Inside the expert network training every frontier AI model | Garrett Lord (Handshake CEO)

0:00 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 around, 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. 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 costs. 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. 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 the 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. Young people are at a huge advantage. Today my guest is Garrett Lord. Garrett is the co-founder and CEO of Handshake.

1:10 Which is one of the most interesting and incredible AI success stories that you probably haven't heard of. Handshake has been around for over ten years. They're essentially LinkedIn for college students. It's a place for students to connect with companies to find a job. They are the platform of choice for every single Fortune 500 company, over 1500 colleges, over 20 million students and alumni, and over one million companies use them to hire graduates. At the start of this year, Garrett and his team realize 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 training data.

1:48 So they launched a new business from zero to one in January. Four months later, they hit fifty million ARR. They're now on pace to blow past 100 million ARR within just twelve 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? A huge thank you to Garrett for making time for this. His wife just had a baby this week.

2:30 He's also in the middle of scaling this insane new business. So thank you, Garrett. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. Also, if you become an annual subscriber of my newsletter, you get a year free of a bunch of incredible products. Including Lovable, Replit, Bolt, N8M, Linear Superhuman, Descript, Whisperflow. Gamma, perplexity, warp, granola, magic patterns, raycast, chap PRD, and Mobbin. Check it out at Lenny's Newsletter.com and click bundle.

2:58 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.

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4:57 That's O R K E S dot IO slash Lenny. Garrett, thank you so much for being here. Welcome to the podcast. Yeah, thanks for having me. 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

5:19 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

5:35 I don't know, fastest growing companies in the world today, including you guys are just are are this is what you do. Clearly there's something really important here. I sort of understand it. Probably not really. 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?

5:55 Yeah. So I I think it's helped'em take like a step back of like what what does training a model look like? So there's really two primary functions. There's a Pre training and a post training. Process in training a model.

6:08 And for a long time these AI providers or LLMs or Frontier Labs We're 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. Not just written, but like every YouTube video, every book. Basically, the pursuit of sucking up everything that was on the internet. And that was the pre training side.

6:30 And there's a lot of gains from pre training, like models continue to get better. And about eighteen months ago, twenty four months ago, we started to really see like an asymptoting of gains coming from Because they had essentially like sucked up. All 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.

6:50 And what post training is is it's augmenting the and improving the data they have across every discipline or capability area that they care about. So take coding or mathematics or law or finance. You know, they are focused on collecting High quality data.

7:10 That really improves the state of art capabilities of their models. And you can see a lot of these popular benchmarks. on on what are called monopards, you know, 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 They're running experiments.

7:33 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. Um and it can it can look like reinforcement learning environments, it could look like trajectories.

7:53 The audio and multi modal It can be tax based, like prompt response pairs. Um, it can also be like reinforcement learning with human feedback, which is like, you know, preference ranking data. Um so that's the that's the state of art of models and Most of the gains that are happening for models right now are are coming from the push training side of the house. And there's just an in an incredible amount of demand.

8:17 to stay at the absolute frontier of where models are going. So training, pre training is feeding it, say the entire internet. Here's like all the data that the humans have ever created. Uh figure out. knowledge and facts and how to reason and all these things. Post training.

8:33 Is it correct to say there's essentially two buckets of things to do? There's Reinforcement learning. human feedback, R L H F, and then there's kind of this bucket of fine tuning. I mean yeah yes and no because like like take for example like trajectories or like we wanna be able to do people use flight search or like an accounting end to end process.

8:50 Where you want to be able to like conduct biological like experiments, like You need actual trajectory. Data. Like you you need to Th there's still very much a lot of the labs are still

9:01 that points of view on what they to collect. It's evolving very quickly. But I think, you know, reinforced learning is really like preference ranking, right? Like which which question do you like more? Question A or question B. S S T 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 need to actually have like

9:23 the step by step instructions of which when you interact with a lot of these frontier models They're the you know, they struggle in very advanced domains. And so Yeah. I I think there's a variety of D is the

9:35 that they're working, you know, working with to improve capabilities in their miles. What I'm hearing is there's other ways to Uh post train. Which of these are you guys focused on? Where do you help models most of these three ish buckets? Are like real unique

9:51 proposition as a business is the fact that we like have an engaged audience. We have eighteen million professionals uh across, you know, we have five hundred thousand PhDs We have three million master students. we're a global platform and so You know.

10:07 D Depending on kind of what you're looking for across any area 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 defending your thesis. Defending your thesis means generally speaking, like

10:22 You have proven that you have extended Uh the world's knowledge in a particular domain. And so the ability to like hyper target this audience into chemistry, math, physics. biology coding

10:35 And really touch parts of human knowledge that have never before made it to the internet. is really where we we excel. And I would say that when you talk about the labeling market, something to to make it more abstract is like

10:50 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 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

11:11 Across every area that the models are focused on. And and really you could think about these model b model builders as They're focused on like the most Ekonomically valuable Capability areas.

11:25 in the economy, right? And so that generally speaking, right now is focused on You know, advanced STEM domains. advanced science in math domains, and then the kind of derivative functions of like Accounting, law, medicine, finance. uh where they want to make the models more capable.

