Inside the expert network training every frontier AI model | Garrett Lord

Garrett Lord is co-founder and CEO of Handshake, which started as a career network for college students and new grads but recently discovered something extraordinary: they were sitting on the world’s largest network of academic experts—exactly what frontier AI labs desperately needed. With 500,000 PhDs and 3 million advanced degree holders creating training data, in just eight months they’ve built a new business that hit $50 million in revenue in its first four months and is on track to blow past $100M in the first 12 months. *What you’ll learn:* 1. How Handshake found an opportunity to leverage their proprietary network of experts to launch a data-labeling business that’s on track to blow past $100 million ARR in 12 months 2. Why AI models need human experts (e.g. physics PhDs) to improve, and what this “data labeling” actually involves 3. Inside the actual work: what a biology PhD does for 8 hours that makes GPT-5 smarter 4. The playbook for building a startup inside a startup: separate teams, separate offices, separate everything 5. Why the shift from “generalist” to “expert” data labeling created a once-in-a-lifetime business opportunity 6. Why AI won’t eliminate entry-level jobs—it’s creating “Iron Man suits” that make junior employees 10x more productive *Brought to you by:* CodeRabbit—Cut code review time and bugs in half. Instantly: https://coderabbit.link/lenny Orkes—The enterprise platform for reliable applications and agentic workflows: https://www.orkes.io/ Claude.ai—The AI for problem solvers and enterprise: https://www.claude.ai/lenny *Transcript:* https://www.lennysnewsletter.com/p/inside-handshake-garrett-lord *My biggest takeaways (for paid newsletter subscribers):* https://www.lennysnewsletter.com/i/171410958/my-biggest-takeaways-from-this-conversation *Where to find Garrett Lord:* • X: https://x.com/garrettlord • LinkedIn: https://www.linkedin.com/in/garrettlord/ • Email: [email protected] *Where to find Lenny:* • Newsletter: https://www.lennysnewsletter.com • X: https://twitter.com/lennysan • LinkedIn: https://www.linkedin.com/in/lennyrachitsky/ *In this episode, we cover:* (00:00) Introduction to Garrett Lord (05:00) Understanding data labeling and its importance (13:08) The role of experts in AI model training (15:35) The future of AI and human collaboration (24:17) Why AI won’t eliminate entry-level jobs (27:58) The continuous improvement of AI models (33:05) The emergence of Handshake’s new business model (37:07) Incubating new ideas in established companies (40:42) Handshake's competitive advantage (45:43) Scaling up and meeting market demand (48:38) Overcoming challenges and adapting (53:08) The importance of separate teams and ownership (57:26) The future of job matching with AI (01:00:30) The biggest bottlenecks to advancing models further (01:02:37) Lightning round and final thoughts *Referenced:* • GPQA: https://github.com/idavidrein/gpqa • Handshake: https://joinhandshake.com/ • OpenAI’s CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai • Inside Bolt: From near-death to ~$40m ARR in 5 months—one of the fastest-growing products in history | Eric Simons (founder and CEO of StackBlitz): https://www.lennysnewsletter.com/p/inside-bolt-eric-simons • Goldman Sachs: https://www.goldmansachs.com/ • General Motors: https://www.gm.com/ • Google: https://about.google/ • Sahil Bhaiwala on LinkedIn: https://www.linkedin.com/in/sahil-bhaiwala-459b0354/ • Francisco “Paco” Guzman on LinkedIn: https://www.linkedin.com/in/guzmanhe/ • Avery Yip on LinkedIn: https://www.linkedin.com/in/averyyip/ • Game of Thrones on HBO: https://www.hbomax.com/shows/game-of-thrones/4f6b4985-2dc9-4ab6-ac79-d60f0860b0ac • SNOO: https://www.happiestbaby.com/products/snoo-smart-bassinet • Careers at Handshake: https://joinhandshake.com/careers/ *Recommended books:* • Zero to One: Notes on Startups, or How to Build the Future: https://www.amazon.com/Zero-One-Notes-Startups-Future/dp/0804139296 • The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers―Straight Talk on the Challenges of Entrepreneurship: https://www.amazon.com/Hard-Thing-About-Things-Building/dp/0062273205 _Production and marketing by https://penname.co/._ _For inquiries about sponsoring the podcast, email [email protected]._ Lenny may be an investor in the companies discussed.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  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, make the late night push, check the data six times over again.

• 0:15Lenny Rachitsky?

  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.

• 0:24Speaker 00

  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.

• 0:32Lenny Rachitsky?

  You guys come in after the models train to tweak the weights based on additional data that you create.

• 0:38Speaker 00

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

• 0:43Lenny Rachitsky?

  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.

• 0:51Speaker 00

  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 like Google search on as a skill on your estimate because you like grew up with Google being like AI native. Young people are at a huge advantage.

• 1:05Lenny Rachitsky?

