What AI changes is not efficiency, but people's jobs.

What AI truly changes may not be efficiency, but jobs.

In the past, a company hired someone because a position required a person. In the future, before hiring, a company may first ask: Is it really necessary to hire a person for this task?

In fact, many positions have already started to change.

Writing code, writing articles, creating graphics, editing videos, making PPTs, writing scripts... These tasks that were previously considered to be completed by professionals are being rapidly compressed by AI.

Not everyone will lose their jobs tomorrow. Many people will first find that the company will no longer be willing to evaluate you according to the original work standards. The workload covered by the new evaluation standards is likely to exceed ten times that of the previous ones.

To be precise, all companies will use AI increment as the position evaluation standard. By replacing the parts of a position that are easiest to standardize, disassemble, and toolize, they can save job positions.

For example, for a position with a workload of 100 points originally, 40 points are for repetitive execution, 30 points are for data organization and preliminary judgment, 20 points are for communication and coordination, and 10 points are for final responsibility. AI will first take away the 40 points and then continuously erode the 30 points. Finally, the company will find that this position does not require three people. One person plus several sets of AI tools are enough.

It's not that AI suddenly turns the company into a no - human company. Instead, the company begins to find that people are not as "necessary" as before.

The one - person company used to be the lifestyle of a few people.

In the AI era, it may become the cost model that all enterprises need to learn.

01 For office workers in front of the screen

The first to be impacted by this round of AI are office workers sitting in front of the computer.

What large models are best at handling are exactly what office workers deal with every day:

Text, pictures, code, spreadsheets, videos, emails, documents, meeting minutes, data, and proposals.

In other words, as long as your work is completed in front of the screen, as long as your output can be digitized, and as long as your deliverables are documents, pictures, code, spreadsheets, videos, or reports, you are within the direct influence of AI.

Category 1: Code.

Writing code is the scenario where AI has the strongest substitution ability.

It's not because programmers are unimportant, but because code is naturally suitable for being disassembled, generated, tested, and repaired. Requirements can be split into modules, modules can be generated, errors can be fed back, and AI can be continuously iterated.

In the past, scheduling for a small function might involve front - end, back - end, and testing teams. Now, a skilled engineer can complete prototypes, interfaces, pages, test cases, and documents faster with the help of AI.

This means that ordinary programmers will not disappear immediately, but junior programmers, those who only copy requirements, and those who only write repetitive business code will find it increasingly difficult.

Category 2: Text.

Writing articles, reports, scripts, Xiaohongshu copywriting, WeChat official account articles, press releases, and marketing plans are all being directly impacted by AI.

In the past, copywriters could at least make a living by "being able to write".

But now, "being able to write" is no longer scarce; instead, it is over - abundant.

What is scarce are more personalized topics, judgments, viewpoints, structures, fact - checking, personal experiences, and styles.

Water - filling writing will become less and less valuable.

Category 3: Pictures and design.

Posters, e - commerce pictures, illustrations, covers, PPT visuals, logo drafts, and product concept pictures used to require designers to create the first drafts. Now, AI can provide different effects in multiple directions within a few minutes.

It's not that good designers have no value. Maybe good designers can work faster and cover more workload.

Those who only execute requirements, change sizes, use templates, and do basic visual work can only hope for the best.

Category 4: Videos.

Scripts, storyboards, voice - overs, subtitles, editing, covers, and material generation are being gradually disassembled.

Previously, a short - video team required at least a director, an editor, an operator, a designer, and a marketer. In the future, an experienced content creator can complete most of the basic production, and only a few key links need to be polished by professionals.

Short videos will not disappear, but low - quality mass production will.

Category 5: Data and analysis.

Investment research, consulting, market analysis, competitor analysis, user interview organization, meeting minutes, and industry data collection used to rely heavily on assistants and junior analysts.

Now, AI can already complete data summaries, comparisons, first drafts, spreadsheet organization, and reverse viewpoint sorting.

What will really be retained are not those who "organize data", but those who can judge the authenticity of data, find key variables, and put forward independent conclusions.

This round of AI has the greatest impact not on factories, but on offices.

The greatest crisis is not for blue - collar workers, but for white - collar workers.

02 For organizational structure

According to the current popularity of AI, the speed of technological progress will only get faster, not slower.

For enterprises, the most direct consideration is not how cool AI is, but how many fewer people can be hired, how much less salary can be paid after using AI, and whether the same number of people can complete several times or even ten times the workload as before.

