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By TJ SoftUpdated:

Will programming skills no longer be needed? The influence of planning will increase, while the role of coding will decrease.

Czy umiejętność programowania nie będzie już więcej potrzebna? Wzrośnie wpływ planowania, zmniejszy się rola kodowania.

Just a decade ago, programming was considered the most important skill of the future. Anyone with even a basic knowledge of Python, C++, or Java could expect a well-paid and stable job. Today, however, technology is changing faster than ever, and with it, the meaning of “software development” itself is shifting. Increasingly, people are saying that soon there will no longer be a need to know how to program. It will be replaced by the ability to design—creating secure, efficient, and intelligent solutions using artificial intelligence.

Of course, this does not mean that the programming profession will disappear completely, but its nature will change significantly. However, one can speak of the decline of code typists, as evidenced by the smaller number of offers for juniors in companies.

A typical programmer’s job will increasingly involve planning and designing the entire program, doing the things that AI is unable to do, and fixing errors. Gradually, more and more code will be written by artificial intelligence.

The importance of programmer-scientists, whose task would be to create new technologies, will increase instead. This includes both the creation of artificial intelligence and new programming languages and libraries—in other words, the “building blocks” that AI then uses to assemble complete programs.

Table of Contents

WordPress Example. Fewer and fewer people are writing website code.

We can never be certain what the future will look like. Although we are certain that artificial intelligence will significantly impact the work of programmers, we are not certain exactly how this will happen.

It is therefore worth looking at interesting examples from the past. WordPress is a very good example, as are other website-building systems such as Webflow, Framer, and Drupal. The change in how they operate is slower and less spectacular, but based on it, we can predict how AI will affect the work of programmers as a whole.

Change in Website Creation.

Currently, 43% of websites are powered by WordPress, while 70% of websites in total are built on some kind of CMS (a website-building and content management system). And most likely, quite a few of the remaining 30% of websites are based on some kind of ready-made solution.

Fifteen years ago, the situation was the opposite, and as many as 76% of websites were created manually. This meant that creating a website required knowing at least HTML. Today, you can create attractive, well-functioning websites without even knowing that something like HTML exists.

How Did WordPress Change the Process of Creating Websites?

The existence of WordPress has made creating websites easier. Anyone can create a website without having to spend months—or several months—first learning how to write website code. In the case of WordPress, getting a basic understanding of how everything works takes about a week, and after that you can already create simple websites that work reasonably well and more or less serve their purpose. Learning to create a website is much more enjoyable when you already have an initial version that you can show to others.

Developers who created websites have become designers and marketers.

When creating websites, the main challenges involve writing good copy, selecting graphics, and logically organizing the whole thing. The technical challenges are much less problematic because, in most cases, ready-made solutions already exist for them.

Technical knowledge is still needed, especially when, for example, you want a website to load quickly. But most such activities involve “clicking” and configuring settings.

The process of making website creation easier took many years

WordPress was created in 2003. A few years later, it was made available as a website-building tool in the form we know today (previously, it was a typical blogging platform), but the 40% share of websites based on WordPress was not reached until more than a decade later. And WordPress is still an unnecessarily complicated tool, being a mix of solutions that are more than a dozen years old.

Complex websites still require coding skills.

Not everything can be done by clicking through ready-made systems. Creating websites with many functions, websites that are applications or portals, requires writing code manually. Intermediate solutions are often used, where a custom plugin is written for a WordPress website. Sometimes using custom code is simpler than testing dozens of plugins.

Creating Programs Without Code, Before ChatGPT Existed.

Before the era of widely available language models that write code, there were many tools for creating applications, such as Bubble, Microsoft Power Apps, Zoho Creator, and a range of game development tools. Because they allowed users to create functional programs without knowing programming languages, they were excellent tools for learning programming and were also used to create prototypes.

However, they had quite a few limitations: a given tool enabled you to create specific types of software, such as only mobile applications, games, or databases/business applications. Thus, with less conventional projects, you could quickly hit a wall and have to switch to another solution.

Creating programs was not always easy either. Although drawing event diagrams is more intuitive for many people than writing code, it was still possible to encounter problems resulting, for example, from using the wrong block.