  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 10 years. They're essentially LinkedIn for college students. It's a place for students to connect with companies to find a job. They are the platform of choice for every single Fortune 500 company, over 1,500 colleges, over 20 million students and alumni, and over 1 million 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.

• 1:48Lenny Rachitsky?

  So they launched a new business from zero to one in January. Four months later, they hit 50 million ARR. They're now on pace to blow past 100 million ARR within just 12 months. 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 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. His wife just had a baby this week. He's also in the middle of scaling this insane new business.

• 2:33Lenny Rachitsky?

  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, Raplit, Bolt, N8N, Linear, Superhuman, Descript, Whisperflow, Gamma, Perplexity, Warp, Granola, Magic Patterns, Raycast, ChatPRD, and Mobbing. Check it out at lennysnewsletter.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 co-pilot,

• 3:16Lenny Rachitsky?

  providing instant code review comments and potential impacts of every pull request. 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. CodeRabbit also provides free AI code reviews directly in the IDE. It's available in VSCode, Cursor, and Windsurf. CodeRabbit has so far reviewed more than 10 million PRs, installed on 1 million 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. That's coderabbit.ai. This episode is brought to you by Orcus,

• 4:00Lenny Rachitsky?

  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. With Orkis 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

• 4:43Lenny Rachitsky?

  deliver smarter outcomes faster. Whether modernizing legacy systems or scaling next-gen AI-driven apps, Orkis accelerates your journey from idea to production. Learn more and start building at orkes.io/leni. That's O-R-K-E-S dot I O slash leni.

• 5:03Lenny Rachitsky?

  Garrett, thank you so much for being here. Welcome to the podcast.

• 5:07Speaker 00

  Thanks for having me, a longtime subscriber.

• 5:09Lenny Rachitsky?

  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 AI 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? 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, this is what you do. Clearly, there's something really important here. I sort of understand it, probably not really. 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?

• 5:54Speaker 00

  Yeah. So I think it's helpful to take, I guess, a step back of like, 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 pre-training side of the house. And that's basically the entire corpus of like, written human knowledge. 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. And that was the pre-training side. And there was a lot of gains from pre-training, like models continue to get better.

• 6:34Speaker 00

  And about 18 months ago, 24 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 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 to their models. And you can see a lot of these popular benchmarks

• 7:18Speaker 00

  on what are called model parts, you know, when Llama 4 is 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. Almost like the scientific process, 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 could, it could look like reinforcement learning environments, it could look like trajectories, 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 like, you know, preference ranking data.

• 8:03Speaker 00

  And so that's the, that's the stated art of models. And

• 8:08Speaker 00

  most of the gains that are happening from models right now are, are coming from the post-training side of the house. And there's just an incredible amount of demand to stay at the absolute frontier of where models are going.

• 8:20Lenny Rachitsky?

  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?

• 8:41Speaker 00

  I mean, yes and no, because like, what, take for example, like trajectories, or like, you want to be able to do, people use FlightSearch, or like, an accounting end-end process, or you want to be able to like, conduct biological like, experiments. Like, you need actual trajectory data, like, you need to, there's still very much, a lot of the labs are still, they have points of view on what data to collect, it's evolving very quickly. But I think, you know, reinforcement learning is really like, preference ranking, right? Like, which question do you like more, 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,

• 9:25Speaker 00

  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 they're working, you know, working with to improve capabilities in their models.

• 9:39Lenny Rachitsky?

  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 three-ish buckets?

• 9:47Speaker 00

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

• 10:27Speaker 00

  And so, the ability to like, hyper-target this audience into chemistry, math, physics, biology, coding, and really touch-

• 10:38Speaker 00

  Parts of human knowledge that have never before made it to the internet is really where we excel. And I would say that when you talk about the labeling market, something to make it more abstract is like, it used to be generalist's 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. And really, you could think about these model builders

• 11:17Speaker 00

  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 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

• 11:58Speaker 00

  you are geographically in the country, what do women versus men prefer, all the way to the most advanced PhD STEM domains out there.

• 12:08Lenny Rachitsky?

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

• 12:22Speaker 00

  Yeah. And I often say, we hope point out where the models are weak. 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. 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 is actually breaking. Either breaking in its reasoning steps, or it's where it's broken in its ground truth right answer, or you start throwing tools in there or needing to, you know, follow some step by step process. And it's, it's, it's, uh, I wouldn't say it's easy for them, but the average person cannot break the models. Uh, and that's where we really come in.

• 13:02Lenny Rachitsky?

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

• 13:22Speaker 00

  A great example is a public paper called like GPQA. So for the engineers out there that want to 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 non deterministic, like the model can get the answer right once, but it might not get the answer right, you know, three out of five times. So you actually prove where the model is 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, right?

• 14:06Speaker 00

  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, 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. And so these PhDs come into a community. We have an instructional design team and an assessments team that's going through and basically iteratively helping them understand.

