Lenny's Podcast: Product | Career | Growth - Inside the expert network training every frontier AI model | Garrett Lord (Handshake CEO)

Starting point is 00:00:00 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 or now, six months for all, you have like no regrets? Get on the plane to go talk to a customer. Make the late night push.

Starting point is 00:00:13 Check the data six times over again. 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. We're the largest expert network in the world. We have this massive strategic advantage, which is like no customer acquisition cost. The only moat in human data is access to an audience. You guys come in after the models train to tweak the weights based on additional data that you create.

Starting point is 00:00:38 The models have gotten so good that the generalists are no longer needed. What they really need is experts. There's this tension between all these students training models to become smarter. And then there's that they will have harder time potentially finding jobs. 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'll like grew up with Google being like AI native. Young people are at a huge advantage.

Starting point is 00:01:05 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 linked in 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. 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 students, is extremely valuable to AI labs to help them create and label high-quality

Starting point is 00:01:47 training data. 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 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?

Starting point is 00:02:25 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. 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,

Starting point is 00:02:45 including lovable, replet, bolt, N8M, linear superhuman, Descript, whisper flow, gamma, perplexity, warp, granola, magic patterns, Raycast, chat, PRD, and Mobben. Check it out at Lenny's newsletter.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 shift faster with AI without sacrificing code quality. Code reviews are critical, but time-consuming.

Starting point is 00:03:14 CodeRabbit acts as your AI co-pilot, 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 code rabbit also provides free AI code reviews directly in the IDE it's available in vScode cursor and windsurf code has so far reviewed more than 10 million PRs installed on one million repositories and is used by over 70,000 open source projects get code rabbit for free for an entire year at codrabbit. a.I using code lennie that's code rabbit.com. AI. This episode is brought to you by Orkis, the company behind Open Source Conductor,

Starting point is 00:04:03 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. Orkis 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 Force 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.

Starting point is 00:04:45 Whether modernizing legacy systems or scaling next-gen-gen-gen-a-i-driven apps, Orcus accelerates your journey from idea to production. and start building at orcus.io slash Lenny. That's ork-es. dot-io slash Lenny. Garrett, thank you so much for being here. Welcome to the podcast. Thanks for having me.

Starting point is 00:05:08 A longtime subscriber. 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

Starting point is 00:05:23 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? Yeah. So I think it's helpful to take

Starting point is 00:05:57 I guess, 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 and 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

Starting point is 00:06:29 pre-training side. And there was a lot of gains from pre-training, like models continue to get better. And about 18 months ago, 24 months ago, we started to really see like an asymptotting 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 of their models. And you can see a lot of these popular benchmarks on what are called model cards, you know,

Starting point is 00:07:20 when Lama Force 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 the running experiments, almost think 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 can, And it could look like reinforcement learning environments. It could look like trajectories. It could be audio and multimodal.

Starting point is 00:07:55 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. And so that's the state of art of models. And most of the gains that are happening from models right now are coming from the posturing side of the house. And there's just an incredible amount of demand to stay at the absolute frontier. of where models are going. So training, pre-training is feeding it, say, the entire internet. Here's

Starting point is 00:08:24 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, RL, HF, and then there's kind of this bucket of flying tuning. I mean, yes and no, because like, take, for example, like trajectories or like you want to be able to do, people use flight search or like an accounting. and process, where 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 that points of

Starting point is 00:09:02 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 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

Starting point is 00:09:24 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 data that they're working, you know, working with to improve capabilities in their models. 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? Are like real, you know, 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 students. We're a global platform. And so, you know, depending on kind of what you're looking for across any area

Starting point is 00:10:10 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. Depending your thesis means generally speaking, like you have proven that you have extended 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 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 generalists work. Like a lot of the market before the model started to get better was leveraging talented international lower cost

Starting point is 00:10:59 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 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,

Starting point is 00:11:45 like we're doing work across so many domains. I mean, 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. Okay. So is it fair to say essentially all the data that is available has been trained on

Starting point is 00:12:14 and your company creates new data and new knowledge to continue advancing the intelligence of models. Yeah. And I also say we help 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 it in a correct response. But if you're a PhD in physics, like you can go 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 and it's ground truth, right answer, or we start throwing tools in there or needing to, you know, follow some step-by-step process.

