five nines is this crazy high bar, but it just seems like a very small movement.
一个九基本上是宿舍里的一个网络服务器，就像我们在加州大学洛杉矶分校那样，然后五个九是这个疯狂的高标准，但这看起来只是一个非常小的动作。
Jason Droege: So you have a similar dynamic going on here where you have a bunch of people, one of the reasons why the POCs have failed, one, there's a denominator effect because it's so easy to do, "Hey, I spun up a project, I spun up a project, I spun up a project." So it's really easy for people to try.
Jason Droege：所以这里也有类似的动态，有一群人，这是 POC 失败的原因之一，一，存在分母效应，因为它很容易做到，“嘿，我启动了一个项目，我启动了一个项目，我启动了一个项目。”所以人们很容易尝试。
So I don't necessarily know that the 95% number, I think is a bit of clickbait in a way.
所以我不一定知道 95% 这个数字，我认为在某种程度上有点像标题诱饵。
It tells the right story, but it is a little bit hyperbolic because if you take the efforts that happen in the company where they actually get a quality partner like we are, or if you do it yourself, if you have engineers who've worked with models before and they put in the time, and I'm talking about months, not like minutes like you see in these videos to actually get legal approval, policy approval, regulatory approval, change managements like an accuracy that everybody's comfortable with.
它讲述了正确的故事，但它有点夸张，因为如果你在公司中付出努力，他们实际上得到了像我们这样的优质合作伙伴，或者如果你自己做，如果你有以前与模型合作过的工程师，他们投入了时间，我说的是几个月，而不是像你在这些视频中看到的那样，真正获得法律批准、政策批准、监管批准、变更管理，比如每个人都满意的准确性。
If you actually do that, these things take 6 to 12 months to get them truly robust enough where an important process can be automated.
如果你真的这么做了，这些事情需要 6 到 12 个月的时间才能让它们真正足够强大，从而可以实现重要流程的自动化。
Jason Droege: So I think that's where the hype is right that when you do it, the impact is like, whoa, I never would've figured that out myself, and I'm one of the most educated doctors in the world as an example.
Jason Droege：所以我认为这就是炒作的正确之处，当你这样做时，产生的影响就像是，哇，我自己永远也想不到这一点，举个例子，我是世界上受教育程度最高的医生之一。
But the time to get there is just longer than what people are selling.
但到达那里的时间比人们销售的时间要长。
Lenny Rachitsky: It's such a good point that it's not only is it easy to try these things, it's just like everyone's doing it so everyone's feeling FOMO like, "I got to try these things.
Lenny Rachitsky：这是一个很好的观点，不仅很容易尝试这些事情，而且就像每个人都在这样做一样，所以每个人都感到害怕，“我必须尝试这些事情。
I got to try all these prototyping tools, Cursor, all these things." Just goes, "Everyone's doing it," and then you just rush into it and it doesn't actually work out.
我必须尝试所有这些原型工具，光标，所有这些东西。”只是说，“每个人都在这样做”，然后你就急于去做，但实际上并没有成功。
Jason Droege: Easy to learn, hard to master.
Jason Droege：易学，难精通。
That's my summary.
这就是我的总结。
Lenny Rachitsky: This episode is brought to you by Mercury.
Lenny Rachitsky：本集由 Mercury 为您带来。
I've been banking with Mercury for years, and honestly, I can't imagine banking any other way at this point.
我多年来一直使用 Mercury 进行银行业务，老实说，目前我无法想象以任何其他方式进行银行业务。
I switched from Chase and holy moly, what a difference.
我从 Chase 和 Holy Moly 换来的，有什么不同。
Sending wires, tracking spend, giving people on my team access to move money around so freaking easy.
发送电汇、跟踪支出、让我的团队成员能够轻松转移资金。
Where most traditional banking websites and apps are clunky and hard to use, Mercury is meticulously designed to be an intuitive and simple experience.
大多数传统银行网站和应用程序都笨重且难以使用，而 Mercury 则经过精心设计，提供直观、简单的体验。
And Mercury brings all the ways that you use money into a single product, including credit cards, invoicing, bill pay, reimbursements for your teammates and capital.
Mercury 将您使用金钱的所有方式整合到一个产品中，包括信用卡、发票、账单支付、队友报销和资本。
Lenny Rachitsky: Whether you're a funded tech startup looking for ways to pay contractors and earn yield on your idle cash, or an agency that needs to invoice customers and keep them current, or an e-commerce brand that needs to stay on top of cashflow and access capital, Mercury can be tailored to help your business perform at its highest level.
Lenny Rachitsky：无论您是一家资金雄厚的科技初创公司，正在寻求向承包商付款并利用闲置现金赚取收益的方式，还是一家需要向客户开具发票并保持最新状态的机构，或者是一家需要掌控现金流并获取资金的电子商务品牌，Mercury 都可以量身定制，帮助您的企业发挥最高水平。
See what over 200,000 entrepreneurs love about Mercury.
了解超过 200,000 名企业家对 Mercury 的喜爱。
Visit mercury.com to apply online in 10 minutes.
10 分钟内访问mercury.com 进行在线申请。
Mercury is a FinTech, not a bank.
Mercury 是一家金融科技公司，而不是一家银行。
Banking services provided through Mercury's FDIC-insured partner banks.
通过 Mercury 的 FDIC 承保合作银行提供的银行服务。
For more details, check out the show notes.
欲了解更多详情，请查看演出笔记。
Lenny Rachitsky: Okay, let's move on from AI.
Lenny Rachitsky：好的，让我们继续讨论人工智能。
This could be an endless discussion about AI, but you've got a lot more lessons to teach us.
这可能是关于人工智能的无休止的讨论，但你还有很多东西可以教给我们。
You've helped build Uber Eats, you've had a couple startups in the past.
您帮助创建了 Uber Eats 优食，过去也曾创办过几家初创公司。
We talked about Scour for a bit.
我们讨论了一些关于 Scour 的事情。
I've talked to a bunch of people that have worked with you over the years, and I got a lot of really interesting insights into the stuff that you're extremely good at.
我和很多多年来与你共事的人交谈过，我对你非常擅长的事情有很多非常有趣的见解。
So I'm just going to go through a bunch of these.
所以我将简单介绍其中的一些内容。
One is your obsession with being close to customers, talking to customers, and I love this topic because it's something everybody thinks they're great at, and they feel like they completely understand how this is important, why this is important.
一是你对接近客户、与客户交谈的痴迷，我喜欢这个话题，因为每个人都认为他们很擅长，他们觉得他们完全理解这有多重要，为什么这很重要。
They all feel like I'm doing this, don't worry about...
他们都觉得我在做这个，不用担心...
Everyone else is not doing this, but I am.
其他人都没有这样做，但我却这样做。
Talk about just what you think maybe people miss about how this looks when you're doing it well, and just why this is so important.
谈谈你的想法，也许人们会怀念当你做得好的时候它的样子，以及为什么这如此重要。
Jason Droege: I mean, I probably fall in the category of what you just described, which is maybe part of the hubris you need to start anything new.
Jason Droege：我的意思是，我可能属于您刚才描述的类别，这可能是您开始任何新事物所需的狂妄自大的一部分。
But I mean, I don't think it's a clean process.
但我的意思是，我认为这不是一个干净的过程。
I think my process is I'm constantly questioning every single thing that I'm hearing at the beginning of anything.
我认为我的过程是不断质疑我在任何事情开始时听到的每一件事。
I don't take what a customer says literally.
我不会按字面意思理解顾客所说的话。
And there's been a lot talked about on this topic from a product management standpoint in terms of like, oh, don't do what they say, do what they mean, and look at the real problems and underlying things.
从产品管理的角度来看，关于这个话题已经有很多讨论，比如，哦，不要照他们说的做，做他们的意思，看看真正的问题和根本的事情。
I think the way that I look at it that might be additive to the discussion is I look at the underlying incentives of the customer.
我认为我看待这个问题的方式可能会补充讨论，那就是我着眼于客户的潜在动机。
And the underlying incentives of customers are not always financial.
客户的根本动机并不总是金钱上的。
Sometimes it's ego, sometimes it's career growth.
有时是自我，有时是职业发展。
Jason Droege: If you're selling enterprise software to someone, there's an executive sponsor as an example, that person needs to trust that you're going to do a good job for them.
Jason Droege：如果你向某人销售企业软件，有一个执行发起人作为例子，那个人需要相信你会为他们做好工作。
How do you get them to jump with you on this big project?
你如何让他们与你一起参与这个大项目？
Well, that's part of the journey of not just the product, but what do they need to hear from us?
嗯，这不仅是产品旅程的一部分，而且他们需要从我们这里听到什么？
What do we need to supply them?
我们需要为他们提供什么？
What do we need to do to actually unlock the opportunity to implement the product?
我们需要做什么才能真正释放实施该产品的机会？
So I think there's an incentives alignment baseline.
所以我认为存在一个激励调整基线。
I'm a big believer that it's cliche, but show me the incentive and I'll show you the outcome.
我坚信这是陈词滥调，但向我展示动机，我就会向你展示结果。
I think that's absolutely true.
我认为这绝对是真的。
And even when customers will tell you things, I'll give you an example.
即使客户会告诉你一些事情，我也会给你举一个例子。
I've been out of the game for a while so I can be open about it, Uber Eats.
我已经退出游戏一段时间了，所以我可以公开谈论 Uber Eats 优食。
Jason Droege: So when we launched Uber Eats, I looked at the business in terms of being close to the customer.
Jason Droege：因此，当我们推出 Uber Eats 优食时，我从贴近客户的角度来看待这项业务。
We actually couldn't get a restaurant tour.
我们实际上无法参观餐厅。
I knew nothing about this industry.
我对这个行业一无所知。
So at Uber, my job was to figure out what other businesses we should get into.
因此，在 Uber，我的工作就是弄清楚我们还应该涉足哪些其他业务。
And so, we looked at a billion businesses and Uber Eats, food delivery was the one that we thought was most interesting, which turned out to be right so good for us.
因此，我们研究了 10 亿家企业，其中 Uber Eats 食品配送是我们认为最有趣的一项，事实证明这对我们来说非常有利。
Jason Droege: And we couldn't get a restaurant tour to help us understand their unit economics.