11:42 Um and then the work that we're doing, I think to come full circle to your question, like we're doing work across Across so many domains. I mean we have We have millions of bachelor students that are being used for Work in like audio. work in customizing a model depending on the voice and tone where you are geographically in the country.

12:01 Uh 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

12:13 trained on. And your company for creates new data, new knowledge. Two continue advancing the intelligence of models. Yeah.

12:23 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.

12:40 And prove where the model's actually breaking. either breaking in its reasoning steps or it's where it's broken in its ground truth right answer. Or we start throwing tools in there or needing to, you know, follow some Step by step. Process.

12:55 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. So essentially it's just like catching mistakes that the model has made.

13:06 Um 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's 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?

13:22 A great example is a public paper called like GP QA. 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.

13:39 You provided step by step reasoning степ. So You know, you can we 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's failing. You actually break down into like where is it failing? You know, maybe you can get the

13:57 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 ten steps in a math problem. Right? Like Step six through ten is wrong. And so like how do you fix the actual steps? Um and uh what are they doing? So they're going in we put them you know, we're really focused on calling this like a Uh uh branding the experience and treating people like experts.

14:21 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 a instructional design team and an assessments team that's going through and basically Iteratively helping them understand. how to use the tools that we built and how to interact with the latest models.

14:42 Then they go in and start actually creating data and not 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 a gentleman from Meta that went a lot of on the post training over there and hope you paid him well.

14:59 Yeah, so war for AI talent is uh very expensive. But super, super privileged and proud to be working with him. And so, you know, each unit of data, you know, we have to build an environment for them to actually create the data. Then we have to understand at a at a in a unit level. We're trying to approximate the actual gain from that piece of data and whether it can improve in a particular capability area. Uh and then we're also focused on, you know o evolving the use cases to also follow what the model builders want, which is

15:26 They want more They they 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 it this is really interesting'cause Just like people hear so much about all of this and they barely understand what the hell it actually is. So this is for me really interesting. I think it's gonna help a lot of people. So essentially

15:45 A PhD, say a biologist. Biology PhD is just their job is find flaws in what say 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 like here's something you're doing cor incorrectly, here's the correct answer, and that improves the model. Is that a second way of thinking about it? Please correct anything I'm saying that isn't correct because I don't want people to misunderstand it.

16:08 Um A great example, let's take like a non verifiable domain like education. So there's like a PhD student, uh 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, uh, in the field of education. And so

16:31 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

16:43 Yeah. How do you How do you spot? incorrect issues in a model in the way that they're like training people. And help

16:51 The models understand the forefront of educational design with the hands on experience of being an eighth grade teacher for Ten plus years. And having a PhD in education. So that's an example of like, you know, you can have that all the way down to like a verifiable

17:06 engineering problem that you're seeing the latest, you know. You know, seeing the latest models fail on. So you have Yeah, I I think it gives you a uh you know, the the gamut. You also have Yeah, we talk about professional domains, like these reinforcement learning environments, like You know.

17:21 There's a bunch of papers out there that's 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 by 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.

17:50 You mentioned this term trajectory. Can you just explain what that actually means?'Cause 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 or that Is collecting what you're doing. Um so it's your screen, it's your mouse. Oh well. Yeah.

18:05 Including this voiceover. Okay. And then I this might be too technical, but what is the output of all this work this say teacher? Is it just like a JSO file, an X ML file, like a text file? Yeah, think about it's J Son data. J Son data, okay. And then You also have like multi modal work, like Audio like

18:23 Classifying music and understanding we're engaging like Thousands or not thousands, like probably hundreds of uh top music students at you know the weak music schools in the country. who are improving model's understanding of music. And you also have the thing called which we haven't talked about here, like a rubric.

18:43 And a rubric like Models are you can You can put a model in as a judge. Like you you can if you What is a good What is a good educational design? Or what what's a good

18:54 MRI results. And instead of having some of these In some of these domains you actually don't have a A guaranteed correct right answer. And so models can sit in the middle as a judge and actually understand

19:08 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 Five thousand word paper. Well there's like A great introductory statement and there's scientific proof, you know, like so you can build a rubric that allows a model to sit in the middle and actually like auto evaluate.

19:25 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 is the market speaks for itself. If these models are being used more and more and people love them and value them, I imagine there's 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

19:46 Tell you if the data you're providing is Correct at what people want. Is there something more there? You know, I didn't get a PhD in in AI. Or math or physics, and I haven't trained myself be a frontier mouth, but you know, there is a lot.

20:00 to each unit of data, whether it's improving. If it You know, there's a ton of science and research out right now around like How do you make sure that the data that you're producing is improving the model? And it's very hard for Modelabolers to understand

20:18 You know, they they can really care about to to zoom out, they care about three things. They care about like quality first and foremost. You have to have high quality data. And if you you imagine you're training a model, like teaching the student, and you're giving it the wrong data, it's extremely, you know. Challenging to overcome that. So qualities first and foremost. And then the other huge problems you have is like volume. Mike.