This meant that the application of these tools was quite limited and focused mainly on creating prototypes and internal tools. Although commercial programs available to the public were created in this way, their development was “even more limited.” For example, if someone learned to create 2D games this way, they could create simple games, but making something else required learning practically from scratch. Working this way required finding a niche and having a good understanding of customers’ needs. Another example could be creating internal programs based on Microsoft Access databases.

In this context, websites can be considered one type of program (that is, things based on code). Websites are not particularly complex, so a relatively small number of “building blocks” is enough to create a wide variety of sites. On the other hand, the uses of websites are very broad, as practically every company or larger undertaking needs a website.

Artificial intelligence will be to applications what WordPress is to websites.

Artificial intelligence will become for applications what WordPress was for websites. It will make it easier to create tools without programming, enable the development of functional solutions based on ready-made models and simple interfaces, and shift the entire process from the level of code to the level of ideas. However, not everything can be created this way; more complex systems will still require traditional programming, architecture, and the work of experts. AI will open the door, but it will not replace all the foundations.

What Will the Transformation of the IT Market Driven by the Development of Artificial Intelligence Look Like?

The programmer profession will not disappear. The profession of code typists will disappear.

For decades, programming has been the foundation of the IT world. Every application, website, and system required someone to write the code manually. Programmers were the alchemists of the digital world. Their work required precision, logical thinking, and patience.

However, when language models such as GPT, Claude, and Gemini emerged, the dynamics of the entire sector changed. Today, it is enough to describe what we need, and artificial intelligence can generate working code on its own.

Tools such as GitHub Copilot, Replit Ghostwriter, and ChatGPT can write a function, fix bugs, and even suggest optimizations. Programming has become a semi-automated process. As a result, the importance of “writing code” itself is declining drastically.

New Profession: AI Solutions Designer

With this change comes a new type of specialist – a designer of AI-based solutions. This is not a traditional programmer. They do not write code line by line, but rather design the operating concept for the entire system: determining what data will be used, how the models should communicate, what safeguards need to be implemented, and how to integrate the whole system with the existing infrastructure.

This is a shift from the syntactic level (how to write something in a programming language) to the semantic level (what the system is supposed to achieve and why). In other words, the designer does not write code but creates an environment in which code is generated and runs automatically.

Artificial Intelligence as a New Language of Communication

The most revolutionary change is that natural language replaces programming languages. Modern models can understand context, intent, and constraints. This means that in the future, it will be enough to “talk” to an AI system to create a new application.

Instead of learning syntax and data types, it will be necessary to learn how to formulate thoughts precisely. This is a new form of communication—between humans and machines, but based not on commands, but on goals.

From Coder to Architect

In practice, an AI solutions designer becomes a systems architect. Their task is to understand the flow of information, oversee model performance, ensure security, and integrate everything into a coherent whole.

This requires systems thinking. Knowledge of code is no longer the goal—it merely becomes one of the tools. What matters is the ability to anticipate consequences and design interactions between humans and machines.

Security as a Key Pillar

Alongside coding automation comes a new priority – security.

AI models can generate solutions in the blink of an eye, but they can just as quickly introduce serious errors. Model “hallucinations,” unpredictable responses, excessive data usage, or uncontrolled connections to external APIs—all of these can pose a threat to organizations.

Therefore, the future creator of AI solutions must think like an auditor, analyst, and ethicist at the same time. They must understand where the data comes from, how it is processed, and what risks it generates.

The Fullstack of the Future

Until now, a “full-stack developer” meant someone capable of working with both the frontend (user interface) and the backend (server logic and database). In the world of AI, this term is taking on a new meaning.

The full stack of the future is a person who understands the entire ecosystem in which artificial intelligence operates – from data acquisition to presenting results to the user. It is no longer about knowing libraries or frameworks, but about understanding how individual models work together, what data they need, and how to maintain their operation over time.

Such a specialist:

  • selects appropriate models (e.g. GPT, Gemini, Claude, Mistral, and specific models within these groups),
  • designs data flows between systems,
  • integrates solutions via APIs and cloud computing,
  • ensures compliance with regulations (GDPR, eIDAS),
  • and analyzes model performance in real time.

