Inside the expert network training every frontier AI model | Garrett Lord (Handshake CEO) · Lenny's Podcast: Product | Career | Growth summary

Key takeaways

Handshake has evolved from a career network for students to a powerful force in the AI data-labeling industry, targeting expert talent over generalist labor.
AI models rely on high-quality data produced by human experts, rendering generalist approaches insufficient.
Companies must be agile and willing to separate new ventures from existing operations to foster innovation and meet market demands.
AI tools enhance productivity rather than eliminate entry-level jobs, empowering workers and transforming the job market.
Building a community and trust within a platform significantly boosts conversions and user retention.

Summary

Introduction to Garrett Lord and Handshake

Garrett Lord is the co-founder and CEO of Handshake, which started as a career network for college students and new graduates. Recently, they discovered their potential to leverage the world’s largest network of academic experts, including 500,000 PhDs and 3 million advanced degree holders, to create training data vital for frontier AI labs. In just eight months, they built a new business that generated $50 million in revenue within its first four months and is projected to reach over $100 million in the first year.

The Shift from Generalist to Expert Data Labeling

The conversation highlights a significant shift from using generalist labor to expert talent for data labeling. Garrett explains how this transition opens a rare business opportunity by filling the gap where AI models require high-quality, specialized labelers to improve their functions. Handshake's large network of experts positions them uniquely to cater to this growing demand.

The Importance of Human Experts in AI Training

In order to improve AI models, human experts play an essential role in data labeling. Garrett discusses the intricacies of training models, explaining that experts are needed to identify and correct flaws in AI outputs. The relevance of human feedback ensures that AI continues to learn and evolve, as generalist data labeling methods no longer suffice.

Scaling the New Business within Handshake

Garrett details how Handshake managed to launch a new business internally. Focusing on separate teams, offices, and methodologies enabled them to optimize operations. They understood the necessity of quickly scaling high-quality data production to meet unprecedented demand from AI labs, comparing this dynamic to running a startup within a more substantial company.

Addressing the Future of Job Markets and AI

Garrett argues that AI is not a threat to entry-level jobs but rather a force that enhances productivity. He draws an analogy of AI tools to 'Iron Man suits' that empower junior employees. As AI democratizes skills and enables workers to become more effective in their roles, the job landscape is transformed, facilitating new career opportunities. He expresses optimism about how platforms like Handshake can assist in skill development and connect students with relevant job opportunities.

Show notes

Garrett Lord is co-founder and CEO of Handshake, which started as a career network for college students and new grads but recently discovered something extraordinary: they were sitting on the world’s largest network of academic experts—exactly what frontier AI labs desperately needed. With 500,000 PhDs and 3 million advanced degree holders creating training data, in just eight months they’ve built a new business that hit $50 million in revenue in its first four months and is on track to blow past $100M in the first 12 months.

What you’ll learn:

1. How Handshake found an opportunity to leverage their proprietary network of experts to launch a data-labeling business that’s on track to blow past $100 million ARR in 12 months

2. Why AI models need human experts (e.g. physics PhDs) to improve, and what this “data labeling” actually involves

3. Inside the actual work: what a biology PhD does for 8 hours that makes GPT-5 smarter

4. The playbook for building a startup inside a startup: separate teams, separate offices, separate everything

5. Why the shift from “generalist” to “expert” data labeling created a once-in-a-lifetime business opportunity

6. Why AI won’t eliminate entry-level jobs—it’s creating “Iron Man suits” that make junior employees 10x more productive

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Transcript: https://www.lennysnewsletter.com/p/inside-handshake-garrett-lord

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My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/171410958/my-biggest-takeaways-from-this-conversation

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Where to find Garrett Lord:

• X: https://x.com/garrettlord

• LinkedIn: https://www.linkedin.com/in/garrettlord/

• Email: Garrett@joinhandshake.com

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Where to find Lenny:

• Newsletter: https://www.lennysnewsletter.com

• X: https://twitter.com/lennysan

• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/

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In this episode, we cover:

(00:00) Introduction to Garrett Lord

(05:00) Understanding data labeling and its importance

(13:08) The role of experts in AI model training

(15:35) The future of AI and human collaboration

(24:17) Why AI won’t eliminate entry-level jobs

(27:58) The continuous improvement of AI models

(33:05) The emergence of Handshake’s new business model

(37:07) Incubating new ideas in established companies

(40:42) Handshake's competitive advantage

(45:43) Scaling up and meeting market demand

(48:38) Overcoming challenges and adapting

(53:08) The importance of separate teams and ownership

(57:26) The future of job matching with AI

(01:00:30) The biggest bottlenecks to advancing models further

(01:02:37) Lightning round and final thoughts

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Referenced:

• GPQA: https://github.com/idavidrein/gpqa

• Handshake: https://joinhandshake.com/

• OpenAI’s CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai

• Inside Bolt: From near-death to ~$40m ARR in 5 months—one of the fastest-growing products in history | Eric Simons (founder and CEO of StackBlitz): https://www.lennysnewsletter.com/p/inside-bolt-eric-simons

• Goldman Sachs: https://www.goldmansachs.com/

• General Motors: https://www.gm.com/

• Google: https://about.google/

• Sahil Bhaiwala on LinkedIn: https://www.linkedin.com/in/sahil-bhaiwala-459b0354/

• Francisco “Paco” Guzman on LinkedIn: https://www.linkedin.com/in/guzmanhe/

• Avery Yip on LinkedIn: https://www.linkedin.com/in/averyyip/

• Game of Thrones on HBO: https://www.hbomax.com/shows/game-of-thrones/4f6b4985-2dc9-4ab6-ac79-d60f0860b0ac

• SNOO: https://www.happiestbaby.com/products/snoo-smart-bassinet

• Careers at Handshake: https://joinhandshake.com/careers/

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Recommended books:

• Zero to One: Notes on Startups, or How to Build the Future: https://www.amazon.com/Zero-One-Notes-Startups-Future/dp/0804139296

• The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers―Straight Talk on the Challenges of Entrepreneurship: https://www.amazon.com/Hard-Thing-About-Things-Building/dp/0062273205

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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.

Lenny may be an investor in the companies discussed.

To hear more, visit www.lennysnewsletter.com

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