Author: LatePost
17-year-old AI intern earns RMB 5,500 per day; those born in 1998 are considered "Zhongdeng"
A well-known venture capital firm, established for over 15 years, hosted a dinner where the invited guests were not dressed in suits and ties; most wore black, white, or gray T-shirts or hoodies with cartoon prints on the front, their hair casually styled. Many carried backpacks, as if attending a class reunion.
They are post-2000s AI professionals who mostly use anime or cartoon characters as profile pictures, frequently use emojis and exclamation marks, and during breaks, order a marshmallow hot chocolate amid a string of iced Americanos. Before meeting a post-2000s AI entrepreneur, an investor advised us that bringing two bubble teas is the best way to quickly build rapport.
Just graduated with a bachelor’s degree, and major tech companies or investment firms have already offered them annual salaries of 2 million, 5 million yuan, or $1 million. Yet, when talking about these multi-million-yuan salaries, the young researcher sipping marshmallow hot cocoa speaks casually, as if discussing last semester’s class schedule.
“I don’t really care; a couple million more or less doesn’t matter,” said another young researcher with a similar mindset. “Either way, I’m going to start a business—I won’t be earning a salary for long.” Some major tech companies and investment firms also want to recruit him.
Large models have enabled the industry to mass-produce hundreds or thousands of young elites earning millions per year. Major companies have broken through traditional limits of age, rank, and experience, offering high salaries to attract young talent.
Several headhunters and HR professionals say that recent graduates who graduated from top universities, interned in core large model teams at major tech companies, published in top-tier journals, and secured positions in leading talent programs at major firms typically earn over 1.5 million yuan annually. One person involved in Seed recruitment said that in 2024, the annual salary for TopSeed campus hires was around 1.5 million yuan; in 2025, it rose to 3 to 5 million yuan; and in 2026, core position campus hires could be offered up to 6 million yuan, with some even higher.
Even before graduation, these talents are already being snapped up—starting at 2,000 yuan per day, with top offers exceeding 5,500 yuan daily. Some have secured internships at Meta, earning $20,000 per month with accommodation and meals provided. While in most industries a daily internship pay of 200 yuan is considered excellent, these figures defy common sense, often prompting listeners to double-check: “Daily or monthly? Yuan or dollars?”
According to industry statistics, the average monthly salary for food delivery workers in Beijing in the first quarter of 2026 is just over 10,000 yuan, meaning a daily wage of 5,500 yuan for an intern is equivalent to the earnings of 10 delivery workers. An AI researcher earning 3 million yuan per year accomplishes in one year what a diligent undergraduate graduate from 2025 would need 39 years to achieve—assuming they remain employed.
Even at major internet companies known for high salaries, to reach an annual salary of 3 million, you need a master’s degree, 8 to 12 years of continuous work, at least three promotion evaluations where you rank in the top 30% each time, and to be in a core business or catch a period of rapid growth, so that by age 40 you can successfully reach levels such as ByteDance 3-2, Alibaba P9, or Tencent T11 and above. By then, you will already be managing a team of dozens and have gained some recognition in the industry.
Today, a 22-year-old AI researcher who has just graduated with a bachelor’s degree, has never led a team, made business decisions, or gone through a single performance cycle, is earning the same income.
Besides, they’re really young. “Born in 1998, I’m already considered ‘middle-aged’ in the Kimo team,” said an intern at a major tech company’s large model team, feeling a bit down because he was older than most interns at his company. One night, while working late, he looked up and saw an intern who was “much younger,” “bouncing around while coding,” he emphasized, then repeated it again, “literally bouncing around.”
The youngest is only 17—large companies are increasingly loosening age restrictions, making it hard to say who’s the youngest. She was proactively contacted by an HR representative from a major company, interned with a large model team while preparing for her high school final exams, and celebrated Children’s Day at her desk.
“Getting a Ph.D. is such a waste of time!” a young researcher tried to persuade his friends from Tsinghua’s “Yao Class.” “Why should the smartest minds learn so slowly?” He cited the example of a 19-year-old Stanford student who left school before finishing sophomore year and quickly raised $4.5 million in funding for his AI startup: “Worst case, you can always go back to Stanford.”
A recruiter once advised three or four PhD candidates to drop out and take full-time positions, offering attractive titles and salaries. “If you’re studying to land a good job, you already have one—and it might not even be available in two years.” Several young people chose to leave their programs midway, making a bold leap to catch the AI wave.