Starting point is 00:12:55 And 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. So essentially it's just like catching mistakes that the model is made. Okay. So what are these people actually doing? 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.

Starting point is 00:13:17 Like what, say a PhD person is sitting there doing stuff, what are they actually doing? 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 might imagine like, because models are nondeterministic, 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's failing. You actually break down into like, where is it failing? You know, maybe it can get the, it knows the

Starting point is 00:13:57 question, but it can get the right answer, but the actually. 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? Like step six through 10 is wrong. And so like how do you fix the actual steps? And, uh, what are they doing? So they're going in, we put them, you know, we really focused on calling us like a, branding the experience and treating people like experts. Like PhD students expect to be treated different than 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

Starting point is 00:14:35 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 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 a lot of the post training over there and hope you paid them well. Yeah, so war for AI talent is very expensive,

Starting point is 00:15:02 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 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,

Starting point is 00:15:25 which is they want more, they want more, they want more real world tool use and trajectory-based data as well. 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 fine flaws in what, say,

Starting point is 00:15:51 chat GPT is producing and then come up with here's the correct answer. and that is used to fine tune the model. 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. I mean, like a great example.

Starting point is 00:16:11 Let's take a non-fairifiable 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 spot incorrect issues in a model and the way that they're like training people

Starting point is 00:16:49 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're going to have that all the way down to like a verifiable engineering problem that you're seeing the latest, you know, seeing the latest models fail on. So you have, yeah, I think that gives you a, you know, the big gamut. You also have, you know, we talk about professional domains,

Starting point is 00:17:18 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 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. You mentioned this term trajectory.

Starting point is 00:17:51 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. 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. Oh, wow. Yeah.

Starting point is 00:18:05 Including this voiceover. Okay. And then this might be too technical, but what is the output of all this work, say, teacher? Is it just like a JSON file, an XML file, like a text file? Yeah. Can you imagine JSON data?

Starting point is 00:18:17 And then you also have like multimodal work like audio like classifying music and understanding. And we're engaging like thousands or not thousands like probably hundreds of top music students at, you know, the lead 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 put a model in as a judge. Like 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.

Starting point is 00:19:03 And so 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 it was a model to sit in the middle and actually it's auto-evaluate responses. We're seeing a lot of rubric's work as well. And you would think, like, why would you trust this one teacher's opinion that this is the right way to do it? But that'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's steps in between

Starting point is 00:19:41 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? I didn't get a PhD in AI or math or physics and I haven't trained myself to the front of your mouths, but there is a lot to each unit of data, whether it's improving. 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 boldish 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.

Starting point is 00:20:25 And if you imagine you're training a model like teaching 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. 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, we 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.

Starting point is 00:20:58 And so our ability to get to scale or volumes of data with that, it's produced very high quality data 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 me for pipelines. And so you might have like three or four bets going at once. And then as soon as one is actually showing a game, imagine you're a researcher or, you know,

Starting point is 00:21:20 you're scientific processes once in 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 period of days and then get to high volumes of data that are high quality is the number one thing

Starting point is 00:21:41 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. 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 what data that we're creating and 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 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,

Starting point is 00:22:14 there's kind of this concept of fine tuning. There's also e-vowels and stuff like... SFT, yeah. SFT, which is supervised fine-tuning. Yeah. So the stuff you've been describing is that, would you mostly describe that as supervised fine-tuning? Yes.

Starting point is 00:22:29 I mean, we're kind of doing all the above. We don't do the auto-eval. We produce rubrics, which are used in auto-e-dowels. Yeah. Okay, awesome. So essentially, there's a model strained 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.

Starting point is 00:22:56 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 in improving the intelligence of models significantly. Oh, yeah. I mean, I think, like, maybe a funny joke. It's like, all the MBAs think this is all just, like, going to go away.

