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

25:46And that's an example, we're like taking an intern and 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 be historical to your dev environment set up and like, figure out where to add value. He just took a bug and squashed it. And so I'm really a believer that 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, good job to evolve. Like people will come this place, they'll have to upscale and reskill.

26:18And I think handshake is a huge role to play in helping now it's workers evolve. This has come up a couple of times this point that I think is really good that younger people coming out of school are actually 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. Do you remember? Do you remember me? I mean, I've had them. I still haven't predates me. Like, you used to put like Google's soap on as like a skill on your estimate, right? Like, you're, you're like good at Google, right?

26:53Because you like, we're up with Google. It's like, I think being like, AI natives and having your Iron Man suit on and understanding how to watch these tools is like young people are at a huge advantage. Yeah. Especially if they're involved in trading these models. They imagine there's some other cool advantage there. Yeah. Well, I mean, just to hit on that, like, Well, we're getting from like our thousands of fellows is like they're in the classroom. They're actually producing research. Like we're talking about, you know, PhDs at the top institutions of the country. And like they can make like 100, 150, 200 hours an hour in their area and their field of expertise.

27:29It's pretty sweet. Like you can make like 25 bucks an hour being a teacher as assistant or you can actually make 150 hours an hour breaking the latest models. and like you're learning what we're hearing from our fellows is like they're bringing a lot of those insights into the classroom to help them be more effective at teaching. Yeah, and currently they're starting to learn how to leverage these tools to actually advance the 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.

27:58Before 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 don't fully understand or you think that is really important? There's just so much happening. Like I said, some of the faster companies in the world are in the space scale, was just like quiet for 30, like sort of a quiet for $30 billion. Just like what else is there if there's anything that you think people need to understand? Generally speaking, like any time that you're interacting with a model, and you're asking to do really advanced things and it's not performing your expectations.

28:35Like somewhere, there's probably an expert that is, you know, the top mind in that domain working directly for the best researchers in the world at the Frontier Labs trying to understand and go to the scientific iteration process of how to make that better. And that, the assumption there is that like they already have the entirety of human knowledge that's written and recorded. And so, first of all, as long as there are problems in solving any problem with AI, any human problem, there will need to be humans in the loop helping advance that. And like models don't generalize, I mean, obviously they feel they'll advance a lot and the type of data they'll collect a lot will evolve a lot, but it's pretty exciting at the frontier.

29:20Kevin Wheel is on the podcast, the CBO at OpenAI. And he made this point that really stuck with me that the model of today is the worst model you will ever use. I love that. Well, only get better. 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 met it. Are they still like a big player in this labeling space or they kind of out of it? And that's. That's the whole scale team at what a respect for what they built is many great companies operating in the space.

29:58I think to the quarter question, it's like, I think if you were building the most, if you viewed your research team and your model building team and they experienced the 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 invested in by a peer. I mean, this is generally what we hear in this space. And so we have seen an incredible search and demand and are, I think, extraordinarily well -positioned. We like to say that the only moat in human data is access to an audience. Basically, there are many, many small players in this space.

30:42Some mid -sized players in this space, and they're basically running TikTok ads, running Instagram ads, paying money for Google search, display ads, YouTube ads, and they will be like, can you get me 200 physics PhDs? What do they do? They only can do one thing. They have 100 recruiters on staff, they all get on LinkedIn, they all send messages, they spend a couple million bucks on performance advertising campaigns, somebody scrolling their Instagram feed that's a physics PhD of what you can't target them that well. And they like, see, come train a model. It's like, I've never heard of this brand before.

31:17The huge advantage that we've had, and why we've resonated so fast in the marketplaces, like we built a decade of trust with 18 million people and they trust us. And we built a patent brand affinity and they use Handshake, may 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 the marketplace. Today's episode is brought to you by Anthropic, the team behind Claude.

31:53I 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 newsletter, for getting feedback on my writing and all kinds of stuff. Just last week I was preparing for an interview with a very fancy guest. And I had Claude tell me, what are all the questions that other podcast hosts have asked this guest so that I don't ask them these questions. How much time do you spend every week trying to synthesize all of your research insights, support tickets, sales calls, experiment results, and competitive intel? Cloud can handle incredibly complex multi -step work.

