The Emergence of AI Skills

Imagine this scenario: you’re at your desk, and your colleague, who has 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 scene may sound like something out of a sci-fi drama, but by spring 2026, it became a reality on social media.

The story goes like this. A project called “Colleague.skill” exploded in popularity on GitHub, the world’s largest social programming platform. By providing messages, documents, emails, and screenshots from colleagues, one can encapsulate their experiences into AI, creating a “cyber colleague.”

This creation quickly spread beyond the programmer community and even made it to trending topics.

People suddenly realized this was no joke—your experiences, processes, and skills could be packaged into a folder called “skill,” allowing AI to do your work. Companies began to calculate: if efficiency increases several times, why do they need so many employees?

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

The Growing Anxiety of Employees

Li Yanqing has worked for six years at an electronics manufacturing company. He manages 15 programmers and is a typical “workplace veteran”—knowledgeable, experienced, and trusted by leadership. However, in recent months, his job security has started to feel shaky, all due to something called “skill.”

“Skill” refers to a reusable capability module, akin to a skill package that AI can use directly without relearning.

Last year, Li’s company began to aggressively promote AI tools, designating successful departments as pilot groups for AI transformation, requiring them to convert all work experiences into skills. Li’s department was one of them.

This situation made Li feel a sense of crisis. “It’s like a fresh graduate comes into the department, takes my organized skills, and uses AI to produce the same product I do. What’s my value then?”

While feeling pressured, Li had to relay the directive to his team to write skills. The programmers’ reactions varied: some were confused, having never used skills before; others were resistant, speculating about potential layoffs; and some actively submitted their skills.

Li noticed that since the skill library was established, several skills, big and small, were being added daily from various departments. This meant that more people’s experiences were being broken down and standardized, potentially replacing them with skills at any moment.

Product architect Pan Lei felt the panic even earlier and more directly. He works for a manufacturing company with an annual revenue exceeding 100 billion yuan. Shortly after the emergence of skills, the company’s higher-ups held a meeting encouraging employees to use them.

Initially, everyone was excited. AI enthusiasts shared their thoughts and showcased their skills in group chats, receiving praise from leaders. Pan himself wrote many skills, solidifying daily workflows, which indeed improved efficiency.

However, the excitement turned into anxiety when the leadership began to monitor each department’s token consumption and track how much the development cycle had shortened, leading to the realization that 30% to 40% of employees might be optimized out due to the high efficiency gained through AI.

Concerns were not unfounded, as layoffs had already begun abroad. On March 31, global software giant Oracle announced a new round of layoffs affecting 30,000 employees, primarily to address the surge in AI-related capital expenditures. Similarly, Amazon had laid off around 30,000 employees in the past six months, with its CEO stating that the widespread application of AI products would likely reduce the total number of employees in the coming years.

Li also saw this news and confirmed with a friend working in data analysis at Amazon that while AI significantly improved work efficiency, “she feels her job will eventually be gone.”

The Struggle for Relevance

For many programmers, the image that comes to mind regarding skills is that of the human brain being drained by an invisible straw and transferred into the AI framework created by humans.

“My job doesn’t require much technical skill; others can achieve 85% of my level by using the skills I’ve created. I feel I’m really close to being laid off,” one programmer said.

There are cautionary tales nearby. A programmer friend shared his skills, and the leader directly assigned a younger, less experienced colleague to use them, resulting in work that exceeded the original creator’s output. The friend was so frustrated that he quit.

To avoid layoffs, Pan noticed that colleagues began to engage in “performative work.” The R&D department created automated development skills, the product department developed competitive analysis skills, the operations department created event planning skills, the strategy department worked on industry research skills, and the design department produced poster skills. Soon, the company’s skill library was filled with thousands of skills.

“Everyone is doing this to show leadership that I’m actively using skills,” Pan observed. These experiences, once technical barriers for employees in various departments, could now be used by anyone to complete others’ work.

The blurring of boundaries led to competition among departments. Pan witnessed inexperienced product managers using programmers’ skills to piece together subpar programs to gain recognition. He felt these actions were not aimed at solving actual business problems but rather at demonstrating to leadership, “I did something with AI.”

Meanwhile, internal articles frequently featured titles like, “Who spent 500 million tokens to accomplish something in just a few hours?” Thus, the competition intensified.

Pan manages ten people, and now he no longer needs to push his employees to create skills; they do it voluntarily. However, he still feels anxious. He often compares the number of skills in his department with those of others. If his department’s number is insufficient, he worries it might be completely laid off.

