Today I came across an article from 36Kr, and I recommend everyone to check it out. Although it contains elements of creating anxiety and exaggeration, we have to admit that this is the current reality and will be for a long time to come—2026 is just the first step.

Even if you embrace AI, you may still be eliminated by AI, and this process is accelerating. In the face of AI, all white-collar jobs have no moat.

The original text is as follows:

The first batch of big tech workers cut by AI: High salary, high performance, high seniority

"Now the company has a (layoff) list, and you're on it." One day in mid-May, Lin Yue was called into a meeting room by his team leader, who got straight to the point.

Lin Yue's first reaction was calm; he had already anticipated it. As early as March and April this year, rumors of layoffs were circulating inside some internet companies. Since the start of the year, major Chinese internet companies have been aggressively pushing AI efficiency through token races, training sessions, and hidden assessments, which are everywhere. When everyone is swept up in an "all in AI" movement, the consensus is clear: "Layoffs will definitely happen."

But standing at the HR door, he still had a moment of emotional breakdown: his hands started trembling, he hesitated for a long time, thinking about how to start and how to adjust his demeanor and expression. "I never want to go through that again."

Lin Yue earned a monthly salary of 25,000 RMB. A year ago, he graduated with a bachelor's degree and joined Ctrip as a backend engineer—at the time, he was extremely lucky. The hiring boom in internet companies was over; out of thousands of resumes, Ctrip accepted fewer than 500 people, but he entered the company's most profitable hotel department, responsible for writing code for commercial products.

But now, a junior programmer with a monthly salary of 25,000 RMB and only one year of experience—who else would be laid off? First, the compensation cost is low; second, compared to veteran employees who are more familiar with the overall business, newcomers are often less efficient at using AI. "With business experience as a foundation, veteran employees are clearer about what to do with AI and what impact it will have," Lin Yue said.

In a paper titled "Canaries in the Coal Mine?" Stanford University used "canaries" as a metaphor for young people just entering the workplace. The study shows that since the widespread adoption of ChatGPT in 2022, employment among the youngest workers has dropped significantly. By September 2025, employment for software developers aged 22–25 had fallen nearly 20% from its peak at the end of 2022.

In the past year, AI has made everything more intense. Ctrip was once a famous "retirement factory" for internet workers: programmers started work at 10:30 a.m., had a two-hour lunch break, and left on time at 7 p.m., with the main app iterating every two weeks. But soon after Lin Yue joined, the AI coding capabilities exploded, and it became so intense that the app was iterating every week, "working until 10:30 every night."

But this acceleration wasn't due to explosive business growth, "but because if you don't find things to do, you become a marginal department, and marginal departments get cut," Lin Yue told 36Kr. In the end, he couldn't avoid being "cut."

However, the "culling" could also be indiscriminate.

Cang Shu never expected to be one of the first people on the layoff list.

One Friday in May, half an hour before work, "the department suddenly called an all-hands meeting, and HR directly announced the results, telling everyone about this."

Before joining Meituan, Cang Shu was an SSP (Super Special Offer) campus recruit at ByteDance, joining with a high salary and eventually becoming the highest-paid employee at the same level within the team. After moving to Meituan, almost all the core projects in the team were handed to him, and this year was supposed to be his promotion milestone.

In this wave of layoffs, the protective shields of "high performance" and "senior roles" failed. In the team next to Cang Shu's, two laid-off employees had received "above expectations" performance ratings last year. By the end, Cang Shu's team was almost entirely "wiped out," "the team exists in name, but there's virtually no one left."

When Lin Yue learned he was laid off, he realized that two frontend engineers he often worked with "had their profile pictures grayed out at some point"; a large Meituan user growth group originally had hundreds of members, but now only about half remain; and Alibaba's Amap and Fliggy businesses are also in turmoil.

"630" has become a hot word on social media. It marks the end of the first quarter when AI truly entered the internet workplace on a large scale in China. From the end of June to mid-July is not only the usual time for many companies to replace personnel but also the commonly set "last day" in this wave of layoffs.

Silicon Valley, as a bellwether, has already started laying off people en masse. In May, Meta announced layoffs of 8,000 employees, with 7,000 transferred to AI departments, making it the most turbulent among Silicon Valley tech companies. Executives admitted that "company morale is the lowest in 20 years." Earlier, Amazon announced layoffs of 16k white-collar jobs, redirecting the saved funds to AI.