The founder of an AI company has a more radical idea: he plans to have his high school freshman take a leave of absence to work and learn simultaneously. “How could he possibly learn more at school than he would here?”
After surveying over 3,500 unicorn founders, global investment firm Antler found that in 2024, the average age of AI unicorn founders worldwide was 29, compared to around 40 in 2020. This number is likely to continue falling—AI enables sufficiently smart young people to either land higher-paying jobs or elevate their net worth to hundreds of millions of dollars.
AI Native: Youth is more valuable than experience
Senior executives at internet companies are a small minority at the top, managing tech teams of hundreds or having achieved success in specific business areas; they hold high ranks, enjoy strong reputations, and have stable positions, allowing them to avoid the "35-year-old curse."
Past experience no longer applies. Someone who previously worked at ByteDance Seed said that when ByteDance first entered the large model space, it followed its usual practice of assigning experienced “seniors” who had delivered results in their businesses to lead the new initiative. Two or three such leaders were appointed, each bringing their own core researchers, but the outcomes fell short of expectations. Later, the younger Zhou Chang joined and rapidly advanced the development of multimodal capabilities.
“This made us realize that our previous hiring strategy was wrong,” he said.
In terms of resources, DeepSeek is no match for giants that routinely invest tens of billions. Its workforce is less than one-tenth the size of those giants, and its employees work only half as many hours on average. It has never received any external investment, yet it entered the global top tier of large models earlier than China’s leading internet companies. We analyzed 84 publicly available resumes among the 172 researchers involved in DeepSeek’s first three models, and over 70% of them are under 30 years old.
One of the conclusions ByteDance reached after studying OpenAI, Anthropic, and DeepSeek is that in the AI field, what truly drives business progress is key researchers—past management experience and business achievements are less important. A senior leader may not necessarily hinder researchers, but they cannot outperform researchers in directly determining what to do; it’s better to let talented young technical professionals lead.
A source said that to build the Seed team, ByteDance transferred a TikTok growth product lead to oversee Seed’s hiring. The hiring logic focuses solely on how much money is invested and what return it generates—when recruiting, the question is how much salary is offered and how much value the candidate brings to the company; past titles and compensation are irrelevant. “Previously, people rated 3-1 were mostly master’s graduates with around five years of experience; today, new graduates can achieve the same or even higher ratings.”
The new rule is that the more AI-native you are, the more opportunities you have.
Several researchers attempted to explain this concept that has become popular in job postings from major companies, fundraising prospectuses, and founder speeches. One said, “The thought process is perfectly aligned with the input and output of large models—always turn to AI first, and know exactly what to ask next.” Another drew an analogy: “Why do seniors need to learn how to use smartphones, but children don’t? Because children understand that tapping the screen will produce a result. The same applies to large models.”
An investor focused on AI put it more simply: “The younger, the better.” They’re all Gen Z.
From OpenAI making large language models mainstream in 2022 to models gaining multimodal, deep reasoning, and programming capabilities, the industry has seen new technologies emerge almost periodically.
Just four years later, someone who chose the popular PhD focus of "computer vision" found the landscape had changed before they even graduated—if they didn't switch fields fast enough, they became part of the "previous generation" of AI professionals.
For more than four years, the longer someone has worked, the more passive they become. A recruiter from a large model company said that in 2024, they hired for AIGC text-to-image and text-to-video roles and, out of habit, looked for candidates with experience in visual algorithms. They quickly realized that these hires also had habits—they first applied proven techniques from their past, and if those worked well, they stuck with them. In contrast, recent graduates and those more “AI-native” didn’t copy old solutions; after replacing the hires, the results improved several-fold.
Someone with five or six years of experience might adapt quickly, but why should a company take the risk when there are younger candidates available? After having dozens of candidate resumes rejected, a headhunter working with a large model company understood the unspoken rule: “33 is probably the upper limit.”
Headhunters have some screening techniques. If a candidate asks about the company’s revenue, immediately consider them not AI-native. Most AI companies aren’t profitable yet; they care more about compute power, models, and data—revenue is seen as a financial metric from the previous generation of companies.
“A ‘genius’ manager only wants to hire people like themselves. Would a 30-year-old technical lead want to hire someone older and less skilled than themselves?” asked a headhunter who has worked with ByteDance.