Starting point is 00:23:19 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 it and collect. But, you know, there will be humans needed in the 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'll 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 domains, but they'll also be,

Starting point is 00:23:57 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. 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. 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?

Starting point is 00:24:39 is a real problem or not? Where do you think this goes? 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 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 basically power the vast majority of how young people find jobs. And a lot of people are kind of hyperbolic at 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, pick like social media marketing. Like before you needed like somebody that could do

Starting point is 00:25:19 Photoshop and take pictures and have created videos, they needed somebody that understood like marketing analytics platforms to track, you know, you're posting on different social media forms. 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. We're 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 challenged that would have been historically

Starting point is 00:25:57 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 and create more impact. And yeah, like, of course, like hundreds of millions of jobs will become, you know, the jobs will evolve. Like, people will become displaced. They'll have to upscale and resale. And I think Handshake has a huge role to play in helping knowledge workers evolve. This has come up a couple 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.

Starting point is 00:26:36 and are much more native to all these advanced tools. And so they just come in as beasts just doing so much more. Did you remember? Do you remember what? I mean, I just a little predates me. But you used to put Google search on as like a skill on your resume. Right? Like you were in person, you were like good at Google.

Starting point is 00:26:53 Like, right? Because you like grew up with Google. It's like I think being like AI native and having your Iron Man suit out and understanding how to leverage these tools is like young people are at a huge advantage. Yeah. Especially if they're involved in trading these models. I imagine there's some other cool advantage there. Yeah.

Starting point is 00:27:09 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 of the country. And like 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.

Starting point is 00:27:36 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 in teaching. More importantly, 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. And so it is quite cool to get kind of paid to learn a skill. 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 very, 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 fast growing companies in the world are in the space. Scale was just like acquired for a 30, like sort

Starting point is 00:28:19 of acquired for $30 billion. Just like what else is there if there's anything that you think people need to understand? 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 to 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.

Starting point is 00:29:00 And so, you know, for as long as there are problems in solving any problem with AI, any human problem, there will need to be humans in the loop helping advance that. And like models don't generalize, I mean, obviously the field will advance a lot and the type of data they'll collect a lot will evolve a lot,

Starting point is 00:29:17 but it's pretty exciting at the frontier. Kevin Wheel was on the podcast, the CPO at OpenAI. And he made this point that really stuck with me that the model of today is the worst model you will ever use. I love that by. We'll only get better. Just boggles the mind.

Starting point is 00:29:32 And now we know why. These are getting better because all the work you guys are doing. 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? Are they kind of out of it? And that's a big opportunity.

Starting point is 00:29:50 Yeah, I mean, who is the whole scale team, a lot of respect for what they've built. There's many great companies operating the 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 a buyer. I mean, this is generally what we hear in this space. And so we have seen an incredible search and demand. and are, I think, extraordinarily well-positioned. We like to say, like, the only moat in human data is access to an audience.

Starting point is 00:30:39 Basically, there are, you know, many, many small players in the space. So 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 they will be like, can you get me 200 physics PhDs? 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 say messages. They spend a couple million bucks on performance advertising campaigns. Somebody scrolling on 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 the marketplaces. Like, we built a decade of trust with, you know, 18 million people. And they trust us. And we built a ton of brand. in affinity and they use handshake, they have an active profile, and we have a ton of

Starting point is 00:31:33 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 the marketplace. 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,

Starting point is 00:32:08 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, You can throw a hundred-paid strategy document at it and ask it for insights,

Starting point is 00:32:32 or you can dump all your user research and ask it to find patterns. With Cloud 4 and the new integrations, including Cloud4 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 want to try it out, get started at clod.aI slash Lenny, and using this link, you get an incredible 50% off your first three months of the pro plan. That's clod.a.ai slash Lenny.

Starting point is 00:33:06 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.

Starting point is 00:33:27 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. 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.

