专家网络如何训练每一个前沿AI模型 | Garrett Lord（Handshake CEO）

这样的时刻前所未有。我从未见过类似景象。我怀疑在商业领域再难遇到这种需求无限的情况。如何确保三个月、六个月后回首时毫无遗憾？登上飞机去见客户吧。

There will never be a time like this. I've never seen anything like it. I doubt I'll ever feel anything like this in business again where there's unlimited demand. How do you make sure that three months from now, six months from now, you have, like, no regrets? Get on the plane to go talk to a customer.

熬夜冲刺。把数据反复核对六遍。

Make the late night push. Check the data six times over again.

贵公司通过创造新数据持续提升模型智能。这是在既有业务基础上构建的新业务。

Your company creates new data to continue advancing the intelligence of models. This is a business that you built on top of a business you've already had.

我们是全球最大的专家网络。拥有零客户获取成本的巨大战略优势。人类数据领域唯一的护城河就是受众触达能力。

We're the largest expert network in the world. We have this massive strategic advantage, which is like no customer acquisition costs. The only moat in human data is access to an audience.

你们在模型训练完成后介入，根据新生成的数据微调权重参数。

You guys come in after the model's trained to tweak the weights based on additional data that you create.

模型已进化到不再需要通才。市场真正渴求的是专家。

The models have gotten so good that the generalists are no longer needed. What they really need is experts.

这里存在矛盾：学生们训练模型使其更智能，却可能面临更严峻的就业形势。

There's this tension between all these students training models to become smarter, and then there's the they will have harder time potentially finding jobs.

从雇主那里我们听到的并非如此。AI只是让人类生产力更上层楼。就像你们这代人会把谷歌搜索技能写进简历，原生AI世代年轻人具有巨大优势。

That's not what we're hearing from our employers. This is just enabling human beings to be even more productive. You used to put a Google search on a skill on your resume because you grew up with Google. Being AI native, young people are at a huge advantage.

今天我的嘉宾是加勒特·罗德。作为Handshake联合创始人兼CEO，他缔造了你可能尚未听闻的最精彩AI成功案例。Handshake运营超十年，本质上是大学生版领英，连接学生与企业的求职平台。

Today, my guest is Garrett Lord. Garrett is the co founder and CEO of Handshake, which is one of the most interesting and incredible AI success stories that you probably haven't heard of. Handshake has been around for over ten years. They're essentially LinkedIn for college students. It's a place for students to connect with companies to find a job.

所有财富500强企业、1500多所高校、超2000万学生校友及百万企业都通过他们招聘毕业生。今年初，加勒特团队意识到其庞大的专有学生网络（含数万硕博）对AI实验室创建高质量标注训练数据极具价值。他们从零开始的新业务一月上线，四个月后ARR达5000万美元，现正以年化超1亿美元的速度增长。

They are the platform of choice for every single Fortune 500 company, over 1,500 colleges, over 20,000,000 students and alumni, and over 1,000,000 companies use them to hire graduates. At the start of this year, Garrett and his team realized that their huge proprietary network of students, including tens of thousands of PhDs and master's students, is extremely valuable to AI Labs to help them create and label high quality training data. So they launched a new business from zero to one in January. Four months later, they hit 50,000,000 ARR. They're now on pace to blow past 100,000,000 ARR within twelve months.

他们将在不到两年内超越经营了十年的业务的收入。这是一个真正令人难以置信且罕见的故事，我认为许多团队都能从中学习，因为AI既创造了大量机遇，也带来了潜在的颠覆。而这家公司基本上是自我颠覆的典范。本期节目充满洞见，包括关于人们标注和创建数据训练模型时究竟在做什么的入门知识。非常感谢加勒特抽空参与。

They'll exceed the revenue that they're making with their decade old business in under two years. This is a truly incredible and rare story, and one that I think a lot of teams can learn from because AI is creating a lot of opportunity but also a lot of potential disruption. And this is an amazing story where the company basically disrupted themselves. This episode is packed with insights, including a primer on what the heck are people actually doing when they're labeling and creating data to train models. A huge thank you to Garrett for making time for this.

他妻子这周刚生下宝宝，同时他还在忙着扩展这个疯狂的新业务。所以谢谢你，加勒特。如果你喜欢这个播客，别忘了在你喜欢的播客应用或YouTube上订阅关注。此外，如果你成为我新闻通讯的年度订阅用户，你将免费获得一系列出色产品的一年使用权，包括Lovable、Replit、Bolt、n eight n、Linear、Superhuman、Descript、WhisperFlow、Gamma、Perplexity、warp、granola、magic patterns、raycast、chat PRD和momen。

His wife just had a baby this week. He's also in the middle of scaling this insane new business. So thank you, Garrett. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. Also, if you become an annual subscriber of my newsletter, you get a year free of a bunch of incredible products, including Lovable, Replit, Bolt, n eight n, Linear, Superhuman, Descript, WhisperFlow, Gamma, Perplexity, warp, granola, magic patterns, raycast, chat PRD, and momen.

详情请访问Lenny's newsletter.com并点击bundle。接下来有请加勒特·洛德。本期节目由CodeRabbit赞助，这个AI代码审查平台正在改变工程团队在不牺牲代码质量的前提下用AI加速交付的方式。代码审查至关重要但耗时，CodeRabbit作为你的AI副驾驶，即时提供代码审查意见和每个拉取请求的潜在影响。

Check it out at Lenny's newsletter dot com and click bundle. With that, I bring you Garrett Lord. This episode is brought to you by CodeRabbit, the AI code review platform, transforming how engineering teams ship faster with AI without sacrificing code quality. Code reviews are critical but time consuming. Coderabbit acts as your AI copilot, providing instant code review comments and potential impacts of every pull request.

除了标记问题，CodeRabbit还提供一键修复建议，并允许你使用AST grep模式定义自定义代码质量规则，捕捉传统静态分析工具可能遗漏的细微问题。CodeRabbit还直接在IDE中提供免费的AI代码审查，支持VS Code、Cursor和Windsurf。CodeRabbit已审查超过1000万次PR，安装在100万个代码库上，被7万多个开源项目使用。使用优惠码Lenny可在coderabbit.ai免费获得一整年服务。

Beyond just flagging issues, Coderabbit provides one click fix suggestions and lets you define custom code quality rules using AST grep patterns, catching subtle issues that traditional static analysis tools might miss. Codebabbit also provides free AI code reviews directly in the IDE. It's available in Versus Code, Cursor, and Windsurf. Codebabbit has so far reviewed more than 10,000,000 PRs, installed on 1,000,000 repositories, and is used by over 70,000 open source projects. Get Coderabbit for free for an entire year at coderabbit.ai using code Lenny.

网址是coderabbit.ai。本期节目由Orcus赞助，该公司是开源Conductor的背后推手，这个编排平台为现代企业应用和智能体工作流提供动力。传统自动化工具已跟不上步伐，孤立的低代码平台、过时的流程管理和脱节的API工具在当今事件驱动、AI赋能的智能体环境中力不从心。Orcus改变了这一切。

That's coderabbit.ai. This episode is brought to you by Orcus, the company behind Open Source Conductor, the orchestration platform powering modern enterprise apps and agentic workflows. Legacy automation tools can't keep pace. Siloed low code platforms, outdated process management, and disconnected API tooling fall short in today's event driven AI powered agentic landscape. Orcus changes this.

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With Orcus Conductor, you gain an agentic orchestration layer that seamlessly connects humans, AI agents, APIs, microservices, and data pipelines in real time at enterprise scale. Visual and code first development, built in compliance, observability, and rock solid reliability ensure workflows evolve dynamically with your needs. It's not just about automating tasks. It's orchestrating autonomous agents and complex workflows to deliver smarter outcomes faster. Whether modernizing legacy systems or scaling next gen AI driven apps, Orkus accelerates your journey from idea to production.

了解更多并开始构建，请访问orkus.i0/lenny。网址是0rkes.i0/lenny。加勒特，非常感谢你的到来。欢迎来到播客。

Learn more and start building at orkus.i0/lenny. That's 0rkes.i0/lenny. Garrett, thank you so much for being here. Welcome to the podcast.

是的，谢谢邀请。我是长期订阅用户。

Yeah. Thanks for having me. Longtime subscriber.

非常感谢。好的，在我们深入探讨你们数据标注业务的惊人发展轨迹之前——这个故事非常精彩，我认为许多正在应对当前颠覆浪潮的创始人和产品团队都能从中获益——我想先帮助大家理解数据标注到底是什么。就像，人们实际上在做什么？

I appreciate that. Okay. So before we get into the insane trajectory that your data labeling business is on, which is just an amazing story that I think a lot of founders and product teams that are trying to navigate this disruption that's happening will have a lot to learn from. I want to first help people understand what the hell data labeling actually is. Just like what are people actually doing?

为什么这如此有价值？包括你们公司在内的当今世界上一些增长最快的公司，这就是你们所做的。显然，这里有些非常重要的东西。我大概了解一些，但可能并不深入。

Why is this so valuable? Some of the most, I don't know, fastest growing companies in the world today, including you guys are just are are this is what you do. Clearly, there's something really important here. I sort of understand it. Probably not really.

我想很多听众都有同感。那么让我直接问你，数据标注的实际工作是什么样的？人们具体在做什么？为什么这对前沿AI实验室如此宝贵？

I think a lot of listeners feel the same way. So let me just ask you this. What is data labeling actually like? What are people actually doing? And then just why is this so valuable to frontier AI labs?

是的。

Yeah.

我认为有必要先退一步思考——训练一个模型究竟意味着什么？这主要包含两个阶段：预训练和后训练。长期以来，AI提供商或大语言模型实验室都专注于在预训练阶段不断吸收更多信息，这基本上涵盖了人类书面知识的全部范畴。

So I I think it's helpful to take, I guess, step back of, like, what what does training a model look like? So there's really two primary functions. There's a pre training and a post training process in training a model. And for a long time, these AI providers or LLMs or Frontier Labs were focused on basically sucking up more and more information on the pretraining side of the house. And that's basically the entire corpus of, like, written human knowledge.

不仅是文字，还包括每个YouTube视频、每本书。本质上就是在互联网上吞噬一切内容——这就是预训练。预训练带来了巨大提升，模型性能持续进步。

So that's not just written, but, like, every YouTube video, every book. Basically, you know, the pursuit of sucking up everything that was on the Internet. That was the pretraining side. And there was a lot of gains from pretraining. Like, models continue to get better.

约18到24个月前，我们发现预训练带来的增益开始趋于平缓，因为他们几乎已吸收完互联网所有知识。于是实验室将重心转向后训练阶段——通过提升各学科领域的数据质量来增强模型能力，比如编程、数学、法律或金融领域的高质量数据收集。

And about eighteen months ago, twenty four months ago, we started to really see, like, an asymptoting of gains coming from because they had essentially, like, sucked up all of the knowledge on the Internet. And so labs really shifted towards most of the gains now coming from the post training side of the house. And what post training is is it's augmenting the and improving the data they have across every discipline or capability area that they care about. So take coding or mathematics or law or finance. You know, they are focused on collecting high quality data that really improves the state of our capabilities of their models.

你可以在模型卡片上看到各类流行基准测试。比如LlamaForce发布时，会展示跨领域基准数据。实验室里每个研究团队都有不同用例，他们像进行科学实验般运作。

And you can see a lot of these popular benchmarks on on what are called model cards. You know, when LamaForce released, you'll see, like, the benchmarks across various domains. And each one of the research teams inside of the labs are have different use cases. Basically, they're running experiments. They almost think like the scientific process.

他们会提出改进模型的假设，收集小规模数据验证假设。若假设成立，就扩大该方向的数据收集规模。这可能表现为强化学习环境、轨迹数据、多模态音频，或是基于文本的提示-响应对，也包括人类反馈强化学习（即偏好排序数据）。

They have, like, a hypothesis around how to improve the model. They're trying to collect small pieces of data to see if that hypothesis works out. If that hypothesis is proving true, then they expand the overall collection of the data in that effort. And it can it can look like reinforcement learning environments. It can look like trajectories.

这就是当前最先进的模型状态。现在模型的大部分进步都来自后训练阶段，为了保持在模型发展的最前沿，市场需求极其旺盛。

It could be audio and multimodal. It can be text based, like prompt response pairs. It can also be, like, reinforcement learning with human feedback, which is, you know, preference ranking data. And so that's the that's the state of the art of models. And most of the gains that are happening from models right now are are coming from the push training side of the house, and there's just an an incredible amount of demand to stay at the absolute frontier of where models are going.