She quickly cited several examples: Zhang Chang, who champions ByteDance’s multimodal capabilities, is in her early 30s; Yang Zhilin was also 30 when he founded Kimi; Lin Jinyang, former head of Alibaba’s Qwen large model, was born in 1993; Luo Fuli, head of Xiaomi’s MiMo large model, was born in 1995; and Yao Shunyu, head of Tencent’s Hunyuan large language model team, was born in 1998.
Moreover, most young people can work longer hours. A 21-year-old AI intern typically works from 11 p.m. until over 1 a.m., taking a break for a meal and walking around a bit to clear their mind, and on weekends they “work a little, play a little.” “It has nothing to do with the company—it’s my own standard,” he added. “Otherwise, it’s hard to stand out among peers.” Another 22-year-old AI researcher doesn’t find this unusual; he sometimes works straight through the night from 9 p.m. until noon the next day because he feels more “immersed.” They are still far from bearing responsibilities or concerns related to family care.
Enter high school, charter a cruise, find younger people
Large model companies achieved success by empowering young people, and this insight quickly spread—companies aiming to become AI-driven must first become more youthful. Beyond AI researchers, products, design, marketing, and HR also need more young talent.
Li Auto has announced that 2026 is the final window to become a leading AI company. This year, founder Li Xiang posted on his Moments that without sufficient deep training and learning, most people with a decade of experience perform significantly worse than those with just one year of experience, and their gap from top-tier graduates in the 90th percentile is at least tenfold—akin to “not using gold that’s already available, but instead opening blind boxes to extract gold from ore.”
In March this year, Geely Holding Group and Xinyi Technology announced the establishment of a talent development program specifically targeting high school students to build a talent pipeline for Geely Intelligence and other businesses.
Hiring young people isn’t just about telling a story of company transformation—it’s also driven by real operational needs. A payment company undergoing an AI transformation says its media roles essentially only consider candidates born after 1998, because the most active tech KOLs are getting younger and require similarly young communicators. At venture capital firms, younger investors are better able to connect with entrepreneurs.
Ultimately, pressure has reached the upper echelons of the internet industry. The AI organizational structure recognized today must be sufficiently flat and transparent. Young talent dislikes traditional high-pressure management and hierarchical pyramids, and instead believes in meritocracy.
In June, Alibaba replaced Wu Zhao, the former president of DingTalk who had taken over a year to rehire, with Chen Yusen, born in 1992. A former business partner of Wu Zhao said that Wu Zhao remains the same as before, determined to accomplish something big, but “he understands that times have changed, perhaps without realizing that people and society have changed too.”
Everyone wants young talent, but there are only so many truly smart young people—it’s crucial to find and connect with them before they graduate. Several HR professionals from major companies say they’ve noticed that if a “prodigy” had a positive internship experience at their firm, they are far more likely to join after graduation. “Smart people are limited—what matters is building connections with them early.”
In Denver, USA, on the day of CVPR (the IEEE/CVF Conference on Computer Vision and Pattern Recognition), one of the "big three" conferences, NVIDIA, ByteDance Seed, and Intel hosted dinners for young scholars; the next day, Tencent Qingyun, Alibaba Star, and MiniMax did the same. Half a month later, in Seoul, South Korea, at another top academic conference, ICML (International Conference on Machine Learning), Alibaba, Kuaishou, and Tencent again chose the same day to host dinners.
Tencent stated that at least 12 executives will attend one of its events this year. Kuaishou chartered a cruise on the Han River and customized a fireworks display over water, allowing its core business leaders to engage in direct, close-range conversations with attendees. Alibaba’s dinner was held on the 38th floor of the Grand Hyatt, where Warren Buffett once gave a speech.
To show sincerity, some companies invite department heads, vice presidents, and key interns to connect on social media, meet for coffee, and spend one to two hours discussing their views on technology and the industry, as well as their life goals. Even if you don’t attend, some HR representatives will still check in on your progress and send small gift boxes during Mid-Autumn Festival or Chinese New Year, mentioning that they’d consider you for a formal role—“The top of others’ salaries is our starting point.”
A Seed representative said that around 2026, Seed established a dedicated "Student Affairs Department" to identify and target interns and recent graduates. Their database encompasses nearly all outstanding current and recent students in China, including lists of students from key universities, key laboratories, and key supervisors, along with their competition records and internship experiences.
Theoretically, if you are an outstanding student with exceptional performance in a key high school, Seed’s HR may know more about where you attend school, when you graduate, and where you interned than your relatives do.
