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Inside the Expert Network
Training Every Frontier AI Model

Garrett Lord
Co-founder & CEO, Handshake
AUG 24 2025
The Hidden Layer

Expert Networks Are
the Invisible AI Infrastructure

DOMAINEXPERTSAI TRAININGFRONTIER MODELS
"The only moat in human data is access to an audience."
  • Most model gains now come from post-training, not pre-training, since the labs already sucked up the internet
  • Handshake's network: 500,000 PhDs, 3 million master's students, 18 million professionals
  • Expert pay: PhDs earn $100–$200 an hour for AI work versus $25 an hour as a teaching assistant
  • The type of data keeps evolving: CAD files, scientific tool use, trajectories, multimodal, audio
Framework

The Expert Network Model

EXPERTRECRUITINGCREDENTIALCHECKAI TRAINING WORK
500K
PhDs on Handshake
$50M
ARR reached in 4 months
  • Recruited from top schools: PhDs at Stanford, Berkeley, MIT in physics, math, chemistry, biology
  • Community, cohorts, and an instructional design and assessments team teach experts the tools
  • Handshake runs its own post-training team to check the quality of each unit of data
  • Direct-to-lab relationships, cutting out the middleman recruiting platforms
The network moatA decade of trust with 18 million people means no customer acquisition cost. Rivals run 100–200 recruiters on LinkedIn and buy Google, YouTube and Instagram ads to find experts.
The Expert AI Training Market

Where Expert Evals Create Value

  • Advanced STEM: physics, math, chemistry, biology PhDs break model reasoning steps
  • Education: a Miami PhD and long-time 8th grade teacher shapes educational design
  • Audio and multimodal: top music students improve model understanding of music
  • Derivative professional domains: accounting, law, medicine, finance
The generalist-to-expert shift

Models got so good that generalist labor is no longer needed. What labs really need now is experts.

A new income stream

PhDs make $100–$200 an hour breaking the latest models, versus $25 an hour as a teaching assistant.

Playbook

Access Expert Knowledge for AI

  • Don't build your own expert network, partner with existing ones
  • Define your quality requirements before recruiting: what credentials, how many examples, what QA process?
  • Pay expert rates: cheap annotation produces cheap models
  • Invest in expert onboarding: an instructional design and assessments team, cohorts, and iterative training
The expert career opportunityDomain experts who understand AI training workflows will command premium rates for the next decade. This is a career skill worth investing in.
Contrarian

Expert Knowledge Myths

✗AI will replace domain expertsINSTEAD →✓ AI trained by domain experts will replace the tasks domain experts hate. It will amplify what domain experts do uniquely.
✗Crowdsourcing works for specialized domainsINSTEAD →✓ Crowdsourcing produces average judgments. Specialized domains require specialized expertise.
✗The best models don't need expert feedbackINSTEAD →✓ The best models require the best expert feedback. The bar rises with model sophistication.
✗Expert networks are too slow to scaleINSTEAD →✓ Expert networks scale slowly and compound over time. That's the point, it's a defensible moat.
Based on Garrett Lord's episode on Lenny's Podcast. All ideas on this page are from the episode.Watch on YouTubeFollow @garrettlord on X
GO DEEPER IN THE EPISODE
05:00 Understanding data labeling and its importance13:08 The role of experts in AI model training15:35 The future of AI and human collaboration24:17 Why AI won’t eliminate entry-level jobs27:58 The continuous improvement of AI models33:05 The emergence of Handshake’s new business model37:07 Incubating new ideas in established companies40:42 Handshake's competitive advantage45:43 Scaling up and meeting market demand48:38 Overcoming challenges and adapting53:08 The importance of separate teams and ownership57:26 The future of job matching with AI01:00:30 The biggest bottlenecks to advancing models further01:02:37 Lightning round and final thoughts
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