所以预训练是喂给它整个互联网——人类创造的所有数据，让它学习知识、事实和推理能力。而后训练是否可以理解为两大方向：人类反馈强化学习（RLHF）和微调？

So training pre training is feeding it, say, the entire Internet. Here's, like, all the data that the humans have ever created. Figure out knowledge and facts and how to reason and all these things. Post training, is it correct to say there's essentially two buckets of things to do? There's reinforcement learning, human feedback, RLHF, and then there's kind of this bucket of fine tuning?

可以说是，也不完全是。比如轨迹数据——无论是航班搜索、端到端会计流程，还是进行生物实验，你都需要真实的操作轨迹数据。

I mean, yeah. Yes and no. Because, like, what take, for example, like, trajectories or, like, you wanna be able to do people use flight search or, like, an accounting end to end process, or you wanna be able to, like, conduct biological, like, experiments. Like, you need actual trajectory data. Like, you you need to.

目前许多实验室对数据收集仍持有不同观点，这一领域发展非常迅速。但我认为强化学习本质上是一种偏好排序，对吧？比如，你更喜欢哪个问题？

There there's still very much a lot of the labs are still they have points of view on what data collect. It's evolving very quickly. But I think, you know, reinforcement learning is really, like, preference ranking. Right? Like, which which question do you like more?

问题A还是问题B。监督微调数据就是提示与回应的配对，显然实验室非常关注这些思维或推理模型。要提升推理模型，实际上需要分步指导——当你与这些前沿模型互动时，会发现它们在高级领域表现吃力。因此，他们正在利用多种数据来提升模型能力。

Question a or question b. SFT data is, like, a prompt and a response, and, obviously, the labs are very focused on these, like, thinking or reasoning models. So in order to improve a reasoning model, you'd actually have, like, the step by step instructions of which when you interact with a lot of these frontier models, they're you know, they struggle in very advanced domains. And so, you know, I think there's a variety of datas that that they're working, you know, working with to improve capabilities in their models.

我理解还有其他后训练方法。你们主要专注哪些方面？在哪些环节最能帮助模型？

What I'm hearing is there's other ways to, post train. Which of these are you guys focused on? Where do you help models most of these buckets?

我们企业真正的独特优势在于拥有活跃用户群体——1800万专业人士，包括50万博士和300万硕士生，我们是全球性平台。无论您需要哪个学术领域的数据，我们都能匹配。

Our, like, real unique proposition as a business is the fact that we, like, have an engaged audience. We have 18,000,000 professionals, across you know, we have 500,000 PhDs. We have 3,000,000 master's students. We're a global platform. And so, you know, depending on kinda what you're looking for across any area of academic knowledge.

博士学位的本质是什么？就是要拓展人类在特定领域的认知边界。我们最擅长的就是精准触达化学、数学、物理、生物、编程等领域的专家，挖掘那些从未被互联网收录的人类知识。

You know, what is the definition of a PhD? It's essentially to, like, be at the how do you get your how do you get your PhD? You defend your thesis. Defending your thesis means, generally speaking, like, you have proven that you have extended the the world's knowledge in a particular domain. And so the ability to, like, hyper target this audience into chemistry, math, physics, biology, coding, and really touch parts of human knowledge that have never before made it to the Internet is really where we we excel.

关于数据标注市场，抽象来看，过去依赖通才型劳动力。但随着模型进步，现在真正需要的是每个专业领域的专家——模型构建者聚焦的正是经济价值最高的能力领域。

And I would say that when you talk about the labeling market, something to to make it more abstract is, like, it used to be generalist to work. Like, a lot of the market before the model started to get better was leveraging talented international lower cost labor to do basic generalist tasks. But really what's happened is the models have gotten so good that the generalists are no longer needed. Like, what they really need is experts. Experts across every area that the models are focused on.

目前主要集中在STEM领域和衍生学科（会计/法律/医学/金融），旨在提升模型能力。回到您的问题——我们工作覆盖数百万本科生参与的语音语调地域化定制，到最前沿的博士级STEM领域研究。

And and, really, you could think about these model model builders as they're focused on, like, the most economically valuable capability areas in the economy. Right? And so that, generally speaking, right now is focused on, you know, advanced STEM domains, advanced science and math domains, and then the kind of derivative functions of, like, accounting, law, medicine, finance, where they want to make the models more capable. And then the work that we're doing, I think, to come full circle to your question, like, we're doing work across, across so many domains. I mean, we have we have millions of bachelor students that are being used for work in, like, audio, work in customizing a model depending on the voice and tone where you are geographically in the country, what do women versus men prefer, all the way to the most advanced PhD STEM domains out there.

那么是否可以理解为：现有数据已被充分训练，而贵司通过创造新数据/新知识来持续推动模型智能进化？

Okay. So is it fair to say, essentially, all the data that is available has been trained on, and your company procreates new data, new knowledge to continue advancing the intelligence of models?

没错。我们还能定位模型弱点——普通人很难让模型出错，但物理学博士就能在其细分领域找出模型推理缺陷或事实错误。

Yeah. And I also say we help point out where the models are weak. Mhmm. So in order to break a model, you know, it's pretty tough for the average person to break a model and get an incorrect response. Mhmm.

当需要工具辅助或分步验证时，普通人无法突破的模型局限，正是我们发挥价值的领域。

But if you're a PhD in physics, like, you can go in in multiple kind of subdomains of physics and prove where the model's actually breaking. Either breaking in its reasoning steps or it's where it's broken in its ground truth right answer, or we start throwing tools in there or needing to, you know, follow some step by step process. And it's it's it's, I wouldn't say it's easy for them, but the average person cannot break the models. And that's where we really come in.

所以本质上，这就像是捕捉模型犯下的错误。好的。那么这些人具体在做什么？具体是什么？我知道有各种不同的类型。

So essentially, it's just like catching mistakes that the model has made. Okay. So what are these people actually doing? What is it? I know there's all kinds of different types.

你描述了数据生成的所有方式，以及哪些数据是有用的。也许可以举些最常见的例子，比如假设一个博士生坐在那里工作，他们实际上在做什么？

You described all the ways that data is generated, what kind of data is useful. So maybe just, like, the most common examples, like, let's say a PhD person is sitting there doing stuff. What are they actually doing?

一个很好的例子是一篇名为GPQA的公开论文。对于想了解细节的工程师来说，论文的核心是你要‘破坏’模型——提供问题的标准答案和逐步推理步骤。由于模型具有非确定性，它可能偶尔答对，但未必能五次中答对三次。

A great example is a public paper called, like, GPQA. So for the engineers out there that wanna read about it, like, essentially, the the crux of the paper is you break the model. You provide a ground truth, the right answer to the question. You provide the step by step reasoning steps. So, you know, you can you might imagine, like, because models are nondeterministic, like, model can get the answer right once, but it might not get the answer right, you know, three out of five times.

这样你就能证明模型在哪些环节出错。比如它可能知道问题，也能给出正确答案，但中间推理步骤是错误的。研究者特别关注这些步骤——比如数学题的10个步骤中，第6到10步出错时该如何修正。

So you actually prove where the model's failing. You actually break down into, like, where is it failing? You know? Maybe it can get the it knows the question, but it can get the right answer, but the actual steps to get there are wrong, and they're really focused on, like, the steps to get there. So there's, like, 10 steps in a math problem.

对吧？关键在于如何修正具体步骤。我们非常注重打造专家级体验——博士生与低成本国际劳工的工作预期不同，需要差别化对待。

Right? Like, step six through 10 is wrong, and so, like, how do you fix the actual steps? And what are they doing? So they're going in we put them you know, we're really focused on calling this, like, a branding the experience and treating people like experts. Like, PhD students expect to be treated different than a lower cost international labor with a different work expectation.

这些博士生加入后，我们的教学设计和评估团队会迭代式指导他们使用工具、对接最新模型。随后他们开始生成数据——我们模型构建方需要确保数据高质量，因此配备了研究团队和训练后团队。我从Meta高薪聘请了负责训练后的专家。

And so these PhDs come into a community. We have a instructional design team and an assessments team that's going through and basically iteratively helping them understand how to use the tools that we built and how to interact with the latest models, then they go in and start actually creating data and that, you know, that process is on our side, the model builders, they wanna know that the data we're producing is high quality. So we have our own research team, our own post training team. I hired a a gentleman from Meta that went along along the post training over there and then Hope you paid him well. Yeah.

AI人才成本极高，但能与他们共事令我倍感荣幸。每份数据单元都需要专门环境来创建，我们还要评估其对特定能力领域的提升效果。同时我们也在跟进模型构建者的需求——他们需要更多现实场景的工具使用数据和轨迹数据。

So Warfare AI talent is, very expensive, but super super privileged and proud to be working with him. And so, you know, each unit of data, you know, we have to build an environment for them to actually create the data, then we have to understand at a at a in a unit level. We're trying to approximate the actual gain from that piece of data and whether it can improve in a particular capability area. And then we're also focused on, you know, evolving the use cases to also follow what the model builders want, which is they want more they they they want more real world tool use and trajectory based data as well.

信息量太大了。虽然可以无限深入，但我觉得特别有意思——人们常听说这些却不明就里。这些内容对很多人都会有帮助。

Okay. There's so much here. And, like, we could go infinitely down here, but I think that this is really interesting because just like people hear so much about all of this and they barely understand what the hell it actually is. So this is, for me, really interesting. I think it's gonna help a lot of people.

简单来说，比如生物学博士的工作就是找出ChatGPT产出的错误，提供正确答案用于微调模型？‘这里你做得不对，正确答案是这样’——这样就能改进模型，可以这么理解吗？

So, essentially, a PhD, say a biologist, biology PhD is just their job is find flaws in what say ChatGPT is producing and then come up with here's the correct answer. And that is used to fine tune the model. Here's, like, here's something you're doing incorrectly. Here's the correct answer, and that improves the model. Is that a simple way to think about it?

如果我说的有误请纠正，我不希望造成误解。

And please correct anything I'm saying that is incorrect because I don't want people to misunderstand it.

我是说，举个很好的例子，比如像教育这种无法验证的领域。网络上有位博士生叫瑞秋，她在迈阿密大学获得博士学位，花了二十年时间教八年级学生，还在当地社区学院担任教育领域的兼职教授。她正在与教育设计领域最先进的模型互动，试图真正理解什么是最佳的教学方式，以及如何发现模型中训练方法存在的问题，并帮助这些模型理解教育设计的前沿知识——凭借她十多年八年级教学经验和教育博士学位的一手经验。

I mean, like, a great example, let's take, like, a nonverifiable domain like education. So there's, like, a PhD student, Rachel, on the network. She got her PhD from the University of Miami, spent two decades as a teacher teaching students in the eighth grade, and she was an adjunct professor at a local community college, in the field of education. And so she is interacting with the state of the art models in educational design. So actually trying to understand what is the best way to teach people and, like, how do you frame the how do you how do you spot incorrect issues in a model in the way that they're, like, training people and help the models understand the forefront of educational design with the hands on experience of being an eighth grade teacher for ten plus years and having a PhD in education.

这就是个例子，你知道，你可以一直延伸到可验证的工程问题，看到最新模型在这些问题上的失败。所以...是的。我觉得这涵盖了整个范围。还有，我们谈到的专业领域，比如这些强化学习环境。你知道，有很多论文基本上就是在记录人们逐步使用工具时的口述过程。

So that's an example of, like, you know, you can have that all the way down to, like, a verifiable engineering problem that you're seeing the latest, you know, you know, seeing the latest models fail on. So you have yeah. I I think that gives you a, you know, the the gamut. You also have you know, we talk about professional domains, like these reinforcement learning environments. Like, you know, there's a bunch of papers out there that's basically speak to, like, people narrating over their step by step tool use.

当他们从头到尾解决问题时，会与多个不同服务区域互动，使用多种不同工具。你知道，有些论文专门讨论这个——比如他们会边做边解说，实际记录鼠标移动轨迹和问题解决过程。遇到障碍时他们会怎么做？研究者们真的很想理解人类的思考方式。

So as they go to solve a problem from start to finish, interact with multiple different service areas, interact with multiple different tools, you know, they're like you know, there's papers that talk about this, like, you know, talking over what they're doing, actually following and screen recording where their mouse is going, how they're problem solving. When they run into a roadblock, what do they do? They really wanna understand how humans think.

你提到'轨迹'这个术语。能解释下具体含义吗？因为你已经提到好几次了，感觉这对清单很重要。

You mentioned this term trajectory. Can you just explain what that actually means? Because it feels like you've mentioned that a few times, and that feels important to the list.

轨迹本质上就是收集你所有操作的整体环境。包括你的屏幕、鼠标移动...

A trajectory is basically just like the entire environment that is collecting what you're doing. So it's your screen. It's your mouse.

哇，明白了。连这个语音解说也算。那可能有点技术性，但这位教师的所有工作输出是什么形式？

Oh, wow. Yep. Including this voiceover. Okay. And then this might be too technical, but what is the output of all this work of this, say teacher?