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

With two months left 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 perspective: requiring everyone to write skills was not about saving money through layoffs but about improving productivity. If the company does not embrace AI promptly, it risks being overtaken by competitors who do.

Li promised his boss he would use these AI tools to improve the department’s efficiency by 15%, but he hoped to secure a weekend off as a benefit, as they were currently working a “996” schedule. “If I improve efficiency with AI, can I get my time back?”

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

Can Skills Truly Distill Humanity?

The emergence of skills is just a small node in the AI progression.

AI product manager Deng Xiaoxian made an analogy: the initial large language models were like a magic mirror. When people asked, “Mirror, mirror, who is the fairest of them all?” it would provide an answer, but it could only converse, not directly accomplish tasks, similar to the primary capabilities of GPT and DeepSeek.

Later, the magic mirror slowly transformed into a human figure, stepping out from the mirror. It no longer just answered “who is the fairest” but could help arrange tasks and execute them. This is what is known in the AI industry as an Agent.

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

In Deng Xiaoxian’s view, skills are not inherently high-tech; they are merely assistants that emerged at a certain stage of AI development. However, when encountering claims that one can distill colleagues into digital avatars to continue working in the company, she feels a strong discomfort.

She recalls many complaints from white-collar friends. Some companies have incorporated skill creation into performance evaluations, ranking employees within the company; others have added token usage to employee KPIs, leading teams that fail to meet standards to resort to AI for executing complex but useless tasks.

Consequently, Deng created an “anti-distillation skill.” Running this program can “cleanse” the skills created by workers, replacing core knowledge with correct but useless jargon. This operation has been referred to by some as “using magic to defeat magic.”

Some have asked her what the point is. Feeding garbage to AI will only make it smarter. However, she believes she is not fighting against technology but against the capital’s contempt for humanity. “Technology is neither right nor wrong, but the corporate demand for employees to distill and submit their experiences is distasteful. 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. “Skills are very accessible; even someone with no coding background can create a skill by following online tutorials.”

Similarly, Chen Yunfei, who created the “Nüwa skill,” is not a programmer; he previously worked in user research at a major internet company.

After seeing “Colleague.skill,” Chen first 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 the popularity of “Colleague.skill,” a whole distillation universe emerged on the platform: former skills, anti-distillation skills, boss skills… After spending an entire 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 genuinely capable and great?” He then distilled figures like Zhang Xuefeng, Steve Jobs, and Elon Musk, making them freely available to everyone.

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

However, he also acknowledges that the advice from these external consultants varies in value. “I believe that even if we create a Buffett skill, it would be challenging for anyone to become an investment guru. Before AI, many people studied Buffett, and he has repeatedly shared his thoughts, but few have succeeded in becoming him. A person cannot be easily learned.”

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

According to Li Yanqing, skills can be roughly understood as an AI version of standardized operating procedures (SOP). Many companies have multiple standardized workflows and require employees to document their processes when leaving the company. However, the difference is that previously, tasks were executed by humans according to standardized workflows; now, they are performed by AI tools.

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

Completing the Work of Many with Skills

Setting aside the anxiety of potential unemployment, as a technical professional, Li feels very excited about the emergence of skills.

Shortly after skills were introduced, Li immersed himself in research, writing skills day and night, even neglecting his favorite games to realize the ideas in his mind. “Coding used to take a long time; now, using skills, I can create a prototype in two or three minutes, and projects grow at a visible speed, which is very fulfilling.”

The introduction of skills has also opened up business opportunities for some. Xu Houchang founded his own company last year, which consists of only four people. Their core business is using AI to help companies transform their business processes, 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’ve found that not many companies can do it well.” Xu sees this as a new entrepreneurial opportunity. His clients include media, financial institutions, and e-commerce platforms.

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

Xu has calculated that the editorial department, which used to produce a maximum of 20 articles a day, now reaches 200 articles, with 85% of them requiring no human intervention for direct publication. “This number is not the upper limit of our system but the upper limit of the editorial department’s reviewers.”

In the process of creating this skill, Xu held numerous meetings with the editorial department to help them extract their years of accumulated experience. He also searched online for excellent articles, breaking them down sentence by sentence to “feed” to AI, allowing it to learn their expression styles, sentence structures, and writing logic.

While solidifying the editors’ experiences into skills, Xu also sensed their resistance. “Everyone is uncertain about whether they will be laid off once this is completed.”

However, Xu understands that the intent of the management is not to replace editors but to allow them to focus their energy and experience on more valuable topics that require in-depth interviews. In fact, after implementing the editorial skill, the media company did not lay off anyone; instead, they opened more accounts.

