专家撰写AI评估如何催生史上增长最快的公司 | Brendan Foody（Mercor首席执行官）

世界上最富有的公司愿意不惜一切代价提升模型能力。我们正在进入评估时代。我们开始与所有顶级AI实验室合作。实验室需要的是一个劳动力市场。他们实际上需要能够衡量模型能力的杰出专业人士。

The wealthiest companies in the world are willing to spend whatever it takes to improve model capabilities. We're entering the era of evals. We started working with all of the top AI labs. What the labs need is labor marketplace. They actually need extraordinary professionals that can measure model capabilities.

他们发现这个领域可能是历史上最大的商业机会。

They found this pocket maybe the biggest business opportunity in history.

我们在十六个月内从1美元增长到4亿美元的收入运行率，这是历史上最快的增长记录。

We grew from 1 to 400,000,000 in revenue run rate in sixteen months, fastest ascent in history.

为什么这如此有价值？

Why is this so valuable?

市场受限于人类能做而模型不能做的事情的数量。实验室改进模型的主要瓶颈在于如何有效地衡量模型成功的标准。

The market is bound by the amount of things where humans can do something that models can't. The lab's primary bottleneck to improve models is how they can effectively have some way of measuring what success looks like for the model.

有一条你转发的推文。如果你仔细想想，我们被放在地球上就是为了为实验室创造强化学习训练数据。

There's this tweet that you retweeted. If you really think about it, we were put on Earth to create reinforcement learning training data for labs.

整个经济很可能将变成一个强化学习环境机器，构建所有这些世界和场景。我认为过去三年AI领域的主流叙事几乎完全是关于工作岗位被取代的。但很少有公司和人们谈论这个正在被创造的新工作岗位类别。

It's highly likely that the entire economy will become an RL environment machine, building out all of these worlds and contexts. And I think the narrative in AI over the last three years has almost entirely been one of job displacement. But very few companies and people have talked about this new category of jobs that's being created.

我经常和很多人讨论：我应该学习什么？我应该在哪些方面提升自己？

I talk to a lot of people about what should I be studying? Where should I be getting better?

他们如何利用这项技术实现更多可能？我们会进行这样的面试：使用任何可用工具来构建一个网站，看看你能在一小时内打造出什么样的产品。

How can they leverage this technology to do so much more? We'll give people interviews where we say, use whatever tools are available to build a website, and let's see what product you're able to build in an hour.

今天我的嘉宾是Merkor的首席执行官兼联合创始人Brendan Fudi。Merkor是历史上增长最快的公司，从1美元收入增长到5亿美元。他们在17个月内就实现了这一目标，不到一年半时间。Brendan也是最年轻的独角兽创始人。他们刚刚以20亿美元的估值完成了1亿美元的融资。

Today, my guest is Brendan Fudi, CEO and cofounder of Merkor. Merkor is the fastest growing company in history to go from 1 to $500,000,000 in revenue. They did this in seventeen months, less than a year and a half. Brendan is also the youngest Unicorn founder ever. They just raised a $100,000,000 at $2,000,000,000 valuation.

如果你还没听说过Merkor，他们帮助AI实验室和AI公司雇佣专家，利用AI来训练他们的模型。他们从未流失过客户。净留存率超过1600%，并且正在以九位数的收入运行率增长。在我们的对话中，我们讨论了评估日益增长的价值和重要性，像Merkor这样的AI培训公司的格局及其变得如此重要和有价值的原因，Brendan如何发现这个机会，他对产品市场匹配的见解，他在组织中灌输的核心原则使他建立了历史上增长最快的公司，为实验室编写评估的人日常实际做什么工作，随着AI兴起哪些技能和工作最持久，为什么他认为短期内不会出现AGI或超级智能，以及更多内容。这期节目非常精彩。

Merkor, if you haven't heard of them, helps AI labs and AI companies hire experts to help them train their models using AI. They've never had a customer churn. Their net retention is over 1600%, and they're on a 9 figure revenue run rate. In our conversation, we talk about the increasing value and importance of evals, the landscape of AI training companies like Merkor and why they've become so important and valuable, how Brennan discovered this opportunity, his insights on what product market fit looks like, the core tenets he's instilled within his organization that have allowed him to build the fastest growing company in history, what people writing evals for labs are actually doing day to day, which skills and jobs are gonna last the longest with the rise of AI, why he doesn't think we'll see AGI or super intelligence anytime soon, and so much more. This episode is incredible.

你一定要听听这期节目。如果你喜欢这个播客，别忘了在你最喜欢的播客应用或YouTube上订阅关注。这对我们帮助巨大。另外，如果你成为我通讯录的年度订阅者，你可以免费获得15款优秀产品的全年使用权，包括Lovable、Replit、Bolt、N8N、Linear、Superhuman、Descript、WhisperFlow、Gamma、Perplexity、Warp、Granola、Magic Patterns、Raycast、Chappy RD和Mobin。请访问lenny'snewsletter.com并点击product pass查看详情。

You need to hear this. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. It helps tremendously. Also, if you become an annual subscriber of my newsletter, you get 15 incredible products for free for one year, including Lovable, Replit, Bolt, N8N, Linear, Superhuman, Descript, WhisperFlow, Gamma, Perplexity, Warp, Granola, Magic Patterns, Raycast, Chappy RD, and Mobin. Check it out at lenny'snewsletter.com and click product pass.

接下来，有请Brendan Foody。本节目由WorkOS赞助播出。如果你正在构建SaaS应用，你的客户迟早会开始要求企业级功能，比如SAML认证和SCIM配置。这时WorkOS就能派上用场，让你快速无痛地为应用添加企业功能。他们的API易于理解，让你能快速交付，然后继续构建其他功能。

With that, I bring you Brendan Foody. This episode is brought to you by WorkOS. If you're building a SaaS app, at some point, your customers will start asking for enterprise features like SAML authentication and SCIM provisioning. That's where WorkOS comes in, making it fast and painless to add enterprise features to your app. Their APIs are easy to understand so that you can ship quickly and get back to building other features.

如今已有数百家公司使用WorkOS，包括你可能熟悉的Vercel、Webflow和Loom。WorkOS最近还收购了精细权限授权服务Warrant。Warrant的产品基于突破性的Zanzibar授权系统，这个系统最初是为Google设计，用于支持Google Docs和YouTube。它能在保持灵活模型适应最复杂场景的同时，实现大规模快速授权检查。如果你正在构建基于角色的访问控制或其他企业功能，如单点登录、SCIM或用户管理，你应该考虑使用WorkOS。

Today, hundreds of companies are already powered by WorkOS, including ones you probably know, like Vercel, Webflow, and Loom. WorkOS also recently acquired Warrant, the fine grain authorization service. Warrant's product is based on a groundbreaking authorization system called Zanzibar, which was originally designed for Google to power Google Docs and YouTube. This enables fast authorization checks at enormous scale while maintaining a flexible model that can be adapted to even the most complex cases. If you're currently looking to build role based access control or other enterprise features like single sign on, SCIM, or user management, you should consider WorkOS.

它是Auth0的完美替代品，免费支持多达100万月活跃用户。请访问workos.com了解更多信息，就是workos.com。你当初爱上产品开发是有原因的，但有时日常现实与想象略有不同。你不是在构思大创意、与客户交流或制定战略，而是淹没在电子表格和路线图更新中，整天基本上都在救火。

It's a drop in replacement for Auth0 and supports up to 1,000,000 monthly active users for free. Check it out at workos.com to learn more. That's workos.com. You fell in love with building products for a reason, but sometimes the day to day reality is a little different than you imagine. Instead of dreaming up big ideas, talking to customers, and crafting a strategy, you're drowning in spreadsheets and roadmap updates, and you're spending your days basically putting out fires.

更好的方式是可能的。向您介绍Jira产品发现——由Atlassian为产品团队打造的全新优先级排序和路线图规划工具。通过Jira产品发现，您可以将所有产品想法和见解集中在一处，自信地进行优先级排序，最终取代那些没完没了的电子表格。几秒钟内即可创建并与任何利益相关者分享自定义产品路线图，而且这一切都构建在Jira之上——您的工程团队已经在使用的平台，因此真正的协作终于成为可能。优秀的产品是由优秀团队打造的，而不仅仅是工程师。

A better way is possible. Introducing Jira Product Discovery, the new prioritization and road mapping tool built for product teams by Atlassian. With Jira Product Discovery, you can gather all your product ideas and insights in one place and prioritize confidently, finally replacing those endless spreadsheets. Create and share custom product road maps with any stakeholder in seconds, and it's all built on Jira, where your engineering team's already working, so true collaboration is finally possible. Great products are built by great teams, not just engineers.

销售、支持、领导层，甚至是财务部的Greg——任何您希望邀请的人都可以免费在Jira产品发现中贡献想法、反馈和见解。没有隐藏条款。而您每月只需支付10美元。告别电子表格和无休止的对齐工作吧。

Sales, support, leadership, even Greg from finance. Anyone that you want can contribute ideas, feedback, and insights in Jira product discovery for free. No catch. And it's only $10 a month for you. Say goodbye to your spreadsheets and the never ending alignment efforts.

旧的产品管理方式已经结束。通过Jira产品发现重新探索可能性。请在atlassian.com/lenny免费试用，就是atlassian.com/lenny。Brendan，非常感谢您能来到这里。

The old way of doing product management is over. Rediscover what's possible with Jira product discovery. Try it for free at atlassian.com/lenny. That's atlassian.com/lenny. Brendan, thank you so much for being here.

欢迎来到播客节目。

Welcome to the podcast.

非常感谢您的邀请，Lenny。我是您的忠实粉丝，非常兴奋能进行这次对话。

Thank you so much for having me, Lenny. I'm a huge fan and so excited to have a conversation.

我也非常期待这次对话。我是您的忠实粉丝，很高兴能让更多人了解您和您正在构建的产品。我想从您固定在Twitterfeed顶部的一条推文开始。就是这条推文。

I'm really excited to have this conversation as well. I'm a huge fan of yours. I'm excited for more people to learn about you and what you're building. I wanna start with a tweet that you have pinned at the top of your Twitter feed right now. And here's the tweet.

引用，'我们现在正与七巨头中的六家合作，所有前五名的人工智能实验室，以及大多数人工智能应用层公司。每个客户都有一个共同的趋势。我们正在进入评估时代。'这一点引起我注意的原因是，这是本播客中最常出现的趋势之一。人们也在谈论学习如何进行评估的价值，以及评估对公司的价值。

Quote, we are now working with six out of the magnificent seven, all of the top five AI labs, most of the AI application layer companies. One trend is common across every customer. We are entering the era of evals. The reason this caught my attention is that's one of the most recurring trends on this podcast. People talking about the increasing value of learning how to do eval as well and the value of evals for companies.

感觉人们仍然不知道这到底是什么，我们在谈论什么，为什么这如此重要。谈谈你认为人们仍然遗漏了什么，他们需要知道什么，这个评估时代意味着什么。

Feels like still people don't know what the hell this is, what we're talking about, why this is so important. Talk about just what you think people are still missing, what they need to know, what this era of evals means.

如果模型是产品，那么评估就是产品需求文档。研究人员日常的工作方式是，他们会运行数十个实验，在评估集上做出小的改进。而强化学习变得如此有效，一旦他们有了评估，就能帮助提升它。对吧？如果你看看人们一旦专注于奥林匹克数学，就能多快地达到饱和，甚至我们专注于SWE基准测试后，饱和速度有多快。

If the model is the product, then the eval is the product requirement document. And the way that researchers day to day look is that they'll run dozens of experiments where they'll make small improvements on an eval set. And reinforcement learning is becoming so effective that once they have an eval, they can help climb it. Right? If you look at just how fast people were able to saturate Olympiad math once they focused on it, how fast we're even saturating SWE bench once we focus on it.

所以在很多方面，应用智能体、整个经济体系来自动化每一个工作流程的障碍是，我们如何衡量成功？我们如何评估它，并为我们希望智能体做的一切编写产品需求文档，而Merkor显然是实现这一目标的重要组成部分。

And so in many ways, the barrier to applying agents, the entire economy to automate every workflow is how do we measure success? How do we eval it and write the PRDs for everything that we want agents to do, which Merkor is obviously a huge part of doing.

所以听到这里的人会想，是的，好吧，该死，我真的得重视这个评估的东西了。关于如何学好这个有什么建议吗？那些做得好的公司有什么不同之处？比如帮助人们在这方面变得更好。

So people hearing this are like, yeah, okay, shit, I gotta really pay attention to this eval stuff. Any advice about learning how to do this well? What companies that are doing this well are doing differently? Like help people get better at this thing.

