The leader of Meta from AI data, who is 29-year-old Alexander Wang

Alexander Wang once dropped out of university to start a business preparing data for AI. Meta later invested $14.3 billion in that company and took a 49 percent stake. Now the 29-year-old entrepreneur is playing a key role in defining the AI ​​strategy of one of the world's largest tech companies. His rise is not just a story of personal success; AI also reflects the growing importance of data, talent and investment in the industry.

When artificial intelligence or AI is discussed, chatbots, powerful computers or complex algorithms usually come to mind. But another thing is essential to make these technologies work—high-quality data. It is difficult to reliably train an AI model unless the data is correctly identified and classified.

Alexander Wang saw this need as a business opportunity. In 2016, he founded Scale AI with his friend Lucy Guo while studying at the Massachusetts Institute of Technology (MIT). After that, he left university studies and focused on business.

Scale AI's job was to prepare the data needed to build AI models. The company initially worked on identifying and classifying data for technologies such as self-driving cars. Later, various services were added including providing data suitable for AI training, validating models.

As the use of AI increases, this task becomes more important. Because, building better models is not just about increasing computing power; The model also needs useful data to be trained. This demand was the foundation of Scale AI's business.

Billionaire in 24
Scale AI's rapid expansion has made Wang one of the tech-world's most talked-about entrepreneurs. After the company's valuation increased in 2021, his net worth reached the billion dollar mark at a young age. At the age of 24, he became the world's youngest self-made billionaire.

But the significance of his success is not limited to personal wealth. As the global competition for AI increases, the importance of tasks such as data preparation and evaluation has become clear. This work, once hidden behind technology, has become the foundation of big business.

Meets from Scale AI
In 2025, Wang's career will undergo major changes. Metascale invested $14.3 billion in AI and took a 49 percent stake in the company. At the time of the deal, Scale AI's valuation stood at $29 billion. Wang joined Mater's AI program and was tasked with leading the organization's superintelligence initiative.

Meta's move was driven by AI's strategic goal of staying ahead of the competition. Organizations like OpenAI, Google and Anthropic were increasing investments in developing advanced AI models. In this scenario, assembling skilled researchers and engineers and building the necessary infrastructure for AI development becomes a big priority for tech companies.

Wang's responsibilities are therefore not limited to technology development. He is also instrumental in building Mater's AI research team, attracting skilled workers and building advanced modeling capabilities.

The fight is not just about technology
In the race for AI, there is now competition for talent. Big tech firms are spending huge sums to bring experienced researchers and engineers to their teams. At the same time, there is increasing pressure to rapidly develop advanced models, make them practical and stay ahead of competitors in the market.

Wang's role also came to the fore with talk of Meta's new AI model and Muse initiative in 2026. The company aims to create more effective AI assistants for personal and professional tasks. How successful such technology will be will depend on model capabilities as well as practical use, reliability and safety.

With success comes questions
Wang's career, however, cannot be left out of controversy. The role of data collection and preparation of scale AI and the role of contract workers have been questioned. In a 2026 report by the British newspaper 'The Guardian', some employees working for the company raised concerns about the nature of their responsibilities and their experience working with personal information found online. Scale AI has also made statements about these job opportunities and employee roles.

Such reports remind us that collecting large amounts of data for AI training also raises questions about the work environment, personal privacy, and the ethics of data use. As technology advances, so will the need for transparency and accountability in these matters.

What is there for young people to learn?
Giving advice to his 18-year-old self at a Y Combinator event in July 2026, Wang emphasized the importance of having a clear vision of the future and trusting that vision in the face of others' opinions. Identifying the potential in a fast-changing field like AI and starting to act on that potential is also an important aspect of his speech.

But it would be wrong to take the lesson from his career that success requires everyone to drop out of university and start a business. Rather, it is important to learn about changing technologies, identify real problems and find effective solutions to them.

In 2016, when Wang saw the need for data as a business opportunity, the subsequent expansion of the AI industry increased the importance of that decision. At the age of 29, he is now at the forefront of the race to shape the future of Mater AI.

His journey therefore brings up a question, where will the big opportunities in future technology be created? Just inventing new technology, or solving the problems necessary to make that technology work?