How a 1997-Born Entrepreneur’s Scale AI Became a $19 Billion Company

Scale AI Becomes a $19 Billion Company Through Data Labeling

A startup founded by a 1997-born entrepreneur has grown into a $19 billion company. His name is Alexander Wang. The company he founded, Scale AI, provides the data necessary for training AI models and currently counts OpenAI, Microsoft, and the U.S. Department of Defense among its major clients. Today, we’ll explore the business model and the secrets behind this company’s success.

Scale AI’s Business Model

Scale AI’s core business involves labeling the data needed to train AI models. To train AI models, diverse datasets are required. Accurately labeling this data is crucial. Scale AI handles this process and charges its clients for the service.

Scale AI’s main clients include OpenAI, Microsoft, and the U.S. Department of Defense, among others. Many AI startups and large corporations also use Scale AI’s services.

The Importance of Data Labeling

AI models need to learn from a large amount of data. For instance, to create an AI model that recognizes apples, you need to provide numerous apple images and tell the model, “This is an apple.” This labeling process, while simple, is critical because it involves handling vast amounts of data.

Here’s an example of the process involved in developing an AI model:

• Orchard Robotics collects crop data to develop AI models.
• Cameras are mounted on tractors to take pictures of the crops.
• Labeling work is done so that the AI model can determine the necessary fertilizers and pesticides for the crops.

In this process, Scale AI supports AI model development with its business model by handling the labeling work.

The Secrets Behind Scale AI’s Growth

How did Scale AI manage to grow so quickly? They achieved success through several key strategies.

1. Focusing on a Specific Field

In the early stages, they focused on “AI for autonomous vehicles.” By focusing on one area, they were able to increase their expertise and gain customer trust.

2. Developing Labeling Tools

Scale AI developed software tools to make labeling work easier. This improved work efficiency and allowed them to provide high-quality labeling services.

3. Building a Global Workforce

They hired part-timers from low-income countries to perform the labeling work. This helped reduce costs while processing large amounts of data quickly.

Scale AI’s Social Responsibility

Scale AI employs part-timers from low-income countries to perform data labeling work. These workers earn 3,000 won per hour, and without them, Scale AI’s business model wouldn’t be viable. This structure creates a unique collaboration between highly trained computer scientists and part-timers to produce an AI service, but it also comes with risks in terms of social responsibility.

Conclusion

Alexander Wang has shared advice for aspiring entrepreneurs in various interviews. Here are a few of his tips:

1. If you don’t do it now, you may never do it.

One of the reasons he decided to start his company was the story of Eric, the founder of OpenDoor.

As you get older, you’ll tend to avoid risk more. Now is the time to pursue risk.

2. Find the bottleneck.

If you’re looking for a business idea, identify the bottlenecks in an industry you’re interested in. There are always bottlenecks that can be solved with a new approach.

3. Produce a lot.

Those who have achieved great feats often try many things. It’s important to implement the core features quickly and execute them fast to try multiple times.

4. To win, you must endure pain.

If you want to win, you need to build tolerance for pain.

Scale AI’s success lies in recognizing the importance of data labeling and building a business model around it. Alexander Wang’s entrepreneurial journey is an inspiring story. Decide to start your business now, find the bottlenecks, try new approaches, produce a lot of ideas, and endure the pain to achieve victory.