It is precisely this kind of versatility—not only technical but also conceptual—that will become the most valuable skill in the market.

Will Programmers Disappear?

No. But the role they play will change.

The programming profession will not disappear, but it will become more specialized. Experts in low-level systems, model optimization, infrastructure security, and the development of new algorithms will still be needed. However, most of the work currently performed by programmers will be automated.

This means that in the future, the programmer will become an architect-controller—someone who does not write code but oversees its creation, verifies its correctness, and ensures compliance with the project’s assumptions.

AI Won’t Make the Software Development Profession Disappear.

The claim that programming skills will no longer be needed sounds impressive, but it is quite exaggerated.

Artificial intelligence itself did not emerge from nothing. Every model, every neural network, and every training system is the result of the work of thousands of engineers who understood code, hardware architecture, and the principles governing data.

Without this foundation, no “easy” layer of design would exist. If everyone becomes merely a user of AI tools and no one understands how they work under the hood, we will fall into a new form of dependence on a few tech giants.

In practice, then, programming skills will not disappear – their function will change. They will no longer be a means of creating code, but rather a way of understanding how the digital world works.

The Boundary Between Creator and User

Thanks to AI tools, anyone can “design” an application, but that doesn’t mean everyone can create something durable and secure. Understanding programming logic still provides an advantage—it allows you to assess whether the result generated by AI makes sense or merely looks good.

Without basic coding skills, it is easy to become a passive consumer of technology instead of a conscious creator of it.

The Human Dimension of New Technology

Paradoxically, the more technology advances, the more people with a broad perspective will be needed. An AI solutions designer is not just a technician – they are a strategist, psychologist, and ethicist all in one. They must understand how algorithmic decisions affect users and how to minimize the consequences of machine errors.

In an era when AI creates text, code, and images, it is the human who gives meaning.

The Future of Technology Education

Schools and universities are already beginning to notice this change. Instead of learning a single programming language, “AI literacy” courses are emerging—focused on understanding, controlling, and collaborating with models.

Soon, learning to code may resemble learning to read sheet music—necessary for understanding music, but not necessarily for creating it.

Instead, education will focus on:

  • designing interactions between humans and machines,
  • analyzing data and its impact on decisions,
  • thinking critically about model outputs,
  • and creating ethical principles for collaboration with AI.

A New Technological Alphabet

For decades, technical education focused on learning programming languages. Students were taught how to write loops, create classes, and manage memory. In the new world, understanding how AI models work, their limitations, how they learn, and how to interpret their outputs will be crucial.

This requires a change in mindset. Instead of “what should I write to make something work,” the question is: “how do I define the problem so that the machine can find the right solution?”

The education of the future will resemble a course in logical thinking rather than training in a specific language.

The Labor Market After the Revolution

A change in competencies will trigger a revolution in the labor market.

Companies will no longer be looking for “frontend developers” or “backend developers,” but rather AI solution creators—people who can integrate tools, understand business processes, and implement automation safely and in compliance with the law.

Those who combine technical, analytical, and communication skills will gain the greatest importance. A fully qualified specialist of the future is someone who understands technology but can also talk to people about its consequences.

Limits of Automation

However, not everything can be automated. Even the most advanced model needs a person to define the goal. AI has no awareness of social context, emotions, or intuition. Many decisions require empathy and experience that cannot be programmed.

Therefore, the claim that programming will no longer be needed is an oversimplification. In reality, the nature of this skill will change – from technical to strategic.

A New Digital Elite, and at the Same Time Greater Democratization of Software

In a world where anyone can create an app with a single command, those who understand the technical foundations will be the most valuable. People who can not only use AI, but also oversee and improve it.

They will become the new digital elite—not necessarily by writing code, but by understanding its meaning and consequences.

On the other hand, creating a working project will be easier. This means that more people will be able to reach the point at which learning how programming works will become worthwhile.

