3 million vying for PhDs, post-95s are already "old": AI recruitment is "burying" middle management

The prosperity of the talent market is a lie; the illusion of liquidity is real.

By Ada, Deep Tide TechFlow

“An internet giant offered over 60 fresh PhDs with AI backgrounds a salary of 3 million this year.” When TTC founder Xiao Mafeng, who has served over 1,500 AI companies, mentioned this figure, his tone was flat, like reporting the day’s weather.

In the same month, Maimai data showed AI job postings skyrocketed 29 times, and Zhaopin reported a 200% surge in job seekers. 29 times more jobs, 200% more applicants—these numbers look as beautiful as a bull market’s candlestick chart.

But these figures hide a secret: a large influx of capital and attention is pouring into a funnel with an extremely narrow opening. The few dozen at the top are inflating the entire market’s salary expectations, while the hundreds of thousands at the bottom are bearing all the anxiety.

Meanwhile, those in the middle of the funnel, who have been in the workforce for five or ten years, are being quietly drained.

The prosperity of the talent market is a lie; the illusion of liquidity is real.

A Difficult Search for Top Talent, a Battle of Thousands

According to Liepin’s report, 47% of AI positions require master’s or doctoral degrees, and nearly half of companies only recognize 985/211 universities.

Headhunter Eva is more direct: “Big companies are hiring, and 211 is barely acceptable; at least it has to be 985. Resumes without vertical project experience are basically not considered.”

What do the top-tier talents look like?

On the day Alibaba’s Qianwen news broke, “people from major companies came to us asking if we could help contact Lin Junyang,” Xiao Mafeng recalls.

There are probably only dozens of such talents nationwide. To find them, headhunters have long stopped browsing resumes. They scour GitHub for code commits, track paper authors on Google Scholar, infiltrate podcast listener groups and AI startup communities. Eva even joined a Tsinghua AI startup competition group, filled with 21-22-year-olds. “Now we chat early, two or three years ahead, when they might need a job, just to reserve a spot.”

Another headhunter, Steve, who started AI recruiting in 2022, said something profound: “I highly doubt there will be resumes in the future.”

He gave an example: In January this year, a company wanted to hire someone familiar with OpenClaw. The field is so new that no one would list it on a resume. His approach was to break down the requirements—it’s essentially a multi-agent framework problem. Has anyone built a similar framework? Is it open source? Who are the contributors in the open-source community?

Resumes are devaluing, and traditional recruitment channels are failing.

Some have seized opportunities from this crack.

Sam, co-founder of DINQ, started with a similar observation: The top authors of OpenAI’s most influential papers often aren’t from prestigious schools; some even dropped out, young, without titles, and not obviously outstanding to non-technical people. LinkedIn’s logic of looking at education and experience doesn’t work for AI talent.

So, Sam created DINQ, a “LinkedIn for AI scientists and developers,” which focuses on achievements—top conference citation counts, GitHub contributions, and whether collaborators are big names—rather than resumes. HR can input “Sora 2,” and the platform will extend to related paper authors, not just those with experience related to Sora 2, uncovering hidden talent.

Xiao Mafeng’s alternative is build in public: directly showcasing your product as the best proof of capability.

Although 621 universities in China now offer undergraduate AI programs, McKinsey predicts a talent gap of 4 million by 2030. But the word “gap” is misleading; what’s missing are experimental scientists who have trained on hundreds of thousands of data cards—people who understand the limits of large models and can find commercial applications. The market is never short of people saying, “I’m very interested in AI” after listening to two podcasts.

Ye Xiangyu, founder of Niuke, summarizes accurately: “Top-tier talent is hard to find,” while “at the bottom, thousands of newcomers are fighting.” Maimai’s statement that “one suitable candidate for every two AI positions” refers to the top. As for the bottom? No one tracks it because those resumes never make it into the system.

Leverage Pricing: The Closer to the Model, the More Valuable

So, where is the money flowing?

Eva provides some numbers. For top-tier companies at P7 level, non-technical roles have a ceiling of about 1 million yuan. For AI technical roles at the same level, salaries range from 1.5 to 2 million. The pay increase when switching jobs is even more significant—50% is common, with some doubling; non-technical roles see only 10-20%, rarely exceeding 30%.

Steve explains this pricing logic with one word: leverage.

Imagine the model as the sun. The closer you are to the core, the greater your leverage and value. A core researcher’s improvements to a model can impact a company’s market value by billions. The cost of running 100,000 data cards far exceeds their salary. From this perspective, paying a billion for such a researcher isn’t expensive.

What about those farther from the sun? Product managers, operations, sales—they don’t have such direct leverage, so their salaries are naturally limited. Steve estimates that at the application layer, the salary gap between technical and non-technical roles can be two to three times.

Xiao Mafeng added a key variable: he believes this “disdain chain” is fundamentally about supply and demand, with two layers. On a macro level, only a few people have trained on hundreds of thousands of data cards, so their salaries are sky-high. But micro-level, it depends on the founding team’s DNA. If the founders are professors from Tsinghua, with many technical talents in the lab, then those who can commercialize are more valuable.

The scarcity of a few dozen people defines the entire industry’s salary narrative. Others take this narrative as a benchmark, but what’s measured is only the gap.

A Purge of the “Old-Timers”

“AI era rejects old-timers,” Xiao Mafeng offers a sharp critique.

The previous AI wave, involving companies like Megvii and SenseTime, now mostly in their 40s, has become a burden due to their experience.