Before the previous wave of layoffs in 2021, major Chinese internet companies were aggressively expanding their boundaries, forming new business units one after another, quickly recruiting a batch of people and then quickly eliminating them.

But this year's layoff wave doesn't follow a single internal theme. AI efficiency improvements, slow growth or deep competition in large and heavy old businesses, and cash pressure from investing in new AI businesses are all intertwined. Many people notified to leave find it hard to weigh the importance of these factors.

The author of "Hassabis: The Google AI Brain" said that just as Oppenheimer created the atomic bomb but couldn't control its use, scientists pursuing truth are also "destroyers of all things": our work, our way of thinking, and even our existence may be "destroyed." Ten years ago in Seoul, AlphaGo delivered the initial "destruction" to human Go player Lee Sedol. Ten years later, from Silicon Valley to Beijing, this destruction is spreading again.

For big companies, AI is a ticket to new businesses like large models or AI applications. But whether new businesses can succeed and when is anyone's guess. Faced with old businesses that are no longer growing, big companies have no choice but to become more resolute in improving efficiency and laying off people in every certain and uncertain direction.

When Lin Yue confided in a friend about being laid off, he was comforted: "It's okay; we'll all face this day eventually. Yours just came earlier." But more important than self-consolation might be how people choose and act after being replaced by AI or laid off from big tech.

Anxious senior management, escalated middle management, frantic grassroots

"Previously, it took two months at ByteDance to create a product demo; now we can do it in two weeks," a former ByteDance product manager, now an executive at an AI startup, told 36Kr. With tools like Claude Code and Codex, his team can now build a demo in three hours and validate an idea within a week.

"A product (manager) is like a CEO," he said. The organizational structure can be significantly compressed, with far less information loss than in big tech—a perfect "entropy reduction."

When startups move quickly with AI, do internet giants look back at themselves and feel like slow-moving behemoths?

Statements from the top of a big company are often a signal.

In March this year, Meituan CEO Wang Xing shared his views on AI at an executive meeting: "The impact of AI Agents on me is greater than ChatGPT. AI is destined to create enormous productivity and will inevitably bring big changes to organizations and work models."

Shortly after that meeting, Meituan held a company-wide online meeting focused on promoting the installation and use of "Longxia," encouraging every colleague to install it and to write daily work as reusable Skills as much as possible.

After the meeting, Chen Yujia, who works in Meituan's core local commerce operations, received a notice to add a section to her weekly report, detailing how she used AI to improve efficiency and what Skills could be promoted to the entire team and department. "Then I felt everyone was frantically trying to integrate AI into their work."

One day in April, an Alibaba algorithm engineer unexpectedly received the token consumption ranking for his department from the previous month. He was ranked first with 17 billion tokens consumed and was publicly praised. The department head said that future annual KPIs and promotion reviews would reference this ranking. But a month later, the new token consumption ranking didn't arrive as expected—"maybe the boss realized this ranking method wasn't reliable."

New rules followed one after another. The department head soon proposed that employees needed to upload hourly "timesheets" from 11 a.m. to 6 p.m. on workdays, with plugins on the Agent automatically recording code and conversation content to generate work summaries—meaning employees couldn't modify their timesheet content. The next day, HR, almost in a confrontational manner, dissuaded the leader from implementing this absurd system.

Incidents like this are no longer surprising. AI anxiety from the top trickles down, with middle managers adding their own pressure, subtly implying that this is an invisible competition of reporting, arms races, and elimination contests.

Although no one was forced to write Skills, Chen Yujia's department head closely monitors each subordinate's token usage, frequently asking about specifics. "He doesn't know exactly what AI can do, but he says he won't allow anyone on his team to fall behind in this AI wave." Sometimes, during after-work dinners, the boss subtly conveys a sense of crisis: "You must use AI, otherwise when the time comes, I won't be able to pull you along even if I want to."

An Alibaba AI coding product engineer told 36Kr that some business heads in the group have requested their product team to add more data tracking points, "so he can clearly see the specific AI usage trajectory of team members every day."

Some Meituan middle managers, after receiving layoff targets, even submitted a more aggressive and higher-percentage layoff list—fewer people and higher AI involvement, in a sense directly equating to "management achievements" in the new era.

