Will AI make programmers unemployed? 2026 industry trends and real data

The topic of programmer unemployment in 2026 is once again on the screen. Salesforce CEO publicly stated that the company will not hire junior engineers this year due to the improvement in AI coding efficiency. Google internal data 25% of new code is generated by AI. Meta shrinks its engineering team by 12%. But at the same time, a Stack Overflow survey showed that 68% of developers believe that AI will make them more valuable rather than unemployed. This article answers this question using real data.

To judge whether AI will put programmers out of work, you can’t just look at the CEO’s slogans or Twitter jokes. It depends on recruitment market data, salary changes, and job structure changes. The following is expanded with 7 dimensions.

What will be the state of the programmer recruitment market in 2026?

U.S. Bureau of Labor Statistics BLS data. The number of software development job openings in the first quarter of 2026 fell by 18% compared with the same period in 2024. However, among the positions released during the same period, the number of "Senior Engineer plus AI experience" positions increased by 47%. The absolute number has not declined, but the structure has changed.

LinkedIn March 2026 Report. The total number of programmer jobs is 250,000 fewer than the peak in 2023. However, there are 180,000 more AI-related positions in the three categories of ML Engineer, AI Application Engineer, and AI Infrastructure. The net decrease was 70,000, mainly concentrated in the three categories of junior front-end, junior back-end, and QA testing.

Chinese market. Zhaopin Recruitment April 2026 data. The total number of jobs in China's Internet industry will drop by 38% compared with 2022. However, AI-related jobs bucked the trend and rose by 65%. The unemployment rate of programmers in Beijing, Shanghai, Shenzhen and Hangzhou will rise from 3.2% in 2022 to 8.7% in 2025, and will fall back to 6.5% in 2026. The fall is mainly absorbed by AI positions.

in conclusion. The overall job pool has shrunk but is structurally shuffled. There is no shortage of jobs for engineers who can use AI. Juniors who can't use AI and still want to "write CRUD" will be eliminated.

Which type of programmers will be replaced first?

Alternative risks are ranked from highest to lowest.

The first category of junior front-end development. HTML CSS JavaScript basic work. Tools such as Cursor, Copilot, and V0 can directly generate usable code, and the core workload of the primary front-end is compressed by 70%. The number of entry-level front-end jobs in 2026 will be 52% lower than in 2022.

The second type of primary test QA. Automated test case generation, regression testing, and UI testing. Now AI tools can generate hundreds of test cases in 5 minutes. 60% of junior testing positions will disappear from 2024 to 2026.

The third category of primary data analysis. SQL query, report generation, visualization production. ChatGPT directly writes SQL for business personnel to use. 80% of entry-level BI positions dedicated to writing SQL have been replaced.

The fourth category of content site maintenance. WordPress secondary development, corporate website maintenance, e-commerce template application. Low-code plus AI is almost a complete replacement.

The fifth category is outsourcing simple projects. A small project with simple functions and clear logic. Customers can directly use AI tools to do their own work without looking for outsourcing. 30% of low-end outsourcing companies in Southeast Asia and India went bankrupt in the past year.

Alternative risks are ranked from lowest to highest.

Category 1 Senior Architect. System design, performance optimization, technology selection. AI gives advice but lacks decision-making capabilities and requires experienced humans.

Category II Infrastructure Engineer. Kubernetes, distributed systems, and database bottom layers. AI tools cannot handle underlying bugs well, and senior SREs are irreplaceable.

Category 3 Safety Engineer. Pen Test, Compliance, Zero Trust Architecture. AI can handle static scanning, but security decisions involving business scenarios still require people.

Category 4 Machine Learning Engineer. Model training, tuning, and deployment. AI Tools Write Your Own AI It’s still early days.

The fifth type of entrepreneurial full stack. A small team that can handle everything from demand to launch and can use AI to accelerate development is more efficient than a large company.

Will programmer salaries rise or fall in 2026?

The overall trend is polarizing.

Average programmer salary. For mid-level programmers with 3 to 8 years of experience, both China and the United States have begun to cut their salaries. The three major companies in Silicon Valley, Google, Meta and Apple, will see an average 8% reduction in salary packages for entry-level L3 and intermediate L4 in 2025. China's major Internet companies will cut junior and mid-level salaries by 10% to 15% in 2025.

Salaries for programmers who know AI. For engineers with the same experience and the same position, who are proficient in using Cursor Copilot Claude and can build RAG and Agent systems, the offer is generally 25% to 40% higher. The market is extremely hungry for "AI plus X" type engineers.

ML Engineer. OpenAI's top ML engineers package an annual salary of US$200 to US$5 million. Frontier labs like Anthropic Google DeepMind Mistral give senior researchers US$2 to 4 million. 5 to 10 times more efficient than traditional software engineers.

