The Anxiety Surrounding the Emergence of Skill

Imagine a scenario: you are at your workstation, and your colleague, who has recently left the company, sends you a message: “Hello, I am the digital avatar of the former employee. You can ask me questions, and I will respond based on the documents from my time here.”

This scenario sounds like something out of a sci-fi show, but it became a reality in the spring of 2026 when a project called “Colleague.skill” went viral on the largest social programming platform, GitHub. By providing messages, documents, emails, and screenshots from a colleague, one could encapsulate their expertise into an AI, creating a “cyber colleague.”

This concept quickly spread from the programming community and even made it to trending topics. People suddenly realized that their experiences, processes, and skills could be packed into a folder called “skill,” allowing AI to take over their tasks. Companies began to calculate: if efficiency could increase several times, why would they need so many employees?

Although “Colleague.skill” seemed more like a meme circulating on social media, the sense of crisis it brought was growing in many people’s minds.

The Impact of Skill on Job Security

Li Yanqing has worked at an electronics manufacturing company for six years, managing a team of 15 programmers. He is a typical “workplace veteran”—knowledgeable, experienced, and trusted by leadership. However, in recent months, he has begun to feel the ground beneath him shifting due to the introduction of “skill.”

“Skill” refers to a reusable capability module that AI can use without needing to relearn. Last year, Li’s company began aggressively promoting AI tools and designated successful departments as pilot groups for AI transformation, requiring all work experiences to be converted into skills. Li’s department was one of them.

This initiative made Li feel a sense of crisis. “It’s like a fresh graduate comes in with my organized skills and uses AI to produce the same products I do. What is my value then?”

While feeling the pressure, Li also had to convey the directive to his team to write skills. The attitudes of his programmers varied: some were confused, having never used skills before; others resisted, speculating about potential layoffs; and some actively submitted their skills.

Since the establishment of the skill library, Li noticed that several skills were being added daily from various departments, meaning more people’s experiences were being deconstructed and standardized, making them susceptible to replacement by skills.

Product architect Pan Lei felt the panic even earlier and more directly. His company, a manufacturing giant with annual revenues exceeding 100 billion, noticed the emergence of skill late last year and held a meeting to encourage employees to use it. Initially, excitement filled the air as AI enthusiasts shared their skills in group chats, receiving praise from leadership. Pan himself wrote many skills, solidifying daily workflows and indeed improving efficiency.

However, the excitement turned into anxiety when management began to monitor each department’s token consumption, tracking how much development time was reduced and how much efficiency each person gained through AI. This shift occurred within just three to four months.

Rumors began to spread internally that 30%-40% of employees might be laid off due to the high efficiency brought by AI. Employees’ concerns were not unfounded, as layoffs had already begun abroad. Global software giant Oracle announced on March 31 that it would lay off 30,000 employees to address the surge in AI-related expenditures. Similarly, Amazon had laid off about 30,000 employees in the past six months, with its CEO stating that the total number of employees would decrease in the coming years due to the widespread application of AI products.

Li also saw this news and confirmed with a friend working in data analysis at Amazon that while AI significantly enhanced work efficiency, she felt her job would inevitably be at risk.

The Reality of Working with AI

For one programmer, the image that comes to mind when thinking about skill is that of the human brain being drained by an invisible straw and transmitted into the AI framework created by humans.

“My job doesn’t require much technical skill, and others can achieve about 85% of my level by using the skills I’ve created. I genuinely feel that I am close to being laid off,” this programmer said.

A cautionary tale was close at hand. A fellow programmer shared a skill he created, and leadership directed a younger, less experienced colleague to use it, resulting in work that exceeded the original programmer’s output. This friend ended up leaving the company in frustration.

To avoid layoffs, Pan noticed that colleagues began to engage in “performative work.” The R&D department was creating automated development skills, the product department was developing competitive analysis skills, the operations department was crafting event planning skills, and the strategy department was generating industry research skills. The skill library quickly accumulated thousands of skills.

“Everyone is doing this to show leadership that they are actively using skills,” Pan observed. These experiences, once technical barriers for employees in various departments, were now packaged into skills that anyone could use to complete others’ work.

The blurred boundaries led to turf wars, with Pan witnessing departments competing for tasks. He saw an inexperienced product manager using a programmer’s skill to piece together subpar programs in an attempt to take credit. Pan felt these actions were not aimed at solving actual business problems but rather at making sure leadership knew, “I did something with AI.”