AI efficiency improvement has become something any business or function can "try their hand at." But regarding what AI can actually do and how to implement it, a long crack remains between the grassroots and management—bosses at all levels have infinite hopes for AI, while the grassroots desperately try to realize them but never reach the envisioned goal, ultimately resulting in a tired "performance."

Jiang Ling works in customer operations at Alibaba's Taobao and Tmall Group, responsible for aligning consumer demand with merchant supply. In her view, bosses always "imagine AI to be very intelligent and simple."

Take the common e-commerce anomaly scenario of "order explosion" for example. Senior management expects to identify all "hot sellers" in advance through full inspections. However, the platform has tens of millions of products per day, far exceeding the capacity of current manpower and tokens. So they can only test on a small scale, selecting hundreds of thousands of products. Due to the small sample size, the hit rate is often very low.

"As an employee, you can't refute the boss's expectations, you know?" Jiang Ling said, both agitated and helpless.

Many times, Jiang Ling feels like a donkey being whipped from behind. "Exhaustion isn't scary; the scariest thing is having no direction or positive feedback. You just keep grinding, not knowing where you're going."

"You can't treat AI like a wishing well," a CTO of an AI company told 36Kr. AI efficiency improvement has many prerequisites, with data being the foundation, but many companies haven't even properly digitized. Additionally, many process bottlenecks are "human" and cannot be solved by AI alone.

"Each generation has its own civil engineering"

Product managers, operations, and other roles in big tech feel uncertain anxiety, but programmers are the first to accept their fate.

Li Chuan, a Baidu frontend engineer, was first shocked by AI capabilities earlier this year when he used Claude Code. "For the same complex requirement, some domestic large models might need five or six rounds of dialogue, while Claude handles it in two or three rounds and does a better job."

The second time he was impressed by AI was in April this year. Chinese large-model company Zhipu released the GLM-5.1 model, "first, it's cheap; second, its capabilities can completely replace Claude Code."

Li Chuan immediately realized his job was at risk. By May, he indeed appeared on the "list."

Like two sides of a coin: on one side, by May 2026, Claude Code's parent company Anthropic had achieved an annualized revenue (ARR) of around $47 billion, growing four or five times in half a year; Zhipu also recently hit a trillion-dollar valuation.

On the other side, the rapid maturation of AI coding capabilities made programmers the hardest hit in this layoff wave. "Almost every company's first targets are product and R&D teams, especially frontend development and test development roles, which bosses often consider no longer valuable," an internet company HR told 36Kr.

In 2025, Li Chuan entered Baidu as a campus hire, becoming a frontend engineer. During his campus interview a year ago, AI only played the role of a search engine, assisting programming through simple Q&A, and the interviewer never mentioned AI.

"Frontend" was Li Chuan's ideal career because it's a WYSIWYG job—code quality directly reflects in every detail of the product interface. Every Chinese New Year, telling his family "open the Baidu app; the thing on top was made by me" gave him a sense of achievement and "the meaning of work."

For years, programmers at big companies were clearly divided into functions like algorithm, frontend, backend, and testing. Frontend required higher soft skills like aesthetics and interaction, while backend required more rigorous technical skills. The salary levels and "pecking order" in this industry directly corresponded to "technical content"—frontend paid more than testing but less than algorithm and backend engineers.

In just one year, everything Li Chuan was familiar with has been turned upside down. AI has largely taken over writing and modifying code, blurring the boundaries between the various programmer functions. Even product managers can now cross into programming.

An Alibaba development department received a notice from the department head in May, asking everyone to suspend all non-urgent requirements. Each team was to develop an Agent, and for any future business requirements, product colleagues could only interface directly with the Agent. Programmers could only modify the Agent, not touch the code. The boss also hinted that by October, teams that performed well would take over maintaining Agents from underperforming teams.

Tencent's CSIG technical team developed a pipeline to fix bugs in the company's apps—AI fixes bugs, and programmers only need to check after the bug is resolved and click the "confirm" button for the code to merge. Its bug-fixing accuracy currently reaches 50%.

In May, Alibaba internally established a number of full-stack teams, converting frontend, backend, and test engineers into "full-stack engineers," becoming "super individuals." Starting in June, Meituan has also been promoting the merger of frontend and backend development internally.