32:29You can throw 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 Cloud 4 and the new integrations, including Cloud 4 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. Cloud just becomes part of your workflow. If you want to try it out, get started at clod .ai slash Lenny. And using this link, you get an incredible 50 % off your first three months of the pro plan. That's clod .ai slash Lenny.

33:06Okay, this is an awesome segue to where I wanted to go, which is just how this business emerged. This is a business that you built on top of a business you've already had. From what I understand, you were at like $150 million in revenue, you've been at this for a long time. You found this opportunity. And now that I, you know, looking back, it's like, obviously, this is an amazing idea. Labs need data. You guys have the supply of incredible experts. What an opportunity. Talk about just how you first realized this was something that you could be doing and should be doing. and then how you started to kind of execute down this path.

33:40Yeah. I think it's been a pretty natural extension from like helping people jump start, restart, start their career. Like, you know, monetizing your skills and this new employment ecosystem is going to look very different in the future. And we want to, you know, to zoom into like how we discovered it's like, we, because we have such a large access to this audience, It has the world shifted from generalist to experts, and with the largest expert network in the world. We have more PhDs, either a thousand of them use hand -shake than any other platform. We have 3 million master students who are in -score alumni.

34:18We started to see all the what I would call middleman companies reaching out to us, saying, can we recruit your PhDs in master students? And like any great marketplace, we started setting them to these different platforms and started to really realize that, you know, from, hearing from our users, that like the experience was really frustrating. Like, training was very transactional. The payments were, you know, there was very amorphous how you could get paid. Like, there was immense amount of drop off in the process to actual project, like completion and these other platforms. So we started to think, the company was, you know, making tens of millions of hours from helping these other platforms.

34:58And we started to realize like, what really kicked it off was like hearing also from the Frontary Labs, they started to reach out to us and started to go direct. And Trent and I, Tomas kind of caught out the middleman. And we started to realize, well, we could really serve our fellows, our PhDs, our experts. We could treat them. We just believe there's like, there will need to be a platform in experts first platform in the pursuit of ASI and advancing AI. And there will need to be a place that everyone in the world could go to to monetize their skills and their knowledge as these labs are focused on improving in these, you know, in all these multi -disciplinary outcomes.

35:38And yeah, we entered the business in really like, I started doing it over Christmas and New Year's. That's when I started like, flying around. Family come, thought it was a little while that I was like on planes trying to chase different leaders, but we built an incredible team of people that came from the human data world. And really started building on our platform in January, and then started really monetizing their 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 in building the best large language models and the team is exploded and revenue is exploded and it's been really an incredible ride kind of like running back a new company inside of a company for the second time over again.

36:28And just to share some numbers tell me if this is a correct or if you're sharing these but I heard that you hit $50 million in revenue just four months into this. Today we're at eight months in and you're in track to hit $100 million in revenue in the first year. I think we'll blow to that number, but yeah. Okay. Incredible. And I didn't even know there are seven frontier labs. That's a zero 50s, pretty good in four months, I think. Zero to 50 million in four months. That's something. It's like the bar has been shifting constantly. Like, you know, a year ago, that'd be legendary. Now it's like, all right, well, another one of these.

37:03It's 50 million in four months. No big deal. It's truly insane. Just to zoom out one second for people to, that don't know a ton about handshake, the original business, what was that? Like what was actually this network that you had that you sat on top of? Yeah, that network does about 200 million. This will do about 200 million. Yeah, so that's, we have like 600 issues, like super passionate teammates that work on on the core business, which is, you know, I would separate do that. I was like, these aren't two businesses. I think it's like, it's one business, but what is that business? It's the, if you're a young person in America That's graduated in the last five, six, seven, eight years.

37:42You probably have had a check on your phone. You like definitely know what an cheek is. It's like a, it's a, it's a, for both young people and America. It's a verb with people that like are in college and their PhD or master's, you know, program. And it is, I call it an unconnected graph, meaning like you don't need to, you know, LinkedIn's very focused on like who you know and like what your experience is. The first question to LinkedIn is like, what's your job? And a lot of young people start off like, they've never had a job before, right? They don't have like 500 connections to add to their to their to their graph.