• 14:38Speaker 00

  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 want to know that the data we're producing is high quality. So we have our own research team, our own post-training team. I heard a gentleman from Meta that went along, that went on the post-training over there and they.

• 14:57Lenny Rachitsky?

  I hope you paid him well.

• 14:58Speaker 00

  Yeah, so war for AI talent is very expensive, but super, super privileged and proud to be working with him. And so, you know, each unit of data, we have to build an environment for them to actually create the data. Then we have to understand 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 want more real world tool use and trajectory-based data as well.

• 15:32Lenny Rachitsky?

  Okay, there's so much here and like we could go infinitely down here, but I think 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 going to help a lot of people. 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 of thinking about it? Please correct anything I'm saying that is incorrect because I don't want people to misunderstand it.

• 16:08Speaker 00

  I mean, like, a great example, let's take like a non-verifiable 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 and the way that they're like training people?

• 16:50Speaker 00

  And help the models understand the forefront of educational design with the hands-on experience of being an eighth grade teacher for 10-plus years and having a PhD in education. 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 think that gives you a, you know, 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 basically speak to like people narrating over their step-by-step tool use. So as they go to solve a problem from start to finish, interact with multiple different service areas, interact

• 17:33Speaker 00

  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 want to understand how humans think.

• 17:50Lenny Rachitsky?

  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 all this.

• 17:56Speaker 00

  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.

• 18:04Lenny Rachitsky?

  Oh, wow.

• 18:05Speaker 00

  Yep.

• 18:05Lenny Rachitsky?

  Including this voiceover. Okay. And then this might be too technical, but what is the output of all this work, this, say, teacher? Is it just like a JSON file, an XML file, like a text file?

• 18:15Speaker 00

  Yep. I think it might be JSON data.

• 18:17Lenny Rachitsky?

  JSON data, okay.

• 18:18Speaker 00

  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 a 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's a good MRI result? And instead of having some of these, in some of these domains, you actually don't have a guaranteed correct right answer. And so

• 19:03Speaker 00

  models can sit in the middle as a judge and actually understand. You know, what is, you know, kind of 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 their scientific proof, you know, like, so you can build a rubric that allows a model to sit in the middle, and actually it's auto-evaluate responses. We're seeing a lot of rubrics work as well.

• 19:28Lenny Rachitsky?

  And you would think, like, why would you trust this one teacher's opinion that this is the right way to do it? But it's cool, as 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. There's, it feels like the market dynamics will tell you if the data you're providing is correct and what people want. Is there something more there?

• 19:52Speaker 00

  You know, I didn't get a PhD in AI or math or physics, and I haven't trained myself, be at the front of your mouths, but, you know, there is a lot to each unit of data, whether it's improving. If, 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, you know, 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 imagine you're training a model, like, teach them a student, and you're giving it the wrong data, it's extremely, you know, challenging to overcome that. So quality is first and foremost. And then the other huge problems you have is, like, volume.

• 20:37Speaker 00

  Like, 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, like, say in physics, we just reach out to students at Stanford and Berkeley and MIT, and like, they're at the top GPA, at the best physics schools in the country. And so, our ability to get to scale or volumes of data with that, to produce very high-quality data, is, is something they care deeply about. And then the other thing I'd say model builders care about is speed, because they have all these hypotheses, and they're constantly testing people for pipelines. And so you might have, like, three or four bats going at once. And then as soon as one is actually showing a game, imagine you're a researcher, you know,

• 21:20Speaker 00

  you're signed to the processes once running again, 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 of the other projects you had that weren't showing improvement. So your ability to quickly turn around for them in a period of days and then get to high volumes of data that are high quality is the number one thing to 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. 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 it could improve the model, how they could best train with it. So hopefully that helps.

• 22:02Lenny Rachitsky?

  Going back to the types of post-training, just because I 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 evals and stuff like-

• 22:18Speaker 00

  SFT, yeah.

• 22:19Lenny Rachitsky?

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

• 22:28Speaker 00

  Yes. We're kind of doing all of the above. We don't do the auto-e that we produce rubrics, which are used in auto-e nows. Yeah.

• 22:39Lenny Rachitsky?

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

• 23:13Speaker 00

  Oh, yeah. I mean, I think like, maybe a funny joke, it's like, all the MBAs think this is also 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 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'll be humans needed in this space for the next decade until we reach like full ASI. So, yeah, it's, I mean, you think about like- You know, a lot of them, I will 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

• 23:54Speaker 00

  on academic domains, but there'll also be, you know, far, far more demand for professional domains as well, across basically every, every trajectory or step-by-step kind of problem that a knowledge worker solves in the workplace. You know, 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.

• 24:17Lenny Rachitsky?

  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 that 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? Where do you think this goes?