Switching to "full-stack" is theoretically feasible, but in practice, it's a painful process that strips a layer of skin.

Han Zhi, suddenly turned into a full-stack engineer, doesn't have much time to learn and soon has to start her first "full-stack" project, handling frontend, backend development, and testing all by herself. "Now all my requirements are 'backward-scheduled,' with deadlines set for specific dates." Recently, her workload has been maxed out; at 9 p.m., she still has tasks unfinished. "I'm so tired."

But the trend is unstoppable. From late last year to early this year, several top Chinese companies have been spending as much as possible to push programmers to consume tokens, gradually phasing out "ancient programming methods."

At its peak, Tencent CSIG team members enjoyed a token allowance of $2,000 per month. As long as the request was reasonable and produced corresponding code output, they could request a double increase when used up. Token usage was also incorporated into performance reviews. "When your usage is very low, your leader will ask why." Consequently, some people would lend their unused token quota to others.

For years, being a programmer at a big tech company meant high pay and prestige. They were the foundation of internet companies; the "programmer spirit" meant open source and sharing, clean and elegant code, results-only without noise, and the excitement of seeing characters dance on the screen.

But times have changed. Almost every programmer interviewed told 36Kr the same thing: "You can't work without AI. If AI 'crashes,' I'd rather spend a lot of time finding a new coding plan than read and modify the code myself"—talking about the so-called "programmer spirit" seems out of place.

Li Chuan said that the cultivation of a good programmer in the past involved continuous learning and iteration, because programming languages have been changing over the decades, and if you don't learn, you fall behind the cutting edge. He and his friends often went to cafes on weekends to study new technologies. "This group has always been quite competitive." But AI's terrifying iteration speed has left people speechless.

"If AI coding could just stay at the 2025 level, it would level the technical skills between someone with one or two years of experience and someone with seven or eight years, without truly replacing humans, leaving plenty of work outside the 'dialog box,'" Lin Yue lamented. But technology won't stop for anyone. Now he has no doubt that programmers are already becoming extinct, "like textile workers after the invention of the spinning jenny."

Old growth is gone, new race begins

When technology injects multiples of efficiency into a company, two things inevitably follow—either the same people do more work, or a company no longer needs as many people.

"We're not laying off people," a software company CEO told 36Kr. After finally "training" these programmers who have deep knowledge of the industry and development methods, each one is a company asset. When AI coding improves programming efficiency by five times, what he needs to do is not lay off 4/5 of the people, but expand the business by five times.

The wish is nice, but the question is, is there enough market growth left?

Before being laid off, Lin Yue briefly experienced the "liberation" of AI writing code, but soon he became even busier. Previously, when the business needed iterative changes to app details, it always had to wait for scheduled development. Now, business demands pile up faster, and regardless of feasibility or importance, they tell the R&D team to "just build it and try."

But in Lin Yue's view, many of these demands are somewhat "useless"—the smallest "banner" changing a few words, or converting a pop-up ad from "cancel for free" to "redeem with points." "Product managers keep tweaking this and that; we do A/B tests, and really, not many changes lead to better results."

"The departments with less growth are the ones going all in on AI; they always need to find new stories to tell," Cang Shu said. He has worked in both the food delivery business and the drone business, and from his personal experience, the atmosphere of going all in on AI in the former is much stronger than in the latter.

A former Meta infrastructure engineer who recently went through mass layoffs told 36Kr that after learning to squeeze AI, he and his colleagues now want to "do things they didn't have time for before." But now that many people have left, the remaining colleagues are cutting out low-priority tasks.

The reality facing everyone is that the star products born in the mobile internet era are now struggling to significantly boost growth by "doing more work." Some companies are not only not growing but are also bleeding due to fierce external competition.

In 2025, the food delivery war burned 200 billion RMB, dragging Meituan's profits and cash flow into the mire. This pushed Meituan, which already had low per-capita profit contributions, into the layoff cycle first. But from another perspective, Meituan's business relies heavily on offline fulfillment, and the space for AI efficiency improvement is smaller compared to more online-oriented companies. "If even Meituan can reduce headcount through AI efficiency, other companies will follow. It's a bellwether," a Meituan employee said.

Baidu, whose traditional cash cow of advertising continues to shrink, and Alibaba's Fliggy and Amap, which have long been marginal with minimal contributions, are in similar situations.

