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

Garrett Lord:There will never be a time like this I've never seen anything like it I doubt I'll ever feel anything like this in business again where there's unlimited demand how do you make sure that three months from now six months from now you have like no regrets get on a plane to go talk to a customer make the late night push check the data six times over again

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

Garrett Lord: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

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

Garrett Lord:The models have gotten so good that the generalists are no longer needed what they really need is experts

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

Garrett Lord: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 Google search on a skill on your resume cause you grew up at Google being AI native young people are at a huge advantage

Lenny Rachitsky:Today my guest is Garrett Lord Garrett is the co founder and CEO of Handshake which is one of the most interesting and incredible AI success stories that you probably haven't heard of Handshake has been around for over 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 1,500 colleges over 20,000,000 students and alumni and over 1,000,000 companies use them to hire graduates at the start of this year Garrett and his team realized that their huge proprietary network of students including tens of thousands of PhDs and master's students is extremely valuable to AI labs to help them create and label high quality training data so they launched a new business from zero to one in January four months later they hit 50,000,000 ARR they're now on pace to blow past 100,000,000 ARR within 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 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 actually doing when they're labeling and creating data to train models a huge thank you to Garrett for making time for this his wife just had a baby this week he's also in the middle of scaling this insane new business so thank you Garrett if you enjoy this podcast don't forget to and follow it in your favorite podcasting app or YouTube also if you become an annual subscriber of my newsletter you get a year free of a bunch of incredible products including lovable Replit Bolt n eight M Linear Superhuman Descript Whisperflow Gamma Perplexity Warp Granola Magic Patterns Raycast Chat PRD and Mobbin check it out at Lenny'snewsletter.com and click bundle with that I bring you Garrett Lord this episode is brought to you by Coderabbit AI code review platform transforming how engineering teams ship faster with AI without sacrificing code quality code reviews are critical but time consuming Coderabbit acts as your AI copilot providing instant code review comments and potential impacts of every pull request beyond just flagging issues Coderabbit provides one click fix suggestions and lets you define custom code quality rules using AST grep patterns catching subtle issues that traditional static analysis tools might miss Codebabbit also provides free AI code reviews directly in the IDE it's available in versus code cursor and windsurf Codebabbit has so far reviewed more than 10,000,000 PRs installed on 1,000,000 repositories and is used by over 70,000 open source get Coderabbit for free for an entire year at coderabbit.ai using code Lenny that's coderabbit.ai this episode is brought to you by Orcus the company behind open source Conductor the orchestration platform powering modern enterprise apps and agentic workflows legacy automation tools can't keep pace siloed low code platforms outdated process management and disconnected API tooling fall short in today's event driven AI powered agentic landscape Orcus changes this with Orcus Conductor you gain an agentic orchestration layer that seamlessly connects humans AI agents APIs microservices and data pipelines in real time at enterprise scale visual and code first development built in compliance observability and rock solid reliability ensure workflows evolve dynamically with your needs it's not just about automating tasks it's orchestrating autonomous agents and complex workflows to deliver smarter outcomes faster whether modernizing legacy systems or scaling next gen AI driven apps Orcus accelerates your journey from idea to production learn more and start building at orkus.i0/lenny that's 0rkes.i0/lenny

Lenny Rachitsky:Garrett thank you so much for being here welcome to the podcast

Garrett Lord:Yeah thanks for having me longtime subscriber

Lenny Rachitsky:I appreciate that okay so before we get into the insane trajectory that your data labeling business is on which is just an amazing story that I think a lot of founders and product teams that are trying to navigate this AI disruption that's happening will have a lot to learn from I want to first help people understand what the hell data labeling actually is just like what are people actually doing why is this so valuable some of the most I don't know fastest growing companies in the world today including you guys are just are 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

Garrett Lord:So I I think it's helpful to take I guess step back of like what what does training a model look like so there's really two primary functions there's a pre training and a post training process in training a model and for a long time these AI providers or LLMs or frontier labs were focused on basically sucking up more and more information on the pretraining side of the house and that's basically the entire corpus of like written human knowledge so that's not just written but like every YouTube video every book basically you know the pursuit of sucking up everything that was on the internet that was the pretraining side and there was a lot of gains from pretraining like models continue to get better and about eighteen months ago twenty four months ago we started to really see like an asymptoting of gains coming from because they had essentially like sucked up all of the knowledge on the internet and so labs really shifted towards most of the gains now coming from the post training side of the house and what post training is is it's augmenting the and improving the data they have across every discipline or capability area that they care about so take coding or mathematics or law or finance you know they are focused on collecting high quality data that really improves the state of our capabilities of their models and you can see a lot of these popular benchmarks on on what are called model parts 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 use cases basically they're 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 it can look like reinforcement learning environments it can look like trajectories it could be audio and multimodal it can be text based like prompt response pairs it can also be like reinforcement learning with human feedback which is like you know preference ranking data and so that's the that's the state of the art of models and most of the gains that are happening from models right now are are coming from the push training side of the house and there's just an an incredible amount of demand to stay at the absolute frontier of where miles are going

Lenny Rachitsky:So training pre training is feeding it say the entire internet here's like all the data that the humans have ever created figure out knowledge and facts and how to reason and all these things post training is it correct to say there's essentially two buckets of things to do there's reinforcement learning human feedback RLHF and then there's kind of this bucket of fine tuning