是的，我认为尤其是对企业来说，核心的思考方式是，他们如何建立一个测试或系统性的方法来衡量人工智能自动化其核心价值链的效果？所以如果是一家建筑公司，生产这些提供给最终客户的建筑图纸，他们如何有效地衡量这一点？每个公司都有自己的价值链，或者如果是多产品公司，可能有几个。思考如何衡量这一点，是在整个业务中有效应用人工智能的前提。

Yeah, I think that for enterprises especially, the core way to think about it is how can they build a test or a systematic way to measure how well AI automates their core value chain? So if it's an architecture firm that's producing these architecture diagrams of what they provide to their end customer, how can they effectively measure that? And each company has its own value chain, or maybe a handful of them if it's a multi product company. Just thinking about how they measure that is the prerequisite to really effectively applying AI throughout their entire business.

我看到你在No Priors播客上与Sarah和Elad谈论这个，我不知道是在这之后还是之前，但Sarah发推说，'评估等于你的新营销'。你，这是什么意思？你认为她在那里说什么？

I saw you talking about this on the No Priors podcast with Sarah and Elad, and I don't know if it was after this or before this, but Sarah tweeted, Evals equals your new marketing. What do you, what does that mean? What do you think she's saying there?

是的，这与我之前所说的相关——如果模型是产品，那么评估就是产品需求文档（PRD），但随后也成为了销售材料，对吧？因为评估是你给研究人员展示他们应该构建什么和继续做什么的依据，同时也是你展示能力有效性的方式。历史上，大家都在关注这些学术评估，比如博士级推理的GPQA、人文学科期末考试或奥林匹克数学，但现在正转向人们实际关心的能力：如何让模型自动化我们构建软件平台的方式，或自动化我们进行投资银行分析的方式。我认为实验室将越来越多地使用实验室，应用层公司也将越来越多地使用EFL来展示其模型和产品的能力。

Yeah, well, ties to what I said earlier about how if the model is the product, evals are the PRD, but also subsequently the sales collateral, right? Because evals are what you give to researchers to show them what they should be building and going on, but they're also the way that you demonstrate the efficacy of capabilities. And historically, everyone's been pointing to these academic evals of PhD level reasoning with GPQA, Humanities Last Exam, or Olympiad Math, but now it's moving towards the capabilities that people practically care about, of how do we get models to automate the way that we build a software platform or automate the way that we do an investment banking, analysis. And I think labs will increasingly use labs as well as application layer companies will increasingly use EFLs to demonstrate the capabilities of their models and their products.

好的。那么让我们在此基础上展开，稍微放大视角，谈谈你所在市场的格局。我在准备这次对话时就在思考这个问题。如果你想想历史上增长比任何公司都快的公司，基本上可以分为三类：基础模型公司、Vibe编码应用（如Cursor、Lovable、Bolt、Replit以及所有这些v0），然后就是像你们这样的数据标注数据公司。

Okay. So let's kind of build on this and zoom out a little bit and talk about the landscape of the market that you're in. And I was just reflecting on this as I was preparing for this conversation. If you think about the companies growing faster than any company's ever grown in history, there's essentially three buckets. There's the foundational model companies, there's Vibe coding apps, Cursor and Lovable, and Bolt and Replit and all these v0, and then there's data labeling data companies like you.

所以我在播客中邀请过Handshake的CEO，Scale的CEO也即将来做客，还有Surge，以及你们。帮我们理解一下这整个格局是怎么回事，因为我觉得人们其实不太清楚到底发生了什么，只看到这些公司疯狂增长。

So I've had the CEO of Handshake on the podcast, I have the CEO of Scale coming on, there's also Surge, there's you guys. Help us just understand the landscape of what this is all about, because I think people don't really know what the hell is going on and see all these companies growing like crazy.

是的，我来稍微讲讲起源故事以及它如何勾勒出这个格局。因为我们创办公司时，我和联合创始人在14岁时就认识了。我们在19岁时一起创办了公司，最初于2023年1月开始招聘，最初在国际上招聘人员，将他们与我们的朋友匹配，并自动化了我们完成这一切的所有流程。类似于人类如何审查简历、进行面试并决定雇佣谁，我们用语言模型自动化了所有这些流程，在大学辍学之前将公司 bootstrap 到百万美元的收入运行率。然后，发生了一些其他事情，但我们遇到了OpenAI，并看到人类数据市场发生了巨大转变，从众包问题——如何找到低至中等技能的人为早期LLM版本编写勉强语法正确的句子——转向了 sourcing 和 vetting 问题。

Yeah, I'll give a little bit of the origin story and that and how it sort of frames the landscape. Because when we started the company, I met my co founders in when we were 14 years old. We started the company together when we were 19 initially hire in January 2023, initially hiring people internationally, matching them with our friends and automating all the processes of how we did that. So similar to how a human would review a resume, conduct an interview and decide who to hire, we automated all of those processes with LMs, bootstrapped the company to a million dollar revenue run rate before we dropped out of college. And then, a handful of other things happened, but we met OpenAI and we saw that there was this enormous transition in the human data market, where it was moving away from this crowdsourcing problem of how do you find low and medium skilled people that can write barely grammatically correct sentences for early versions of LLMs, and moving towards this sourcing and vetting problem.

我们如何寻找和评估最优秀的专业人士，经验丰富的软件工程师、投资银行家、医生和律师，这些人实际上可以帮助评估和解释人们希望模型具备的所有能力。因此，从那里开始，我们开始与所有顶级AI实验室合作。我们在十六个月内将收入运行率从100美元增长到4亿美元。这是一段非凡的旅程，非常令人兴奋。

How do we source and assess the best professionals, the experienced THANGS software engineers, the investment bankers and doctors and lawyers that can actually help to evaluate and interpret all of the capabilities that people want models to have. So from there, we, started working with all of the top AI labs. We grew from 100 to $400,000,000 in revenue run rate in, sixteen months. And it's been an extraordinary journey, and super exciting.

好的。首先，这简直太疯狂了。我不知道人们是否低估了——我想这是你第一次分享这个数字。我知道正在录制这个。你们现在应该已经宣布了，十六个月内收入从1美元增长到4亿美元。

Okay. First of all, that is out of control. I don't know if people have under I think this is the first time you're sharing that number. I know recording this. You'll have announced it by now, 1 to $400,000,000 in revenue in sixteen months.

没错。所以是历史上最快的增长，这是一个令人兴奋的统计数据，我们非常自豪。

Exactly. So fastest ascent in history, which is an exciting statistic we're very proud of.

好的。那么这里正在发生一些大事。为什么这如此有价值？到底是怎么回事？简单总结一下你们的工作就是：你们帮助实验室招聘人员来训练他们的模型，不仅帮他们寻找通用劳动力，更是寻找专家来帮助解决模型知识中非常具体的空白。

Okay. So something big is happening here. Why is this so valuable? What is going on here? So it's just to try to summarize what you guys do simply is you help hire people for labs to train help them train their models, and you help them find not just generalist, labor, but experts helping them with very specific gaps in the model's knowledge.

是的，完全正确。这实际上与你最初关于评估时代的问题密切相关，这个框架贯穿了所有这一切——实验室提升模型能力的主要瓶颈在于如何有效衡量模型成功的标准，既要将其作为衡量进展的测试评估标准，也要作为强化学习环境中的验证器来奖励模型、提升能力等等。他们需要在每个领域、针对模型尚未掌握的每种能力都做到这一点。世界上最富有的公司愿意不惜一切代价提升模型能力，而Merkur正处在这一前沿，可以说是主要瓶颈所在。

Yeah, precisely. And so it really ties to your first question around the era of evals that's framing all of this, which is that the lab's primary bottleneck to being able to improve models is how they can effectively have some way of measuring what success looks like for the model, both to use it as the eval for the tests that they're measuring their progress against, as well as the verifiers in an RL environment to then reward the model, improve capabilities, etcetera. And they need this across every domain for every capability that models don't know how to use. And the wealthiest companies in the world are willing to spend whatever it takes to improve model capabilities where Merkur is sitting at the forefront, and sort of the primary bottleneck.

好的。这些人实际在做什么？能否举个例子说明什么样的人是被需要的，然后他们具体在做什么？比如就坐在电脑前工作吗？

Okay. What are these people actually doing? So what's an example of a kind of person that is sought after, and then what are they doing, like sitting there at the computer?

实际上，这个市场的边界在于人类能做而模型不能做的事情的数量。让我具体说明一下。假设你有一个模型，你希望它能像律师那样起草合同的红线条款，但它犯了一些错误，遗漏了许多关键点。你可以做的是让律师创建一个评分标准，就像教授创建评分标准来设定交付物要求一样，明确我们希望模型能够做到哪些事情，这样就能有效进行评分，对吧？比如，识别出这个要点加多少分，或者XYZ关键点等等。

Effectively, the market is bound by the amount of things where humans can do something that models can't. So I'll make that very concrete. Say you have a model that you want to write, like, a red line for a contract in the way that a lawyer would, and it makes a handful of mistakes, misses a bunch of key points in doing so. What you could do is have a lawyer create a rubric, similar to how a professor might create a rubric to create a deliverable for what are the things we want the model to be able to do, so it can effectively score that, right? Like, you know, plus however much of it identifies this, or, you know, XYZ key point.

这确实是衡量模型进展的基础。要知道，这个模型是否达到了这些专业人士期望的能力？同时我们如何将这些作为训练数据来奖励和强化人们希望模型实现的诸多能力。

And that's really the foundation to measuring what does progress look like for models. You know, is this model achieving the capabilities that these professionals want? As well as how do we use this as training data to reward and to reinforce a lot of the capabilities that people want models to achieve.

好的。所以他们本质上是在编写评估标准，这与我们最初的讨论联系起来了。

Okay. So they're essentially writing evals just to connect it back to original conversation.

正是如此。不过有趣的是，每个人都在谈论强化学习环境。我觉得两个热点话题就是强化学习环境和评估。但Andre Carpathi在推特上多次提到的一点是：其实在数据类型上并没有细微差别，更多只是描述其用途的不同语义方式。

Exactly. Well, that's an interesting thing is everyone talks about RL environment. I feel like the two hot button things are RL environments and evals. But one thing Andre Carpathi has tweeted out about a bunch is there's not actually a nuance in the data type. It's more just a different semantic way of describing what it's being used for.

但最终它只是一个衡量标准点，用来判断什么是好的表现。你可以像Sarah说的那样，将其作为销售材料的基准，说明为什么我们的模型是世界上最好的模型，以及我们一直在努力实现的能力。或者你可以在训练后阶段使用它来奖励某些模型轨迹并实现这些能力。

But ultimately it's just some stasis point for how do you measure what good looks like? And you can use that either as the benchmark to the sales collateral, as Sarah was saying, to say, here is why our model is the best model in the world, and here's the capabilities that we've been working towards. Or you can use it on the post training side to reward certain model trajectories and achieve those capabilities.

好的。那么假设这位律师，这个人在写：这是一份优秀的红线合同应该是什么样的，这是优秀的标准。然后他们是否也提供数据，比如实际的红线文件示例作为其中的一部分？

Okay. So say this lawyer, so this person is writing, here's what a great red line contract looks like, and here's the rubric of what excellent is. And then are they also providing data, like actual examples of Redline documents as a part of that?

他们可能会。历史上数据景观包括两种数据。第一种是监督微调数据，即输入输出。当人们从历史意义上思考微调时，就是这样。第二种是RHF，模型会生成几个示例，然后选择最受欢迎的示例。

They may. So the data landscape historically has included two kinds of data. The first is supervised fine tuning data, which is input output. When people think about fine tuning in the historical sense, that's what it is. The second is RHF, where the model will generate a couple of examples, will choose which is the most popular example.

大家普遍都在朝着从AI反馈而非人类反馈进行强化学习的方向发展，即人类定义某种成功标准，某种衡量方式。在代码示例中，可以是一个单元测试，对吧？我们可以规模化地衡量成功。在其他领域，可以是一个评分标准。然后你用这个来激励模型能力的发展。

What everyone is generally moving towards is reinforcement learning from AI feedback instead of human feedback, where you have instead the human defined some sort of success criteria, some way to measure that. And examples in code, it could be a unit test, right? We can scalably measure success. In other domains, it could be a rubric. And then you use that to incentivize model capabilities.

这种方式更具可扩展性和数据效率，这就是为什么市场上更广泛的趋势是全面转向RLAIF，既用于评估模型也用于提升能力。

And it's far more scalable and data efficient, and so that's why a lot of, you know, the broader trend in the market across the board is moving towards RLAIF to both eval models as well as improved capabilities.

