On the evening of July 6, a former Huawei “genius youth” posted online about his second-round interview at DeepSeek. The interviewer stared at his left monitor and said, “You’re copying code, right? If you can’t prove you’re not, the interview is over.” He felt publicly accused of cheating, walked out on the spot, and shared the story. It shot to the top of trending topics the next day. That same afternoon, a well-known investor jumped in and called him “the most untrustworthy founder I’ve ever met.” The former Huawei talent fired back overnight, saying the investor had only wired half the promised money and then stopped.
Two shiny labels — “Huawei genius youth” and “idealistic, national-level AI model company DeepSeek” — crashed into each other and went viral. But the real story isn’t about who won the argument. It’s a mirror that shows what often happens when AI companies get big funding and try to scale fast: systems break, the wrong people get interviewed for the wrong roles, and everyone ends up frustrated. The core issue isn’t that one interviewer was rude. It’s that DeepSeek’s hiring process hasn’t kept up with its growth, and we keep treating both “genius” individuals and big-name companies as if they’re perfect.
1. What actually happened in that interview
Li Bojie, born in 1992, is now chief scientist at a startup called Pine AI (he specifically clarified he is not a co-founder). He wanted to do research work at DeepSeek. Instead, the first two rounds were coding tests. When the interview finally happened, the interviewer was late and seemed uninterested in his actual research. The only question that kept coming up was “What engineering challenges have you solved?”
The most explosive moment was the copying accusation. Because it was a remote interview, he had two monitors on his desk — a completely normal setup for programmers. The interviewer accused him of looking at the left screen to copy code and threatened to end the interview if he couldn’t prove otherwise. Li explained it was just dual monitors. The interviewer didn’t believe him and turned it into a cheating allegation.
A lot of people’s first reaction was sympathy: even a Huawei genius youth gets treated this way, so how hard must it be for everyone else? But if you look closer, this isn’t mainly about one interviewer’s attitude. It’s about deeper problems in how DeepSeek is building its team right now.
2. Why live coding tests feel outdated
These days, a lot of code is written with AI assistance. Asking someone to write large chunks of code from scratch on a blank screen, with no tools, has become less useful for judging real-world ability. Turning that directly into a “you’re cheating” accusation makes it worse.
This kind of mismatch has happened before. In older programming interviews, candidates were sometimes asked to hand-write a keyword that the development environment automatically generates. People who actually wrote code every day never typed it manually, so the test ended up measuring the wrong thing. In the AI era, clinging to old “prove you can code blind” methods often misses the point and can unfairly penalize strong candidates.
3. When interviewers skip the resume, bigger problems follow
In normal hiring, you read someone’s background first, then talk about what they’ve done and whether it fits the role. In this case, the interviewer clearly hadn’t looked at Li Bojie’s resume. His history as one of the first eight Huawei genius youths, his top-conference papers, his training at a top university — none of it seemed to matter. He was just another person going through the same standardized process.
Other candidates have reported the same pattern at DeepSeek: multiple HR people adding them on WeChat at once, interviewers arriving late and acting impatient, and questions that have nothing to do with the candidate’s actual experience. When hiring turns into “run everyone through the same script,” you waste time and often end up with poor matches.
4. The “Huawei genius youth” label cuts both ways
The program started in 2019 when Huawei’s founder personally pushed for it. It offered very high salaries — up to around 200 million RMB a year for the top tier — to attract exceptional young talent. Li Bojie was in that first group of eight. He later left Huawei reportedly turning down a 3 million RMB annual package.
But a flashy label only proves one thing: strong research ability. It doesn’t automatically mean someone is also great at engineering execution, leading teams, or running a company as CEO. Some former genius youths have stayed and contributed quietly inside big organizations. Others left to chase bigger dreams. The public tends to notice the dramatic exits more than the steady work happening behind the scenes. Treating the label as proof of overall perfection sets everyone up for disappointment.
5. Research and engineering are very different jobs
Li Bojie wanted a research role. The interview kept pushing toward engineering tasks. This is a common mismatch.