For high-level competitions, they can sponsor GPUs, tokens, or other resources needed by competition coaches, and beyond the list of winners, they can gain insight into each participant’s specific performance. For example, a participant with a low overall score may not necessarily be unskilled—perhaps one of the three judges gave an unusually low rating. “A semi-public secret,” said one HR professional. “Ask around; other companies know too.”
For competing companies, HR at large corporations are required to assign labels to relevant teams as thoroughly as possible, including daily performance, output, contribution to the team, and technical strengths—consulting and verifying feedback with enough people to ultimately determine alignment with their team’s needs. If a mentor’s former interns performed exceptionally well, that mentor’s team will also receive heightened attention; most mentors are happy to collaborate with big companies, and some students jokingly refer to getting hired together with classmates as “packing into the company.”
An intern who was approached by several major companies said that when choosing an internship, first consider the reputation of the team: whether it focuses on large models or multimodal tasks, pre-training or post-training, Team A or Team B—find out in advance if you’ll be doing “grunt work”; second, check how many GPUs are available—without them, it’s hard to get anything done; third, assess the team culture and whether you’ll have opportunities to directly interact with top experts; only fourth comes the pay.
Big companies don’t lack money. ByteDance established a special talent program called Top Seed for its Seed department; last year, the average daily internship pay was 2,000 yuan. This year, the Top Seed program has been officially discontinued, but salaries have no upper limit. Tencent’s Qingyun program covers the entire group, with the largest number of slots allocated to AI teams such as the HunYuan large model team. Internships follow a monthly salary structure, ranging from over 20,000 yuan to over 80,000 yuan per month, with some receiving around 110,000 yuan—this is also a competitive strategy. Daily pay means you earn only for the days worked, but monthly pay provides income even during holidays.
Interns often say, “If you have Seed, choose Seed; if you have Tencent, choose Tencent.” If those aren’t suitable, there’s a whole series of “Star” programs: Meituan’s “Beidou Plan,” Alibaba’s “Alibaba Star,” Kuaishou’s “Kuaishou Star,” and Xiaohongshu’s “REDstar.”
Job postings are becoming increasingly earnest—beyond salary, they must emphasize what the company offers researchers, such as “lead core projects,” “unlimited compensation,” and “join now to take on key responsibilities earlier.” To enhance appeal in the talent war, startup Kimi has publicly announced it will grant stock options to interns who pass its top talent program a year ahead of schedule—Zhipu’s stock has risen 20-fold in less than six months, making the potential value of these options highly compelling.
After joining the company, these young people will also be granted far greater freedom than typical new graduates.
Some campus hires brought in through the top talent program are directly managed by business leaders and have the autonomy to decide what’s worth pursuing—initiating projects around new directions, presenting proposals, and building teams, rather than optimizing existing business lines by 1% or 0.1%. Yao Shunyu invites interns from Tencent Hunyuan to join meals and regularly organizes exchange activities. One intern said they felt the company “wants to cultivate long-term potential and expects you to make an impact at Tencent.”
Some companies have promised candidates that they can join with peers who have also received talent programs, first building a small team to explore new directions. After joining, one campus recruit felt that the computing power was insufficient, so he included the request in his weekly report and copied it to the group’s top executive. Three days later, his department received over ten million yuan in computing resources.
The利益chain behind "youth"
In investment circles, "Generation Z" has become a key label for projects.
A 27-year-old researcher, who doesn’t feel young anymore, has just started a startup. To secure a stake, an investment firm sent an expression of interest with the amount left blank—meaning “you name the terms.” Who knows if the next OpenAI, Anthropic, or DeepSeek isn’t hiding in a young person today carrying a backpack? It’s far more imaginative than starting a business at 40.
“We’ve finally reached the point where we can benefit from the era’s advantages,” said a 2003-born AI entrepreneur who completed a two-year graduate program in just six months and devoted the rest of his time to starting his business. He secured millions in seed funding, his partners are only two or three years older than him, and the team consists of over twenty people, including some junior classmates as interns. The company is located in an AI community near Tsinghua University, where many similar startups are clustered.
“That’s not much.” His senior PhD classmate raised hundreds of millions of yuan within a few months. Among his peers, someone launched a startup and closed four funding rounds in a single month, “doubling the valuation on the spot.” He asked, “Do you even know what ‘on the spot’ means?”
Nothing has changed—only the amounts on the business plan are different. There are still many investors interested.