Garrett Lord:I mean yeah yes and no because like what take for example like trajectories or like you wanna be able to do people use flight search or like an accounting end to end process or you wanna be able to like conduct biological like experiments like you need actual trajectory data like you need to there there's still very much a lot of the labs are still they have points of view on what data collect it's evolving very quickly but I think you know reinforcement learning is really like preference ranking right like which which question do you like more question A or question B SFT data is like a prompt and a response and obviously the labs are very focused on these like thinking or reasoning models so in order to improve a reasoning model you'd actually have like the step by step instructions of which when you interact with a lot of these frontier models they're you know they struggle in very advanced domains and so you know I think there's a variety of datas that that they're working

Garrett Lord:know working with to improve capabilities in their models

Lenny Rachitsky:What I'm hearing is there's other ways to post train which of these are you guys focused on where do you help models most of these buckets

Garrett Lord:Our like real unique proposition as a business is the fact that we like have an engaged audience we have 18,000,000 professionals across you know we have 500,000 PhDs we have 3,000,000 master's students we're a global platform and so you know depending on kinda what you're looking for across any area of academic knowledge you know what is the definition of a PhD it's essentially to like be at the how do you get your how do you get your PhD you defend your thesis defending your thesis means generally speaking like you have proven that you have extended the the world's knowledge in a particular domain and so the ability to like hyper target this audience into chemistry math physics biology coding and really touch parts of human knowledge that have never before made it to the internet is really where we we excel and I would say that when you talk about the labeling market something to to make it more abstract is 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 across every area that the models are focused on and and really you could think about these model model builders as they're focused on like the most economically valuable capability areas in the economy right and so that generally speaking now is focused on you know advanced STEM domains advanced science and math domains and then the kind of derivative functions of like accounting law medicine finance where they want to make the models more capable and then the work that we're doing I think to come full circle to your question like we're doing work across across so many domains I mean we have we have millions of bachelor students that are being used for work in like audio work in customizing a model depending on the voice and tone where you are geographically in the country what do women versus men prefer all the way to the most advanced PhD STEM domains out there

Lenny Rachitsky:Okay so is it fair to say essentially all the data that is available has been trained on and your company creates new data new knowledge to continue advancing the intelligence of models

Garrett Lord:Yeah 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 and get an incorrect response

Lenny Rachitsky:Mhmm

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

Lenny Rachitsky:So essentially it's just like catching mistakes that the model has made okay so what are these people actually doing what is it I know there's all kinds of different types you described all the ways that data is generated what kind of data is useful so maybe just like the most common examples like let's say a PhD person is sitting there doing stuff what are they actually doing

Garrett Lord:A great example is a public paper called like GPQA so for the engineers out there that wanna read about it like essentially the the crux of the paper is you break the model you provide a ground truth the right answer to the question you provide the step by step reasoning steps so you know you can you might imagine like because models are nondeterministic like 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 question but it can get the right answer but the actual steps to get there are wrong and they're really focused on like the steps to get there so there's like 10 steps in a math problem right like step six through 10 is wrong and so like how do you fix the actual steps and what are they doing so they're going in we put them you know we're really focused on calling this like a branding the experience and treating people like experts like PhD students expect to be treated different than a lower cost international labor with a different work expectation and so these PhDs come into a community we have a instructional design team and an assessments team that's going through and basically iteratively helping them understand how to use the tools that we built and how to interact with the latest models then they go in and start actually creating data and that you know that process is on our side the model builders they wanna know that the data we're producing is high quality so we have our own research team our own post training team I hired a a gentleman from Meta that went along on the post training over there and

Lenny Rachitsky:I hope you paid him well

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

Lenny Rachitsky:Okay there's so much here and like we could go infinitely down here but I think that this is really interesting because just like people hear so much about all of this and they barely understand what the hell it actually is so this is for me really interesting I think it's gonna help a lot of people so essentially a PhD is a biologist biology PhD is just their job is find flaws in what say ChatGPT is producing and then come up with here's the correct answer and that is used to fine tune the model here's like here's something you're doing incorrectly here's the correct answer and that improves the model is that a simple way to think about it and please correct anything I'm saying that is incorrect because I don't want people to misunderstand I

Garrett Lord:Mean like a great example let's take like a nonverifiable domain like education so there's like a PhD student Rachel on the network she got her PhD from the University of Miami spent two decades as a teacher teaching students in the eighth grade and she was an adjunct professor at a local community college in the field of education and so she is interacting with the state of the art models in educational design so actually trying to understand what is the best way to teach people and like how do you frame the how do you how do you spot incorrect issues in a model in the way that they're like training people and help the models understand the forefront of educational design with the hands on experience of being an eighth grade teacher for ten plus years and having a PhD in education so so that's an example of like you know you can have that all the way down to like a verifiable engineering problem that you're seeing the latest you know you know seeing the latest models fail on so you have yeah I I think that gives you a a you know the the gamut you also have know you we talk about professional domains like these reinforcement learning environments like you know there's a bunch of papers out there that's basically speak to like people narrating over their step by step tool use 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

Lenny Rachitsky:They do they really wanna understand how humans think you mentioned this term trajectory can you just explain what that actually means because it feels like you've mentioned that a few times and that feels important to all of

Garrett Lord: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

Lenny Rachitsky:Yep including this voice over. Okay, and then this might be too technical but what is the output of all this work of this say teacher? Is it just like a JSON file, an XML file, like a text file? Yep, think about

Garrett Lord:It as JSON data.

Lenny Rachitsky:JSON data, okay.