20:38 How how do you generate thousands of pieces of data in the most advanced domains of chemistry and mathematics and physics. And how do you ensure that 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, the at the best physic schools in the country. And so our ability to get to scale or volumes of data without it to produce very high quality data is

21:05 is something they care deeply about. And then the other thing I'd say model builders care about is speed. 'Cause they have all these hypotheses and they're constantly testing different pipelines. And so you might have like three or four bats going at once. And then as soon as one is actually showing a a game, imagine you're a researcher or you know, you're scientific processes once running again, then you're trying to grow that pipeline and grow that piece of

21:25 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 and in in a period of days And then get to high volumes of data.

21:38 That are high quality is the number one. 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.

21:50 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 how it could improve their model, how they could best train with it. Um So hopefully that helps. Going back to the types of

22:05 post training just'cause 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 Uh reinforcement learning, human feedback. There's kind of this concept of fine tuning. Uh there's also Evals and stuff like SFT, yeah. SF T which is supervised fine tuning. Okay. So the stuff you've been describing, is that would you mostly describe that as

22:26 uh supervised fine tuning. Uh yes. And we're doing preference rate. I mean, we're kind of doing all of the above. Uh we don't do the auto email. We we produce rubric which are used in auto emails. And um

22:38 Yeah. Okay, awesome. So essentially There's a model strained on all this amazing data. Uh You guys come in after the model's train to tweak the weights.

22:49 Based on additional Data that you Great. What's interesting is that This is a scalable system. I w I wanna talk about just like the supply of amazing people.

22:59 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.

23:13 Oh yeah. I mean I think like Maybe a funny joke is like All the MBAs think this is all just like gonna go away. It's like

23:20 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.

23:34 There there will be yeah, 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 in a lot of them I'll struggle Two

23:46 So Yeah, right now. People are very focused on academic domains and I think they'll continue to be focused on academical academic domains, but they'll also be Yeah. Far, far more demand for professional domains as well.

24:01 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.

24:17 So let me ask you about this. There's this tension I imagine people might feel between Uh all these students training models to become smarter and smarter and smarter. And then there's the They will have harder time

24:30 potentially finding jobs if models are So smart that people at entry level. uh 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

24:41 I'm probably in the camp of like GDP growth over like universal basic income. Like I I like very much like believe that this is going to improve and accelerate every human's ability to like create an impact in the economy and the world. And uh You know, we're hearing from there's like a million companies that use handshake, like

25:00 We have One hundred Well hundred percent of the Fortune five hundred 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 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

25:15 Take like social media marketing. Like before you needed like Somebody that could do Photoshop and take pictures and move created videos. They needed somebody that understood like marketing analytics platforms to track you know, you're posting on different social media forums. It's like Now one person one like young

25:32 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 exact or or like take an intern uh in our in our company like he had a

25:50 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 to get your dev environment set up and like figure out where to add value. You just took a bug and and squashed it. And so I'm really a believer this is just like Enabling human beings to be even more productive and create more impact. And yeah, like of course, like

26:10 Like m 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 Henshake has a huge role to play in in in helping uh knowledge workers evolve.

26:24 This has come up a couple of times this point that I think is really good that uh younger people coming out of school are actually gonna be much more likely to be successful because they're kind of uh 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. Do you remember when like

26:44 I mean I I still don't predates me, but like You used to put like Google search on as like a skill on your Same, right? Like you were saying you were like good at Googling, right?'Cause 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 watch these tools is like Uh young people are at a huge advantage. Yeah. Uh especially if they're involved in training these models, I imagine there's some other cool advantage there. Yeah. Well, I mean, trust the hit on that, like What we're getting from like our thousands of fellows

27:14 It's 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. I'm like They they they can make like hundred, hundred and fifty, two hundred dollars an hour. in their area, in their field of expertise. It's pretty sweet. Like you can make like twenty five bucks an hour being a teacher's assistant.

27:33 Or you can actually make hundred fifty dollars an hour breaking the latest models. And like you're learning what we're hearing from our 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, there are 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.

27:55 And so uh it is quite cool to get kind of paid to learn a scale. Before we get to the story of how this all emerged,'cause that is an incredible story. Is there anything else about this whole field of labeling of reinforcement learning? Uh that you think people just kind of don't fulfill or you think that's really important. There's just like so much happening. Like I said, some of the fastest growing companies in the world are in this space. Scale was just like

28:18 acquired for thirty like sort of acquired for thirty billion dollars. Uh 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.

28:32 And it's not performing your expectations. Like Somewhere. There's probably an expert. That is

28:40 Yeah, the the 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

28:55 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, the any human problem. There will need to be humans in the loop. helping advance that. And like models don't generalize. I mean they're obviously the field will advance a lot.

29:14 And the type of data they'll collect a lot. W will evolve a lot. But it's it's pretty exciting at the frontier. Kevin Weel is on the podcast, the CPO at uh Open AI. Yeah.

29:24 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 line. We'll only get better. Just just boggles the mind. And now we know why these are getting better because 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 superintelligence and meta are they still like a big player in this labeling space or are they kind of out of it and

29:49 And that's Yeah, and we got the whole scale team, uh. What of respect and For what they built, um There's many great companies operating in the space.