是JSON文件、XML文件还是纯文本文件？

Is it just like a JSON file, an XML file, like a text file?

对，可以理解为JSON数据。

Yep. Think about it as JSON data.

JSON数据。好的。

JSON data. Okay.

还有多模态工作，比如音频领域的音乐分类和理解。我们正在与全国顶尖音乐院校的数百名优秀学生合作，提升模型对音乐的理解。另外还有我们没提到的评分标准（rubrics）——你可以把模型当作评委，比如判断什么是好的教育设计，或者什么是合格的核磁共振结果。

And then you also have, like, multimodal work, like audio, like, classifying music and understanding. We're engaging, like, thousands or not thousands, like, probably hundreds of top music students at, you know, the weed music schools in the country who are improving models understanding of music. And you also have the thing called, which we haven't talked about here, like rubrics. And a rubric, like, models are you can you can put a model in as a judge. Like, you you can if you what is a good what is a good educational design, or what what's a good MRI result?

与在某些领域中存在部分正确答案不同，实际上你并没有一个绝对正确的保证答案。因此模型可以居中作为裁判，真正理解——回想一下你的学生时代——比如怎样才能在5000字的论文中拿到A？需要有出色的开篇陈述和科学论证，明白吗？

And instead of having some of these in some of these domains, you actually don't have a a guaranteed correct right answer. And so models can sit in the middle as a judge and actually understand, you know, what is you know, kinda like think back on your school days. Like, what it how do you get an a on your 5,000 word paper? Well, there's like a great introductory statement, and there's scientific proof. You know?

就像你可以建立一个评分标准，让模型居中实现自动评估回答。我们目前也看到很多评分标准相关的工作。而你...

Like, so you could build a rubric that allows a model to sit in the middle and actually, like, auto evaluate responses. We're seeing a lot of rubrics work as well. And you

可能会质疑：凭什么相信某位教师认定这就是正确方法？但妙处在于市场自有公论。如果这些模型被越来越广泛使用且深受欢迎，我想中间必然存在验证流程来确认其有效性，并得到他人认可。市场动态自会表明你提供的数据是否符合人们需求。这其中是否还有更深层逻辑？

would think, like, why would you trust this one teacher's opinion that this is the right way to do it? But what's cool is the market speaks for itself. If these models are being used more and more and people love them and value them, I imagine there are steps in between to verify this is good and other people think this is a good idea. It feels like the market dynamics will tell you if the data you're providing is correct at what people want. Is there something more there?

要知道，我并未获得AI、数学或物理的博士学位，也没有通过前沿实验来训练自己。但每个数据单元都蕴含大量信息——无论它是否在改进。当前有大量科学研究关注如何确保所产数据能优化模型，这对模型构建者而言极难把握。

You know, I didn't get a PhD in in AI or math or physics, and I haven't trained myself via frontier mouse. But, you know, there is a lot to each unit of data, whether it's improving. Yep. If it you know, there's a ton of science and research out right now around, like, how do you make sure that the data that you're producing is improving the model? And it's very hard for model builders to understand.

明白吗？若宏观来看，他们真正关注三点：首要的是质量。必须拥有高质量数据——想象你在训练一个学生模型，若输入错误数据，后续修正将极其困难。

You know? They they can really care about to zoom out, they care about three things. They care about, like, quality first and foremost. You have to have high quality data. And if you you imagine you're training a model, like, a student and you're giving it the wrong data, it's extremely, you know, challenging to overcome that.

因此质量是首要任务。另一个重大难题是数量——如何在高阶化学、数学和物理领域生成数千条数据？如何确保其高质量？我们的解决方案是：比如物理领域，就直接联系斯坦福、伯克利和MIT的顶尖学生。

So quality is first and foremost. And then the other huge problems you have is, like, volume. Like, how how do you generate thousands of pieces of data in the most advanced domains of chemistry and mathematics and physics. And how do you ensure that it's high quality? Well, for us, we say in physics, we just reach out to students at Stanford and Berkeley and MIT.

这些全美顶尖物理院系的GPA优等生，能帮助我们既保证数据规模又维持极高品质——这正是模型构建者极度重视的。第三点则是速度，因为他们同时测试多个假设管道，可能并行推进三四个项目。

And, like, they're at the top GPA, the at the best physics schools in the country. And so our ability to get to scale or volumes of data with that it's a pretty very high quality data is is something they care deeply about. And then the other thing I would say model builders care about is speed. Because they have all these hypotheses, and they're constantly testing different pipelines. So you might have, like, three or four bets going at once.

当某个管道开始显现成效时——想象你是科研检察官发现某个方案有效——就会立即扩大该数据管道，同时可能放弃其他两三个未显示改进的项目。因此能否在数天内快速交付高质量海量数据，是他们最看重的。为此我们开发了大量技术来评估每个数据单元，还建立了专属的后训练团队。

And then as soon as one is actually showing a a game, imagine you're a researcher or, you know, your scientific prosecutors once running a game, then you're trying to grow that pipeline and grow that piece of data that's actually improving it. And you're maybe ditching two or three other projects you had that weren't showing improvement. So your ability to quickly turn around for them in a in a period of days and then get to high volumes of data that are high quality is the number one thing they care about. And so there's quite a bit of technology we built on our side to assess each unit of data. We have our own post training teams.

我们租用专用GPU，确保能与研究人员直接协作，分享对所创数据的观察：如何优化模型、如何高效训练。希望这些解释有帮助。回到后训练类型的话题...

We're renting our own GPUs, and we're trying to make sure that we can sit directly with these researchers and help share, like, what we're seeing with the data that we're creating and how how it could improve their model, how they could best train with it. So, hopefully, that helps. Going back to the types of post training, just because I

我认为建立这样的认知框架或许有益——至少对我而言：存在预训练和后训练阶段，后训练中又包含强化学习的人类反馈、微调概念，还有评估体系...

think this might be helpful, at least for me, the mental model of there's pre training, there's post training, within post training, there's reinforcement learning, human feedback. There's kind of this concept of fine tuning. There's also eval

还有SFT之类的。

and stuff SFT. Like

是的。SFT，即监督微调。这样可以吗？所以你刚才描述的内容，主要可以归类为监督微调吗？是的。

Yeah. SFT, which is supervised fine tuning. Is that okay. So the stuff you've been describing, is that would you mostly describe that as supervised fine tuning? Yes.

我在处理偏好率。我是说，我们某种程度上

I'm working preference rate. I mean, we're kind

在做所有上述工作。我们不进行自动评估。我们会制定评分标准，这些标准用于自动评估。是的。

of doing all of the above. We don't do the auto eval. We we produce rubrics, which are used in auto evals. Yeah.

好的。太棒了。所以本质上，有一个模型是在所有这些惊人数据上训练的。你们在模型训练完成后介入，根据你们创建的额外数据调整权重。有趣的是这是一个可扩展的系统。

Okay. Awesome. So essentially, there's a model trained on all this amazing data. You guys come in after the model is trained to tweak the weights based on additional data that you create. What's interesting is that this is a scalable system.

想谈谈你们拥有的这些产出数据的优秀人才供给，但人类能做到这点真的很神奇。你会以为这需要是无限扩展的东西，但实际上人类坐在那里创造数据确实有效，并且显著提升了模型的智能。

Want to talk about just like the supply of amazing people that you have producing this, but it's amazing that humans can do this. Like, you would think it needs to be this infinitely scalable thing, but, like, humans sitting there adding creating data is working and improving the intelligence of models significantly.

哦，是的。我想说，可能有个好笑的说法是，所有MBA都认为这些都会消失。但我觉得只要模型还在改进，这个过程就需要人类参与。当你和这些实验室的首席科学家、研究员交谈时，会发现数据类型会进化，他们试图捕捉和收集的内容也会变化。但未来十年里，在这个领域仍然需要人类，直到我们实现完全的ASI（人工超级智能）。

Oh, yeah. I mean, I think, like, maybe a funny joke is, like, all the MBAs think this is all just, like, gonna go away. It's like and I think for as long as models are improving, humans will be needed in this process. And when you talk to the the lead scientists and researchers at these labs, it's like the data types will evolve and what they're trying to capture and collect. But, you know, there will be there will be humans needed in this space for the next decade until we reach, like, full ASI.

所以，是的，我是说——想想看，现在很多模型连基本轨迹都处理不好。目前人们非常关注学术领域，我认为他们会继续关注学术领域，但对专业领域的需求也会大幅增加，基本上涵盖知识工作者在职场上解决的每个步骤式问题。这些实验室的目标就是确保他们收集数据，尽可能为人类在这个过程中增添价值。

So, yeah, it's I mean, you think about, like, you you know, a lot of the models struggle to do basic trajectories right now. So, you know, right now, people are very focused on academic domains, and I think they'll continue to be focused on academic academic domains, but they'll also be, you know, far, far more demand for professional domains as well across basically every trajectory or step by step kind of problem that a knowledge worker solves in the workplace. It's the pursuit of these labs to make sure that they're trying to collect the data to help add as much value in that process for humans as possible.

那我问问这个。我能想象人们可能会感受到这种矛盾——所有这些学生在训练模型变得越来越聪明，而如果模型太聪明，初级岗位的人可能更难找到工作。你怎么看待这种矛盾？你认为这是个真实存在的问题吗？

So let me ask you about this. There's this tension, I imagine, people might feel between, all these students training models to become smarter and smarter and smarter. And then there's the they will have harder time potentially finding jobs if models are so smart that people at entry level, aren't being hired as much. How do you think about just that tension? Do you think this is a real problem or not?

你觉得这会如何发展？

Where do you think this goes?

我可能属于支持GDP增长而非全民基本收入的阵营。我非常相信这将提升并加速每个人在经济和世界中创造影响力的能力。我们有上百万家公司使用Handshake——实际上所有财富500强企业都在使用。我们基本上支撑了年轻人求职的主要渠道。

I'm probably in the camp of, like, GDP growth over, like, universal basic income. Like, I I, like, very much, like, believe that this is going to improve and accelerate every human's ability to, like, create an impact in the economy in the world. And that, you know, we're hearing from there's, like, a million companies that use Handshake. Like, we have 100 well, 100% of the Fortune 500 uses Handshake. So we we basically power the vast majority of how young people find jobs.

很多人夸张地说所有年轻人都将失业，但雇主们反馈并非如此。以社交媒体营销为例：过去需要会Photoshop、拍照、制作视频的人，后来需要懂营销分析平台来追踪不同社交媒体的发布效果。而现在，一个具备AI能力的年轻人就能单枪匹马完成所有工作——

And a lot of people are kind of hyperbolic in saying that all young people won't have jobs. And, like, that's not what we're hearing from our employers. What we're hearing is, like, take, like, social media marketing. Like, before you needed, like, somebody that could do Photoshop and take pictures and have created videos, then you needed somebody that understood, like, marketing analytics platforms to track, you know, your posting on different social media forms. It's like, now one person, one, like, young, talented, AI native, Iron Man suit enabled young person can get on.

他们能自制视频、生产创意素材、跨平台发布内容、运行数据分析，根本不需要数据科学学位。再比如我们公司一个实习生，入职当天下午就提交了首个PR代码。

Like, they can build their own videos, produce their own creative assets, post across multiple social media platforms, run all of their own analytics. They don't need a data science degree to be able to do that. And that's an or or, like, take an intern in our in our company. Like, he had his first PR up, like, I think, like, the afternoon he started. Right?

你当过产品经理就知道，以前光是搭建开发环境找准贡献点就够头疼的。而他直接定位并修复了一个漏洞。我坚信这能让人类更高效地创造价值。当然，数亿工作岗位会演变转型——

Like, you were a PM. Like, you realize how how challenged that would have been historically to get your dev environment set up and, like, figure out where to add value. He just took a bug and and squashed it. And so I'm really a believer this is just, like, enabling human beings to be even more productive and create more impact. And, yeah, like, of course, like, like, hundreds of millions of jobs will become you know, the jobs will evolve.

部分人将面临职业更替，需要技能升级。我认为Handshake在帮助知识工作者转型方面大有可为。

Like, people will come displaced. They'll have to upscale and rescale. And I think Handshake has a huge role to play in in helping, knowledge workers evolve.

这个观点很有见地——在校生反而更可能成功，因为他们从小接触这些工具，使用起来得心应手。他们入职后简直能以一当十。你还记得当年...

This has come up a couple times, this point that I think is really good that younger people coming out of school are actually gonna be much more likely to be successful because they're kind of growing up with these tools and are much more native to all these advanced tools. And so they just come in as beasts just doing so much more. Would you remember do you remember when,

虽然我那时还没毕业，但以前有人会把'擅长谷歌搜索'写进简历对吧？就像现在年轻人天生会驾驭AI这套'钢铁侠战衣'，优势太大了。

like I mean, I I used to a little bit predates me, but, like, you used to put, like, Google search on as, like, a skill on your resume. Right? Like, you were, like, good at Google.