Starting point is 00:33:53 And we want to, you know, to zoom into like how we discovered it. 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 three million master students who are, you know, in school or 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

Starting point is 00:34:32 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 an immense amount of drop off in the process to actual project like completion on these other platforms. So we started to think the company was making tens of millions of hours 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 kind of caught out the middleman. And we started to realize, well, you know, we could really

Starting point is 00:35:12 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 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 all these multidisciplinary outcomes. And yeah, we entered the business in really like I started doing it over like Christmas and New Year's.

Starting point is 00:35:46 Like that's when I sort of like flying around. My family kind of thought it was a 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 on 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 work and building the best large language models. And the team has exploded and revenue is exploded. And it's been really a incredible ride kind of like running back new company inside of a company for the second time over again.

Starting point is 00:36:28 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. I think we'll blow to that number, but yeah. Okay. Incredible. And I didn't even know there are seven-fant-year-lapse. That's a-year-old. You're a 50's pretty good in four months, I think. 0 to 50 million in four months. That's something. It's like the bar has been shifting constantly.

Starting point is 00:36:58 Like, you know, a year ago, that'd be legendary. Now it's like, all right, well, another one of these 50 million and four months. No big deal. It's truly insane. Just to zoom out one second for people that don't know a ton of handshake the original business, what was that? What was actually this network that you had that you sat on top of? Yeah, that network does about 200 million.

Starting point is 00:37:20 This will do about 200 million. Yeah. 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 simply do that. 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 handhake is.

Starting point is 00:37:45 It's like a verb with young people in America. It's a verb with people that like are in college and their PhD or master's. program. And it is, I call it an unconnected graph, meaning like you don't need you, 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? And a lot of young people start off like they've never had a job before, right? They don't have like 500 connections to add their to their graph. Whereas on Handshake, you start off like trying to discover and explore

Starting point is 00:38:16 and figure out how to navigate through a school and figure out, I'm an engineer. Maybe I want to be a PM. Maybe don't want to start. out. Maybe we want to get a larger company. Like, what are the pros and cons? You want to learn from near peers and young alumni. And so handshakes 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. And how long has that business been around? It's been around 10 years. 10 years. Okay. So it's just like again, it just feels like

Starting point is 00:38:56 such holy shit. You guys are in the right place and the right time with the right network that is extremely valuable now. What an interesting story. 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, 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 a sudden AI and they're like, oh, what if we build AI apps in the browser and just generate

Starting point is 00:39:32 products for you with AI? And now it's, I don't know, one of the fastest during 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. I think also like, as you your company grows in size and head count 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

Starting point is 00:40:14 around for 10 years with hundreds and hundreds of people. So I've really, had a ton of fun and it's been fun, it's been fun, 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. 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. 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 here. can come from. This isn't a really clear, simple, understandable one, which is just experts

Starting point is 00:40:55 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? I think if you care about building a really high-quality business and having, like, good gross margin and like high quality growth like you know the the ecosystem here is like one of the leading players has like they have like 200 recruiters it's like 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 in hours a month on performance advertising

Starting point is 00:41:38 Google ads to find experts and to find folks and it's experts mostly at this point and then they put him 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 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 a experience that's rooted in community that's rooted in, like, high-quality training. Like, if you're getting your Ph.D. 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

Starting point is 00:42:21 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. 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 background. You're being taught on how to interact and there's like a trial and error and we have an instructional design piece. You can't 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

Starting point is 00:42:59 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-trainting 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 you get put on a project like that. Once you're doing a really, really good job on our projects, oftentimes that we'll 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

Starting point is 00:43:39 huge customer acquisition. I mean, it's a basic, you know, you all 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, cost to customer acquisition, right, your CAQ. 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? 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

Starting point is 00:44:18 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, 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 part with these fellows, unless we treat them off. 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 and hours

Starting point is 00:45:01 a month on performance marketing. So that's, I think, why we've seen so much success. 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. 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.

Starting point is 00:45:43 Nice work. I would say it's not been easy building business two inside a business one. 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.

