Garrett Lord

Co-founder and CEO at Handshake

Co-founder and CEO of Handshake, the largest platform connecting college students with employers used by every Fortune 500 company, which launched an AI data labeling business that went from zero to 50 million ARR in four months.

The Visionary The Operator

Dimension Profile

Strategic Vision 85%

Execution & Craft 70%

Data & Experimentation 50%

Growth & Distribution 60%

Team & Leadership 55%

User Empathy & Research 50%

Key Themes

AI data labeling business model self-disruption through AI pivot expert network as moat post-training data for frontier labs urgency in unlimited demand markets audience access as competitive advantage

Episode Summary

Garrett Lord shares how Handshake, a 10-year-old platform connecting college students with employers, discovered that their network of tens of thousands of PhD and master's students was incredibly valuable to AI labs for creating expert training data. They launched the new business in January and hit 50 million ARR in four months, on pace to surpass their decade-old core business within two years. The conversation covers the mechanics of data labeling, why expert networks are the real moat, and the intense urgency required to capture an unprecedented market opportunity.

Leadership Principles

→ The only moat in human data is access to an audience — customer acquisition cost advantages win in data labeling
→ There will never be a time like this — when there's unlimited demand, make sure you have no regrets in three to six months
→ The models have gotten so good that generalists are no longer needed — what AI labs really need is domain experts

Notable Quotes

"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."
— On the unprecedented market opportunity in AI data labeling

"The only moat in human data is access to an audience."
— On why Handshake's network of 20 million students is their strategic advantage

"The models have gotten so good that the generalists are no longer needed. What they really need is experts."
— On the shift in AI training from general data to expert knowledge

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