我采访过Anthropic的一位联合创始人，他也说了完全相同的话。他们在Anthropic做的就是转向AI驱动的强化学习。所以本质上，如果我理解正确的话，作为外行代表观众来理解这个：律师就像是定义了红线修改的正确标准，然后AI几乎就是自主地尝试达到这个标准。

I had the one of the co founders of Anthropic on, he said exactly the same thing. That's what they've done at Anthropic is move towards AI driven reinforcement learning. So essentially, if I can understand this correctly, I'm the lay person here trying to understand this on behalf of the audience. So essentially, a lawyer is like, here's what correct looks like for redlining. And then it's AI is just on its own almost just like, here's all the I'm gonna try to get this.

我会尝试改进这个。而且我知道根据给我的这个评估标准，我是否在朝着正确的方向前进。

I'm gonna try to improve on this. And I know if I'm getting heading the right direction based on this evalrubric I've been given.

没错。应用所有优秀标准，就像助教可能会应用教授的标准来判断学生的回答是否符合这个标准或那个标准，加上多少分等等。

Exactly. Applying all of the criteria of what good looks like, similar to how, you know, the TA might apply the professor's criteria of does the, you know, student's response meet this criteria or this criteria, plus however many points, etcetera.

太棒了。好的。让我转向谈谈更广泛的劳动力市场。这个问题有两个部分。第一个是我们需要这样做多久？

Awesome. Okay. Let me shift to talking about the broader labor market here. So there's kind of two parts to this question as we talk about this. One is just how long will we need to do this?

是否存在我们不再需要的时刻？你们增长得如此之快。是否会有这样一个时刻：好吧，我们不需要了。人类已经达到极限了。让我们从这里开始，然后我会问一个更广泛的问题。

Is there a point where we don't need? Like you guys grew so incredibly fast. Is there a point of like, okay, we don't need. Humans are we're tapped out. So let's start there, and then I'll ask a broader question.

所以关键问题是，经济中人类能做而AI不能做的事情会持续多久。确实有一部分人认为，三年内我们将拥有超级智能，人类在经济中将不再扮演角色。这是一种观点。我们的视角非常不同。我们认为这些模型非常出色，能快速自动化很多事情，但也有很多事情它们做得非常糟糕。

So the key question is how long there's going to be things in the economy that humans can do that AI can't do. And I think there's certainly a bucket of people that say, we're going to have superintelligence within three years and humans won't play a role in the economy. And that's one school of thought. Our perspective is very different. Our perspective is that these models are extraordinary and automating a lot of things very quickly, but there's a lot of things that they're horrible at.

比如到现在，它还不能为我的日历安排时间，不能为我起草电子邮件，不能使用基本工具。我们需要对一切进行评估。对于模型不能做的每件事，我们需要工具使用评估、长时推理评估。

Like even still, it can't schedule time on my calendar. It can't draft emails for me. It can't use basic tools. And we need evals for everything. For everything that the models can't do, we need evals for the tool use, evals for the long horizon reasoning.

想象一下十年后，当我们希望模型能够出去用三十天时间创建一家初创公司。我们需要为此设计评估来有效奖励它。我认为改进模型的这条道路将持续到经济中任何人类能做而模型不能做的事情存在为止，这将构成未来工作的很大一部分。因此我们的使命是创造未来工作，我认为这是一个非常令人兴奋的行业，让我们得以窥见一切发展的方向。

Like imagine in ten years when we want models to be able to go out and build a startup for thirty days. Like we need evals for that to effectively reward it. And I think that that road to improving models, will last for as long as there is anything in the economy that humans can do which models can't, and be a huge portion of what the future of work looks like. And so our mission is creating the future of work, and I think that this is a really exciting industry in giving us a glimpse into the direction that everything is headed towards.

你转发的一条推文我想问问你。如果你仔细想想，我们被放在地球上是为了为实验室创造强化学习训练数据。是的。这对你意味着什么？这个人想表达什么？

There's a tweet that you retweeted that I want to ask you about. If you really think about it, we were put on earth to create reinforcement learning training data for labs. Yeah. What does that mean to you? What is this person implying?

基本上你的意思是，我们只是在帮助训练模型。

And it's basically what you're saying is we're just helping train models.

这呼应了我和TOP实验室许多研究人员及高管的对话，即整个经济极有可能变成一个强化学习环境机器，构建所有这些世界和情境，让我们随后拥有评分标准或其他类型的验证器。这在很多方面都令人非常兴奋。因为我认为可以类比其他革命，比如工业革命时期，每个人都担心失业。但那时出现了一整套全新的工作岗位：我们如何制造机器？我们如何进行知识工作？

It speaks to conversations I've had with a lot of researchers and executives at TOP Labs, which is that it's highly likely that the entire economy will become an RL environment machine, building out all of these, worlds and contexts for us to then have rubrics or other kinds of verifiers. That is really exciting in so many ways. Because I think let's draw analog to other revolutions where when we had the industrial revolution, everyone was freaking out about losing their jobs. But there was this whole new class of jobs of how do we build the machines? How do we have knowledge work?

我们如何创造一切新事物？我认为过去三年AI领域的叙事几乎完全集中在岗位替代上。ChatGPT发展迅速，非常酷，大家都爱用。但从经济角度来看，人们大量讨论岗位替代。但很少有公司和人员谈论正在被创造的新岗位类别，这意味着什么，以及人们如何为此做好准备和提升技能。

How do we create everything new? And I think that the narrative in AI over the last three years has almost entirely been one of job displacement. ChatGPT is growing fast and it's very cool and everyone loves using it. But from an economic standpoint, people talking a lot about job displacement. But very few companies and people have talked about this new category of jobs that's being created, and what that's going to mean and how people can prepare and upskill for that.

我认为最令人兴奋的可能性是创造那样的未来：人类如何融入经济，以及这将如何随时间演变。

And I think that the most exciting thing possible is creating that future of how do humans fit into the economy and how will that evolve over time.

我和很多人聊过，比如我该学什么？我该在哪些方面变得更好？现在在校的学生都在想，未来什么才是有价值的？你处于很多需求最大的职位以及招聘如何演变的核心。所以让我问你一个非常具体的问题。

I talk to a lot of people about just like, what should I be studying? Where should I be getting better? People in school right now are just like, what is even going to be valuable in the future? You're at the center of a lot of just what jobs are most in demand, how hiring is evolving. So let me just ask you a very concrete question.

你认为哪些工作在未来会保留？尤其是对年轻人来说，哪些技能仍然值得投入？

What jobs do you think will remain in the futurewhat skills are still worth investing in for younger people especially?

就工作而言，我的回答是需求弹性非常大的类别将会非常令人兴奋。因为当我们让人的生产力提高10倍时，我们将建造10倍甚至100倍的软件，举个例子，对吧？所以我认为那些现在能做更多事情的产品经理将会处于极其有利的位置。而且

In terms of jobs, I would respond with a category of things that have very elastic demand are going to be super exciting. Because when we make people 10 times more productive, we'll build 10 times, if not a 100 times as much software as an example, right? And so I think the product managers that can now do so much more are going to be extremely well positioned. And

所以

so

就技能而言，我认为关键在于人们能否利用AI来优化他们的日常工作流程。比如我曾与几位教师交流过，他们询问我对学生评估方式的看法，因为我们最初开始策划所有这些AI面试和评估方案时，对此进行了深入思考。我们意识到，不应该阻止他们使用这些模型。这有点像计算器刚出现时的情形——你不想布置一大堆算术作业，然后纠结如何让学生不用计算器完成。你应该告诉他们：使用工具，让我们看看你能做出什么。

far as the skills, I think it's people that can leverage AI to do whatever their day to day workflows are. Like I have had couple of conversations with teachers where they get my thoughts on how they should be assessing their students, because we originally started out curating all of these AI interviews and assessments for people and have thought about this immensely. And what we realized is that you don't want to fight against them using the models. It's sort of similar to when the calculator came out, you don't want to give people all of this arithmetic homework of how do you get them to do it and not use the calculator? You want to tell them, use the tools and let's see what you can do.

因此我们会设计这样的面试：使用ChatGPT和Codecs，使用Cloud Code，使用任何工具、光标以及所有可用工具来构建一个网站。让我们看看你在一小时内能打造出什么样的产品。我举这个人才评估的例子，是因为我认为这也关系到人们应该重点培养的技能——如何运用这项技术在自己所处的行业或领域实现更大成就。

And so we'll give people interviews where we say, use ChatGPT and Codecs, use Cloud Code, use whatever tool, cursor and whatever tools are available to build a website. And let's see what product you're able to build in an hour. And so I think that I give that example insofar as talent assessment, because I think it pertains also to the skills that people should be honing in on of how can they leverage this technology to do so much more in whatever industry or vertical they're operating in.

当你谈到弹性，具有弹性，是指通才擅长多种不同事物吗？你怎么说？当你思考弹性时具体指什么？

When you talk about elastic, being elastic, is it like generalists being good at just a bunch of different things? What do you say? What do mean when you think elastic?

我更指的是该行业的需求容量有多大。举几个例子：比如会计行业，现实地说，世界需要的会计服务就这么多，对吧？也许有些领域我们可以做得更多，这很好，但感觉世界并不需要增加100倍的会计服务。相反，在软件开发领域，我认为我们可以为产品发布100倍的功能，提速100倍，构建更多东西。

So I more mean how much capacity for demand there is in that industry. So I'll give a couple of examples. Like in accounting, I think realistically, we only need so much accounting in the world, right? Like maybe there's areas where we can do more and that'll be good, but it doesn't feel like the world needs a 100 times more accounting. On the other hand, in software development, right, like I think we can ship a 100 times more features for our products, move a 100 times faster, build so much more.

感觉这个行业的需求是无限的。马克·安德森最近发推谈到这一点，说软件是所有行业中最具弹性的——当我们提高生产力时，将会构建出更多东西。这显然也是许多其他领域的特点。因此我会重点关注那些领域：如果我们让每个人的效率提高10倍，需求会增加而不是减少。

There's just, it feels like there's unlimited demand for the industry. I think, Mark Andreessen tweeted about this recently that software is the most elastic industry of all, where when we increase productivity, there's so much more that will be built. And it's definitely characteristic of a lot of other domains as well. And so I would, I would focus on those domains where if we make everyone 10 times more productive, that'll increase demand, not reduce it.

好的。所以你属于认为学习编程仍然有用的阵营。学习计算机科学。那么在有弹性的职业类别中，听起来工程、产品管理都属于这一类。

Okay. So you're you're in the bucket of learn to code still useful as a skill. Take computer science. Okay. And so in terms of elastic categories of jobs, sounds like engineering, product management is in that bucket.

很好。很多听众都是产品经理。还有哪些方面，比如设计、用户研究，我不知道，根据你的观察，你觉得还有哪些属于这个范畴？

Great. A lot of people listening to this are PMs. What else, like design, user research, I don't know, what else do you feel is in that bucket from what you've seen?

是的，我认为在很多领域，整个公司建设的价值链都存在大量可变成本，甚至包括运营或咨询的大部分工作，对吧？想象一下如果我们能拥有10倍数量的麦肯锡顾问。我们在研究和分析等方面能实现什么可能性。但我认为那些能够成功的公司和个人，是那些拥抱这种丰裕叙事的人——我们如何做得更多，而不是抗拒它，试图阻止替代。

Yeah, think that there's a lot of things where the whole value chain of, building companies has a lot of these like variable costs, even large portions of operations or consulting, right? Like imagine if we could have 10 times as many McKinsey consultants. What would be possible insofar as the research we could do, the analysis, etcetera. But I think the companies and people that are going to succeed are those that lean into this narrative of abundance, of how do we do so much more, rather than fighting back against it of how do we try to stop displacement.

顺着这个思路，想想你的第二个类别，即那些将会最成功的人。这不是某种特定技能，而是擅长使用AI，利用AI变得更强，在你已经在做的事情上做得更好。这让我想起埃隆的Neuralink项目，我不确定他是否这样表述，但我一直听到的说法是：他之所以要打造Neuralink，是因为未来当通用人工智能和超级智能出现时，我们需要一种竞争方式，而最好的竞争方式就是将我们的大脑接入超级智能，这样我们才有机会。感觉这就是AI的意义所在，擅长使用AI工具本质上就是拥有这种

So along those lines, think about your second bucket, which is the people that will be most successful. It's not like a specific skill, but it's being good with AI, using AI to become more, become better at what you're already doing. This reminds me of Elon's whole thing with Neuralink, which I don't know if this is how he put it, the way I've always heard it is he wanted to build Neuralink because in the future when AGI and super intelligence is around, we need a way to compete and the best way to compete is plug our brains into a super intelligence so we have a chance. And it feels like that's what AI is, like getting good at AI tools is essentially, is having the

超级能力。弄清楚如何利用并整合它们绝对至关重要。

super Figuring out how to leverage them and incorporate it will definitely be of paramount importance.