38:13Whereas on Henshake you start off like trying to discover and explore and figure out how to navigate through a school and figure out, hold, I'm an engineer, maybe I want to be a P .M. Maybe I want to start out, maybe I want to go to a larger company like What are the pros and cons you want to learn from near peers and young alumni? And so Henshake's this, 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.

38:48And how long has that business been around? It's been around 10 years. 10 years. 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 Stackplates, which was building for seven years, this browser -based OS, where you could run an OS in the browser.

39:25And they're like, I don't know. No one needs this. Why are we doing it? 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 it. And now it's, I don't know, one of the fastest during companies in the world. Yeah. So interesting. And so I think this is just an interesting time for all people to think about, what do we done that may give us a new opportunity to build something huge based on this unfair advantage that we have? I think also like as your company grows in size and headcount and maturity, it's also like hard to like incubate something new inside of a business.

39:58Like it's hard to,

40:02you 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 more mature business that has been around for 10 years with hundreds and hundreds and hundreds of people. So I've really had a ton of fun and been funneled, ton of passion in like running it back again for the second time inside the business. And then yeah, we have this massive strategic advantage, which is like no cost -requisition costs. And we have like much higher conversion rates and retention than like any other platforms by a large margin because we have such consumer affinity.

40:42There's actually two threads here. I'm going to follow. I'm going to follow the second one first. This idea of where this data labeling work can come from. This isn't a really clear, simple, understandable one, which is just experts sitting there creating data. Another one that I know a lot of other companies in the space use scale. I know especially it was just like low -cost labor in a nationally. 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, you know, the ecosystem here is like one of the leading players has like, they have like 200 recruiters.

41:25It's like unsustainable. They're like 200 people on LinkedIn sending individual messages to acquire these people because there's no brand There's no trust they spend you know They're spending tens of millions of hours a month on performance advertising Google ads to find experts and to find folks and it's experts mostly at this Yeah, and then they put them on to an experience that like Is treating them like they're drawing like boundary boxes around stop signs in the Philippines like you know the but the frontier tax accountants don't want to be treated like low -cost international labor, right?

41:59And 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 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.

42:29It's very connected to our core business of starting, jump starting, or restarting your career. And like, you come in, you build a profile, you see the community, there's groups and a feed of, here's how people are learning. Like you come into actual, individual cohort with peers that look like you and have your similar background, you're being taught on how to interact. and there's like a trial and error, and we have an instructional design key, so you can't do it. Then you're put on the projects, we're building like, there's certain swim lanes where we're actually pre -building data and selling that data to all the labs.

43:03So we can do this thing where we produce one unit of data, ourselves, we pay for it, also a movie production, we pay for a unit of data, and then we make sure it's very high quality, we run our own push 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 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 they only want the best of the best people in machine learning, right? And then they go from our projects to their projects.

43:38And so there's a huge customer acquisition. I mean, it's a basic, you know, you will go deep on your podcast, just to talk about it. It's like, you really have a couple of things the matter of cost to customer acquisition at your CAQ. And then you have your LTV, like the lifetime value of a user. And an LTV is kind of 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 1600 universities, power 92 % of the top 500 schools in the country.

44:13We power almost every institution and they committed to 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 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, there's school, pies handshake, like we need to treat, we care about things these people will have, but like the universities would not tolerate our partnership with these fellows unless we treat them out. So you put them into this process where our LTVs and repeat Engagement rate and retention rate on different projects is really high and so these structural advantages are quite significant When you contrast like a leading provider that has like 200 individual Contributing recruiters and are spending tens of millions an hour a month on performance marketing You know, so that's I think why we've seen so much success That's extremely interesting.

