Founder & CEO, Surge AI; teaches AI right from wrong
DEC 7 2025
The Thesis
Human Feedback IS the AI Moat
RLHF quality determines model quality more than architecture choices
The labelers are not a commodity, domain expertise + good judgment = rare combination
Surge built the expert network that taught AI what's good and bad
The invisible layer: most people don't know this work exists, but it determines what AI does
Framework
How RLHF Actually Works
$1B+
Surge revenue last year
<100
people on the team
10×
better product vs competitors
Not just "is this good?", expert raters understand nuance, context, and edge cases
Domain expertise matters: physicists probe their own research, teachers build lesson plans, coders stress-test the model at work
Scale + quality: Surge's bet is expert-quality feedback at production scale
The eval loop: better feedback → better model → harder evals → need better feedback
The hidden variableThe most important people in AI development are often not the researchers or the engineers, they're the expert labelers.
The RLHF Industry
What Most People Don't Know
Quality: Bad RLHF produces confidently wrong, helpful-sounding AI
Specialization: Code RLHF, reasoning RLHF, safety RLHF require different experts
The problem: Most AI companies underinvest in RLHF quality, and it shows
Why Claude feels different
Claude's helpfulness and safety balance comes from deliberate, expert human feedback, not just prompting.
The eval arms race
As models get better, the humans rating them need to be smarter. The bar keeps rising.
Playbook
Think About AI Quality
Ask: where does the AI's "judgment" come from? Trace it to the training data.
Build eval datasets with real domain experts, not just crowdsourced workers
The best product insight: what does the AI confidently get wrong in your domain?
Invest in evals before you invest in prompts, you can't improve what you can't measure
Edwin's missionSurge exists because good AI requires good humans. The quality of human feedback is the quality of the AI.
Contrarian
AI Quality Myths
✗Bigger models need less human feedbackINSTEAD →✓ Bigger models need better human feedback. The bar rises with model capability.
✗Crowdsourced labeling is fineINSTEAD →✓ Crowdsourced labeling is fine for simple tasks. Expert labeling is required for nuanced judgment.
✗RLHF is just annotationINSTEAD →✓ RLHF is judgment at scale. Edwin calls it closer to raising a child than data labeling, and his best raters are experts at the top of their fields.
✗Models will soon self-improve without humansINSTEAD →✓ Models self-improve with AI feedback loops. But the root signal is still human values.