Handshake's $2 Billion Pivot: Garrett Lord on Building the Data Layer for AI

What if the company students use to find their first internship became one of the most important players in training AI?

Garrett Lord, co-founder and CEO of Handshake, joins Immad Akhund and Raj Suri to break down Handshake's unlikely pivot. Handshake started as a way to help college students — regardless of where they went to school — find internships and jobs, and grew into a $200M+ ARR business used by most students in America. But over the last 18 months, Garrett has built a second business inside Handshake: using the company's network of 30 million students and alumni to help AI labs train their models on real, high-quality, professional-domain data — from oil and gas to finance to scientific research. That business alone has gone from zero to nearly $2 billion in revenue in about a year.

The conversation goes deep on how this actually works: recruiting domain experts, building task environments that function like video games, scoring model performance against expert-validated tasks, and why 70% of the money spent training a model today goes toward reinforcement learning rather than pre-training. Garrett, Immad, and Raj also cover the open-weight vs. frontier model debate, why China may already be ahead on robotics, and what jobs might look like in a world where AI models can eventually learn continuously, on the job.

The episode closes with a genuinely open-ended debate between Garrett and Immad about what humans will actually do for a living, and for meaning, if knowledge work is mostly automated — and how disruptive that transition might be along the way.

What you'll learn:

1. How Handshake used its network of 30 million students and alumni to build a second, multi-billion-dollar business training AI models
2. Why 70% of AI training spend now goes toward reinforcement learning, not pre-training
3. How AI labs identify gaps in their models and commission the specific data needed to close them
4. Why data, not algorithms, may be the real long-term moat for AI companies
5. Why computer use — AI navigating real software and websites — has recently gotten dramatically better
6. Why China may already be ahead of the U.S. in deploying real-world robotics
7. How enterprises like Mercury are likely to use a mix of frontier and open-source models going forward
8. What Handshake learned scaling a data business from zero to nearly $2B in under two years
9. Garret and Immad's differing views on what human work and meaning look like if knowledge work becomes automated

Timestamps:

(00:19) Introduction and Handshake's origin story

(01:39) Handshake's new business: training AI models on real-world data

(02:30) How Handshake's 30M-person network became a moat

(04:24) Inside the "video game" environments used to train agents

(05:00) Why 70% of AI training spend now goes to reinforcement learning

(08:18) How AI labs commission specific data from Handshake

(12:10) Why computer use has finally gotten good

(14:11) What models are still bad at, and why

(17:00) The "8 people can agree" test for what AI can be trained to do

(18:23) China's 2 million working robots, and why the US is behind

(22:12) Open-weight vs. frontier models, and how enterprises will use both

(31:15) Scaling from zero to $2B: what broke along the way

(34:02) Handshake's "Olympic pace" culture value

(39:41) Why continuous learning is the next frontier for AI

(42:07) Bill Gates' essay on AI, job loss, and taxing tokens

(45:23) Immad and Garret debate what jobs and meaning look like in an AI-driven future