没错吧？

Like, right?

正是如此。掌握这些工具就像穿着战衣出生，年轻人优势明显。

Because you, like, grew up with Google. It's like, I think being, like, AI native and having your Ironman suit on and understanding how to leverage these tools is, young people are at a huge advantage.

特别是如果他们参与过模型训练，肯定还有额外优势。

Yeah. Especially if they're involved in training these models. I imagine there's some other cool advantage there.

是的。我想强调的是，根据我们从数千名研究员那里了解到的情况，他们在课堂上实际进行着研究。我们谈论的是国内顶尖学府的博士们。在他们擅长的领域，每小时能赚100、150甚至200美元。

Yeah. Well, I mean, just to hit on that, like, what we're hearing from, like, our thousands of fellows is, like, they're in the classroom. They're actually producing research. Like, we're talking about, you know, PhDs at the top institutions in the country. And, like, they they can make, like, $100,150, $200 an hour in their area, in their field of expertise.

这相当不错。当助教可能每小时赚25美元，但运用最新模型工作每小时能赚150美元。研究员们反馈说，他们正将许多这类洞见带入课堂，提升教学效果。更重要的是，他们开始学习如何利用这些工具推动自己的研究领域发展——他们认为这些工具能通过优化时间效率来促进研究突破。

It's pretty sweet. Like, you can make, like, $25 an hour being a teacher's assistant, or you can actually make $150 an hour breaking the latest models. And, like, you're learning what we're hearing from our fellows is, like, they're bringing a lot of those insights into the classroom to help them be more effective at teaching. More importantly, they're they're starting to learn how to leverage these tools to actually advance their area of research. So they believe that these tools can help them advance their area of research by helping them be more effective with their time.

所以，能边学习技能边获得报酬确实很酷。

And so, it is quite cool to get kind of paid to learn a skill.

在我讲述这个领域如何兴起之前（那确实是个精彩的故事），关于强化学习标注这个领域，你觉得还有什么大众尚未充分理解或特别重要的点吗？这个领域发展太快了——就像我说的，全球增长最快的公司中有不少涉足其中，Scale刚以300亿美元被收购。你觉得还有哪些关键认知是大众需要了解的？

Before I get to the story of how this all emerged, because that is an incredible story, is there anything else about this whole field of labeling, of reinforcement learning that you think people just kind of don't fully understand or you think that's really important. There's just like so much happening. Like I said, some of the fast growing companies in the world are in the space scale was just like acquired for 30, like sort of acquired for $30,000,000,000 Just like what else is there if there's anything that you think people need to understand?

总体而言，当你要求模型执行高级任务却未达预期时，通常意味着某个领域顶尖专家正在前沿实验室与世界顶级研究者合作，通过科学迭代改进模型。前提是他们已掌握人类所有书面记录的知识。只要AI在解决人类问题过程中存在障碍，就需要人类参与推动发展。模型不具备泛化能力——尽管这个领域会快速进步，收集的数据类型也会演变，但前沿探索依然令人振奋。

Generally speaking, like, anytime that you're interacting with a model and you're asking it to do really advanced things and it's not performing to your expectations, like, somewhere, there's probably an expert that is, you know, the top mind in that domain working directly for the best researchers in the world at the frontier labs, trying to understand and go through the scientific iteration process of how to make that better. And that the assumption there is that, like, they already have the entirety of human knowledge that's written and recorded. And so, you know, for as long as there are problems in solving any problem with AI, you know, that any human problem, there will need to be humans in the loop helping advance that. And, like, models don't generalize. I mean, they're obviously, the field will advance a lot, and the type of data they'll collect a lot will will evolve a lot, but it's it's pretty exciting at the frontier.

OpenAI首席产品官Kevin Weil在播客中说过让我印象深刻的话：你今天使用的模型将是你用过最差的版本。

Kevin Weil is on the podcast, the CPO at OpenAI. Yeah. And he he made this point that really stuck with me that the model of today is the worst model you will ever use.

这话太棒了。

I love that.

为什么？因为模型只会越来越好，这简直不可思议。现在我们明白原因了——正是你们的工作推动着进步。关于Scale公司还有个简单问题：

Why? Will only get better. Just just boggles the mind. And now we know why. These are getting better because all the work you guys are Just one quick question on this whole scale thing.

他们曾是这领域的主要参与者，现在被收购后Alex负责Meta的超智能项目。他们在这个标注领域还保持重要地位吗？还是已经退出竞争了？

I guess they were like, I don't know, the main company doing this. Now they're swallowed up, and Alex is running super intelligence and meta. Are they still like a big player in this labeling space? Are they kind of out of it and and that's Yeah. And so we've to

整个Scale团队都备受尊敬。这个领域有许多优秀公司。你问题的核心在于：如果你将研究团队和模型构建团队视为改进的关键，就不会希望竞争对手掌握你正在攻关的最新研究——这确实是业内普遍共识。

use the whole scale team. A lot of respect in for what what they built. There's many great companies operating in this space. I think to the core of your question, it's like, I think if you were building the most, if you viewed your research team and your model building team and the experiments they're running to be, you know, really the cornerstone of how you're improving, you probably wouldn't want the latest research of what you're trying to work on being, you know, being invested in by a by a peer. I mean, this is generally what we hear in this space.

因此我们见证了需求的惊人增长，我认为我们处于极其有利的位置。我们常说，人类数据唯一的护城河就是触达受众的能力。这个领域有许多小玩家和一些中型参与者，他们基本上都在投放TikTok广告、Instagram广告，花钱买谷歌搜索展示广告、YouTube广告。他们会问：'能帮我找到200个物理学博士吗？'他们能做什么？

And so we have seen a, an incredible search in demand are, I think, extraordinarily well positioned. We we like to say, like, the only the only moat in human data is access to an audience. Basically, are, you know, many, many small players in this space, some mid sized players in this space, and they're basically, you know, running TikTok ads, running Instagram ads, paying money for Google search display ads, YouTube ads. And they will be like, can you get me 200 physics PhDs? They what do they do?

他们只能做一件事。比如雇佣100名招聘专员，全部上LinkedIn发消息，再花几百万美元做效果广告投放。

They only can do one thing. They're like, you know, they have a 100 recruiters on staff. They all get on LinkedIn. They all send messages. They spend a couple million bucks on performance advertising campaigns.

当某个物理学博士刷Instagram时——其实你很难精准定位这类人群——突然看到'来训练模型吧'的广告，可能根本不认识这个品牌。我们快速获得市场共鸣的巨大优势在于：我们与1800万用户建立了长达十年的信任关系，他们信赖我们。我们积累了强大的品牌亲和力，用户活跃使用Handshake平台，我们掌握着他们学业表现和在校经历的丰富数据。

Somebody's scrolling their Instagram feed that's a physics PhD of which you can't target them that well, and they, like, see you know, come train a model. It's like, I've never heard of this brand before. The huge advantage that we've had and why we've resonated so fast in the marketplace is, like, we built a decade of trust with, you know, 18,000,000 people, and they trust us. And and we built a ton of brand affinity, and they use Handshake. They have an active profile, and we have a ton of information around their academic performance and what they've done in school.

因此我们能高效精准地触达目标人群，以远超同行的速度获取大规模优质数据。这种受众触达的竞争优势正在市场上产生强烈共鸣。

And so we're able to really target people really effectively and get to scale and volume of high quality data faster than anyone else. And I think that competitive advantage of access to an audience is really resonating in the marketplace.

本期节目由Anthropic赞助，他们是Claude背后的团队。我每天至少使用Claude十次：研究播客嘉宾、为播客和通讯稿构思标题、获取写作反馈等等。就在上周，我在准备采访一位重要嘉宾时，让Claude列出其他播客主持人问过的问题，避免重复提问。你每周要花多少时间整合研究洞察、客服工单、销售通话、实验结果和竞品情报？

Today's episode is brought to you by Anthropic, the team behind Claude. I use Claude at least 10 times a day. I use it for researching my podcast guests, for brainstorming title ideas for both my podcast and my newsletter, for getting feedback on my writing, and all kinds of stuff. Just last week, I was preparing for an interview with a very fancy guest, and I had Claude tell me what are all the questions that other podcast hosts have asked this guest so that I don't ask them these questions. How much time do you spend every week trying to synthesize all of your research insights, support tickets, sales calls, experiment results, and competitive intel.

Claude能处理极其复杂的多步骤工作。你可以扔给它100页战略文档要求提炼见解，或导入所有用户研究让它寻找模式。Claude 4及其新集成（包括全球最佳编程模型Claude 4 Opus）提供语音对话、高级研究功能、直接Google Workspace集成，现在还能通过MCP连接你的定制工具和数据源。Claude将成为你工作流的一部分。试用请访问claude.ai/lenny。

Cloud can handle incredibly complex multi step work. You can throw a 100 page strategy document at it and ask it for insights, or you can dump all your user research and ask it to find patterns. With Claude four and the new integrations, including Claude four Opus, the world's best coding model, you get voice conversations, advanced research capabilities, direct Google Workspace integration, and now MCP connections to your custom tools and data sources. Claude just becomes part of your workflow. If you wanna try it out, get started at claude.ai/lenny.

通过此链接注册，前三个月专业版可享五折优惠。网址claude.ai/lenny。好，这完美衔接到我想要探讨的话题：这个业务是如何诞生的。这是你在既有业务基础上开拓的新业务。

And using this link, you get an incredible 50% off your first three months of the pro plan. That's claud.ai/lenny. Okay. This is an awesome segue to where I wanted to go, which is just how how this business emerged. This is a business that you built on top of a business you've already had.

据我所知，你们原有业务年收入已达1.5亿美元，经营多年后发现了这个机遇。如今回看，这显然是个绝妙主意——实验室需要数据。

From what I understand, you were at like $150,000,000 in revenue. You've been at this for a long time. You found this opportunity. And now that I you know, looking back, it's like, obviously, is an amazing idea. Labs need data.

你们拥有顶尖专家资源，多好的机会啊。请谈谈你是如何意识到这可以且应该成为新业务方向，以及后续如何推进执行的。

You guys have the supply of incredible experts. What an opportunity. Talk about just how you first realized this was something that you could be doing and should be doing and then how you started to kind of execute down this path.

是的，我认为这是帮助人们开启、重启或推进职业生涯的自然延伸。在这个新的就业生态系统中，技能变现的方式将大不相同。要说到我们如何发现这个机会——因为我们拥有庞大的受众触达能力，随着世界从通才转向专才，我们已成为全球最大的专家网络平台。Handshake拥有超过50万博士用户，数量远超任何其他平台。

Yeah. I I think it's been a pretty natural extension from, like, helping people jumpstart, restart, or start their career. Like, you know, monetizing your skills in in this new employment ecosystem is gonna look very different in the future, and we wanted you know, to zoom into, like, how we discovered it, it's like we, because we have such a large access to this audience, and as the world shifted from generalists to experts, we're the largest expert network in the world. We have, you know, more PhDs. 500,000 of them use Handshake than any other platform.

我们有300万名在校及校友硕士生。于是我们开始看到各种中介公司联系我们，询问能否招聘我们的博士和硕士生。就像任何优秀的市场平台一样，我们开始将他们推荐到这些不同平台，但通过用户反馈发现体验非常糟糕——培训过程像交易般机械，付款方式模糊不清。

We have 3,000,000 master students who are, you know, in school alumni. And so we started to see all the, what I would call, like, middleman companies reaching out to us saying, can we recruit your PhDs and master students? And like any great marketplace, you know, we started sending them to these different platforms and started to really realize that, you know, from hearing from our users that, like, the experience was really frustrating. Like, training was very transactional. The payments were you know, there is very amorphous how you could get paid.

这些平台的项目完成率极低，流失严重。当我们发现公司通过协助这些平台已创造数千万小时的收益时，前沿实验室也开始直接联系我们试图绕过中介。这让我们意识到：我们完全可以直接服务我们的学者、博士和专家群体。

Like, there's immense amount of drop off in the process to actual project, like completion on these other platforms. So we started to we started to think the company was, you know, making tens of millions of hours from helping these other platforms. And we started to realize, really kicked it off was hearing also from the Frontier Labs. They started to reach out to us and started to go direct and trying to almost cut out the middleman. And we started to realize, well, you know, we could really serve our fellows, our PhDs, our experts.