The post-00s founders of a company had just signed a funding agreement when one of the co-founders quit in frustration: “This is kids starting a business,” said the investor. But what if this company succeeds one day? Who would care if Zuckerberg showed up to meet investors in pajamas and a T-shirt?
Cao Xi, formerly Sequoia Capital’s youngest partner and an investor who backed DeepSeek after launching his new fund, said at the end of last year that this is the era of 90s-born founders. Six months later, the entrepreneurs he encountered were born between 2000 and 2002. “Sometimes, I even wish I weren’t a 90s-born myself.”
Similar to Jiqi Chuangtan, which focuses on early-stage funding for young people, some investment firms have begun establishing funds specifically dedicated to young founders. For example, Yunqi Capital’s Y Transformer invests exclusively in founders born after 1998, with a budget of 100 million yuan, planning to invest in approximately 20 to 25 projects, solely in their first round, with an average investment of about $600,000 per deal and a decision-making cycle of 2 to 3 weeks.
In the business world, the unspoken norm used to be the “old boy’s club,” where seasoned tech experts, successful entrepreneurs, and investment managers overseeing billions of dollars supported one another—“big brothers helping big brothers”—with opportunities, trust, and capital circulating among a small circle. Core projects in most fields remained in the hands of the previous generation of investors, leaving young people unfamiliar with key entrepreneurs and without decision-making power. A Gen Z investor said he had to adapt to the “big brothers’” rules: he needed to be sharp at dinners, know when to raise a toast, read the room, and beg for guidance from his seniors.
AI has created opportunities for young investors—established investors often don’t fully understand it, and most entrepreneurs are young, so “seniors” are willing to listen more closely to their junior investment managers. The founder of a traditional investment firm said they will give interns greater responsibility: “Just as the potential of many AI companies depends on the talent and effort of interns, the future of investment firms may also be determined by them.”
It’s not just young investors and entrepreneurs who help each other. AI researchers command high salaries, move quickly between companies, and are in strong demand by firms, some of which adopt a “defensive hiring” strategy—even if no position is currently open, they won’t let competitors hire them, and they offer generous packages. Aside from having somewhat high hiring standards and a limited pool of eligible candidates, everything else aligns perfectly with the headhunter’s business model.
They scour and recruit smart young people like hunters. One headhunter was offered a reward of 10,000 yuan for every qualified researcher brought in for an interview, regardless of whether they were hired. Another company was willing to pay a headhunter fee of 30% for specified researcher candidates—a rate typically reserved for recruiting CEOs in other industries. “If someone’s salary is $1 million, the bonus could be at least 2 million RMB,” calculated one headhunter.
AI talent is becoming increasingly young, and beyond the fact that younger generations are more AI-native and more “effective,” everyone benefits from the advantages youth brings. A larger theme is that young people are banding together, building collective influence, and jointly “pushing back against the older generation.”
Young researchers achieve results and prove their capabilities, leading them to join major companies or start their own firms and gain management authority; they place greater trust in peers or even younger individuals. Younger researchers and interns are motivated to explore, demonstrate their value to management, or catch the attention of young investors; young investors who back successful projects advance more quickly.
Of course, peers connect more easily! One researcher spent time in the San Francisco Bay Area, the epicenter of the AI storm, where 20-year-old founders hired 18-year-old employees and were selected by 19-year-old investors. They had never met before—they simply sent emails: “I’m very interested in your paper; my idea is xxx—let’s chat?”
He said that some investors in China still stick to the old routine—handing out business cards with a large photo on the left and a list of titles on the right. Younger people rarely do that: “We don’t really have any titles.” As long as the ideas are interesting, he’s happy to connect with new people through a simple email. The next moment, “I know a few people like you—you’d get along,” and gradually, a network forms. Ideas spread like wildfire; a few smart young people can start a startup, secure funding, and go head-to-head with well-resourced big companies.
No one can stay young forever
In the intense atmosphere that idolizes young talent, a former "Huawei Genius Youth" faces a full-scale shock. At the time of his PhD graduation, his salary as a Huawei Genius Youth far exceeded that of his peers and was considered a coveted destination even among top universities. Two or three years later, the salaries of his junior classmates completely surpassed his earlier expectations for fresh graduates—ByteDance began aggressively recruiting talent for foundational model development with no cap on positions, often offering salary increases of double the previous level.