Starting point is 00:46:01 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? 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, we could. get built a way better experience for our fellows. We could serve them directly to the labs

Starting point is 00:46:35 and build a direct cost of 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 are million companies in the network. And you know, you might think about just like upskilling and reskilling and what's going to happen there. So we want to enter the space. We started in, you know, really December exploring and learning more about it on like expert calls. calls and hammering down, you know, I hired like three expert firms, Alpha in the Alpha sites and like GLG and sort of doing a bunch of calls with the latest researchers. Because we had

Starting point is 00:47:13 resources. Like one of the cool things about being larger companies. Like we have financial, you know, our core business is $200 million error. 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. Yeah. I wonder who it is. Bening with Ben for our key different answers. Working with the number one lab and, and it's just, you know, now we're working with Zeven on the frontier labs. And the number one thing we're trying to do is just focus on like scaling up. 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

Starting point is 00:47:58 start last Monday. It's like, where are? you know, we are so bottlenecked on just meeting this opportunity because in this market, there's essentially like unlimited demand. Like if you can produce high quality volumes of data, you most likely will be able to sell whenever 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. Yeah, but it, uh, you know, it's, it's been, it's been fun. You said it's also been really hard to start those business within an existing business.

Starting point is 00:48:43 What it's been, what's been, what's been hardest? You touched on a couple of these elements already, but what else? 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 warn how to be like the best. We are the fastest growing higher education company in like history. So we signed up to 600 schools. Then we had to build an employer business.

Starting point is 00:49:11 Or, you know, we had to figure out how to sell the 100% of, you know, 70, you know, all these workforce for our companies use it and like 70% of it pay for it. So I had to learn about upmarket sales to like Goldman Sachs and General Motors and Google and the biggest companies 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? You know, so we had, I felt a little bit of familiarity in this like kind of zero to ones.

Starting point is 00:49:39 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. And we had like Warren 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. be. And so one of my warnings in spitting up these three different businesses in starting handshake was like, you know, I was pretty hands on. So like, you know, everyone reported directly to me.

Starting point is 00:50:08 I really did not try to be like, I really said in a lot of me, it's like, I'm not trying to be the boss. I'm just trying to get another smart guy in the room. Like I hired, I was just, we've hired an incredible team of people that 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.

Starting point is 00:50:44 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 was a lot of people that like try to get involved. right like everyone wants to say not everyone this is a stretch 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 existence set of priority so you know i essentially with exception of a few things like everyone just came straight into this new work 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

Starting point is 00:51:29 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 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 to and based on like hurdles in this due 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 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

Starting point is 00:52:11 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 cadence for a long time. this weekly, monthly, quarterly operating cadence. With Handshake AI, we've been way more focused on, like, operating with data and metrics and rigor from an early stage. This is a gentleman named Sahel on our team, who's been doing an incredible job with that. Shout out Sahel, shout out Young, shout out Paco.

Starting point is 00:52:40 Yeah. 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 million a year in revenue with the traditional business. and if, 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 of the run rate of a business that took you 10 years to build. Incredible.

Starting point is 00:53:09 Two, 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 weren't delegating it to someone. Hey, go start this thing. you dedicated people here.

Starting point is 00:53:24 We're going to pick people. You have nothing else going on. This is your new job. You're going to work on this stuff. You worked in a different part of the office. There's a different, there's a metrics-based cadence. So it's just like, let's stay really diligent about here's how it's going. Here's where we're going.

Starting point is 00:53:37 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 going to try to do this, I imagine. And so I'm curious what else you found important to make this work. Yeah, I mean, I should 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

Starting point is 00:54:01 one job and one job only that was making Hensh guy successful. We had a couple integration points more and I have an incredible executive team on the core part of the business and now there's becoming more and more involvement, but like, you know, I, our executives that have built handshake for a long time, like, ran the core business. And I, 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 focused on hiring a lot of entrepreneurs. We have a lot of entrepreneurs, 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,

Starting point is 00:54:41 like, only worked at early stage companies before that feel super comfortable with ambiguity. We were also, like, way more upfront around this is going to be chaotic, like, just like owning that narrative, like in front of all hands at the core company, owning it directly at 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've got great people in the core business. We took some great people from the core business and basically said, sorry, like, I know you love your old team. I know you love what you're doing.