Steve’s words are more tactful but aligned: “We don’t believe you can find new lands with old maps. People who have worked in an industry for too long have too much inertia. Their instinctive reactions are results of reinforced training, but times have changed, and the right response might be completely opposite.”

Age anxiety has penetrated every level. Some investment firms are seeking post-00 entrepreneurs, and “post-95s are already old,” such remarks are emerging.

It sounds absurd, but the signals from the job market are real: when resources are limited, the scale tilts decisively toward the young.

“Now, the competition is about speed of execution and implementation. Everyone is training special forces, not a big army,” Steve says. Special forces don’t need many commanders.

But there’s an unspoken contradiction no one wants to face.

Those who truly implement AI products and turn technology into business value rely precisely on industry experience, tacit knowledge, and learned lessons. Steve admits these tacit skills are found in more mature people—they may not know exactly which path to take, but they know which ones are dead ends.

The industry needs the drive of young people and the judgment of veterans. Everyone can say this, but the flow of money only favors the former.

The Middle Layer Is Being Swallowed

All three headhunters mention a common change: management layers are shrinking.

“Pure management roles are probably already difficult. Many established systems are being overturned; what you built might be dismantled tomorrow,” Steve says.

Organizations are becoming extremely flat, no longer needing hierarchical pyramids with layers of reporting, but small teams capable of fighting on their own. Relying on people to do tasks is less effective than deploying an Agent. Previously, strong management skills and complex teams were valued, but that’s being challenged.

The boundaries between product managers, operations, front-end, and back-end engineers are blurring. One person can now develop an MVP of a product using AI.

Chen Lei (pseudonym), a product director at a mid-sized AI company, managed an eight-person team for three years. Earlier this year, after a restructuring, her team was disbanded—four moved to Agent products, two were laid off. Her title changed from “Director” to “Senior Product Manager,” reporting to a technical lead five years her junior.

“I wasn’t laid off, but I feel worse than being fired,” she says. “What I built in three years was gone with just an organizational change. And I can’t complain because people will say, ‘You’re still here, aren’t you?’”

This is the cruelest part of the liquidity illusion. At the top of the funnel, dozens of geniuses are fiercely contested with sky-high salaries. At the bottom, hundreds of thousands of newcomers can’t even get through the door. In the middle, those who have been in the workforce for five, ten, or even fifteen years are being internally sidelined.

The career ladder has lost several middle rungs. Instead of climbing step by step, it’s now a parachute jump—either land at the top or fall freely.

Who Is Creating This Illusion?

Who benefits from this liquidity illusion?

Recruitment platforms use “AI job postings surge 29 times,” “talent gap of 4 million” to attract traffic, each share pushing more anxious job seekers into the funnel.

Companies use AI as a shield. Forrester Research found that 55% of employers regret layoffs driven by AI, often because the AI capabilities they replaced were unprepared. Resume.org’s survey is more direct: 59% of companies admit to disguising layoffs as “AI-driven,” claiming it’s better for stakeholder explanations. Saying “because of AI” sounds strategic; citing poor performance sounds like management failure. AI has become the best excuse.

Klarna laid off 700 employees claiming AI replaced customer service, but service quality plummeted, and customers rebelled, leading to re-hiring. This isn’t an isolated case. Forrester predicts that half of AI-driven layoffs will be quietly reversed, often with lower salaries or outsourced overseas.

Steve accurately summarizes the current mindset of bosses: “Their first question now is whether to hire at all, then what kind of talent to hire.”

According to Forrester, only 16% of global employees are highly AI-ready. Companies don’t invest in training; employees self-learn. Generation Z has the highest AI readiness at 22%, yet they are the first to be pushed out of entry-level positions—precisely the roles most vulnerable to AI. Mercer’s survey shows anxiety about AI causing unemployment soared from 28% in 2024 to 40% in 2026.

AI is both a reason to hire and a reason to fire. Whoever controls the definition wins the game.

The Funnel Won’t Widen

Returning to those initial numbers.

29 times more jobs, 200% more applicants, 3 million yuan salaries, 4 million talent gap—all true. But when combined, they tell a completely different story: jobs are increasing, but the opening is extremely narrow; applicants are flooding in, but most can’t even pass screening; salaries are soaring, but only for the top few dozen; the gap is widening, but what’s missing and what’s supplied are mismatched.

But this funnel won’t widen. AI technology iterates every six months; today’s hottest direction may become obsolete in half a year. You might think you’re close to the sun, but with each new model release, you could be pushed to the periphery.

Steve said something that could serve as both a eulogy and an entry ticket: “Using tenure to measure experience may no longer be enough. What matters is the density and depth of your interaction with AI. Someone entered the industry four years ago but only used it casually. Someone else joined last year but is fully immersed. Who has deeper experience?”

Even the three headhunters are being reshaped by this industry. Eva is studying algorithm principles, Steve is researching Agent frameworks, and Xiao Mafeng just left a meeting with a post-00 entrepreneur, exclaiming, “Their understanding has already reached the next level.” Even those selling tools must keep pace with the gold rush.

Chen Lei recently started a small project on GitHub—an automated legal document generator using an Agent framework. No one asked her to do it, and no one paid her. She realized: instead of waiting to be filtered by the funnel, it’s better to carve out her own path.

This might be the only somewhat optimistic part of the entire article, but only just.

Most people aren’t among those 60 PhDs earning 3 million, nor are they like Chen Lei, capable and willing to carve their own way. They are the silent majority in the middle of the funnel—less top-tier to be fiercely contested, less determined to start over.

This funnel will not widen.