Inside the expert network training every frontier AI model | Garrett Lord (Handshake CEO)
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What is data labeling and why is it critical for frontier AI models?
There will never be a time like this. I've never seen anything like it. I doubt I'll ever feel anything like this in business again where there's unlimited demand. How do you make sure that three months from now, six months around, you have like no regrets? Get on the plane to go talk to a customer, make the late night push, check the data six times over again.
Your company creates new data to continue advancing the intelligence of models. This is a business that you built on top of a business you've already had.
We're the largest expert network in the world. But we have this massive strategic advantage, which is like no customer acquisition costs. The only moat in human data is access
to an audience. You guys come in after the models trained to tweak the weights based on additional data that you create. The models
have gotten so good that the generalists are no longer
needed. What they really need is experts. There's this tension between all these students, training models to become smarter, and then there's that they will have harder time potentially finding jobs. That's not what we're hearing from
our employers. This is just enabling human beings to be even more productive. Use to put like Google search on a skill on your SMA. Cause you like grew up at Google. Being like AI native, young people are at a huge advantage.
Today, my guest is Garrett Lorde. Garrett is the co founder and CEO of Handshake, which is one of the most interesting and incredible AI success stories that you probably haven't heard of. Handshake has been around for over 10 years. They're essentially 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 million students, and Alumni, and over 1 million companies use them to hire graduates. At the start of this year, Garrett and his team realized that their huge proprietary network of students, including tens of thousands of PhDs and master'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 0 to 1 in January. Four months later, they hit 50 million ARR. They're now on pace to blow past. 100 million ARR within just 12 months. They'll exceed the revenue that they're making with their decade-old business in under two years. This is a truly incredible and rare story, and one that I think a lot of teams can learn from because AI is creating a lot of opportunity, but also a lot of potential disruption. And this is an amazing story where the company basically disrupted themselves. This episode is packed with insights, including a primer. On what the heck are people actually doing when they're labeling and creating data to train models? A huge thank you to Garrett for making time for this.
His wife just had a baby this week. He's also in the middle of scaling this insane new business. So thank you, Garrett. If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube. Also, if you become an annual subscriber of my newsletter, you get a year free of a bunch of incredible products, including love. Replit, Bolt, N8M, Linear, Superhuman, D Script, Whisperflow, Gamma, Perplexity, Warp, Granola, Magic Patterns, Raycast, ChatPRD, and Mobbin. Check it out at lenny's newsletter.com and click bundle. With that, I bring you Garrett Lord. This episode is brought to you by CodeRabbit, the AI code review platform, transforming how engineering teams ship faster with AI without sacrificing code quality.
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Chapters
8 chapters
1
What is data labeling and why is it critical for frontier AI models?
0:00–9:16
2
How do expert PhDs actually create training data for models like GPT‑5?
9:16–18:56
3
Why has Handshake’s expert network become a moat for AI data‑labeling?
18:56–28:24
4
What challenges did Handshake face building a startup inside an established company?
28:24–37:20
5
How does the shift from generalist to expert labeling unlock new revenue streams?
37:20–45:43
6
What does the future of AI‑human collaboration look like for entry‑level jobs?
45:43–54:05
7
What are the biggest bottlenecks to scaling high‑quality model training data?
54:05–1:02:00
8
How can listeners get involved or help Handshake’s fast‑growing AI business?
1:02:00–1:09:46
Speakers
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