是的，这又回到了现在几乎成陈词滥调的一句话：AI不会取代你，但擅长使用AI的人会取代你。

Yeah, just comes back to this almost cliche quote now. It's AI won't replace you. People that are really good with AI will replace you.

我认为这句话完全正确。在企业层面我也确实看到这种情况，有些我们接触的企业最为恐惧，不愿参与，不愿评估他们的业务，因为那会证明他们的价值链正在被自动化。而另一些企业，我是说，一些最知名、最成熟的财富500强企业，就持有这种心态。还有一些企业则积极拥抱它：我们有能力做10倍甚至100倍更多的事情，那意味着什么？我们如何迎接那个未来？

I think it's totally spot on. And I've definitely seen this at the enterprise level as well, where there are certain enterprises we talk to that are most fearful, not wanting to engage, not wanting to eval their businesses because that'll provide the evidence that their value chain is being automated. There's others that, I mean, some of the most recognized sophisticated fortune 500 businesses that, that have this mentality. And there's others that are leaning into it of, we have the ability to do 10 or a 100 times more, what will that mean? And how do we lean into that future?

因为未来十年将发生太多变化。我认为这些才是将会成功的企业类型。

Because there's so many things that are gonna change over the next ten years. And I think those are the kinds of businesses that are gonna be successful.

让我们更广泛地谈谈劳动力市场。你们这些人很有趣，虽然你们最初并不是为AI实验室提供数据来训练模型，而是帮助人们找工作、帮助企业招聘，然后你们发现，哇，这整个机会。你们对劳动力市场和招聘的未来有着非常独特的见解。

Let's talk about labor markets more broadly. You you guys so it's interesting, though. You started not feeding people to AI labs, training models. It was just like help people find jobs, help companies hire, and then you're like, oh wow, this whole opportunity. You have this really interesting view on the future of just labor markets and hiring.

详细说说这个。

Talk about that.

是的，这很有趣。我记得我们创办公司时，正如我提到的，我们才19岁，只是有一种直觉，觉得劳动力市场如此分散，效率极低。我的意思是，当我们跨国招聘时，求职者会申请十几份工作；而我们作为湾区公司，只会考虑市场上可用人才中极小的一部分。原因在于每个人都在手动解决这个匹配问题——手动筛选简历、手动面试、手动决定聘用谁。

Yeah, it's interesting. I remember when we started the company, as I mentioned, we were 19 and just had this gut intuition that it felt so wildly inefficient that labor markets are so disaggregated. And what I mean by that is when we would hire someone internationally, they would apply to a dozen jobs. When we, as a company in The Bay Area, were considering candidates, we would consider a fraction of a percent of candidates that were available in the market. And the reason for that is that there is this matching problem that everyone's solving manually, where they'll manually review resumes, they'll manually conduct interviews, and manually decide who to hire.

但当我们能够以软件成本自动化这个匹配问题时，就为全球统一的劳动力市场铺平了道路——所有求职者在这里申请，所有企业从这里招聘，促进经济中信息的完美流动。我认为这无疑是我们正在迈向的未来。但随着时间推移，我们意识到工作的性质也在急剧变化。构建这个未来的一部分，是在十年时间跨度内创造未来的工作形态，包括我们为客户构建评估体系和强化学习环境等更具体的措施，积累这些惊人的数据集。

But when we're able to automate that matching problem at the cost of software, it makes way for this global unified labor market that every candidate applies to and every company hires from facilitating a perfect flow of information in the economy. And I think that that future is undoubtedly what we're heading towards. But what we've realized over time is that the nature of work is also changing dramatically. And part of building that future over a ten year time horizon is creating that future of work. And all of the more tactical things we do and building these incredible data sets across evals and RL environments for our customers.

我从招聘方式的变化中观察到——我正在与合作伙伴Gnome研究这个现象——现在申请工作变得如此容易，以至于每个人都在向数百家公司投递简历。AI让人们能轻松调整简历和求职信，给人一种‘我针对性地申请了更多职位’的感觉，但实际上只是海投之一。另一方面，招聘经理被申请淹没，现在他们需要AI来筛选。所以即使我们不想走到这一步，也几乎是被推着向这个方向前进——双方都面临巨大流量，我们需要真正智能的系统来筛选并帮助我们招聘和选择。

What I've seen in how hiring has changed, I'm doing research on this with a partner Gnome, It's so much easier to apply for companies that everyone's just applying out to hundreds of companies. AI is just making it easy to adjust their resumes and cover letters and make it feel like, oh, I applied to more courses very specifically, but it was one of a 100 places. And then on the flip side, hiring managers are getting flooded with applications. And so now they need AI to filter. So even if we didn't want to get to this place, we're almost being pushed into this direction of so much volume on both sides, and we need something really smart at filtering and helping us hire and select.

而这正是你们长期以来一直在构建的东西。

And this is exactly what you guys have been building for a long time.

完全正确。有趣的是，很多人问：我们认为自己是劳动力市场平台还是数据公司？我认为这个问题有意思的原因在于，我们从实验室的需求中意识到，他们实际上需要一个劳动力市场平台，他们真正需要的是这些异常高素质的人才。

Precisely. Yeah. And the fascinating thing, like a lot of people ask, do we think about ourselves as a labor marketplace or do we think about ourselves as a data company? And I think that the reason it's an interesting question is our realization from what the labs need is that they actually need a labor marketplace. They actually need these exceptionally high caliber people.

当然，我们还会叠加一些项目管理和相关的软件平台。但他们真正想要的核心是，如何找到这些跨领域的杰出专业人士，能够衡量模型能力并共同努力构建未来的工作。

And of course, we'll layer on some project management and some software platform associated with it. But the really core thing that they want is how do they find these extraordinary professionals across all of these different domains that can measure model capabilities and work to build that future work together.

本节目由Interpret赞助播出。Interpret是一个客户智能平台，被Canva、Notion、Perplexity、Strava、Hinge和Linear等领先的CXN产品团队使用，以利用客户之声打造一流产品。Interpret实时统一所有客户对话——从Gong录音到Zendesk工单再到Twitter讨论串——并让您的团队能够进行分析和采取行动。Interpret的独特之处在于它能构建和更新客户专属知识图谱，提供最精细准确的客户反馈分类，并将这些反馈与收入、CSAT等关键指标关联。如果将您的客户之声项目现代化升级为代际革新是2025年的重点，就像以客户为中心的行业领导者Canva、Notion、Perplexity和Linear那样，请联系interpret.com/Lenny团队。

This episode is brought to you by Interpret. Interpret is a customer intelligence platform used by a leading CXN product orgs like Canva, Notion, Perplexity, Strava, Hinge, and Linear to leverage the voice of the customer and build best in class products. Interpret unifies all customer conversations in real time from Gong recordings to Zendesk tickets to Twitter threads and makes it available for your team for analysis and for action. What makes Interpret unique is its ability to build and update a customer specific knowledge graph that provides the most granular and accurate categorization of all customer feedback and connects that customer feedback to critical metrics like revenue and CSAT. If modernizing your voice of customer program to a generational upgrade is a 2025 priority, like customer centric industry leaders like Canva, Notion, Perplexity, and Linear, reach out to the team at interpret dot com slash Lenny.

网址是interpret.com/Lenny。回到这一切如何运作以及你们为模型所做的工作，我有个朋友脚踝扭伤或者说脚部疼痛，他拍了X光片，然后把X光片输入Chachi PT，然后问它，比如‘给我分析这张具体的X光片’，它就说‘好的，没问题’。然后它给出了诊断结果‘这是你的问题’。他跟我聊天时说，‘互联网上有什么资料可以训练这个模型懂得这些知识？’

That's e n t e r p r e t dot com slash Lenny. Going back to just how this all works and what you guys do for models, I was talking to a friend who had an ankle sprain or her his foot was hurting, and he got an x-ray, and he fed the x-ray into Chachi PT and then asked him, like, give me this specific x-ray, and he's like, okay. Sure. And then he gave him, here's what you have. And he was talking to me, he's like, what is out there on the internet to train this model to know this stuff?

我就说，不，实际上是有真人坐在那里帮助模型理解这些，一旦他们发现模型没有完全掌握这些知识。实际上是人类在帮助它们学习这些东西。

And I was like, no, it's actually somebody sitting there helping the model understand this once they recognize it doesn't fully understand this. Like humans are actually helping them learn these things.

没错。运作方式是这样的，至少大多数人的理解是：模型运作中有很多复杂性，预训练将大量关于世间万物的知识注入模型，而后训练和强化学习则负责所有推理工作——哪些知识准确、哪些不准确，以及在任何给定时刻优先考虑哪些来做出决策。在这背后，会有放射科医生参与后训练数据集的工作，建立某种基准点：这是诊断结果，以及相关的奖励和惩罚机制。最终ChatGPT做出的决策和建议质量，实际上取决于这些参与人员的素质。

Exactly. Well, so the way it works, at least what most people's understanding is there's a lot of complexity in how the models work, that pre training gets a lot of the knowledge into the model of what are all the different things that sort of see it in the world, and then post training and reinforcement learning is for all of the reasoning of what are the pieces of knowledge that are accurate, what are inaccurate, and what to prioritize, at any given time to make a decision. And so behind that, there would have been radiologists that, worked on the post training data set to create some stasis point for here's the diagnosis and rewards and penalties associated with it. And it's really the quality of those people that went into the quality of the decision and recommendation that ChatGPT ultimately made.

那么让我们顺着这个思路继续，因为这真的很有趣，而且我不知道有多少人理解这一点，我算是有点了解。所以你们和这些专家做的工作是后训练。不是向模型输入训练数据，而是我们已经有了这个模型，比如GPT-5。现在这里列出了它所有缺失的能力。

So let's actually follow that thread, because that's really interesting, and I don't know how many people understand it, I sort of understand it. So the work that you do and these experts do is post training. It's not feeding data into the model that it's trained on. It's we have this model, GPT-five. Now here's all the things it's missing.

让我们来完善它。

Let's add to it.

确实如此。是的。这真正实现了解锁，让模型能够专注于预训练中的所有正确标记和模型上下文中的所有正确内容，升级有效的推理链，使模型能够以更通用的方式进行更好的推理。

Exactly. Yeah. It's really unlocking, allowing the model to focus on all the right tokens from pre training all the right things in model context, upgrading the effective reasoning chains, to enable the models to reason better in a more generalized way.

从事这方面工作的人员规模有多大？是数千人、数万人还是数十万人？

What's the scale of people just working on this stuff? It like thousands, tens of thousands, hundreds of thousands?

在任何特定时间都有数万人，更普遍地说有数十万人。我的意思是，规模非常庞大。最令人兴奋的是它正在快速增长。关于你提到的竞争格局问题，历史上有很多众包公司会招募大量低技能人员。我认为Scale和Surge是开创这个行业的主要公司。

Tens of thousands at any given time, hundreds of thousands more generally. I mean, it's huge. And the most exciting thing is that it's growing really quickly. I mean, I think that to your question also about the competitive landscape, historically there were all these crowdsourcing companies that would get these super high volumes of low skilled people. I think like Scale and Surge were the primary companies that pioneered that industry.

然后在这种向高技能劳动力转型的过程中，人们意识到实际上通过招募更高素质的人才，即使最初数量较少，也能取得更大进展，现在在达到质量标准后重新扩大规模。我认为在我们成功和去年初开始的快速收入增长之后，有很多公司开始追随这个方向，这是有道理的，对吧？看到市场变化很快，我们起飞了并试图在市场上追求类似的理念。

And then in this transition to higher skilled labor, what people realized is that actually you can go a lot further with just getting higher caliber people, even in smaller amounts initially, and now subsequently scaling that back up once they're able to meet the quality bar. I think that there's a bunch of companies that after our success and very rapid revenue growth that sort of started early last year have chased after that, which makes sense, right? And seeing that the market was changing very quickly, we were taking off and trying to pursue a similar thesis on the market.

这很有趣。一直都有这样的公司，比如AlphaSight和GLG，在AI之前就做类似的事情，或者说是付费连接专家并向他们提问。本质上，事实证明这对模型非常有用。我们不需要中间人。

It's interesting. There's always been these companies, AlphaSight and, GLG, that like sort of did this before AI, or is like paid to connect to an expert and ask them questions about stuff. And essentially, okay, it turns out this is really useful for models. We don't need the person in the middle.