Enterprises are not charities. When a boss finds that one person plus AI can complete the work of ten people in the past, what he first thinks about is not whether AI will change the world, but how to arrange the other nine people and how to redo the future human - resource budget. This is the real motivation for all enterprises to use AI.

It won't be immediately popularized in all enterprises, but it's just a matter of time. Simply put, if five people in a peer company can complete a task while we need fifty, there is obviously a problem with our cost structure; if a peer content team tests 100 pieces of material a day while we still produce 20 pieces a day using the traditional process, our competitiveness will also be affected.

In the short term, the company will not immediately lay off 90% of its employees. There are many resistances in the real world: organizational inertia, customer relationships, data security, employee emotions, legal responsibilities, management complexity, boss awareness, industry supervision, etc. But in the long run, the organizational structure will inevitably be redefined around AI.

This change will not be manifested as large - scale layoff news at the beginning. Instead, it will first be manifested as an increase in position standards. In the past, an operator only needed to be responsible for event execution. In the future, he may need to be responsible for topic selection, copywriting, data, marketing, and review at the same time; in the past, a programmer only needed to write business code. In the future, he may need to complete prototypes, testing, documents, and deployment at the same time. AI not only makes employees more relaxed, but also makes the company redefine "how much work a qualified employee should complete".

Next, the changes in many companies will probably occur in sequence: first stop expanding recruitment, then increase the output requirements per person, then merge some positions, then outsource non - core links, and finally redesign the organizational structure. Many positions will not disappear suddenly. Instead, after an employee leaves, the company will find that there is no need to recruit a new one, and AI and the existing team can fill the gap.

This is the real crisis at the organizational level. In the past, companies solved growth problems by increasing the number of employees. In the future, companies will give priority to using AI, tools, outsourcing, and project - based methods to solve growth problems. People are no longer the first choice for organizational expansion, but the cost item to be filled in last.

03 For one - person companies

A one - person company used to be like an upgraded version of a freelancer.

In the AI era, a one - person company definitely doesn't mean one person taking on all the work alone. It's more like one person standing in the middle, coordinating the entire set of external capabilities and more cooperation opportunities, turning oneself from an isolated node of a company into an ecological node with a lightweight structure.

More and more people will use AI for basic production. In many fields, the cost savings compared to before are roughly between 30% and 90%. A one - person company can not only use AI to complete the business links it focuses on, but also cooperate with more similar companies to integrate into a more complete product or industrial service link.

This collaborative network will reconstruct all value chains, and individuals will leap from isolated nodes to elastic hubs. A one - person company does not pursue full - stack self - construction. It only relies on professional judgment and the AI tool chain to efficiently connect with the standard modular services in the industrial chain. When the marginal cost of collaboration becomes lower and lower, the scale effect will shift from "the number of internal employees" to "the quality of external connections".

In the past, for a company to grow, it had to hire people first. Design, development, operation, sales... One position for each link, one department for each process... In the AI era, most capabilities no longer need to be built - in. As long as one knows how to coordinate AI and review and modify capabilities, the core of a one - person company is the coordination ability. If one can find suitable modules, judge the quality of modules, and recombine modules into products, one can use a small organization to complete the work that used to require a large team.

Competitiveness depends on the individual's sensitivity in coordinating ecological resources and long - term credit accumulation. The one - person company is not just a topic for entrepreneurs. It will also have a reverse impact on all companies. There will be a large number of low - cost, high - efficiency, and highly collaborative nodes in the market, forcing large companies to reposition their bloated positions and processes.

Organizational ability will change from "employment relationship" to "call relationship". All companies will become lighter. Large companies will split into small teams, small companies will reduce positions, and start - up companies will become individual nodes...

04 For company positions

For any position, AI will not take away the entire position at once, but will re - price the position.

The prices of positions that can be generated by AI will decline; the prices of positions that can be delivered through standard processes will decline; the prices of positions that can be replaced by external modules will also decline. Only the personalized parts that rely on judgment, responsibility, aesthetics, trust, and complex collaboration may see an increase in position prices, and the position functions and evaluation standards will definitely be different from the current ones.

The content position will still exist, but data organization, title generation, first - draft writing, summary rewriting, and distribution copywriting will all change. Topic selection, viewpoints, fact - checking, style, and personal writing style will become more important. Programmers will still exist, but routine business code, interface generation, test cases, documents, and simple bug fixes will change. Architecture judgment, complex problems, system security, and product understanding will become more important.