29:58 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

30:08 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.

30:20 Being You know? being invested in by a uh by a peer. I mean, that's just generally what we hear in this space. Uh, and so we have seen a uh incredible surge in demand.

30:31 Um And R I think extraordinarily well positioned. We we like to say like the only The only moat in human data is access to an audience.

30:39 Basically there are, you know, many, many small players in the space. Uh so mid sized players in the space and they're basically, you know, running tick tock ads, running Instagram ads. Paying money for Google search display ads, YouTube ads. And uh They will be like, Can you get me

30:55 Two hundred physics PhDs. They What do they do? But only can do one thing. They like Yeah, they have

31:01 hundred recruiters on staff, they all get on LinkedIn, they all send messages. They spend couple million bucks on performance advertising campaigns. Somebody scrolling at Instagram feed that's a physics PhD, but you can't target them that well and they like C Yeah.

31:15 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 marketplace is like We built a decade of trust with You know, eighteen million people.

31:26 Um they trust us and and we built a ton of brand affinity and they use handshake and 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 fashion than anyone else. And I think that competitive advantage of access to an audience is really resonating in the marketplace.

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33:04 Dot AI slash Lenny. Okay, this is an awesome segue to where I wanted to go, which is just how how this business emerged. This is a business that you Built on top of a business you've already had. From what I understand you were at like a hundred fifty million dollars in revenue. You've been at this for a long time.

33:20 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.

33:32 Talk about just h 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 jump start, restart or start their career, like You know.

33:48 Monetizing your skills and This new employment ecosystem is gonna look very different in the future and we wanna Yeah. It's like we Because we have such a large access to This audience.

34:02 And as the world shifted from generalist The experts. We're the largest expert. Network in the world. We have, you know, more PhDs.

34:11 Five hundred thousand of them using a shake than any other platform. We have three 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, 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

34:34 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's an 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 uh helping

34:57 these other platforms and we sort of realize like W 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 tr trying like almost kind of cut out the middleman. And we started to realize, well, you know, we could really serve

35:13 our fellows, our PhDs, our experts, we could treat them We we just believe there's like a 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.

35:28 to monetize their skills and their knowledge as these labs are focused on improving in these you know in all these multidisciplinary outcomes. And yeah, we we entered the business in Really like I started doing it over like Christmas and New Year's, like that's when I started like flying around family kinda thought it was a little wild that I was like on

35:51 on planes trying to chase different leaders, but we we built an incredible team of people that came from the humidity world. And really started building out our platform in January. And then started really monetizing the relationships about five months ago. Uh

36:07 Fast forward to today we're working with Seven of the Frontier Labs. Basically, every lab that's doing That's do that's doing work. Yeah. In building the best large language models?

36:18 And the team has exploded and revenue's exploded and it's been it's been really a incredible ride kind of like running back a new company inside of a company for the second time over again. And just to share some numbers, tell me if this is correct or if you're sharing these, but Uh, I heard that you hit fifty million dollars in revenue just four months. into this. Today we're at eight months.

36:37 in and you're on track to hit a hundred million dollars in revenue uh in the first year. I think we'll blow through that number, but yeah. Okay. Incredible. Uh And I didn't even know there were seven Frontier laps. That's uh Zero fifty's pretty good in four months, I think. Uh zero to fifty million in four months. That's something. It's like the bar has been

36:57 Shifting constantly. Like you know, a year ago that'd be legendary. Now it's like all right, well, another one of these Fifty million in four months, no big deal. Uh 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 had that you sat on top of?

37:18 Yeah, that that network does about two hundred million. This will do about two hundred million dollars. Yeah. So that's we have like six hundred ish, like super Passionate teammates. that work on on the core business, which is you know, I I would separate these out, like these aren't two businesses. I think it's like it's a one business. But uh that what is that business? Um it's the numb if you're a young person in America that's graduated in the last five, six, seven, eight years. You probably have handshake on your phone. You like definitely know what handshake is.

37:45 It's like a it's a verb with young people in America. It's a verb with people that like are in college in their PhD or master's You know program. And it is yeah, I call like an unconnected graph. Uh meaning like you don't need to You know.

37:59 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? A lot of young people start off like they've never had a job before, right? They don't don't have like five hundred connections to add to their to their to their craft. Whereas on Handshake, you start off like trying to discover and explore and figure out how to navigate through a school and figure out

38:19 Oh, I'm an engineer, maybe I want to be a PM, maybe I wanna work a startup, maybe I want to work at a larger company like What are the pros and cons you wanna learn from mere peers and young alumni? And so handshakes this I I call like a very like social platform with like groups and messaging and profiles and short form video and

38:36 Feed. All focus on your interests and helping pro Really like build your confidence. in your early career to find your first job, your second job. And to manage, you know, kind of

38:47 eighteen to thirty, I would say. And how long have you that has that business been around? Been around ten years. Ten years. Well. We can So it's just like

38:55 Again, it just feels like such uh holy shit, you guys are in the right place in the right time with the right network that is extremely valuable now. Uh what an interesting story. 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.