• 24:42Speaker 00

  I'm probably in the camp of like GDP growth over like universal basic income. Like, 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 and the world. And that, you know, we're hearing from, there's like a million companies that use Handshake. Like, we have 100, 100% of the Fortune 500 uses Handshake. So we, we basically power the vast majority of how young people find jobs. And a lot of people are kind of hyperbolic in saying that all young people will 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 need somebody that understood like marketing analytics platforms

• 25:25Speaker 00

  to track, you know, you're posting on different social media forums. It's like, not one person, one like young, talented, AI-native, Iron Man suit-enabled young person can get on. 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 example. Or like, take an intern in our company. Like, he had his first PR up, like, I think like the afternoon he started, right? Like, you were a PM, like, you realize how, how challenging that would have been historically, your dev environment set up and like, figure out where to add value. He just took a bug and squashed it. And so- I'm really a believer, this is just like enabling human beings to be even more productive

• 26:07Speaker 00

  and create more impact. And yeah, like of course, like, like hundreds of millions of jobs will become, you know, the jobs will evolve. Like, people will come displaced, they'll have to upscale and rescale. And I think handshake has a huge role to play in helping knowledge workers evolve.

• 26:24Lenny Rachitsky?

  This has come up a couple of times, this point that I think is really good, that younger people coming out of school are actually going to 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.

• 26:42Speaker 00

  Do you remember, do you remember, like, I mean, I still, it predates me, but like, you used to put like Google search on as like a skill on your resume, right? Like, you were in person, you were like good at Googling, right? Because you like grew up with Google. It's like, I think being like AI native and having your Iron Man suit on and understanding how to leverage these tools is like, young people are at a huge advantage.

• 27:03Lenny Rachitsky?

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

• 27:08Speaker 00

  Yeah. Well, I mean, just to hit on that, like, what we're getting 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. It's pretty sweet. Like, you can make like 25 bucks 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 starting to learn how to leverage these tools to actually advance their area of research. So they believe

• 27:50Speaker 00

  that these tools can help them advance their area of research by helping them be more effective with their time. And so it is quite cool to get kind of paid to learn a skill.

• 27:58Lenny Rachitsky?

  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 is really important? There's just like so much happening. Like I said, some of the fastest growing companies in the world are in the space. Scale was just like acquired for, like, sort of acquired for $30 billion. Just like, what else is there, if there's anything that you think people need to understand?

• 28:26Speaker 00

  Generally speaking, like, any time that you're interacting with a model. And you're asking it to do really advanced things and it's not performing 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, any human problem, there will need to be humans in the loop helping advance that.

• 29:10Speaker 00

  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 evolve a lot, but it's, it's pretty exciting at the frontier.

• 29:20Lenny Rachitsky?

  Kevin Weil was on the podcast, the CPO at OpenAI.

• 29:23Speaker 00

  Yeah.

• 29:23Lenny Rachitsky?

  And he, he made this point that really stuck with me, that the model of today is the worst model you will ever use.

• 29:29Speaker 00

  I love that.

• 29:30Lenny Rachitsky?

  Will only get better. This just boggles the mind. And now we know why these are getting better because of all the work you guys are doing. Uh, just one quick question on this whole scale thing. 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? Or are they kind of out of it? And, and that's.

• 29:49Speaker 00

  Yeah.

• 29:50Lenny Rachitsky?

  And so.

• 29:50Speaker 00

  Who does the whole scale team? What a respect and for what they built. There's many great companies operating in this space. I think to the core of your question, it's like, uh, 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. Uh, and so we have seen a, uh, incredible search and demand.

• 30:31Speaker 00

  Um, and 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 there are, you know, many, many small players in the space, uh, some mid-sized players in the space. And they're basically, you know, running TikTok ads, running Instagram ads, paying money for Google search, display ads, YouTube ads. And, uh, they will be like, can you get me 200 physics PhDs? They, what do they do? They only can do one thing. They like, you know, they have a hundred recruiters on staff. They all get on LinkedIn. They all send messages. They spend a couple of million bucks on performance advertising campaigns.

• 31:08Speaker 00

  Somebody's scrolling an 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 to marketplaces, like we built a decade of trust with, you know, 18 million 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. 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.

• 31:48Lenny Rachitsky?

  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 user research insights, support tickets, sales calls, experiment results and competitive intel? Claude can handle incredibly complex, multi-step work.

• 32:28Lenny Rachitsky?

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• 33:06Lenny Rachitsky?

  Okay, this is an awesome segue to where I wanted to go, which is just how this business emerged. This is a business that you built on top of a business you've already had. From what I understand, you were at like $150 million 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 this is an amazing idea. Labs need data. 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.