完全正确。是的。一个核心区别是，alpha sites通常是一次性通话，而我们的很多工作实际上是雇佣人员参与项目，对吧？他们如何长期从事某项工作？我认为这就是一些传统专家网络难以进入这个领域的原因之一。

Exactly. Yeah. Well, the one core difference is that alpha sites would generally be a one off call versus a lot of our work is really hiring people for projects, right? Of how do they work on something for a longer period of time? And so that's, I think, one of the reasons that some of the traditional expert networks have struggled to get into this.

此外，如何留住这些人并考虑所有激励机制，在某些方面实际上更类似于Uber或DoorDash这样的传统劳动力市场，只是技能水平更高的人才得到了特别好的待遇。

And also, how do you retain those people and think about all the incentives where it actually looks more similar in some ways to one of the traditional labor marketplaces of an Uber or DoorDash, just with much higher skilled talent that's treated exceptionally well.

这对我来说是个绝佳的学习机会。我正好想问些问题。是的，当然。这太有趣了。专家们有多少精力是集中在具体专业知识上，又有多少是关注个性和软技能方面？

It's such a good opportunity for me to learn so much about this. I'm just going to questions. Yeah, sure. It's so interesting to me. How much of the experts are focused on specific concrete knowledge versus personality and like softer skills?

有多少是像'这是做检查的方法'、'这是拍X光片的方法'这样的内容？

How much of it is like, here's how you do an exam, here's how you do an x-ray?

这取决于实验室。两方面都很多。我认为以前可能更侧重软技能，但现在很多实验室都专注于他们的商业模式，即

It depends on the lab. It's a lot of both. I think that previously it might've been more softer skills, but now a lot of the labs are focused on their business models of what

是

are

那些能够创造收入、具有经济价值的核心能力，并且大量倾注于这些专业领域。但我认为创意方面对每个人来说仍然非常重要。所以我们看到两者都有相当的分量。比如几个月前我们聘请了哈佛讽刺文社的所有成员，他们的喜剧俱乐部来帮助让模型变得更有趣。我们还做了各种类似的事情，聘请艾美奖获奖编剧，以及在创意能力方面全方位寻找人才。

the economically valuable capabilities that drive revenue and lean a lot into these professional domains. But I think the creative side is also still really important to everyone. And so we're seeing a meaningful amount of both. Like we hired all the people from the Harvard Lampoon a couple months ago, their comedy club to help with making models funnier. And so do all sorts of stuff like that, hiring Emmy award winning screenwriters and, everything across the board on creative capabilities that you'd look for.

太棒了。真是个很酷的故事。是的。我很期待这个开始见效。这些事情的转变速度有多快？

That is amazing. What a cool story. Yeah. I'm excited for this to kick in. How fast do these things turn around?

比如说，你们聘请了这个团队，我们大概多久能看到效果？是几个月？还是几年？

Like, say you hired this team, like, fast are we gonna see the impact potentially? Is it like months? Is it years?

嗯，这要看情况，因为有些模型或实验室会迭代式发布，他们只是在后台不断改进模型，基本上是每

Well, so it depends because some models, or some labs will release iteratively where they'll just improve the model behind the scenes, sort of every

而不宣布新模型。

Without announcing a new model.

没错，基本上是每隔几周改进一次，而其他公司则会进行大型发布。所以这很大程度上取决于具体情况。我们虽然落后于所有这些公司，但我们的速度非常快。比如客户给我们一个需求，说需要这些获奖编剧，我们能在24小时内就找到专家并完成交付。

Exactly, as sort of every couple of weeks versus others do these big releases. And so it depends a lot. We're behind all of them, but the, I mean, we move really fast. It would be a customer gives us a request of we need these, you know, award winning screenwriters. And within twenty four hours, we'll turn around, the experts.

还有一个非常有趣的现象：在我们招聘的100人中，通常前10%的人会推动模型的大部分改进。这就像一家公司，对吧？如果你有100人的公司，通常前10%的员工会产生大部分影响。这意味着，当我们能够在识别这前10%的人才方面建立专有优势——既包括如何让他们加入我们的平台，也包括如何有效识别和匹配他们——这就能为客户创造巨大价值，让竞争对手难以匹敌。这实际上又回到了公司的创立理念：如何找到这些非凡的人才并识别他们，从而能够可靠地为客户提供前10%或10倍顶级的体验。

And there's also this really interesting dynamic where in a set of a 100 people that we hire, oftentimes the top 10% of people will drive majority of the model improvement. It's sort of like a company, right? If you have a 100% company, oftentimes the top 10% of the company, will drive majority of the impact. And what that means is that when we're able to build proprietary advantages in identifying who are those top 10% of people, both insofar as how do we have them on our platform, but also identify and match them effectively, it creates so much value for customers that it's difficult to compete against. And so it really does tie back to the founding thesis of the company, which is like, you know, how do we find these extraordinary people and identify them so that we can reliably deliver these top 10% or top 10x experiences for our customers.

那么关于这一点，想法是你们雇佣Jane，她编码能力惊人，现在她为Anthropic工作，这是她的全职工作，还是这更像兼职，主要是项目制的？

So on that, so is the idea you hire Jane, she's incredible at coding, and she now works for Anthropic, and that's her full time job doing this, or is this like a part time thing, is this a project thing mostly?

有时是兼职，有时是全职。我认为大多数情况下是兼职，比如有人可能在发展较慢的公司工作，未被充分雇用，每周有额外的20小时，然后他们可以兼职做这个，或者在不同行业中有类似的情况。但我们也有很多每周40小时的全职角色。

It would sometimes be part time, sometimes it would be full time. I would say most often it's part time, where it's like, you know, someone might work at fan company where they're underemployed, maybe one of the ones that's moving slower, where they have an extra twenty hours a week, and then they're able to do this on the side, or, you know, whatever the equivalent is sort of across a bunch of different industries. But we also do a lot of, you know, forty hour a week, roles as well.

那这些人赚多少钱？是否足够有意义，让FENG工程师愿意花时间在这上面？

And these, how much are they making? Is it like, like meaningful enough for FENG engineers to spend time on this?

是的，非常有意义。我的意思是，我们市场上的中位数薪酬是每小时95美元，但根据专业知识的深度，可以灵活上涨到每小时500美元左右。与许多众包公司相比，突显这一差异的一点是，如果你看看众包公司的经济模式，他们通常平均支付给Talon之类的公司大约是每小时30美元。那么想想你能以30美元雇佣的人——本科生，对比高盛银行家、麦肯锡分析师、FANG软件工程师。最终归结为实验室希望他们的模型具备哪些能力？

Yeah, very meaningful. I mean, so our median pay rate in the marketplace is $95 an hour, but it can flex up well up into like $500 an hour based on the depth of someone's expertise. And one thing that highlights this difference relative to a lot of the crowdsourcing companies is if you look at the economics of the crowdsourcing companies, oftentimes they would pay like $30 an hour to Talon as sort of the average. And so think about the people that you can hire, the undergrads for $30 now versus the Goldman bankers, the McKinsey analysts, the, FANG software engineers. And ultimately it comes down to what are the capabilities that labs want their models to have?

而且它更倾向于后者而非前者。

And it much more falls in the latter bucket than the former one.

我知道关于这些事情能谈的有限，但Anthropic的Claude在编码方面一直表现得非常出色，历史上比其他模型好得多。我也用它来写作，提供写作反馈。是什么让他们在这方面如此出色并能持续保持优势？

I know there's only so much you can talk about with this stuff, but so Anthropic Claude has been so good at coding, so much better historically than other models. I also use it for writing, giving feedback on writing. What is it that allowed them to get so good at this and continue to be so good at this?

嗯，我不能过多透露客户工作的细节，但我认为这是强化学习的趋势，以及非常谨慎地定义正确的奖励，这一点我们在各个领域都看到了，如何减轻奖励黑客行为、设置正确的奖励，这非常有影响力。

Well I can't go too much into detail about customer work, but I think that it's this trend of reinforcement learning and being very thoughtful about defining the right rewards that we're really seeing across the board, and how we can mitigate reward hacking, set up the right rewards, that's super impactful.

评估，再次强调，回归评估就是一切。

Evals, again, Back evals is all you to evals.

客户说过我最喜欢的一句话是：模型的好坏完全取决于它们的评估，这一直是真理。

One of my favorite quotes from customers is that models are only as good as their evals, which has always held true.

我记得Greg Brockman曾经发推说过，评估就是一切。

I think Greg Brockman tweeted this once, evals are all you need.

是的，不，真的。

Yeah, no, truly.

那么，再谈谈马克·戈特吧。也许甚至不是也许。我相信数据告诉我们，这是历史上增长最快的公司。

Well, talk about Mark Gort a little bit more. One of the maybe not even maybe. I believe the data tells us it's the fastest growing company in history.

是的。

Yeah.

我想了解你做了什么来实现这一点。所以让我直接问，你认为你建立Morcord的一些核心原则是什么，这些原则对取得如此成功贡献最大？

I wanna understand what you did to make this happen. So let me just ask, what do you think are some of the core tenets of how you built Morcord that most contributed to being this successful?

我认为最重要的是关注快速变化市场中的领先指标。我记得以前在风投领域，每个人都在谈论为什么是现在。我过去更多是从产品角度思考为什么是现在，而不是从市场角度，比如现在我们可以自动化简历审查或面试等方式。但最终，这个传统市场有所有这些现有参与者且相对停滞。真正重要的是找出哪些是新市场、哪些是需求快速变化的新领域，世界上最富有的客户愿意不惜一切代价来提升模型能力。

I think the most important thing is looking at the leading indicators in fast moving markets. Like I remember when I used to think everyone in venture talks about the why now. And I used to think about the why now of how from a product standpoint, less from a market standpoint of like, now we can automate the way that we review resumes or the way that we conduct interviews, etcetera. But ultimately, there is this legacy market that has all these incumbents and is relatively stagnant. What matters a ton is actually figuring out what are the new markets, the new pockets of demand that are changing very quickly, where the wealthiest customers in the world are willing to pay whatever it takes to improve model capabilities.

我们如何关注这些市场的领先指标，确保我们为市场中的旗舰客户提供最佳解决方案，并围绕这一点优化一切？我发现这在业务建设中影响最大。也许这是一点，就是市场中的领先指标。如果必须选择另一点，那就是客户痴迷。过去一年半，我们开始有几位产品经理帮助上市，但业务上没有任何销售和营销人员。

And how do we focus on the leading indicators of those markets to make sure that we have the best solution for the flagship customers in the market and optimize everything around that? And that's what I found has been most impactful in building the business. I think that's, maybe that's one thing is like leading indicators in markets. If I had to choose another, it's customer obsession. Like we have had for the last, we're starting to like have a couple of, product managers help out with go to market, but like for the last year and a half of the business, we've had no one in sales and marketing.

所以从销售和营销的角度看，我们有点不成熟，因为我们把100%的公司资源集中在如何为客户打造优秀的产品和体验上。仅靠口碑，在其他企业与我们合作过的人希望继续合作，并致力于创造那些卓越的体验。这就是我花费所有时间的地方。我认为一些创始人可能会陷入如何在他们真正找到驱动大量客户喜爱并创造你习惯打造的六星级体验之前，就非常擅长营销。

And so we're sort of like immature from a sales and marketing standpoint because we've focused a 100% of company resources on how do we build great products and experiences for our customers. Just getting word-of-mouth, people that have worked with us at other businesses want to keep working with us and leaning into creating those great experiences. And so that's where I spend all my time. I think that some founders can get caught up in like, how do they get really good at marketing before they've figured out the thing that really drives a lot of customer love and creates the six star experiences that you're used to building.

我想回到第一个观点，就是，好吧，你发现了这个机遇，可能是历史上最大的商业机会。你最初是怎么发现的，那个时刻是怎样的，让你觉得这可能，这可能真的很大？

I'm gonna go back to that first point, which is like, okay, you found this pocket, maybe the biggest business opportunity in history. How did you first find, what was that moment of like, this could be, this could be really big?

这里有一些疯狂的故事。我记得我们是在2023年1月创办的公司，正如我提到的，然后在2023年8月，当时我还在上大学，我们的一位客户通过Zoom电话把我们介绍给了xAI的联合创始人，说我们拥有这些非常聪明的印度软件工程师，他们擅长数学和编程。所以我们见了面，我们解释说我们的软件工程师之所以在数学和编程上非常出色，是因为他们不受所有人文学科的干扰。他们不必学习历史、英语等等其他东西，而且他们热爱这个。所以他们两天后就邀请我们去了特斯拉办公室，我们见到了整个XAI联合创始团队，除了埃隆，而当时我还是一名大学生。

So there's some crazy stories here. I remember we started the company, as I mentioned, in January 2023, and then in August 2023, when I was still in college, one of our customers introduced us to the co founders of xAI over a Zoom call, saying how we had these really smart Indian software engineers that were great at math and coding. So we met them and we explained how the software engineers we had were really good at math and coding because they weren't distracted by all the humanities. They didn't have to study history and English and all these other things, and they loved it. So they had us in, two days later to the Tesla office, and we met the entire XAI co founding team, except for Elon, while he was still a college student.

