Will AI replace programmers? The truth about fears, new roles, and the future of the IT industry

MarGib August 20, 2026

🌐 🇵🇱 Polski · 🇬🇧 EN

Tools like github Copilot or Devin spark hope for faster coding, but simultaneously trigger fears of job loss. Do programmers really have reasons to worry? We analyze data, research, and expert recommendations to separate facts from myths.

Programista współpracujący z holograficznym asystentem AI przy tworzeniu kodu — Illustration of human and artificial intelligence collaboration in programming

Introduction: Why does the topic of AI in programming stir so many emotions?

Over the last three years, AI-powered tools have revolutionized the way programmers write code. github Copilot, Devin, or alphacode not only accelerate work but also provoke fundamental questions: Will machines replace us at work? Which skills will lose value? What new roles will emerge in the industry?

The emotions surrounding this topic are understandable. According to the "State of Software Engineers 2024" report, as many as 42% of programmers fear that AI will take over their jobs within the next five years. At the same time, tech companies – from Microsoft to Google – assure that artificial intelligence will create more jobs than it eliminates. Who is right?

In this article, we will look at concrete data, research, and examples that show how AI is changing (but not eliminating) the programming profession. We will also discuss the psychological effects of these changes and adaptation strategies.

Which programmer skills are losing value?

AI tools handle repetitive, boilerplate, or pattern-based tasks the fastest. Here are the areas where their impact is already visible:

1. Writing boilerplate code

github Copilot, introduced in 2021, can generate entire functions based on a short description in a comment. A 2022 Microsoft and github study showed that programmers using this tool completed tasks 55% faster. The problem is that this mainly applied to:

Standard algorithms (e.g., sorting, searching).
CRUD (Create, Read, Update, Delete) code in web applications.
Unit tests (e.g., generating tests for existing code).

This means that junior developers who are just learning these basics may have difficulty finding their first job – because their tasks are the easiest to automate.

2. Debugging simple bugs

Devin, presented by Cognition AI in March 2024, goes a step further: it can independently find and fix bugs based on reports in systems like github Issues. In tests conducted by the tool's creators, Devin handled 30% of tasks without human intervention. However, its effectiveness dropped when:

The problem required understanding business context (e.g., why a given function should work in a specific way).
The bug resulted from unclear requirements or conflicts in the system architecture.

This suggests that the skill of root cause analysis will remain crucial – but it may be performed at a higher level of abstraction.

3. Documentation and comments

AI tools can generate documentation based on existing code, which reduces the need for manual commenting. However, the problem is that AI-generated documentation is often superficial. It lacks:

Explanations of why a piece of code was written in a specific way.
Information about limitations and assumptions (e.g., "this function only works for inputs meeting condition X").
Historical context (e.g., "this variable was added in version 2.1 to solve problem Y").

Therefore, the ability to write valuable documentation – the kind that helps other programmers understand the system – will continue to be valued.

What AI cannot (yet) do?

Despite progress, there are areas where humans still have an advantage:

System architecture design – AI can suggest solutions, but it cannot evaluate which ones will be scalable, maintainable, and aligned with the company's long-term strategy.
Understanding user needs – AI tools lack empathy or experience in working with clients, which is crucial when creating useful products.
Ethics and responsibility – decisions about what data to collect, how to process it, and what risks are acceptable still require human judgment.

It is worth noting that the above limitations are not permanent. AI models are learning at a breakneck pace, so in a few years, the situation may look completely different.

Do programmers really have reasons to worry? Data vs. emotions

The fear of job loss due to AI is not unfounded, but it often stems from misunderstandings. Let's look at what the research says.

Statistics that cause concern

In the "AI Anxiety in Tech" report (Dice, 2023), 58% of programmers admitted to feeling increased stress related to the development of AI. One in four is considering changing industries within two years.
The "Developer Nation Q3 2023" survey found that 29% of programmers believe that AI is already reducing the demand for their skills.
In an April 2024 Harvard Business Review study, 63% of respondents reported an increase in imposter syndrome related to AI. Junior developers feel particularly threatened.

What do tech companies say?

Large corporations have an interest in reassuring programmers – after all, they are the ones building their products. Here are the official stances:

Microsoft: Satya Nadella stated in July 2023 that "AI will not replace programmers, but programmers who use AI will replace those who do not." The company is investing in reskilling programs, such as Microsoft Leap.
Google: Sundar Pichai announced in May 2024 that AI will increase the demand for software engineers, but will change the nature of their work. Google has launched courses such as "Google AI Essentials".
Meta: Mark Zuckerberg emphasized in March 2024 that AI will be a key tool for developers, but he does not plan to reduce headcount in the engineering department.
IBM: In May 2023, the company announced that AI would take over 30% of back-office tasks by 2025, but at the same time, it plans to hire 20,000 new employees in the technology department.

