Handshake CEO Garrett Lord: frontier AI labs are hungry for PhD-level expert data — and Handshake supplies it

we have Garrett from Handshake coming in. He was mentioned in the information. We've been mentioned in the information. It's a bunch of information boys hanging out on the chat. We love the information. We love We love the information. Thanks so much for joining.

How you Garrett Lord, the nominative determinism is insane. Yeah. I think we love something also in common. Sonning. I'm a big big sauna guy. No way. There we go. Yeah. Yeah, the sauna is important.

You'll be devastated to hear that when we moved into this new studio, we we don't have a good We don't have a good sauna set up, but we'll figure it out eventually. The cold plunge can be can fit nearby, though. I mean, that's there's still opportunities. Be good. Yeah, maybe we got to get in the cold plunge game.

Uh anyway, in full suit. Anyway, uh kick us off with a little introduction on the business. Uh obviously, it's in the news today. We covered a little bit about it earlier, but I'd love to get you to explain the business, a little bit of the history, and the positioning of the company. Yeah, for sure.

So, I mean, the business started way back when I was in college. Um, I started Handshake out of a personal pain that I faced in breaking into find my first internship and first job. Um, I went to a no-name school in the middle of nowhere called Michigan Tech.

It's awesome if you love to ski or love the cold, but uh if you wanted to break into Silicon Valley, nobody had really recruited there before. Uh fast forward to today, Handshake is the number one place that young people in America start, jump start or restart their career.

Uh we're like kind of an 18 to30 early career network. There's a million employers that use Handshake. So it's where the vast majority of employers recruit undergrads and interns and people after school. Uh and then there's 18 million students and young professionals use the network.

And we also power uh about 1,600 universities in the country. Mhm.

And the background I think uh that's important for right now in this very moment is about 18 months ago many of the frontier labs as well as the large annotation uh engine companies started reaching out to us with basically asking us beating down the door saying like do you have access to PhDs?

Do you have access to master students? And uh for us that was incredible. I mean we have 500,000 PhDs in the network. We have 3 million master students on the network.

There's tens of millions of undergrads in the network and we started serving these players with uh uh experts really as this the world has evolved from training frontier models.

It's moved from generalists like drawing kind of boundary boxes around stop signs y to today experts and experts are in law, finance, medicine, mathematics, physics, chemistry, biology.

These labs uh really are hungry for reasoning data to help improve with human in the loop the actual uh you know frontier of uh what their models are capable of delivering uh yet alone in the future and you talk about like tool use or trajectory.

So they started reaching out to us and saying do you have access to these PhDs and master students and we started providing we were the leading provider of all this talent and we really started to realize is that people weren't getting paid on time. They were really confused.

they would go through training and kind of get dropped out of a leaky bucket. Um, we heard from students that were successful on it that they they loved the money, they love learning more about some of this AI tooling. They wanted to use AI tools in the classroom. They wanted to use it in their research.

And so given that we have this huge supply and zero customer acquisition costs, we started building a human data business. Um and really in the con construct of building that business, the the focus is really around like how can you also think about evolving and automating a lot of the recruiting practices.

Recruiting is still you know it's sourcing, it's screening, it's scheduling. There's a lot that AI can bring to bear on that. And so we now fast forward to today in the last 6 months have been working now with six of the frontier labs. We provide them tens of thousands of It's a lot of them.

I didn't even know there were six. They're only five, the big six. Count them up. You got them all. And uh we provide them with experts to help make their models uh more effective. Very cool. Um talk to me about like what the how how are the Frontier Labs thinking about human data annotation and answer generation.

It feels like we might be at the end of that story soon or maybe we're shifting into uh more of a focus on the areas that are less verifiable uh less like write the answer to an IMO level math problem and more in the biology and legal context where the models are falling behind like like where where are the pockets of value?

Where's the most demand within the human data generation industry and where do you see it going over the next couple years? Yeah. So maybe I'll go like from the latter part of the question to the first. So like where we see it going over the next couple years. It's definitely going to evolve into audio.

It's definitely going to evolve into tool use. It's definitely going to evolve into trajectories. Uh and experts will be needed to uh provide data. Um imagine almost like recording your screen as you're conducting a task. Maybe you're building a slide deck and doing it.

you know, if you're an investor construct like doing a DCF and doing competitive research, they want more data to be able to help uh improve these models, especially as you think about like agents, right? And step-by-step problem solving.

Uh as where the puck is right now and where the puck will continue to be, if you talk to a lot of the frontier researchers, is they need expert data and uh expert data is in basically every esoteric area of human knowledge.

They want to, you know, they'll the models have already kind of sucked up the entirety of books and YouTube and, you know, human knowledge. And what they really need is they need special data to be able to make and understand the step-by-step reasoning that's required in order to be able to to kind of fuel the future.

And so, if you think about academia, these PhDs, like what is the definition of getting a PhD? The definition of getting a PhD is like pushing forward an area of research that nobody else has done before as peer-reviewed by your peers. That's how you get your doctorate.