Designers will still exist, but basic posters, covers, material extension, and style drafts will change. Brand aesthetics, visual systems, business understanding, and key creativity will become more important. Video teams will still exist, but first - draft scripts, storyboards, subtitles, voice - overs, rough cuts, and covers will change. Account positioning, hit - judgment, shot language, and business conversion will become more important.

Positions will be continuously thinned out. Most of the easiest execution parts in a position will be handed over to AI, and only a part of high - value capabilities will become more important. What white - collar workers really need to worry about in the future is not the disappearance of enterprise position requirements, but the higher gold content of position requirements. For the original positions, the company will no longer use the original evaluation standards or the original ability models.

05 For the ability model

In the past, more people meant stronger ability. In the future, more people mean higher costs.

In the past, only a few departments such as the marketing department were facing the outside world, and the enterprise's value chain mainly stayed within the company. In the future, many value chains of the company may be split into the external network. People in any department can use AI to complete the entire set of solutions, use external design for visuals, use technical collaboration for key development, use professional platforms for distribution, and form a temporary team for delivery.

The organizational structure will be more amoeba - like, personal work will cover a more full - stack scope, the product process will be more agile, the cost will be lower, and the trial - and - error will be faster. The market will not give a high valuation to a company just because its process is more complete. Instead, it will only compare who can deliver faster, at a lower cost, and with better results. Many fixed positions will become optional, and enterprises will pay more attention to full - stack talents.

Enterprises will no longer naturally believe that "one more position means one more value". Instead, they will increasingly believe that "one less position means one more callable module". People will be re - stratified. The differences between different people will no longer be about education, work experience, and platform, but about whether they have the new ability structure formed with the help of AI.

The ability models for the future can be roughly divided into four types of talents:

1. Enhanced talents are suitable for both large companies and one - person companies. These people will not be replaced because AI makes them stronger. A writer can not only write articles but also complete the entire process of topic selection, data collection, framework construction, first - draft writing, revision, illustration, distribution, and review; a programmer can not only write code but also complete the entire process of product prototype, deployment, testing, documentation, and data analysis; a designer can not only create graphics but also control the style, build a visual system, generate materials, and make the final aesthetic judgment.

2. Network - type talents may not be good at everything, but they are very good at connecting resources. They know who can do design, who can do technology, who can do delivery, who can do channels, and who can do sales. They can connect AI, tools, outsourcing, partners, and customer needs to form a small network that can deliver results. What a one - person company sells is not personal labor but the collaborative network behind it.

3. High - judgment talents. AI will generate many answers but will not bear the consequences. Therefore, the truly scarce ability in the future is judgment: what to do, what not to do, which customers are worth serving, which requirements should be rejected, which products have long - term value, which projects are just short - term noise, what risks cannot be taken, and what opportunities are worth gambling on.

4. Strong - relationship and strong - on - site talents. Not all work will be taken over by AI. Sales, consulting, education, medical care, elderly care, complex business, organizational management, and offline services are scenarios that highly depend on trust, emotions, and responsibilities. AI can only play an auxiliary role and is difficult to replace the trust relationship and on - site judgment between people.

Except for the above four types of talents, those who only do replication, organization, transfer, and primary execution, lacking industry understanding, customer relationships, aesthetic judgment, resource connection, and delivery responsibilities, are likely to become victims of AI. In the future, it will not be a competition between humans and AI, but a competition between those who can use AI and those who cannot, and between those who have a collaborative network and those who only have single - point skills.

07 Future solutions

Currently, the worst thing to do is to stick to just one execution action.

Positions will be disassembled, tasks will be reorganized, and values will be re - evaluated. Everything described in this article is still in the early stage. There are just people who choose to change and those who don't.

First, establish an AI workflow, not just use AI for chatting. Given the current popularity of AI, people who can ask questions to AI are not scarce. What is scarce are those who really integrate AI into their work processes. In the future, the competition will not be about whether one can use AI, but about whether one can use AI to produce stable delivery results.

Second, retain the professional main axis. Don't fantasize that full - stack ability means cross - border without a threshold. AI can make up for weaknesses, but it cannot replace the long - term accumulated understanding and judgment ability in a field. People without financial experience are likely to make mistakes when judging risks, valuations, and cycles based on AI investment research. The best strategy is not to cross - border, but to use AI to maximize the radiation scope of your own profession.

Third, train judgment ability. The stronger AI becomes, the more precious judgment ability is. It will become easier and easier to generate content, but more and more difficult to screen the correct content. The truly valuable people are not those who can generate the most content, but those with more accurate judgment.