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

Aug 24, 2025 1h 9m 24 insights Episode Page ↗
Garrett Lord, co-founder and CEO of Handshake, discusses their new AI data labeling business, which leverages their vast student network to provide expert-level training data for frontier AI labs. This venture rapidly scaled to $50M ARR in four months, demonstrating how companies can disrupt themselves and capitalize on AI opportunities.
Actionable Insights

1. Identify Unfair Advantages for New AI Ventures

Reflect on existing company assets, networks, or capabilities to identify “unfair advantages” that can be leveraged to build new, significant businesses in the AI era. This encourages internal disruption and innovation.

2. Separate New Ventures from Core Business

Acknowledge the inherent difficulty of incubating new ventures within a mature business, as the zero-to-one approach differs significantly from running an established company. This necessitates distinct operational models and structures.

3. Create Dedicated, Focused New Business Unit

Establish a completely separate organizational structure for a new venture, ensuring all team members have no responsibilities in the existing business and clear Directly Responsible Individuals (DRIs). This fosters intense focus and avoids dilution of effort.

4. Maintain Full Separation for New Business

Ensure full separation of all functional teams (engineering, design, operations, finance) for a new business, with each person having only one job: making the new venture successful. This prevents resource contention and maintains singular focus.

5. Foster Startup Culture for New Ventures

Cultivate a distinct, intense startup culture for new ventures, including separate physical spaces, high work expectations (e.g., 24/7 commitment), and compensation tied to new business hurdles. This attracts entrepreneurial talent and drives rapid growth.

6. Hire Entrepreneurs for New Ventures

Prioritize hiring individuals with entrepreneurial backgrounds or those who have previously started companies for new ventures. These individuals are comfortable with ambiguity and possess the drive needed for early-stage growth.

7. Be Transparent About Startup Chaos

Be upfront and transparent about the chaotic nature and high expectations of a new venture, communicating this clearly in separate all-hands meetings and onboarding processes. This sets realistic expectations and attracts suitable talent.

8. Recruit Top Talent from Core Business

Actively recruit top, entrepreneurial talent from the core business to staff the new venture, even if it means asking them to forgo existing responsibilities. This provides critical expertise and leadership for rapid scaling.

9. Adopt Metrics-Driven Operating Cadence Early

Implement a rigorous, data- and metrics-oriented operating cadence (weekly, monthly, quarterly) from the early stages of a new venture. This provides clarity, accountability, and rapid feedback for decision-making.

10. Cultivate Urgency for Unique Opportunities

Instill a sense of urgency and shared mission within the team, emphasizing that a current opportunity is “once in a lifetime” and requires collective commitment to “meet the moment.” This motivates high performance and execution.

11. Leave Nothing to Chance

To avoid regrets in a fast-moving, high-opportunity environment, commit fully by engaging directly with customers, making late-night pushes, and meticulously verifying data. This ensures maximum effort against unlimited demand.

12. Focus on Meaningful AI Solutions

For aspiring entrepreneurs, especially in AI, focus on building solutions that address meaningful societal problems and genuinely help people, such as improving learning or connecting talent with opportunities. This approach can lead to both impactful and successful ventures.

13. Anticipate AI-Driven Hiring Transformation

Recognize that AI will fundamentally reinvent the job search and hiring process, automating tasks like resume review and cover letter creation. This highlights the need for platforms that can effectively match talent with opportunities using AI.

14. Anticipate Continued Human Role in AI Training

Expect that humans will remain essential in the AI training process for the foreseeable future, as models continuously improve and data types evolve. This suggests a long-term opportunity for human expertise.

15. Leverage Expert Data for AI Models

Understand that advanced AI models require expert-level data, not generalist input, to improve capabilities in specialized domains like STEM, accounting, law, or medicine. This shifts the focus from low-cost labor to specialized knowledge.

16. Prioritize Data Quality, Volume, and Speed

When providing data for AI models, prioritize quality above all else, followed by the ability to generate high volumes of data, and then speed of turnaround. These three factors are critical for model builders to test hypotheses and scale improvements.

17. Build Audience Access for Data Moat

Recognize that access to a proprietary, engaged audience is the primary competitive moat in the human data market. This allows for efficient targeting and acquisition of high-quality experts without high customer acquisition costs.

18. Treat Expert Data Labelers as Valued Professionals

Create a “brandy experience” for expert data labelers, treating them with the respect and work expectations appropriate for their high qualifications (e.g., PhDs). This fosters a community and ensures higher quality contributions compared to generalist labor.

19. Address Model Reasoning Weaknesses

Identify and fix where AI models fail in their reasoning steps or ground truth answers, especially in advanced domains, by providing step-by-step instructions and correct solutions. This helps improve the model’s underlying thought process.

20. Leverage AI Data Work for Skill Development

Engage in AI data creation and labeling as a way to get paid while simultaneously learning and advancing skills in your field of expertise. This offers a unique opportunity for professional growth and research advancement.

21. Embrace AI-Native Skills for Career Advantage

Understand that young people who are “AI native” and skilled at leveraging AI tools gain a significant advantage in the job market, enabling them to be more productive and impactful. This suggests a need for current professionals to also become AI-native.

22. Recognize Expert-Driven AI Improvement

Understand that when AI models fail at advanced tasks, it’s often due to the absence of expert-level data, which is actively being addressed by top minds in specific domains. This highlights the ongoing, iterative, and human-centric process of AI advancement.

23. Focus on Post-Training Data

Recognize that most AI model gains now come from post-training, which involves augmenting and improving the data across specific disciplines or capabilities. This is where high-quality, expert-generated data is crucial.

24. Trust Founder Intuition in New Ventures

In new, ambiguous ventures, rely more on founder intuition, especially when leveraging past experiences in building and scaling. This can guide rapid decision-making where established processes may not exist.