45:08And it feels like, as you said, there used to be a big focus on generalists, which is people anywhere in the world for low -cost can do the work, like draw bounding boxes around things. And essentially, the market has shifted from low -cost generalists to experts. And a lot of these companies, like Scale, were optimizing for general work model training data and you guys are set up to be extremely good at expert -based data and so you're in the right place at the right time with the right supply. What a business. Nice work. I would say it's not been easy building business two inside of business one, but - So let me actually, yeah, so let me follow that thread.

45:49That's where I wanted to go. What was just that like? So you started noticing that model companies were coming to your people that people were having hard times with some of these other companies in this space. And you're like, oh, maybe we should do we doing this sort of thing. How did that just like initial inception start? And how did you start to explore that idea and to see if it was a real thing? Tactically, you know, we were working with many of the middle man 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 middle man in their pursuit of getting higher quality data.

46:26And we started to put together the dots on we, we could build a way better experience for our fellows. We could serve them directly to the labs and build a direct customer relationship with the labs and basically cut out the middleman and provide a better experience to the labs, provide a better experience to our fellows and provide a better experience long term to our like our million companies on the network and you know, and you might, you might think about just like upskilling and re -skilling what's going to happen there. So we want that into the space. We started in, you know, really December exploring and learning more about it.

46:59I'm like expert calls and hammering down, you know, I heard like three expert firms alpha in the alpha sides 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 larger companies. Like we have financial, you know, our core business is $200 million a year. So it's like, you know, we had resources to be able to like accelerate the learning curve here. And then we started working with the arguably like the number one lab about five months ago. I wonder who that is. Yeah. I'm like, yeah, I wonder who it is. But I'm going to spend for our key, get different answers.

47:40Working with the number one lab and and have just, you know, now we're working with seven of the frontier labs and And the number one thing we're trying to do is just focus on scaling up and what 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 are, you know, we are so bottlenecked on just meeting this opportunity because in this market there's essentially like unlimited demand. Like if you could produce high quality values of data, you most likely will be able to sell whenever you produce.

48:19And so on our side, it's like we're really focused on making sure that we picked the right longer term strategy, making sure that we don't grow too fast as to a road to trust that we built up with these frontier labs. Yeah, but it's been fun. You said it's also been really hard to start those business within an existing business. What's been hard? What's been hardest? You touched on a couple of these elements already, but what else? I think I just kind of followed a lot more of my intuition around this doing this. The story of Handshake was we had to sign up 1600 universities. So I didn't want to be like, the fastest growing higher education company in my history.

49:06So we signed up 16 -year -old schools. And then we had to build an employer business where we had to figure out how to sell the 100 % of the, you know, all these works for our company's use it. Like 70 % of the pay for it. So I had to worry about like up market sales to like Goldman Sachs and General Motors and Google and the biggest companies in the world, which is totally different than selling universities. And then we had to learn how to build like an incredible student, like kind of social network. Like what does the best feed look like? What does group messaging look like? You know, so we had, I felt a little bit of familiarity and there's like kind of zero to ones.

49:39Oh, sometimes like marketplaces are like many zero to ones. Sometimes I dream that we just like, I actually don't dream, but I make a joke that like, I just wish we were like a cybersecurity company and we had like Warren buyer and just like one product and it was just like, you know, we had to, in a marketplace you have to serve free 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, I was pretty hands on. So, like, you know, everyone reported directly to me. I really did not try to be like, I really said in a lot of me, it's like, I'm not trying to be the boss.

50:13I'm just trying to get another smart guy in the room. Like, I hired, I was just, we've hired an incredible team of people that have spent a lot of time in the space and have been big leaders at a lot of the human data companies in the space. And so everyone saw very clearly the structural of the energy 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 expanded 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 just checks and balances that it was a lot of people that like try to get involved, right?

50:54Like 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 can't privatize that this week or this month. I have an existence at a priority So you know, I Sentially with the exception of a few things like everyone just came straight into This new work that I built Everyone did not have any responsibilities in the existing part of the business It was extremely clear who was like the directly responsible individual across each area the new cow and And now we've got deeper, couple and integration points across the rest of the business, but we sat in a separate part of the office.