Garrett Lord:And then you also have like multimodal work, audio like classifying music and understanding. We're engaging like thousands or not thousands, like probably hundreds of top music students at, you know, the top music schools in the country who are improving models' understanding of music. And you also have the thing called, which we haven't talked about here, like rubrics. And a rubric like models are, you can put a model in as a judge. Like you, you can if you, what is a good, what is a good educational design or what's a good MRI result? And instead of having some of these in some of these domains you actually don't have a guaranteed correct right answer. And so models can sit in the middle as a judge and actually understand, you know, what is, you know, kinda like think back on your school days, like how do you get an A on your 5,000 word paper? Well, there's like a great introductory statement and there's scientific proof, you know, like so you can build a rubric that allows a model to sit in the middle and actually auto evaluate responses. We're seeing a lot of rubrics work as well.

Lenny Rachitsky:And you would think like why would you trust this one teacher's opinion that this is the right way to do it? But what's cool is the market speaks for itself. If these models are being used more and more and people love them and value them, I imagine there are steps in between to verify this is good and other people think this is a good idea. There's, 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?

Garrett Lord:You know, I didn't get a PhD in AI or math or physics and I haven't trained myself via frontier models but, you know, there is a lot to each unit of data whether it's improving. Yep, if it, you know, there's a ton of science and research out right now around like how do you make sure that the data that you're producing is improving the model and it's very hard for model builders to understand. You know, they, they can really care about to zoom out they care about three things. They care about like quality first and foremost. Like you have to have high quality data and if you, 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, 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, the at the best physics schools in the country and so our ability to get to scale or volumes of data with that to produce very high quality data is 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 different 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, your scientific prosecutors once running a game then you're trying to grow that pipeline and grow that piece of data that's actually improving it and you're maybe ditching two or three other projects you had that weren't showing improvement. So your ability to quickly turn around for them in a, in a period of days and then get to high volumes of data that are high quality is the number one thing they care about. And so there's quite a bit of technology we built on our side to assess each unit of data. We have our own post training teams, we're renting our own GPUs and we're trying to make sure that we can sit directly with these researchers and help share like what we're seeing with the data that we're creating and how, how it could improve their model, how they could best train with it. So hopefully that helps.

Lenny Rachitsky:Going back to the types of post training just because I think this might be helpful at least for me the mental model of there's pre training, there's post training within post training there's reinforcement learning human feedback, there's kind of this concept of fine tuning, there's also eval.

Garrett Lord:And stuff SFT.

Lenny Rachitsky:Yeah SFT which is supervised fine tuning is that okay so the stuff you've been describing is that would you mostly describe that as supervised fine tuning? Yes, I'm working with preference rate.

Garrett Lord:I mean we're kind of doing all of the above. We don't do the auto eval, we produce rubrics which are used in auto evals. Yeah.

Lenny Rachitsky:Okay awesome so essentially there's a model it's trained on all this amazing data you guys come in after the model is trained to tweak the weights based on additional data that you create. What's interesting is that this is a scalable system 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 you would think it needs to be this infinitely scalable thing but humans sitting there adding creating data is working in improving the intelligence of models significantly.

Garrett Lord:Oh yeah I think, mean like maybe a funny joke is like all the MBAs think this is all just like gonna go away. It's like and I think for as long as models are improving humans will be needed in this process and when you talk to the lead scientists and researchers at these labs it's like the data types will evolve and what they're trying to capture and collect but you know there will be, there will be humans needed in this space for the next decade until we reach like full ASI. So yeah it's I mean you think about like you, you know a lot of the models struggle to do basic trajectories right now so you know right now people are very focused on academic domains and I think they'll continue to be focused on academic domains but there'll also be far far more demand for professional domains as well across basically every trajectory or step by step kind of problem that a knowledge worker solves in the workplace. 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.

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

Garrett Lord:Think this goes I'm probably in the camp of like GDP growth over like universal basic income like I I like very much like believe that this is going to improve and accelerate every human's ability to like create an impact in the economy in the world and that you know we're hearing from there's like a million companies that use Handshake like we have 100 well 100% of the Fortune 500 uses Handshake so we basically power the vast majority of how young people find jobs and a lot of people are kind of hyperbolic in saying that all young people won't have jobs and like that's not what we're hearing from our employers. What we're hearing is like take like social media marketing like before you needed like somebody that could do Photoshop and take pictures and have created videos they needed somebody that understood like marketing analytics platforms to track you know your posting on different social media forms it's like now one person one like young talented AI native Ironman 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 or or like take an intern in our in our company like he had his first PR up like I think like the afternoon he started right like you were a PM like you realize how how challenged that would have been historically your dev environment set up and like figure out where to add value he just took a bug and and squashed it and so I'm really a believer this is just like enabling human beings to be even more productive and create more impact and yeah like of course like like hundreds of millions of jobs will become you know the jobs will evolve like people will come displaced they'll have to upscale and rescale and I think Handshake has a huge role to play in in helping knowledge workers evolve.

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

Garrett Lord:Do you remember do you remember when like I mean I I I still predates me but like you used to put like Google search on as like a skill on your resume right like you were like good at Google like right because you like grew up with Google it's like I think being like AI native and having your Ironman suit on and understanding how to leverage these tools is young people are at a huge advantage.

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

Garrett Lord:Yeah well I mean just to hit on that like what we're hearing from like our thousands of fellows is like they're in the classroom they're actually producing research like we're talking about you know PhDs at the top institutions in the country and like they they can make like $100,150 $200 an hour in their area in their field of expertise it's pretty sweet like you can make like $25 an hour being a teacher's assistant or you can actually make $150 an hour breaking the latest models and like you're learning what we're hearing from our our fellows is like they're bringing a lot of those insights into the classroom to help them be more effective at teaching more importantly they're they're starting to learn how to leverage these tools to actually advance their area of research so they believe that these tools can help them advance their area of research by helping them be more effective with their time and so it is quite cool to get kind of paid to learn a skill.