Starting point is 00:55:21 will you join us in Hinchiki Eye? And they like completely foregoed their historical response to as it 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'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.

Starting point is 00:55:53 I don't want to be on the number of 2 a.m. 3 a.m. nights we've done in this business. It's quite regular. Like, people sometimes don't want to commit to that, but we've been up front. Like, here are the expectations for this team. 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.

Starting point is 00:56:19 like owning this outcome and like we have we have this model like leave nothing a 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 a chance like how do you how do you make sure that three months or not six months or you have like no regrets like get on the plane to go talk to a customer like mid 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,

Starting point is 00:57:01 it's very flat, right? So there really isn't this principle of, you know, 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, it's been awesome. Believe nothing to Chance piece, I imagine speaks partly to the value of trust in what you're doing. People are going to, 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.

Starting point is 00:57:29 I understand, like, it's, they're 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. No. Dude, cannot, cannot. Yeah, it's, handshake should be a. business does billions of dollars revenue as a public company

Starting point is 00:57:52 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

Starting point is 00:58:06 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

Starting point is 00:58:15 it's where people spend most of their time and energy, just hours of their life, they spend an at work. The process of like searching for a job, applying to a job is going to be completely reinvented with AI. We've been leading to 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 to me. I don't know the last time you've done this, but like the hiring manager process of like reviewing 200 resumes. Are you kidding me?

Starting point is 00:58:48 I'm going to sit there and review 200 resumes. Not a chance by your shenau, 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 talent with opportunity. And we think and get psyched about, like,

Starting point is 00:59:09 the opportunity for impact here. Like, that's my story. Like, I went to community college, a payment with your school. I went to a no-name school in the Upper Peninsula at Michigan. I worked at Palantir as an intern. It totally changed my life.

Starting point is 00:59:22 And I started handshake because I wanted to make it easier for 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

Starting point is 00:59:36 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. I think that's going to improve outcomes for employers,

Starting point is 00:59:50 save them in the aggregate, like billions of dollars over time. And I think it makes the experience way better for students. So it's just like we have to meet the moment. Like, you know, 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.

Starting point is 01:00:08 And that's a lot of the messages we've been sharing internally. It's like it's time to amp it up. It's time to like, this is a once-in-a-time opportunity to be positioned this well. I'm like, we are going to need the moment as a team. 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 model stop advancing? Are we going to

Starting point is 01:00:35 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 in 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 model neck to advancing models faster and further. 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, you know, 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.

Starting point is 01:01:13 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 tax and audio. Like, audio is just a huge demand for audio data right now. So the type of data is going to evolve. Yeah, I use voice mode all the time. That's my default chat dbt experience. It's amazing.

Starting point is 01:01:41 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 off voice mode.

Starting point is 01:01:56 And yes, 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 would consistently hear from companies is like, you know, synthetic data is not going to dominate. Like, it's not going to be like, there, there's a, 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. Let me first say just huge kudos to you for just having a kid, your wife, just having a kid a few days ago,

Starting point is 01:02:29 and building this business that is growing bananas and doing this podcast conversation. I really appreciate you. Of course. Is there anything else that we haven't covered that you think might be helpful for folks to hear or part of your story that you think might be helpful 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. I mean, the thing I always love talking,

Starting point is 01:02:54 I'm really passionate about people starting companies and helping them do so. And like, I just think in this moment right now, today I like, for young entrepreneurs that listen to read this podcast, because I've been a reader since 2020, we lucked. Yeah, we did check. That's incredible. You're a long-term reader.