Think of it this way: research is like working in a lab — exploring new methods, running experiments, publishing papers, and focusing on “can we make this work at all?” Engineering is like running a restaurant that serves customers every day — the food has to be consistent across different kitchens, handle rush hours, fix problems quickly, and keep customers happy long-term. The skills don’t overlap as much as people assume. Code written for a research paper can “run” in a demo, but it often falls apart when you try to make it stable for real users. Putting pure research talent into an engineering-heavy interview without understanding this difference leads to exactly the frustration we saw here.
6. The investment side showed another expectation mismatch
On the same day, a different story broke. An investor (co-founder of Huobi) publicly accused Li Bojie of taking funding for a project called Metagent and then going silent — not sharing updates or financials. Li replied that the investor had promised $1.5 million but only sent $500,000 and then stopped.
Both sides seem to have had unrealistic expectations. Investors who back young research stars know the risk is high: great at papers doesn’t automatically mean great at being a CEO or handling investor communications. Smart investors usually release money in stages — give some upfront, then more after clear milestones and transparency. This is called milestone-based funding. It protects the investor while giving the founder a chance to prove progress.
Li apparently felt the full amount was promised and got frustrated when it didn’t arrive. The investor felt the founder wasn’t communicating properly and chose to limit further risk. Neither side had aligned expectations or built enough trust. The result was public finger-pointing instead of a productive relationship.
7. Why he actually wanted to join DeepSeek
Li later posted a video clarifying his situation. His current company is running fine — he wasn’t job-hunting because of failure. He wanted to join DeepSeek because almost everyone is now building “harness agents” (AI systems that can use tools, write code, and handle complex tasks on top of large models). Many of the most useful capabilities, however, are locked inside the model companies: how they build training data, how they evaluate performance, and the long system prompts that guide behavior.
He wanted to go inside and see how things actually work. This isn’t unusual. Even high-profile people in Silicon Valley have left startups to join base model companies just to understand the foundations better. Without an easy external collaboration path at DeepSeek, he tried the normal job route — and ran straight into the hiring issues described above.
8. What happens when a company rushes hiring after funding
DeepSeek completed its first external funding round on June 16. After that, it clearly wanted to quickly build its own harness agent capabilities. The normal way to grow a new team is to first design the structure, identify the right experienced people, and let them help build the rest of the team layer by layer. Instead, it appears they went straight to headhunters for stacks of resumes and ran everyone through a fast, uniform interview process.
This “get it done quickly” approach often backfires. You can end up with mismatched roles, interviewers who don’t understand the work, and a team that looks busy but struggles to deliver real results. A famous earlier example is what happened at JD.com during one of its fast-growth periods. They hired a lot of “good enough for now” people to keep the website running. It worked short-term, but later, when they needed to level up and bring in stronger talent, many key positions were already occupied by people who couldn’t handle the next stage. The company had to spend years fixing the gaps.
DeepSeek risks repeating a version of this story if it keeps prioritizing speed over thoughtful team building.
9. A few final thoughts
This whole episode really comes down to three simple reminders:
- Don’t treat job titles or company names as proof of overall perfection.
- They only show strength in one area. Don’t imagine companies like Huawei or DeepSeek as single, flawless personalities. Every organization has strong parts and weaker parts.
- Nothing in the real world is perfect. Even the most impressive systems have rough edges and rough periods.
DeepSeek has the money now. Whether it builds a strong team or ends up carrying hidden problems forward depends on how it handles hiring and role matching in the next phase. The world is basically one big makeshift team — some groups know they’re still figuring things out and keep improving, while others act like they’ve already built a perfect temple. The first kind usually travels farther.
**Huawei Genius Youth Program** A high-profile recruitment initiative started in 2019 by Huawei’s founder to attract top young talent (especially fresh PhDs) with very high salaries — up to around 2 million RMB per year. It proves strong research ability but does not automatically guarantee engineering execution, leadership, or business skills.
回复删除Research roles vs engineering roles
回复删除Research focuses on exploring new ideas, running experiments, and publishing results (“can we make this work?”). Engineering focuses on building stable, maintainable products that work reliably for real users across different situations. The two require very different skills and mindsets.
“Grass stage troupe” (makeshift team) metaphor
回复删除A way of saying that most real organizations and teams are imperfect and still figuring things out — like a small, temporary theater group putting on a show with limited resources. Teams that admit they’re still learning tend to improve faster than those that pretend everything is already perfect.