我们坚信需要打造一个专家优先的平台来追求人工超级智能（ASI）和推动AI发展。世界需要这样一个地方——当实验室专注于跨学科突破时，每个人都能在此变现自己的技能与知识。实际上我在圣诞和新年期间就开始行动，当时我像个空中飞人到处奔波。

We could treat them. We we just believe there's there will need to be a platform, an expert's first platform in the pursuit of ASI and advancing AI. And there will need to be a place that everyone in the world could go to to monetize their skills and their knowledge as these labs are focused on improving in these, you know, in all these multidisciplinary outcomes. And, yeah, we we entered the business in really, like, I started doing it over, like, Christmas and New Year's. Like, that's when I sort of, like, flying around.

家人觉得我疯狂追逐各界领袖的行为有点野，但我们组建了来自人类数据领域的顶尖团队。一月份启动平台建设，五个月前开始商业化。如今我们合作的前沿实验室已达七家——基本涵盖所有研发顶级大语言模型的机构。团队和营收都呈爆发式增长，这就像在母公司内部二次创业的奇幻旅程。

My family kinda thought it was a little wild that I was, like, on on planes trying to chase different leaders, but we we built an incredible team of people that came from the human data world and really started building out our platform in January and then started really monetizing the relationships about five months ago. Fast forward to today, we're working with seven of the Frontier Labs, basically every lab that's doing that's doing work and building the best large language models. And the team has exploded, and revenue's exploded. And it's been it's been really a incredible ride kind of like running back new company inside of a company for the second time over again.

分享些数据，请纠正我的信息——听说你们四个月就实现5000万美元营收？现在八个月过去，首年有望冲击1亿美元？

Just to share some numbers, tell me if this is correct or if you're sharing these, but I heard that you hit $50,000,000 in revenue just four months into this. Today, we're at eight months in, and you're on track to hit $100,000,000 in revenue in the first year.

实际数字可能略低，但差不多。

I think we're below to that number, but yeah.

太惊人了！我甚至不知道有七家前沿实验室存在。

Okay. Incredible. And I didn't even know there are seven Frontier Labs.

四个月从零到五千万还算不错吧。

That's Zero to 50 is pretty good in four months, I think.

四个月五千万！标准线一直在刷新——放在去年堪称传奇，现在大家却觉得'还行吧'。

0 to 50,000,000 in four months. That's something. It's like the bar has been shifting constantly. Like, you know, a year ago, that'd be legendary. Now it's like, alright.

又来一个四个月五千万的案例，简直疯狂。顺便为不熟悉Handshake原始业务的观众做个背景补充...

Well, another one of these. It's 50,000,000 in four months. No big deal. It's truly insane. Just to zoom out one second for people to that don't know a ton about Handshake, the original business.

那是什么？就是你实际建立的那个网络，它是基于什么构建的？

What was that? Like, what was actually this network that you have that you set on top of?

是的。那个网络大约价值2亿美元。这个大概值2亿美元。

Yeah. That that network does about 200,000,000. This will do about $200,000,000.

好的，明白了。

Yeah. Okay.

所以我们有大约600名非常热情的团队成员专注于核心业务，简单来说，我认为这不是两个业务，而是一个整体。这个业务是什么？如果你是美国过去五到八年毕业的年轻人，你的手机上很可能有Handshake应用。

So that's we have, like, 600 ish, like, super passionate teammates that work on on the core business, which is you know, I I would simply do that. Was like, these aren't two businesses. I think it's, like, it's one business. But that what is that business? It's the numb if you're a young person in America that's graduated in the last five, six, seven, eight years, you probably have handshake on your phone.

你肯定知道Handshake是什么。在美国年轻人中它已经成为一个动词，大学生、博士生或硕士生都在用。我称之为'非连接图'——不像领英那样强调人脉和经验，领英第一个问题总是'你的工作是什么？'

You, like, definitely know what handshake is. It's like a it's a verb with young people in America. It's a verb with people that, like, are in college in their PhD or master's, you know, program. And it is I call it an unconnected graph, meaning, like, you don't need to you know, LinkedIn's very focused on, like, who you know and, like, what your experience is. The first question on LinkedIn is, like, what's your job?

但很多年轻人刚开始根本没有工作经历，也没有500个联系人可以添加到个人资料。而在Handshake上，你从探索开始：如何适应校园生活，发现自己是工程师但可能想成为产品经理，

And a lot of young people start off like they've never had a job before. Right? They don't they don't have, like, 500 connections to add to their to their to their craft. Whereas on Handshake, you start off, like, trying to discover and explore and figure out how to navigate through a school and figure out, oh, I'm an engineer. Maybe I wanna be a PM.

可能想去初创公司或大企业，通过同龄人和年轻校友了解利弊。Handshake是个高度社交化的平台，有群组、私信、个人资料、短视频和兴趣推送，全部聚焦于帮助18到30岁的用户建立职业自信，找到第一份、第二份工作。

Maybe I wanna work at a startup. Maybe I wanna work at a larger company. Like, what are the pros and cons you wanna learn from near peers and young alumni? And so Handshake's this, like, I call, like, a very, social platform with, like, groups and messaging and profiles and short form video and feed, all focus on your interests and helping really, like, build your confidence in your early career to find your first job, your second job, and to manage, you know, kind of 18 to 30, I would say.

这个业务运营多久了？

And how long have you that has that business been around?

已经十年了。

It's been around ten years.

十年啊。真是...你们正好在正确的时间占据了正确的位置，构建了现在极具价值的网络。这故事太精彩了。

Ten years. Okay. So it's just like, again, it just feels like such a holy shit. You guys are in the right place in the right time with the right network that is extremely valuable now. What an interesting story.

我觉得这就像是另一个有趣的例子——你长期从事某件事，突然AI出现，为你开辟了全新的方式来利用这些积累。这让我更看好BOLT和Stackblitz，他们花了七年打造浏览器操作系统，当时人们质疑‘谁需要这个？我们在干嘛？’结果AI时代来临，他们顿悟‘何不在浏览器用AI构建应用，直接生成产品？’

I feel like I feel like it's just another interesting example of you've been doing something for a long time and then all of a sudden AI is just like opens up a whole new way of leveraging something that you have been doing for a long time. It makes me think a little better about BOLT and Stackblitz, which was building for seven years this like browser based OS where you could run an OS in the browser. And they're like, I don't know, no one needs this. What are we doing? And then all of a sudden AI and they're like, oh, what if we build AI apps in the browser and just generate products for you with AI?

现在他们成了全球增长最快的公司之一。这太有意思了。我认为当下正是我们思考的契机：过往的积累能否借助独特优势，孕育出新的巨大机遇？

And now it's, I don't know, one of the fastest growing companies in the world. Yeah. So interesting. And so I think this is just an interesting time for our people to think about what have we done that may give us a new opportunity to build something huge based on this unfair advantage that we have.

随着公司规模、员工数和成熟度增长，内部孵化新事物其实非常困难。从零到一寻找产品市场匹配、快速扩张团队的方式，与运营拥有数百人的十年成熟企业截然不同。

I think also as your company grows in size and headcount and maturity, it's also, like, hard to, like, incubate something new inside of a business. Like, it's hard to you know? It's hard in so many ways. Right? Like, the way that you build zero to one and find product market fit and scale a team very quickly and is very different than the way that you run a a more mature business that has been around for ten years with hundreds and hundreds and hundreds of people.

我特别享受第二次在企业内部重走创业路的过程。我们拥有巨大战略优势：零获客成本，转化率和留存率远超其他平台——这源于深厚的用户认同。

So I've really had a ton of fun and and been spent a ton of passion in, like, running it back again for the second time inside the business. And then, yeah, we have this massive strategic advantage, which is, like, no customer acquisition costs. And we have like much higher conversion rates and retention than like any of the other platforms by a large margin because we have such consumer affinity.

这里有两个方向值得探讨，我先说后者。关于数据标注工作的来源，最直接的是专家人工创建数据，另一个常见方式是像Scale等公司采用的国际廉价劳动力。

There's actually two threads here I wanna follow. I'm gonna follow the second one first. This idea of where this data labeling work can come from. This isn't a really clear, simple, understandable one, which is just experts sitting there creating data. Another one that I know a lot of other companies in the space use scale, I know, especially is just like low cost labor internationally.

除了这两种，还有其他方法吗？业界是怎么解决的？

Are there other methods for doing this that isn't one of those two? How are other companies doing this?

如果想打造高毛利的高质量业务，行业领头羊的做法不可持续——他们雇200名招聘专员在LinkedIn逐个挖人，每月烧数千万美元投广告，只因缺乏品牌信任。

I think if you, like, care about building a really high quality business and having, like, good gross margin and high quality growth, the ecosystem here is one of the leading players has they have 200 recruiters. It's unsustainable. There are 200 people on LinkedIn sending individual messages to acquire these people because there's no brand. There's no trust. They spend you know, they're spending tens of millions of dollars a month on performance advertising, Google Ads.

就为了找专家？现阶段主要是找专家对吧？

To find experts and to find folks. To find experts. And it's experts mostly at this point.

然后他们给这些专家提供的体验，就像让菲律宾人画停车标志框——顶尖税务会计师可不愿被当作廉价劳工。我们打造的体验植根于社区和高品质培训，比如MIT博士生其实并不完全掌握工具使用。

And then they put them onto an experience that, like, is treating them like they're drawing, like, boundary boxes around stop signs in The Philippines. Like, you know, the the frontier tax accountants don't wanna be treated like low cost international labor. Right? And I I don't I mean, I don't think anyone enjoys that process. And so, you know, the ability to build a experience that's rooted in community, that's rooted in, like, high quality training.

其他平台耗费数千小时获取的用户，直接扔进项目毫无培训。我们从第一天就专注构建专家网络，这与我们‘职业启航/重启’的核心业务将产生深度协同效应。

Like, if you're getting your PhD at MIT, chances are you're just not being taught well enough on how to use the tools. Now you can't break the models. It's just like, you know, the other platforms, you know, they're spending thousands of hours to acquire an individual user, and then they're put right into a project with no training. So we just started from day one at building like this expert. We believe there'd be a deep network effect here that's very connected to our core business of starting, jump starting, or restarting your career.

就像，你知道的，你进来后建立一个档案，看到社区里有各种群组和学习动态。你会加入一个由背景相似的同龄人组成的实际学习小组，学习如何互动。我们提供教学设计工具包，过程中会有试错。如果不行，就会被安排到项目中去。

And, like, you know, you come in, you build a profile, you see the community, there's, you know, groups and a feed of here's how people are learning. Like, you come into actual individual cohort with, like, peers that that look like you and have your similar background. You're being taught on how to interact. And there's, like, a trial and error, and it's we have an instructional design kit, so you can't do it. Then you're put on the projects.

我们正在构建特定的数据通道，预先准备数据并出售给所有实验室。我们可以自己生产一单位数据，几乎像电影制作那样付费获取。我们确保数据质量极高。

We're building, like you know, there's certain swim lanes where we're actually prebuilding data and selling that data to all the labs. We So can do this thing where we produce one unit of data ourselves. We pay for it almost like a movie production. We pay for a unit of data. And then we make sure it's very high quality.

我们自行进行后期训练，生成一系列数据规格，并将这些独立数据包卖给多个实验室。表现优异者会被推荐到客户项目，这些客户只要机器学习领域的顶尖人才。

We run our own post training on it. And then we produce a bunch of specifications of the data, and we actually sell that individual package of data to, like, many different labs. And so that you get put on a project like that. Once you're doing a really, really good job on our projects, oftentimes, that will put you on customer projects where we they only want the best of the best people in machine learning. Right?

从我们的项目过渡到客户项目，形成了强大的客户获取链。深入探讨的话，关键在于几个核心要素。

And then they go from our projects to their projects. And so there's a huge customer acquisition. I mean, it's a basic. You know, are going deep on your podcast. So just to talk about it, it's like, you know, you really have a couple of things that matter.

客户获取成本（CAC）和用户终身价值（LTV）至关重要。在这个行业，LTV计算很简单：取决于人员留存率和参与项目数量。善待并培训好人员是关键。

You have cost cost to customer acquisition at your CAC, and you have your LTV, like the lifetime value of a user. And an LTV is calculated pretty simply in this business. Like, it is based on the retention of a person and how many projects they can participate in. So if you treat people really well, you train them really well. Right?

我们与1600所大学合作，覆盖全国92%的顶尖500所学校及几乎所有社区学院，客户获取成本为零。凭借长期建立的品牌信任，转化率极高。满足他们的期望是我们的责任。

Like, well, a, we have no customer acquisition cost because we partner with 1,600 universities, power 92% of the top 500 schools in the country. We power almost every institution and community college in the country. We have no customer acquisition cost to acquire the people. We have ton of brand and trust with them built up, so they convert at, you know, really, really high rates. And then if you treat them really well and because that's what they expect from us.