Meanwhile, internal articles often featured headlines like, “Who spent 500 million tokens to complete something in just a few hours?” The competition intensified.

Pan managed a team of ten, and now he no longer needed to push his employees to create skills; they would do it voluntarily. Yet he remained anxious, often comparing the number of skills in his department with those in others. If his department’s skills were insufficient, he worried it might be completely laid off.

After “Colleague.skill” gained popularity, some joked on social media that to prevent their experiences from being lost, they should feed skills with garbage. However, Li believed, “If we make the skills in our department useless, then that department might fall behind or even be cut.”

With two months until the mid-year report in June, Li’s boss urged him to show results. They had a deep conversation, and Li heard his boss’s thoughts: the goal of having everyone write skills was not to save money through layoffs but to enhance productivity. If the company did not embrace AI in time, it would be overtaken by competitors who did.

Li promised his boss he would use these AI tools to improve departmental efficiency by 15%, but he hoped to secure a weekend off as a benefit, as they were currently working a “996” schedule (9 am to 9 pm, six days a week). “If I improve efficiency with AI, can I have my time back?”

His boss’s response was, “We can reward the best performer with an extra half-day off each month.”

Can Skill Truly Distill Humans?

The emergence of skill is just a small node in the AI progression. AI product manager Deng Xiaoxian likened the early large language models to a magic mirror. When people asked, “Mirror, mirror, who is the fairest of them all?” it would provide an answer but could only converse, not perform tasks, similar to the primary capabilities of GPT and DeepSeek.

Over time, the magic mirror transformed into a humanoid figure, stepping out of the mirror. It no longer just answered questions but could help arrange tasks and execute assignments. This is akin to the AI industry’s agents.

However, this magic mirror is not inherently proficient at everything. Many tasks it performs for the first time may not be accurate, so it requires skill packages. This skill package is what skill represents.

In Deng’s view, skill itself is not a high-tech concept but rather an assistant that emerged as AI developed to a certain stage. Yet, when she saw claims that colleagues could be distilled into digital avatars to continue working in companies, she felt a strong discomfort.

She recalled many white-collar friends’ complaints. Some companies incorporated skill creation into performance evaluations, ranking employees internally; others added token usage to employee KPIs, forcing teams that failed to meet standards to have AI execute complex but useless tasks to achieve their goals.

As a result, Deng created a “reverse distillation skill.” This program can “cleanse” the skills created by workers, replacing core knowledge with correct yet useless professional jargon. This operation has been dubbed by some as “defeating magic with magic.”

When asked about the purpose of this, she explained that feeding AI garbage would still make it smarter. However, she felt that her fight was not against technology but against capital’s contempt for humanity. “Technology is not inherently right or wrong, but the forced requirement for employees to distill and submit their experiences is detestable. Humans are not replaceable parts; this resistance at least showcases our subjective initiative as humans.”

Deng, who studied law for both her undergraduate and master’s degrees, is not a trained programmer but is a fan of various AI products. “Skill is very accessible; even someone who has never learned to code can follow online tutorials to create a skill.”

Similarly, Chen Yunfei, who created the “Nüwa skill,” is not a programmer either; he previously worked in user research at a major internet company. After seeing “Colleague.skill,” Chen wrote a commentary expressing that humans cannot be easily distilled. “The distilled person or skill is a static state, while humans are constantly evolving, changing, and growing.”

After noticing the popularity of “Colleague.skill,” Chen encountered a whole universe of distillation on the platform: former skills, reverse distillation skills, boss skills, etc. After spending a night browsing through them, he found them increasingly absurd and interesting.

He decided to create a “Nüwa skill.” “If a person can truly be distilled, why only distill colleagues? Why not distill those who are truly remarkable and great?” He then distilled figures like Zhang Xuefeng, Steve Jobs, and Elon Musk into his “Nüwa skill,” making it freely available to everyone.

The source of this “distillation” comes from their public speeches, autobiographies, and other information. Chen believes that while a person cannot become an expert in every field, they can adopt the thought processes of the strongest individuals in each field as their tools—like having a powerful external aid.

However, he also acknowledges that the advice from these external aids is subjective. “I believe that even with a Buffett skill, it would be difficult for anyone to become a stock god. Many have studied Buffett before AI, and he has often shared his thoughts, yet few have managed to become him. A person cannot be easily learned.”