Layoffs in old businesses are inevitable, but is there hope in new areas?

When discussing layoffs, some management tells employees, "The company is also working on AI now; try to find projects you can do." A Meituan employee told 36Kr. Recently, Meituan's core local commerce established a new AI Transformation department, focusing on exploring using AI to streamline internal processes. Additionally, many core senior and middle managers are personally leading AI-related projects.

Wang Yue, a ByteDance product manager, told 36Kr that he is internally starting a venture to create an AI efficiency product for B-end clients. "The company encourages us to explore like this." From the beginning, they not only eliminated the "design" and "testing" functions but also emphasized to the review committee how much labor cost this product would save. Another colleague of Wang Yue is developing an AI customer service Agent product, with its 2026 OKR being "help the company cut xx% of customer service staff."

Today, in every big tech company, there are a dozen or dozens of small teams doing such projects. "Sometimes several teams work on the same direction; whoever stands out, the company will focus resources on promoting them."—A new race has begun.

What is changing, besides business priorities, is organizational structure, such as removing more middle managers.

Tencent started implementing a project-based system this year, weakening management ranks and restoring professional ranks for managers. Meituan, during its mid-year review, laid off some L9 (business unit director level) and recently completely eliminated the X1 node (previously the lowest management tier), reducing management layers.

Let's bid farewell to the past

Where the AI wave will take people is still unclear to most.

By mid-June, before the end of his severance period, Lin Yue was already busy interviewing for positions at Taobao, Kuaishou, and ByteDance. Continuing a career as a "big tech programmer" was still his preferred path. But these companies haven't extended offers yet. "It's so hard," Lin Yue said.

"Finding a job is easy, but once you leave a big tech for a mid-sized or small company, you can never go back to big tech," Lin Yue said. In his mind, giving up big tech means a permanent fall, and he doesn't want to "settle for less."

Some have let go of their "big tech obsession." Three days after leaving Baidu, Li Chuan seamlessly joined a startup. Naturally, his role changed from "frontend engineer" to "full-stack engineer." The startup's main product is an office AI Agent, and he even got a pay raise.

Although everyone says times have changed and programmers' skills are no longer reliable, Li Chuan still has some "technical aspirations," hoping to participate in a product loved by users as a technical professional, which doesn't necessarily have to happen in big tech.

After leaving Alibaba, Jiang Ling joined a traditional automotive company. Her work now doesn't have to be forcibly tied to AI. She no longer has to worry daily about "whether the boss's AI tasks can be completed" and doesn't have to "perform frantically." Jiang Ling's current project is due on September 30, "These tasks fall within my comfort zone, and with ample time, I'm really much more relaxed."

Recently, whenever her department posts a job opening, "a bunch of people from Alibaba come to interview, crazily flocking to manufacturing."

Perhaps only 10% of programmers will remain in the end, but Cang Shu doesn't want to find another big tech job. "Go compete for that desperate 10%."

After being laid off by Meituan in May, he decisively embarked on the entrepreneurial path. Before the AI wave, he had already tried building something on the side. At that time, just by creating communities and selling skills, he had tasted earning 100,000 RMB a month.

In March and April this year, some "students" in Cang Shu's community had already ridden the wave to start AI ventures, "started their own companies, hired many people, and I was still working miserably—was that right?" he asked himself.

Now, Cang Shu's startup is targeting overseas markets, developing systems and independent products around the needs of rare disease users. He also shares progress on his Xiaohongshu account "Cang Shu (Quit the Monthly Salary Version)" and overseas social media. Besides the main product, he is working on several small products in parallel to keep his touch. "A small tool takes at most three or four days; a complex system might take half a month."—All much faster than the typical big tech development pace.

AI might be the most powerful intellectual lever in human history. It can amplify personal capabilities N times, support the realization of most startup products, and allow every good idea to be quickly seen and priced.

Born in 2000, Cang Shu says he was destined to start a business, but without this layoff, he might not have acted now. "The company made the decision for me."

"Don't dwell on the past, go all in forward" is the last sentence in Meituan's farewell message to every departing employee, and it's also a phrase many big tech workers mention when leaving. In this complex change brought by AI, whether leaving big tech or staying, the old path can no longer be continued.

A brief "breakdown" doesn't mean lying down. Changing careers or starting a business—those who accept change first might see a different world.