$1B in Revenue. Zero Sales Team.

Estimated Reading Time: 3 minutes

🌟Editor's Note:

Welcome to Behind the Books! A 3-minute read with stories, tools, and lessons from real companies—what worked, what didn't, and what founders can learn.

🚀 The Story:

While Scale AI burned through $1.3B in funding…

While startups chased the next shiny AI trend…

While others hired armies of salespeople and growth hackers…

Edwin Chen was quietly building something different.

Surge AI.

No investors. No sales team. No glassy offices.

Just 120 people. And $1 billion in revenue.

Never heard of them? That’s the point.

It started in 2020. Chen, then at Facebook, hit a wall: He needed thousands of companies categorized.

The vendor told him: “Four months.”

Four months for data labeling.

That’s when it clicked: The bottleneck wasn’t compute or code. It was people.

High-quality human judgment. Done right. Done fast.

So Chen left Facebook.

No VC pitch decks. No growth hacks. No “move fast and break things.”

He built Surge AI with one obsession: Solve the human layer of AI—the part foundation models still can’t.

Red-teaming. Hallucination detection. Nuance labeling.

The boring work that actually makes AI work.

Today, Surge powers the world’s top AI labs. OpenAI. Anthropic. Google.

While competitors burn cash, Surge stacks it.

The Lesson:

Sometimes the best strategy is saying no.

No to investors who don’t get it. No to meetings that don’t matter. No to hype that doesn’t help.

Chen’s philosophy? “90% of Big Tech is solving useless problems.”

He focused on the 10% that actually mattered.

From Behind the Books:

The most profitable companies often fly under the radar. They’re too busy building to brag.

Your Weekly Prompt:

What are you saying yes to that you should be saying no to?

What would happen if you cut everything except what actually moves the needle?

Write down 3 things you’ll stop doing this week. Then watch what starts to happen.

See you next Friday,

– Yan

P.S. Chen says AGI might take until 2040 not for technical reasons, but human ones. Sometimes the biggest breakthroughs come from understanding people, not just algorithms.