Anderson Chen’s $80,000 AI Bet and His College Exit Plan

Daniel Okafor

Anderson Chen, 22, put about $80,000 of his personal savings into Operon Solutions and now plans not to return to Nanyang Technological University in Singapore.

The Taiwanese founder and CEO leads the San Francisco-based, Y Combinator-backed industrial AI company, which automates tasks for human operators and specialists in manufacturing, oil and gas, and construction.

Business Insider’s as-told-to account, based on a conversation with Chen and edited for length and clarity, is an anecdotal company profile rather than a national labor-market survey: its scale markers are about 20 clients, a five-person team and a $700,000 pre-seed round.

What made a degree feel less decisive?

Chen started Operon during his first gap year (time away from university) and initially intended to return to his chemical engineering and business studies at NTU. He then described the company’s growth near that year’s end as exponential, which prompted a second gap year.

During the second year, he concluded that completing a degree would not guarantee that he would find his way. Chen said he was frightened of dropping out because, in his view, a bachelor’s degree remains the Asian standard.

His parents initially worried that he was doing badly at school, but a fellowship covered his gap-year expenses, which he said left him with no financial risk during his leave. They now support the plan; Chen said they respond: “Okay, go for it.”

His case for questioning a degree also rests on AI’s pace. He cited OpenAI’s recent solution to a version of the Navier–Stokes problem (equations used to model fluid dynamics), recalling that his fluid-dynamics professor called the problem unsolvable during his sophomore year. Chen believes effective AI use could let a practitioner outperform that professor.

How did he find the industrial AI opportunity?

He began after internships at manufacturing companies and through connections with manufacturers and founders in Taiwan.

He watched engineers feed engineering PDFs into ChatGPT to have the documents understood and turned into engineering documentation. Chen said frontier AI models (the leading general-purpose AI systems) couldn’t understand those files because the confidential data had never been used to train them. He called that failure the moment he knew he had to start the company.

He diagnosed a broader problem: manufacturing requires workers to follow rigorous standard operating procedures (prescribed step-by-step instructions), but heavy-industry data is too messy for AI. Operon structures that information, builds a context layer (a structured, company-specific information base) and enables agentic workflows (AI systems that carry out multi-step tasks).

What did the financing and client numbers show?

In May, Operon received Y Combinator funding as part of a $700,000 pre-seed round (early-stage fundraising) that also included other investors; the company closed it in about three days.

Operon reports about 20 clients. Some engagements are proofs of concept (limited trials), while others are already full contracts.

Before raising funds, Chen put almost all his personal savings—about $80,000—into Operon. He takes no salary, although the company pays its employees.

He generated that money through earlier businesses: at 14, he sold sneakers and ran Amazon dropshipping (online resale without stocking inventory). In college, he built a tutoring agency connecting Malaysian students with parents of children at international schools in Singapore; he handled marketing and hired tutors.

What does being a young founder change?

Operon’s five-person team is entirely under 30: the oldest member is 26 and the youngest is 16. Chen contrasts it with competitor founders he says are all in their 40s and 50s.

He treats that age mismatch as an advantage, saying: “Skepticism brings exposure, and people will naturally be intrigued.” He believes it prompts prospects to investigate how a 22-year-old founder entered the space, which he says has helped attract clients.

What does this mean for workers and students?

Chen’s market position rests on a specific proposition: he can pair heavy-industry context with AI deployment and sell the result to clients without a completed bachelor’s degree.

For students and early-career workers, the narrow lesson from a 22-year-old founder with about 20 clients is concrete: demonstrable workflow expertise and client-facing delivery can outweigh a credential in this niche.

It does not show that the wider labor market has downgraded degrees. The account contains no applicant counts, hiring or layoff totals, salary benchmarks, degree-requirement comparisons or comparison group; its evidence is one founder’s account of about 20 clients and a five-person team.

The next test for Chen’s argument is whether the proof-of-concept portion of Operon’s roughly 20-client portfolio becomes more full-contract work while he proceeds without a salary or a completed degree.