• 33:40Speaker 00

  Yeah, 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 this new employment ecosystem is going to look very different in the future. And we wanted, you know, to zoom into like how we discovered 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. We have 3 million 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,

• 34:23Speaker 00

  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 was very amorphous how you could get paid. Like, there was 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 dollars from helping these other platforms. And we started to realize like, You know, what really kicked it off was like hearing also from the Frontier Labs, like they started to reach out to us and started to go direct and trying to like almost kind of cut

• 35:07Speaker 00

  out the middleman. And we started to realize, well, you know, we could really serve our fellows, our PhDs, our experts, we could treat them. We just believe there's like, there will need to be a platform, an experts 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 entered the business in, really like, I started doing it over like Christmas and New Year's. Like, that's when I started like flying around. My family kind of thought it was a

• 35:49Speaker 00

  little wild that I was like on planes trying to chase different leaders. But 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 has exploded. And it's been, it's been really a incredible ride, kind of like running back a new company inside of a company for the second time over again.

• 36:28Lenny Rachitsky?

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

• 36:42Speaker 00

  I think we'll blow through that number, but yeah.

• 36:44Lenny Rachitsky?

  Okay, incredible. And I didn't even know there were seven Frontier Labs.

• 36:49Speaker 00

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

• 36:52Lenny Rachitsky?

  Zero to 50 million 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, all right, well, another one of these, 50 million 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, what was that like? What was actually this network that you have that you sat on top of?

• 37:18Speaker 00

  Yeah, that that network does about 200 million. This will do about 200 million.

• 37:21Lenny Rachitsky?

  Yeah.

• 37:21Speaker 00

  Okay. So that's, we have like 600 ish, like super passionate teammates that work on on the core business, which is, you know, I would separate these out. I was like, these aren't two businesses. I think it's like, it's a one business, but that, what is that business? It's the, 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. You like definitely know what handshake is. It's like, it's a verb with young people in America. It's a verb with people that like are in college and 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 is very focused on like who you know and like what your experience is. The first question on LinkedIn is like, what's your job? And a lot of young people start off like they've

• 38:06Speaker 00

  never had a job before, right? They don't, they don't have like 500 connections to add to their, to their, to their graph. 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 want to be a PM. Maybe I want to work at a startup. Maybe I want to go to a larger company. Like, what are the pros and cons? You want to learn from near peers and young alumni. And so handshake is this, I call like a very like social platform with like groups and messaging and profiles and short form video and feed, all focused 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.

• 38:49Lenny Rachitsky?

  And how long has that business been around?

• 38:51Speaker 00

  It's been around 10 years.

• 38:52Lenny Rachitsky?

  10 years. Okay. So it's just like, again, it just feels like such, 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. 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 bit 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. Why are we, 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? And now it's, I don't know, one of the fastest growing companies in the world.

• 39:37Lenny Rachitsky?

  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?

• 39:49Speaker 00

  I think also, like, 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 more mature business that has been around for 10 years with hundreds and hundreds and hundreds of people. So I've really had a ton of fun 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 cost to acquisition costs.

• 40:33Speaker 00

  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.

• 40:43Lenny Rachitsky?

  There's actually two threads here I want to follow. I'm going to 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. Are there other methods for doing this that isn't one of those two? How are other companies doing this?

• 41:10Speaker 00

  I think if you like care about building a really high quality business and having like good gross margin and like high quality growth, like, you know, the ecosystem here is like one of the leading players has like, they have like 200 recruiters. It's unsustainable. They're like 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.

• 41:39Lenny Rachitsky?

  To find experts and to find folks.

• 41:41Speaker 00

  Find experts.

• 41:42Lenny Rachitsky?

  And it's experts mostly at this point.

• 41:43Speaker 00

  Yeah. 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

• 41:54Speaker 00

  Frontier tax accountants don't want to be treated like low-cost international labor, right? And I don't think anyone enjoys that process. And so, you know, the ability to build an experience that's rooted in community, that's rooted in like, high-quality training, 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. It's not you can't break the models. It's just like, you know, the other platforms, you know, they're spending thousands of dollars to acquire an individual user, and 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. And like, you know, you come in, you build a profile, you see the community,

• 42:36Speaker 00

  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 look like you and have your similar backgrounds, you're being taught on how to interact, and there's like a trial and error, and we have an instructional design team to teach you how to do it. Then you're put on the projects where we're building like, you know, there's certain swim lanes where we're actually pre-building data and selling that data to all the labs. So, we can do this thing where, you know, 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, you know, we make sure it's very high-quality, we run our own post-training on it, and then we produce a bunch of specifications of the data,

• 43:18Speaker 00

  and we actually sell that individual package of data to like many different labs. And so, you get put on a project like that, once you're doing a really, really good job on our projects, oftentimes they will put you on customer projects where, you know, they only want the best of the best people in, you know, machine learning, right? And then they go from our projects to their projects. And so, you know, there's a huge customer acquisition. I mean, it's a basic, you know, you 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. You have a cost to customer acquisition, right, your CAC, and then 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.