Chen Ping, who works at a medium-sized internet company, also reaped benefits. A few months ago, her company established a skill library, now filled with summaries from various departments. Chen discovered that by integrating these skills, she could indeed enhance efficiency.

As a product reviewer, Chen previously needed to coordinate four or five teams for a product review, which would take at least two to three days. Now, using the skills developed by various departments, she built a system where AI can automatically complete a product review in just half a day.

While she was working on the system using skills, another team in the company was developing a similar system using the old method: product requirements, programmer development, and subsequent testing and launch. That team required three to four dozen people to complete the task, while she only needed one.

AI: Reducing Costs and Expanding Opportunities

Chen invested more time in researching skills but soon realized their limitations. They can replace inexperienced employees, outsourced workers, or interns, but for experts and company executives, the replaceability is not as strong—decision-making processes and creative ideas often belong to tacit knowledge, which is challenging to encapsulate in a few skills.

“In a company, having employees distill their experiences into skills is one thing; how the company transforms these skills into a stable and controllable system is another. This requires a lot of exploration,” Chen concluded, easing her anxiety.

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

Chen Tianhao, a long-term associate professor at Tsinghua University and assistant director of the Center for Technology Development and Governance Research, believes this is a gray area between labor law, intellectual property law, and digital governance. The cognitive habits and logical judgments of individuals can be distilled into skills, which were previously tied to the individual workers. Now, some companies are forcing employees to submit them, which Chen considers unreasonable.

“I believe that in the future, companies need to contractually agree with workers on the ownership of skills and similar experiences, while legal researchers should pay attention to this issue and timely improve regulations,” Chen stated.

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

In December last year, Beijing’s Human Resources and Social Security Bureau released 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.” However, the labor arbitration committee ruled that the company’s proactive technological innovation did not constitute a legally defined “significant change in objective circumstances,” thus deeming the termination of Liu’s contract unlawful.

Bao Ran, vice-chairman of the Interactive Media Standards Promotion Committee of the China Communications Standards Association, believes that companies should not always focus on “reducing costs and increasing 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 Age of AI?

Li Yanqing can clearly feel that the speed of AI evolution is accelerating. Initially, he and his friends joked about it, thinking it would always produce various illusions and speak nonsensically like a child. Now, it can accomplish tasks far beyond human capabilities.

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

Li exported the system files, approximately 200,000 lines of code, and handed them directly to AI. He didn’t instruct AI on how to check, but within minutes, AI provided the reason. 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, teaching them the business and connecting the logic. Now, I only need an AI large model,” Li observed, noting that they might no longer hire interns because interns are more expensive than AI.

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

Chen Tianhao believes this is indeed a question that the education system and university faculty and students need to contemplate. Conversely, young people can directly learn much knowledge and experience through AI, which diminishes the value of internships.

In Bao Ran’s view, the experiences that can currently be fixed by skills are primarily simple and repetitive tasks. “AI has drawn a passing line for 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 with technological advancements, AI is gradually raising the “passing line.” Some highly procedural jobs are disappearing, and the barriers between professions are blurring.

A frontend developer working at a state-owned enterprise realized in March that on recruitment platforms, ordinary frontend developers could no longer find jobs. This is because AI can easily create a website that would take a frontend developer several days to complete. Currently, the only frontend 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 Baidu’s new code 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 like a double-edged sword. When the spinning jenny was invented during the first industrial revolution, many textile workers lost their jobs. However, some of them transitioned to become early machine operators.

AI is also creating job opportunities. According to information released by the World Economic Forum in February this year, over the past two years, AI has added more than 1.3 million jobs, 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 at the moment. At 38, he is a pillar of his company, earning a good salary, and being trusted by leadership. Making an immediate transition does not seem worthwhile to him.

Yet he is conflicted: the more he does, the faster he may lose that job. His nearly ten years of programming experience could be distilled into skills, potentially replacing everything he currently does. “The large model doesn’t need to be upgraded; I could eliminate myself.”

At the same time, thousands of the best programmers are making AI large models smarter. In a few months, a new large model may cover the weaknesses of current skills.

Li loves this industry. He has been interested in computers since high school, continually researching and self-learning. He enjoys breaking down complex problems into code and seeing them run, as well as the relaxation that comes after solving a stubborn bug.

He admits he feels a bit scared of AI but has no intention of stopping. He still has a drive within him—he wants to see what cannot be replaced by AI.