Perhaps unsurprisingly, something rather absurd has happened in the AI industr… · @GenAI_is_real · TwKit

@GenAI_is_real

Perhaps unsurprisingly, something rather absurd has happened in the AI industry over the past two or three years: young researchers have increasingly impressive resumes, more top-tier publications, and more internships at leading model companies, yet their bargaining power in the job market has started to decline. Just a few years ago, companies were willing to offer million-RMB annual salaries to young researchers who had yet to demonstrate any significant industrial achievements, betting that they might eventually become exceptional talents. Today, even PhD graduates with stronger research records may struggle to get an interview at those same companies. I think the most interesting part of this change is that AI talent hasn't suddenly become less valuable. Rather, the industry is shifting from paying a premium for intelligence and potential to paying for capabilities that have already been demonstrated. The problem, of course, is that young researchers often need an opportunity to prove themselves before they can demonstrate those capabilities. From an economic perspective, this shift is understandable. Over the past few years, foundation model companies were racing to build their teams while technical directions remained highly uncertain. They were willing to pay a premium for intelligence and long-term potential, essentially purchasing an option on someone's future contributions. Now that these teams are maturing, their needs in Pretraining, Post-training, RL, Inference, and other areas have become much clearer. Hiring is no longer simply about bringing smart people into the company and figuring out what they should work on afterward. Instead, companies first identify the problems they need to solve and then look for people capable of solving them. This is certainly good for short-term efficiency, but it also means that young researchers without specific industrial experience can no longer rely on potential alone to get opportunities. Meanwhile, traditional signals such as academic credentials, publications, and internships are rapidly losing their ability to distinguish candidates. Having several top-tier papers or participating in the training of an important model is not necessarily the same as possessing independent technical judgment. I've always believed that one of the most important ways to evaluate engineers and researchers is to understand why they chose a particular technical approach, whether they can explain their experimental results, and whether they can independently solve unfamiliar problems. These qualities are difficult to assess from a resume. You often only begin to understand them after actually working with someone. As a result, personal recommendations and previous industry collaborations are becoming increasingly valuable. There are perfectly reasonable explanations for this, but it also risks reinforcing existing professional circles. And this brings me back to something I've said before: whether in hiring or academic peer review, our AI community seems to be gradually moving backward from the imperial examination system to the Nine-Rank System of ancient China, where social standing and personal connections played an increasingly important role in determining opportunity. To me, this is a step backward. What's even more unfortunate is that talent development itself depends heavily on opportunity. If a young researcher has never had the chance to participate in large-scale model training, how are they supposed to demonstrate that they are capable of doing it? And if hiring increasingly favors candidates who already have industrial experience, we end up with a self-reinforcing cycle: those who entered core teams earlier continue to grow because they have access to compute and important projects, while those who come later struggle to obtain those same resources because they lack the required experience. We tend to attribute past achievements entirely to individual ability, overlooking how profoundly the environment itself shapes the development of talent. I've always believed that open-source communities offer something uniquely valuable here. In AI Infrastructure, many engineering capabilities can be demonstrated through public PRs, benchmarks, design discussions, and sustained maintenance. Even without an internship at a prestigious model company, someone can establish their reputation through actual contributions. Of course, open source cannot entirely overcome the compute barriers in areas such as Pretraining, but it can at least create additional opportunities for young researchers outside established circles. I think it's perfectly reasonable for the market to become more selective, and I don't believe companies should be expected to invest unconditionally in every promising young researcher. But I still hope that, while pursuing people who can contribute immediately, the industry retains some patience for developing young talent. After all, many of the highly experienced researchers being pursued by companies today were once young people with no industrial experience, who needed someone to believe in them and give them a chance.

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