39:15 It makes me think a little bit about s uh bolt and stack blitz. Which was building for seven years this like browser based uh 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 you with AI and now it's uh I don't know, g one of the fastest growing companies in the world.

39:36 Yeah. So interesting. And so I think this is just an interesting Time for our people to s think about. What have we done that it may give us a new opportunity to build something huge.

39:47 Based on this unfair advantage that we have. I think also like as 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.

40:01 Okay. it's hard in so many ways, right? Like the way that you build zero to one and find product market fit and scale team very quickly and is very different than the way that you run a a more mature business that has been around for ten years with hundreds and hundreds and hundreds of people. Um

40:19 So I've really had a ton of fun in and been it's been fun of passion in like running it back again for the second time inside the business. And then yeah, we have this massive

40:30 strategic advantage, which is like no cost or 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'm gonna follow. I'm gonna follow the second one first.

40:47 Uh this idea of where Does 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

40:59 uh companies in the space use scale I know especially is just like low cost labor internationally. Um, w are there other methods for doing this that isn't one of those two? How are other companies doing this? 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

41:18 You know, the The the ecosystem here is like one of the leading players has like they have like two hundred recruiters. It's like unsustainable. They're like Two hundred people on LinkedIn sending individual messages to acquire these people because there's no brand, there's no trust. They spend

41:33 You know. They were spending tens of millions of dollars a month. on performance advertising. 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

41:46 Is treating them like they're drawing like boundary boxes around stop signs in the Philippines, like You know the Yeah. Frontier tax accountants don't want to be treated like

41:57 low cost international labor, right? And I I don't I mean I don't think anyone enjoys that process. And so, you know, the ability to build a experience that's rooted in community. It'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. Not you can't break the models. It's just like, you know, the other platforms

42:18 You know, they're spending thousands of dollars to acquire an individual user and then they're put right into a project with no training. So We just started from day one at building like this expert we believe there'd be a deep network effect here. That's very connected to our core business. of starting, jump starting, or restarting your career. And like You know, you come in, you build a profile, you see the community, there's

42:37 You know, groups and a feed of here's how people are learning, like you come into actual individual cohort with like peers that that look like you and have your similar background. You're being taught on how to interact. And there's like a trial and error. And it's we have an instructional design piece, you can't do it. Then you're put on the projects where building like You know, there's certain swim lanes where we're actually pre-building data.

43:01 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 We know we make sure it's very high quality. We we run our own post training on it.

43:16 And then we produce a bunch of specifications of the data and we actually sell that individual package of data to like many different labs. And so that you get put on a project like that. Once you're doing a really, really good job on our projects Oftentimes then will put you on customer projects where, you know We they only want the best of the best people in

43:34 You know, machine learning, right? And that they go from our projects to their projects. Uh and so You know, there's a huge customer acquisition. I mean, it's a basic, you know, you all go in deep on your podcast, so just to talk about it's like You know, you you really have a couple of things that matter. You have a cost cost of customer acquisition, right? Your CAC. And then you have your L T V, like the lifetime value of a user.

43:52 And an L T V is colour. 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

44:05 Well, A, we have no customer acquisition cost. We partner with sixteen hundred universities. power ninety two percent of the top five hundred schools in the country. We power almost every institution and community college in the country.

44:17 We have no customer acquisition cost to acquire the people. We've had ton of brand and trust with them built up. So they convert Uh In really, really high rates. And then if you treat them really well and'cause that's what they expect from us, like they know handshake, their school

44:32 Piece handshake. Like We we need to treat we we care about fingers well, but like the universities would not tolerate our partnership with these with these fellows Unless we treated them well. See, you put them into this process. Where our L T Vs and repeat

44:46 engagement rate and retention rate on different projects is is really high. And so these structural advantages are quite significant. When you contrast like a leading provider that has like two hundred individual contributing recruiters. And are spending tens of millions of hours a month on performance marketing.

45:03 You know, so that's I think why we've seen so much success. Extremely interesting. And it feels like as you said, there will 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.

45:19 And And s essentially the market has shifted from low cost generalists to experts. And a lot of these companies like Scale were optimizing for general

45:29 Work. Model. Training data. And you guys Are set up.

45:34 to be extremely good at Expert. based data and So you're in the right place at the right time with the right supply. Uh, what a business.

45:43 Yeah. Nice work. I would say it's not been easy building business two inside of business one, but I want it to go. What was just that like? So you started noticing that model companies were coming to your people. that people are having hard times with some of these other companies in this space, and you're like, oh, maybe we should be doing this sort of thing. How did that just like initial inception start? And how did you start to

46:06 explore that idea and to see if it was a real thing. Tactically um And we were working with many of the middle man companies doing work. We started to see the demand as I

46:17 We we started to see direct outreach from the frontier labs reaching out to us trying to cut out the middleman in their pursuit of getting higher quality Data. When we started to put together the dots on we we could build a way better experience for our fellows

46:34 We could serve them directly to the labs and build a direct cost 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 uh

46:49 You know? And they and It you might you might think about just like upskilling and reskilling what's gonna happen there. So we want to enter the space. We started in You know, really December exploring and learning more about it. Um like Expert calls and Hammering down.