Jason Droege：我们无法参观餐厅来帮助我们了解他们的单位经济效益。
And they'd say like, "Oh, it'd be this percentage or that percentage, or Why do you want to know?" And then we'd go to a different restaurant tour and they would explain it, but they were a little suspicious of why are these Uber guys talking to me about how much my ham costs?
他们会说，“哦，会是这个百分比或那个百分比，或者你为什么想知道？”然后我们会去另一家餐厅参观，他们会解释，但他们有点怀疑为什么这些 Uber 的人要和我谈论我的火腿要多少钱？
And so, what we did is we ordered just a bunch of food from these places, and then we got a restaurant supplier to give us a base catalog, and we just matched up how much does the ham weigh?
所以，我们所做的就是从这些地方订购了一堆食物，然后我们让一家餐厅供应商给我们一个基本目录，我们只是匹配火腿的重量是多少？
How much does the cheese weigh?
奶酪的重量是多少？
How much does the bread weigh?
面包有多重？
How many pieces of lettuce were on there?
那里有多少片生菜？
And we tried to actually just compose our own independent view of what's the ingredients cost versus what's the labor cost?
我们实际上试图就原料成本与劳动力成本是什么形成我们自己的独立观点？
And then, we triangulated what was our ground truth, and then what are we being told by restaurant tours, and then what is the site guys telling us about restaurant economics?
然后，我们三角测量了我们的基本事实是什么，然后餐厅之旅告诉我们什么，然后网站人员告诉我们有关餐厅经济学的什么？
Jason Droege: And if those things all overlapped, and we're like, okay, we have an insight about what to do here and how does this relate to Uber Eats?
Jason Droege：如果这些事情全部重叠，我们就会想，好吧，我们对这里要做什么以及这与 Uber Eats 优食有什么关系有了深入的了解？
Well, what we found as part of this is that roughly a restaurant pays 20 to 30% of every meal to ingredients, and they pay roughly 20 or 30% to labor, and they pay roughly 10% to real estate and a bunch of other, anyway, so goes down the chain.
好吧，我们发现，大约一家餐馆每顿饭的 20% 到 30% 都用来支付食材费用，大约 20% 或 30% 用来支付劳动力费用，大约 10% 用来支付房地产和其他费用，无论如何，所以要沿着链条往下走。
But the important parts is what's the value of incrementality?
但重要的是增量的价值是什么？
Jason Droege: And so, we came in and we said, "We're going to charge you 30% of the bill." And they were like, "Oh my God, is this group on all over again?
Jason Droege：所以，我们进来后说：“我们将向您收取 30% 的费用。”他们就像是，“天哪，这个乐队又开始了吗？
This is way too high.
这太高了。
Oh my gosh." And we explained the economics to them and they were like, "Okay, we'll give it a try, but this is way too high." And they were right, the real number, the real clearing prices aren't 25%, but we weren't that far off.
哦天哪。”我们向他们解释了经济学，他们说：“好吧，我们会尝试一下，但这太高了。”他们是对的，真实的数字，真实的清算价格不是 25%，但我们也相差不远。
And so, when you go to find product market fit or be close to the customers, it's a combination of what's the most valuable thing.
因此，当你寻找适合市场的产品或贴近客户时，这就是最有价值的东西的组合。
Well, in a restaurant tours case, give me incremental demand.
好吧，在餐厅旅游案例中，给我增量需求。
Because if you were to take a restaurant location and triple demand based on the same labor but you're just scaling ingredients, you've got a 70, 80% incremental gross margin product.
因为如果你要在相同的劳动力基础上选择一家餐厅，并将需求增加三倍，但你只是扩大原料规模，你就会得到 70%、80% 的增量毛利率产品。
Jason Droege: Restaurant tours would hate when we would say this because it doesn't work out exactly like that in reality.
Jason Droege：餐厅之旅会讨厌我们这样说，因为它在现实中并不完全像那样。
But because we had that insight, we had confidence that we could go to market with, we need to charge you this so that the delivery fee can be that.
但因为我们有这样的洞察力，我们有信心进入市场，我们需要向您收取这个费用，这样运费就可以了。
And then, if the delivery fee is that and we charge you this, then we think the consumers will adopt, and that's what you need to get your incremental demand, and then we could pay the driver this.
然后，如果送货费是这样的，我们向你收取这个费用，那么我们认为消费者会采用，这就是你需要获得增量需求的费用，然后我们可以向司机支付这个费用。
And so, you fit this whole puzzle together without totally satisfying, in the case of a marketplace, you're not totally satisfying any individuals 100% of their needs.
因此，你将整个难题拼凑在一起，但并没有完全满足，就市场而言，你并没有完全满足任何个人 100% 的需求。
What you're satisfying is is you're getting a clearing rate for them to participate in the market in the case of a marketplace.
您满意的是您获得了他们在市场中参与市场的清算率。
So that's one example.
这就是一个例子。
Lenny Rachitsky: Yeah.
莱尼·拉奇茨基：是的。
I love this example as you almost you figure out how to help them with something they don't even fully themselves know yet.
我喜欢这个例子，因为你几乎想出了如何帮助他们解决他们自己还不完全知道的事情。
So as you think through their goals for them as if you were them, break down the economics and then here's the solution versus, hey, what can we do for you guys?
因此，当您像他们一样思考他们的目标时，请分解经济因素，然后这是解决方案，而不是，嘿，我们能为你们做什么？
Jason Droege: Yeah.
杰森·德罗奇：是的。
I mean, if you walked into a restaurant, they would tell you a bunch of things.
我的意思是，如果你走进一家餐馆，他们会告诉你很多事情。
They would say, "Oh, labor schedule is an issue." They would say, "My rent is an issue." They would say, "All these, my ingredients prices are an issue, that's 20 or 30%." If you could shave off 3% of that, that would be huge.
他们会说，“哦，劳动安排是一个问题。”他们会说：“我的房租是个问题。”他们会说，“所有这些，我的原料价格都是一个问题，有 20% 或 30%。”如果能削减 3%，那就是巨大的了。
You might then take that and go, "I'm going to go build a business.
然后你可能会说：“我要去建立一家企业。
It's going to save you 10% of your ingredients costs."
这将为您节省 10% 的原料成本。”
Jason Droege: Well, but that doesn't actually get into their head on what's truly important day-to-day.
Jason Droege：嗯，但这实际上并没有进入他们的头脑，什么才是真正重要的日常事务。
That might be important for them on an annual basis, but on a daily basis, what are they doing?
这对他们每年来说可能很重要，但每天，他们在做什么？
They're looking at their numbers, they're looking do people show up.
他们正在查看他们的数字，他们正在查看人们是否出现。
Did I make money yesterday?
昨天我赚钱了吗？
Am I going to make money tomorrow?
明天我能赚钱吗？
So the urgency, I think the biggest thing people miss when they're building new products is the urgency of the buyer part of it.
因此，紧迫性，我认为人们在开发新产品时最忽略的就是买家部分的紧迫性。
You can build something that provides a lot of value, but if it's not the top thing that the customer is thinking about in their busy days, then you're just going to have a long road to a small town.
您可以构建提供大量价值的东西，但如果这不是客户在忙碌的日子里最关心的事情，那么您将有很长的路要走才能到达小镇。
Lenny Rachitsky: This touches on just the theme I heard a lot about, this idea of independent thinking and how much you value that, and this feels like a really good example of that.
Lenny Rachitsky：这触及了我经常听到的主题，独立思考的想法以及你对它的重视程度，这感觉就像一个很好的例子。
Is there anything else along those lines of just why this way of thinking is so critical?
是否还有其他原因可以解释为什么这种思维方式如此重要？
Jason Droege: I think as a founder's job, and I mean I stretched that term because at Uber we had all of the benefits of Uber so I wasn't really a founder.
Jason Droege：我认为作为创始人的工作，我的意思是我延伸了这个术语，因为在 Uber，我们拥有 Uber 的所有好处，所以我并不是真正的创始人。
I just started the business there.
我刚刚开始在那里做生意。
But there are some elements of founding there is you're looking for alpha in the market.
但创业有一些要素，那就是你要在市场上寻找阿尔法。
When we started our first company in '97, it wasn't that cool.
当我们在 97 年创办第一家公司时，情况并没有那么酷。
It might've been cool in Silicon Valley, but it was definitely not cool in LA.
在硅谷可能很酷，但在洛杉矶绝对不酷。
Now, it's super cool to start a business.
现在，创业真是太酷了。
So as a result, everyone's trying everything.
所以结果是，每个人都在尝试一切。
So how do you get alpha on that market.
那么如何才能在这个市场上获得阿尔法呢？
If your research is highly influenced by what the world is saying around you, you're not going to have an independent insight.
如果你的研究很大程度上受到周围世界言论的影响，那么你就不会拥有独立的见解。
You have to go off and do your own thing.
你必须离开去做你自己的事情。
Jason Droege: And this is why from an entrepreneurship standpoint, I have very strong feelings about what the approach to founding a company should be and is probably very particular to me.
Jason Droege：这就是为什么从创业的角度来看，我对创建公司的方法应该是什么有非常强烈的感觉，并且可能对我来说非常特别。
But it truly is about what insight do I have, because why am I so lucky to have this insight?
但这确实是关于我有什么洞察力，因为为什么我这么幸运有这种洞察力？
Why in a world of a million entrepreneurs who are thinking, who are smart, who are trying everything, why am I in the position where I likely have an insight that others do not?
为什么在一个有上百万企业家思考、聪明、尝试一切的世界里，为什么我可能拥有别人没有的洞察力？
And then, why am I the one to do it?
那么，为什么我是这样做的人呢？
Jason Droege: And the answer might be I'm in this narrow, far-flung place.
Jason Droege：答案可能是我在这个狭窄、遥远的地方。
The other answer might be, I am inherently a contrarian personality type, so I'm just constantly looking for the thing that's true that people don't believe is true, which sometimes worked.
另一个答案可能是，我本质上是一个逆向人格类型，所以我只是不断地寻找人们不相信的真实事物，这有时会起作用。
But then, the second part of that's super important, which is why do I want to work on this problem for 5 to 10 years?
但是，第二部分非常重要，这就是为什么我想花 5 到 10 年的时间来解决这个问题？
And people get this wrong all the time.
人们总是弄错这一点。
They go and talk to a customer and they go, "They have a problem.
他们去和顾客交谈，然后说：“他们有问题。
I'm going to go solve it." And it's just not a great way to start a business.