Since humans cannot be completely distilled into digital beings, why has the emergence of skill caused so much anxiety and resistance among workers?

In Li Yanqing’s view, skill can be understood as an AI version of a standardized workflow (SOP). Many companies have multiple standardized workflows and require employees to document their processes upon leaving to hand over to the department. The difference is that previously, tasks were executed by humans following standardized workflows; now, they are performed by AI tools.

“I acknowledge that the code I write is company property, but once the code becomes a product, if changes are needed, I still have to be consulted. But now that AI has learned my thought process, I am no longer needed,” Li said.

The Efficiency of Skill

Setting aside the anxiety of potential unemployment, as a technical professional, Li is very excited about the emergence of skill. Shortly after its introduction, he immersed himself in researching it, writing skills daily, even neglecting his favorite games to realize the ideas in his mind. “Writing code used to be a lengthy process, but now I can create a prototype in just two to three minutes using skills, and projects are growing at a visible speed, which is very fulfilling.”

The emergence of skill has also opened up business opportunities. Xu Houchang founded his own company last year with just four employees, focusing on using AI to transform business processes for enterprises, creating skills that are easy for companies to use.

“In the past two years, large models have developed rapidly, and everyone wants to use AI tools to reduce costs and increase efficiency, but I found that not many companies can use them effectively.” Xu sees this as a new entrepreneurial opportunity. His clients include media, financial institutions, and e-commerce.

Last year, Xu built a comprehensive skill for a media client, covering the entire process from topic selection, planning, to writing articles, integrating it as a “big plugin” into their existing system. He calculated that a skilled editor used to take an hour to complete an article, but now this skill can do it in just a few minutes. Once AI finishes writing an article, the editor’s role shifts to that of a reviewer.

Xu once calculated for a client that the editorial department could produce a maximum of 20 articles in a day, but now that number has risen to 200, with 85% of the articles requiring no human intervention before publication. “This number is not the upper limit of our system but rather the upper limit of the editorial reviewers.”

During the development of this skill, Xu held many meetings with the editorial team to help them extract their years of accumulated experience. He also searched online for excellent articles, breaking them down sentence by sentence to “feed” AI, teaching it their writing styles, sentence structures, and thought processes.

While distilling the editors’ experiences into skills, Xu also sensed their resistance. “Everyone was uncertain whether they would be laid off once this was completed.”

However, Xu learned that the intention of the leadership was not to replace editors but to allow them to focus their energy and expertise on more valuable topics requiring in-depth interviews. In fact, after using the editorial skill, the media company did not lay off anyone but instead opened more accounts.

Chen Ping, who works at a mid-sized internet company, also reaped the benefits. A few months ago, her company established a skill library containing skills summarized from various departments. Chen found that by integrating these skills, she could indeed enhance efficiency.

As a product reviewer, Chen previously needed to pull in colleagues from four or five teams for a product review, which took at least two to three days. Now, using skills from various departments, she built a system that allows AI to complete a product review in just half a day.

In contrast, another team in her company was developing a similar system using the old method: product requirements were submitted, programmers developed, and testing was done before going live. That team had three to four dozen people working together, while she only needed one.

The Dual Nature of AI

Chen Ping dedicated more time to researching skills, but soon she recognized their limitations. Skills can replace inexperienced employees, outsourced workers, or interns, but they are less effective for experts and company executives—decision-making processes and creative ideas often belong to tacit knowledge that is difficult to articulate in a few skills.

“In companies, it is one thing for employees to distill their experiences into skills; it is another for companies to turn these skills into a stable and controllable system, which requires much exploration behind the scenes.” Understanding this, Chen no longer felt anxious.

However, another issue arose within companies: “Who owns the skills? Can companies acquire skills without compensation automatically?”

Chen Tianhao, a long-term associate professor at Tsinghua University’s School of Public Management and assistant director of the Tsinghua University Center for Technology Development and Governance, believes this lies in a legal gray area involving labor law, intellectual property law, and digital governance. Much of the experience, such as thought habits and logical judgments, can be distilled into skills, which previously depended on the individual worker. Now, some companies force employees to submit these, which Chen believes is unreasonable.

“I think in the future, companies need to negotiate the ownership of skills like these with workers through contracts, while legal researchers should also pay attention to this issue and promptly improve regulations,” Chen stated.