47:03 You know. I heard like Three expert firms, Alpha in Alpha sites and like GLG and sort of 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 two hundred million dollars error, so it's like

47:21 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.

47:32 About five months ago. Wonder who that is. Yeah. Um Yeah, wonder who it is. Uh working with the number one lab and uh and and have just

47:44 You know, now we're working with Devon on the Frontier Labs and Uh 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 seventy five plus people working on it. Uh we're trying to I think we had like twelve people start last Monday. It's like we're you know, we are so bottlenecked on just

48:04 meeting this opportunity because In this market. There's There's unless essentially like unlimited demand. Like if you can produce High quality volumes of data.

48:15 Uh you most likely will be able to sell whatever you produce. 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

48:29 grow too fast as to erode the trust that we built up with these frontier labs? Yeah, but And uh You know? It's it's been it's been fun.

48:38 You said it's also been really hard to start this business within an existing business. What it's been what's been hard, what's been hardest. You touched on a couple of these. Elements already, but What else? I think I just

48:52 Kind of. Followed. A lot more of my intuition around this doing this. The story of Handshake was where you had to sign up Sixteen hundred universities.

49:02 I had to learn how to be like the best We're the fastest growing higher education company in like history. So we Find up the six hundred schools. Then we had to build an employer business. Uh We had to figure out how to sell the

49:13 Hundred percent value, you know. Seventy You know, all these four five hundred companies use it and like seventy percent of it pay for it. So I don't worry about like up market sales. 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.

49:29 like kind of social network. Like what is the what does the best feed look like? What is group messaging look like Yeah, 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.

49:42 Sometimes I dream that we just like Actually don't dream but I make a joke that like I just wish we were like a cybersecurity company and we had like one buyer and just like one product and it was just like You know, we had to In a in a marketplace you have to serve three different sides, you know, from your time at Airbnb.

49:56 And so one of my warnings in spinning up These three different businesses. In starting Handshake was like Yeah, you I was pretty hands on.

50:05 It's like, you know. Everyone reported directly to me. I really did not try to be like I really said in a lot of me is like I'm not trying to be the boss, I'm just trying to get another smart guy in the room. Like A hired

50:17 Oh, we've just we've hired an incredible team of people that have have spent a lot of time in this space and have been big leaders at a lot of the human data companies in the space and so Everyone saw very clearly the structural of the energy that we had. And a lot of the focus was

50:34 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

50:49 Rightfully so, but like there's just checks and balances that Oh, so a lot of people that like try to get involved. Right, like Everyone wants to say not everyone, this is stretch, but You know, it's easy to say

51:00 No, right? It's easy to I I can't prioritize that this week or this month. I have an existing set of priorities. So You know. I Esse actually with the exception of a few things, like everyone just came

51:12 Straight into This new or bij belt. 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 cow.

51:27 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. Yeah, we are You know, everyone's in the office five days a week. Mm a lot of weekends. There's a totally different expectation in hiring talent too, where it's like

51:42 Hey, this is uh This is a twenty four seven job, right? Like this is an early stage company. We the compensation was also different too, and based on like hurdles in this new business. So people felt like owners. Creating the new co. Um Yeah, it's like

51:57 It's still Extremely nimble. very, very flat. you know, just because you want run one function doesn't mean you're the directly responsible individual on a project. We pick the best person who's most capable of driving an initiative forward.

52:12 Regardless of the function to be the DRI. We're a lot more metrics oriented. Yeah, when I When I built handshake we We we resisted this like operating cadence for a long time, like this

52:24 Weekly, monthly, quarterly operating. 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 Sahel on our team who's been doing an incredible job with that.

52:37 Shout out Sahel, shout out Young. Shout out Paco. Um Yeah. 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 two hundred million A year in revenue with the

52:52 Traditional business, you're gonna As you said, blow past a hundred. 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 uh the sizes

53:05 the run rate of a business that took you ten years to build. Incredible. to make this successful, a few of the things I noted as you were talking. One is clear you were just like in founder mode. You're the CEO of this comp you're like the lead of this new business. You were taking you weren't delegating it to someone to go start this thing. You dedicated people here. We're gonna pick people you have nothing else going on. This is your new job. You're gonna work on this this stuff.

53:28 You worked in 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 just felt really important.

53:41 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. Yeah, I mean I just really believe it's separate and everything, like separate engineering team, separate design team. Sh Separate

53:56 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 Hinchi Guy successful. We had a couple integration points more and I have it. 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

54:15 the our executives that have built handshake For a long time like ran the core business. And I focus eighty plus percent of my time and attention on just this. Um

54:27 You know. We hired an incredible engineering leader like Avery who Yeah, we we focus on hiring a lot of entrepreneurial we have a lot of entrepreneurs, people that are starting companies inside the company. Or pardon me, people that started companies before. Like that was huge. Uh, a lot of familiarity with hiring talent that have like only worked at early stage companies before that feel super comfortable with ambiguity.