Junior programmer. This is the worst group. The median offer price for fresh graduates in computer science in North America in 2026 is US$120,000, a 33% drop from the US$180,000 in 2022. The starting salary for China's 985 computer graduates is 250,000 yuan, a 28% drop from 350,000 yuan in 2022. The reason is that AI tools significantly compress the primary work load.

judge. With AI, your salary can keep up with or even exceed that before AI. Without AI your salary will slowly decline until you lose your job.

What should programmers learn most now?

Arrange by priority.

First AI programming tool proficiency. Master at least 2 of Cursor, Claude Code, Windsurf, and GitHub Copilot in depth. Being able to use it is the basis, and being able to adjust prompt and context projects is a master. The state of using AI to write 80% of the code every day is the baseline.

Second RAG and Agent system design. Retrieval Augmented Generation Retrieval Augmented Generation. Agent workflow orchestration. These two are the core technology stacks for enterprise AI implementation in 2026.

Third vector database. Pinecone, Weaviate, Chroma or PostgreSQL plus pgvector. AI applications all require vector retrieval infrastructure.

Fourth LLM integrated development. Frameworks such as LangChain, LlamaIndex, and Vercel AI SDK. Ability to integrate LLM into business systems.

Fifth model Fine-tuning. LoRA fine-tuning, SFT supervised fine-tuning, RLHF. Open source model fine-tuning capabilities are currently a scarce skill.

Sixth Prompt Engineering. Few-shot prompting, Chain of Thought, ReAct mode. These are soft skills but companies are clearly willing to pay for them.

The seventh basic ML knowledge. Transformer architecture, Attention mechanism, Diffusion model principle. You don’t have to do research, but you need to understand the basics.

No need to learn. Complex algorithm competition. Redundant design patterns. Outdated framework. Spending your time on AI tools and engineering has the highest returns.

Real Cases 5 Transformation Paths for Programmers Who Know AI

Case 1. 5 years of experience as a BAT backend engineer in Beijing. Starting to use Cursor fully in 2024, the efficiency of writing code will increase by 3 times every day. In 2025, I will switch to an AI startup company as an AI Application Engineer, and my salary will increase from 700,000 to 1.2 million.

Case 2. 3 years of front-end experience in an outsourcing company in Hangzhou. In 2025, companies will lay off employees and lose jobs. I learned LangChain and RAG in 3 months, and joined an AI ToB entrepreneurial team as a technical lead. Although the initial salary was only 500,000, I got 1% options.

Case three. 8 years of client experience in a gaming company in Shenzhen. In 2025, I used Cursor and Claude Code to develop an AI tool SaaS product myself. Within half a year, the monthly turnover was 500,000 yuan, completely out of the salary system.

Case 4. ML engineer at a major Internet company in Shanghai for 6 years. Recruited by Anthropic's Beijing office in early 2026, the offer package was US$800,000, which was 8 times higher than the domestic salary of RMB 600,000. This cross-border AI talent flow will accelerate significantly in 2026.

Case five. 10 years of back-end development for a foreign bank in Guangzhou. In 2025, the company laid off employees after using Copilot. After self-studying LLM, I switched to "AI plus finance" consulting, with a unit price of 3,000 to 8,000 yuan per day, and a monthly freelance income of 80,000 yuan in 2026.

common ground. They all actively embrace AI tools instead of resisting them. Adding AI to the original field does not mean changing the track from scratch. All have continuous learning capabilities and can complete the transformation within 6 months.

Recruitment and layoff trends of major domestic manufacturers in 2026

Alibaba. In Q1 of 2026, 5% of employees will be laid off, mainly in non-core business departments. AI team Tongyi Qianwen’s recruitment expanded by 40%. The overall trend in technical positions is to reduce the application layer and strengthen the infrastructure layer.

ByteDance. The overall number of jobs decreased slightly but AI-related jobs increased sharply. Doubao Big Model Team’s recruitment will expand by 80% from 2025 to 2026. The video generation model doubles the number of people in the dream team.

Tencent. The Hunyuan large model team is actively recruiting. WeChat’s AI team will expand from 100 people in 2024 to 500 people in 2026. The game department will use AI to replace some art and coding positions and lay off 15% of its employees.

Huawei. Hongmeng adds AI strategy and Ascend AI chip team recruits on a large scale. But the consumer BG app development department is streamlined.

Meituan. The unmanned delivery and AI dispatch system team expanded. 10% layoffs in traditional business units such as store SaaS.

JD.com. The AI ​​customer service project will replace 35% of manual customer service by 2025. However, there are serious layoffs in the core backend of e-commerce.

Millet. Automotive BU’s smart driving AI team is expanding the fastest. The traditional software positions of mobile phone BU are shrinking.

Overall, it seems that major manufacturers are making structural adjustments of "reducing tradition and adding AI". Engineers who know AI have more opportunities to change jobs across departments.

5 misunderstandings about programmers’ unemployment anxiety

Myth 1: AI will replace all programmers. wrong. What AI tools can replace is the link of "typing the code according to the requirements document". AI is far from reaching requirements analysis, system design, performance tuning, bug diagnosis, and cross-team coordination.