我这就去解决。”这并不是创业的好方法。
You really have to have this burning desire to constantly be questioning yourself.
你真的必须有这种强烈的愿望来不断地质疑自己。
Jason Droege: The other thing about independent thinking is is that you can't fall in love with your ideas.
Jason Droege：独立思考的另一件事是你不能爱上自己的想法。
And I do not proclaim to be the world's greatest thinker for what it's worth, this is what you've been told, but is just part of that is basically throwing away who you are, who you've been, all your ideas for the mission that you're on, which is trying to accomplish something for our customer.
我并没有宣称自己是世界上最伟大的思想家，这就是人们告诉你的，但这只是其中的一部分，基本上抛弃了你是谁，你曾经是谁，以及你所肩负的使命的所有想法，即试图为我们的客户完成一些事情。
Lenny Rachitsky: This is great.
莱尼·拉奇茨基：这太棒了。
I'm glad you went here.
我很高兴你去了这里。
This touches on the other theme I heard often about you is just how high of a bar you set for new businesses.
这涉及到我经常听到的关于您的另一个主题，即您为新业务设定的标准有多高。
And I think this advice is useful both for founders, as you said, and also people starting companies within companies, new business lines.
正如您所说，我认为这个建议对于创始人以及在公司内部创办公司、新业务线的人都很有用。
So you've talked about this a bit already, but is there anything more there, just how high that bar needs to be for it to likely work out when you're starting something new?
所以你已经讨论过这个问题了，但是还有什么更多的事情吗？当你开始新事物时，这个标准需要多高才能实现？
Jason Droege: Look, if you want to give yourself the best chance, and this isn't always how it works, but if you're in my position 25 plus years in their career, if you want to give yourself the best chance, I think there's two ways that companies end up working out.
Jason Droege：听着，如果你想给自己最好的机会，事情并不总是这样，但如果你处在我的位置，已经有 25 年以上的职业生涯，如果你想给自己最好的机会，我认为公司最终有两种解决办法。
And the first way, which is probably the most important, quite frankly, is that the founder is just a force of nature over a long duration of time.
第一种方式，坦率地说，这可能是最重要的，创始人只是很长一段时间内的自然力量。
Because you're going to have to pivot, you have to have that energy to pivot.
因为你必须转变，你必须有能量来转变。
You have to go years and years and years with it being hard, and that's probably the most important thing.
你必须经历很多年、很多年、很多年，这可能是最重要的事情。
Jason Droege: But the second most important thing is that you can easily educate yourself on what are good business models, what are bad business models, what are good markets, what are bad markets?
Jason Droege：但第二重要的是，你可以轻松地自我教育什么是好的商业模式，什么是坏的商业模式，什么是好的市场，什么是坏的市场？
And even if you're this force of nature, having the knowledge, if you're going to go into a bad market with all your energy, you should at least know, maybe ignorance is bliss because you just throw yourself into it and it just works out with time.
即使你是这种自然力量，拥有知识，如果你要全力以赴进入一个糟糕的市场，你至少应该知道，也许无知是幸福，因为你只要把自己投入其中，它就会随着时间的推移而发挥作用。
But that's not how I would operate, which is marketplaces are good businesses.
但这不是我的运作方式，因为市场是好生意。
SaaS, at least historically, we'll see how this changes, but SaaS, historically, great businesses, recurring revenue businesses, sticky businesses, network effect businesses.
SaaS，至少从历史上看，我们会看到这种变化是如何变化的，但是SaaS，从历史上看，是伟大的企业、经常性收入企业、粘性企业、网络效应企业。
Jason Droege: And if you look at what the top VCs invest in, yes, there is a lot of portfolio building, but there are similarities in terms of the types of business models that they believe could be worth tens of billions of dollars.
Jason Droege：如果你看看顶级风险投资公司的投资内容，是的，有很多投资组合建设，但他们认为商业模式类型有相似之处，这些模式可能价值数百亿美元。
And they have network effects, they have lock-in.
他们有网络效应，他们有锁定。
They are more valuable at scale, a big scale than low scale.
它们在规模上更有价值，大规模比小规模更有价值。
So if you just take a filter on a new business, this is what I did at Uber, which is like if you just have a filtering mechanism on a new business, it doesn't take that long to eliminate the bad ideas.
所以如果你只是对一个新业务进行过滤，这就是我在 Uber 所做的，就像如果你只是对一个新业务有一个过滤机制，不需要那么长时间就能消除坏主意。
And then, of what's left, you can pick, oh, I'm very passionate about this, even though it might have more problems than this other thing that on paper looks better.
然后，在剩下的内容中，你可以选择，哦，我对此非常感兴趣，尽管它可能比纸上看起来更好的其他东西存在更多问题。
And then, you have to have passionate about it.
然后，你必须对此充满热情。
But I think people just miss a basic understanding of what businesses even have a chance of being worth $100 billion.
但我认为人们只是错过了对哪些企业有机会价值 1000 亿美元的基本了解。
Lenny Rachitsky: So you launched Uber Eats, you figured out this is the place to go and bet.
Lenny Rachitsky：所以你推出了 Uber Eats 优食，你发现这是一个可以去赌博的地方。
As an outsider, feels obvious, of course this is going to be a massive success.
作为一个局外人，感觉很明显，这当然会是一个巨大的成功。
Of course, food delivery, such a good idea.
当然，送餐是个好主意。
I know you looked at a ton of ideas in that process.
我知道您在这个过程中考虑了很多想法。
Can you just talk about what you explored and why you ended up picking Uber Eats?
您能谈谈您的探索以及最终选择 Uber Eats 优食的原因吗？
Jason Droege: I am definitely not the smartest person in the room when it comes to figuring these things out.
Jason Droege：在解决这些问题时，我绝对不是房间里最聪明的人。
And so, I keep a very, very wide aperture on ideas for as long as I can until I'm like, okay, everything is coalescing.
因此，我会尽可能长时间地对想法保持非常非常广泛的视野，直到我觉得，好吧，一切都在融合。
And I think there's a bunch of reasons why you have to keep an open aperture on considering ideas that might seem bad at the start, but you just keep digging and see if you're right that they're bad or you're wrong.
我认为有很多原因可以解释为什么你必须对那些一开始看起来很糟糕的想法保持开放的态度，但你只要继续挖掘，看看你是对的，它们是坏的还是错的。
So just as a general philosophical principle, I'll start there.
因此，作为一般的哲学原则，我将从这里开始。
We looked at, we did some crazy stuff.
我们看了看，我们做了一些疯狂的事情。
I went walking around San Francisco one day and I looked down Market Street and there was a CVS, a 7-Eleven, a CVS, a Walgreens, a 7-Eleven, and I'm like, "How many SKUs could possibly be inside one of these things that people want and couldn't you just put that into a van and you hit the button on the van and the van comes around and you get whatever convenience items you have, and they're convenience items, so why would that be a problem?"
有一天，我在旧金山散步，顺着市场街往下看，看到了一家 CVS、一家 7-11、一家 CVS、一家沃尔格林、一家 7-11，我就想，“人们想要的这些东西中可能有多少个 SKU，难道你不能把它放进一辆货车里，然后按下货车上的按钮，货车就会过来，你会得到你拥有的任何便利物品，它们是便利物品，所以这为什么会成为问题呢？”
Jason Droege: And we launched that in DC.
Jason Droege：我们在华盛顿推出了它。
We put 10 of these trucks on the road, we put 250 SKUs in them.
我们将 10 辆这样的卡车放在路上，我们在其中放置了 250 个 SKU。
And I mean, crickets is an understatement of how bad it was.
我的意思是，蟋蟀只是轻描淡写地描述了当时的情况有多糟糕。
I mean, we couldn't get an order to save our lives.
我的意思是，我们无法得到拯救生命的命令。
And what we realized was that we hadn't really done the research on what convenience stores really were.
我们意识到我们还没有真正研究过便利店到底是什么。
It was if you didn't have cigarettes, you didn't have beer, you didn't have Slurpees, you didn't have these things, for example, you didn't bring people in to sell all the other things.
就是如果你没有香烟，你没有啤酒，你没有思乐冰，你没有这些东西，比如说，你没有带人来卖所有其他的东西。
So we didn't know anything about retail.
所以我们对零售一无所知。
We were clueless.
我们一无所知。
So that's one idea.
这就是一个想法。
We looked at grocery, but honestly the unit economics just terrified me of all the pick packing and everything like that.
我们看了杂货店，但老实说，单位经济效益让我对所有的挑选包装和类似的东西感到害怕。
I think Instacart did a remarkably good job at getting the unit economics to a good spot and probably the hardest operational problem you could tackle.
我认为 Instacart 在使单位经济效益达到良好水平方面做得非常好，这可能是您可以解决的最难的运营问题。
Jason Droege: We did generalized delivery, point to point delivery, what's now, I forget what Uber's product is called, but Uber Direct I think it's called, where you have something that needs to go point to point in a city.
Jason Droege：我们做了广义配送，点对点配送，现在怎么样了，我忘记了 Uber 的产品叫什么，但我认为它叫 Uber Direct，你有一些东西需要在一个城市里进行点对点的配送。
That was a flop from the beginning because the truth is is how consumers don't really have this need, business sort of have this need, and in 2014 when we were doing this, no one had this need.
这从一开始就是失败的，因为事实是消费者并没有真正有这种需求，企业有这种需求，而在 2014 年我们这样做的时候，没有人有这种需求。
But we tried 15 versions of all these things before we eventually just said, "Okay, the food delivery thing is just popping off on all signals and we can make the unit economics work.
但我们尝试了所有这些东西的 15 个版本，最后才说：“好吧，送餐这件事在所有信号下都会突然出现，我们可以让单位经济效益发挥作用。
People seem to want it.
人们似乎想要它。
It's a super cool problem because we can enable independent restaurants with all these tools and allow them to compete with the big guys.
这是一个非常酷的问题，因为我们可以使用所有这些工具为独立餐厅提供支持，并让他们与大公司竞争。
We can take the real estate out of the equation.
我们可以将房地产排除在外。
So you can have a real estate location that's non-prime, but if you have prime food, then you get to compete." So we're like, "Oh, this is a very interesting problem and we can really help local economies."