54:45 Um We were also like way more upfront around this is gonna be chaotic, like Just like owning that narrative, like at 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

55:02 You know. Everyone was essentially, you know. I had some connection points, but mostly Sephiroth and I think that was like Absolutely critical. We took some of the top people.

55:13 I mean, we have got great people in the core business. We took some great people from the core business and And basically said, sorry, like I know you love your old team, I know you love what we're doing, like Will you join us in him shaking I and they like completely forgoed their historical responsibility as it came over? That became really critical with engineering when things started to scale and topple and like

55:31 You know, we're growing so quickly. We took some of our top senior engineers who were very entrepreneurial. And principal engineers, staff level engineers like peer shoot them in and You know. That that's been like it's been awesome to be able to like We have

55:43 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. Yeah. most of the weekends. I don't want to be on The number of two AM, three AM nights we've done in this business, it's

55:58 It's it's bad I mean it's quite regular. Like people Sometimes don't want to commit to that, but we've been up front. Like you're Here are the expectations for this team. It's a it's a you know. It's an insane pace.

56:10 If you want to be a part of one of the fastest growing, you know. businesses in Silicon Valley, you can join it. Um Owner the ownership too has also been huge. Like owning this outcome and like we have we have this model like leave nothing a chance like

56:24 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.

56:39 And so we had this motto like leave not for me to chance like How do you how do you make sure that three months and not six months around you have like no regrets? Like get on a plane to go talk to a customer, like Mid to late night push. shack the data six times over again. Like

56:54 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 isn't this Principle of Yeah.

57:04 Yeah. 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 it's 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 gonna Like you win if they can trust that your data's awesome and great and consistent.

57:23 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 Uh advanta you guys have in just like the stress that comes with that burden also, I imagine, is very high.

57:41 I've just like, this is we can't screw this up. No. Did cannot cannot Yeah, it's And she should be uh

57:49 business does billions of dollars of revenue as a public company like You should Yeah, we should be able to continue to I mean it and it also helps our core business. Like A longer term opportunity that we see

58:02 Is It's connecting. It's building the best Job matching marketplace on the internet. It's like

58:09 You know It's probably one of the largest problems in the world like labor supply Matching. Thank you

58:17 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 Search for a job, applying to a job is gonna be completely reinvented with AI.

58:30 We've been leaving the charge there, like you know, an AI interviewer that's collecting the skills and actually asking about your experiences doing a work simulation Experiences that like help employers find the best candidates to meet. I don't know the last time you've done this, but like the hiring manager process like reviewing two hundred resumes, like are you kidding me? Like I'm gonna sit there and review two hundred resumes like

58:51 Not a chance by your chanel, right? Like Hey. students manually making cover what like not a chance, right? So There will need to be a marketplace that wins. In connecting s you know.

59:03 supply and demand and you know town with opportunity. And we think and get psyched about like the opportunity for impact here. Like I was my story. Like I went to community college at Pierre 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.

59:22 And like I started handshake'cause 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 Wait.

59:34 Whole way 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 gonna improve outcomes for employers.

59:50 Save them. you know, in the aggregate like billions of dollars over time. Uh 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.

1:00:08 Uh And that's a lot of the messages 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 long time opportunity. to be positioned as well and like we're we we are gonna need the moment as a team. It really is. This is Very much feels like a once in a lifetime opportunity.

1:00:24 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 gonna hit some plateau and there's not actually gonna be some A GI moment, SGI moment? So well, 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?

1:00:50 Yeah, I mean like it's just the type of data we're gonna need is gonna evolve. It's gonna be CAD files. scientific tool use data As they are trying to automate scientific discoveries and drug discovery. Can it you know.

1:01:07 It's gonna be. Esoteric. you know, operating systems that exist on You know, scientific tools. It's gonna be You know.

1:01:15 So I I love this like trajectory and like stitching together step by step instruction following like you know, there will need the type of data we're gonna need is gonna evolve a lot. And We haven't even talked about like multimodal and video and text and audio.

1:01:31 Mike. Audio is is is huge demand for audio data right now. So the type of data is gonna evolve. Yeah, I use voice mode all the time. That's my default chat D B T experience just talking to it's amazing. It's amazing. I just had a baby on.

1:01:45 We or my wife had a baby on Sunday. And voice mode's been incredible. I mean I'm Every night at you know. Every two hours is speed. It's like I have more questions. Voice mode's been huge. So I uh shot up voice mode. And yes, the type of data's gonna collect a lot or change a lot.