51:34We're, we, everyone's in the office five days a week, a lot of weekends. There's a totally different expectation and hiring talent too, where it's like, hey, this is a 24 or seven job, right? Like this is an early stage company, where the compensation was also different too, and based on like hurdles in those due business, so people felt like owners, creating the new co, And yeah, it's like, it's still extremely nimble, very, very flat. You know, just because you want, run one function doesn't mean you're the director responsible individual on a project. We picked the best person who's most capable of driving an initiative forward, regardless of the function to be the DRI, where a lot more metrics oriented, you know, when I, when I built Handcheck, we, we, we resisted this like operating cadence for a long time, Like this weekly monthly quarterly operating cadence.

52:26With handshake AI, we've been way more focused on operating with data and metrics in rigor from an early stage. This is a gentleman named Sahel on our team who's been doing an incredible job with that. Shahdoud Sahel, Shahdoud Young, Shahdoud Paco. Yeah. Okay. This is incredible. So, if you kind of elements of what allowed this to succeed within a decade old company. And by the way, so you're a 200 million a year in revenue with the traditional business you're gonna, as you said, blow past 100 million in the first year of this new business. So it's wild that in the first couple of years, if things continue to go this way, you'll exceed the size of the run rate of a business that took you 10 years to build.

53:08Incredible. To make the successful, a few of the things I noted as you were talking, one is clearly you were just like in founder mode, you were the CEO of this company. You're like the lead of this new business. You were taking, you weren't delegating it to someone. Take, go start this thing. You dedicated people here. We're going to pick people, even nothing else going on. This is your new job. You're going to work on this stuff. You work 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.

53:39Anything else there that you felt really important to making this work because a lot of companies are going to try to do this, I imagine. And so I'm curious what else you found important to make this work. Yeah, I mean, I just really believe it's separate, 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 I was making it to you guys successful. We had a couple integration points, more and a, I have an incredible executive team on a core part of business, and others 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.

54:20And I focused 80 plus percent of my time and attention on just this. You know, we hired an incredible entrepreneur like Avery who, you know, we, we focused on hiring a lot of entrepreneurs. We have a lot of entrepreneurs, people that have started companies inside the company, or pardon me, people that have started companies before. Like, that was huge. A lot of familiarity with hiring talent that have like only worked at early stage companies but it's so forward that feels super comfortable with ambiguity. We were also way more up front around, this is gonna be chaotic. Just like owning that narrative, like in front of all hands of the core company, owning it directly to the team.

54:56We 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 suffer, 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 Biscuit said, sorry, I know you love your old team, I know you love what you're doing. Like, will you join us in Hinchick? Yeah, I and they completely foregoed their historical or spot spot as it came over. That became really critical with engineering when things started to scale and topple.

55:30And we're growing so quickly. We took some of our top senior engineers who were very entrepreneurial and principal engineers, your staff all one year, it's like parachute them in and, you know, that's been awesome to be able to like, we have, it's been awesome to like ask someone most talented people in the core business like, hey, do you want to come over here and do this? And sometimes they say, no, like they're like, I don't want to work, you know, most of the weekends. I don't want to be on the number of 2 a .m. 3 a .m. nights we've done in this business. It's, it's that, it's quite regular.

56:01Like people sometimes don't want to come into that, but we've been up front. Like here, here are the expectations for this team. It's It's an insane pace. If you want to be a part of one of the fastest growing businesses in Silicon Valley, you can join it. The ownership too is also very huge. Like owning this outcome, and we have this model like leave nothing a chance. For a while there, we 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've never seen anything like this in business again, where there's unlimited demand, and it's just our blood execute against it.

56:39And so we had this model, like, leave nothing but a chance. How do you make sure that three months or not six months, you have no regrets. Like, get on the plane to go talk to a customer, like, made 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 really is in this principle of, 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 has been, it's been awesome.

57:13Believe in nothing to chance piece, I imagine speaks partly to the value of trust in what you're doing. People are gonna, like you win if they can trust that your data is awesome and great and consistent, and I could see why that ends up being such an important part of what you're building. And like just listening to you describe this, I understand, like it's there's so, It's obviously a massive opportunity, obviously a massive advantage you guys have and just like the stress that comes with that burden also Imagine is very high of just like this is we can't screw this up Did cannot cannot yes Handshake should be a Business does billions dollars revenues a couple company like you should you know we should be able to Continue to I mean 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.