学校认可Handshake平台。我们必须善待学员——大学不会容忍我们亏待合作学员。因此我们的LTV、复购率和项目留存率极高。相比那些每月花费数千万美元招聘的领先供应商，这种结构性优势非常显著。

Like, they know handshake. Their school ties handshake. Like, we we need to treat we we care about treating these people well, but, like, the universities would not tolerate our partnership with these with these fellows unless we treated them well. So you you put them into this process where our LTVs and repeat engagement rate and retention rate on different projects is is really high. And so these structural advantages are quite significant when you contrast, like, a leading provider that has, like, 200 individual contributing recruiters and are spending tens of millions of dollars a month on performance marketing.

这就是我们取得巨大成功的原因。

You know? So that's, I think, why we've seen so much success.

这极其有趣。如你所说，过去市场注重低成本通用型人才（如标注数据框的工作），现在已转向专家型人才。Scale等公司曾优化通用训练数据模型，而你们专精于专家级数据领域。

That's extremely interesting. And it feels like, as you said, there used to be a big focus on generalists, which is people anywhere in the world for low cost can do the work, like draw bounding boxes around things. Essentially the market has shifted from low cost generalists to experts. A lot of these companies like Scale were optimizing for general work model training data. And you guys are set up to be extremely good at expert based data.

你们在正确的时间、正确的地点拥有正确的供给。真是了不起的商业模式。

And so you're in the right place at the right time with the right supply. What a business.

干得漂亮。可以说在一个业务内部再建立第二个业务并不容易，但

Nice work. Would say it's not been easy building business two inside of business one, but

让我接着这个话题展开。这正是我想探讨的方向。当时具体是什么情况？你开始注意到模范公司主动联系你的团队，发现人们在这个领域的其他公司那里遇到困难，于是你们就想，或许我们应该涉足这类业务。

So let me actually yeah. So let me follow that thread. That's where I wanted to go. What was just that like? So you started noticing that model companies were coming to your people, that people are having hard times with some of these other companies in this space, and you're like, oh, maybe we should be doing this sort of thing.

这个想法最初是如何萌芽的？你们又是如何开始验证这个想法是否可行的？具体操作上，

How did that just like initial inception start, and how did you start to explore that idea and to see if it was a real thing? Tactically,

要知道，我们当时与许多中介公司合作。正如我之前提到的，我们开始看到需求——前沿实验室直接联系我们，试图绕过中介获取更高质量的数据。当我们把这些线索串联起来，意识到我们可以为研究员打造更好的体验：直接对接实验室建立客户关系，剔除中间环节，既提升实验室体验，也优化研究员体验，长远来看还能惠及我们网络中的百万家企业。

you know, we were working with many of the middleman companies doing work. We started to see the demand as I talked about earlier. We we started to see direct outreach from the Frontier Labs reaching out to us trying to cut out the middleman in their pursuit of getting higher quality data. When we started to put together the dots on we we could build a way better experience for our fellows. We could serve them directly to the labs and build a direct customer relationship with the labs and basically cut out the middleman and provide a better experience to the labs, provide a better experience to our fellows, and provided a better experience long term to our, like, our million companies in the network.

另外你可能也考虑过技能提升的问题——这个领域会如何发展？去年十二月我们开始深入调研，通过专家访谈等方式夯实基础。

And, you know, and and you might you might think about just, like, upskilling and reskilling. What's gonna happen there? So we want that into the space. We started in, you know, really December exploring and learning more about it, like, expert calls and hammering down. You know?

我聘请了三家专业机构（包括Alpha Sites和GLG），利用现有资源与顶尖研究者展开系列访谈。作为规模较大的企业，我们年收入达2亿美元的核心业务提供了加速学习曲线的资本。五个月前我们开始与业内公认的顶级实验室合作——你猜是谁？

I hired, like, three expert firms, alpha ins alpha sites and, like, GLG and started doing a bunch of calls with the latest researchers because we had resources. Like, one of the cool things about being a larger company is, like, we we have financial you know, our core business is $200,000,000 ARR. So it's like, you know, we we we had resources to be able to, like, accelerate the learning curve here. And then we started working with the arguably, like, the number one lab about five months ago. I wonder who that is.

没错，和Ben Farquhar打赌会得到不同答案。现在我们正与Zevran的前沿实验室合作，核心目标就是全力扩大规模。

Yeah. Yeah. I wonder who it is. Betting with Ben Farquhar, you get different answers. Working with the number one lab and have just you know, now we're working with Zevran on the Frontier Labs, and the number one thing we're trying to do is just focus on, like, scaling up.

团队已从最初的4-5人扩展到75人以上，上周一就有12名新人入职。当前市场存在近乎无限的需求——只要能产出高质量海量数据，产品基本不愁销路。因此我们重点确保选择正确的长期战略，避免扩张过快损害与前沿实验室建立的信任。

I mean, we've gone from four or five people working on this to 75 plus people working on it. We're trying to I think we had, like, 12 people start last Monday. It's like we're you know, we are so bottlenecked on just meeting this opportunity because in this market, there's there's essentially, like, unlimited demand. Like, if you can produce high quality volumes of data, you most likely will be able to sell whatever you produce. And so on our side, it's like we're really focused on making sure that we pick the right longer term strategy, making sure that we don't grow too fast as to erode the trust that we built up with these frontier labs.

虽然过程充满挑战，但也很有意思。

Yeah. But it you know, it's it's been it's been fun.

你提到在现有企业内开展新业务非常艰难。具体哪些方面最具挑战性？虽然已经谈到一些，还有其他困难吗？

You said it's also been really hard to start this business within an existing business. What it's been what's been hard? What's been hardest? You touched on a couple of these elements already, but what else?

我觉得在这件事上我更多是跟随直觉行事。Handshake的故事是我们必须签约1600所大学。所以我必须学会如何成为历史上增长最快的高等教育公司。我们签约了1600所学校，然后必须建立雇主业务，想办法向所有财富500强公司推销——其中70%会付费使用。

I think I just kind of followed a lot more of my intuition around this, doing this. I mean, the story of Handshake was we had to sign up 1,600 universities. So I had to learn how to be, like, the best we are the fastest growing higher education company in, like, history. So we signed up to 1,600 schools. Then we had to build an employer business where we had to figure out how to sell the 100% know, 70 you know, all these Fortune 500 companies use it, like, 70% of it pay for it.

所以必须操心如何向高盛、通用汽车、谷歌这些世界顶级企业进行高端销售——这和向大学推销完全不同。接着我们还要学会打造一个出色的学生社交网络——什么样的信息流最理想？群组消息该是什么样子？

So had to worry about, like, upmarket sales to, Goldman Sachs and General Motors, Google, and the biggest companies in world, which is totally different than selling universities. And then we had to learn how to build, like, an incredible student, like, kind of social network. Like, what is the what is the best feed look like? What does group messaging look like? You know?

我对这种从零到一的过程有点熟悉。市场平台往往包含多个从零到一的阶段。有时我开玩笑说，真希望我们是家网络安全公司，只需面对一个买家、一个产品。但在市场平台里，就像你在Airbnb的经历，必须服务三方客户。创办Handshake时启动这三项业务，我的一个警示就是：我必须亲力亲为。

So we had I felt a little bit of familiarity in this, like, of zero to ones. Oftentimes, like, marketplaces are, like, many zero to ones. Sometimes I dream that we just, like I actually don't dream, but I make a joke that, like, I just wish we were, like, a cybersecurity company. We had, like, one buyer and just, one product, and it was just, like, you know, we had to in a in a marketplace, you have to serve three different sides, you know, from your time at Airbnb. And so one of my warnings in spinning up these three different businesses in starting Handshake was, like, you know, you I was pretty hands on.

所有人都直接向我汇报。我常强调：'我不是要当老板，只是想成为房间里另一个聪明人。'我们组建了顶尖团队，成员都是这个领域的资深人士，曾在多家知名人力数据公司担任要职。大家都清楚我们的结构性优势，重点在于先为单一客户提供高质量数据，再考虑扩展。

So, like, you know, everyone reported directly to me. I really did not try to be like I I really said in a lot of meetings, like, I'm not trying to be the boss. I'm just trying to be, like, another smart guy in the room. Like, I hired I was just we've hired an incredible team of people that have have spent a lot of time in this space and have been big leaders at a lot of the human data companies in this space. And so everyone saw very clearly the structural advantage that we had, and a lot of the focus was on making sure that we could deliver high quality data to one customer before we expand to anyone else.

我们必须对很多机会说不。核心业务部门也有很多人——理所当然地——存在制衡机制，总有人想参与决策。当然不是所有人，这么说有点夸张。

Like, we just you had to say no to a lot of things. And then you also had a lot of people in the core part of the business that rightfully so, like, there's these checks and balances that there's a lot of people that, like, try to get involved. Right? Like, everyone wants to say not everyone. This is a stretch.

但拒绝其实很简单。就说'这周/月没法优先处理，已有既定事项'。基本上，除了少数例外，我直接全权负责这个新建的部门。

But, you know, it's easy to say no. Right? It's easy to be like, I I can't prioritize that this week or this month. I have an existing set of priorities. So, you know, I essentially, with the exception of a few things, like, just came straight into this new org that I built.

所有人在原业务部门都不再担责。新公司的每个领域都有明确的第一责任人。虽然现在与集团其他业务有更深度融合，但我们当时在独立办公区——全员每周五天到岗，经常周末加班。招聘标准也不同：明确告知这是份24/7的工作。

Everyone did not have any responsibilities in the existing part of the business. It was extremely clear who was, like, the directly responsible individual across each area, the new co. And now we've got deeper coupling and integration points across the rest of the business, but, like, we sat in a separate part of the office. You know, we are we you know, everyone's in the office five days a week, a lot of weekends. There's a totally different expectation in hiring talent too where it's like, hey.

这是家早期创业公司。薪酬结构也不同，与新业务的阶段性目标挂钩。

This is a this is a twenty four seven job. Right? Like, this is an early stage company. Wait. The compensation was also different too and based on, like, hurdles in this new business.

因此员工都像新公司的所有者。现在依然保持高度敏捷和扁平化管理——负责某个职能不代表就是项目第一责任人。我们会选择最能推动计划的人担任DRI，不论其原本职能。

So people felt like owners creating the new co. And, yeah, it's like it's still extremely nimble, very, very flat. You know? Just because you want run one function doesn't mean you're the directly responsible individual on a project. We pick the best person who's most capable of driving an initiative forward regardless of the function to be the DRI.

我们更注重数据指标。当初做Handshake时，我们长期抵触那种周度/月度/季度的运营节奏。但做Handshake AI时，我们从早期就严格以数据和指标驱动。团队里的Sahil在这方面做得非常出色——特别表扬下Sahil。

We're a lot more metrics oriented. You know, when I when I built Handshake, we we we resisted this, like, operating cadence for a long time, like this weekly, monthly, quarterly operating cadence. With Handshake AI, we've we've been way more focused on, like, operating with data and metrics and rigor from an early stage. This gentleman named Sahil on our team who's been doing an incredible job with that. Shout out Sahil.

向Young致敬。向Paco致敬。没错。

Shout out Young. Shout out Paco. Yeah.

好的。这太不可思议了。那么，有几个关键因素让我们在一家成立十年的公司里取得成功。顺便说一句，你们传统业务的年收入已达2亿美元。正如你所说，这项新业务第一年就将突破1亿美元。

Okay. This is incredible. So a few kind of elements of what allowed us to succeed within a decade old company. And by the way, so you're at 200,000,000 a year in revenue with the traditional business. You're gonna, as you said, blow past 100,000,000 in the first year of this new business.

所以疯狂的是，如果势头持续，头几年内你们就会超越花了十年时间打造的核心业务规模。太惊人了。要实现这一点，我注意到几个关键：首先你完全处于创始人模式，作为CEO亲自领导新业务。

So it's wild that in the first couple of years, if things continue to go this way, you'll exceed the size, run rate of a business that took you ten years to build. Incredible. To make this successful, a few of the things I noted as you were talking. One is clearly you were just like in founder mode. You were the CEO of this company.

你没有委派他人，而是亲自组建专职团队，明确告诉他们'这就是你们的新工作'。你们在独立办公区运作，建立指标化节奏——严格追踪进展、目标和KPI等。还有哪些你认为至关重要的成功要素？

You're like the lead of this new business. You were taking, you weren't delegating it to someone, hey, go start this thing. You dedicated people here, we're gonna pick people, you have nothing else going on, this is your new job, you're gonna work on this stuff. You worked in different part of the office, there's there's a metrics based cadence, it's just like let's stay really diligent about here's how it's going, here's where we're going, here's our track, here's our KPIs, things like that. Anything else there that you felt really important to making this work?

因为想必很多公司都会尝试这种模式。我很好奇你们还发现了哪些关键成功因素。

Because a lot of companies are gonna try to do this, I imagine. And so I'm curious what else you found important to make this work.