Paradox: More code, fewer programmers?

According to the "Future of Jobs 2023" report (World Economic Forum), the demand for programmers will grow, but the profile of required skills will change. Companies will need:

Fewer people to write simple code.
More people for system design, AI integration, and solving complex problems.

This means that junior developers may have difficulty entering the market, but experienced programmers – especially those who know how to collaborate with AI – will be in high demand.

New roles and specializations: How is the programming profession evolving?

The development of AI not only changes existing positions but also creates entirely new ones. Here are the most interesting examples:

1. AI-Augmented Developer

This is a role that has been gaining popularity since 2022. An AI-Augmented Developer does not write code from scratch, but:

Supervises and optimizes AI-generated code.
Integrates AI tools (e.g., Copilot, Devin) with devops processes.
Solves problems that AI cannot overcome on its own.

Example: The github Next team is working on "AI-powered workflows" intended to automate entire stages of software development – from code generation to deployment.

2. Prompt Engineer

Prompt Engineering is the skill of designing queries (prompts) for AI models so they generate high-quality results. This role gained significance in 2023 when companies like Anthropic began hiring specialists to:

Create prompts that generate code meeting specific standards.
Optimize queries for performance and security.
Test and iteratively improve prompts.

It is worth noting that prompt engineering is a temporary skill. As AI models become better at understanding natural language, the demand for this role may decrease.

3. AI Ethics Engineer

A specialization developed since 2021, focusing on:

Auditing AI algorithms for bias and discrimination.
Designing systems compliant with regulations (e.g., the EU AI Act).
Educating teams on the responsible use of AI.

Example: Google launched the "Responsible AI" program in 2021, hiring engineers to work on the ethical aspects of artificial intelligence.

4. MLOps Engineer

MLOps (Machine Learning Operations) is a field combining devops with machine learning. MLOps Engineers deal with:

Deploying and maintaining AI models in production environments.
Monitoring model performance and updating them.
Integrating AI with existing systems.

This is one of the fastest-growing specializations – according to a linkedin 2024 report, the number of job postings for MLOps Engineers has increased by 90% over the last two years.

How is AI changing programmer tasks? Case studies

Although AI tools can generate code, in practice, they rarely work completely autonomously. Here are concrete examples showing what human-machine collaboration looks like:

1. "AI for Accessibility" project (Microsoft)

Since 2018, Microsoft has been developing AI-based tools that help people with disabilities. In projects such as Seeing AI (an app describing the world for the blind) or Soundscapes (audio navigation for the visually impaired), programmers collaborate with AI on several levels:

Prototyping: AI suggests code based on functionality descriptions, but programmers must adapt it to specific user needs.
Testing: AI generates test cases, but humans evaluate whether they are realistic and cover all scenarios.
Optimization: Programmers analyze the performance of AI-generated code and make improvements.

In this case, AI accelerates work, but key design decisions are made by humans.

2. github Copilot in open-source projects

The "How Developers Use github Copilot" (github, 2023) study showed how programmers use this tool in practice. Here are the most interesting findings:

88% of users believe that Copilot increases their productivity.
However, only 12% allow AI to write entire functions without supervision.
The most frequently used feature is generating unit tests – but programmers still have to verify them manually.

Example: In the React (Meta) project, Copilot is used to generate tests, but the library's architecture is still created by humans. As one maintainer says: "Copilot helps us write tests faster, but it won't replace our knowledge of how React should work."

3. Devin AI in practice

In March 2024, Cognition AI reported on tests of Devin in 12 open-source projects. The results were mixed:

30% of tasks were completed without human intervention.
The biggest problems concerned understanding business context (e.g., why a given function should work in a specific way).
In one project, Devin generated code that worked but was suboptimal and difficult to maintain. Programmers had to rewrite it.

This shows that even advanced AI tools have limitations – especially when it comes to long-term code quality.

Psychological effects of working with AI: Imposter syndrome, burnout, and anxiety

The development of AI not only changes programmer tasks but also affects their well-being. Here are the most common psychological problems reported in studies:

1. Imposter syndrome

The "AI and Imposter Syndrome in Tech" (Harvard Business Review, 2024) study found that:

63% of programmers feel an increase in imposter syndrome related to AI.
45% of junior developers feel "incompetent" compared to AI tools.
Programmers fear that their skills are becoming obsolete faster than ever.