58:07It'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 an 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 a charge there. Like, you know, an AI interviewer that's collecting skills and actually asking about your experiences, doing work simulation experiences that like help employers find the best candidate to me. I don't know the last time you've done this, but like the hiring manager process like reviewing 200 resumes, like, are you kidding me?

58:48Like, 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, supplying demand and you know, talent 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, a pavement with her school. I went to a no name school and I'll propanate and sell up Michigan. I worked at Palantir as an intern. I told you 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.

59:31And I think AI will, like, holdably step function improvement in matching. And I think that our human data business is really serving as, like, the foundation for improving matching. Like, a lot of things that we're doing in the human data business are being integrated to our core business. I think that's going to 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 just like we have to meet the moment. Like, you know, we still have this stamina and the excitement and the passion internally in our core and in the new business to like go charge after this.

1:00:08And that's a lot of the message that we've been sharing internally. It's like it's time to amp it up. It's time to like, this is a once in a life that my opportunity to be positioned as well. I'm like, we are gonna need the moment as a team. It really is. This is very much feels like a once in a lifetime opportunity. Let me ask a few other questions along these lines that are something I've been thinking about, something that a lot of people think about just while I have you. There's always this question of, will we run out of data? Will models stop advancing? Are we going to hit some plateau?

1:00:35And there's not actually going to be some AGI moment, SGI moment. So first of all, do you think we'll run out of data? There's a point at which we just can't produce more knowledge and data to feed these models. And kind of along these lines, what do you think is the biggest bottleneck to advancing models faster and further. You know, 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, you know, scientific tool use data as they are trying to automate scientific discoveries and drug discovery. It's gonna, you know, it's gonna be esoteric, you know, operating systems that exist on, you know, scientific tools.

1:01:14It's gonna be, you know, so I love this like trajectory and like stitching together step -by -step instruction following, like, you know, theirable need, the type of data we're gonna need is gonna evolve a lot. And we haven't even talked about like multi -modal and video and hacks and audio, like, audio is just 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, DBT experience, just talking to it's amazing. It's amazing. I just had a baby on, where my wife had a baby on Sunday, and voice mode has been incredible.

1:01:49I mean, every now you get, you know, every two hours is speedy. It's like, I have more questions. Voice mode has been huge. So I shot out voice mode. And yes, the type of data is gonna collect a lot, or change a lot. I sing synthetic data has a role to play in verifiable domains, but what would consistently hear from companies is like, you know, synthetic data is not gonna dominate. Like it's not gonna be like, there's billions and billions and billions of dollars are so valued to extract as a company over the next decade and following the frontier of AI development. Let me first say just huge kudos to you for just having a kid, your wife just having a kid a few days ago, and building this business that is growing bananas and doing this podcast conversation, I really appreciate you thinking that.

1:02:36Of course. Is there anything else that we haven't covered that you think might be helpful for folks to hear or part of your story that you think might be helpful folks to learn from or something you may want to just double down on that we've talked about before we get to a very exciting lightning round. I mean, the thing I always love talking, I'm really basher about people starting companies and helping them do so. I'm like, I just think in this moment right now with the eye, for young entrepreneurs that read this podcast, because I've been a reader since 2020 we looked at it. Yeah, we did check.

1:03:07That's incredible. The long term reader, I'm just so curious and love talking about your interviews. But it's like, it's just focused on doing something like a meaning that really helps people. And I think with AI, there's going to be so many opportunities to improve the way people learn. 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 really helps solve a societal problem that matters. And yeah, it's even my one shot out here. if anyone wants advice on how to do that or wants to reach out on my capy to chat.

1:03:43Okay, so this is an offer to share advice on starting companies within AI. Is that the offer here? Just some folks. Yeah, it'd be great. 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. Anything else before we get to a very exciting lightning round? No. Well, with that, Garrett, we reached a 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? I'm a must sucker for Peter Teal's zero to one.