And so this kind of perpetually reoccurring stream of of PhD students and master students are really valuable in this very moment. And it's also to zoom out to their experience like you can make I don't know if you remember when you were in school, but you can make like 23 bucks an hour being a teacher assistant.

You know, you could dive you could drive Door Dash. And we're paying these students like 60 70 80 h 100red plus dollars an hour. And they're also we can connect it to actually getting jobs.

So, we envision a world where like you get badges on your profile and there's like leaderboards by school and we're actually I mean what better way to articulate your skill than actually proving it by being able to break the model or or by being able to provide the model feedback.

And so, we believe that we can help you get more jobs with the million employers in the network, help you build your professional reputation and articulate your skills all the while while while making like $100 an hour uh when you want to. I mean, it's it's a gig job. Yeah.

How do you think about financing Handshake going forward? I'm sure you're making uh generating a lot of revenue. You're clearly uh paying a bunch of your your network out quite a lot. Um uh we were just learning about Surge AI earlier and what they were able to do while bootstrapped.

I I imagine even in the last week, you've had investors reach out trying to, you know, say, "Hey, scale's out of the game. You wanna you want a 100 million? you want to dance. Uh but how how are you thinking about the business going forward?

Um yeah, I mean one of the the ways we think about this market is like you know if you don't have an audience there's no moat.

What our competitors are doing is they're at some of these companies they'll have hundreds of people who are recruiters sitting on top of platforms sending messages on companies like Handshake or spending tens of millions of dollars a month doing performance advertising trying to acquire experts on Instagram.

You can imagine if you're like a physics PhD and you get an ad on Instagram for a company you never heard of before claiming they could pay you $100 an hour. It's kind of a jarring experience. And so because we built a decade of trust in adding a ton of value to these users lives, we have no customer acquisition costs.

And what that means is that we can pass along all those savings by paying contributors. We call them fellows. It's the move fellowship program. We can actually pay you more than any other vendor on the market. Um we can also pass along those savings to the frontier labs.

So as you think about our overall P&L like our gross margin and ability to scale this business considering you know the moat is the network that we've built we sit in an amazing position to you know to to grow extraordinarily quickly and that's what we've been seeing.

I mean in the last you know month we've grown by over 3x and you know there seems like there uh there's a lot of demand uh continue to be out there. I can imagine. I had no idea it was that big though. Let's go. Let's go. Three hits in the conference. That's incredible. Uh are there any last question, we'll let you go.

Um are there any like weird areas that you think we'll see this type of human data generation pop up? I'm imagining like AI seems to be at like 150 IQ. It can write code and yet it can't like book me a flight.

Do we need to take like flight uh like travel uh what travel agents and have them go through the workflow so that they don't get hung up on should I sign up for the credit card or do I want you know insurance on this flight so that we have a whole bunch of data specifically about that task.

I'm just interested in this concept of like these economically valuable but highly niche tasks that don't seem to be we don't seem to be getting closer and closer and closer to like oneshotting them with the current models.

And I'm wondering if we're going to see this long tale of different hyperspecific business use cases like what we saw in SAS where there would be uh Hipmunk just help you book flights better.

Is there going to be a flow where there's a new startup that's doing AI agents for flight booking and then they're coming to you for a ton of data generation around how to actually book the correct flight because it learns whether or not you're okay with uh with a layover or how price sensitive you are.

All the things that you would get from the interaction with a human flight uh uh travel agent. Is that something that you think we'll see or or is that kind of just completely tangential? No, I think that's totally something we'll see. Interesting.

what what you just described is like a trajectory called a a browser trajectory. Sure. And that's basically like you have a goal in mind. Yeah.

And you you you know you have like a step by step kind of thoughts in your mind around how you accomplish that and you navigate tools, you navigate the browser, you stitch together your own intuition to be able to accomplish that task. Yeah. You might look at your own calendar. When do I get off work?

How look up how long it takes to get to the airport? It takes me a different amount of time to get to Burbank than LAX. What's the parking like? Like there's so it's such a simple task cuz you think about like anyone can do that job for you and yet to do it well is actually really hard. Totally.

And you talk about just being able to talk to a model, right? Like totally even need to log in, right? So you're going to need audio data, you're going to need trajectory data, you're going to be able to interact with APIs.

Uh humans experts will be needed for the next several years to to be able to make that data happen. Interesting. In order to be able to power the frontier of where you want to see it going. Well, that's exciting. I want to book a flight with an AI. It still hasn't happened. That's my own personal touring test.

Hopefully you can make it happen. Uh but thank you so much for stopping by. This was fantastic. Sure. Time. We'll talk to you soon. Great to meet you. Cheers. Uh coming in next, we have uh Tan uh coming into the studio to the TVPN Ultra Dome. Massive round. Oh. Oh, we're going to hit the gong again.

The 10th time of the show. Always a good time. There he is. Welcome. You got news for us? Hit us with an introduction. Hit us with the news. What's going on in your world? Think we might be muted. Donnie, are you there? Can you hear us? Are you there? I'm I'm I'm itching to hit the gong for you.

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