• 44:01Speaker 00

  So, if you treat people really well, you train them really well, right? 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 a ton of brand and trust with them built up, so they convert at really, really high rates. And then if you treat them really well, and because that's what they expect from us. Like, they know handshake. Their school pies handshake. Like, we need to treat, we care about treating these people well, but like, the universities would not tolerate our partnership with these fellows unless we treated them well.

• 44:41Speaker 00

  So you put them into this process where our LTVs and repeat engagement rate and retention rate on different projects 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. So that's, I think, why we've seen so much success.

• 45:06Lenny Rachitsky?

  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. And essentially, the market has shifted from low-cost generalists to experts. And 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, and so you're in the right place at the right time with the right supply. What a business.

• 45:42Speaker 00

  Yeah.

• 45:43Lenny Rachitsky?

  Nice work.

• 45:44Speaker 00

  I would say it's not been easy building business two inside of business one, but...

• 45:47Lenny Rachitsky?

  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 were 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? 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?

• 46:09Speaker 00

  Tactically, 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 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 provide a better experience long-term to our like our million companies in the network and you know, and you might, you might think about just like upskilling and reskilling and what's going to happen there.

• 46:54Speaker 00

  So we want that into the space. We started in, you know, really December, exploring and learning more about it on like expert calls and hammering down, you know, I hired like three expert firms, Alpha, Alpha sites and like GLG and started doing a bunch of calls with the latest researchers. Cause we had resources, like one of the cool things about being larger companies, like we, we have financial, you know, our core business is $200 million ERR. So it's like, you know, we, we, we had resources to be able to like accelerate the learning curve here. Uh, and then we started working with the arguably like the number one lab about five months ago.

• 47:34Lenny Rachitsky?

  I wonder who that is.

• 47:35Speaker 00

  Yeah. Um, yeah, I wonder who it is. But I mean, we've been far key, different answers, uh, working with the number one lab and, uh, and, and it's just, you know, now we're working with Zevon, the frontier labs and, uh, the number one thing we're trying to do is just focus on like scaling up. And we've gone from four or five people working on this to 75 plus people working on it. Uh, 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, uh, you most likely will be able to sell whatever you produce.

• 48:19Speaker 00

  Um, and so on our side, it's like, we're really focused on making sure that we pick the right longer-term strategy. Uh, making sure that we don't grow too fast as to erode the trust that we built up with these frontier labs. Yeah, but it, uh, you know, it's, it's been, it's been fun.

• 48:38Lenny Rachitsky?

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

• 48:50Speaker 00

  I think I just kind of followed a lot more of my intuition around this, doing this. The story of Handshake was we had to sign up 1600 universities. So I had to learn how to be like the best. We were the fastest growing higher education company in like history. So we signed up to 1600 schools. Then we had to build an employer business. Where we had to figure out how to sell the 100% value, you know, 70%. You know, all these words about what our companies use it and like 70% of it paid for it. So I had to learn about upmarket sales to like Goldman Sachs and General Motors and Google and the biggest companies in the 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 best feed look like? What does group messaging look like?

• 49:34Speaker 00

  You know, so we had, I felt a little bit of familiarity in this, like kind 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 like one product. And it was just like, you know, we had to, in a marketplace, you have to serve three different sides. You know, from your time at Airbnb. And so one of my learnings in spinning up these three different businesses in starting Handshake was like, you know, you, I was pretty hands on. So like, you know, everyone reported directly to me. I really did not try to be like, 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.

• 50:15Speaker 00

  Like I hired, I was just, we hired an incredible team of people that have, have spent a lot of time in the space and have been big leaders at a lot of the human data companies in the 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. 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, but like there's just 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,

• 50:57Speaker 00

  but you know, it's easy to say no, right? It's easy to be like, 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 everyone just came straight into this new org that I built. 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 of 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, you know, everyone's in the office five days a week, a lot of weekends. There's a totally different expectation in hiring talent

• 51:41Speaker 00

  too, where it's like, hey, this is a, this is a 24/7 job, right? Like this is an early stage company. The compensation was also different too, and based on like hurdles in this new business. 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 picked the best person who's most capable of driving an initiative forward, regardless of the function to be the DRI. We're a lot more metrics oriented. You know, when I, when I built Handshake, we, we resisted this like operating

• 52:21Speaker 00

  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. There's a gentleman named Sahil on our team who's been doing an incredible job with that. Shout out Sahil, shout out Young, shout out Paco. Yeah.

• 52:42Lenny Rachitsky?

  Okay, this is incredible. So a few kind of elements of what allowed this to succeed within a decade old company. And by the way, so you're at 200 million a year in revenue with the traditional business. You're gonna, as you said, blow past 100 million in the first year of this new business. So it's wild that in the first couple of years, if things continue to go this way, you'll exceed the size, the run rate of a business that took you 10 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're the CEO of this company. 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. If nothing else going on, this is your new job. You're

• 53:26Lenny Rachitsky?

  gonna work on this, this stuff." You worked in a different part of the office. There's a different, 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? 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?