所以你可以拥有一个非黄金地段的房地产，但如果你有优质的食物，那么你就可以参与竞争。”所以我们想，“哦，这是一个非常有趣的问题，我们真的可以帮助当地经济。”
Lenny Rachitsky: And this ended up being, if I remember correctly, this basically saved Uber during COVID.
Lenny Rachitsky：如果我没记错的话，这基本上在新冠疫情期间拯救了 Uber。
Lyft didn't have something like this.
Lyft 没有这样的东西。
And how big is this business at this point?
目前这项业务有多大？
Anything you share about just how important this turned out to be for Uber?
您能分享一下这对于 Uber 来说有多重要吗？
Jason Droege: Yeah, of course.
杰森·德罗奇：是的，当然。
Well, we launched it in December of 2015 in Toronto and within two hours we had done $20,000 for the sales.
嗯，我们于 2015 年 12 月在多伦多推出了它，两个小时内我们的销售额就达到了 20,000 美元。
It was crazy how quickly we saw that it was the right idea and the unit economics were good.
令人疯狂的是，我们很快就发现这是正确的想法，而且单位经济效益也很好。
And then, four and a half years later, I was at Uber for about six years, but it took us about a year and a half to figure this out.
然后，四年半后，我在 Uber 工作了大约六年，但我们花了大约一年半的时间才弄清楚这一点。
Four and a half years later, it was about $20 billion.
四年半后，这一数字约为 200 亿美元。
So it was 0 to 20 billion in four and a half years, which is pretty good.
所以四年半时间从0到200亿，已经很不错了。
Uber was very good at scaling things, but competitive market.
Uber 非常擅长扩展业务，但市场竞争激烈。
Others did well.
其他人做得很好。
We beat a lot of people.
我们打败了很多人。
Some people beat us.
有些人打败了我们。
And then, now I think it's pushing 80 billion, and that's been for another four and a half years since I left.
然后，现在我认为它正在推动 800 亿美元，自从我离开以来，这已经过去了四年半。
I think COVID turned it from 20, I left right before COVID, total coincidence, 20 to 50 in a year.
我认为新冠疫情把它从 20 岁变成了，我在新冠疫情之前就离开了，完全巧合，一年之内从 20 岁变成了 50 岁。
So I mean, ride-sharing went this and food delivery just went to Pluto.
所以我的意思是，拼车是这样的，而食品配送则去了冥王星。
Jason Droege: Luck is part of the game.
Jason Droege：运气是游戏的一部分。
That's the other thing that's important to realize.
这是另一件需要意识到的重要事情。
Luck is part of the game, so do not begrudge people for luck.
运气是游戏的一部分，所以不要嫉妒别人的运气。
This industry is hard.
这个行业很难。
All these things we're doing are really, really hard.
我们正在做的所有这些事情都非常非常困难。
Luck is just part of the game.
运气只是游戏的一部分。
Lenny Rachitsky: Maybe speaking that maybe not.
Lenny Rachitsky：也许这么说也许不是。
One of your colleagues, Stephen Chau, who I am an investor in his new company, he worked with you at Uber Eats for a long time.
你的一位同事史蒂芬·周（Stephen Chau），我是他新公司的投资者，他在 Uber Eats 优食上与你共事了很长时间。
He told me to ask you about the McDonald's story.
他让我向你询问麦当劳的故事。
I imagine that was just a big milestone, a big moment enough for you guys.
我想这对你们来说只是一个重要的里程碑，一个足够重要的时刻。
So why'd you decide put McDonald's in Uber Eats and there's apparently a story of how you won that deal.
那么，你为什么决定将麦当劳纳入 Uber Eats 优食，显然有一个关于你如何赢得这笔交易的故事。
Jason Droege: So it was interesting, and this just goes to maybe where sometimes ignorance leads you to accidentally the right answer.
Jason Droege：这很有趣，这可能会导致有时无知会导致你意外地得到正确的答案。
So we had launched Uber Eats and Uber had a global footprint and we were the only food delivery network with a global footprint excluding China.
因此，我们推出了 Uber Eats 优食，Uber 的业务遍及全球，我们是除中国以外唯一一家业务遍及全球的食品配送网络。
Everything at Uber needed to be launched globally.
Uber 的一切都需要在全球范围内推出。
That was a very big part of the culture, et cetera, which is a lot of work and you can spread yourself too thin and cause other problems.
这是文化的一个非常重要的组成部分，等等，这是大量的工作，你可能会把自己分散得太小并导致其他问题。
But in this way it was good.
但这样就很好了。
My vision was, okay, let's help the little guy compete with all these chains.
我的愿景是，好吧，让我们帮助这个小家伙与所有这些链条竞争。
They have these systematized food systems and food is what makes a city amazing.
他们拥有系统化的食物系统，食物让城市变得令人惊叹。
And no one talks about the chain restaurant that they visited in Paris.
没有人谈论他们在巴黎参观过的那家连锁餐厅。
They talk about the local place that they found and let's be part of that.
他们谈论他们发现的当地地方，让我们成为其中的一部分。
That's who we want to be.
这就是我们想成为的人。
Jason Droege: And so, McDonald's actually approached us and they said, "Hey, we'd love to do food delivery with you." And I said, "No." And they're like, "Hold on a second.
Jason Droege：因此，麦当劳实际上联系了我们，他们说：“嘿，我们很乐意与您一起提供送餐服务。”我说：“不。”他们就像，“等一下。
We have 80 million consumers a day.
我们每天有 8000 万消费者。
You don't want to do this together?" I'm like, "It's not really our vibe right now." And so, I pushed them off for four or five months until my team is like, "You're insane.
你不想一起做这件事吗？”我想，“这不是我们现在的氛围。”所以，我把他们推迟了四五个月，直到我的团队说，“你疯了。
These people are going to put marketing behind it.
这些人将把营销放在后面。
They really want to do this.
他们真的很想这么做。
They want to lean in." So we actually had, because of that, I think it's hard to correlate these things, we ended up with this exclusive relationship with them, got an insane number of customers of...
他们想要靠拢。”所以我们实际上有，正因为如此，我认为很难将这些事情联系起来，我们最终与他们建立了这种排他性的关系，得到了疯狂数量的客户......
Chains at this point actually weren't really on food delivery networks because everybody was so worried about the unit economics, because they're so sensitive to the basket size.
此时的连锁店实际上还没有真正进入食品配送网络，因为每个人都非常担心单位经济效益，因为他们对篮子大小非常敏感。
Jason Droege: And my approach was like, eh, figure it out, which is a very Uber culture thing.
Jason Droege：我的方法是，呃，弄清楚，这是一个非常 Uber 文化的东西。
Okay, the basket's $17, it's our job to make that work, reduce the radius on the delivery, figure out the economics, maybe mark up some of the food someplace.
好吧，一篮子 17 美元，我们的工作就是让这个工作发挥作用，减少送货半径，弄清楚经济效益，也许在某个地方给一些食物加价。
There's always a way to figure it out.
总有办法解决这个问题。
So we did it and then three months later the business just started hockey sticking again at a different level.
所以我们就这样做了，三个月后，我们的业务又开始达到不同的水平。
And my team is just like, "Dude, you were so stubborn on this point," but I think it actually ended up being in net benefit because we got a great deal with them.
我的团队就像是，“伙计，你在这一点上太顽固了，”但我认为它实际上最终获得了净收益，因为我们与他们达成了很大的协议。
Lenny Rachitsky: So the fact that you pushed him out helped you get a better deal is what I'm hearing.
Lenny Rachitsky：所以我听到的是，你把他赶了出去，这帮助你达成了更好的协议。
That's amazing.
太棒了。
Jason Droege: Yeah, I think that's the story he would be referencing.
Jason Droege：是的，我想这就是他引用的故事。
And then, the onboarding of it was crazy because we basically went global with them in six months, and at this point the business was less than two years old.
然后，它的加入是疯狂的，因为我们基本上在六个月内与他们一起走向全球，而此时该业务还不到两年。
So activating this, I don't even know, an 80-year-old company that expects processes to be in place and we have two of our office managers in New York managing it.
所以激活这个，我什至不知道，一家有 80 年历史的公司，期望流程到位，我们在纽约有两名办公室经理来管理它。
It's just mayhem.
这只是混乱。
Lenny Rachitsky: I'm still sad In-N-Out is still not on any of these apps.
Lenny Rachitsky：我仍然很遗憾 In-N-Out 仍然没有出现在这些应用程序中。
Lenny Rachitsky: I remember someone was hacking it.
Lenny Rachitsky：我记得有人正在入侵它。
There's all these ways people found a way around and they're like, "No, no. Okay, you're Postmates.
人们找到了各种各样的方法，然后他们说：“不，不。好吧，你们是邮差。
We know we're not going to give you any food."
我们知道我们不会给你任何食物。”
Lenny Rachitsky: You've touched on this idea of gross margins and margins, how obsessed you are with this.
Lenny Rachitsky：您已经谈到了毛利率和利润率这个概念，您对此是多么着迷。
I wanted to spend a little time on here.
我想在这里花一点时间。
I've heard just you're obsessed with understanding gross margins before going in on anything.
我听说你在做任何事情之前都热衷于了解毛利率。
Most founders have no idea what they're doing here.
大多数创始人不知道他们在这里做什么。
What have you learned about just what people should be paying attention to, what they might be forgetting when they think about just the feasibility of a business?
关于人们在考虑企业可行性时应该注意什么以及他们可能会忘记什么，您了解到了什么？
Jason Droege: Yeah, look, it's one filter like many filters.
Jason Droege：是的，看，它是一个像许多过滤器一样的过滤器。
There are certainly businesses that have low gross margins that are great businesses.
当然，有些毛利率较低的企业也是伟大的企业。
Costco, Walmart, et cetera.
好市多、沃尔玛等。
Amazon talks about this all the time of there's companies that increase prices and there's companies at lower prices.
亚马逊一直在谈论这个问题：有的公司提高价格，有的公司降价。
But I would say that by and large, high gross margins combined with healthy churn curves are a very healthy sign for the business.
但我想说，总的来说，高毛利率与健康的客户流失曲线相结合对于该业务来说是一个非常健康的迹象。
I mean, think about it.
我的意思是，想一想。
If I were to sell you something and I can't mark it up a lot, how much value am I adding beyond what's in my hand?
如果我要卖给你一些东西，但我无法加价很多，那么除了我手中的东西之外，我还增加了多少价值？
And if I'm not adding that much value, then what am I in the business of doing?