是的。我认为关键在于完全独立——独立工程团队、设计团队、客户运营团队、财务团队。早期所有部门都独立运作，每个人只有一个使命：让NGAI成功。

Yeah. I mean, I just really believe it's separate and everything. Like, separate engineering team, separate design team, separate accounts and operations team, separate finance team. Like, early on, everything was separate. People only had one job and one job only, and that was making NGAI successful.

后期才逐渐有些整合点。我的核心业务高管团队非常出色，现在参与度越来越高。但那些长期经营Handshake的高管们负责主业，而我80%以上的精力都专注在新业务上。我们还聘请了像Avery这样优秀的工程主管，重点招募了很多创业者。

We had a couple integration points more in that. I have it. I have an incredible executive team in the core part of business, and now there's becoming more and more involvement. But, like, you know, I the our executives that have built handshake for a long time, like, ran the core business, and I focused 80 plus percent of my time and attention on just this. And, you know, we hired an incredible engineering leader like Avery who you know, we we focus on hiring a lot of we have a lot of entrepreneurs.

就是那些曾创立过公司的人——这点非常重要。我们特别青睐有早期公司经验、适应不确定性的优秀人才。

People that have started companies inside the company. Or pardon me. People that started companies before. Like, that was huge. A lot of familiarity with hiring talent that have, like, only worked at early stage companies before that feels super comfortable with ambiguity.

我们也更直白地告知'这会很混乱'——在全员大会上直面这个事实，与团队坦诚沟通。我们有独立的全员会议、独立入职培训、独立招聘团队。

We were also, like, way more upfront around this is gonna be chaotic. Like, just, like, owning that narrative, like, in front of all hands at the core company, owning it directly with the team. We have a separate all hands. We have separate onboarding. We have a separate recruiting team.

基本上完全独立运作，只有少量连接点。我认为这绝对关键。我们抽调了核心业务的部分精英，直接告诉他们：'抱歉，虽然你们热爱原团队...'

Like, you know, everyone was essentially you know, I had some connection points, but mostly separate, and I think that was, like, absolutely critical. We took some of the top people. I mean, we've got great people in the core business. We took some great people from the core business and and basically said, sorry. Like, I know you love your old team.

我知道你热爱你所做的事。比如，你愿意加入我们Hingeck AI吗？他们彻底摒弃了以往对语音的响应方式，转而采用新方案。当业务开始规模化并面临挑战时，这点对工程团队变得至关重要——我们发展得太快了。我们抽调了一些最具创业精神的首席工程师、骨干工程师组成特遣队空降支援，能邀请核心业务中最优秀的人才加入，这种感觉实在太棒了。

I know you love what you're doing. Like, will you join us in Hingeck AI? And they, like, completely forego their historical response to voice and came over. That became really critical with engineering when things started to scale and topple and, like, you know, we're growing so quickly. We took some of our top senior engineers who are very entrepreneurial and principal engineers, staff of engineers, like, parachute them in, and, you know, that that's been, like it's been awesome to be able to, like we have it's been awesome to, like, ask some of the most talented people in the core business.

就像说'嘿，想过来一起干这个吗？'有时他们会拒绝。他们会说'我不想周末总加班，不想像在这个项目里那样熬那么多凌晨两三点'。

Like, hey. Do you wanna come over here and do this? And sometimes they say no. Like, they're like, I don't wanna work, you know, most of the weekends. I don't wanna be on the number of 2AM, 3AM nights we've done in this business.

这其实是常态。不是所有人都愿意接受这种强度，但我们事先说得很清楚——这就是团队的要求。节奏快得疯狂，但如果你想加入硅谷增长最快的企业之一，这就是机会。

It's it's it's bet I mean, it's quite regular. Like, people sometimes don't wanna commit to that, but we've been upfront. Like, here here are the expectations for this team. It's a it's a you know, it's an insane pace. If you wanna be a part of one of the fastest growing, you know, businesses in Silicon Valley, you can join it.

主人翁意识也极其重要。要完全掌控结果，我们奉行'零侥幸'原则。有段时间我们甚至在白板上倒计时全年天数，因为这样的机遇千载难逢——无限需求等着我们去实现，成败全在执行力。

Owner the ownership too has also been huge. Like, owning this outcome and, like, we have we have this model, like, leave nothing to chance. Like, I always for a while there, we, like, drew the number of days in the year on the whiteboard, and it was like, there will never be a time like this. I've never seen anything like it. I doubt I'll ever feel anything like this in business again where there's unlimited demand, and it's just our ability to execute against it.

所以我们有个口号叫'杜绝侥幸'。如何确保半年后回首时毫无遗憾？比如立刻飞去见客户，熬夜冲刺，反复核查数据六遍，多开发个实用功能——这些就是答案。

And so we had this motto, like, leave not to be a chance. Like, how do you how do you make sure that three months or not, six months early, you have, like, no regrets? Like, get on the plane to go talk to a customer. Like, make the late night push. Check the data six times over again, like, ship the extra feature that helps.

我们还建立了强大的庆祝文化。组织架构很扁平，大家直接公开表扬贡献者，围绕工作成果营造出超有趣的氛围，效果非常好。

And really a huge celebratory culture too, like, calling people out across it's very flat. Right? So there's there really isn't this principle of, you know, the there's so many people putting up points, like, directly calling out the people that are putting up points and creating a really fun environment around impact, I think, has been has been awesome.

你们'零侥幸'的理念，我想部分体现了对所做之事可信度的重视。当人们相信数据绝对可靠时，胜利自然水到渠成。这确实是你们构建的核心价值。听你描述就能感受到——既是巨大机遇又是显著优势，但随之而来的压力肯定也大得可怕，毕竟'绝不能搞砸'。

Believe nothing to chance piece, I imagine, speaks partly to the value of trust in what you're doing. People are gonna like you win if they can trust that your data is awesome and great and consistent. And I could see why that ends up being such an important part of what you're building. And like just listening to you describe this, I understand like it's there. So it's obviously a massive opportunity, obviously a massive advantage you guys have, and just, like, the stress that comes with that burden also, imagine is very high of just, like, this is we can't screw this up.

没错兄弟，绝对不能。Handshake应该成为营收数十亿美元的上市公司。我们必须持续...这其实也反哺核心业务。长期来看，我们正在打造互联网上最卓越的职位匹配平台。

No. Dude, cannot cannot yes. Handshake should be a business does billions of dollars revenue as a public company. Like, there should you know, we should be able to continue to I mean, and it also helps our core business. Like, the longer term opportunity that we see is it's connecting it's building the best job matching marketplace on the Internet.

这可能是全球最重大的问题之一——劳动力供需匹配。人们生命中最宝贵的时间都在工作，而AI将彻底重塑求职应聘流程，我们正引领这场变革。

It's like, you know, it's probably one of the largest problems in the world, like labor supply matching. Like, it's where people spend most of their time and energy, just hours of their life. They spend it at work. The process of, like, searching for a job, applying to a job is gonna be completely reinvented with AI. We've been leading the charge there.

比如AI面试官收集技能数据、深度了解经历，通过工作模拟帮助雇主发现最佳人选。现在招聘经理还要审200份简历？别开玩笑了！五年后绝对不可能再有这种事。

Like, you know, an AI interviewer that's collecting skills and actually asking about your experiences, doing work simulation experiences that, like, help employers find the best candidates. Mean, I don't know the last time we've done this, but, like, the hiring manager process, like, reviewing 200 resumes. Like, are you kidding me? Like, I'm gonna sit there and review 200 resumes. Like, not a chance five years from now.

对吧？比如让学生手动制作封面，根本不可能。所以需要有一个市场平台来高效连接供需双方，将人才与机遇匹配起来。我们对此充满激情，认为这里蕴藏着巨大的影响力机会。

Right? Like, students manually making cover, like, not a chance. Right? So there will need to be a marketplace that wins in connecting, you know, supply and demand and, you know, talent with opportunity. And we think and get psyched about, like, the opportunity for impact here.

这就是我的故事。我上过社区大学，自费支付学费，就读于密歇根上半岛一所默默无闻的学校。后来我在Palantir实习过。

Like, that's my story. Like, I went to community college. I paid me with your school. I went to a no name school in the Upper Peninsula Of Michigan. I worked at Palantir as an intern.

这彻底改变了我的生活。我创立Handshake的初衷，就是想让每个人——无论你认识谁、父母做什么、就读什么学校——都能更容易找到好机会。我认为AI将实现匹配效率的阶跃式提升，而我们的人力数据业务正为优化匹配奠定基础。许多人力数据业务的成果正在整合到核心业务中。

It totally changed my life. And, like, I started Handshake because I wanted to make it easier for, like, anyone regardless of who you knew, what your parents did, what school you went to to find a great opportunity. And I think AI, like, totally step function improvement in matching. And I think that our human data business is really serving as, like, the foundation for improving matching. Like, a lot of things that we're doing in the human data business are being integrated to our core business.

这将改善雇主招聘效果，长期来看能为他们节省数十亿美元。同时也让学生获得更好的体验。我们必须把握这个时代机遇——我们核心团队和新业务团队仍保持着充沛的精力、热情与斗志。这也是我们内部反复传达的理念。

I think that's gonna improve outcomes for employers, save them, you know, in the aggregate, like, billions of dollars over time. And I think it makes the experience way better for students. So it's, it's just like we have to meet the moment. Like, you know, I we still have the stamina and the excitement and the passion internally in our core and in the new business to, like, go charge after this. And that's a lot of the message we've been sharing internally.

现在是时候全力以赴了。这是一生难得的机遇，我们团队必须抓住这个历史时刻。

It's like, it's it's time to amp it up. It's time to, like this is a once in a lifetime opportunity to be positioned as well. And, like, we're we we are gonna need the moment as a team.

确实如此，这感觉就像千载难逢的机遇。趁此机会请教几个相关问题：很多人都在思考，我们会耗尽数据吗？模型发展会停滞吗？

It really is. This is very much feels like a once in a lifetime opportunity. Let me ask a few other questions along these lines that are something I've been thinking about, something that a lot of people think about just while I have you. There's always this question of, will we run out of data? Will models stop advancing?

我们会遇到技术瓶颈吗？真的会有通用人工智能（AGI）或超级智能（SGI）的突破时刻吗？首先，你认为数据会枯竭吗？是否存在某个临界点，人类无法再产生更多知识和数据来喂养这些模型？顺着这个思路，你认为当前模型发展面临的最大瓶颈是什么？

Are we gonna hit some plateau, there's not actually gonna be some AGI moment, SGI moment? So well, first of all, you think we'll run out of data? There's a point at which we just can't produce more knowledge and data to feed these models. And kind of along those lines, what do think is the biggest bottleneck to advancing models faster and further?

是的。我认为所需的数据类型将不断演进——会是CAD文件、科学工具使用数据（用于自动化科学发现和药物研发）、科学仪器上的特殊操作系统数据...

Yeah. I mean, like, it's just the type of data we're gonna need is gonna evolve. It's gonna be CAD files. It's gonna be scientific tool use data as they are trying to automate scientific discoveries and drug discovery. It's gonna be esoteric operating systems that exist on scientific tools.

我特别看好这种渐进式指令跟随的发展轨迹。未来所需的数据类型将大幅进化，更不用说多模态数据（视频/文本/音频）了。目前音频数据的需求就极其庞大。数据形态必将持续演变。

It's gonna be so I I love this, like, trajectory and, like, stitching together step by step instruction following. Like, you know, there will need the type of data we're gonna need is gonna evolve a lot. And we haven't even talked about, like, multimodal and video and text and audio. Like, audio is is is just a huge demand for audio data right now. So the type of data is gonna evolve.

确实。我经常使用语音模式，那是我默认的ChatGPT交互方式

Yeah. I use voice mode all the time. That's on my default chat GBT experience

刚聊到这件事真是太棒了。太棒了。我妻子周日刚生了孩子，语音模式简直帮了大忙。我是说，每晚每隔两小时，速度就像‘我又有问题了’。语音模式起了大作用，所以我直接启用了语音模式。

just talking to It's amazing. It's amazing. I just had a baby on we or my wife had a baby on Sunday, and voice mode's been incredible. I mean, every night at you know, every two hours, speed is like, I have more questions. Voice mode's been huge, so I shot off voice mode.

而且，是的，这类数据将会大量收集或发生巨变。我认为合成数据在可验证领域有其作用，但我们从企业那里反复听到的是，他们的合成数据不会占据主导。它不会成为未来十年企业能从中榨取数百亿数千亿美元价值的东西。

And, yes, the type of data is gonna collect a lot or change a lot. I think synthetic data has a role to play in, like, in verifiable domains, but, like, what we consistently hear from companies is, you know, their synthetic data is not going to dominate. Like, it's not going to be like there there's an there's there's billions and billions billions of dollars of value to extract as a company over the next decade

在跟随AI发展前沿的同时，首先我要大大祝贺你刚有了孩子，你妻子几天前刚生产，同时你还在经营这个疯狂增长的企业并参与这次播客对话。非常感谢你抽时间。

and following the frontier of AI development. Let me first say just huge kudos to you for just having a kid, your wife just having a kid a few days ago and building this business that is growing bananas and doing this podcast conversation. I really appreciate you taking time.