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

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

Lenny Rachitsky:Kevin Weil is on the podcast the CPO at OpenAI yeah and he he made this point that really stuck with me that the model of today is the worst model you will ever use.

Garrett Lord:I love that.

Lenny Rachitsky:Why will only get better just just boggles the mind and now we know why these are getting better because all the work you guys are 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 or are they kind of out of it and and that's yeah.

Garrett Lord:And so we've to the whole scale team at a lot of respect in for what what they've built there's many great companies operating in this space I think to the core of your question it's like I think if you were building the most if you viewed your research team and your model building team and the experiments they're running to be you know really the cornerstone of how you're improving you probably wouldn't want the latest research of what you're trying to work on being you know being invested in by a by a peer I mean this is generally what we hear in this space and so we have seen a an incredible surge in demand and are I think extraordinarily well positioned we we like to say like the only the only moat in human data is access to an audience basically there are you know many many small players in this space some mid sized players in this space and they're basically you know running TikTok ads running Instagram ads paying money for Google search display ads YouTube ads and they will be like can you get me 200 physics PhDs they what do they do they only can do one thing they're like you know they have a 100 recruiters on staff they all get on LinkedIn they all send messages they spend couple million bucks on performance advertising campaigns somebody's scrolling an Instagram feed that's a physics PhD of which you can't target them that well and they're like see you know come train a model it's like I've never heard of this brand before the huge advantage that we've had and why we've resonated so fast in the marketplace is like we built a decade of trust with you know 18,000,000 people and they trust us and and we built a ton of brand affinity and they use Handshake they have an active profile and we have a ton of information around their academic performance and what they've done in school and so we're able to really target people really effectively and get to scale and volume of high quality data faster than anyone else and I think that competitive advantage of access to an audience is really resonating in the marketplace.

Lenny Rachitsky:Today's episode is brought to you by Anthropic the team behind Claude I use Claude at least 10 times a day I use it for researching my podcast guests for brainstorming title ideas for both my podcast and my newsletter for getting feedback on my writing and all kinds of stuff just last week I was preparing for an interview with a very fancy guest and I had Claude tell me what are all the questions that other podcast hosts have asked this guest so that I don't ask them these questions how much time do you spend every week trying to synthesize all of your user research insights support tickets sales calls experiment results and competitive intel Claude can handle incredibly complex multi step work you can throw a 100 page strategy document at it and ask it for insights or you can dump all your user research and ask it to find patterns with Claude four and the new integrations including Claude four opus the world's best coding model you get voice conversations advanced research capabilities direct Google workspace integration and now MCP connections to your custom tools and data sources Claude just becomes part of your workflow if you wanna try it out get started at claud.ai/lenny and using this link you get an incredible 50% off your first three months of the pro plan that's claud.ai/lenny okay this is an awesome segue to where I wanted to go which is just how how this business emerged this is a business that you built on top of a business you've already had from what I understand you were at like $150,000,000 in revenue you've been at this for a long time you found this opportunity now that I you know looking back it's like obviously this is an amazing idea labs need data you guys have the supply of incredible experts what an opportunity talk about just how you first realized this was something that you could be doing and should be doing and then how you started to kind of execute down this path.

Garrett Lord:Yeah I I think it's been a pretty natural extension from like helping people jump start restart or start their career like you know monetizing your skills in in this new employment ecosystem is gonna look very different in the future and we wanted you know to to zoom into like how we discovered it it's like we because we have such a large access to this audience and as the world shifted from generalists to experts we're the largest expert network in the world we have you know more PhDs 500,000 of them use Handshake than any other platform we have 3,000,000 master students who are you know in school alumni and so we started to see all the what I would call like middleman companies reaching out to us saying can we recruit your PhDs and students and like any great marketplace you know we started sending them to these different platforms and started to really realize that you know from hearing from our users that like the experience was really frustrating like training was very transactional the payments were you know there is very amorphous how you could get paid like there's immense amount of drop off in the process to actual project like completion on these other platforms so we started to we started to think the company was making tens of millions of dollars from helping these other platforms we started to realize what really kicked it off was hearing also from the frontier labs they started to reach out to us and started to go direct and try trying to like almost kinda cut out the middleman and we started to realize well you know we could really serve our fellows our PhDs our experts we could treat them we 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 these you know in all these multidisciplinary outcomes and yeah we we entered the business in really like I started doing it over like Christmas and New Year's like that's when I sort of like flying around my family kind of thought it was a little wild that I was like on on planes trying to chase different leaders but we we built an incredible team of people that came from the human data world and really started building out our platform in January and then started really monetizing the relationships about five months ago fast forward to today we're working with seven of the frontier labs basically every lab that's doing work and building the best large language models and the team has exploded and revenue's exploded it's been it's been really a incredible ride kind of like running back new company inside of a company for the second time over again.

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

Garrett Lord:I think we'll blow through that number but yeah.