如果我没有增加那么多价值，那么我在做什么呢？
And I'm in business of adding value.
我从事的是增值业务。
And it's not quite that simple.
事情并没有那么简单。
This is just a litmus test of when someone comes to me and they go, especially in a new business, and we deal with this.
这只是一个试金石，看看有人来找我然后他们就走了，尤其是在新业务中，我们会处理这个问题。
I dealt with this at Uber, I've dealt with it everywhere.
我在 Uber 处理过这个问题，我在其他地方也处理过这个问题。
Jason Droege: Someone comes up with an idea and they go, "We can get into this business and I think we can charge this and it'll get us to a 40% gross margin." And then, my next question is start at a 60% gross margin.
Jason Droege：有人提出了一个想法，然后他们说：“我们可以进入这项业务，我认为我们可以对此收费，这将使我们的毛利率达到 40%。”然后，我的下一个问题是从 60% 的毛利率开始。
Why does that not work?
为什么那不起作用？
And they go, "Oh, well, the customer..." And immediately, you short circuit to what the real problem is.
他们说，“哦，好吧，客户……”然后你立刻就意识到真正的问题是什么。
Oh, the customer has an alternative.
哦，客户有一个替代方案。
Oh, okay, well who's the alternative?
哦，好吧，那谁是替代者呢？
Oh, it's some offshoring company.
哦，这是一些离岸公司。
Well, what's their gross margin?
那么，他们的毛利率是多少？
Oh, we don't know.
哦，我们不知道。
You go find out.
你去了解一下。
It's like 20% and they've been around for a long time and they have scaled operations.
大约有 20%，他们已经存在很长时间了，并且已经扩大了运营规模。
And you're like, okay, so your gross margin is going to go from 40 to 20 quicker than you think, and you're going to be in a world of hurt unless you do something to differentiate.
你会想，好吧，你的毛利率将从 40 增长到 20，比你想象的要快，除非你采取一些措施来实现差异化，否则你将陷入困境。
Jason Droege: So I take gross margin is just a very coarse instrument, not a perfect instrument to think about, am I adding enough value?
Jason Droege：所以我认为毛利率只是一个非常粗糙的工具，而不是一个值得思考的完美工具，我是否增加了足够的价值？
Am I differentiated?
我有区别吗？
It's not perfect, but it's a very quick short circuit filter to even to see if someone's pitching you an idea, have they thought through this dynamic?
它并不完美，但它是一个非常快速的短路过滤器，甚至可以查看是否有人向您提出一个想法，他们是否考虑过这种动态？
Because if the response is gross margin is super low right now, but here's the dynamic I'm going after.
因为如果答案是现在的毛利率非常低，但这是我正在追求的动态。
And then you're like, "Oh, okay." And sometimes it's like, we'll just make it up with volume and then the gross margin will go negative for a while and you're like, "Wait, this doesn't work."
然后你会说：“哦，好吧。”有时，我们会用销量来弥补，然后毛利率会在一段时间内变为负数，你会想，“等等，这行不通。”
Lenny Rachitsky: So what I love about this is just a lens into is my idea good enough if studying, can I keep a high gross margin?
Lenny Rachitsky：所以我喜欢这个只是为了了解我的想法是否足够好，如果研究的话，我可以保持高毛利率吗？
Is there a reason why people in this space haven't been able to have a higher margin?
这个领域的人们无法获得更高的利润是否有原因？
Jason Droege: Yeah, exactly.
杰森·德罗奇：是的，完全正确。
And like I said, it's meant to disqualify just you're doing these large for larger companies and everybody has ideas.
就像我说的，这意味着取消你为大公司做这些大事并且每个人都有想法的资格。
And so, it's a way to cut through.
所以，这是一种突破的方式。
Do you understand the machine that is going to need to be in place in two or three years?
您了解两三年内需要安装的机器吗？
You might have a 70% gross margin now because the next question is why can't someone else do this?
你现在的毛利率可能是 70%，因为下一个问题是为什么其他人不能这样做？
And if you have an answer of like, "Well, they can now, but they can't in two years, if we run really fast." Okay, we might have something.
如果你的答案是这样的：“好吧，他们现在可以，但如果我们跑得很快的话，两年后他们就不行了。”好吧，我们可能有一些东西。
If they can now and they will be able to in two years, you're going to have margin compression.
如果他们现在可以并且两年后也可以，那么你的利润率将会受到压缩。
Lenny Rachitsky: Along these lines I was just listening to, I think it was the a16z podcast.
Lenny Rachitsky：按照这些思路，我只是在听，我认为这是 a16z 播客。
Alex Rampell I think was sharing this story about Costco, how as you said, their strategy is actually to keep margins very, very low because all their revenue comes from their membership.
Alex Rampell 我想正在分享这个关于 Costco 的故事，正如你所说，他们的策略实际上是保持利润非常非常低，因为他们所有的收入都来自会员资格。
So they have something like 50 million members paying 100 bucks a month and that's their entire business.
所以他们有大约 5000 万会员，每月支付 100 美元，这就是他们的全部业务。
And so, they don't plan and they don't want to make money off the products.
因此，他们没有计划，也不想从产品中赚钱。
Jason Droege: Yeah, that's right.
杰森·德罗奇：是的，没错。
I mean, they're playing a slightly different game, not an expert on Costco, have spent some time with the company, but they use price as a way to get to scale.
我的意思是，他们正在玩一个稍微不同的游戏，不是 Costco 的专家，已经在该公司花了一些时间，但他们使用价格作为扩大规模的一种方式。
And so, they're basically saying if we discount, same with Walmart, we will get so much volume that we will just take the air out of the room for all of our competition.
所以，他们基本上是说，如果我们打折，就像沃尔玛一样，我们将获得如此多的销量，以至于我们所有的竞争都会被排除在外。
And so, then the question of, okay, so if you have a low gross margin today, in two or three years, once you land one of these centers in a market, why won't your margins to get eroded?
那么，接下来的问题是，好吧，如果你现在的毛利率很低，两三年后，一旦你将其中一个中心投入市场，为什么你的利润不会受到侵蚀？
The answer is because we will have already absorbed all of the demand.
答案是因为我们已经吸收了所有的需求。
You try to go to 8% versus 10% gross margin, which I roughly think is what their gross margin is.
你尝试达到 8% 和 10% 的毛利率，我粗略地认为这就是他们的毛利率。
That's going to be a really hard business.
这将是一件非常艰难的事情。
If you already have a habit with a customer, they have already built their weekly trips around you, you already have relationships with suppliers, you already have general managers that know how to stock inventory, that's not a straightforward exercise.
如果你已经与客户建立了习惯，他们已经围绕你建立了每周的旅行，你已经与供应商建立了关系，你已经有了知道如何库存的总经理，这不是一个简单的练习。
So they're first to scale and then good luck competing with them.
所以他们首先要扩大规模，然后祝你好运与他们竞争。
Lenny Rachitsky: Okay.
莱尼·拉奇茨基：好的。
Just a couple more questions.
还有几个问题。
One is there's this term that I've heard that you often say and believe in is this idea of not losing is a precursor to winning.
一是我听过你经常说并且相信的一个术语是，不输是获胜的先决条件。
Jason Droege: Tech is a culture where portfolios are built by investors, and a lot of the narrative is controlled by investors frankly.
Jason Droege：科技是一种由投资者建立投资组合的文化，坦白说，很多叙述都由投资者控制。
Founders obviously participate, but this idea that you should just go for it is consensus.
创始人显然参与了，但你应该去追求它的想法是共识。
Just go for it.
就去做吧。
Who cares?
谁在乎？
Well, I don't know, if it's my life and I only have one moment to take a shot, I might want to just not just go for it.
好吧，我不知道，如果这就是我的生活，而我只有一次机会去尝试，我可能不想只是去尝试。
I might want to think for a little bit, and I think the best entrepreneurs, I have no data to back this up, but just these are my friend, this is my friend group.
我可能想想一下，我认为最好的企业家，我没有数据支持这一点，但这些是我的朋友，这是我的朋友群。
I think the best entrepreneurs and the best business owners look at the risk profile of the decisions that they're making and they try to make asymmetrically positive decisions all along the way.
我认为最好的企业家和最好的企业主会考虑他们所做决策的风险状况，并在整个过程中尝试做出不对称的积极决策。
Jason Droege: And so, oftentimes I feel like we forget about the risk of a decision, and there's more to unpack there because I actually think taking highly risky decisions and then having it work out is a weird cultural thing too, because then how do you train people to do that?
Jason Droege：所以，我常常觉得我们忘记了决策的风险，而且还有更多东西需要解开，因为我实际上认为做出高风险的决策然后让它发挥作用也是一种奇怪的文化事情，因为那么你如何训练人们这样做呢？
Because it's a very hard thing to take high risk decisions and be right enough because it creates a lot of volatility.
因为做出高风险决策并且足够正确是一件非常困难的事情，因为它会产生很大的波动性。
But it goes back to my comment about the most important thing in founders, which is just this ability to persevere through.
但这又回到了我关于创始人最重要的事情的评论，那就是坚持到底的能力。
Survival is just part of the game, and most people just give up before they get their timing right, before they get the right insight with the customer before they get the right product in the market.
生存只是游戏的一部分，大多数人在找到正确的时机之前、在与客户获得正确的洞察之前、在市场上获得正确的产品之前就放弃了。
And life can change quickly in tech.
科技的发展可以让生活迅速改变。
You can go from being a dog to being a hero in a very short period of time, but you're on this very, very long journey, but you have to survive for that condition to be met.
你可以在很短的时间内从一条狗变成一个英雄，但你正处于这个非常非常漫长的旅程中，但你必须生存下来才能满足这个条件。
Jason Droege: And so, then the question is is when you're in a hype cycle, I would argue that we are right now, everyone wants to go for it and then go for it more and then go for it more and go for it more and you don't realize, guys, all of our customers are going to be around in five years.
Jason Droege：所以，问题是，当你处于炒作周期时，我认为我们现在就处于炒作周期中，每个人都想追求它，然后更多地追求它，然后更多地追求它，更多地追求它，但你没有意识到，伙计们，我们所有的客户都会在五年内出现。
They just want us to solve their problems.
他们只是希望我们解决他们的问题。
We have to be around to solve their problem for them.
