This isn’t just a story about technology — it’s a financial case study on how So Young, co-founder of 12 Labs, turned what many called an “impractical idea” into a $100M+ business, now competing head-to-head with giants like OpenAI and Anthropic.
She didn’t invent a new app.
She didn’t chase the chatbot craze.
Instead, So Young saw the hidden stream of money in video — a medium that makes up 90% of global data, yet remains largely untapped.
From that vision, she and her team raised over $100 million, signing contracts with major sports, media, and entertainment corporations across North America.
In a 22-minute video that’s been viewed over 167,000 times (and a 5-minute read here),
below are 5 key financial lessons from her journey — not just about technology, but about how to build a sustainable business model around artificial intelligence.
1. Start from a “Hidden Revenue Stream” Others Don’t See
Most AI companies focus on language (LLMs) — because text data is easier to process and monetize early.
But So Young realized that video is the real gold mine: companies spend billions each year storing, labeling, and searching through videos — yet earn nothing from them.
She and her team identified a simple but powerful financial pain point:
“There’s no Ctrl + F for videos.”
A system that allows businesses to search and extract context from videos could save media networks, sports leagues, and ad agencies millions of dollars annually.
💡 Lesson: The biggest money-makers don’t create new needs — they uncover needs that are already being wasted.
2. Raise Smart Capital — Sell Belief Before You Sell the Product
When 12 Labs started, no investor believed in them.
No one thought a group of 20-somethings could build a foundation model for video.
Instead of convincing people with words, So Young proved it with results:
They used $50,000 in AWS credits to train their experimental model Moringo, which later won the ICCV competition — an AI contest organized by Microsoft.
That win brought in Index Ventures (early investor in Notion and Figma) with a multimillion-dollar seed round.
Later, they raised Series A and B, pushing the company’s valuation past $100 million.
💡 Lesson: When you can’t sell a product yet — sell provable belief.
3. Build Products That Grow Revenue, Not Just Save Costs
A company succeeds only when its product ties directly to the client’s cash flow.
So Young understood this and positioned 12 Labs not as a “video analytics company,” but as a video AI revenue-growth platform.
Clients like Maple Leaf Sports & Entertainment (owner of the Toronto Raptors) use 12 Labs’ AI to auto-generate highlight clips, enabling their marketing teams to produce 10x more content with the same resources.
A single highlight video that once took 16 hours to edit now takes 9 minutes — and drives millions of views.
This isn’t just good tech — it’s a revenue machine.
💡 Lesson: Investors love products that help clients make more money — not just save some.
4. A Startup’s True Advantage Isn’t Technology — It’s Learning Speed
Tech giants like Google or OpenAI have the money, manpower, and data —
but they lack what So Young’s team has: speed of iteration.
She describes 12 Labs as a closed loop between market → product → data → research.
They collect customer feedback instantly, retrain their model, update APIs, and repeat.
With such fast learning cycles, a startup can pivot faster than any corporation ever could.
💡 Lesson: In the AI era, adaptability is the ultimate competitive edge.
5. When You Control Data — You Control the Future
12 Labs currently earns revenue by providing video AI APIs to enterprises — effectively, “OpenAI for Video.”
But the real value lies in the data network effect:
The more clients use the platform, the smarter the model gets — raising the barrier to entry for competitors.
In the future, 12 Labs won’t just sell APIs; they’ll sell contextual video intelligence to industries from entertainment to security.
Every video clip will become a smart asset — and the company will earn from the data streams the world produces every single day.
💡 Lesson: In AI, the greatest value isn’t in the product — it’s in the data your product collects.
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