Lenny Rachitsky:Okay incredible and I didn't even know there are seven frontier labs that's a

Garrett Lord:Zero to 50 is pretty good in four months I think

Lenny Rachitsky:0 to 50,000,000 in four months that's something it's like the bar has been shifting constantly you know a year ago that'd be legendary now it's like all right well another one of these 50,000,000 in four months no big deal it's truly insane just to zoom out one second for people to that don't know a ton about handshake the original business what was that like what was actually this network that you have that you set on top of

Garrett Lord:Yeah that that network does about 200,000,000 this will do about $200,000,000 yeah okay so that's we have like 600 ish like super passionate teammates that work on on the core business which is you know I I would simply do that was like these aren't two businesses I think it's like it's one business but that what is that business it's the numb if you're a young person in America that's graduated in the last five six seven eight years you probably have handshake on your phone you like definitely know what handshake is it's like a it's a verb with young people in America it's a verb with people that like are in college in their PhD or master's you know program and it is I call it an unconnected graph meaning like you don't need to you know LinkedIn's very focused on like who you know and like what your experience is the first question on LinkedIn is like what's your job and a lot of young people start off like they've never had a job before right they don't they don't have like 500 connections to add to their to their to their graph whereas on handshake you start off like trying to discover and explore and figure out how to navigate through a school and figure out oh I'm an engineer maybe I wanna be a PM maybe I wanna work at a startup maybe I wanna work at a larger company like what are the pros and cons you wanna learn from near peers and young alumni and so handshake's this like I call like a very like social platform with like groups and messaging and profiles and short form video and feed all focus on your interests and helping really like build your confidence in your early career to find your first job your second job and to manage you know kind of 18 to 30 I would say

Lenny Rachitsky:And how long have

Garrett Lord:You that

Lenny Rachitsky:Has that business been around

Garrett Lord:It's been around ten years

Lenny Rachitsky:Ten years okay so it's just like again it just feels like such a holy shit you guys are in the right place in the right time with the right network that is extremely valuable now what an interesting story 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 just opens up a whole new way of leveraging something that you have been doing for a long time it makes me think a little better about bolt and stackblitz which was building for seven years this like browser based OS where you could run an OS in the browser and they're like I don't know no one needs this why are we what are we doing and then all of a sudden AI and they're like oh what if we build AI apps in the browser and just generate products for you with AI now it's I don't know one of the fastest growing companies in the world yeah so interesting and so I think this is just an interesting time for our people to think about what have we done that may give us a new opportunity to build something huge based on this unfair advantage that we have

Garrett Lord:I think also like as your company grows in size and headcount and maturity it's also like hard to like incubate something new inside of a business like it's hard to you know it's hard in so many ways right like the way that you build zero to one and find product market fit and scale a team very quickly and is very different than the way that you run a a more mature business that has been around for ten years with hundreds and hundreds and hundreds of people so I've really had a ton of fun and and been spend funnel 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 no customer acquisition costs and we have like much higher conversion rates and retention than like any of the other platforms by a large margin because we have such consumer affinity

Lenny Rachitsky:There's actually two threads here I wanna follow I'm gonna follow the second one first this idea of where this data labeling work can come from this isn't a really clear simple understandable one which is just experts sitting there creating data another one that I know a lot of other companies in the space use scale 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

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

Lenny Rachitsky:To find experts and to find folks find experts and it's experts mostly at this point

Garrett Lord:And then they put them onto an experience that like is treating them like they're drawing like boundary boxes around stop signs in the Philippines like you know the the frontier tax accountants don't wanna be treated like low cost international labor right and I I don't I mean I don't think anyone enjoys that process and so you know the ability to build a experience that's rooted in community that's rooted in like high quality training like if you're getting your PhD at MIT chances are you're just not being taught well enough on how to use the tools now you can't break the models it's just like you know the other platforms you know they're spending thousands of hours to acquire an individual user and then they're put right into a project with no training so we just started from day one at building like this expert we believe there'd be a deep network effect here that's very connected to our core business of starting jump starting or restarting your career and like you know you come in you build a profile you see the community there's you know groups and a feed of here's how people are learning like you come into actual individual cohort with like peers that that look like you and have your similar background you're being taught on how to interact and there's a trial and error and it's we have an instructional design kit so you can do it then you're put on the projects we're building like you know there's certain swim lanes where we're actually prebuilding data and selling that data to all the labs 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 we run our own post training on it and then we produce a bunch of specifications of the data and we actually sell that individual package of data to like many different labs and so that you get put on a project like that once you're doing a really really good job on our projects oftentimes that will put you on customer projects where you know we they only want the best of the best people in you know machine learning right and then they go from our projects to their projects and so you know there's a huge customer acquisition I mean it's a basic you you are going deep on your podcast so just to talk about it it's you know you really have a couple of things that matter you have cost cost to customer acquisition right your CAC and you have your LTV like the lifetime value of a user and an LTV is calculated pretty simply in this business like it is based on the retention of a person and how many projects they can participate in so if you treat people really well you train them really well right like well a we have no customer acquisition cost because we partner with 1,600 universities power 92% of the top 500 schools in the country we power almost every institution and community college in the country we have no customer acquisition cost to acquire the people we have ton of brand and trust with them built up so they convert at you know really really high rates and then if you treat them really well and because that's what they expect from us like they know handshake their school pies handshake like we we need to treat we we care about treating these people well but like the universities would not tolerate our partnership with these with these fellows unless we treated them well so you you put them into this process where our LTVs and repeat engagement rate and retention rate on different projects is is really high and so these structural advantages are quite significant when you contrast like a leading provider that has like 200 individual contributing recruiters and are spending tens of millions of dollars a month on performance marketing you know so that's I think why we've seen so much success

Lenny Rachitsky:that's extremely interesting and it feels like as you said there used to be a big focus on generalists which is people anywhere in the world for low cost can do the work like draw bounding boxes around things essentially the market has shifted from low cost generalists to experts 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 nice work