• 53:50Speaker 00

  Yeah, I mean, I just really believe in separating 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 Hinge Guys successful. We had a couple integration points, more in it. I have an incredible executive team and a core part of the business, and now there's becoming more and more involvement. But like, you know, I, the, our executives that had 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 focused on hiring a lot of, we have a lot of entrepreneurs, people that have started companies inside

• 54:35Speaker 00

  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. 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, 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 have 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.

• 55:20Speaker 00

  I know you love what you're doing. Like, will you join us in Hinge AI? And they like completely forego their historical responsibilities 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 parachuted 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, like, hey, do you want to come over here and do this? And sometimes they say no, like, they're like, I don't want to work, you know, most of the weekends. I don't want to be on the number of 2 AM, 3 AM nights we've done in this business. It's, it's, it's been, I mean, it's quite regular. Like people sometimes don't want to commit to that, but we've been upfront, like here,

• 56:04Speaker 00

  here are the expectations for this team. It's a, it's a, you know, it's an insane pace. If you want to be a part of one of the fastest growing, you know, businesses in Silicon Valley, you can join it. 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. And so we had this motto, like leave nothing to chance. Like, how do you, how do you make sure that three months or not six months, you have like no regrets?

• 56:46Speaker 00

  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. And really a huge celebratory culture too, like calling people out across, it's very flat, right? So there's, there really is in this principle of, you know, there are 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 it's been, it's been awesome.

• 57:13Lenny Rachitsky?

  Leaving 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's 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.

• 57:45Speaker 00

  Cannot, cannot, you know, it's handshake should be a business does billions of dollars revenue as a public company, like you should, 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. 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 going to be completely reinvented with AI.

• 58:30Speaker 00

  We've been leading the charge there, 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 candidate. I mean, I don't know the last time you've done this, but like a hiring manager process, like reviewing 200 resumes, are you kidding me? Like, I'm gonna sit there and review 200 resumes, like not a chance five years from now, 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 your talent with opportunity. And we think and get psyched about like the opportunity for impact here, like.

• 59:12Speaker 00

  That's my story. Like, I went to community college, a pavement through school. I went to a no-name school in the Upper Peninsula of Michigan. I worked at Palantir as an intern. 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 meshing. Like, a lot of things that we're doing in the human data business are being integrated to our core business. I think that's going to improve outcomes for employers, save them, you know, in the aggregate, like billions of dollars over time.

• 59:55Speaker 00

  And I think it makes the experience way better for students. So it's a, 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. 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 are going to need the moment as a team.

• 1:00:19Lenny Rachitsky?

  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? Are we going to hit some plateau? And there's not actually going to be some AGI moment, SGI moment? So first of all, do 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 you think is the biggest bottleneck to advancing models faster and further?

• 1:00:50Speaker 00

  Yeah, I mean, like, it's just the type of data we're going to need is going to evolve. It's going to be CAD files. It's going to be scientific tool use data. As they are trying to automate scientific discoveries and drug discovery, it's going to, you know, it's going to be esoteric. You know, operating systems that exist on, you know, scientific tools. It's going to be, you know, so 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 going to need is going to evolve a lot. And we haven't even talked about like multimodal and video and text and audio, like.

• 1:01:33Speaker 00

  Audio is is is this huge demand for audio data right now. So the type of data is going to evolve.

• 1:01:38Lenny Rachitsky?

  Yeah, I use voice mode all the time. That's my default chat GPT experience, just talking to it.

• 1:01:42Speaker 00

  It's amazing. It's amazing. I just had a baby on, or my wife had a baby on Sunday and voice mode has been incredible. I mean, every night at, you know, every two hours, it's like, I have more questions. Voice mode's been huge. So I shot up voice mode and yeah, so the type of data is going to 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 like, you know, their synthetic data is not going to dominate. Like, it's not going to be like,

• 1:02:13Speaker 00

  there's, there's, there's billions and billions and billions of dollars of value to extract as a company over the next decade and following the frontier of AI development.

• 1:02:24Lenny Rachitsky?

  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.

• 1:02:35Speaker 00

  Thank you.

• 1:02:36Lenny Rachitsky?

  Of course. 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?

• 1:02:52Speaker 00

  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 this podcast, because I've been a reader since 2020, we lucked.

• 1:03:06Lenny Rachitsky?

  Yeah, we did check. That's incredible.

• 1:03:08Speaker 00

  A long-term reader, I'm just like so curious and love sucking up 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 going to be so many opportunities to improve the way people learn. Like, just, you know, I'm just really passionate about trying to make handshake a platform that is not only an incredible business, but it's also something that like really helps solve a societal problem that matters. And yeah, that's my one, one shout out here. If anyone wants advice on how to do that or wants to reach out, I'm like happy to chat.

