He is the youngest billionaire! From small to large, it is called "the next generation musk".
Today's protagonist is Alexandr Wang, who:
At the age of 17, he became a full-time coder of quora, a famous American Q & a website;
At the age of 18, he was admitted to the Massachusetts Institute of technology to study machine learning;
19 years old, dropped out of school, founded scale AI and served as CEO;
At the age of 25, he became the youngest self-made billionaire in the world.
19 years old, MIT dropout
Alexandr Wang was born in New Mexico, and his parents were physicists at Los Alamos National Laboratory in New Mexico.
This national laboratory is inseparable from the famous Manhattan Project.
During World War II, the first American atomic bomb was secretly developed here.
Wang's childhood was also surrounded by this powerful lethal weapon.
It can be said that the native family has a great influence on him.
Many of Wang's talents in science and technology come from his parents.
After all, can scientists who can make weapons for the government be ordinary people? (in connection with the above, Xiao Bian even suspects that they are actually nuclear physicists)
During school, Wang showed his sensitivity to science and technology and his ability to learn quickly. Wang often participates in math and programming competitions held by the school.
In his sixth grade, he signed up for the national mathematics competition for the first time. The prize was a free ticket to Disney.
That time, he didn't win the prize. But his parents took him to Disneyland to encourage him.
But Wang is not a nerd who only knows how to count. He has been learning to play the violin since he was nine years old.
After that, his interests became more extensive. He loved writing, hiking, programming, and reading the philosophical works of Nietzsche, Sartre, and Kierkegaard. It can be said that these have shaped his thinking.
Later, Wang entered Los Alamos high school. At that time, he began to self-study programming through the network.
After a small success, he signed up for some world-class programming competitions. For example, the United States computer Olympic Games (USACO). With the help of these competitions, Wang has been wandering in the world of technology.
At the age of 17, when most high school students of the same age were still preparing to go to college, Wang had started working full-time as a programmer for quora.
In quora, he met Lucy Guo. At that time, he did not realize that Lucy was the right person. It was they who founded scale AI together.
Next, he went to MIT to learn about machine learning. Just one year after coming to MIT, he founded scale with Guo in the summer vacation just after his freshman year, and got the investment from y combinator, a business incubator.
Wang told his parents, "this is what I do in the summer."
Listen to this tone. To Wang at that time, setting up a company that is already successful was just a summer vacation entertainment.
But obviously, the scale is getting bigger and bigger, and Wang finally didn't go back to school.
His parents have a big opinion about him dropping out of school to run a company. Not only at that time, but now, even though he has made countless achievements, his parents are still very disappointed.
Wang was not affected by this.
When asked about his views on college education, Wang said, "if you have a clear understanding of what you want to do in the future, a college diploma is not important to you. The only important thing is whether your skills are strong enough."
He quoted Apple The founder Steve Jobs said:
"Again, you can't connect the dots when looking forward; you can only connect them when looking back. So you have to believe that what is happening now will connect in some way in your future. You have to believe in something - your courage, destiny, life and fate."
With this sentence as a spiritual force, Wang moved forward firmly in the fog of uncertainty. Everything done at present is doomed by cause and effect, and there will be results in the future.
He has no doubt about it.
Wang is confident in saying so. He grew up to win big prizes.
In addition to the awards mentioned above, he won a bronze medal in the U.S. mathematical talent search (USAMTS) in 2011 and a gold medal in 2012.
In 2013, Wang ranked among the top 30 in the U.S. Mathematical Olympiad. At the same time, he also participated in the national "who wants to be a mathematical" competition and won the third place.
Besides mathematics, he is good at physics. In 2014, he participated in the US National Physics Olympiad (usapho) and reached the semi-finals.
In addition, it must be mentioned that he was already on the "30 under 30" list in 2018 long before he achieved the achievement of the youngest billionaire on earth.
Friends who have seen the big bang in American TV life should be familiar with this list. It refers to the 30 young people under the age of 30, from all fields and with the most remarkable achievements. Among them, it covers a wide range from entrepreneurs, stars to thought leaders.
Starting from 3 people, to the valuation of USD 7.3 billion
Although Alexandr Wang, now worth $1billion, has long been the CEO of one of the largest AI companies in Silicon Valley.
But who would have thought that when he founded scale AI in 2016, some people even thought it was a joke.
After all, the company had only three employees.
However, with continuous financing and development, scale AI has grown into a unicorn enterprise worth $3.5 billion by 2021, with 300 employees.
At the same time, scale AI has made another investment of about US $350million.
In 2022, scale AI, with a revenue of more than $100million, also achieved a valuation of $7.3 billion.
Alexandr Wang, who holds 15% of the shares, has also become the youngest self-employed billionaire in the world.
Although the company seems to be "unknown", when it comes to its more than 300 customers, it is basically well known.
Such as NVIDIA, openai, PayPal, Toyota, general motors and other familiar industry leaders.
Now, the business scope of scale AI has long gone beyond the annotation data required to provide autonomous vehicle companies with training machine learning models.