Garrett Lord:i would say it's not been easy building business two inside of business one but-

Lenny Rachitsky:so let me actually yeah so let me follow that thread that's where i wanted to go what was just that like so you started noticing that model companies were coming to your people that people 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 explore that idea and to see if it was a real thing

Garrett Lord: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 we started to see direct outreach from the frontier labs reaching out to us trying to cut out the middleman in their pursuit of getting higher quality data when we started to put together the dots on we we could build a way better experience for our fellows we could serve them directly to the labs and build a direct customer relationship with the labs and basically cut out the middleman and provide a better experience to the labs provide a better experience to our fellows and provided a better experience long term to our like our million companies in the network and you know and and and you might you might think about just like upskilling and reskilling what's gonna happen there so we want that into the space we started in you know really december exploring and learning more about it like expert calls and hammering down you know i hired like three expert firms alpha ins alpha sites and like glg and started doing a bunch of calls with the latest researchers because we had resources like one of the cool things about being a larger company is like we we have financial you know our core business is $200,000,000 arr so it's like you know we we we had resources to be able to like accelerate the learning curve here and then we started working with the arguably like the number one lab about five months ago

Lenny Rachitsky:i wonder who that is

Garrett Lord:yeah yeah i wonder who it is betting with ben farquhar key different answers working with the number one lab and and have just you know now we're working with zavern 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 start last monday it's like we're you know we are so bottlenecked on just meeting this opportunity because in this market there's there's essentially like unlimited demand like if you could produce high quality volumes of data you most likely will be able to sell whatever you produce and so on our side it's like we're really focused on making sure that we pick the right longer term strategy making sure that we don't grow too fast as to erode the trust that we built up with these frontier labs yeah but it you know it's it's been it's been fun

Lenny Rachitsky: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

Garrett Lord:i think i just kind of followed a lot more of my intuition around this doing this i mean the story of handshake was we had to sign up 1,600 universities so i had to learn how to be like the best we are the fastest growing higher education company in like history so we signed up to 1,600 schools then we had to build an employer business where we had to figure out how to sell the 100%

Garrett Lord:all these four twenty five other companies use it and 70% of it pay for it i had to learn about upmarket sales to goldman sachs and general motors and google and the biggest companies in the world which is totally different than selling universities and then we had to learn how to build like an incredible student like kind of social network like what is the what is the best feed look like what is group messaging look like you know so we had i felt a little bit of familiarity in this like kind of zero to ones oftentimes like marketplaces are like many zero to ones sometimes i dream that we just like i actually don't dream but i make a joke that like i just wish we were like a cybersecurity company we had like one buyer and just one product and it was just like we you had to in a marketplace you have to serve three different sides you know from your time at airbnb and so one of my warnings in spinning up these three different businesses in starting handshake was like you know you i was pretty hands on so like you know everyone reported directly to me i really did not try to be like i i really said in a lot of meetings like i'm not trying to be the boss i'm just trying to be like another smart guy in the room like i hired i was just we've hired an incredible team of people that have had spent a lot of time in this space and have been big leaders at a lot of the human data companies in this space and so everyone saw very clearly the structural advantage that we had and a lot of the focus was on making sure that we could deliver high quality data to one customer before we expand to anyone else like we just you had to say no to a lot of things and then you also had a lot of people in the core part of the business that rightfully so like there's these checks and balances that there's a lot of people that like try to get involved right like everyone wants to say not everyone this is a stretch but you know it's easy to say no right it's easy to be like i i can't prioritize that this week or this month i have an existing set of priorities so you know i essentially with the exception of a few things like just came straight into this new org that i built everyone did not have any responsibilities in the existing part of the business it was extremely clear who was like the directly responsible individual across each area of the new co and now we've got deeper coupling and integration points across the rest of the business but like we sat in a separate part of the office you know we are we you know everyone's in the office five days a week a lot of weekends there's a totally different expectation in hiring talent too where it's like hey this is a 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 and yeah it's like it's still extremely nimble very very flat you know just because you want run one function doesn't mean you're the directly responsible individual on a project we pick the best person who's most capable of driving an initiative forward regardless of the function to be the dri we're a lot more metrics oriented you know when i when i built handshake we we we resisted this like operating cadence for a long time like this weekly monthly quarterly operating cadence with handshake ai we've we've been way more focused on like operating with data and metrics and rigor from an early stage this gentleman named sahil on our team who's been doing an incredible job with that shout out sahil shout out young shout out paco yeah okay

Lenny Rachitsky:this is incredible so a few kind of elements of what allowed this to succeed within a decade old company and by the way so you're at 200,000,000 a year in revenue with the traditional business you're gonna as you said blow past 100,000,000 in the first year of this new business 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 ten years to build incredible to make this successful a few of the things i noted as you were talking one is clearly you were just like in founder mode you're the ceo of this company you're like the lead of this new business weren't delegating it to someone hey go start this thing you dedicated people here we're gonna pick people you have nothing else going on this is your new job you're gonna work on this stuff you worked in different part of the office there's a different there's a metrics based cadence it's just like let's stay really diligent about here's how it's going here's where we're going here's our track here's our kpis things like that anything else there that you felt really important to making this work because a lot of companies are gonna try to do this i imagine and so i'm curious what else you found important to make this work