Misunderstanding 2: Only top experts have a way out. wrong. A large number of mid-level programmers continue to be stably employed at mid-level salaries by quickly mastering AI tools. AI lowers the threshold, not raises it.

Myth 3: Programmers will be eliminated after the age of 35. wrong. 35 Adding programmers with business understanding experience, and adding AI tools, the output will be 5 to 10 times better than that of beginners. Those who are eliminated are the 35 plus who do not learn, not all 35 plus.

Myth 4: A bachelor's degree in computer science is no longer valuable. wrong. Basic computer knowledge is even more important in the AI ​​era. "Vibe coders" who can call AI but don't understand the underlying logic produce low-quality output and are prone to bugs, and are not recognized by the recruitment market.

Misunderstanding 5: There will be no new jobs after AI replacement. wrong. Every technological change creates new jobs. Positions such as AI Agent Engineer, Prompt Engineer, AI Product Manager, AI Security Auditor, and Model Evaluator that do not exist in 2022 will already be large-scale in 2026.

What will the programmer industry look like in 5 years?

The talent structure is an inverted pyramid. A small number of top ML engineers define the model. A medium number of AI application engineers integrate the model into the business. A large number of product/business engineers use AI tools to implement requirements. Traditional "coders" will basically disappear within 5 years.

Work content. 50% of the time is spent reviewing AI code, adjusting AI output, and designing Agent workflow. 30% of the time is spent communicating with the business team on requirements. 20% of the time is writing real code. It’s completely different from 2020, when programmers spend 80% of their time writing code.

Corporate organization. The "two pizza team" principle is further reduced, and an AI-enhanced team of 2 to 5 people can do the output of the previous 50-person team. The organizations of large companies have been flattened, and the threshold for starting a business in small companies has dropped to the lowest level in history.

Salary structure. The salary ceiling for top AI engineers continues to open, with an average of US$5 million to US$10 million per person. An average AI application engineer earns a stable salary of $500,000 to $1 million. The monthly salary of traditional engineers who do not know AI at all has dropped to less than 10,000 yuan.

Geographic changes. AI has greatly improved the efficiency of remote collaboration, and 70% of programmers work remotely. The "programmer overtime culture" in first-tier cities is gradually being eliminated.

learning style. No longer relying on academic education, continuous on-the-job learning has become standard. The technology stack needs to be updated every 6 months.

FAQ

After studying computer science for 5 years, do I still want to continue?

continue. Computer basics are more important than a single programming language. Learning algorithms, data structures, operating systems, networks, and databases is more valuable in the AI ​​era. AI replaces typing, not thinking. It is recommended to spend 8 to 10 hours a week learning AI application tools and LLM engineering while learning the basics well. When you graduate, you will understand both the basics and AI. It will be better than a pure subject class or a pure AI Bootcamp.

How does a programmer who has been working for 10 years transform?

3 step path. Deeply learn Cursor or Claude Code within the first month so that 80% of your daily code is written by AI. Within the second 3 months, learn LangChain or a similar framework to make an AI application side project, put it on GitHub or deploy it as an accessible demo. Third, proactively look for AI project opportunities within the company, transfer jobs internally, or jump to the AI ​​team. 10 years of experience plus AI skills are extremely scarce in the market. As long as you take the initiative to transform, there is a high probability that your salary will not fall but rise within 6 months.

Should fresh graduates submit resumes now or take a gap year to learn AI?

There is no gap between submitting your resume and learning AI at the same time. A gap of one year is a negative sign when applying for a resume. The strategy for fresh graduates in 2026 is to join any relevant company without choosing a position, and to use work scenarios to continue learning AI after joining. You can switch jobs within 6 to 12 months of your first job. There is a direct gap in learning AI at home. What you learn in project scenarios cannot be put into practice.

How to quantify proficiency in AI tools

3 pieces of evidence. 1 GitHub. Disclose 1 to 3 projects developed using AI tools. The README states which AI tools and prompt engineering methods were used. Two technical blogs. Write 5 to 10 practical AI tutorials on Nuggets, CSDN, and Medium. If the traffic is good, the interviewer can search for it. Three portfolios. A PDF of "X real projects I completed using AI", including code screenshots, prompt history, and effect data. These three combined are 10 times more powerful than a resume that says "Proficient in ChatGPT".

Does my poor English affect learning AI?

The short-term impact is small and the long-term impact is large. The current mainstream AI tool ChatGPT Claude Cursor already supports Chinese interaction, and English is not required for daily use. However, most cutting-edge papers, official documents, Reddit discussions, and Twitter industry updates are in English. It is recommended to spend 30 minutes reading English technical content every day, and you will be able to read English papers fluently in half a year. Not reading English at all will put you 6 months behind the industry after a year. The input-output ratio is extremely high.