那时XAI才刚刚起步，他们对我们专注于专家质量的做法超级兴奋。所以当他们还在进行预训练时，当时还没准备好接收人类数据，我们那时也没有开始与他们合作。但从那时起，甚至在我们辍学之前，我们就知道市场即将发生根本性变化，我们需要站在那个前沿。然后快进几个月，一家众包公司找到我们，实际上使用我们的平台雇佣了一千多人，这是一次非常有趣的经历，因为我们开始被关于那些人没有拿到工资的支持工单淹没。我们显然感觉非常糟糕，因为我们把他们推荐给了这个机会。

XAI was just getting started at that point, and they were super excited about our focus on the quality of the experts. And so while they were still doing pre training, they weren't ready for human data at the time and we didn't, start working with them at that point. We just knew from that point forward, before we even dropped out, that the market was about to change radically and we needed to be at the frontier of that. And so then fast forward a few months, one of the crowdsourcing players came to us and actually used our platform to hire over a thousand people, where it was this very interesting experience because we started getting flooded with support tickets about how those people weren't getting paid. And we obviously felt horrible because we had referred them to this opportunity.

那是一家信誉良好的公司。我们意识到，许多现有企业在为他们市场中的专家创造体验以帮助改进模型方面，所需要的东西上，都在吃老本。这里有一个机会，可以直接与实验室合作，以一种保持市场专家尊严、支付极高报酬、并某种程度上剔除中间商的方式。于是我们在五月份开始这样做，然后剩下的就是历史了。

It was this reputable company. And we realized that a lot of the incumbents were resting on their laurels with respect to what was needed in the experiences they were creating for talent in their marketplaces to help improve models. There was this opportunity to work directly with the labs in a way that kept the dignity of the experts in the marketplace, paid them extremely well, and sort of cut out the middlemen. And so we started doing that in May, and then the the rest is history.

哇。从那以后收入达到了数亿美元。所以我在这里听到的是，你非常乐于寻找。你看到了一些拉力。你探索了它。然后一旦你发现那里有真正有意义的东西，就深入下去，尽可能地把它打造成一种不可思议的、极致的体验。

Wow. Hundreds of millions of dollars in revenue since. So what I'm hearing here is you're very open to looking for You saw some poll. Pull, you explored it. And then once you saw that there was something really meaningful there, just went deep on making that an incredible experience as amazing as possible.

没错。如果我要把它提炼成给创始人的建议，我意识到的一点是，我花了很多时间试图强行实现产品市场契合。在某种程度上，你应该坚持。应该对那些你坚信世界将如何改变的论点抱有信念。但有时你只需要从市场中听到它，并且知道它就在那里，那种拉力，从而知道应该关注哪些正确的地方。

Exactly. I think if I had to distill it into advice for founders, one thing I've realized is that I spent a lot of time trying to force product market fit. And in some ways, you should be persistent. Should have these theses that you have conviction about how the world will change. But sometimes you just need to hear it from the market, and like know that it's there, the pull, to know the right places to focus.

因为如果销售很困难，如果争取边际客户极其困难，你就不可能发展出一个巨大的企业。你真正需要找到的是那些出乎意料容易销售的客户，你将能够与他们一起成长。你知道那是一个巨大的痛点。所以这需要一些结合：在关于世界将如何改变的论点上固执己见，但同时在对具体采取什么形式、市场如何发展、以及你的公司将如何融入其中方面，保持非常开放的心态。

Cause if it's difficult to sell, if it's extremely difficult to sell the marginal customer, you're not going to be able to grow a huge business. What you actually need to find is the customer that's surprisingly easy to sell into, where you're going to be able to grow with them. You know that it's a large pain point. And so it's some combination of being stubborn with respect to your thesis around how the world will change, but also very open minded with respect to exactly what form that takes, and how the market's developing, and how your company will fit into it.

这真是一个了不起的洞察。在你描述的那些时刻，感觉就像是xAI会议带来的双重冲击：一方面惊叹'哇，他们真的非常非常想要我们拥有的这个东西'，另一方面意识到'我们做得并不够好'，然后又是上千人争相加入平台。是这两个时刻让你产生'哇'的震撼感吗？

That's an amazing insight. In the moments you described, it felt like it was a combination of this xAI meeting feeling like, oh wow, they really, really want this thing that we sort of have. We're not doing an amazing job. And then it's a thousand people hiring the platform. Was it those two moments that are like, wow.

完全正确。而且需要记住，这些发生时我们还只是一家种子期公司。第一个时刻甚至发生在我们获得种子轮融资之前，那时我们完全靠自筹资金运营。因为我们通过自筹资金将公司做到了百万美元年收入规模，并且始终保持极高的资本效率。

Exactly. And those happened, keep in mind while we were a seed company. Right? Well, so the first one was before we even raised eddy seed funding, we were totally bootstrapped. Cause we bootstrapped the company to a million dollar revenue run rate, and have always remained super capital efficient.

我们从未烧过钱，一直保持盈利。然后在九月份我们从General Catalyst获得了种子轮融资，融资后的另一个经历让我们真正认识到这个市场存在巨大的需求——我们看到了交易量，也看到现有参与者对市场变化和实现这些变化所需人才类型的态度近乎沉睡。

Like we've, we've never burned money. We vote, we're lifetime profitable. And then in, we raised our seed round in September from general catalyst, and it was the other experience after we raised our seed round where we really knew that there was an enormous amount of demand in this market, where we saw the volume, right? And we saw that the incumbents were sort of sleeping with respect to how the market was changing and the kinds of people that were needed to make that change happen.

看到机会并开始执行是一回事，真正在这种规模上取得成功并持续获胜是另一回事。你们在业务中有着非常具体的价值观，谈谈这些吧。感觉这也是你们成功的重要因素。

It's one thing to see this opportunity and start to execute on it, it's another to actually succeed at this scale and consistently win. You guys have very specific values within the business. Talk about those. It feels like that's a big part of your success too.

确实如此。所以我将分享这三个原则，并简要讲述每个原则背后的故事。第一个是保持积极进取的态度，这一点有时会让我有点为难，因为它听起来有点老套，但我们总是设定这些极其雄心勃勃的目标，然后公司的轨迹就会围绕着这些目标形成。我记得在与Benchmark洽谈A轮融资前，我们的年运行收入是150万美元。而我当时说年底要达到5000万美元的运行收入。

It totally is. So I'll give the three and maybe a brief story associated with each of them. So the first one is having a can do attitude, which ever gives me a little bit of a hard time for because it's sort of a funny saying, but we've always set these ridiculously ambitious goals and then somehow the trajectory of the company forms around those goals. Where I remember when we were talking to Benchmark, before they led our Series A, we were at 1,500,000.0 in run rate. And I said we'd be at 50,000,000 in run rate by the end of the year.

他们说我们完全疯了，对吧？任何人都会这么想。但误差不超过两周，我们真的做到了。现在我们已经远远超过了最初为本年度设定的5亿美元运行收入目标。

And they said we were absolutely insane, right? As anyone would. And plus or minus two weeks, we hit it. Right? And then we've now well blown past, you know, the tracking to 500,000,000 in run rate, which was initially our goal for this year.

因此，在业务收入规模、人才体验水准等所有维度上设定这些极其雄心勃勃的目标，首先具备一种'能做到'的态度至关重要。第二点是真正的高标准，这体现在我们雇佣谁以及对他们的期望上。我们设立了极高的招聘门槛，雇佣了大量前创始人以及拥有非凡经验的人才。我们最近聘请或与桑迪普·贾恩合作，他作为总裁加入我们。他此前是优步的首席产品官和首席技术官，加入我们这家在宏大格局中相对较小的公司，帮助扩展所有流程——而优步当然是世界上最大的劳动力市场。

So setting these incredibly ambitious goals with respect to the revenue scale of the business, the caliber of experiences for talent, all those dimensions is super important to first have a can do attitude. The second thing is really high standards, which is who we hire and what we expect of them. We have an incredibly high hiring bar where we hire tons of former founders, people that have incredible experiences. We just hired or partnered with Sandeep Jain, who joined us as president. He was previously the chief product officer and chief technology officer at Uber, and joined our relatively small in the grand scheme of things company to help scale up all the processes where Uber is of course the largest labor marketplace in the world.

所以超高标准至关重要。我们真正非常倚重的第三点是强度。如果你看看那些传奇企业的早期文化，比如Meta、谷歌，它们都有这种令人难以置信的、高强度早期文化，人们竭尽全力、不惜一切代价推动模型能力的前沿。因此仍然非常以产出为导向，关注人们取得了什么成就，而不是以投入为导向关注具体工作时间，但要认识到，打造一家传奇企业需要付出很多，而这正是我们最终优化的目标。

So super high standards is of paramount importance. And then the third one that we really lean on significantly is intensity. And that if you look at the early cultures of businesses, of the legendary companies, thinking of Meta, Google, they have these incredible, intense early stage cultures of people just moving heaven and earth and doing whatever it takes to push the frontier of model capabilities. And so still very much output oriented of what do people achieve rather than input oriented of the specific hours they work, but recognizing that, it takes a lot to build a legendary business, and that's ultimately what we're optimizing for.

我能理解为什么这行得通。能做到的态度加上高标准加上高强度。我能看到这如何带来成功。现在有很多关于996文化的讨论，每周工作六天，早9点到晚9点。你知道，很多人都在问，为什么？

I could see why this works. Can do attitude plus high standards plus intensity. I could see how that leads to success. There's a lot of talk these days about this six ninety nine culture, working six days a week, 9AM to 9PM. You know, a lot of people are like, why?

这太糟糕了。为什么要让人们这样做？但与此同时，我不断从最成功的人工智能公司听到这种说法。这就是成功的必经之路。事情发展得太快了。

That's terrible. Why would you make people do that? But at the same time, I'm just constantly hearing this from the most successful AI companies. This is just the way it is to be successful. Things are moving so fast.

这是一个你再也见不到的机会。谈谈你对这个的看法吧。

This is an opportunity you'll never see again. Just talk about your thoughts on that.

是的，澄清一下，我们从不强制要求。这更多是关心企业发展的副产品，我们非常关心企业的轨迹。所以很多人来办公室工作到很晚，但如果他们需要早点离开陪孩子吃饭或周末旅行，当然完全没问题。对我们来说，更重要的是找到那些有高度主人翁精神并真正投入的人，而不是具体的办公室工作时间，尽管我们发现通常最投入的人——不总是但经常是那些最投入的人——会和我们一起挑灯夜战。

Yeah, well to clarify, we don't, we've never mandated ours. It's more been a byproduct of people that care a lot, where we care a lot about the trajectory of the business. And so a lot of people come into the office and stay late, but if they need to leave early and get dinner with their kids or travel on the weekend, of course that's totally fine. And for us, it's much more about finding people who have a lot of ownership and are really bought in, less so about the specific hours in the office, even though we found that oftentimes it's the people that are most bought in, not always, but oftentimes it's the people that are most bought in that, you know, sort of burn the midnight oil with us.

当你说高标准时，能否分享一个例子说明你的意思？因为很多人认为自己有高标准，但实际上并没有。

When you say high standards, is there something you could share that gives us an example of what you mean there? Because a lot of people think they have high standards and they don't.

如果你非常有耐心，在招聘时总是在速度和质量之间有所权衡。我记得，尤其是对我们前10名员工，我们非常有耐心和纪律性地寻找世界上一些最优秀的人。比如，我们第二名员工Sid，他在美国是我们第二名员工，之前是Scale公司的增长负责人，在我们还是种子阶段公司时就加入了我们。Daniel加入我们之前，曾将两个消费类应用扩展到超过10万用户，我们前10名招聘的员工都有各种非凡的背景。

If you are very patient, there's always some trade off between speed and quality when hiring. And I remember, especially for our first 10 people, we were just so patient and disciplined about finding some of the best people in the world. Like, you know, half of them are our second employee, Sid, as an example, our second employee in The U. S, Sid, was previously the head of growth at scale, you know, who joined us when we were a seed stage company. Daniel, who joined us, was previously scaled two consumer apps to over 100,000 users and all sorts of just extraordinary backgrounds of our first 10 hires.

我认为最初的人才密度在很大程度上决定了组织在扩张过程中的整体面貌。

And I think that that initial talent density shapes so much of what the rest of the org looks like as you scale it up.