Garrett Lord:yeah i mean i just really believe it's separate and everything like separate engineering team separate design team separate accounts and operations team separate finance team like early on everything was separate people only had one job and one job only and that was making ngai successful we had a couple integration points more in a i have an incredible executive team on a core part of business and now there's becoming more and more involvement but like you know i the our executives that have built handshake for a long time like ran the core business and i focused 80 plus percent of my time and attention on just this and you know we hired an incredible engineering leader like avery who you know we we focus on hiring a lot of we have a lot of entrepreneurs people that have started companies inside the company or pardon me people that started companies before like that was huge a lot of familiarity with hiring talent that have like only worked at early stage companies before that feels super comfortable with ambiguity we were also like way more upfront around this is gonna be chaotic like just like owning that narrative like in front of all hands at the core company owning it directly with the team we have a separate all hands we have separate onboarding we have a separate recruiting team like you know everyone was essentially you know i had some connection points but mostly separate and i think that was like absolutely critical we took some of the top people and we have great people in the core business we took some great people from the core business and and basically said sorry like i know you love your old team i know you love what you're doing like will you join us in hinshake ai and they like completely forego their historical response vote 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 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 wanna come over here and do this and sometimes they say no like they're like i don't wanna work you know most of the weekends i don't wanna be on the number of 2am 3am nights we've done in this business it's it's it's bet i mean it's quite regular like people sometimes don't wanna commit to that but we've been upfront like here here are the expectations for this team it's a it's a you know it's an insane pace if you wanna be a part of one of the fastest growing you know businesses in silicon valley you can join it owner the ownership too has also been huge like owning this outcome and like we have we have this model like leave nothing to chance like i always for a while there we like drew the number of days in the year on the whiteboard and it was like there will never be a time like this i've never seen anything like it i doubt i'll ever feel anything like this in business again where there's unlimited demand and it's just our ability to execute against it and so we had this motto like leave nothing with a chance like how do you how do you make sure that three months or not six months from now you have like no regrets like get on the plane to go talk to a customer like make the late night push check the data six times over again like ship the extra feature that helps and really a huge celebratory culture too like calling people out across it's very flat right so there's there really isn't this principle of you know the there's so many people putting up points like calling out the people that are putting up points and creating a really fun environment around impact i think has been has been awesome

Lenny Rachitsky: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 i understand like it's there's so it's obviously a massive opportunity obviously a massive advantage you guys have and just like the stress that comes with that burden also imagine is very high of just like this is we can't screw this up

Garrett Lord:no dude cannot cannot you know it's handshake should be a business does billions of dollars revenue as a public company like there should you know we should be able to continue to i mean and it also helps our core business like the longer term opportunity that we see is it's connecting it's building the best job matching marketplace on the internet it's like you know it's probably one of the largest problems in the world like labor supply matching like it's where people spend most of their time and energy just hours of their life they spend it at work the process of like searching for a job applying to a job is gonna be completely reinvented with ai we've been leading the charge there like you know an ai interviewer that's collecting skills and actually asking about your experiences doing work simulation experiences that like help employers find the best candidates i mean i don't know the last time we've done this but like the hiring manager process like reviewing 200 resumes like are you kidding me like i'm gonna sit there and review 200 resumes like not a chance five years from now right like students manually making cover like not a chance right so there will need to be a marketplace that wins in connecting you know supply and demand and you know talent with opportunity and we think and get psyched about like the opportunity for impact here like that's my story like i went to community college i paid my way through school i went to a no name school in the upper peninsula of michigan i worked at palantir as an intern it totally changed my life and like i started handshake because i wanted to make it easier for like anyone regardless of who you knew what your parents did what school you went to to find a great opportunity and i think ai like totally step function improvement in matching and i think that our human data business is really serving as like the foundation for improving meshing like a lot of things that we're doing in the human data business are being integrated to our core business i think that's gonna improve outcomes for employers save them you know in the aggregate like billions of dollars over time and i think it makes the experience way better for students so it's it's just like we have to meet the moment like you know i we still have the stamina and the excitement and the passion internally in our core and in the new business to like go charge after this and that's a lot of the message we've been sharing internally it's like it's it's time to amp it up it's time to like this is a once in a lifetime opportunity to be positioned as well and like we're we we are gonna need the moment as a team

Lenny Rachitsky:it really is this is very much feels like a once in a lifetime opportunity let me ask a few other questions along these lines that are something i've been thinking about something that a lot of people think about just while i have you there's always this question of will we run out of data will models stop advancing are we gonna hit some plateau and there's not actually gonna be some agi moment sgi moment so what 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 think is the biggest bottleneck to advancing models faster and further

Garrett Lord:Yeah I mean like it's just the type of data we're gonna need is gonna evolve it's gonna be CAD files it's gonna be scientific tool use data as they are trying to automate scientific discoveries and drug discovery it's going to be esoteric you know operating systems that exist on you know scientific tools it's gonna be you know so I I love this like trajectory and like stitching together step by step instruction following like you know there will need the type of data we're gonna need is gonna evolve a lot and we haven't even talked about like multimodal and video and text and audio like audio is is is a huge demand for audio data right now so the type of data is gonna evolve

Lenny Rachitsky:Yeah I use voice mail all the time that's on my default ChatGPT experience

Garrett Lord:Just talking to it's amazing it's amazing I just had a baby on we or my wife had a baby on Sunday and voice mail has been incredible I mean night at you know every two hours speedy it's like I have more questions voice mode has been huge I shout out voice mode and so the type of data is gonna collect a lot or change a lot I think synthetic data has a role to play in like in verifiable domains but like what we consistently hear from companies is you know their synthetic data is not gonna dominate like it's not gonna be like there there's an there's there's billions and billions billions of dollars of value to extract as a company over the next decade 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