Starting point is 01:03:09 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 going to be so many opportunities to improve the way people learn. Like just, you know, I just really passionate about trying to make handshake a platform that is not only in an incredible business, but it's also something that like really helps solve a societal problem that matters. And yeah, it's maybe my one one shot out here. If anyone wants advice on how to do that or wants to reach out, I'm like, happy to chat. Okay, so this is an offer to share advice on starting companies within AI. Is that the

Starting point is 01:03:48 offer here just some folks? They'd be great. Okay. I don't know how much time you'll 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? No. Well, with that, Garrett, we reached our very exciting lightning round. I'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? I'm a, I'm a, sucker for Peter Thiel's zero to one. I read it and I started the company and watched Peter Teals like startup school class at Stanford. He taught back in the days where there wasn't everything

Starting point is 01:04:22 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, so it pitted me of like starting a company. Hard things about hard things, obviously. But these are, these are all quite common books. But also classics. Ben Horowitz is coming on the podcast. Talk about hard things about hard things. Super cool. The heart thing about hard things? Yeah. Okay.

Starting point is 01:04:45 Have you seen a recent movie or TV show? You really enjoy it. I imagine you don't have much time for this. I'm going to get blasted for this, but I did start Game of Thrones to my wife. And I cannot be. For the first time. Yeah. Okay.

Starting point is 01:04:57 So I got a lot of hedge you know up to do. 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 gruesome. That's the only downside of that show.

Starting point is 01:05:07 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 they really love? The snoo, the baby automated snoo is like, has really helped us a lot. So love the shout-out snoo team. Amazing. I had a snoo as well. We never actually turned it on.

Starting point is 01:05:26 We just ended up using it as a best in it. It's not turned on. But a couple of cries that's been turned on. It's been very helpful. Do you have a favorite life motto that you find yourself coming back to sharing with other people? I love that, like, leave nothing chance, like to leave it all out on the fields, you know? grew up in a really hardworking family and dad worked really hard to provide, make it happen for us. And it's like, just give it your all, leave nothing to chance. Okay. So the last question

Starting point is 01:05:52 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 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. Yeah, so 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, we're sleeping out of our car.

Starting point is 01:06:22 We had this like Ford focused. We put 20, 30,000 miles on it, sleeping the back of like McDonald's parking lots. They're well lit and had good Wi-Fi back in the day. And instead of staying in an hotel, way to freshen up ahead of your meeting is like every university has a pool and the pool is almost always, it is always so. 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 freshen up. So the Princeton

Starting point is 01:06:55 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 directory we were selling to being like, who's Garrett Lord? Like, is he really here to like, pitch you software for your crescentor. And it made the start of the meeting with the crescenter like really stimulating and exciting. Because they were like, you showered

Starting point is 01:07:20 in our pool, 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. Fast forward to all these founders now starting to use this growth lever of getting trouble with the campus police to get better meetings with the school

Starting point is 01:07:36 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 have. Like, if I was an investor in Handshake, I'd be like, all right, 10 years, it's going great. And that's like, 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. Where can folks find you if they want to maybe reach out or maybe if you're hiring, let us know. And then how can listeners

Starting point is 01:08:10 be useful to you. I mean, sign up for Handshake. If you want a message me on there, it's the easiest way to reach me. So you just find me at Gaird Lord at Handshake. And you find me on Twitter, love or love Axe, huge, huge X guy. You can email me at Garrett at joinhandshake.com and double R-D-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. If you have friends that be passionate about this, you want them know or you're interested in the 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

Starting point is 01:08:55 handshake, you want to work on our consumer product, if you want to work on our employer product, cool PLG issues or the state of art consumer social experience, like reach out or you want to work on the AI business. We'd love to 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. All right. If you're an engineer,

Starting point is 01:09:13 I want to join when the fast-growing AI companies in the world right now, here we go. We'll link to your careers page in the show notes. Thank you.

Starting point is 01:09:19 Yeah, of course. Gary, thank you so much for being here. This was incredible. Of course. Bye, everyone. Thank you so much for listening. If you found this valuable,

Starting point is 01:09:28 you can subscribe to the show on Apple Podcast, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps

Starting point is 01:09:38 other listeners find the podcast. You can find all past episodes or learn more about the show at lenniespodcast.com. See you in the next episode.

Lenny's Podcast: Product | Career | Growth - Inside the expert network training every frontier AI model | Garrett Lord (Handshake CEO)

There aren't comments yet for this episode. Click on any sentence in the transcript to leave a comment.