我知道你还有个观点：人们常说招聘要慢一点等待合适人选，但这未必是正确的建议。谈谈这个看法。

I know you also have this perspective that people talk about waiting to hire hiring really slowly, but it's actually not necessarily the right advice. Talk about that.

这很痛苦，因为它是一把双刃剑。一方面，我非常高兴我们最初的10名员工都如此出色，这为业务带来了巨大回报。但另一方面，公司确实会发展到需要快速招聘的阶段。有些工作需要大量人手来完成，你必须认识到招聘过程中会存在一定差异，但快速行动才是首要任务。

It's painful because it's a double edged sword. Like on one hand, I'm thrilled that our first 10 people are like so phenomenal. And I think that that has paid dividends for the business. But on the other hand, I think that companies do get to the point where you just need to hire really fast. There are some things where you need a lot of people to do them and you need to recognize that there's going be some variance associated with hiring, but moving quickly is the priority.

我觉得在某种程度上，我们团队扩张的速度过于缓慢。好处是每个人都非常优秀，我们保持着极高的标准并希望长期维持。但缺点是，虽然公司发展极其迅速，如果我们在从10人到100人的扩张阶段能更快一些，本可以发展得更快。

And I think that in some ways we move too slowly with how we scaled out the team. And so the benefit is that everyone is extraordinary. We have this super high bar and we want to maintain that over time. But I think the downside is that while the company has grown incredibly quickly, we likely could have grown even faster if we had moved a little bit more quickly with especially ramping from call it like 10 to 100 people.

好的，我正想问这个问题。所以听起来前10人要非常谨慎，慢慢挑选；10到100人阶段，或许应该加快速度。

Okay, I was going to ask, so it sounds like the first 10, be very careful and take your time. 10 to a 100, maybe speed up a bit.

是的。虽然我不认为一定是10人这个具体数字，而是取决于你明确知道业务真正跑通的时刻。我知道这仍然不是一条明确的分界线，但当你发现需求远超出处理能力时，就应该踩下油门，在多方面优先考虑速度。但在此之前，保持耐心和纪律性，找到最优秀的人才始终很重要。

Yes. Though I wouldn't say it's necessarily 10. It's determined by the point where you know it's really working. And I know that's still not like a bright line, but it's like once you know that there's so much more demand than you can handle, that's when you want to step on the gas and optimize for speed in a lot of ways. But I think especially until then, it's important to be patient, be disciplined, get the best people is always important.

但一旦发现市场机遇或市场空白，速度就会变得更为重要。

But speed becomes more important once you find the market opportunity, the market vacuum.

我知道你过去创办过几家公司，规模都比较小。现在作为这家大规模高速增长公司的CEO，在这个新角色中，你最惊讶的是时间主要花在哪里，或者这个角色包含什么？因为很多人都想创业，梦想处于你的位置，他们可能不太理解你的大部分时间都花在哪些方面？

You've I know you've started a couple companies in the past, much smaller scale. In this new role as CEO of this massive hyper growth company, what's what surprised you most about where you spend the time most, or just what the role involves? Because a lot of people wanna start companies, dream about being in your shoes, what are they maybe not understanding about where a lot of your time goes?

是的，其实并不太令人惊讶。最重要的两件事始终是招聘和与客户相处的时间。我如何真正深入了解客户需求以及如何支持他们，然后如何组建团队并建立相关流程。当然，还有一些我没预料到的临时事务，比如处理人员问题，如何设定职级和薪酬区间等等，这些都是在业务扩展过程中逐渐学会的。但我认为我花费时间的核心领域既符合我的预期，也是我喜欢做的事情，这非常幸运。

Yeah, it's actually not too surprising. The top two buckets are always working on hiring, and time with customers. How do I really deeply understand what customers need and how we can support them, and then how do I build the team and a lot of the processes around that. Of course, there's all the ad hoc things I didn't expect of dealing with the people questions of how do we set up our levels and our comp bands and all of that, which you sort of learn as you scale a business. But I think that the core places that I spend my time are in line with what I expected as well as what I love doing, which is very fortunate.

那么你过去创办的这两家公司，也许可以分享一下它们是什么，因为听起来很有趣。然后它们如何帮助你在现在取得成功？比如它们教会了你什么对当前角色有帮助？

So these two companies you've started in the past, maybe share what they were because they're fun. And then what, how do they help you be successful in this? Like what's something that they taught you that helps you in your current role?

好的，其实我创办过十几家，但我会选我最喜欢的两个。八年级时，我创办了'甜甜圈王朝'，当时我看到Safeway的甜甜圈卖5美元一打。作为一个八年级学生，我觉得这简直太划算了。于是我开始骑自行车去Safeway，以5美元一打的价格购买甜甜圈，然后回到中学以每个2美元的价格出售。当然利润率很高。

Yeah, so I'll, there's been like a dozen, but I'll choose my favorite two. So when I was in eighth grade, I started Doughnut Dynasty, where I saw that Safeway donuts were selling for $5 a dozen. And I was amazed because I felt like as an eighth grader, this was such an incredible deal. And so I started to bike down to Safeway, buy Safeway donuts for $5 a dozen, and then, go back to my middle school and then sell them for $2 each. Running really good margins of course.

很快就售罄了，所以我需要扩大规模。于是我付给妈妈20美元，让她开小货车带我去Safeway，购买10打甜甜圈，到学校全部卖光。后来学校试图制止我，因为我在校园内销售食品，他们不喜欢这样。我被叫到校长办公室，要求停止销售。于是我把甜甜圈摊位移到了50英尺外，这样就不在校园范围内了，我说他们不能再管我了。我记得还出现了竞争对手，他们卖的是Chuck's甜甜圈。

It sold out super quickly and so then I need to scale up. So I would pay my mom $20 to drive me in her minivan, down to Safeway, buy 10 dozen donuts, go to my middle school, sell them all out. And then the school tried to shut me down, and so because I was selling like food on school campus, which they didn't like, so they had me in the principal's office asking me to not do that. And then I moved my donut stand over 50 feet so it was off school campus saying that they could no longer, you know, police me. I remember we had competitors pop up where the competitors were charging.

如果你在湾区就知道，Chuck's甜甜圈比Safeway的更高端，但成本也更高，每个成本1美元。于是我把价格降到1美元持续两周，把他们赶出市场，虽然当时我还不知道什么是反竞争行为。我雇佣了所有朋友，用甜甜圈支付报酬，因为他们认为每个甜甜圈值2美元，可以在学校销售，而我的成本更低。所以在卖甜甜圈的过程中我获得了所有这些有趣的经历。

They bought these Chuck's donuts, which if anyone in the Bay Area knows are, you know, higher end donuts than Safeway donuts, but they have a higher cost basis. They cost a dollar per. And so I dropped my prices to $1 for two weeks to run them out of business before I, before I knew, what anti competitive practices were. I'd, I'd hire all my friends, paying my friends in donuts because, you know, they perceived the donuts as, as $2 each where they could sell them throughout the school, and I could have a lower cost basis on them. So I had all of these like fun experiences in selling donuts.

我还可以多谈谈我的高中生意，规模更大一些。但我觉得最大的收获就是：你可以直接去做。很多人都有想法，但创建更多公司的障碍我认为只是主动性和采取行动来打造客户想要的产品或体验，并投入时间和雄心去扩大规模。所以我认为正是这些实践经历让我意识到以后应该在更大规模上做这件事。

And then I could talk more about my high school business as well, which was, more significant scale. But I think the takeaway from that was just like, you can just do things. Like so many people have ideas, but the barrier to more companies being built, I think is just initiative and taking the steps to build the product or experience that customers want and investing the time and the ambition to scale that up. And so I think it was really getting reps of that that enabled me to realize that I should do it later on at a much larger scale.

真棒的故事。我喜欢这种积极健康的氛围，而不是像毒品那样。你在卖甜甜圈。然后

Amazing story. I love how wholesome that is versus like drugs. You're selling donuts. Then

我妈妈非常担心。她说，哦，这些甜甜圈里有没有大麻？我说，不，妈妈，我向你保证。这些都是纯粹的甜甜圈。

my mom was very worried. She was like, Oh, is there any pot of these donuts? I was like, No, mom, I assure you. These are pure donuts.

我喜欢你付给你妈妈20美元开车费这点。

I love that you paid your mom $20 to drive.

是的，不能让她白帮忙，她花时间开车运送这些，所以需要从中赚点钱。我们还为她的头衔讨价还价，最后她想当全球运营总监，我们觉得特别有趣。

Yeah, it couldn't be a handout that she was taking her time to drive these, so she needed to make a little bit of money off of it. We haggled over her title where eventually she wanted to be head of global operations, which we found very entertaining.

希望这已经写在她的领英上了。

I hope that's on her LinkedIn.

还没有。也许她得

Not yet. Maybe she'll have to

加上去。你说你创办过十几家公司。是的。哇。好吧。

add it. And so you said that you've started a dozen companies. Yeah. Wow. Okay.

嗯，有十几个项目，但我觉得其中真正规模化的是那个以及我的AWS公司，这两者是我真正做大的项目。

Well, a dozen projects, but I I think there were it was that and then my AWS company, were the two that that I sort of scaled up.

Merkor这个名字背后有什么故事？

What's the story behind Merkor as as the name?

Merkor在拉丁语中意为市场或买卖交易，我们想打造世界上最大的市场——一个人人如何找到工作的市场，这确实是吸引我们的核心所在。

Merkor means marketplace in Latin or to buy, sell, trade, and we want to build the largest marketplace in the world, the marketplace for how everyone finds jobs, that was really the draw to it.

好的，最后一个问题。这要回到我们早先的讨论，因为在我们交谈时我一直在思考这个问题。现在有一种从数据作为模型燃料转向专家驱动的转变。你认为下一步会是什么？还是说这就会直接带我们走向AGI超级智能？

Okay, maybe a last question. This is going back to earlier in discussion, because it's something I've been thinking about as we're talking. There's been this shift from data as kind of the fuel for models, and now it's experts. Do you think there's a next step, or is this just will take us to AGI superintelligence?

我不认为这是从数据到专家的根本转变。更多的是实验室意识到需要与专家紧密合作，以帮助理解他们正在构建的评估标准以及如何推动前沿发展。但我认为评估标准是永恒需要的——只要我们想改进模型，就需要专家来为它们创建评估标准，并为它们创建训练后数据来学习这些能力。当然，人们进行强化学习或其他训练的具体方式可能会变化，但他们始终需要评估标准来衡量在他们想要构建的每个领域中成功是什么样子。

I don't think it's necessarily changing from data to experts. It's more just the paradigm of realizing that labs need this close collaboration with experts to help understand what are the evals that they're building and how can they push the frontier. But I think it's very clear that evals are evergreen, that so long as we want to improve models, we'll need experts to create evals for them and to create the post training data for them to learn those capabilities. And of course, there might be changes in the exact way that people do training with RL or otherwise, but they will always need an eval to measure what does success look like across every domain that they wanna build.

好的。那么在此基础上，最近经常出现的一个问题是——我知道我们在聊有趣的事情，但我又要回到严肃话题了——关于扩展定律和模型智能的进展。很多人感觉，我不知道，进展正在放缓。

Okay. So then building on that, a question that comes up a lot these days is, and I know we're talking about fun stuff, but I'm getting to serious stuff again. Scaling laws and just like progression of model intelligence. A lot of people are feeling like, I don't know. It's slowing down.

照这个速度我们可能无法真正实现超级智能。你的看法是什么？

We're not gonna really get to super intelligence at this rate. What is your sense?

我完全同意这一点。比如，我知道一些大实验室的高管说我们三年内就能实现超级智能，但我认为事实是这条路更长。这并非要贬低这些模型的非凡之处。我认为未来十年内我们肯定能够自动化大部分知识工作。但这条长路是由所有那些帮助实现这些能力的评估铺就的。

I totally agree with that. Like, I don't think it's I know there's been some executives at big labs that say we'll have super intelligence in three years, but I think the truth is that it's a longer road. And that's not to diminish from how extraordinary the models are. Like, I think we'll be able to automate a majority of knowledge work tasks in the next ten years for sure. But that long road is paved with all of the evals that help to make those capabilities possible.

这不会是，你知道，再多10倍的预训练数据就能获得这些能力。更重要的是所有那些数据效率更高、更经过深思熟虑的后训练数据集，它们帮助我们达到目标。

It's not going to be, you know, 10x more pre training data that gets those capabilities. It's much more going to be all of the post training data sets that are far more data efficient and thoughtful that help us get there.