A year later, he started his own company, and Tencent and Alibaba also joined the talent war, with salary expectations for top graduates being “shockingly high.” He could only appeal to emotions, emphasizing his reliability and offering more equity to recruit from his alma mater. When seeking funding, the title of “Huawei Genius Youth” still held weight, but it was no longer as compelling as the appeal of young, celebrity entrepreneurs born in the 2000s.
Young people keep coming in waves—there’s no youngest, only younger. Competition has become fiercer than ever; an AI industry professional notes that the number of top academic paper submissions has surged from around one to two thousand in 2020 to as many as seventy to eighty thousand today. Where once publishing two papers at top conferences was considered impressive for a master’s student, that standard has now doubled, and doubled again.
An AI researcher posted interview experiences for top talent programs at major companies on the platform and created an exchange group, requiring applicants to have relevant internship experience; the 500-member group filled up within two days. They discuss interview tips and actual team conditions, and many HR professionals from major companies follow his account “Random Field” to gather information about interns and new graduates.
The unspoken rule is that to land a top talent program, you need a great internship; to get a great internship, you first need a great internship. “So how do you get that first great internship? Through strong referrals from senior students or advisors.” A Gen-Z intern said solemnly, “No referrals? Then you’re left to luck.”
Another candidate selected for the top talent program remarked, “The circle around foundation models has already closed”; interns from several large model companies cycle between roles, transition to full-time positions, and then recommend juniors and classmates—“people inside don’t leave, and people outside can’t get in.”
“There are many truths that are better left unknown—it’s cruel to speak them,” said a person familiar with AI industry hiring. “In the past, an average college graduate earned 100,000 yuan a year, while graduates from Tsinghua and Peking University earned 1 million—people accepted a tenfold difference. But now, Tsinghua and Peking University graduates might earn 5 million yuan a year, while average graduates can’t even make 50,000. Is it not cruel when the gap widens to a hundredfold?”
A Gen Z AI researcher said he feels lucky, "The rewards for the extraordinary have never been greater in this era"—the AI industry's generosity toward young people can make people focus only on the first half, overlooking the second half—"the punishment for the ordinary has never been harsher."
That "Huawei Genius Teen" could at least start a business. Most of his peers spent years progressing from undergraduate to PhD, went through at least five rounds of interviews, eliminated other candidates, and joined major internet companies around 2020, becoming elites in a high-paying industry. Of course, they also face anxiety about turning 35, but they always focus on continuously improving their skills, striving to outperform the bottom 10% of colleagues who might be let go.
AI has arrived. Front-end developers are immediately becoming seen as redundant by companies; other software developers are just a matter of time—most programmers at major corporations live in constant anxiety, forced to work harder than their colleagues to distill their own value, striving to eliminate their peers first, only to be eliminated by AI in the end.
Before the second half of 2025, a software engineer over 30 from a major tech company had never doubted that he was "getting old." He earned his Ph.D. in the U.S., landed a job at a leading firm, and consistently followed advancements in new technologies. But one day, he suddenly felt that updates and information about large models were bursting forth like an uncontrollable faucet, turning his past experience into a "liability."
A wave of intense anxiety hits: “Before, one person couldn’t read 200 articles in a day, but now you can team up with AI to read 300, 500, even 1,000.” The question is, “What if you miss something?” Every night before bed, he assigns tasks to AI, trying to ease some of his unease.
Hearing this, a post-00s AI researcher immediately asked, "What else? It's like cars replacing horse-drawn carriages—advanced productive forces will inevitably replace backward ones."
A few hours later, another researcher, unfamiliar with him, used the exact same analogy: “Why didn’t they switch sooner?”
“But the coachman may find it hard to learn how to drive a car.” “But that’s how society progresses,” he said. “Four words—too narrow-minded.”
After hearing the account, the 30-something programmer fell silent. After a long pause, he spoke: “We all know no one can stop technology—it’s foolish to cover one’s ears and pretend it isn’t there; you can only follow. But it’s hard to explain to them that transformation isn’t easy.” He left the big company, hoping to explore new technologies in a different way.
A few days later, he sent a message saying he once again felt the ruthless confidence of the younger generation. Chen Yusen, born in 1992, has taken over as CEO of DingTalk from Wu Zhao, who interned at Alibaba back in 1992—this transition has many complex dimensions, but the summary from the young people around him is: “Take down the ‘old man,’ bring in the youth, and everything will get better.” He seems out of place in that jubilant new world.