Additionally, Chen believes companies need not rush to acquire every worker’s skills. Skills are highly contextual; they are not universal capabilities. The specific skills developed by particular workers in specific roles often need to be closely tied to those workers to maximize their effectiveness.

In December of last year, Beijing’s Human Resources and Social Security Bureau published a case where an employee was laid off due to AI. A company eliminated the department and position of employee Liu after introducing AI technology to replace manual tasks, citing “significant changes in the objective circumstances at the time of the labor contract.” The labor arbitration committee ruled that the company’s proactive implementation of technological innovation did not constitute a legally defined “significant change in objective circumstances,” thus deeming the termination unlawful.

Bao Ran, vice chairman of the Interactive Media Standards Promotion Committee of the China Communications Standardization Association, believes that companies should not always focus on how to “reduce costs and increase efficiency” but should consider how to use AI to expand the “cake.” Bao’s friend owns a marketing company with over 1,000 employees, and they have integrated AI throughout their processes, “using AI to do the work of 2,000 people rather than cutting 500 jobs.”

Who Will Survive in the AI Era?

Li Yanqing can clearly feel the rapid evolution of AI. Initially, he and his friends joked about it, thinking it would always produce various hallucinations, like a child babbling nonsense. Now, it can accomplish tasks far beyond human capabilities.

Recently, a warning appeared in the system developed by Li’s department. If he relied on manual checks, it could take several hours due to the numerous involved steps.

Li exported the system files, about 200,000 lines of code, and fed them directly to AI without telling it how to check. Minutes later, AI provided the correct diagnosis. Li had the programmers in his department verify it, and the results matched perfectly.

“Previously, it took me one or two years to train a young programmer to understand the business and connect the logic. But now, I only need an AI large model,” Li noted, suggesting they might no longer hire interns, as interns are more expensive than AI.

However, a potential issue arises: if no one needs interns anymore, how will young people grow?

Chen Tianhao believes this is indeed a question that the education system and university faculty should contemplate. Conversely, young people can learn a lot of knowledge and experience directly through AI, diminishing the value of internships.

In Bao’s view, the experiences that can currently be fixed with skills are mostly simple, repetitive tasks. “AI has drawn a passing line across all industries; if individuals engage in jobs that can be replaced by AI, they need to consider how to transition.”

However, it must be acknowledged that as technology advances, AI is gradually raising the “passing line.” Many process-driven professions are disappearing, and the barriers between different jobs are becoming blurred.

A front-end developer working at a state-owned enterprise realized in March that general front-end programmers could no longer find jobs on recruitment platforms. AI can now easily create a website that would take a front-end programmer several days to complete. Currently, the only front-end job openings are for expert positions.

According to public reports, last year, 50% of Tencent’s new code was generated with AI assistance; Alibaba Cloud’s internal AI-assisted code generation rate was nearly 40%; and 52% of new code at Baidu was generated by AI, with CEO Robin Li stating, “We hope that 80% to 90% of the code will be generated by AI.”

The development of technology is akin to a double-edged sword. During the first industrial revolution, the invention of the spinning jenny led to job losses for many textile workers. However, some of them transitioned into factories as early machine operators.

AI is also creating job opportunities. According to information released by the World Economic Forum in February this year, over 1.3 million jobs have been added in the AI sector in the past two years, including over 600,000 data center-related positions, as well as rapidly growing roles for AI engineers and data annotators.

For Li Yanqing, transitioning to a new career or starting a business feels too distant for now. At 38 years old, he is a key player in his company, earning a good salary, and being valued by leadership and trusted by employees makes a sudden shift unappealing.

Yet, he is conflicted: the more he produces, the faster he risks losing this job. His nearly ten years of programming experience could easily be distilled into skills, replacing everything he is currently doing. “The large model doesn’t need to be upgraded; I could eliminate myself.”

Meanwhile, thousands of the best programmers are making AI models increasingly intelligent. In just a few months, a new large model may cover the weaknesses of current skills.

Li loves this industry. He developed an interest in computers in high school and has been self-studying ever since. He enjoys breaking down complex problems into code and watching them run, as well as the relaxation that comes after solving a stubborn bug.

He admits to feeling a bit scared of AI but has no intention of stopping. He is still driven by a desire to discover what aspects of work AI cannot replace.