我们必须在身边为他们解决问题。
And so, survival is a precursor to that.
因此，生存是其先决条件。
So let's not put ourselves in position that could potentially compromise the enterprise along the way.
因此，我们不要将自己置于可能会损害企业发展的境地。
It doesn't mean don't take risks, but think about how you calculate it.
这并不意味着不要冒险，而是要考虑一下如何计算。
Lenny Rachitsky: I love how clear it is that this lesson and many of the lessons along these lines have come from just failure and things not working out and things breaking, which is the best outcome.
Lenny Rachitsky：我很喜欢这一点，这一点以及许多类似的教训都来自失败、事情不顺利和事情破裂，这是最好的结果。
Jason Droege: If you ever get on the other side of a high reward, high risk decision, it is so painful because you are just cooked.
Jason Droege：如果你曾经做出高回报、高风险的决定，那会非常痛苦，因为你已经熟透了。
You are done, and often there's no way out.
你已经完了，而且往往没有出路。
Lenny Rachitsky: Is there a story along those lines that comes to mind or an example of that?
Lenny Rachitsky：有没有想到类似的故事或例子？
Jason Droege: Well, this is where it is together on why I try to be so I think you can spend a little bit of time thinking upfront to save yourself a lot of pain downstream.
Jason Droege：嗯，这就是我为什么要努力做到这一点的地方，所以我认为你可以花一点时间预先思考，以节省下游的很多痛苦。
I had this business not worth detailing it, but after the bubble burst in 2001, I'm like, "I'm going to self-fund a business.
我的生意不值得详细说明，但在 2001 年泡沫破灭后，我想，“我要自筹资金创办一家企业。
I'm going to build a profitable business.
我要建立一个有利可图的业务。
I want to prove that I can do this." And we had started Scour, which had all the things we talked about.
我想证明我能做到这一点。”我们开始了 Scour，里面有我们谈论的所有内容。
And so, what I did is I'm like, I was a golfer and frankly, there was nothing to do in tech.
所以，我所做的是，我是一名高尔夫球手，坦率地说，在科技领域没有什么可做的。
Jason Droege: So I started selling golf clubs on the internet and I was making real money and I might've learned more from this business than any other because I started on eBay and I was 22, and I didn't really understand that my margins would come down because anyone can do this, but I was one of the first ones to do it, so I was making a ton of money and then I built this business and then I just failed to recognize I had a lot of hubris.
Jason Droege：所以我开始在互联网上销售高尔夫球杆，我赚了很多钱，我可能从这项业务中学到了比其他业务更多的东西，因为我从 eBay 开始，当时我 22 岁，我并没有真正理解我的利润会下降，因为任何人都可以做到这一点，但我是第一个这样做的人，所以我赚了很多钱，然后我建立了这个业务，然后我没有意识到我有很多傲慢。
I was like, "Oh, if I could just buy all the used golf clubs in America, I can be the market maker for prices," and don't people do that?
我当时想，“哦，如果我能买下美国所有二手高尔夫球杆，我就可以成为价格的做市商”，人们不这样做吗？
Lenny Rachitsky: I love this ambition.
Lenny Rachitsky：我喜欢这个雄心壮志。
That's great.
那太棒了。
Jason Droege: And it's just like it's madness to actually think about the practicality of that.
Jason Droege：真正考虑它的实用性就像是疯狂的。
And so, I just didn't spend the time thinking and then I ended up in this business.
所以，我只是没有花时间思考，然后我就进入了这个行业。
The business was profitable, it got to a couple million of revenue, whatever, paid me a dividend for a while, but it was painful the entire way.
生意是盈利的，收入有几百万，无论如何，还给我发过一段时间的股息，但一路上很痛苦。
Lenny Rachitsky: I love the spectrum of experiences you've had.
Lenny Rachitsky：我喜欢你所经历的各种经历。
You've sold golf clubs, you're helping achieve AGI, you could say.
你可以说，你卖掉了高尔夫球杆，你正在帮助实现通用人工智能。
There's also a whole part of your career.
这也是你职业生涯的一部分。
We haven't talked about where you built tasers and body cams and drones and all these things.
我们还没有讨论你们在哪里制造泰瑟枪、随身摄像头、无人机以及所有这些东西。
Also, peer-to-peer file sharing before anyone else.
此外，在其他人之前进行点对点文件共享。
Final topic I just want to spend a little time on based on this experience is hiring and building teams, something that I know you have a really strong take on.
根据这次经验，我想花一点时间讨论的最后一个主题是招聘和建立团队，我知道您对此有很强的看法。
That I've been hearing a lot on this podcast recently is this idea of it's more important to build the right team than find the most optimal top talent.
我最近在这个播客上听到了很多这样的想法：建立合适的团队比找到最优秀的顶尖人才更重要。
Talk about that, why that's so interesting and important.
谈谈这个，为什么它如此有趣和重要。
Jason Droege: As of late, I've developed a more nuanced view of this, which is for certain roles, you absolutely need the right experience in this current market.
Jason Droege：最近，我对此有了更细致的看法，对于某些角色，你绝对需要在当前市场中拥有正确的经验。
You see this with researchers, because the market's moving so fast, you don't have time to train up some people, so you actually have to go find people either who have the right relationships with customers that you want to get or you have to, who might not check other boxes but are awesome at that, might not check the classic boxes that I think you're referencing of they're a problem solver, they can grow with the company, they have a high trajectory, et cetera.
你在研究人员身上看到了这一点，因为市场变化如此之快，你没有时间培训一些人，所以你实际上必须去找那些与你想要获得的客户有正确关系的人，或者你必须这样做的人，他们可能不会勾选其他框，但在这方面很出色，可能不会勾选我认为你引用的经典框，他们是问题解决者，他们可以与公司一起成长，他们有很高的发展轨迹，等等。
I would say that's 5% of the roles in the company, but very important whenever speed to market is important.
我想说这占公司职位的 5%，但只要上市速度很重要，就非常重要。
Jason Droege: And then, for interviewing, I just interview for three things and I have to interview across all kinds of expertises, which is hard.
Jason Droege：然后，对于面试，我只面试三件事，而且我必须面试各种专业知识，这很困难。
I can't be an expert in everything.
我不可能成为所有事情的专家。
And so, I reduce it down to just three things, which is like, are you a curious problem solver and can you articulate that verbally?
因此，我将其简化为三件事，比如，你是一个好奇的问题解决者吗？你能用口头表达出来吗？
Can you work across people?
你能跨人群工作吗？
Are you humble enough to work across and are you a good leader?
您是否足够谦虚以适应工作，并且您是一名优秀的领导者吗？
And if you just do those three things, I think you have a pretty high chance of success, at least in an organization that I'm running, because the world's changing.
如果你只做这三件事，我认为你成功的机会相当高，至少在我管理的组织中是这样，因为世界正在发生变化。
So you do need people that are adaptable.
所以你确实需要适应性强的人。
So all the experience is not necessarily one-to-one relevant.
所以所有的经历不一定是一对一相关的。
Jason Droege: And then, the working across to your team point, this actually came up at Uber Eats.
Jason Droege：然后，按照您的团队观点，这实际上是在 Uber Eats 上出现的。
So when I was building the Uber Eats management team, I'm not sure if this was mentioned to you from that group, but whenever I would hire people, I was trying to compose almost like an organism of strengths and then minimize the conflicts.
因此，当我组建 Uber Eats 优食管理团队时，我不确定该团队是否向您提到过这一点，但每当我要雇用人员时，我都会尝试像一个有机体一样组织优势，然后最大程度地减少冲突。
That management team for the most part outside of some of the operations side, but for the most part, that management team was the same management team from day one when we had nothing to $20 billion.
该管理团队大部分都在某些运营方面之外，但在大多数情况下，该管理团队从第一天起就是同一个管理团队，当时我们一无所有，只有 200 亿美元。
And I just believed that the team, knowing each other's strengths and weaknesses and being able to compensate for each other was more important than the classic advice you get around, "Well, that person hasn't seen this much scale." And you're like, "Well, yeah, but can they learn it?" I learned it.
我只是相信团队，了解彼此的优势和劣势，并能够互相弥补，比你得到的经典建议更重要，“好吧，那个人还没有见过这么大的规模。”你会说，“嗯，是的，但是他们能学会吗？”我学会了。
So you do have to kind of believe in people a little bit, which is my job, not necessarily their job.
所以你必须稍微相信别人，这是我的工作，不一定是他们的工作。
And so, I mean, these are people systems.
所以，我的意思是，这些是人员系统。
They're not straightforward rules-based things you can apply.
它们不是您可以应用的简单的基于规则的东西。
Lenny Rachitsky: And I especially love this advice because there's all this talk about what skills will matter in this world of AI doing all our jobs, and it feels like these three buckets are maybe the same thing, just are they good at solving problems?
Lenny Rachitsky：我特别喜欢这个建议，因为大家都在讨论在人工智能完成我们所有工作的这个世界中哪些技能很重要，而且感觉这三个桶可能是同一件事，只是它们擅长解决问题吗？
Are they good leaders?
他们是好的领导者吗？
Can they collaborate well with other people?
他们能与其他人很好地合作吗？
Jason Droege: Yeah, I don't think that the core rise of humanity, it will change, and I think that these things are pretty core to how humans have been successful for a long time.
Jason Droege：是的，我认为人类崛起的核心不会改变，而且我认为这些东西是人类长期以来成功的核心。
Lenny Rachitsky: Speaking of that, I'm going to take us to a recurring segment of this podcast that I call AI Corner, where I ask folks this question, what's some way that you've found a use for AI in your day-to-day life in your work that makes you more effective, get more done, get better stuff done?
Lenny Rachitsky：说到这一点，我将带我们进入这个播客的一个反复出现的部分，我称之为“AI Corner”，我会问人们这个问题，您发现人工智能在日常生活和工作中的用途是什么，可以让您变得更高效、做得更多、做得更好？
Jason Droege: Honestly, when I came into Scale, so my history was in consumer and I've done some application level stuff with government, and this space is moving so quickly.
Jason Droege：老实说，当我进入 Scale 领域时，我的历史是在消费者领域，我与政府一起做了一些应用程序级别的工作，这个领域发展得非常快。
AI is my, I use it as a tutor.
