Three post-2000 entrepreneurs have built a unicorn valued at 70 billion.

Mercor's story will once again show entrepreneurs that choice is far more important than hard work.

It was reported that Mercor, a company founded less than three years ago, is in the process of finalizing a new round of financing worth $350 million, and its valuation has soared to $10 billion, making it the youngest unicorn valued at over $1 billion in the AI infrastructure sector.

Initially, the company focused on recruitment. It evaluated candidates by analyzing interview records, resumes, and personal portfolio websites, and provided talent for data annotation companies like Scale AI. In the process, it inadvertently accumulated a group of professional experts. Subsequently, it took over Scale AI's business and transformed into hiring high - skilled professionals to train AI models, which led to a sharp increase in its valuation.

$10 billion is five times Mercor's valuation before its transformation.

The capital market is willing to invest, and the young unicorn's revenue is also remarkable. It is understood that Mercor's annualized revenue is approaching $450 million, and it achieved a profit of $6 million in the first half of the year. It is expected to reach the $500 million Annual Recurring Revenue (ARR) milestone faster than Anysphere, the startup behind the AI coding assistant Cursor. Meanwhile, in February this year, CEO Brendan Foody revealed that the team has 75 people with an average age of only 22, and the revenue growth rate is faster than the team expansion rate.

You see, the capital is betting not only on the post - 2000 generation but also on a young and efficient money - making machine.

Starting a business at 19, from AI recruitment to data annotation

The three founders, CEO Brendan Foody, CTO Adarsh Hiremath, and COO Surya Midha, were all born in 2004 and are recipients of the Peter Thiel Fellowship. In 2023, they dropped out of Harvard University and Georgetown University. Relying on the friendship they forged in the high - school debate team, they decided to fully commit to entrepreneurship.

The topic of that debate was related to the job market, and their team won the championship of the National Policy Debate Tournament in the United States. Two years later, when they decided to establish Mercor in their dormitory, the company's initial focus was on AI recruitment. It used a self - developed large model to complete resume screening, AI interviews, and job matching in 20 minutes, connecting Indian engineers with remote jobs in the United States. With higher efficiency than traditional headhunters, Mercor achieved a revenue of $1 million without external financing. After initially proving their entrepreneurial ability, the three founders dropped out of school and started their business full - time.

As ChatGPT became popular worldwide, the shortage of high - quality talent has become a key bottleneck in the AI industry. These talents are crucial for training and improving advanced models like ChatGPT. Leading AI labs are competing to expand their outsourcing teams to obtain high - quality human feedback data. Mercor then expanded its platform contractors from programmers to 30,000 experts including doctors, lawyers, and consultants. Its clients include leading AI companies such as OpenAI, Anthropic, and Meta.

Mercor's solution is an AI - driven platform that recruits and manages tens of thousands of industry experts globally, accelerating AI training with carefully selected domain - specific knowledge. Clients only need to upload task descriptions, and the system can assemble interdisciplinary annotation, evaluation, and RLHF training teams within hours. Amid the wave of layoffs in Silicon Valley, the story of data annotation seems more imaginative than recruitment.

In terms of market competition, different from competitors like ZipRecruiter, Otta, and RippleMatch, which mainly focus on general recruitment automation, or AI service providers like Surge AI and Scale AI, which emphasize extensive data annotation, Mercor's competitive advantage lies in its focus on high - value expert tasks that have undergone in - depth review. Its ability to handle flexible, small - batch, and high - difficulty long - tail demands fills the market gap left by companies like Scale.

Don't think that domestic VCs are reluctant to invest in the database business model. These data annotation companies are highly sought after by Silicon Valley VCs. Meta previously spent $14.3 billion to acquire Scale AI. According to Reuters, Surge AI plans to seek up to $1 billion in a new round of financing.

Mercor has also had a smooth financing journey. In 2023, it received $3.6 million in seed funding led by General Catalyst. In 2024, it raised $32 million in Series A financing led by Benchmark, with a valuation of $250 million. In February 2025, it secured $100 million in Series B financing led by Felicis, and its valuation soared to $2 billion. In October, it received another $350 million, and its valuation reached $10 billion. Its shareholder list includes top funds and angels such as Peter Thiel, Jack Dorsey, DST Global, and Menlo Ventures.