As one respondent says: "When I see Copilot generate code in seconds, I start wondering if my work still has any value."

2. Professional burnout

The "Tech Burnout Report 2023" (Haystack Analytics) indicates that:

52% of programmers feel more burnout due to the pressure to adapt to AI.
The main causes are:

The rapid pace of technological change.
Fear of job loss.
The feeling that one must constantly learn to keep up.

Burnout particularly affects mid-career programmers who fear they won't keep up with younger colleagues.

3. Anxiety and depression

The "Mental Health in Tech 2024" (ÓSMI) study showed that:

38% of programmers report an increase in anxiety levels related to AI.
22% are considering therapy for this reason.
The most common concerns are:

Job loss.
Inability to find a new job.
The feeling that the industry will leave them behind.

How to deal with these problems?

Experts recommend several strategies:

Acceptance of uncertainty: The IT industry has always changed – AI is just another stage of this evolution.
Focus on added value: Instead of worrying about what AI can do, it's worth focusing on what only a human can do (e.g., creative problem solving).
Community support: Talking to other programmers helps realize that you are not alone in these fears.
Therapy and mindfulness: Many tech companies offer psychological support programs (e.g., ÓSMI).

How to adapt? Recommendations from experts

Programmers who want to survive in the AI era must change their approach to learning and work. Here are the most important recommendations from industry leaders:

1. Andrej Karpathy: From coder to system architect

Andrej Karpathy, former Director of AI at Tesla, wrote in March 2024:

"Programmers should focus on understanding business problems and system design, not on writing boilerplate. AI will take care of the rest."

His recommendations:

Learn software architecture – how to design systems that are scalable and maintainable.
Gain knowledge in the field of MLOps – how to deploy and maintain AI models in production environments.
Develop critical thinking skills – how to evaluate whether AI-generated code is good, or if it just "works."

2. Martin Fowler: Collaborate with AI, don't fight it

Martin Fowler, author of classic software engineering books, stated in February 2024:

"AI will not replace programmers, but it will shift their role to be more strategic. Those who learn to collaborate with it will have an advantage."

His advice:

Treat AI as a collaborator, not a competitor. Learn to talk to it (e.g., through prompt engineering).
Focus on code quality, not quantity. AI can generate a lot of code, but it's the human who must ensure it is readable and maintainable.
Develop soft skills – communication, project management, team collaboration.

3. Kathy Sierra: From code to user value

Kathy Sierra, author of the book "Badass: Making Users Awesome", wrote in April 2024:

"Programmers should focus on creating value for users, not on code. AI can write code, but it doesn't know what problems really need to be solved."

Her recommendations:

Learn UX/UI – how to design interfaces that are intuitive and useful.
Gain knowledge in cognitive psychology – how people think and make decisions.
Develop empathy – the ability to understand user needs.

4. World Economic Forum: 5 key skills

The "Future of Jobs 2023" report points to five skills that will be most important for programmers in the AI era:

Creative problem solving – the ability to find innovative solutions.
Project management – coordinating team work and AI tools.
AI ethics and responsibility – understanding the risks associated with artificial intelligence.
Collaboration with AI tools – the ability to effectively use AI at work.
Continuous learning – readiness to adapt to new technologies.

Summary: Do programmers have anything to fear?

The development of AI in programming is not a revolution that will eliminate the programmer profession overnight. It is an evolution that changes the nature of work – just as compilers once replaced assembly, and IDEs replaced text editors.

Here are the key takeaways from this article:

AI takes over simple, repetitive tasks, but it cannot (yet) design complex systems or understand user needs.
The demand for programmers will grow, but the profile of required skills will change. Junior developers may have difficulty entering the market.
New roles are emerging, such as AI-Augmented Developer, Prompt Engineer, or AI Ethics Engineer.
The psychological effects of working with AI are real – imposter syndrome, burnout, and anxiety affect more and more programmers.
The key to survival is adaptation – learning new skills, collaborating with AI, and focusing on added value.

As Martin Fowler says: "The future of programmers does not depend on whether AI will replace them, but on whether they will learn to collaborate with it." What seems like a threat today may turn out to be the biggest career opportunity tomorrow.

If you are a programmer, don't panic – but don't ignore the changes either. Start experimenting with AI tools, develop skills that machines cannot replace, and remember: the best way to survive in the AI era is to be better than AI.

You can read more about the evolution of the IT industry in the post "Human compass in the eye of the AI cyclone", where we analyze the vision of the future according to Dario Amodei.