David Sacks在推特上提出了一个有趣的观点，说我们现在所处的情况几乎是最佳情景——AI并没有快速爆发成为超级智能。有很多竞争者相互制衡。模型已经非常有价值，而且只会越来越有价值，但并没有出现那种赢家通吃的超级智能接管世界的情况。

David Sacks tweeted this interesting point that, like, the situation we're in now is almost the best case scenario where AI is not in this fast takeoff to superintelligence. There's a lot of competitors kinda keeping each other in check. Models are already very valuable and only getting valuable, more valuable, but there's not just this like winner super intelligence taking over the world situation.

是的，我认为这是对的。我觉得很多关于超级智能的危言耸听可能被夸大了。但同时，很多人的观点是，即使只有5%到10%的可能性会出现灾难性后果，我们也应该谨慎，这似乎很合理。但我认为这将是硅谷乃至全世界非凡的十年，因为这项技术能够创造富足，给每个人更好的医疗待遇，你知道，最好的法律建议获取途径，以及比以往任何时候都更强的打造优秀产品的能力。

Yeah, I think that's true. I think a lot of the super intelligence fear mongering is probably overrated, But at the same time, a lot of people's framing around that is even if there is a five to ten percent chance of this p doom, then we should be careful, which seems logical. But I think that it's going to be an extraordinary ten years for all of Silicon Valley and all of the world as this technology is able to create abundance and giving everyone better medical treatment, you know, the best access to legal recommendations and the ability to build great products more than we've ever seen before.

而且教育似乎正在发生变革。

And education feels like is transforming.

绝对是的，对吧？就像我在过去十年里已经感受到了这一点，我记得我父母曾经因为我大学不去上课而责备我，我会说，YouTube上有更好的讲座，为什么不在那里听呢？但我可以想象，当模型变得极其擅长传递信息，甚至比最好的教授还要好时，那将意味着什么，对吧，以及获取各种信息来更好地推动人类进步和提升每个人的技能。

Absolutely, right? Like I even have felt bits of this over the last ten years where like I remember ever, my parents would give me a hard time for not going to classes in college and I'd be like, well, there's way better lectures on YouTube, why not just listen there? But I can only imagine as the models get extremely good at conveying information better than the best professor, what that'll mean, right, and access to all sorts of information to better forward humanity and upskill everyone.

那么我就以此作为过渡来问最后一个问题。我将带大家进入AI Corner，这是我们播客的一个固定环节。你个人是如何使用AI来做更好的工作、帮助生活的呢？

So I'll use that as a segue to a final question. I'm going to take us to AI Corner, which is a recurring segment on the podcast. What's some way that you personally use AI to do better work, to help you in life?

嗯，让我想想。正如你所料，我经常用它来写文档。我也会和它交流，获取问题建议。就像，我发现把它当作一个思考伙伴来推理非常有帮助。因为，是的，我也不太确定。

Well, let's see. I use it a lot to write documents, as you would expect. I also talk to it to get advice on problems. Like, I find it helpful to just reason through almost as a thought partner. Because, yeah, I don't know.

我发现，有时候在讨论事情时我的思路会更清晰，但我不可能把所有事情都和同事或身边的人讨论。

I find, I think better sometimes when I'm talking something through, but I can't talk through everything, with colleagues or people around me.

所以这主要是像ChatGPT的语音模式之类的，对吧。

And so this is like ChatGPT voice mode mostly or something Yeah.

我非常喜欢ChatGPT的语音模式。

I like ChatGPT voice mode a lot.

很酷。

There's Cool.

虽然还有改进空间，但我对语音技术的未来感到非常兴奋。

Still room for improvement, but I I am very excited about the future of voice.

让我给你看看我实际构建的一个东西。我本来没打算谈论这个，但有个叫Eric Antinow的人，很多人推荐邀请他上这个播客。他是个创意产品人，现在有点低调。他在Facebook工作了很长时间。他构建了一个名为Parrot GPT的项目，基本上就是把Jet GPT放进毛绒玩具里和它对话。

Let me show you something I built actually. I wasn't planning to talk about this, but there's this guy, Eric Antinow, who reckon who's been recommended by a lot of people to get him on this podcast. He's this creative product person that's kind of under the radar now. He's at Facebook for a long time. He built this project called Parrot GPT, which is you put you basically put Jet GPT into a stuffed animal to talk to it.

所以制作了一个小智能猫头鹰。我现在没戴着。但基本上，你在这里缝入一个小扬声器，下面放一个小磁铁。你可以把它戴在肩膀上然后直接说话。哇。

So built built a little wise owl. I don't have it on right now. But basically, you sew in a little speaker right here, and you put a little magnet underneath. And you could put on your shoulder and then just talk Wow. To

我喜欢这个。我得买一个。你知道，我一直在因为我的公寓里有一些语音助手，但我真的很想要一个Chattyputee语音助手。所以我很期待

I love it. I'll have to get one of those. You know, I've been because I have, like, a some of the voice assistants in my apartment, but I really want a Chattyputee voice assistant. And so I'm excited for

我刚才就在想这个。对啊，来吧。为什么我们不能让ChatGPT语音一直坐在那里听我们说话呢？你可以在手机上用，因为它会休眠，就像，喂？什么？

I was just thinking that. Like, yeah, just come on. Why can't we have ChatGPT voice just sitting around listening to us all the time? And you can on your phone because it goes to sleep, it's like, hello? What?

不行吗？

No?

没错。是的。

Exactly. Yeah.

是的。所以这差不多就是它想实现的东西。嗯，就像他发起了一个Kickstarter众筹项目，我们会附上链接，可以帮你

Yeah. So it's kinda what this is trying to be. Well, like, there's a Kickstarter he started that we'll link to that you could help you

这就对了。

There you go.

这真的很简单。Brendan，在我们进入非常激动人心的快速问答环节之前，你还有什么想分享、想讨论或者想留给听众的吗？

It's really easy. Brendan, is there anything else that you wanted to share or touch on or maybe leave listeners with before we get to our very exciting lightning round?

结合刚才关于主动性的观点，也就是你可以直接去做事、去行动，我鼓励每个人，尤其是在AI让构建变得如此容易的今天，主动去开发产品、与客户交流、迈出那一步。因为我认为这在很多方面都是阻碍经济更多创新的最大障碍，我们应该尽一切可能支持这种主动性。

Tying to the point around initiative and that you can just do it, do things, I encourage everyone, especially with AI and it being so much easier to build, just take the initiative to go out and build products and talk with customers and take that leap of faith. Because I think that that is in so many ways the largest barrier to more innovation in the economy, in any way that we can support that.

是的，有太多人只是——我们不要贬低播客——但只是听播客、读文章，不断地阅读和收听，却不对这些信息采取任何行动。而现在正是实际构建和尝试东西最容易的时候。所以一定要采纳这个建议：你可以做事，你应该把你的甜甜圈摊位移动50英尺，摆脱他们的管辖范围。好了，Brendan，说到这里，我们已经来到了非常激动人心的快速问答环节。

Yeah, there's so many people that just, nothing, let's not bash the podcast, but just listen to podcasts, read posts, just keep reading and listening and don't do anything with that information. And there's never been an easier time to actually build stuff and try stuff. So definitely take that advice just you can do things, you should move your donut stand 50 feet and get out of their jurisdiction. Yeah. Okay, Brendan, with that, we've reached a very exciting lightning round.

我有五个问题要问你。准备好了吗？

I've got five questions for you. Are you ready?

准备好了。

All set.

你最常向别人推荐的两三本书是什么？

What are two or three books that you find yourself recommending most to other people?

让我想想，我会说《高产出管理》是一本关于公司运营的非凡著作。第二本是《从0到1》，这当然是经典之作。第三本是《鞋狗》，我觉得这是一个非常鼓舞人心的故事。

Let's see, I would say In Order, High Output Management is a phenomenal book on running companies. Second is Zero to One, which of course is a classic. And then third is Shoe Dog, where I just find it to be a really inspirational story.

我最近真正喜欢的一部电影或电视剧是什么？

What is a recent movie or TV show I've really enjoyed?

我真的很喜欢《奥本海默》。我最喜欢的电视剧是《金装律师》。我知道这不是最近的，但如果必须选一个最近的，可能是《奥本海默》。

I really liked Oppenheimer. My favorite TV show of all time is Suits. So I know not not recent, but if I had to choose a recent one, probably Oppenheimer.

很酷。《金装律师》，第一次有人提到这个。你最近发现并真正喜爱的产品是什么？

Very cool. Suits, first time someone's mentioned that. Favorite product you recently discovered that you really love?

我喜欢使用Codecs，比如新版本。我知道在版本意义上算是新的。是的，我认为它非常棒，是一个巨大的、巨大的改进。

I love using Codecs, like the new version. I know it's sort of new in terms of version. Yeah, I think it's incredible and just a huge, huge improvement.

那么，你有没有一个生活座右铭，发现自己经常回想起来，与人分享，觉得在工作或生活中很有用？

So yeah. Do you have a life motto that you find yourself coming back to, sharing with folks, finding useful in work or in life?

我认为就是你可以直接去做事，你知道吗？我们之前谈到的，要敢于迈出信仰的一跃。

I think it's you can just do stuff, you know? What we were talking about earlier, take the leap of faith.

我以为你会说'能做到'，这是你推特个人资料里的。它是一个

I thought you were going to say can do, which is in your Twitter profile. It's a

也可以做到，是的。

can do as well, yeah.

两个很棒的观点。最后一个问题。我们之前聊过可以谈论的话题，你分享了一个从未在其他地方透露过的有趣事情——你有阅读障碍。为什么要和大家分享这个？作为历史上增长最快的公司的创始人，你是如何应对这个挑战的？

Two great ones. Final question. So we were chatting before this about things that we could talk about, and you shared this interesting thing that you haven't shared anywhere else, is that you're dyslexic. Why share that with folks, and just how do you get around that, having built the fastest growing company in history?

我完全不隐瞒这一点。我的很多同事都知道。一方面，这确实让我很难每天处理上千封邮件或阅读所有需要看的文件。但另一方面，我觉得这帮助我用不同的方式思考，更有创造力，也许能看到市场变化的趋势，而这些是其他人看不到的。所以到目前为止还算顺利。从管理角度来说，这件事让我意识到我们应该更专注于如何发挥人们的优势，而不是帮助改进弱点，因为有些事情我不擅长，也永远不会成为世界顶尖，而有些事情我希望能够精进并努力做到最好。

I don't hide it at all. Like I think a lot of my colleagues know, and I think on one hand it definitely makes it difficult to go through a thousand emails a day or read every document that I'm supposed to. But on the other hand, I feel like it helps me to think a little bit differently, to be more creative and perhaps see the ways that markets are changing that not everyone sees. And so it's turned out okay so far. And so, you know, I try to, I think one thing it's helped me realize from a management standpoint is that we focus much more on how we can leverage people's strengths rather than helping to improve weaknesses, because there's some things that I'm not great at and I'll never be the best in the world at, and there's others that I can hopefully refine and strive to be.

这也是本期播客中反复出现的主题——专注于优势而非过度关注弱点。Brandon，这太精彩了。我学到了很多。我还有无数问题想问，但你还有重要的事情要处理。最后两个问题。

That's such an also recurring theme on this podcast of just focusing on strengths and not focusing over all your focus on weaknesses. Brandon, this was incredible. I learned so much. I have a billion more questions, but you got shit to do. Two final questions.

大家应该了解你正在做什么以及你们正在招聘哪些职位？然后听众如何能对你有所帮助？

What should people know about what you're doing and roles you're hiring for? Then And how can listeners be useful to you?

当然。我们团队正在全面大量招聘。我们在运营团队招聘战略项目负责人，在工程团队招聘软件工程师，还有研究人员。请访问mercora.com，我们很乐意与你共事。这是你能帮助我们最大的方式。

Absolutely. We're hiring a ton across the board on our team. We're hiring strategic project leads on our operations team, software engineers on our engineering team, as well as researchers. And so please go to mercora.com and we would love to work with you. And that's the largest way that you can help us.

也请分享给你的朋友们。我们市场中超过一半的人来自推荐，因为我们有一个热爱我们平台的用户群体。所以，无论你想申请任何职位或推荐朋友来，我们都非常欢迎。

Share it with your friends as well. Over half of people in our marketplace come from referrals, because we have a platform of people that love us. And so, any jobs that you want to apply to or send your friends to, we would love to have you.

布伦丹，非常感谢你加入我。

Brendan, thank you so much for joining me.

谢谢你的邀请。

Thank you for having me.

再见，各位。非常感谢大家的收听。如果你觉得本期内容有价值，可以在苹果播客、Spotify或你喜欢的播客应用上订阅我们的节目。同时，请考虑给我们评分或留下评论，因为这真的能帮助其他听众发现这个播客。你可以在lennyspodcast.com上找到所有往期节目或了解更多关于本节目的信息。

Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com.

下期节目再见。

See you in the next episode.