Currently, Mercor manages over 30,000 contractors, with a daily total revenue of over $1.5 million. Looking to the future, Mercor plans to expand its platform from the technology sector to industries such as healthcare and legal services, where professional knowledge is crucial and the risks are high. The company is also developing a new AI - driven recruitment market to expand the global expert matching scale and further popularize high - paying and meaningful knowledge - based job opportunities. When Scale AI was acquired by a large company and lost its neutrality, it undoubtedly provided an opportunity for companies like Mercor to accelerate their development.

College students' entrepreneurship in the AI wave

Mercor's rapid development is just a microcosm of the wave of young entrepreneurship in Silicon Valley. In an era when AI has lowered the barriers to programming, design, and writing to just inputting a single sentence, young founders' intuition, enthusiasm, and execution ability regarding technology have become a scarce resource.

The founders of the following AI star companies that have attracted top - tier capital are all post - 1990s or even post - 2000s:

Cursor, an AI coding startup founded in 2022, has a latest reported valuation of $29.3 billion. Its founders, Michael Truell, Sualeh Asif, Arvid Lunnemark, and Aman Sanger, are all post - 2000s and dropped out of MIT to start the business. In its second year of operation, institutions such as OpenAI's startup fund and the startup accelerator Neo led its seed round of approximately $8 million. Cursor has a very high conversion rate of paying users, and by June 2025, its Annual Recurring Revenue (ARR) had exceeded $500 million.

Perplexity, an AI search company founded in 2022, has a latest valuation of $20 billion. Its valuation increased 40 times in a year and a half, making it the unicorn with the fastest - growing valuation in this sector. CEO Aravind Srinivasan was born in 1994. After completing his doctoral studies at Berkeley in 2021, he joined OpenAI as a research scientist and left in August 2022 to found Perplexity.

Pika Labs, an AI video generation platform founded in 2023, is rumored to be sold to Meta, with a valuation of over $500 million. CEO Demi Guo and CTO Chenlin Meng dropped out of their Stanford PhD programs and were only 24 years old when they started the business.

GPTZero, an AI text detection company launched in 2023, has a valuation of $500 million. Its founder, Edward Tian, was 22 years old when he started the business. He received $3.5 million in seed funding before graduating from Princeton University with a bachelor's degree in computer science.

In China, Lingchu Intelligence, an embodied AI company, received investment from Hillhouse Capital and BlueRun Ventures. Co - founder Chen Yuanpei is a post - 2000s. In his sophomore year, after participating in a robot competition, Chen Yuanpei became interested in robots, decided to teach himself, and later visited Stanford University as a scholar, studying under Fei - Fei Li.

The wave is changing rapidly. In the previous wave of hard - tech industries dominated by semiconductors and new energy, investors preferred experienced industry practitioners. Youth was not necessarily an advantage. However, AI investment follows a completely different logic. Chasing young people is not so much a form of kowtowing to the trend as a practical business choice.

Whether in Silicon Valley or in China, investors' reasons for chasing young people generally boil down to the following points: The AI paradigm is changing too fast, and the experience and path dependence of older teams can easily become a burden. Young people are the native digital generation, treating AI as their mother tongue, and their product sense is closer to the next - generation users. Dropping out of school to start a business means a high opportunity cost, which can filter out a group of mission - driven founders.

In Silicon Valley, there is the Peter Thiel Fellowship, a program specifically designed to support young entrepreneurs who drop out of school. In China, many investment institutions are also mobilizing social resources to support young entrepreneurs. For example, Yunqi Capital recently launched the Y Transformers program, using a special fund to support post - 1998 "AI natives" entrepreneurs. It is foreseeable that as the model capabilities continue to improve and the entrepreneurship threshold further decreases, more young AI entrepreneurs will emerge both at home and abroad.

This article is from the WeChat official account "China Venture Capital". Author: Liu Yanqiu. Republished by 36Kr with permission.