Programmer-scientists – those who build the language of the future

Although AI tools will take over most software development work, there is a group of people whose importance will only grow—the research programmers. They are the ones who create the languages, paradigms, and architectures that enable all this automation to function.

Without them, artificial intelligence would have nothing to build on at all. Language models generate code, but they cannot build the environment in which that code runs. Someone has to design languages that work better with AI, create new data structures, and improve compilers and machine learning mechanisms.

It is precisely the programmer-scientists who define the boundaries of what is possible. In the laboratories of companies and institutions such as OpenAI, Google DeepMind, NVIDIA, and MIT, new languages dedicated to human–machine collaboration are being created, in which messages blend programming syntax with natural language.

The goal is to create languages that are intuitive for humans while precise for machines. This is a huge research challenge, combining computer science, linguistics, and cognitive science.

The next task is to create ordinary solutions, not necessarily designed specifically for AI. Artificial intelligence relies on assembling solutions from ready-made building blocks. And although AI is increasingly capable of creating such blocks, we regularly encounter problems that AI cannot tackle. The task of programmer-scientists will therefore be to create these meta-blocks.

Research programmers will become what cathedral architects were to the Renaissance—unseen by most, yet essential to ensuring that the entire structure does not collapse. Their role is not to write applications, but to create the foundations that enable others to build them without knowing how to code.

In a world where everyone can generate programs with a single command, those who understand how the language AI itself uses works will be the most valuable. They will build the “language of languages” – the foundation of communication between humans and machines.

Big Data for Small Businesses as a New Task for Programmers

The demand for big data will grow, and so will the role of programmers who can work with large datasets. Many companies are only just beginning to discover how valuable data is as a resource, while also realizing how difficult it is to process, catalog, and secure. This opens up a new area of expertise that will be highly valuable in the coming years.

Lower infrastructure costs are bringing big data to smaller enterprises. However, the mere availability of tools does not mean that data will start working “by itself.” Someone needs to design how it is collected, ensure its quality, select the appropriate processing mechanisms, and make sure that the entire system is secure and scalable. That is precisely the task of programmers.

In practice, this means combining traditional coding with architectural thinking. A programmer working with big data must understand not only application logic, but also data flows, storage models, streaming systems, and AI integrations. It is a role that will eventually become just as fundamental as backend or frontend development is today.

Big data will no longer be something “for the big players.” Thanks to lower costs, it will become a routine part of work, and developers will play a key role in transforming raw data into real value.

Programming as Mind-Developing Entertainment

Although today you can vibe-code without knowing any programming language, programming remains one of the most intellectually stimulating forms of digital entertainment. Writing code teaches you to organize your thoughts, anticipate consequences, and break complex problems down into smaller steps. It is a workout for logical thinking that comes in handy not only at work, but also in everyday decisions.

Vibecoding is convenient because it allows you to create solutions by talking to AI. However, knowing how to code makes the process significantly easier. Those who understand loops, conditionals, or program structure can formulate commands more precisely, diagnose model errors more quickly, and anticipate their behavior. It is like knowing musical notation, even if you play “by ear.”

Programming is also an enjoyable activity in its own right. It provides a sense of agency and immediate satisfaction from solving a problem. In a world where many tasks are automated, a moment of concentration on your own piece of code can become a refreshing form of intellectual relaxation.

Therefore, even in the age of AI tools, knowledge of coding does not disappear. Instead, it becomes a support that makes vibecoding more conscious and solution design deeper and more precise.

Conclusions: From Code to Concept

The end of programming does not await us, but rather its evolution.

Code will become a hidden layer, much like internet protocols are today. You don’t need to know HTTP to use a browser—but someone must understand how it works so the world doesn’t come to a standstill.

The same will happen with coding. For most people, it will become unnecessary, but for a select few, even more crucial.

Artificial intelligence will not kill programming. It will only change its language. And instead of asking whether it is worth learning, it is better to ask: can we understand what we really want technology to do for us?

The ability to program will not disappear—it will change form. The future belongs to those who can design systems, understand data, and think critically about the effects of artificial intelligence.

The full-stack developer of the future is not someone who knows every framework, but someone who understands how to unite people, technology, and purpose into one cohesive solution.