Lenny Rachitsky:Days ago and building this business that is growing bananas and doing this podcast conversation I really appreciate you taking 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 wanna just double down on that we've talked about before we get to a very exciting lightning round

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

Lenny Rachitsky:I yeah we did check

Garrett Lord:That's incredible you're a long term reader I'm just like so curious and love sucking up your interviews but it's like you just focus on doing something like a meaning like that really helps people and I think with AI there's like gonna be so many opportunities to improve the way people learn like just you know I'm just really passionate about trying to make Handshake a platform that is not only an incredible business but it's also something that like really helps solve a societal problem that matters and yeah that's my one one shout out here if anyone wants advice on how to do that or wants to reach out I'm like happy to chat

Lenny Rachitsky:Okay so this is an offer to share advice on starting companies within AI is that is that the offer here just yeah folks it'd be great okay I don't know how much time you have for the hundreds of thousands of people coming your way but but I appreciate the offer that's very cool anything else before we get to a very exciting lightning round

Garrett Lord:No

Lenny Rachitsky:Well with that Garrett we reached our very exciting lightning round we've got five questions for you are you ready ready what are two or three books that you find yourself recommending most to other people

Garrett Lord:I'm a I'm a sucker for Peter Thiel's Zero to One I read it when I started the company and watched Peter Thiel's like startup school class at Stanford he taught back in the days where there wasn't everything written on the internet about how to start companies and like just think he's was the coolest love love Shoe Dog like I think that you know it's epitome of like starting a company hard things about hard things obviously but these are these are all quite common books

Lenny Rachitsky:But also glassics Ben Horowitz is coming on the podcast talk about hard things about hard things super cool the hard thing about hard things yep okay what have you seen a recent movie or TV show you really enjoy it I imagine you don't have much time for this but I'm

Garrett Lord:Gonna get blasted for this but I I did start Game of Thrones with my wife and I cannot for the first time

Lenny Rachitsky:Yeah okay

Garrett Lord:Cool I got a lot of headshing up to do.

Lenny Rachitsky: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 that's crap 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.

Garrett Lord:The Snoo the baby automated Snoo is like has really helped us a lot so love the shout out Snoo to.

Lenny Rachitsky:You amazing I had a Snoo as well we never actually turned it on we just ended up using it as a best internet yeah mostly time.

Garrett Lord:It's not turned on but a couple of cries it's been turned on it's been very helpful.

Lenny Rachitsky:Your favorite life motto that you find yourself coming back to sharing with other people.

Garrett Lord:I love that like leave mouth and a chance like leave it all out on the field you know grew up in you know like a really hardworking family and dad worked really hard to provide make it make it happen for us it's like just give it your all leave nothing the chance.

Lenny Rachitsky:Okay so last question I've been I was researching you in prep for this podcast and there's a story that I love about your hustle early on is when you were you were going from campus to campus pitching schools to join Handshake and there's a story where you had to shower in the 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.

Garrett Lord: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'd you we're sleeping out of our car we had this like Ford Focus we would put twenty thirty thousand miles on it sleep in the back of like McDonald's parking lots they're well lit and had good Wi-Fi back in the day and instead of staying in a hotel way to freshen up ahead of your meeting is like every university has a pool and the pool's almost always it is always open we never had a situation where it's always open for people to swim in the morning like fitness faculty students and every pool what do they have they have a shower so you could go to any pool at any university in the country and you can get a free shower and freshen up so the Princeton campus security did not appreciate me showering as a nonstudent but I think it meaningfully helped us because the Princeton campus security like called the career service center director we're selling to being like who's Garrett Lord like is he really here to like pitch you software for your career center and it made the start of the meeting with the career center like really stimulating and exciting because they were like you showered in our pool well you drove here yeah we drove here from Michigan you know we like and so I think that showed a level of commitment that was exciting for them.

Lenny Rachitsky:Fast forward to all these founders now starting to use this growth lever of getting in trouble with the campus police to get better meetings with the school school leaders incredible Garrett this is such an insane amazing inspiring story just like what you're building and the opportunity here and just how it's fast it's going and all the advantages have like if I was an investor in Handshake I'd be like alright ten 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 wanna maybe reach out or maybe if you're hiring let let us know and then how can listeners be useful to you.

Garrett Lord:I mean sign up for Handshake if you wanna message me on there it's the easiest way to to reach me so you just find me Garrett Lord at Handshake and you find me on Twitter love or love axe huge huge axe guy you can email me at garrett@joinhandshake.com and r r t t and how can you be helpful like we are trying to hire so many people we have offices in New York and in San Francisco in London in Berlin if you have friends that are maybe passionate about this you want to know or you're interested in learning more like please reach out we'd love to talk to you hiring hiring is like the number one problem we have right now to meet the demand so if you're talented and interested in learning more about Handshake if you want to work on our consumer product if you want to work on our employer product cool PLG issues or the state of the art consumer social experience like reach out you or want to work on the AI business we'd love to talk.

Lenny Rachitsky: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 you want to join one of the best growing AI companies in the world right now here we go we'll link to your careers page in the show notes.

Garrett Lord:Thank you.

Lenny Rachitsky:Yeah of course Garrett 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 you can subscribe to the show on Apple Podcasts Spotify or your favorite podcast app also please consider giving us a rating or leaving a review as that really helps other listeners find the podcast you can find all past episodes or learn more about the show at Lenny'spodcast.com see you in the next episode.