AI是我的，我用它当导师。
As these new concepts come up, I have a lot of people in the company who can educate me on the nuances of the technicals of all of, excuse me, the technical nature of the data and the products, but they only have so much time.
随着这些新概念的出现，公司里有很多人可以教育我所有技术的细微差别，对不起，数据和产品的技术本质，但他们只有这么多时间。
And honestly, there's new concepts coming up all the time and I need to stay on top of it.
老实说，新概念一直在出现，我需要时刻掌握它。
Jason Droege: So it might sound crazy, but a large percentage of my job is not dealing with the engineering issues related to AI.
Jason Droege：这听起来可能很疯狂，但我工作的很大一部分并不是处理与人工智能相关的工程问题。
I'm managing an organization, but I love understanding it.
我正在管理一个组织，但我喜欢理解它。
It's one of the most enjoyable, rewarding parts of my job is to learn from all these AI researchers, but they don't always have the time to do it, so I use it as a tutor.
我工作中最有趣、最有价值的部分之一就是向所有这些人工智能研究人员学习，但他们并不总是有时间这样做，所以我把它当作导师。
I turn on voice mode and talk to it on my way into work.
我打开语音模式并在上班途中与它交谈。
So I think that's probably the most impactful thing that I use it for that's also relevant to this topic.
所以我认为这可能是我使用它的最有影响力的事情，也与这个主题相关。
Lenny Rachitsky: I do exactly the same thing, especially when I'm prepping for this podcast.
Lenny Rachitsky：我也做同样的事情，尤其是当我准备这个播客时。
What exactly is this?
这到底是什么？
I think about when you say this, I did an interview with the founders of Perplexity a few years ago asking about how they work at Perplexity, and the founders said that before, they were ruled, before they ask a question of anyone on the team, they have to ask AI first.
我想，当你这么说的时候，几年前我采访了 Perplexity 的创始人，询问他们在 Perplexity 的工作方式，创始人说，以前他们被规定，在向团队中的任何人提问之前，必须先问 AI。
And I was just like, "That's crazy." Now, it's so obvious.
我当时想，“这太疯狂了。”现在，它是如此明显。
But back then, I was like, "That's an insane way of working.
但当时，我想，“这是一种疯狂的工作方式。
I've never heard of this before." Just a sign of how ahead of the curve they were.
我以前从未听说过这个。”这只是他们走在时代前沿的一个标志。
Jason Droege: Yeah, I think number two would be I'll take internal documents and I'll ask, what's the most important thing in this document?
Jason Droege：是的，我认为第二点是我将获取内部文件，然后我会问，这份文件中最重要的事情是什么？
And I'm shocked, and then I'll read it and just double check, but I'm shocked at how good it is at just pulling out.
我很震惊，然后我会读它并仔细检查，但我对它在退出方面的出色表现感到震惊。
There's so much in organizations that is like, I don't know what you want me to say and I don't know what I need to know, but we each have our own agendas, and so this matching of, and so then there's this huge broadcast problem where it's like, of all of the information you might want to receive, what's actually important to you?
在组织中有很多这样的事情，我不知道你想让我说什么，我也不知道我需要知道什么，但我们每个人都有自己的议程，所以这种匹配，然后就存在一个巨大的广播问题，在你可能想要接收的所有信息中，什么对你来说真正重要？
And so, I use it a lot for that too.
因此，我也经常使用它。
Lenny Rachitsky: Amazing.
莱尼·拉奇茨基：太棒了。
That's a really good tip.
这是一个非常好的提示。
I use it for legal documents, just like what do they know about what they're trying to do here for me or against me?
我将其用于法律文件，就像他们知道他们在这里试图为我做什么或反对我做什么一样？
Jason, is there anything else you wanted to share or leave listeners with, maybe double down on a point before we get to a very exciting lightning round?
杰森，您还有什么想分享或留给听众的吗？也许在我们进入激动人心的闪电回合之前，您可以在某一点上加倍努力？
Jason Droege: Yeah, absolutely.
杰森·德罗奇：是的，绝对是。
I mean, I think the really important, the reason why I'm doing this, the reason why want to spend time here outside of wanting to be on the show for a while and being a long-term listener is, our long-time listener, excuse me, is there's a lot of amazing work going on at Scale.
我的意思是，我认为真正重要的是，我这样做的原因，除了想在节目中呆一段时间并成为长期听众之外，我想花时间在这里的原因是，我们的长期听众，对不起，是 Scale 正在进行很多令人惊叹的工作。
The teams are working super hard, we're delivering a ton of value for our customers.
团队工作非常努力，我们正在为客户提供大量价值。
The public narrative has not represented the work that the people here are doing and the work that our customers are doing with what we're doing for them.
公众的叙述并没有代表这里的人们正在做的工作以及我们的客户正在做的工作以及我们为他们所做的事情。
And I just think that deserves the respect and reward that all those people are putting in, and we'd like people to know that.
我只是认为这值得所有这些人所付出的尊重和奖励，我们希望人们知道这一点。
Lenny Rachitsky: I appreciate you saying all that.
Lenny Rachitsky：我很欣赏你所说的一切。
With that, we've reached our very exciting lightning round.
至此，我们进入了非常激动人心的闪电回合。
We've got five questions for you.
我们有五个问题要问你。
You ready?
你准备好了吗？
Lenny Rachitsky: What are two or three books that you find yourself recommending most to other people?
Lenny Rachitsky：您最常向其他人推荐的两三本书是什么？
Jason Droege: Some of this is going to sound interesting.
Jason Droege：其中一些听起来很有趣。
The Selfish Gene is one of my favorite books.
《自私的基因》是我最喜欢的书之一。
Lenny Rachitsky: Love that book.
莱尼·拉奇茨基：喜欢那本书。
I don't know if anyone's ever mentioned, it was one of the most influential books for me too.
我不知道是否有人提到过，这也是对我影响最大的书之一。
So sorry, keep going.
非常抱歉，请继续。
Jason Droege: Yes.
杰森·德罗奇：是的。
I think Selfish Gene.
我认为是自私的基因。
Road Less Traveled, I've read more than once.
《少有人走的路》，我读过不止一遍。
I mean, it's just one of the classic human psychology book.
我的意思是，这只是一本经典的人类心理学书籍。
And then, I think in business, I think Good to Great.
然后，我认为在商业领域，我认为从优秀到卓越。
It's not the read that you're going to be most excited to enjoy on a vacation, but it's pretty much right, and I think we should take advice from people who have analyzed these business problems before because not a lot's changed, but we keep acting like everything's changed.
这不是你在度假时最兴奋的读物，但它是非常正确的，我认为我们应该听取以前分析过这些业务问题的人的建议，因为没有太多改变，但我们一直表现得好像一切都改变了。
Lenny Rachitsky: What's crazy about that book, you look at all the companies they talk about, I haven't read in a while, but just the whole book is about companies that last, I believe, or maybe that's the other book, I don't know.
Lenny Rachitsky：那本书的疯狂之处在于，你看看他们谈论的所有公司，我有一段时间没有读过，但我相信整本书都是关于持久的公司，或者也许这是另一本书，我不知道。
But anyway, all the companies that they talk about, I don't know if they're still around.
但无论如何，他们谈论的所有公司，我不知道他们是否还存在。
It's so hard for a business to last a long, long time.
一个企业想要长久地持续下去，是非常困难的。
Jason Droege: I would also recommend Thinking Slow and Fast, that's the...
Jason Droege：我还推荐《慢速思考和快速思考》，这就是......
Yes.
是的。
Jason Droege: Thinking, Fast and Slow.
Jason Droege：思考，快与慢。
Excuse me, sorry.
对不起，对不起。
It's been like a decade since I read it, but just in terms of point there being human biases are very important to understand.
我读这本书已经有十年了，但就这一点而言，理解人类偏见非常重要。
Lenny Rachitsky: What's really crazy to me about that book and Kahneman in general, someone asked them just, how's your life been impacted by learning all these biases humans have?
莱尼·拉奇茨基：这本书和卡尼曼总体上对我来说真正疯狂的地方是，有人问他们，了解人类的所有这些偏见对你的生活有何影响？
He's like, "Not much.
他说：“不多。
I have the same biases.
我也有同样的偏见。
Knowing them doesn't really help me avoid them."
了解它们并不能真正帮助我避免它们。”
Jason Droege: See, I find myself checking myself.
Jason Droege：看，我发现自己在检查自己。
Whenever I get super convicted on something now I will be like, okay, what is the list of things that I'm inclined to do to try to catch myself?
每当我现在对某件事感到非常有罪时，我就会想，好吧，我倾向于做哪些事情来试图抓住自己？
Because I think we're most inclined to have these bad decisions impulsively, which is what I think the book is largely about.
因为我认为我们最容易冲动地做出这些错误的决定，这也是我认为这本书主要讲述的内容。
I mean, it's a long book.
我的意思是，这是一本很长的书。
Lenny Rachitsky: So long.
莱尼·拉奇茨基：再见。
Oh, my God.
我的天啊。
It feels like that's where AI can help us in the future.
感觉这就是人工智能未来可以帮助我们的地方。
Just like, "Hey, Jason, are you sure this isn't framing a fact or whatever?"
就像，“嘿，杰森，你确定这不是在捏造事实或其他什么吗？”
Lenny Rachitsky: Okay.
莱尼·拉奇茨基：好的。
Next question.
下一个问题。
Do you have a favorite recent movie or TV show that you've really enjoyed?
您最近有最喜欢的电影或电视节目吗？
Jason Droege: Most of the movies I watch are with my kids, so I wish I had something deep and profound.
Jason Droege：我看的大部分电影都是和我的孩子们一起看的，所以我希望我能看一些有深度的电影。
Lenny Rachitsky: No, kids content also is a very acceptable-
Lenny Rachitsky：不，儿童内容也是非常可以接受的-
Jason Droege: The Formula 1 movie I thought was really good.
Jason Droege：我认为一级方程式电影非常好。
I mean, it's a classic action movie.
我的意思是，这是一部经典的动作片。
I don't think it informs anything in AI or business, but it's good to check out from the craziness of tech once in a while.
我不认为它对人工智能或商业有任何影响，但偶尔看看科技的疯狂还是有好处的。
Lenny Rachitsky: Is there a product you recently discovered that you really love?
Lenny Rachitsky：您最近发现有一款您真正喜欢的产品吗？
Could be an app, could be clothing, could be a kitchen gadget, anything along those lines?