1:04:14I read it and I started the company and watched Peter Teal's like start up school class. It's Dan Ferdittot back in the days where there wasn't everything written on the internet about how to start companies and like just think he was the coolest. Love, love shoe dog like thing. Yeah. So it tited me of like starting a company. Hard things about hard things, obviously. But these are these are all quite common books But also glassics Ben Harrow it's just coming on the podcast talk about hard things about hard things Super cool the hard thing about her things. Yeah, okay What have you seen a recent movie or TV show you really enjoy it?

1:04:48I imagine you don't have much time for this but I'm gonna get blasted for this But I did start chemo thrones my wife and I Can opt for the first time. Yeah, okay, so I got a lot of head she up to do. Why would you get? No, this is great I mean, a little bit of watching. You've loved it so far. Okay, it's quite gruesome. That's not only downside of that show, but 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. The Snew, the baby automated. Snew is like, has really helped us a lot.

1:05:22So, love the shout -out to you. Amazing, honest, new as well. We never actually turned it on. We just ended up using it as a best in it. Mostly, I think. It's not turned on, but a couple of cries, it's been turned on, it's been very helpful. The favorite life motto that you find yourself coming back to, sharing with other people. I love that, like, leave nothing chance, like to leave it all out of my fields, you know, grew up in, you know, like a really hardworking family and that worked really hard to provide, make it happen for us. And it's like, just give it your all, leave nothing chance. Okay, so the 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 going from campus to campus pitching schools to join handshake and there's a story where you had to shower in the Princeton spool to save money because you just didn't have a place to stay.

1:06:09Is there something there? Is there a story there you could share? Yeah, so it was tough one. I mean, I almost got arrested at Princeton because, I guess, for entrepreneurs that are traveling around all the time, we were sleeping out of our car. We had this like Ford focused, put 20 to 30 thousand miles on it, sleeping in the back I'm like McDonald's parking lots, because they're well lit and had good Wi -Fi back in the day. And instead of staying in a hotel, wait a fresh and up ahead of your meeting is like, every university has a pool, and the pool's almost always, but 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?

1:06:48They have a shower. So you could go to any pool, any university in the country, and you can get a free shower in fresh and up. So the Princeton campus security did not appreciate me showering as a non -student, but I think it immediately helped us because the Princeton campus security called the Curse Service Center director, Rissange, to being like, who's Garrett Lord? Like, is he really here to like pitch you software for your Chris Center? And it made the start of the meeting with the Chris Center like really stimulating and exciting. Because they're like, you showered in our pool, you drove here?

1:07:22Yeah, we drove here from Michigan, you know, we like and so I think that showed a level committed that was exciting for them Fast forward to all these founders now starting to use this the growth lever of 10 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 all right 10 years going great and that's like Well, holy shit. This come from Incredible and this just also really meaningful so I'm really happy that you may time for this in spite of the madness you are in right now Two final questions where can folks find you if they want to maybe reach out or maybe if you're hiring let us know And then how can listeners be useful to you?

1:08:12I mean sign up for handshake if you want a message me on there It's the easiest way to reach me. It's each Spine, Make, Get, Lord, at Handshake. And you can find me on Twitter, love, or love, acts, huge, he checks back. You can email me at get rid at trainhingshake .com and double r double t. And how can you be helpful? Like we are trying to hire so many people. We have offices in New York and at San Francisco and London and Berlin. If you have friends that are passionate about this, you wanna know or you're interested in the warning more, Like please reach out, we'd love to talk to you. Hiring is like the number one problem we have right now to meet the demand.

1:08:53So if you're talented and interested in learning more about handshake, you want to work on our consumer product. If you want to work on our employer products, cool PLG issues or the state of the art consumer social experience, like reach out or you want to work on the AI business, we'd love to talk to you. To make it even more clear for folks, what roles are you most hiring for? Is it every role? Is it engineering? Engineering. Engineering, all right. when the fast -seering companies in the world right now. Here we go. We'll link to your career's page in the show notes. Thank you. Yeah, of course.

1:09:20Garrett, 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 a leaving review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at Lenny's Podcasts .com. See you in the next episode.