• 1:03:43Lenny Rachitsky?

  Okay, so this is an offer to share advice on starting companies within AI. Is that, is that the offer here?

• 1:03:49Speaker 00

  Yeah, it'd be great.

• 1:03:50Lenny Rachitsky?

  Okay, I don't know how much time you have for the hundreds of thousands of people coming your way, but, but I appreciate the offer. That's very cool.

• 1:03:58Lenny Rachitsky?

  Anything else before we get to a very exciting lightning round?

• 1:04:00Speaker 00

  No.

• 1:04:02Lenny Rachitsky?

  Well, with that, Garrett, we reached our very exciting lightning round. I've got five questions for you. Are you ready?

• 1:04:06Speaker 00

  Ready.

• 1:04:07Lenny Rachitsky?

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

• 1:04:11Speaker 00

  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 thing that, you know, it's a tit in me of like starting a company, hard things about hard things, obviously. But these are, these are all quite common books.

• 1:04:37Lenny Rachitsky?

  But also classics, Ben Horowitz is coming on the podcast, talk about hard things about hard things.

• 1:04:42Speaker 00

  Super cool.

• 1:04:43Lenny Rachitsky?

  The hard thing about hard things. Yeah. Okay. Uh, what, have you seen a recent movie or TV show? You really enjoy it. I imagine you don't have much time for this, but.

• 1:04:49Speaker 00

  I'm gonna get blasted for this, but I did start Game of Thrones with my wife and I cannot.

• 1:04:55Lenny Rachitsky?

  For the first time?

• 1:04:55Speaker 00

  Yeah.

• 1:04:56Lenny Rachitsky?

  Okay.

• 1:04:57Speaker 00

  So I got a lot of hatching up to do.

• 1:04:58Lenny Rachitsky?

  Why would you get, no, this is great. It's like people that have watched it, you've loved it so far. Okay. It's quite, quite gruesome. That's the only downside of that show. Don't watch it before you go to bed. I don't know how many gruesome scenes you've seen already. Do you have a favorite product you recently discovered that you really love?

• 1:05:14Speaker 00

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

• 1:05:24Lenny Rachitsky?

  Amazing. I had a SNU as well. We never actually turned it on. We just ended up using it as a bass and end.

• 1:05:28Speaker 00

  We mostly find it's not turned on, but a couple of cries, it's been turned on. It's been very helpful.

• 1:05:33Lenny Rachitsky?

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

• 1:05:37Speaker 00

  I love that, like leave nothing to chance, like leave it all out in the field, you know? Grew up in, uh, you know, like a really hardworking family and dad worked really hard to provide, make it, make it happen for us. And it's like, just give it your all, leave nothing to chance.

• 1:05:51Lenny Rachitsky?

  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?

• 1:06:12Speaker 00

  So it was a tough one. I mean, I almost got arrested at Princeton because I mean, I guess for entrepreneurs that are traveling around all the time, you, we were sleeping out of our car. We had this like Ford Focus. We put 20, 30,000 miles on it, sleeping in the back of like McDonald's parking lots. They're well lit and had good wifi back in the day. 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, any university in the country, and you can get a free shower and

• 1:06:53Speaker 00

  freshen up. So the Princeton campus security did not appreciate me showering as a non-student, but I think it meaningfully helped us because the Princeton campus security like called the career service center director we were 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 were like, you showered in our pool? Well, you drove here? Yeah, we drove here from Michigan, you know? We like, and so I think that showed a level of commitment that was exciting for them.

• 1:07:29Lenny Rachitsky?

  Fast forward to all these founders now starting to use this growth lever, 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, all right, 10 years, feeling great. And that's like, whoa, holy shit, where did 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. Where can folks find you if they want to maybe reach out? Or maybe if you're hiring, let, let us know. And then how can listeners be useful to you?

• 1:08:12Speaker 00

  I mean, sign up for Handshake. If you want to message me on there, it's the easiest way to reach me. So you just find me Garrett Lord at Handshake. And you find me on Twitter, love, or love X, huge, huge X guy. You can email me at [email protected] and double R, double 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 and Berlin. If you have friends that are 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 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, if you want to work

• 1:08:56Speaker 00

  on our consumer product, if you want to work on our employer product, cool PLG issues, or the state-of-the-art consumer social experience, like, reach out, or you want to work on the AI business, we'd love to talk to you.

• 1:09:06Lenny Rachitsky?

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

• 1:09:10Speaker 00

  Engineers.

• 1:09:11Lenny Rachitsky?

  Engineering, all right. If you're an engineer, 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.

• 1:09:18Speaker 00

  Thank you.

• 1:09:19Lenny Rachitsky?

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

• 1:09:22Speaker 00

  Of course.

• 1:09:23Lenny Rachitsky?

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