当然。

Of course.

在我们进入激动人心的快速问答环节前，还有什么我们没谈到的、你认为对听众有帮助的内容？或是你想再次强调的某个故事或观点？

Is there anything else that we haven't covered that you think might be helpful for folks to hear, or a part of your story that you think might be helpful for folks to learn from, or something you may want to just double down on that we've talked about before we get to a very exciting lightning round?

我想说的是，我一直热衷于谈论并帮助人们创业。对于正在收听或阅读这期播客的年轻创业者——因为自2020年起我就是这个播客的读者——现在正是AI带来的机遇时刻。

I mean, the thing I always love, like, talking I'm really passionate about, like, people starting companies and helping them do so. And, like, I just think in this moment right now with AI, like, for young entrepreneurs that listen to that read this podcast, because I've been a reader since 2020. We looked.

我们确实查证过，这太不可思议了。

I yeah. We did check. That's incredible.

你是长期读者啊。我充满好奇，特别爱钻研你的访谈。关键在于专注做真正能帮助他人的事。借助AI，改善人们学习方式的机遇将会层出不穷，你懂吗？

You're a long term reader. I'm just, like, so curious and love sucking out your interviews. But it's like, you just focus on doing something like a meaning, like, that really helps people. And I think with AI, there's, like, gonna be so many opportunities to improve the way people learn. Like, just you know?

我真心渴望把Handshake打造成不仅是个卓越企业，更能解决重要社会问题的平台。这是我的呼吁——如果有人想获取相关建议或联系我，我很乐意交流。

I'm just really passionate about trying to make Handshake a platform that is not only, you know, incredible business, but it's also something that really helps solve a societal problem that matters. And, that's my one one shout out here. If anyone wants advice on how to do that or wants to reach out, I'm happy to chat.

好的。所以这是在提供关于AI领域创业的建议对吗？这个邀约？太棒了。好的。

Okay. So this is an offer to share advice on starting companies within AI. Is that the offer here? Just for Yeah, it'd be great. Okay.

我不知道你还有多少时间能留给即将涌来的数十万人，但我很感谢你的提议。这真的很棒。在我们进入激动人心的快速问答环节前，还有什么要补充的吗？

I don't know how much time you have for the hundreds of thousands of people coming your way, but I appreciate the offer. That's very cool. Anything else before we get to a very exciting lightning round?

没有。

No.

好的，既然如此，加勒特，我们就要进入激动人心的快速问答环节了。我们为你准备了五个问题。准备好了吗？准备好了。你平时最常向别人推荐的两三本书是什么？

Well, with that, Garrett, we reached our very exciting lightning round. We've got five questions for you. Are you ready? Ready. What are two or three books that you find yourself recommending most to other people?

我特别痴迷彼得·蒂尔的《从0到1》。创业时读了这本书，还看了他在斯坦福的创业课程视频。在那个互联网上还没有铺天盖地创业指南的年代，他的观点简直酷毙了。也超爱《鞋狗》，你知道的，就是那种创业的真实写照。

I'm a I'm a sucker for Peter Thiel's zero to one. I read it when I started the company and watched Peter Thiel's, like, startup school class at Stanford. He taught back in the days where there wasn't everything written on the Internet about how to start companies and, like, just think he's was the coolest. Love love Shoe Dog. Like, I think it you know?

还有《创业维艰》，当然啦。不过这些都是比较常见的书。

It's a pit of me of, like, starting a company. Hard things about hard things, obviously. But these are these are all quite common books.

但也是经典之作。本·霍洛维茨马上要来上播客了，正好聊聊《创业维艰》。太酷了，《创业维艰》。

But, also classics. Ben Hurwitz is coming on the podcast. Talk about hard things about hard things. Super cool. The hard thing about hard things.

好的。最近有看过什么特别喜欢的电影或电视剧吗？我猜你可能没太多时间看这些。

Yep. Okay. What have you seen a recent movie or TV show? You really enjoy it. I imagine you don't have much time for this.

不过

But

说出来可能要被吐槽，但我确实和妻子开始补《权力的游戏》了。我简直无法相信是第一次看？是的。

I'm gonna get blasted for this, but I I did start Game of Thrones with my wife, and I cannot For the first time? Yeah.

好吧。

Okay.

酷。我也被狠狠折腾了一番。

Cool. I got a lot of hesshing up too.

你怎么会不喜欢呢。这太棒了。就像我超爱看这个节目。你到现在都很喜欢对吧。好的。

Why would you get no. This is great. That's like I love watched it. You've loved it so far. Okay.

相当相当血腥。这是这部剧唯一的缺点。太糟糕了。千万别睡前看。不知道你已经看过多少血腥场景了。

It's quite quite gruesome. That's the only downside of that show. It's crap. You don't watch it before you go to bed. I don't know how many gruesome scenes you've seen already.

你最近有没有发现特别喜欢的什么产品？

Do you have a favorite product you recently discovered that you really love?

那个SNU，婴儿自动SNU真的帮了我们大忙。所以要特别感谢SNU。

The SNU, the baby automated SNU is like, has, really helped us a lot. So love the shout out SNU to you.

太棒了。我也有个SNU。但我们其实从没打开用过。最后就当个摆设了。是啊。

Amazing. I had a SNU as well. We never actually turned it on. We just ended up using it as a best in it. Yeah.

大部分时间都是。

Mostly time.

是没怎么开过。但宝宝哭几次后就会打开。确实很有帮助。嗯。

It's not turned on. But a couple of cries, it's been turned on. It's been very helpful. Mhmm.

你有什么特别喜欢的人生格言吗？会经常和别人分享的那种。

Do you have a favorite life motto that you find yourself coming back to sharing with other people?

我很喜欢这句话：不留遗憾。就像在球场上拼尽全力那样。懂吗？我成长在一个，你知道的，非常勤劳的家庭，爸爸为了让我们过上好日子工作特别拼命。

I love that. Like, leave nothing chance. Like, leave it all out on the field. You know? Grew up in a, you know, like, a really hardworking family, and dad worked really hard to provide make it make it happen for us.

就像这样，全力以赴。不给任何机会留下遗憾。

And it's like, just give it your all. Leave nothing a chance.

好的。最后一个问题。我在为这期播客做准备时研究过你，有个关于你早期奋斗的故事我很喜欢——当时你穿梭于各个校园推销Handshake，有次为了省钱不得不在普林斯顿大学的游泳池洗澡，因为你根本没地方住。这背后有什么故事可以分享吗？

Okay. So last question. I've been I was researching you in prep for this podcast, and there's a story that I love about your hustle early on is when you were you were going from campus to campus pitching, schools to join handshake, and there's a story where you had to shower in the the Princeton's pool to save money because you just didn't have a place to stay. Is there something there? Is there a story there you could share?

是啊，那段挺艰难的。我差点在普林斯顿被抓——对于四处奔波的创业者来说，我们当时就睡在车里。有辆福特福克斯，跑了两三万英里，晚上停在麦当劳停车场过夜，因为那里灯光明亮还有WiFi。

Yeah. So it was tough one. I I mean, I almost got arrested at Princeton because I mean, I guess for entrepreneurs that are traveling around all the time, you you we're sleeping out of our car. We had this, like, Ford Focus. We would put twenty, thirty thousand miles on it, sleep in the back of, like, McDonald's parking lots because they're well lit and had good Wi Fi back in the day.

比起住酒店，我们发现在大学游泳池洗漱更划算。每个大学都有游泳池，而且清晨总是开放的，教职工学生都能用。每个泳池都有什么？淋浴间。所以你可以在全美任何大学的泳池免费冲澡焕新。

And, instead of staying in a hotel, way to freshen up ahead of your meeting is, like, every university has a pool, and the pool's almost always it is always open. We never had a situation where it's always open for people to swim in the morning, like fitness, faculty, students. And every pool, what do they have? They have a shower. So you could go to any pool at any university in the country, and you can get a free shower and freshen up.

普林斯顿校警不太欢迎非学生人员洗澡，但这反而帮了我们。他们打电话问就业服务中心主任：‘这个加勒特·洛德真是来推销就业中心软件的？’这让会议开场特别热烈，他们惊讶地问：‘你在我们泳池洗澡？开车来的？’

So the Princeton campus security did not appreciate me showering as a nonstudent, but I think it meaningfully helped us because the Princeton campus security, like, called the Career Service Center director we're selling to being like, who's Garrett Lord? Like, is he really here to, like, pitch you software for your Career Center? And, it made the start of the meeting with the Career Center, like, really stimulating and exciting because, they're like, you showered in our pool? You drove here? Yeah.

我们从密歇根一路开过来的。这种拼劲让他们很受触动。

We drove here from Michigan. You know? We like and so I think that showed a level of commitment that was exciting for them.

现在创始人们怕是要开始效仿这个增长秘籍——故意惹校警注意来获得校领导接见了。太疯狂了。加勒特，这故事和你正在打造的事业一样令人震撼，发展速度、现有优势...作为Handshake投资人我都要喊：十年计划稳了！

Fast forward to all these founders now starting to use this growth lever of getting in trouble with the campus police to get better meetings with the school school leaders. Incredible. Garrett, this is such an insane, amazing, inspiring story, just like what you're building and the opportunity here and just how it's fast it's going and all the advantages you have. Like, if I was an investor in handshake, I'd be like, alright, ten years. It's going great.

简直像突然爆发的黑马，太不可思议了。而且意义深远。非常感谢你在百忙中抽空。最后两个问题——

And that's like, woah, holy shit, where should this come from? Incredible. And it's just also really meaningful. So I'm really happy that you made time for this in spite of the madness you are in right now. Two final questions.

大家去哪里能找到你？如果你们在招聘也请告知。另外听众们怎样能帮到你？

Where can folks find you if they wanna maybe reach out or maybe if you're hiring, let let us know. And then how can listeners be useful to you?

注册Handshake就能联系我，搜Garrett Lorde就行。推特上我也活跃，账号是loveorloveaxe。

I mean, sign up for handshake. If you wanna message me on there, it's the easiest way to to reach me. So you just find me, Garrett Lorde at handshake. And you find me on Twitter. Love or love axe.

超级巨斧男。你可以发邮件到Garrett@joinhandshake.com联系我，还有r r t t。你能帮上什么忙呢？我们正在努力招聘很多人。我们在纽约、旧金山、伦敦和柏林都设有办公室。

Huge huge axe guy. You can email me at Garrett@joinhandshake.com, and r r t t. And how can you be helpful? Like, we are trying to hire so many people. We have offices in New York and in San Francisco, in London, in Berlin.

如果你有朋友可能对此充满热情，你想了解或你有兴趣了解更多，请随时联系我们。我们很乐意与你交流。招聘是我们目前为满足需求面临的头号难题。如果你才华横溢且有兴趣了解更多关于Handshake的信息，想从事我们的消费者产品、雇主产品、酷炫的PLG问题或尖端消费者社交体验的工作，或者想参与AI业务，我们非常欢迎

If you have friends that are maybe passionate about this, you want to know or you're interested in learning more, like, please reach out. We'd love to talk to you. Hiring hiring is like the number one, problem we have right now to meet the demand. So if you're talented and interested in learning more about Handshake, you wanna work on our consumer product, you wanna work on our employer product, cool PLG issues, or the state of the art consumer social experience, like, out, you or want to work on the AI business, we'd love to

与你交流。为了让人们更清楚，你们主要招聘哪些职位？是所有职位吗？是工程类。工程类。

talk to you. To make it even more clear for folks, what roles are you most hiring for? Is it every role? Is it Engineering. Engineering.

好的。如果你是工程师，想加入目前全球发展最快的AI公司之一，机会就在这里。我们会在节目备注中附上你们的招聘页面链接。

All right. If you're an engineer and want to join one of the best growing AI companies in the world right now, here we go. We'll link to your careers page in the show notes.

谢谢。

Thank you.

当然。Garrett，非常感谢你今天的参与。这次对话太棒了。再见，各位。

Yeah, of course. Garrett, thank you so much for being here. This was incredible. Of course. Bye, everyone.

非常感谢大家的收听。如果你觉得这期节目有价值，可以在Apple Podcasts、Spotify或你喜欢的播客应用上订阅我们的节目。也请考虑给我们评分或留下评论，这能帮助其他听众发现这个播客。你可以在lenny'spodcast.com找到所有往期节目或了解更多关于节目的信息。下期节目再见。

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 lenny'spodcast.com. See you in the next episode.