可能是一个应用程序，可能是服装，可能是一个厨房小工具，类似的东西？
Jason Droege: VO3.
杰森·德罗奇：VO3。
Not totally new, but when I was in high school, I wanted to be a screenwriter.
并不是全新的，但当我在高中时，我想成为一名编剧。
I actually grew up in the Bay Area and everybody was an engineer, but I wanted be a screenwriter.
事实上，我在湾区长大，每个人都是工程师，但我想成为一名编剧。
And so, I went back and I got the first page of one of my old scripts, which not good scripts, but I got the first page.
所以，我回去了，我得到了我的一个旧脚本的第一页，这不是好的脚本，但我得到了第一页。
I took a picture of the script and I fed it to VO3, and I said, "Make this scene," and it got it right.
我拍了一张剧本的照片，然后把它输入到 VO3，然后我说，“制作这个场景”，结果就成功了。
Jason Droege: I was shocked.
杰森·德罗奇：我很震惊。
I was just absolutely shocked that you could just take a picture of a script.
我真的很震惊你竟然可以给剧本拍张照片。
And so, now I'm thinking about that for how do I use these tools for family videos?
所以，现在我在思考如何使用这些工具来制作家庭视频？
Some of the grad tools now with making live images more active, I think are really interesting.
我认为现在的一些毕业工具可以使实时图像更加活跃，这真的很有趣。
I think they need one more step of iteration, but I think those are going to be really emotionally life-changing for people because just a little bit of movement in an image from a grandparent or a relative or whatever you haven't seen in a while, it really does make a big emotional impact on you.
我认为他们需要再进行一步迭代，但我认为这些将真正改变人们的情感生活，因为祖父母或亲戚或任何你一段时间没有见过的图像中的一点点动作，确实会对你产生很大的情感影响。
Lenny Rachitsky: I love that when you play with these tools, you probably can think about, oh, here's the people that help train this thing.
Lenny Rachitsky：我喜欢当你使用这些工具时，你可能会想到，哦，这是帮助训练这个东西的人。
Here's the people that helped on the problem that it had.
这是帮助解决问题的人。
Jason Droege: I was actually talking to someone who was working on VO3, and I told him the script thing and he goes, "Oh, actually scripts.
Jason Droege：我实际上正在和一个正在制作 VO3 的人交谈，我告诉他剧本的事情，他说：“哦，实际上是剧本。
Yeah, no, the way the data is formatted in a script, that would actually be very good." Because they start with set looks dark interior, this character says it in this raspy voice, and so it gives you all the instructions in the script.
是的，不，数据在脚本中的格式化方式实际上非常好。”因为他们一开始的场景看起来很黑暗，所以这个角色用沙哑的声音说出来，所以它给了你剧本中的所有说明。
Lenny Rachitsky: Oh, man, just unlocked a whole new business unit right there.
Lenny Rachitsky：哦，伙计，刚刚在那里解锁了一个全新的业务部门。
Two more questions.
还有两个问题。
One is do you have a favorite life motto that you often think about, find useful in work or in life?
一是您是否有一条您经常思考、认为在工作或生活中有用的人生格言？
Jason Droege: Yeah.
杰森·德罗奇：是的。
The end is never the end.
结束永远不是结束。
That's my favorite internal saying, and it goes to the comments before about survival being a precursor, surviving being a precursor to thriving.
这是我最喜欢的内心说法，它适用于之前关于生存是先决条件的评论，生存是繁荣的先决条件。
You got to survive before you thrive, which is your brain tells you, and along these entrepreneurial journeys, I think this is most applicable.
你的大脑告诉你，你必须先生存，然后才能发展，在这些创业之旅中，我认为这是最适用的。
I mean, this is the hardest journey anyone can go on.
我的意思是，这是任何人都可以经历的最艰难的旅程。
If you go on this journey for five years, you are mentally harder than 99.9% of the population.
如果你在这条路上坚持五年，你的心智比99.9%的人还要辛苦。
People don't understand the Chinese water torture of having self-doubt and having things go wrong, et cetera.
人们不理解中国人因自我怀疑、出问题等而遭受的水折磨。
Jason Droege: And so, more tactically, you get this when you're working out like in a day like, "Oh, I'm too tired.
Jason Droege：所以，从战术上讲，当你在一天中锻炼时，你就会明白这一点，“哦，我太累了。
I need to stop." But the truth is is you can keep going and the world's going to keep spinning.
我需要停下来。”但事实是，只要你继续前进，世界就会继续运转。
So I find in the moments where it's just the hardest or you have this hard decision that seems impassable and your body, you're having this visceral reaction to this is impassable, just to remind yourself that I'm going to wake up tomorrow.
所以我发现，在最困难的时刻，或者你做出了这个似乎无法逾越的艰难决定，而你的身体，你对此产生了无法逾越的本能反应，只是为了提醒自己，我明天要醒来。
This isn't the end.
这还不是结束。
There's another end somewhere.
某处还有另一个终点。
I just find that to unlock me to be like, okay, there might not be a perfect solution, there might be an imperfect solution, but it's a solution so let's just keep going.
我只是发现解锁我就像，好吧，可能没有一个完美的解决方案，可能有一个不完美的解决方案，但它是一个解决方案，所以让我们继续前进。
Lenny Rachitsky: Final question.
莱尼·拉奇茨基：最后一个问题。
You helped create Uber Eats.
您帮助创建了 Uber Eats 优食。
I imagine you're still a power user of Uber Eats.
我想您仍然是 Uber Eats 优食的高级用户。
You have a favorite restaurant on Uber Eats that maybe people should know about, maybe that you order most from?
您在 Uber Eats 优食上有一家最喜欢的餐厅，也许人们应该知道，也许您订购最多的餐厅？
Jason Droege: I order a shocking amount of McDonald's actually.
Jason Droege：事实上，我点了数量惊人的麦当劳。
Despite my original story, it's the family treat in the house.
尽管我的故事是原创的，但这是家里的家庭款待。
I would say that that's probably the top thing that we order.
我想说这可能是我们订购的最重要的东西。
Lenny Rachitsky: Oh, man, I'm worried for your health, but I love, I haven't had McDonald's so long.
莱尼·拉奇茨基：哦，伙计，我担心你的健康，但我爱，我已经很久没吃麦当劳了。
This is like, maybe I should give it another-
这就像，也许我应该再给它一个-
Jason Droege: I mean, more practically we will order mixed greens or tender greens or something like that on a day-to-day basis, but I think that the more notable, surprising thing is is that despite my initial aversion to working with a global chain, it's a good treat once in a while.
Jason Droege：我的意思是，更实际的是，我们每天都会订购混合蔬菜或嫩蔬菜或类似的东西，但我认为更值得注意、令人惊讶的是，尽管我最初不愿意与全球连锁店合作，但偶尔还是一种不错的享受。
You just shouldn't have it all the time.
你只是不应该一直拥有它。
Lenny Rachitsky: Jason, this was incredible.
莱尼·拉奇茨基：杰森，这太不可思议了。
I really appreciate you making time for this.
我真的很感谢你抽出时间来做这件事。
I'm really honored to be the first chat you've had since taking over at Scale.
我很荣幸成为您接手 Scale 以来的第一个聊天对象。
Where can folks find you online if they want to maybe reach out, learn more about what you're, I don't know, maybe join Scale.
人们可以在哪里在线找到您，如果他们想联系您，了解更多关于您的信息，我不知道，也许可以加入 Scale。
Where do you want to point people to and how can listeners be useful to you?
您想将人们引向何处？听众如何对您有用？
Jason Droege: Yeah, absolutely.
杰森·德罗奇：是的，绝对是。
I'm @jdroege, J-D-R-O-E-G-E on X.
我是 @jdroege，X 上的 J-D-R-O-E-G-E。
That's probably the easiest way to follow me, keep up with things and you can shoot me a DM if you like.
这可能是关注我、了解最新动态的最简单方法，如果您愿意，您可以给我发私信。
And so, I think that's how you would keep in touch and, sorry, what was your other question?
所以，我认为这就是您保持联系的方式，抱歉，您的其他问题是什么？
Sorry.
对不起。
Lenny Rachitsky: If you're hiring, I don't know, where should people go check it out if you are, and then also just-
Lenny Rachitsky：如果你在招聘，我不知道，如果你在招聘，人们应该去哪里查看，然后-
Jason Droege: Absolutely.
杰森·德罗奇：当然。
Just go to scale.com, go to our careers page, and we have 250 open roles.
只需访问scale.com，进入我们的职业页面，我们就有 250 个空缺职位。
To the point about we're in business and we're growing, we're hiring a ton of people.
就我们的业务和发展而言，我们正在招聘大量人员。
Our data business is growing, our applications and services business is growing like crazy, and so we're going to need a lot of people to help us on that journey.
我们的数据业务正在增长，我们的应用程序和服务业务正在疯狂增长，因此我们需要很多人来帮助我们完成这一旅程。
Lenny Rachitsky: You guys just signed some insanely large contracts with the government I was reading.
Lenny Rachitsky：你们刚刚与我读到的政府签署了一些非常大的合同。
Jason Droege: 100, yeah.
杰森·德罗奇：100，是的。
We didn't sign just one.
我们不只签了一份。
We signed two in one month, so yes, no, our federal business is doing well.
我们在一个月内签署了两份合同，所以是的，不，我们的联邦业务表现良好。
Our enterprise business is doing well.
我们的企业业务做得很好。
Our international government's business is doing well.
我们的国际政府业务进展顺利。
There's a lot of demand out there.
那里有很多需求。
Lenny Rachitsky: Some salespeople are getting some great commissions.
Lenny Rachitsky：一些销售人员获得了丰厚的佣金。
Good job.
好工作。
Jason, thank you so much for being here.
杰森，非常感谢你来到这里。
Jason Droege: Yeah, thank you.
杰森·德罗奇：是的，谢谢。
Honor to be a guest here.
很荣幸成为这里的客人。
Super excited to be with you, especially so early in the journey, or at least my journey here leading Scale.
非常高兴能和你们在一起，尤其是在旅程的早期，或者至少是我在这里领导 Scale 的旅程。
Lenny Rachitsky: Appreciate it.
莱尼·拉奇茨基：很欣赏。
Thanks for coming.
感谢您的光临。
Thanks for joining us.
感谢您加入我们。
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。