1:02:00 Um, I think synthetic data has a role to play in like in verifiable domains, but like what would consistently hear from companies is like Yeah. There synthetic data's not gonna dominate. Like it's not gonna be like Th there there there's an there's there's

1:02:15 billions and billions and billions of dollars of value to uh 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 uh Just having a kid, your wife just having a kid a few days ago and

1:02:30 Building this business that is growing bananas and doing this podcast conversation. I really appreciate you thinking time. 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. I mean the thing I always love like talking I'm really passionate about like

1:02:55 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 that that read this podcast. 'Cause I've been r a reader since twenty twenty. We looked. I yeah, we did check. That's exactly a long term reader. I'm just like so curious and love. your interviews. But it's like

1:03:13 Can you just focus on doing something like a meaning, like that really helps people? And I think with AI there's like gonna be so many opportunities to Improve the way people learn. Thank you. Just

1:03:24 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 uh

1:03:36 Yeah, it's maybe my one One shout out here. If anyone Once advice on how to do that or wants to reach out, I'm like happy to chat. Mm. Okay. So this is uh an offer to share advice on starting companies within AI. Is that is that the offer here? Just for folks. It'd be great.

1:03:50 I'm Okay. I don't know how much time you have for the hundreds of thousands of people coming your way, but I appreciate the offer. That's very cool. Um Anything else before we get to a very exciting lightning round? No. Well with that, Garrett, we reached our very exciting lightning round. We've got five questions for you. Are you ready? Ready?

1:04:07 What are two or three books that you find yourself recommending most to other people? I'm uh I'm a sucker for Peter Thiel, zero to one. I read it. And I started the company and watched Peter Teal'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

1:04:25 was the coolest. Um Love. Love shoe dog, like Yeah.

1:04:31 It's a pit in me of like starting a company. Hard things about hard things, obviously. But these are these are all quite common books. But uh also glassics. Uh Ben Horowitz is coming on the podcast, talk about hard things about hard things. Super cool. 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 I'm gonna get blasted for this, but I I did start Game of Thrones with my wife and I uh For the first time. Yeah.

1:04:56 Okay. So I got a lot of hatching up to do. Why would you get No, this is great. That'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. You 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?

1:05:14 The snew. The baby Automated Snew it's like has uh really helped us a lot. So

1:05:22 Love the shout outs to New Team. Amazing and a s new as well. We never actually turned it on. We just ended up using it as a best and it's not turned on. But a couple of cries it's been turned on. It's been very helpful. Your favorite life motto that you find yourself coming back to, sharing with other people? I love that like leave nothing and chance, like leave it all out on the field, you know.

1:05:42 You know, like a really hard working family and That work really hard to Provide f make it make it happen for us and it's like Just Give it your off. Leave nothing the chance.

1:05:51 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're You were going from campus to campus pitching Uh schools to join Handshake, and there's a story where you had to shower in the The

1:06:06 Princeton's pool. To save money,'cause you just didn't have a place to stay. Is there something there? Is there a story there you could share? Yeah, so it was a tough one. I I mean I almost got arrested at Princeton because I mean I guess For entrepreneurs that are traveling around all the time, you

1:06:20 Do you we were sleeping out of our car. We had this like forward focus. put twenty, thirty thousand miles on it, sleep in the back of like McDonald's parking lots. They're well lit and had good wifi back in the day. And uh

1:06:33 Instead of staying in a hotel. way to freshen up ahead of your meeting is like Every university has a pool and the pools almost always it is always open. We never had a situation where it's always open. for people to swim in the morning, like fitness, faculty, students. And every pool, what do they have? They have a shower. So you could go to any pool at any university in the country.

1:06:52 And you can get a free shower. And freshen up. So The Princeton Campus security did not appreciate me showering as a non student, but I think it meaningfully helped us because

1:07:04 The Princeton Camp of 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 Christenter? And Uh, it made the start of the meeting with the Christian to like really stimulating and exciting. He guys.

1:07:19 Uh, they were like, You showered in our pool, you drove here? Yeah, we drove here from Michigan, you know? Uh we like and so I think that uh showed a level of commitment that was exciting for them. Fast forward to all these founders now starting to use this growth lever of School school leaders.

1:07:38 Incredible. Garrett, this is such a 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, doing great. And now it's like, Whoa holy shit, where should this come from? Uh incredible. Uh and it's just also really meaningful. So uh I really uh

1:07:59 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 wanna maybe reach out or maybe if you're hiring like Let us know. And then how can listeners be useful to you?

1:08:12 I mean sign up for handshake. Uh, if you want to message me on there, it's the easiest way to to reach me. Uh spine make airlord at handshake. And you find me on Twitter, Love uh or Love Axe, huge uh huge X guy. Uh you can email me at

1:08:26 Garrett at trainhandshake.com. Um And double R double T. And uh how can you be helpful? Like we are trying to hire so many people. Uh we have offices in New York and in San Francisco, in London and Berlin.

1:08:39 If you have friends that are maybe passionate about this, you want to know, or you're interested in learning more, like please reach out. We'd love to talk to you. Uh hiring hiring is like the number one Uh. problem we have right now to meet the demand. So if you're talented and interested in learning more about Handshake.

1:08:56 You wanna work on our consumer product, if you want to work on our employer product. cool P L G 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. 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 and want to join one of the fastest growing companies in the world right now, here we go. Uh we'll link to your careers page in the show notes. Thank you.

1:09:19 Yeah, of course. Gary, thank you so much for being here. This was incredible. Course. 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.

1:09:40 You can find all past episodes or learn more about the show at Lenny's Podcast.com. See you in the next episode.

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